diff --git a/README.md b/README.md
index 162a98c1d..8bc0c9951 100644
--- a/README.md
+++ b/README.md
@@ -126,12 +126,12 @@ These are examples of how DeepTrack2 can be used on real datasets:
-- DTEx213 **Multi-Particle tracking**
--
+- DTEx213 **[Multi-Particle tracking](https://github.com/DeepTrackAI/DeepTrack2/blob/develop/tutorials/2-examples/DTEx213_multi_particle_tracking.ipynb)**
+
Detecting quantum dots in a low SNR image.
- DTEx214 **Particle Feature Extraction**
--
+
Extracting the radius and refractive index of particles.
- DTEx215 **Cell Counting**
diff --git a/tutorials/2-examples/DTEx204_multi_molecule_tracking.ipynb b/tutorials/2-examples/DTEx204_multi_molecule_tracking.ipynb
deleted file mode 100644
index 89f635dc2..000000000
--- a/tutorials/2-examples/DTEx204_multi_molecule_tracking.ipynb
+++ /dev/null
@@ -1,13106 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:03.888292Z",
- "iopub.status.busy": "2022-06-30T10:46:03.888292Z",
- "iopub.status.idle": "2022-06-30T10:46:07.410150Z",
- "shell.execute_reply": "2022-06-30T10:46:07.409651Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- ]
- }
- ],
- "source": [
- "%matplotlib inline\n",
- "!pip install deeptrack"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Example 4. Multi-particle tracking"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 1. Setup\n",
- "\n",
- "Imports the objects needed for this example."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:07.433650Z",
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- "shell.execute_reply": "2022-06-30T10:46:16.462098Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\backend\\_config.py:11: UserWarning: cupy not installed. GPU-accelerated simulations will not be possible\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\backend\\_config.py:25: UserWarning: cupy not installed, CPU acceleration not enabled\n",
- " warnings.warn(\"cupy not installed, CPU acceleration not enabled\")\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset already downloaded.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "import deeptrack as dt\n",
- "\n",
- "from deeptrack.extras import datasets\n",
- "datasets.load('QuantumDots')\n",
- "\n",
- "IMAGE_SIZE = 128"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2. Defining the dataset\n",
- "\n",
- "### 2.1 Defining the training set\n",
- "\n",
- "The training set consists of simulated 128 by 128 pixel images, containing multiple particles each. The particles are simulated as point scatterers. Their position in the camera plane is constrained to be within the image, and is sampled with a normal distribution with standard deviation of 5 pixel units in along the axis normal to the camera plane. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:16.465590Z",
- "iopub.status.busy": "2022-06-30T10:46:16.465090Z",
- "iopub.status.idle": "2022-06-30T10:46:16.468590Z",
- "shell.execute_reply": "2022-06-30T10:46:16.468590Z"
- }
- },
- "outputs": [],
- "source": [
- "particle = dt.PointParticle(\n",
- " position=lambda: np.random.rand(2) * IMAGE_SIZE,\n",
- " z=lambda: np.random.randn() * 5,\n",
- " intensity=lambda: 1 + np.random.rand() * 9,\n",
- " position_unit=\"pixel\",\n",
- ")\n",
- "\n",
- "number_of_particles = lambda: np.random.randint(10, 20)\n",
- "\n",
- "particles = particle ^ number_of_particles"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The particles are imaged using a fluorescence microscope with NA between 0.6 and 0.8, illuminating laser wavelength of 500 nm, and a magnification of 10."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:16.471091Z",
- "iopub.status.busy": "2022-06-30T10:46:16.471091Z",
- "iopub.status.idle": "2022-06-30T10:46:16.474590Z",
- "shell.execute_reply": "2022-06-30T10:46:16.474098Z"
- }
- },
- "outputs": [],
- "source": [
- "optics = dt.Fluorescence(\n",
- " NA=lambda: 0.6 + np.random.rand() * 0.2,\n",
- " wavelength=500e-9,\n",
- " resolution=1e-6,\n",
- " magnification=10,\n",
- " output_region=(0, 0, IMAGE_SIZE, IMAGE_SIZE),\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The noise is simulated as poisson noise with a signal to noise ratio between 4 and 7. The image is finally normalized by rescaling it to be contained between two random numbers within (0, 1)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:16.477090Z",
- "iopub.status.busy": "2022-06-30T10:46:16.476590Z",
- "iopub.status.idle": "2022-06-30T10:46:16.480091Z",
- "shell.execute_reply": "2022-06-30T10:46:16.479652Z"
- }
- },
- "outputs": [],
- "source": [
- "\n",
- "normalization = dt.NormalizeMinMax(\n",
- " min=lambda: np.random.rand() * 0.4,\n",
- " max=lambda min: min + 0.1 + np.random.rand() * 0.5,\n",
- ")\n",
- "\n",
- "noise = dt.Poisson(\n",
- " snr=lambda: 4 + np.random.rand() * 3,\n",
- " background=normalization.min\n",
- ")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We now define how these objects combine"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:16.482590Z",
- "iopub.status.busy": "2022-06-30T10:46:16.482091Z",
- "iopub.status.idle": "2022-06-30T10:46:16.485090Z",
- "shell.execute_reply": "2022-06-30T10:46:16.484597Z"
- }
- },
- "outputs": [],
- "source": [
- "imaged_particle = optics(particles)\n",
- "\n",
- "dataset = imaged_particle >> normalization >> noise"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 Defining the training label\n",
- "\n",
- "Each particle is represented by a disk with a radius of 3 pixels."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:16.487590Z",
- "iopub.status.busy": "2022-06-30T10:46:16.487090Z",
- "iopub.status.idle": "2022-06-30T10:46:16.490590Z",
- "shell.execute_reply": "2022-06-30T10:46:16.490590Z"
- }
- },
- "outputs": [],
- "source": [
- "CIRCLE_RADIUS = 3\n",
- "\n",
- "X, Y = np.mgrid[:2*CIRCLE_RADIUS, :2*CIRCLE_RADIUS]\n",
- "\n",
- "circle = (X - CIRCLE_RADIUS + 0.5)**2 + (Y - CIRCLE_RADIUS + 0.5)**2 < CIRCLE_RADIUS**2\n",
- "circle = np.expand_dims(circle, axis=-1)\n",
- "\n",
- "get_masks = dt.SampleToMasks(\n",
- " lambda: lambda image: circle,\n",
- " output_region=optics.output_region,\n",
- " merge_method=\"or\"\n",
- ")\n",
- "\n",
- "def get_label(image_of_particles):\n",
- " return get_masks.update().resolve(image_of_particles)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 Visualizing the dataset\n",
- "\n",
- "We resolve and show 16 images."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:16.493090Z",
- "iopub.status.busy": "2022-06-30T10:46:16.493090Z",
- "iopub.status.idle": "2022-06-30T10:46:17.353090Z",
- "shell.execute_reply": "2022-06-30T10:46:17.353090Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.20368483555245 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:276: RuntimeWarning: invalid value encountered in sqrt\n",
- " * np.sqrt(1 - (NA / refractive_index_medium) ** 2 * RHO),\n"
- ]
- },
- {
- "data": {
- "image/png": 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4sVhoPp9rsViEGlKj0Yh9kQ4UGM8muQPEs5ynT2aBTM3l5WWs+11ZEHeq8tx/si2cD86612rgqFKfwV5PJpOglRFxp6h7sVjowYMHR5kfnMhms6mTk5Oj589mM0kKlSTPJEgKWViyQe4ocx30O97Jbrd7ROsj00CEzR2wD4q0JUuWLNk3wmazmf7O3/k7arfb+v2///d/oAPwuc99Tj/1Uz91FKBKluybZffCASBaKCmAX7FYDE450fpCoRCADt46YAFAB60BQOR0Fq7L00GcC+28aYA/dJ1arRbACfDpEVbXQJ9Op0dFq5ICDEqHqH0e0ADot9ut+v3+0TiZB0otZDw8GurzLxQKOjk5UbvdDsUdagyWy2U4XDgUOCDwwLvd7tEzAO4AXlefYezsFd9H0pPPsmY4d16sWq1WtV6vY+1YN9bM9wtw69F75k39QLVa1dnZmSTF/nimAKdDOvSI4GelUkknJyfxWfYJqtZut4uMDvUCmINh73tAbQQRexweaEte8M39nO6CY8i+OJ2JDAVnFeeDbIL3gcDx8aJ35opqjysTlUolbbfbcG5xSBgzz+XzXl/AvGezmcrlcpwx3gG+9mJlzh+qUO7cQUXLFyUnS5Ys2TfTlsul/tpf+2v6B//gH7zvNZ/5zGeOJKyTJftm2r1wAJwqQVTYI5BOx2i321osFkca/nm+shdWwo+HYoA8I5Fm14onOu3KLfDsoZ6cnp5G1B4gulwuA1yTiXCnBIDnevaSgmoBkHOVI0ATz6WWgH4Ek8kkqFGeAQDU4gCcnZ0FF92bdBHpRue/Wq0GrYp1Bbwzf5yt8XgcvHTpEE33egkKnk9PT0MZBhAJwCNbQ/aj1+tpNBrp6urqJcUXnsPaLJfLiFr3er3YO79fs9nU+fn50RlijTw6L+kIwMNn73a7Wi6XevHihaRbzfosyzSZTLRarY5kNt1Rw5FkLO50IEnr8rXlcvmoJsTBP0AaWdRKpaJHjx6FswRohtLloD9/r7v6UzDWk5MTVavVI1qY06W4f7lcDkoY9yUT4PUPgHbO/XQ6jSwC7wcZAHcQLy8vNZvN1Gg01Gw2437ucOCU3NWfIVmyZMm+GbZarfQzP/Mz3+xhJEv2Fdu9cQDg/rsyCuDZASZGpB7Vl36/r91uF3xtB1hQMKBrOA8aWoKDby/alQ68coAwjZ4oOnaKR7vdVqlUeun76LW7s+LRcjIT0sExcErIdrsNOgo1C16girMEqIafDRXJi3xxJLg/zpWkoHYADJHuBOixBg5aG41GNCSj3oHoLdF8ngVYBmiyztPpVOPxOJy0fHTX6yAYL+tUrVaD8sLesr7QlmgQx33d8WJczI3nEslmX5gDTd1cuWixWKjRaBzRcjgfkqLYlX4MOCM8lz9e8O01KmS/PJu0XC6DIsX+43hKCiWh+XwelCjeD886ePG8S9/izJEtYF0orPbziZNF7YBncLyPAONk7Pk6C84/Z5G9xNEhO8jYkyVLlixZsmSvbvfGAXCpztFopOVyGfKGd2l/E8F//PixVquVLi8vtVwuo4EQABwQ0Wg01Ov1dHV1paurq6PGRu50IBkpHfTh6WwL5WSxWEQ3WqgN3W5XtVpNjx49UrFY1LNnz7RcLtVut4+085kjMoaAVmQYiV5DH5Gk6+trzefzKH4GZHuDMEkvdXqVFA4J44a2AuBy2U1oPMPhMO6Jk1UoFELthzWez+eaTCZqNBrqdrsajUa6vr6OcUpSv9/Xfn/bAMsLTyeTSXQv9nNAgzO+Dx3K6SFEjwHE9FzYbDYaj8dRKEqTM7IWNLwCkLK+nBEi35w1p6KwvpKi2Rmfp7C8VCoFh71Wq2k6nYYcKFmFTqejUql01KiOupTlcqlOpxPyn2Sf5vO5Go2GHj16dORckIXqdDqh6LRcLgPEn5yc6NGjR7q5uYkuuqgPMcebm5s4G5vNRqenp2o2myEfy9q7E8e9WEvvMcAa48xDGaLDMA45NQms8cnJSTgOXkiM84HT4JSpvIJSsmTJkiVLluwrs3vhAEgHDrnLY7nEI9FFL26VFMDcOc0eueQPyjAoiZAlAOBLB7oGQEdSAD2+R5Q4z/kGhHqEXDoUghLhB1g6R5uxzmazo0ZcXi8AR5rIqdOlXBYTx8Ejxa5ixB+P6HIvntVqtY4UhVya0bMTLs9JhJ2fOw8cB499w8lC7QUQmHcG2BfWEfCXl0/zIleApwNlMiPsD2fHOfF3yZZi+WwElBW+hxY+crW73W2XXCL6DqJxgHgGWRWPyruzSRYHJ47zyRrgKPjakrmRFJQr5u97TY0MFC16VXgNgKTIgPB1vhEb93ag7o4IWRTW2WtEvCOzv1Pu2DLuVqt11A/DG8slS5YsWbJkyb5yuxcOAJQP7/rpdQCLxSI4xETHAUCDwSAAXaFQOALoDkBHo1HQQRxoAV5R7oEeUywWA6zwvd1uFxFbL8CkgLZYLIbKCtmL+XweKkTw94fD4ZFCDtHr6+trlUolnZ+fR3MwaBkAQJpnQTWhLwJjojiy2+1GRNZ53RSTAnyR9WRNKpWKnjx5ovl8rs9//vMB5L2ZF9FzQOxyudTl5WVEwFFSgifOWjvNptFo6OTkRM+fP9eLFy+OFIFwKshE4FScnZ2pVCoFqGUP+v2++v1+1Gh4gTbF0jSrevfdd6MAl3kUi8VQmOp2u7q4uDhyMl3b2YuFAaydTketVkudTkftdlv9fl/Pnz8P0Iozw7P4gxM4nU5DnpUsBjSzdrutx48fBxXIVaDIjiAnx3kiE7RarfTs2bOjYnjkY6lH6fV6kqRnz55pOp3GdTghFL5Td8MeSzrqj+B7O5lMor4EWlSn0wllJTIVqCJxtr1uAUeN96Zer6vVasWelkqlaLqXLFmyZMmSJXs1uzcOgMsnugSkRzUlRfQQAEikE5pNvigwH1nOG1xy6RCt9462XAN3GU63ZwlcNhFgxT3zBZ8ASKKwOBco+2BEmAHGnt3AvADUFWzy83Otdi+u5WdEbon4u4Plikd8zfh8ToBlosOeNXBjPbyLK/UCXnMAzSdfnOuFsXyPDIlnCPg8mRCyQuwLY2Z8nt3Ir59z/SkSlxTnhM9D16FgGqDtRel5JSIvcPW1hUOP40UWxbn+7qDm95Nn5DNN7BPn1D9zVy2MpHA88tkD7uHOYJ5K5+8N6+VSpcyXOfFuuFOPswStj/XGuU+WLFmyZMmSvZrdCwcA3jtAA978eDyOTsDdbjcUewDpNI2CWw1odwoRQAOw4A2YAJx8djAYBIUBx8BpG0RFW61WyCbinBCJB5AT5QX4cA/PQEi3HP0HDx5oOp3qc5/73BG4zfPVB4NBUG0AYKwD3H+cCMAsmQl47B719kg20dlCoRANt6AsOe0H6pSDRKfrQNUisguIhBIDSKaJ2OnpqT760Y8egVaPvAMKJQVP3ptKUVhLh2Oi1dQCnJ6earPZ6PLyUrvdLpqvsZbQtWg4B9h0Bwd+/fX1dUTg6fFAXQWN0a6urlSv13VxcaHJZKJ33nkn9qNcLms2mwVvnroQ9tOdkXq9Hso8AOlWqxU1GsvlUldXV1oul1F/0mw2j5wg/qzXaw2HwyNg7Rr+XlAv3QL+Wq2mVqulwWCgt99+OxSVOMNE4qVDUbT3raDGwWtm2FPm52eJcdXr9XDm2ZtKpaLZbKbnz5+rVqsdqVMlS5YsWbJkyV7d7oUDAFB3jjo8cqgTgHvXNJeOI9FEsjFXMMl/zyUbnW8vHUf8PRJOdNOjmDzXJRm9BoBnEFn1aG8+mu568a56lK9pyF+bj1r7PF2nH/Dl68G68kynVADunZbjUVevJSCSzPeIWufH7pKZ/oc95HoyID4e6hxYf6d6eYbEo9BeV8K+uwPkoNtpPqxzPuPgY/Hv5TM0TrFiv1gfP8esFXQeIt44reyNO5cu1epn2fXy2S+XhOUaHw/n17NTvi48J59V4Fqn2nFvp955psb30p10N392vvaCzwP8/R1LlixZsmTJkn3ldi8cAKg8o9EoVFNqtZoGg4Emk0lEE9Hbp4AUyk2+cBOpTopvoU4A1MkqQL25urqSpIiedrtdFQoFXV5eBg+7Vqsdaf/PZrPgJa9Wq4jKDofDAEdEfYn8LpfLAGl0EV6v13r33XdjrNJttJw5uXa+c8gBfy6R6tFq6CrL5TLoLd4ADZCMAk29XtfZ2VkALoAzgH632+nm5iYoUJJiDVFkoncBAG2328V6AHK9mNNlJZ3CRHQ6T1vKGxkQQDq8efaHtcRwaFAbIgNULBajSJwzwHnZ7XbR48GVg5h/uVyO4l93Zui5cHZ2dkRnQo2I/el0Our1ejEXMhmz2UxXV1eRhVmv17q+vlaxWNSDBw9Ur9f1kY985KguZTKZaDweq9VqRf0JNRBkuchAnJ6eRnahWCyq3+8fvRd08a1Wq/rIRz5yVMztdRzsM0pIeWcac+oVZ6tQKAT9i5/hwPj7PJ/PozZmtVrp5uYmMh53Ob/JkiVLlixZsg+2e/Hb0yOu0jE32iUZ+Z5H9T3qyN93ARCeIx2is+4wOD/8/ZRgvFDVx+10HMaWzx44iPUoMeDOo+M4NYBv1uOurIFTeTziDNDyeeYju6wX4/Hma/notl/rUV3u7x1bpQM/3vfRAT3gmyxPPuuS57Qzfv72CLNHnD3j4GvtRbPOeWdMeToJzpVnE3i2d9X1bILXdpB58T3jXBCVd/pYvijcuftOk/FxEJkHkHPdXdkJrzmhZsKLgn2N3RlzJR+vY8CpY618vH4fP3Oehcl/n3Hm9/iuOeQL/JMlS5YsWbJkr2b3wgFA8eXx48eSpOFwqNlsFqouKOkQMSwUCqEiAlBG4QfgR8QbkAf/3Lu3cm29Xg9NdhR8qtVqqLsAVE5OTnR2dhbj3m63evr06dFnT09PA9RlWRZNk+r1utrttobDoUajURR5kpEga+BFmBQ90tUW/jtgEM18B+yVSuUIKLZarVBZwYmQDtKKDsoBZTglk8lE+/0+MiDw36lrIMPhfQfITAwGAxUKhVBqYY48k7qC2WymwWCgZrOpbrcbP5MOVBIH8TgakgLEsi7w+Debjer1uiQdRa6JgJNhwgnh316bwblkH9y5QIq11WpFBgHL10jgKJBVYL/Q32+32+p2u8qyLLIV3J97Qyl78uSJpEPmg/WCg08mgzF71JzsxqNHj+Ln1I8wft4Jsm3I0qK/32g0ogNzlh2azPX7/aDq8d7hOLA/NGiDxlWv17Xb7TQajeKMM5dSqRT1P/V6XfV6PVSIqIXwfU+WLFmyZMmSvZrdCweACDpNlADz5XI5aDLwmEn5O9+bDIErhEg6inR6ZNI14r3BEfQWvk/jKp5DQyOPyLrGPoo+zk1mnFCCeFaeT+10HudWI1dZqVSieyzPRiEGSgkOkQNS5uuReM+AeJaD8WK+Zt7Z1alBzMG7tLIfzrf3z3A/riVqzXg8mu/Rdo9Ss1537a1nMhifdODIex2Frxl1C3k+u2cAXNUpH332qLdnJ3w/oCFxDnCivCbBswuePaBBmNOa8vPgvswRB411pE8BakVeH+AZmLvqR1y+1J/JmuDcUVTNmvJ+e40DRfOegfKzwRx4tqToZ4Cz44pYyZIlS5YsWbKv3O6FA+BRbyLwko4iiSjdnJ6ehpKMf57PesSbyGW1Wj2izBClhpfOPd9+++3gxTugAdSgcU50FOWU7XYbGQDAHACL763Xa02nU+12u4hSX15eBmgD7JF5kA5NrDzy78pGZE52u4OEZ77oFvUX7p+Xe6SuolAohFoRwAoN++FwqPF4HNFdFHTyRbTSscxnoXDb6IpMBY6AU7hQcIIO5PcCEHqjNua92WyOpFadfkQNCPUGkiIr0ul0tN/vI7uCc+kF54BXxksdBbUh1HIQ2easdDoddTqdOB/c1ylprpyDo8A9pFuVJnj/7P1sNtNisQgu/MnJSezher2OrtTUlbhTOp/PY189U8Oa9vv9mCvRd86hW76pl0fvUTJCr597UIuxXq9jDQH3OAQ4HCg8Uf9CnQ/PdIeY7EaeppcsWbJkyZIl+8rsXjgA0oGqIR10xT0iDiiAhgAYcGWdLMsiKj6ZTI74xq6n78AVkAiA8swC4wJouKwhRcDtdjuipTzHaSA4BERBeSbFrmQFPMpKcSWZEJ8j8+Se9EUAvAIkJQWAlG4BJw6PA3HnjgPMPVpMYS+Fy66Bz5rl1Vqcl8+90bT3ImHWwvXiMd8jr7Ngjnn+d/7f7IfvuaQYvxeKZ1l2RCeBvsUfCqwBt+5g+lyKxWI4oNIhg+K8f65vt9uRKWI+7CsFxXyN9CuUHTJCOA6LxUKLxSIKmHmOc/ZZF7JcrBeOI+Px982zbT4en79nRHCeOKO+xlB/2FPvoeBrxbVnZ2dHHYj9fczXqCRLlixZsmTJXs3uhQMA0IL7PJ/PI6pYKpUCtKxWK43HY0mHIkGA3tnZWUTAPWKMszCfzyNKTwYAUCsdpDehpfjYJpOJRqPRUV0BYBCgxb0BOET7Xa/epSLRr99ut6FZjzY64yc6zWcBfvCjvVEUQJf5eAdajyYD8hgnHWjzzgNzLxaLoQ/PunmWxTnYRPP5t+8TIJZ5zmaz2BPqGeDt46CgmMO9iJp7NHg6ncY58aJuzo8rD2VZptFodETdwpEoFApRGwJohVaFk8Z+AF4ByDgV0+lUT58+jYwRmSqyP5KCwgJvH8cL4L/b7SIKTnEyc+MczWYzrVarAOutVit6ZeDoQRmjwJqOv5w/ovetViuyZJVKJbr45gu/XZqWDACUNNah0WjE3lI34eo/+XtKiq7Ci8XiqHgZhS3OSKlUiiySFx8nGlCyZMmSJUv26nZvHAA4vcvlMv4QnQUUAMqg1UgHYNFut1UsFnV9fX3URZQI9Gw203Q6PXIAnLbAOJwHTSSUCGun0wkpTaLpRGY9ogn9gQwE5jKUADqoHZ6pAFR7hBTgU6vVgv7EOhCh9ugtlCXWzXngHq2n4Jl58CzfG+YMjYZC6tFoFA2eAILsi8szeo0GTd4AmkSRJUUmoFQ6NJPieWQKXG2IxmyuwsO6eQS8Wq2q3W6rUChEQTkGAKcBFeuwXC41Ho9VLpd1fn4ekqzeV8DXSbqV/qR4vdfrhQNGIyyKxKHY1Gq1o0yVpHi28+f9bOCg4DThOJTLZTUaDTWbzaN1mE6nIZkJZYhsB3Nwmh3Oimc68jUaROZ5VzhPnKW8bC3vm79f6/X6qEjc+xN4vQ3/D+B049zg/Pk7kixZsmTJkiX7yuxeOACAOVckKRaLOjk5UavVUr/f12AweIlrLB2oBtPpNKLLRCGdngG4hO9MlNgLdtF5d6ALCEPrnmisU1aoBcBQcCF6DZDyDIQXv/I9ONhETb3rb57u5IorRKXduZlMJpE9KJfLQfHIq+pA7SCLQLfl3W4XEXfGCKgmei5JnU4n6CAATtab+zpoJarthbuSjrrVUriK8pBnVzwSXywWj3ob0IfBC2Y5M9fX1+GwcQayLIs5esQfGgtcc6dIAV73+33sFz8jkr7dbnV5eRnzLBaL6na7MV7mgBWLRU2nU83n8yNZV6/l4KxLhwZY/X5f2+02OhGzt9PpNH7GfRgjjioZIupXcDqk26i899FgL53G5FQ6fo5DiNPi9ROcvbxzzc85i+604uBRL8DZoX4jn7FKlixZsmTJkn1ldm8cAOgpgN9isaher6eLi4toggSVh59Lh4jheDxWqVTSxcVFAFnABfQZmg4BJJwbLh0KHYnSE6Ul+k1UlGgowBDevgNO11kn0k9BLA4AwIb5EEGlcNUjyESdXQ2JjIBnH5ByxAGgwPbs7EyNRkP9fj8KUZ2T7dzufr8fEX2X5CR6DGCk6BWpU+nAKSdaD1gFtJGdcABMxsQj3y6dCaiE/w73vdPphCNYKBQ0mUz09OlTtdttvfHGG0EZQ/KSc+DPaDQaqlarsS44l7vdLmoBAM84ZGQmcMBw5IhSj0YjXV1dHTkt0Fc4c+ynZ5mGw6GazaaazWZkvgC6gGj2frfbhVNcrVbD4dlsNppMJrq+vo7xSQcFJEAzDgAUKuZ4cXGhTqej4XAYDhBN3jiH0I9wiMlEXF1daTabBRVpOp1qOp2qXq+HY8TzeX/J6OFsk5kgKIAjAp2Ls+j3SJYsWbJkyZK9mt0LB4DiSYARUU+oCLvdLjqZugqJ0yMAIWikf1B0MB+F9mg0NQjQVSQddQ926UqoSc6Hdl65Kw/V6/UYJ1r6REPh7zsdxucFVcNBmHSonSBK6/x0MgPMaTKZxNo43UpSKBrRQ4DCZpwrAB/RceaIhKWDVMbua80auQynqyvxDJwHuPDwwnEi3Onjc8vlMvjrrkIDjer09PSoGJqxMF749L6uFIPDYweow6OfTCbh+G02m3AsoT1Vq1X1er2jWgt3FrMsC5UkHBLW251Sz9RIh4wADgiUGEnh7PIZgD97B/WL7Ab3ctlQHE4oUtSHNBqNeF+casXnqDNAtpdsEZknxsf7gVPEuFzy1etl2CcyM2RHKpVKnI9UDJwsWbJkyZK9ut0LBwBpw+FweFRUOhqNNJvNVK1WdXZ2dgSq8w4AEo39fj9oA1zvnHdXhoHKwjMBTdPpVPv9Ppp+OQiBnuIOhhfbAsA8sl8o3DYwo+Ay35uAa5HjJBLe7XYjUk2EnIJonw/gC9nF/X6vZrMZ995utxoMBkFbIQIPxQIqFPzxk5MTSQfQBngmQ8G8AIcO8l3/nr3lZx7J9X4JRO/hjTcajWj8BNAFAEoKAEhEmUwNqkxkFGq1mi4uLrTb7fT8+fMAw8wtv39kJXDWiD4zrsePH0ekGxoQjmKn04l6jlqtFlmcLMu0WCz0/PnzALjSoecE9B3OslNhvOGZR885r/DnOZ84iNC3VqtV1JfgSHHGeb4rXOGIURPRbrePmtC5c+7qVNyr0WiEStdisVC73Var1dJkMlG/3493yWtFOCeeoeFMMx+KgBk/jipSq8mSJUuWLFmyV7N74QA4d92LPolSA4Lhg2NEFKHYALY8Cp+X/ZQODYa4r6T3LW4kok4U3iOrTqnwXgQ+PkA09Qd8ZrvdHmUCvMgTcw15L+rF0YA3T8EkRZ5cz9xLpdKRGhB/E00FcBHl9wJrn6Nz/8mGwMd3Z0xSAH3GT32Fa8QTBScSjAqS13owF19bxgm1B7DtlCVJ0beBObBHrAlnQVJIr1YqlSPZU+mgUENnXEmxl6zLZDKJKDn0LjIAzMWdV+9NgWOAM8UeubF27pCR7ej3+5E9ASj7O5NXR5IOfRF8HfKdgXEsuN5paIxJ0tHZ8nWBMuaZBn+eOxzMHecYhwd1I/aeDIFnnJIlS5YsWbJkr2b3wgFw8A1vvdVq6cWLFxqPx8HFns/nurm5CfAAEITmkmVZcJqhVuBcvN8zHQwT+QUwwXcnm1CtVtXtdrXf7zUajeLn0nGkH8cB0Difz4+69m42GzWbzZDMJDrqmvXQdqRbzXjWAMUh+NuXl5fqdDp68803A+BlWRZNlJyuARhjPqyB6/17FBZa0Xg8jkzBbrfT6emp2u12qCuxH97DgDG6ShOAlPVz6lG9Xlev19N8Ptf19bXK5bJ6vV7UX0jHevnMZzKZHMmUNhqNmCNFwew38/XzgDJSq9WKaDX8+ZOTExWLxchOXF9fB53L5S3H47Fubm4CfKMa5YDWsypkLwDzq9Uq6DaAZklHtCCkTLfbbczp9PRU1WpVV1dXIVNbr9eP1HgonmX9yG6xHtSIkBXy7BgF3Rj0N5exxWHznhAYDiNUJ5xuCpVxYCVFrY2Pfb1eq9Pp6OzsLDJWrAUOQHICkiVLlixZsle3e+EAELl2ioGDPac2kCnAvPEW93IuNdFYIqiuhOJqPM6f9nsiVwgvHF13SVEgKenomfwsn3XwKLRH5wGJDvId/HE9RhSUaxhXPtqb55N7BPwu+URXx+Ez3q3V5+FSmE75AfTni3zz1C1kKwHr3vGVa4liA5gZdz5TgiPAmFw5B0DKfnjBLrx9l9dkDR3I+rrh5JTL5bgeUAwgdcCLYwio9zoI5kJEmzm4UhH0LtbZ15pMCUW0niXwM876u+PhUXmn1uXfI89+uZKP11X4e8vakpXzDsCMhf0HzDuNh2wLDiTr6ueZ96bRaCQKULJkyZIlS/ZV2L1wADabjW5ubgLQEKWEKrFYLEKhJV9cyeddTx1lklKppMlkosVioV6vF7zoyWRypG7iToak6CkwHo81nU714MEDdbtdzedzvf322wFcaFYkHYCdK8Q4WMbxABwBqOr1us7OzkJKdLvd6ubmRpIiogqokxTgEi404O3y8jIiqKjKOI0E4Oc8fh+XpKOGYq4Q4/ei0Ho0Gqler0fEnwJmlI3IXvizXEueLMLz58/17NkzSQqA7gXHnjVg/gBizsZgMIhMEU4FQB6aDPtxeXmp5XKpXq+nWq0WUrNXV1d68eLFUbM3HCAcUaL0jx8/VrVa1dOnT6MxHXMku3N2dhbjWC6Xur6+jj2FAiXdSm5Wq1W98847cc1+v1e73dbp6anK5bI6nU4UJQPst9ut3nrrLUm3dQTUi9AobDabRV0H+0I9w13OL84ujrhLmvJ1r9eLDMVms9Hl5eURAGdOJycnajQaeuutt/Ts2bNwnKiN4D3m3jgd6/Vao9FIq9Uqem6QCUABioJ3Vzj61V/91Vf6/yZZsmTJkiX7Vrd74QBgHuV0pRsABBFxALhf55Ferud7Dl7zHH2eIR344IC+PGfcqUaeYXD1H4yx5hV7XPGEz+QjvH5vrqMokugwkVmP1t9Fh/CIOo4O8/PiXcyjuT4XehrcRbsAsDMOHDG+5vMevfZ197oBqF0fNCeMLALg1usVfOz+LI/652UkGRufycur+t4RoeZnTrXKZ37IQjFm3xP23O+VP7vMDSfLz5JH9r2ewr/P5/N75YXwrJtnJ5zr72vFXPPZN18nMgguq+v1J/kah/x6+Tvi6kn8zP9OlixZsmTJkr2a3QsHgEitdAB9/NLPN96i6Jdr4BDTaClfSAxlxyO71Wo1Op5iDtIGg0GMi4gl0XoHQvQngAvP9UScqVtwpRq00QFNnonguRSIch3zJTpbLpej2yr0GXTdoWXAUYfv72pC1CQQFQcEIhHqBcLQpgqFgi4uLlSv1zWdToPjzn6hh8/3W61WKMS4E4RRO7DZbGL+NPzyxlLucAAkAX4PHz5UpVLRzc2Nrq+vg/riINdpUcViUaenp7HGzJU+A61W6wjUEi2nCHk6nWq5XOqdd96JZl5E072uoFAoxL6zR6745LQpZEipeWg0GlHQDein/wBqOCcnJ6rX67G2Nzc32mw26vV6arVacUagRrEeWZZFnQvO4LNnz7TZbHR6ehrZI+mWijOdTqNWhfoaHLRarRZZjhcvXmi5XIYSE5+tVCr6tm/7Nk0mE41Go1AbyrIs+lSQXaKo3DNb8/n8SA2J8+b1A8kBSJYsWbJkyV7d7oUDIB0i99IhCpnXfXclEowouPPXiT7yWY+Yci/nrPMzAIZHgv2zRHr9s1BenCvv3GUMyo9H8uktwL28tsAjqcyX58AZ92wEhZGetcgbBbUARICor6Wbc659jV3Nx6/lHl5k6tFmfwYORz6LkXf4WA9+zvlgzlCaKFi+y9lwY23JKEFNyj/HqVv5ObDnd9VRMG5Xe+Js+Tz5LNdRfO0qOEThKSh3Hnw+k+Xz5hzzLHcA8gXx3F86FLJXKpVwLlx5ir+d08/c3DHmnhQXQ9lx9aV8doVn+znOj93n6nUoyZIlS5YsWbJXs3vhAJABcC14Vxwher7f7yNyDPikOZODdoAu4KBarWq5XOrm5iYimkhYoieeZVlw//NKLXSUBRACEFEmKZVuOxBLCs73cDgMecNGo6FmsxkqL0R9r66ugkNPtBxATyScKDyOCxkBIvP+tXSgZbhyksuJukMhHdM7AOz5Rlg4KePx+Ij770CXNSNyTQYHpSNAM8XcRLe9+Hu/v5Wl9B4GXoANB96zRZyPs7OzUP7h3Ph5QNkIsOsAGUnWRqMR/Hmi96XSbRdf19Anus91rC3Ra3ceyL48ePAgahC4L+eFyHypVNJsNtNwOIzuvr4/1JuQ1SFCTi3K2dmZut1uZJmovyAjxf5ut1uNRqOoV2g2m2o0GqpUKmo2myoWi7q8vNRsNosaC/j7nEvUiPb7W/lWshZkCSTFv+v1ut588804w/RF2O9v+1KgnkXWAieAswiNyAu4C4VC9Ab54he/+FX9v5MsWbJkyZJ9q9q9cAAAvADrvEZ7nmPNz5xjTMTUee15/jbNoXieK894calHkgH80ELcAXC+O4B7MplENH+73QbA8eJfosTr9fqIv+41CERYidr6GvCzfKbC1/Ku9XLnwn8OMIQm5Dx1V4sB8EKfcWciz+/H8jxxou6MJ1+jIB13viUCnH8O+8DeEI3Oqxa5rKtnZ7xGwaP0Pj7GTyQbQI1j5E6idGhylf8+ykH5ffZxcYYA3R5RZw2hLHnvBcZNpN2zYKyLR+g9Q+FqPH4GyEKw7hSvs0dOr9rtdkeF5/7+8s5Ba+I98PfLn8desR7MDacqryx1V5YrWbJkyZIlS/bl7V44AJvNRv1+X+PxOCKARL75OcAaQA0IcU186bYWIC8V6gZ3HoWUYrEYkVQUXbgnqi+AZiK7XrNA51n/OVFZVHlKpVJwweGaSwcwRcTT18NpI3x9c3Oj8Xisk5OTyHxQGzAejwPwAfIKhYKazabq9XqsR6fTUaPRiCgxRqR1t9tpPB5HRiIPpFGwIcq/Xq9DTWe5XGo8Hh/Rsdi3fJEwKjNOZSHajzOIhCQOx6NHj7Tf7/X8+XMNBoOIwqOlD5h0sM7av/POO9psNlGjwfjIQHC9pNijx48fhyoTXZbZH0lRMwDYZz2I6EPzIuvjTpArQbnzValU1O12g0+PbbfbeD7OE+eItb25udHNzY2m06nm83lkLah3IbuSL0CnLgYFIc7vxz/+cY1GI/X7/cjOVSqVo74LnBPqGTabTSgr4SSRuVqv11GTg8N9fn6uUqmk6+vr2MMsu5VkZf89u8Vacf4SBShZsmTJkiV7dbsXDgD0DuQFpeNINnQRougepSYa6kWW+Yi5RwqRMWw2m+p2uwGU77oXzbSkY4UgB6oexfW/AVTeuRSg6KDvLgDjFBaeDdCaTCZHgB6w7A3AvH4A+gz3a7Va6nQ6R02YpOMOrYBiz4L4da7Q4gXW0q2Dxbqwr6wHY/UiY8Cg1ww4V94jwjSeev78uZbLZewb1J96vX6UxWAcm80mCngBu0TImQPPYq0rlYouLi40m810dXUVtBvWnIg9dBiKmP2ccj1j9GJW7kMfCJxIvuYP5wEHw2tRuJY1xRlaLpdaLBZqNBqqVqtB0aJmIr+fOI04RIvFQqenpzo5OdF6vdbV1ZWkg5Qr++CZAJ7hkX0v4EaKlOJf5koHYJwlvp+vw+A9dofx/eo8kiVL9q1h+X47eXM1s2TJkh3bV+0AZFn2EUn/d0kPJe0l/cR+v//xLMtOJf20pG+T9AVJf2i/3/c/6F5ENzudTgDq+Xx+BAzhb8/nc9XrdbXb7YisOqAB7AJEnPtN9B6wS+Ml53AzHknB26c5ESDnrqJEsgZ5SoOPBwAHrcadHC985Xur1UrFYjG04uHUo97j5o2tUBaSFLULjAeQ5cCc7sVQSgDizMnXjRoJFIN4zmAwCIcj7xTtdruoieAZXozc6XTiP2qn+3gPAUA82QR05CkmZT98jQHeu91t92Lu778w4M/XarWjPR4MBvr85z8fWQbp1nkqFApRZwB//vLyUsPhMLI8ANpSqRQKOoyLWhaA8Onp6VFtAud2NptpPB6rXC5H12XPIHE+vKYCp4Rz0Gg0onswDhhryzjYX/aHny8WC41Go6D/cC00KVSxiMRLih4dNAEjA8HeokBVKBQiUzcYDFQoFNRut9VsNkNhirqf1WqlFy9eqFarqdlsHr2/KQOQ7D7b6/wdmexl63a7+tSnPqWPf/zj73vNz/3cz+mnf/qn35cRkCzZt7J9LRmAjaQ/ud/v/z9ZlrUl/VKWZf9vST8o6b/b7/c/kmXZn5b0pyX98AfdiOj2xcWFqtWqRqNRAG1+4dOUCGqDR+b95XYQCeDiWgoL+TmcZT5PJJavq9WqqtXqEeXFr/fnE5F2HrYr/njNAGCNqDOA2dVQuLekcHiyLAvde+Q+nasO+IcPDrhjbF4cjZPjzaKg/HitgINpVGqI9AIC883V7trf2WwWTgO0GbI1KDytVqtwmiQF8HSaFzQeB4vSodswIJZ1n0wmKhQK4Vw6FUdSqAh5DQqUmefPnx8VWuPYkQ0AlD579izui7O02dzKm56cnGizuZXw3O/3cY6hLbVareC4e00FdCpAPM6MO4uAf+fi4/ACvgHgnHnmjkws55LCZp8jTmDeMcURpBB8OBxqs9lEIS976YpDnP3VahXgvlAoRMbo9PRUpVIpMjUUZV9eXmowGKjdbqtarR7VF+Tf/WTJ7pm9tt+RyV62ZrOpH/iBH9D3fu/3vu81u91Of+Nv/I30/0SyZHfYV+0A7Pf7p5KevvfvcZZln5H0hqR/TtLvf++y/1TS39eX+c8NlRRJAXLyuurSQTMfgOIFhx6l9OZGRNur1ap6vV7oqhOpdGAlKegjAErAJ9FVABqA1/nIDsIZi9MiJIWyT77QWFLUGxBFB8yh5lOv19VsNmNu8/lc4/E41HPe2wutViv1+/2YmyvzeAEpdB+i6BSZesYDp4Y9gZPvBZvsEfN16gdZFbIWXtAK6PXx5KlYTrfiOdSIsH6oEhFhdgcHukpe/hLDCXSJU8Boq9XSer2O/gzuBBI1h1ImKWpGiNZLtxkY5iAdskudTifmPR6PVa/XIwvBPm42m9DHdxUiwDj3dKob55f3in3BweGs1+v1yPpkWaZut/tSgTDZI8bPWLwAmz0mI0GzMs4cUrc4htSu4JTyzpFV4t1F679YLEZgALqTd19OhcDJ7qu9zt+RyQ52cXGhf/Ff/Bf17d/+7froRz/6gdf+4//4P65/79/79/Qrv/Ir+lt/62/F/8vJkiV7TTUAWZZ9m6TfK+l/lPTwvf/4JOmZbtOfH2g4AFdXVwEgARr2jADXOACuUONRZekA7HAS6vW6er2e+v2++v1+RDEBRURIHYA7gHZe+Wq1ikZHnl2QDpxqwB2caqLyLjfpUXz42TRDgj5SKpU0HA41m8308OFD9Xq9APKDwSAoPZKOnJ/pdHqUYYB3PZlMQrq0VCodRd4pRsZpgePNPHkO3HdX8yFbwH+wrC/N07xwNMsyjcfjiDo7iLvLAfBncw8KfbMs09nZmU5PT9Xv9zWfz4+i9jzfawn8eWQQmGO5XNbJyYkqlYparVbIcrLHXuw9HA6jdkVSSHFOp9MoUh6Px6pWqzo9PVWWZUEn6nQ62u/3sYf1ej0cTLIYFCdzxnEAJIV0qdN+8jQ3z3Y5Rcp/zlw6nY6q1WrIxk4mE00mk3CKqtWqut1uOMXslRe6z2YzzefzGCPrcnp6ql6vd+RU0/SNOfGerVarmAdUL+Q+WROam0FdS5bsvtvX+jsy2cHOzs70r/1r/5q+67u+68te+8lPflKf/OQn9Tf/5t/Uf/1f/9fJAUiWzOxrdgCyLGtJ+puS/sR+vx/5L+T9fr/PsuxOkm6WZZ+S9CnpFtg4lx9aAkpArmzz3n1f+kPUEbBDZ2DpEDEF3HgxpxfyEpFGyQVADl3DI+geVZcOnYg9au+RZcZPsSN0EKK70qHoVdJRdBRuNZ1eJ5PJkZIK13thMOPj63wTL6geAHLn5Us66sOAM8W8PMKfl3Xk+0hZ8nPWHUDrfRqIOEPfIorOOUCtCIWavNb+YrEIJSe47YBo1oAsQrfbjXsul8vYA+bkz4XKA40G7j/PZX2ITqMolc+6cF+cSBwt1HZ8Tdh3aEKcfQfz0MxwVJ0yttls4ue73S4cIq/v4NywP5LiXLsTwfl3p5tsAJQsnisdambc8eYdvLq6CspYqVQK54D3j/MAjYk1xWkgu0MxNVkgl0tNluw+2uv4HZnsYHkxgGTJJOnx48f6Z//ZfzZ6K+Vtv9/r53/+5/Vrv/Zr3+CR3V/7mhyALMvKuv2P7b/Y7/f/z/e+/TzLssf7/f5plmWPJb2467P7/f4nJP2EJLVarf1oNAogQQTaqQOuJuIAL+8EkCk4Pz9Xu93WYrGI4szRaBRA0ak63BtaAgDu5OQkioDhn+Mc8ByitL1e74i/z5h5zntzPtJKr9frqlQqajQaocoD+HKwfXFxEVrqUI5ms1lEcgGIXlwMGERpCNAHjcJBFvQQj7y6Pr5TpvibyDM1Aa6wAz3J6VQ3Nzdar9fqdrtBB/IMD7QZMjVEobkXc8aZI/qdZVlIXO52u3j52cd+vx8ZCCQ2Jemtt97SfD5XtVpVs9kM+gqUHug1PGuz2Wg0GsWauIOH2g4qTYB7QLykkLj0ounNZhPjIvLNGSJK7iAXRw4pU0C807pQZcJxnU6ncT5watgnHByKwp1+5AAb53K5XKpcLkdh8bNnz4L7T8aqWq2Gc0VWZTgc6tmzZ+r1ejo/P9d6vdZkMol94t3b7/fqdDqq1WohVzsYDOK9vbm5ORofCkLJkt1Xe12/I9/PSUiWLNmtffzjH9d/8B/8B3r8+PGdP1+v1/qhH/qh5ACYfS0qQJmk/0TSZ/b7/f/FfvT/kvQvSfqR9/7+O1/uXgB6fplDTfCfSQeQCI+e6C/AF7AJ+IPuAsc9LyfoGQOPRpMJ8EyEK+g4xxx6DYCciDydcaFJOOWG8QCeqtXqUR2B892J/JJdwCGBhuF8fUD8/j2FGq9ZkA6FmL4OTsdgboB81+5/b89fKrbFXPEIR8xlUHGCvIgVEO3SqtvtVldXV5ERkBRryj0onIUGRtEu4/MaEuZA92W6QPveOfh35yJPZ2o0GuHEAUI9w+Hr5Go5LrPp68U58QZXrmrU7XZDihZqUrFYjL8prOZ5qCYxLz9DOHq8V/zci9r5HPuHg8CZYA5c45kGMlt+vrlntVoN5wCHnDl7LwjOD7Q5SUeN8thPnBN/TrJk981e5+/IZMmS3W1vvPGGPvnJT+p3/+7frXa7/b71flmW6R/7x/4x3dzc6Fd/9Vf12c9+9hs80vtnX0sG4J+W9Mck/X+zLPvl9773Z3X7n9rPZFn2L0v6oqQ/9OVuBKCEckCU3AElAGMymajVakUUEhnB8/PzAIrSbfFlv9+PexHxBpBLB0Ds9Bzp0AgMCcR2u61arabxeBzNyoiK89nLy0sVCoVoVOa1AxSU1uv1aObFvPg+tAkvqnWwU61WY96lUinoFtBZBoPBEZi/uLhQlmUh5Sgd9OiJtJfLZd3c3Ojp06fB8fZaBKLg+XoCxknUnfECjvna70VBLWAYUIt6DvPs9/v6whe+oHK5rLOzsyh+ZR+IehcKhWhQRVMzADn8/f1+H6o0l5eXUd/BmJw3D82r0+no7OxMi8VC19fXUcdQqVT08OEtVff58+exhrVaLYqxfd8A5FCOaNSGcXbYn1arddQJuNvt6qMf/WjsdbFY1IMHD1StVvX48WPt93t95jOfiag8e1Ov1yMjIynWrlqtBjiXboE1xbdO1+EcYp6JgIqGY4Q60Wg0indzt9sFdQ+no9Vqqd1uR0bGVaOoc+F84qiRMfHCYNa00+lIOjjZyZLdU3ttvyOTJUt2t33yk5/UX/7Lf1m9Xu+oTi5vxWJRP/iDP6g/8kf+iP7CX/gL+pEf+ZFv4Cjvp30tKkCflvR+RLw/8Cr3Irqab5DkIEJSRLSJ0OYVagAwHsXnZ0Tpc3MIEAI4BgwB0OkA68ougGMHLjgrAKt8FNQjqfnGTzgigGMvjHU+PwWWgDZ04HlWPtqfr3EAxHvxM+vKH/bAiz0Zl9ObXDPfHRfX8SdazP1YCy+w9iJi/u2NsHyNyQrk1Wq8ToT7sNeeDYBLjrPiheasMc+gXsFrI3AWXdUHagyF1d6jwdeBfUaliHm6GhJZMC/q5Syxj4yZDIEXDtMBmCg6jhJUJG+Iwz383NzFq/V3kZ/7nvFzSbGfrCEOh+8vY/Lv+fz5GyeAObOu3hzNHZVkye6bvc7fkcmSJbvbCA7eJUGeN3AOv7+/1e1edAKuVqv62Mc+pmfPnkW0kah5s9k84qZ799xCoaCzszNlWaZ6va79/rbBEhrkFDISLR4MBqrX6xFBJAL/xhtvaL/fR4dZp+osFgu9ePFCm80mIvBEIR1Ec7/hcHgEZgCTTgehA7GkI611otYoBF1cXAQtZrlcBr+cCPP19bXeeeedIxoTf1OgSfSXGgaixJvNRldXV6rVanrjjTdC055o9Xq91mg0knRbXNNoNCKbgqOEUgz7QcQfcEbhNU6NOyDj8TgkIn2OhUJBDx48iIjzfr/X9fV1rKlzvzkXKMVQKDqfz/Xs2TPVarWIljN/aGFEqqGkAI49On1ychL3IutTLBb18Y9/XJ1OR5vNbZOwwWCg8XisXq8XNBwAOACY9fviF7+o7Xarj3zkI7FW1KKsVis1Gg01m00tFgvd3NyoUqmo3W6r0WhE/cJwOFS5XNYnPvEJ1Wq1UD/6jd/4jfg3/TJqtdpRZsidDklHDgDvFE4gzjDvkJ9zrs2yTO12W/v9XqPRKNYSNS/eAdabLA6ZIjIVOJI8h6wCHYkrlUqcTxwNL+hPlixZsmTJkn3ldi8cAKKsrlQiHZRQ8r/kXYnFFWmIKBJV9CJZgLDTVjzam4/WEiX28fBcIp5wr6WDvKXz/r2uQFJE1D1q6px4rmVecNMp/PVC0XK5HM4A4JjxeHTUKRL+LI/wMzevD/AxshcOFgGRgEGPsvMszDMhPhaMPSFrgQNIpJ8otkeHyWK40hMOkNcw+H7wGa8ryVNIWBfWybNSHoVH8QcAC7D29SEDwt/sUX5tcJb8TObX22sqaG7HOnlU3c8y82G+nlXyaL8X1PNz5uBnw7++a+0YH9F7dwg98/J+oN3Xxb/nMq4+n2TJkn3r2WKx0K//+q+rUCjozTfffF/VF0nq9/t6+vSp3nrrrfR/RrJkObsXDgAvNMAD6s/V1ZXm87m63a46nU5wfqkHgIoBuJcO4GU0GoW+OR1Znzx5otFoFHx9aBlPn95KMk+nU+12O7Xb7QB4KP845caLkYnaQ8XAMXFaBCCZKCzZAIqZ6/V6qAA5UHv+/LmKxeIR1cNBriS9+eabms/n0ayKz/JcsgasE+BxPp+HHOPNzc1RpJf1f+ONNyTdRpzH43GAWighZCqc+kKUnKZm0oFqRQSaiD/ZCK5FOhQwDVhHhhPwT00CykiLxSLqGNiTXq8XayopKEUUTbOPZCC80ByqVavVUrFYVLPZVL1e19nZWWQgCoWCrq+vNRwO9fDhQz1+/DjOCTUb0Lagw0jS+fl5gFo48ePxOFKYk8lEo9Eo9gOVKObabrf18Y9/XOVyWc+fP4+aEi8Md2lZIu0U27J3nFsabnF2WAcUuGazmcbj8VF9Dc4l1/N5MhU3Nzd69uyZfttv+21qNpsxPne6cNBxitwx5/nUa+AA8zmfZ7Jkyb617O2339af+BN/Qm+++aZ+9Ed/VL/v9/2+9732Z3/2Z/UX/sJfiHrAZMmSHexeOAAAVTTC8wo0eeDAHwcAAGMsH00EfDrXnPtD4+FvVz3xSL3r2Xuk3KP3ruCDMW4vpOU5zmUHWFEjwDNwfPJFpPkaCSKr7iR4FgMQ7pFs6E53jd/51hRGE4l2JyTf5yBv+TX5INqGZ2D4HNKl+XszJ5wuHC1XxGH+ns3wSLLfx/cr/xzUbFhDvweA26P5OJesm/cB8IxGPurOPcgmOJ0Mg0oGRS1fb3JXJD1vRNTzGTTWx8/L++2Zz8EzUPn9uevMM1cyJFzP2eJM+XuQz1okS5bsW89Wq5U+97nPaTqd6ktf+pIePHjwvtd+4Qtf0Gc/+9lUL5Qs2R12LxwA+Ohvvvlm8ISJ5i6XyyMZQ+kAUCgO9GixdJtBePDgQdQL7Pd7zedz9fv9iM6j/+6Sgui8u5KJy3BCDfEosMswOkUCAEvkFacFbfTBYKBnz57F3AA7jI11KBaLGg6H0ZXVnwXnXVIUi6JO1G63lWVZKAWhHjSfzzUajSJ70mw2gycPICNaC70FnXnvocC9z87OYs7SAcCyb0T3WRu49HC+ieh6wXGz2TyqaQCcA3RZWy8w9swJ3HzUnxwsUijUarUkHZwXP4vr9Tp08rk/XHYyFYVCQQ8fPtTFxUXUnuDk1Wq14N7jVHrdBRmjQqGg09PT+AXmNSNo5bNOi8VCrVZLm81GX/ziF1UoFPT2229rOp2GDn+1Wo2eEpw1VIHY82azKelQJI4KFe/R5eWlptNpqAdR80KkH1WkzWajfr8vSUe9HZbLpU5PT9VoNHRxcRHnzhu0UZPgRfP+LtLDgHmvVitVq9UIEDAn5GKTJUv2rWf9fl//7r/770Zt1F329OnTI/GDZMmSHexeOADOWXcKSr1eP4qEYkTxpQOA8wZNFP9Wq9UoWHXakCsM4Ug4GAX4uia/jzUf9Xf+Ncb4oJ4wN1e2ccUgdyr4AxUG0OR1Dfv9PqQXiXgDfAFpOE3uyBBJZ/7IiwLeAO/MgT/skasrSbfUknx0Pg/IPQLsDcf8T15Dnz1mHTyjQVSZveWZfJ6xuiPGdTgvPMOj+DhVPnafD/9mrGQEptNpOE2ehXB1JPaM+VLrQG8HV0SSdHQdjgTndzQaKcuykKQFNHvk/K5z6mfZx5V3Qr2egvXKU2882wSti3odnFxX6PJeFx71938zXt5J1pqaD86AZwfTL/dkyb41bbVa6Vd+5Ve+2cNI9k02grdQivOYzY2AEhjnW93uhQNQLpf1+PHj0NuHh000fjQaBdAtlUpaLBYaDoeq1WqhqEP0nmgoXwNgAFqACgCw0zcAHwANAJofGh8XKivSASzDvQcQUoPAPQFti8UiHAOoIejTz2az4F4TqW+1WhoMBprNZuHczOfzAIVE6SUdNSYjA4Fajyu4OMij2zH7kS/S3e/3R112iSbXajXNZrPo1ptXX2Jc7sxRvFsoFEL1hsyAdHCMJEX2BiUdAPd6vT6SU2UeAFG4/zwP/me+i+7z5881Go10cnIScrA4C65Jzx6zHq7yxL3guuNgjcdjPX36VJVKRY8ePYp6DrIEOGSsHWvr56FarardbqtQKOjZs2cql8vxn91kMjnKWHE274rSNxqNI7qPF9fy/qDCdH5+HvUPvpZ5x4jzQDaGouhGoxHjQVEKR6XZbKpQKITT6XQgzopnw6A5sebU3xSLxej4+O677361//UkS5YsWbIPsf3iL/6i/tV/9V/V7/k9v0d/8k/+SZ2cnNx53Xa71V/9q39Vf/fv/l195jOf+QaP8n7avXAAisVidAp15Rais0gzUhQIoHDeNtFrqATIPUJl4LNEqx0AedSRn+VpPV5/UCgUwoMksgmQ4f6AMSLxPAs5T69Z8OLaLMsCEBIdpckZEX7nvuflHLkvIKlSqSjLsqgjcGUbN1e/wWlwegUOkYNI5iUdF4aSYeH7eQ63U7iIFHskP19kikOAI+WdYimOZlw0j8pnJPzrfP3DeDwOeVj2ksizq03dlRnw7AHzJTqO7CjjJlOz2+2CAkR032seuNYzVbvdrc4/a8JnyYhAo3KnzsEzDoCbZ0Ioru52uwHgyax5czfn6ed7A/h5pIh+PB6HelW9Xg+nIV+T4Y61pHCQuG++dgDn8Supd0iWLFmyZL817d1339W7776rwWCgT33qU0Hvzdt6vdYv/dIv6W//7b/9jR3gPbZ75QAg+4m5zGK32w2QNpvNItI8nU6Djy4d04McHEynU41GowBrKPCsVitdX19H5N5Bx2w2OwJWrVbrqAmZ02dQDBqNRtrtDl2NAVeMl2j+YrHQYrGI8W63W/X7/SPuO87LYDDQYDAIlRwHQ3k5UQ4/tQlQPIhWQxfBAJbQkPb7ffD3UYoZj8dH4A6Kx2KxCLDb6/WOClidP09vAcAraw89yotcndLhHP8syzQYDGL/4KaXy+XoBNxsNo8Apmc8WH/qK4gkl8tlnZycRKdcb0AFJQWef75IHMeAdanVajo/P1eWZXr+/HkoJ223W11eXh4VtxNtZw1wJhiXdKBXPX36NM4OmQY3zm1eLrNcLodTw56yPpeXl3GNdND8B9CPx2NdXV3F+XfqGObzZr3J0Ewmk3i+U9W8Zsb3mDOAShPnA4eEdxgHLe+gJkuWLFmyb137zd/8Tf07/86/E3Vuedvtdvr0pz/9DR7V/bZ74wA0m00Nh8MACEgUzufzAHbtdjuaLREZhvMOZUE6RAid/+y0GyLc0i2Iubm5OQJAADGyChiShPycwkTvzAuNBYAKcHL+MrKffB+w6aDdo7/j8ThkO/O1Bw7IiPRKB7Dk0W6i+C6riPPiGQm43ijpsE55igbX81yXu3QuORFeX0M68kkvq/IQ8fVMgI+Llt/MZTweazabRXc/5ux1Be6AIWGK0wMtyKP/jIOmb/v9Xv1+/yXOuavgULdCwTlZGEkh7dnpdCLTxBoji8ofHJNyuazVaqXBYKBSqRSN0XyNWRcoUmREpEOGZrFYxLlkXQaDgTabTUTROSPc06VlpUNPDpfcdeqSq/1QE3FycqJutxvAHqpQ/tx6AflyuYxibzJEjUYjsgl3rXuyZMmSJfvWtqdPn+o//8//82/2MD5Udi8cAACaq8dIh6JZoqzz+fyIpuMggugnIPUumUV41a1W6yj6DHh1iorLO7p6yWKxONJ5Hw6HR0WwROCdOiIpgA9FwESeeR5zwFFgfhS2kJngXjg+TrXZbDYaDodBj0Cxxh0BL9501RnWiLmSGcGghOSjr6wRDguR9PF4rCzL1Ol0jmhN0i3Qo87BKSROUUE5xrnhODEe5c7LTzqILxQKarfb2u126vf7cYbYU18Txk4myLMnHnWWdFQk7VH85XKp6XQaBbuS1Gw2w5lhT50yBOCeTCZR68D+ENnHufXiZ+lAk2HMrAl7AYjnLLJeXkjtNRucPxxT1p/xkC1g/s7Tx4EmE8B96NlA1oeMBNkjwL5/lneJs5uvG8lflyxZsmTJkiV7Nbs3DoDrqDuFB945oOEuMMu18I5RGnGQCPAikwDgoCDTI4nwkQF3gDdAa6vVisZk4/E4AIpTcCggBWABIpHFJHsAtYRnuQEOiXbDSacGArlJB+1Ewk9OTkJCdLFYRFrMo/JwruH+83zGSgGpq8RIOlLtAcy6mgwR4EKhoAcPHoTDg/yjyzt6AS80Lu7XbDa12+00GAxifE5fcgfP6S+cIwpo1+t10Fl4Pvvvuv0UVvvP4cH7WfPu1ID/crmsyWSiwWAQz8fZ9KJsd9rcmZnNZhoMBtGAi30iO0b2xs8pGRAoal6cXi6XNZvNglaGw5pXJXIHAFoXGQUyKq4oREZCunV2C4VCvJfsH7ZerzWdTqMGBwof8rVOd8IBYb15h1w+l+wNBc5kuZIlS5YsWbJkr2b3wgHYbrehNAIoc9ABSM03yPJopxeaEr10UASI9ohlnnbjlBSnBPF9wApjlhTOAz8DJDPWRqMRnXdxCHBeXA4zD7L9Xh7B9SJP6ViZhqirz1E6yDbiCNVqtYhUU5iKI+JUIJfOhPLDuJfLZTTo8voJQCTrOxwOj54P8He6khfXQhdxmU7OA3/G47F2u110nqVAGCcNh2az2ej6+joyQh5dB7TizLD/UG0A5tSCeDEqZxDnhzVx0Eytg8u+SrfOA4o4XsjLWcGp8/PoRcm+H4wd54g95PrtdhvFxjzPi5Q900SWxaP50Nt8/pLCIWFerg4FFcm/x1kig+bvd6PROFL38bXjs2TBms1mOFNeMJwsWbJkyZIlezW7Fw7Aer3W9fV1gB7AAoB9Pp9HVF5SRJAdrLkSj3PoUSGhadV8PtdkMjniegNmoKtA6SGS6vKjAEOaItGcyeU1pQN//uTkRLVaTTc3NzE2gB9gCOfGVU1cbUc6bhLl1A2yEzgiACtve+4SigA7pDsbjYa63W7QRbgX+7Lf7wOQOh1juVxGjQPgEQdguVyGM3F1dRWZkUKhEFSXi4sL9Xq9I315AOx0Oj3iyQPecSKurq40nU5DBtKLTUejUazfcrnU8+fPj84Ea4pKlFNiiII/ePAglKfcucsXXAPufQ4ujekgnrqPy8vLo8Zs0+k0uPjUt3DuPQLvc+v1epHt2u12qtfrqlarUefidBqaZ3mxrwNnHFLqWHAO6vW6ut2uhsOhRqNRrEetVtODBw+itkA6ZAKI6uOYcQ78DwpYOH3tdlv7/V7Pnj2LGh2cP2/y12w2dXZ2psFgEHtKdjBZsmTJkiVL9mp2LxwAj/Tv9/soEAUoIA/qwJc/AGAihq5243x6gKUXUPJMj+wCgOEmU/jokWJJQduRjpsZAbQAz1CBvLg5L5lI5JmIM/dDcQhQSQddd3agiTAnQKODVcYNqKYg1ItGPaLqtQfSgZ4B9YWvS6VSRJz9HuwZ6+H7wveZL44YIDUPTvO1HkSHcXSc/sLe8llXEXKqkgNznBfn6fu/fS3z6lIUtub3z4195zOM3aVhOU/5Hg1er0GPC9aX88oaELnPZ8I8i+Jr6dF1xuf0KkA4n4FaxDuDU8f988XRnHGcBJwaB+w8G2cJh8rlYb2+wRWd+Ds5AMmSJUuWLNmr271wAACbo9FIm81GFxcXajQaoe4CUHXNdLqn3tzcBEimiZZTV+BNLxaLUNIBsAH0oThwPYDp5ubmSHJUUvCPUXUBdLrOu6vpoAZDdNmVXuhTgFIKYJRMA9FgZCqvrq5CKcnrHHhesViMGgOKV11WkeZM0+lUrVZLFxcX4YgwLp7LvSQdRY83m4263a5qtVrIk/J8HK1SqRTNqxws73Y7NZtNtdvtiOxC/ahUKmq325IOnHMaYZ2enirLMr377rtaLBZqtVoROfZMB2AVMOlODeCSfWAvZ7NZ3ANnwoE+TsN+v4/GW+zxcDjUarWKJnbuAJFtITPCZ3q93lHRMjz2yWSizWajTqdzNB/p4PScnp4GXY4eE4wDRxWJW1cD4rxRhAu4Z86AewfUDrK9aRmytoVCQY8ePVKhUIh9Ycw4Y7PZTMPhMLIJrCcOnzsdOLuMrdFohANK7YufZeqGnDKXLFmyZMmSJfvK7F44AIBfj+oSbc7zzwEy/lkUWjwaSMTXswDSoWsphcUecQbM5wG2g0Iv+N3vD5r5qBVxHZQKnBFAH0AbhwPARJ2Dq7XAZSdzQYEmlBMHUJ7F4Hnw1B2U8scjxvlIvPPt3RzoA7YB0+yd69HfBc4cXPse3MXn5vvuZJF1yKsCOVj2Z1Bonc/4YDh90E3880S6AfIOkMkqwL3PR+CdvgNlyiVf76qzALw7ZYt5OGWNcZB98XoEsmIUp3t9jHSoXWHfGCe8en+f/AwCuBlzvhkf7yfrllfB4j3xebtT4dk9/g/g3fB3DWfI1zZZsmTJkiVL9mp2LxwAou3wlQeDgbbbrbrdbhT+oeAyHo8jMpyP1LrCCp1/0Z/nazTRnV8sKYpLUTVBktSLbmneRRYBzjUKJ2Qgdrudbm5utFqtohMwlAY48OVyOZqIjUajAJHSAeCNx+NoyMQ6eYFno9EIVRUaLVH0CbCiWRZjd543tQcudUpU3otOHWR7cSbZBq/JQG3oLmDmsqR0m807JYBZny+yk5VKRY1GQ1dXVxqPx+r1esGlr1QqcUac5kITL/jy0J+wer0e2QooUjgYAFz2H2eCcwEFZre7lYNlHqj/MB6i4ewtc8OKxWI0uqMIeDQaRZaJgnaUhJAW/cIXvqDFYqFer6dGoxH37vf7urm5Ubfb1aNHj6KOgTNeKpXU7XZVLpejhuaLX/xiZCF872q1Wty72WxquVxqMBjEGSbDhloWc+PftVotMmb+vuLoQIUim0G0n+yB90OYz+eqVqs6OTnRarWKeopkyZIlS5Ys2avZvXAAMICW85gxIp35SL9/Nl8067KHko4iqn5/IonIUnqk1Tn2ziF3eocrE+XNI+nOlfci3vcbF9/3KG++SJjPe9TaaRoALFfiyTdj8jXKc88lHUWG+Txg3Y3orc+FMeFsOc+cCLsDfrIhTgfxe/vYPLuTzzawDuyhZ4F8njhK3NvPTb4eALCbV6DJ8/Lzc5cOZym/1r4/XlPiZ5fvExVnHNzPZVh9nVxBKW+cAefXe/+D/LllXnyO73v9C+/BXVkd/u3qTl5/g+Pq++RzvGu98+93smTJkiVLluwrs3vhAKD4AT8bvjCgBDoNqjtESSUdAZw8952aAUkRifVGYNVqVYvFIiK60HLIRBClhHvcbDYjWu4R+M1mo8lkokKhEM29ms1mFA9D/9hsbhtRIXMJiCGLcH5+Lumgf+6dd5G6RO6yUChoPB7rxYsXsY6Ayf3+touyJJ2dnR3RStDobzabOj8/j8iq01OQ8MTIojBeorlOqwFs0qwNShR7AqhzZ2W/38e+b7fbUJuhQy9ZAq8jYA/JIsAdzxcwS8eOi0fyJWk8Hmu5XIbST6vVUqvVigwRz99sNur3+yqXy/rYxz4WylKLxUKDwSD2zmsPyHQw11KppNPT08gUbLfbo8g3mRCi32Sh2BO0/pHKJZtAhoeeEjgSnElJevHiRZxpr+vgHFJvQvSfrAp7N5vNNBqNgsfPO8Pz/d3jzHBfV9GSFH0ZOCfb7fYoc+WF22SuyKKQ/dntdpEhTPz/ZMmSJUuW7Kuze+MA5NV9AAMOFiW99Evfo/ge6c1HUb1AMx9FdwDp2Ye8CgwAKy/5mY+Eu4JK3phbvgbBI+GsgUdj3QCa0I2wfMMqf77z8u8aWz4ynY+K4/R4tNg52w4A83vGWJyTTrQfrXyP6t4V1fd74ZzlI9EfFIHPZyZ83z0azR+/hwN27+TMuPLPuKt2wutRpENnZc9y5M+gvw+emfI996yLO3G8A8yFZ+ef4eYZBH92/nkUGHNPzrNnrHzeXJvviSAdZ7n8/eXfTpXi7NFFOGUAkiVLlixZsq/O7pUDQNHhdDrVfr8/UmwpFAoRjUSpxxVZnIOeZVko+ACwqtWqGo1GqKjU6/WIIJ+fn78EiIg8brdb9Xo9tdttDYfDiLh7EWixWNTJyUnMg4wAijVEaL3bKfeqVqs6OzuLwkdJwa0+OztToVDQ9fW15vN58MLPz8/VaDSiyywa8USRS6WSHj9+HNKd3nFXugXQw+FQk8kkwHSeisQ8pINjQaaE7Ik3ZyJrgIM0n8+PijSZOxHy0WgU2YJ6vR70IOaVZVlkMYg40yfh9PRU0q2Czng8jkxPr9cLZSOyFnnOPRmhk5MTlUqlyC6RCSHr4uvRarWixkA6yLbO5/MjmpUrOxFx56xcX19HDUKxWAzN/36/H5/hXOMo0X8BB4mzTJfnq6srrVaro5oEms5dXV3FWjabTZ2enmqxWOitt96SdNtLwGlDJycncdaeP3+udrutVqul/X4f79RwOFShUDhSKioWizo/P1epVNI777wT2SXWjP3GkUCy1AvevQkYZ38+n0fDNDo0X15exnvp2ZxkyZIlS5Ys2avZvfkN6tFcFFUAA0QEnY8NGAMUAdagyLiKCtcApNbrdTgOcKo9kosiEeAQcArYlg78bVdIyavaOJfdC4ErlUrQfABJzscGqCPH6RFW7/BLNNajpMhPAlKXy2WM3ylSLr/pWQZXT/Lob74ugDnjPHgU2jMFHuH16LJHbz1yDR2EwlVfc+g+AG7WkCZT0G08G5Q3rw2gFwBrzz0wLwB3frur1vCHZ5Ed8WyO39sBPucxr2xFUS1n1P949sP5/n7mcF54DnPl7NyVTcPZuLq6imJbp1UxB+hEzIv9Yky73XEHZNYOyVyKwDE/D2Ra8rUkrOVyuQz1IT9vyZIlS5YsWbJXs3vjAEg6AkkAzFqtFtF0urRKB3CWp4IQaXV9eIAlYJpoa6Fwq1s/GAxULBZ1cXERHVZXq5Wur68jSg5w63a7mk6n0ZW10+nEfTabTfCTW61WKPRMp9MAbNAXPDtAJBPAPBgMAsgBzDqdTnDTZ7OZ3nnnHVUqlYjAw4uXDv0HAPuSgmvujpRHvguFW133crkcSj7w2F3dhsj3ZDI52js6w7Je7Xb7iK+NkRnwbreuWOR7haEH7wXPAEeizQDBy8vLANVYoXDobgwA9QZv+/1tx9wHDx5EvwhqTwCvXsBNd2mi2ABz5kPmhXGwl1mWRQ8EePLUtdDrgue4c0UmpNfrSbqtXwAwe13MZDJRv99XoVDQkydPjmonnj17pkKhoI9+9KPh9GZZFtkDuvdyxp0yxBqhVMUz2EPOGs4n68ucmYufj4uLiyhWl25rZorFYmR1cJpxPAqFgnq9XlzvykHJkiVLlixZsleze+UAADaI+jkQpPiz0WhEJFE6UDwAZ94ITDrmfDun3p0GIpsAOL6mgBdQSLRzPp9HZsB50lB/druder1eNCvzvgJOf/DuqK64gmRlvosqQBrZTYqlGbuDVIpIcXQ8cgzw8qZkFEe7tn1eJpVIMvOHeuP685PJRLVaTScnJ3dG4VmHarUaMqLsT75/AGuD40KhqnPMyaxQEwE49jPB3P35nBHWHaoMnHWoU5wV585TOO5zc0eB57iCDRQe9oB5kMXJdwbm7OP0QI/CqeL+APFqtarBYKDxeKxGo6FutxvRcv6G7sY7BVDH4UASlcwR8/f1JIPmdSQ4kK7Zn1c8ImsHmG82m0Hr44xDg5vP51GIDyUry7LoiM1aedO9ZMmSJUuWLNlXbvfCAcjTQgC8m81Gw+FQs9ksgAr0EqKLHj0HpBYKhYioOuBDI57I4mAwkKTozorKCsDm/Pw8IraujV+pVPTkyZOjYlBA6OPHjwOAT6fTUJMpFosBnIjW4+wQRYYexM9ns1lETAGE/Axn4unTp/F85umykAArxgTo9zXB0eDv8XgsSQGGiWoD8sgSEOmG3+9Amyju+fl5gEJXIcK8A62DufV6HVkG7k9mYjabHdFZGBuUHi+G9r+ZC+cEB4dIOk4R58qzKpLizOWLermXdFBKcgUin4NnL5gn6kZkKnguWQT6D1DX4BkR6bYWgnfEnT1Ujsikkd1iboXCbZO55XIZUX/GMp1ONZ1OY19xVjjPULEKhUJkdTjDrKkXjJNNYu7u2LNekmIP2Dd38D1b4PNPlixZsmTJkr2a3QsHQNKRAwCwBuRBJ8jrgTtNBIBHBLndbqterwedB0BO4S+gpFwuR7T65uYm6DvQbihYddBaq9WiEBLg41KHm81Gb7/9djRpcslS+NJOi3GuOZKR2+02Gj3h1EAZArzTLKlWqwWA8/E4B9uj/V7oCjAjOuuZh1ardQT0vIjYo9Ne6+BR4Wq1qgcPHqhSqWg6nR412HL9e6f9OE8ewMsYiE77HJyXDy3EAaWkozoCLwrHySRy7XUJPBNnk/swP1fJyasVeVGrF017nQDzpdjVHTen3gCq2W/OARH17XYbIJ7zz72hmrGXUH52u11kE5A9nc1mL71zZHNcBctrANgvOlVT+Mx5cCfAC9G5j2cUcAQ8wyMdsjxe++Lvb1IBSpYsWbJkyV7d7oUDQHSPSDFAx0GnpKNoJPQEPkf01znXy+XySHe/0+kcRZy5J0CWgllkDuH1oy4EWNzv9xHdJ6LsBaRw8gFOjNGLHakNcLoMYBxwRKQWsEohMMDX6ySazWbUM0gHSgr/xkGZTCYB9rxAFDBcKBTCmcjLmQLipUNzLxwMshvdbjeAnVM40PjHscIBINKNeTErjod3bXZHzxtiTSaTyKBAeZF0dF6gA7H33muBsbFu+eJapyp58birV7E3cN6JpFerVV1cXBzRuKBRUTzu7wJA1zMjZED8rHPeWq2W6vV6gOftdqubmxttNht1u93IKvEZ6gpYR5zK5XIZFLDxeBzzYXxQozj7dFD27BiOOs4S5455kpEic8V7w1ljrDgrZNY4874/XryeLFmyZMmSJfvK7d44AKj6ZFkW+vb1ej2isNKxPKUXg5ZKpYiKOoDEAZhOp9HoKR+1BcAAGh3M0yzq7OxM9Xo9JBcBS4Bzxu/KQ7VaLUA9QMaLiZfLpWazmSqVitrttlarlcbjcUSMXamGdQBkupILjaCazWZEzV2JBfAImJ7NZhoMBrEeHrVHlcgLm5mLdEu9wZkBBPv8i8Wi2u32EYh1B2C9Xqvdbsdn2Cd32gCP0J1capUsBvsGaFwsFkf0ECLwHqXGuXLATTbBC4Yp3vbsAsCT8+nZHmQ3PXJNlHwymQQQPj8/P8reXF9fh/ynF79D7WLO7qh6kTTXAppd8rXf72swGESjOjJq7BO1GoyVNVqtVmo2m0EPYq3Za9Z1OBweOY5+pt2ZcYUkP8/L5VLPnj2Ls+WOaqPRiDVdrVbhALBGvu9ec5EsWbJkyZIl+8rtXjgAeR61c47vygJIBx4wAJYoP4ADKU/oHoAeV1dBVafdbqtcLgfwA2Q3m0212+2g7HgkWDoU47qMpUsxSofaAB87vHW+P51OwwmBrw+dxQtl+TwA0KPUdKcF6DMnDPBcqVTU6/WOClt9XBj3xSFiDETdPQPglBN3onwviUzDMWcfoGd5ES3AFiCaH9tyudRyuQwA6g4dz6VglDlCefFaA7IMXoCLhj9j8FoNngEnXzr0i8CZgx8v3YLbBw8eqFwu6/nz53HWvOjb1W74Hs4K54S6CrIaLotJ4bvTfyqVStDYcEygGHk0n/uzb+wdWajz83PVajW12+1QLULJiH0nC8XXZIN4h+jqzFmE7sTeMk7+H/DMG/8HIJtLnQTvGo5ksmTJkiVLluzV7N44AKiQeMGt836lQ9QXaowXBcN1psETEX2un0wmGo/HQZdZLpfBn6cpEo7C06dPtV6vdXFxoW63q/F4HFKMACYADnSIVqsl6dBxGADjTaScTkFEV5KGw+FR1JrroFgQYWYN/A/Pur6+1n6/j6j06elpKOPgqOx2OzWbzZAyxWnyfZAO4BCaFZFggBr3cspGo9EIeVSeJymAH4CWyDONsKBC4ew4/QXeOlkN1p6sDmN2Osh8Po+6jvV6rcFgoP1+H9kf9gVKULPZDBUdKD2cA5e09GdRAD0cDqO2ZDwe6/T0NJzI9XqtVquldrut6XSqL3zhCyoUCvrYxz72ksY+Z55xkTECKEPvYc44BJyL0Wik5XKpk5OTmE+tVosMmKTIClG7An+ePR0Oh8HF5x35yEc+Eg7KaDTS9fW1CoVCNKjDKEommj8ej6MoeTabRbYJSlSWZQH8efd4b8i68TWZoEKhEO8z++29E5IlS5YsWbJkX7ndCwdA0kvUnrxkpmcCcBiI/sHtlg4AGeBJ9LlarQZoIQpJ9JjOotJB7pJoJCotFOMC2JzOAV/ZCzC5l4NNV4YBkDlP2nsaSApaDZ+F/uG0FbIZZDwYI18TTWVdoQZJCkUj1oY/AM08BSSfMeAaHAIoG/lrAc7UTjAedxIcnGPsu1OFUPLh+vF4HOCda6RDzwDPxADmcTB5hhvOHeCVLIhneXAIGC8glc7V1LIQnYcag3PHmWb9/Ex4ETTjoUCb9fKCZ+aAs0JdCVx71gnAz1w8M8D7h/MDpcjPsGe/iMRjfg/WCCddUqwLGQd/13CMOUM4rI1GI+prBoNBZHx4XxlvsmTJkiVLluzV7V78BgWwU3iIwe32glZoG0glOvUBwAO1AMA5n88jMjoYDHR1dXWURbi+vpZ0UOOhSHW73WoymWg4HGoymUQkE1WX9Xqt8XgcmQoUfNBslxTNxDDGSWSX8RK9B/R6VoNoKJSXwWCgm5sbdTodnZ+fR2Mu1rFYvO2SCz/eC4dRRxoMBtGgDBoMdQ5kE+Dge/EyzhZAl2gttB4yIR7llg51BJeXl9HYbbFYqFqtqtVqHYFUHCvnxZMh2e/36nQ6qtVq+vznP6/nz5/r4cOHarfbsQ4Axbv6RQAmcSI4Rw7IoYddXV0dKQbhpE6n0yPFIAB3u91Wq9UKpZ31eq3r6+sjhZ98vYP/zbNZYxwGiq+RqWV9Hzx4EP0UFouFLi8v1e/3dXJyctT/YbFYaDgcql6v6+TkJOo7cA4l6ezsLBSyvMjY1x8nDu1+si4UFOM0UBtSrVbV7XaPzkt+X/kszdVYj4uLCz18+FBPnz7Vl770pVCBIquDHGnqA5AsWbJkyZK9un3NDkCWZUVJvyjpnf1+//1Zln1M0l+XdCbplyT9sf1+v/qge0iHyD3/dlqHc4wdkOQ563nlHv4APon6Ao68sFNScL3hp0sH6kSz2QwA6ON1ioukiLz7XJwHj5NB1JgIq48vH5X2+oK83KPTovLXMjeX28SxIGrs+vUeGYcXzjOYO9Ff5957/QLPZRwUd3sGw+sIPMLM+jMup0zBBWf/WD+v0QDQu5qPg1nmzLnB2cJp8nXA0fS5eC0ADgZgnfvSeAunCAfE1wrzzEp+L5zi4rQ36RBpZ20ZL/P1DAzrwDvkZ9Cj6J65wYHJ35P3j3u74+SGs+jX4dQyJr/W7+Nr7VQzf1eZf7JkHwZ7Xb8jkyVLlux12uvIAPyQpM9I6rz39f9Z0o/u9/u/nmXZ/1XSvyzpL3/QDaD0ADIAK9AG6vW6qtVqdHqF2pEH4KjNSLe1AHDTC4WCRqORhsOhCoVCyG5C3yBaTRQd3v5kMtF6vdbZ2ZlqtZouLy91c3NzpIN+enqq9Xqt2Wx29HxAU71eV7PZjCwC4IViXBpeEeGWDpKJADUi8UTti8Wier1eKKbAcb8LkKEcgwNzfX2tm5sbSYroLJF9nAvAPnMB+FE3AEgjWkw0GcBIhsTBJBQpsg3efwCgt9vtoscCUWpXMWo0GkeqM48fP9bDhw9D6rVSqejBgwdxpvb7fXSOvry81GazibqCwWAQFCi65jJOuj6fnp4eRemZO3UmRPeh/6zXa7148SLWnj4N1CRA31qv19EcjnnjhNB1ul6vq9vtBmAnY+Rz6/f7R8XD1Hc4+CZSLikyNi4f2+12gx4FBx/q03a7Va/X04MHD0LZyGlVrAuOA1Qv6Eo4Q6hN4dCRVdntdkf1LZJij2ezWdQvULfA+SLy745BsmT32L7m35HJkiVL9rrta/rtmWXZm5L+oKSffO/rTNL/StLfeO+S/1TS//bL3cdVY5wfnOf9+/WYR63zRaxkC7yIFXCKZrw3pJIOtAdJR5F1V71xJRnnhjMe57J7hN651VBEKPokauw0EMwLovN8a38O0V7nRjstinm6koqvmRdeMl53zDCnXAHQ/RnMz6k7vnfsra+N00QAo6wHc/doMVFlACBj53qAdl42lXl5LQTXkT1yAJ1XqPJx+Xxd2cfXnvX3OfJcz3D4+c0D27xjx7qi3AP4dlUm/yzF2lCKOEOuIuXP53P+/vi55jOcIdaBMblUK2vg2TfodX5fSUfniAwAz2QOrKFT05Ilu6/2un5HJkuWLNnrtq81A/Bjkv6UpPZ7X59JGuz3e0J6b0t648vdZLVa6fnz59HpFkDsgIpGQ0QBURVBmYTIIUaBIQ2o2u22Hjx48JKTAABx/jFcdKKko9FIl5eXqtVqevTo0RFtxcck3SqzELGVDvSHZrMZnYnn87mGw6HG43EAVugr0oFOhHJNv9+PgmgalRHZd342UVqn+QCoiBRDkUG60sEiPO56va4sy15SeyEy3Ol0IhrtspHValWdTieKcxeLhfr9viSFvvxwODxqukVNBUYHWqf8QKNZLpdH2vLsA4W2y+VSg8HgqGjVgTvZHuejr1YrPX36NIC9078owiZaDT0IZaF6va5erxff98JcSUF7IWpdLpdDvvby8jIUd/I0MsA0fQT4fv7MsR/Idfpe+fxYS+7J2drtdnrx4oX2+31kudxBBuzf3NxosVhEnwrOOw3jOJeDwSCyLI1GI1S9OCes13w+1xe+8IWj4nV3NskMoIZUrVbVbrd1cnISa03mLO90JUt2z+zH9Bp+RyZLlizZ67avOgOQZdn3S3qx3+9/6av8/KeyLPvFLMt+0aP0DtA92uv8YYCORyn5rAMOABCAySOyzi3ns86fz0dc4Vu7iol0aHTFvVzxhvHluc/O5ydi6rx+jHsQWfU55M1VY+5aO1cSAmi9nzEW18j3tc9nM2xPjzIhvoa+LkTPAeP8IcKLcX+cNJwgL5z1mgzWygtqvdDV50y0nEyMZwmYg/PlnfvPWLgnzqZnNny9fN+ZK5/LA1iv7eBzZDTuisIzF8/q+HnIj8G/B9feu1h7RgdHy5W5yI7k6wh8LT1DkL+Hf+2ZrrvGxzh8Lvn/HzxTlizZfbLX+TvyNQ8tWbJkyb6mDMA/LekHsiz730iq6Zbf+OOSelmWld6LcLwp6Z27Przf739C0k9IUqfT2V9cXAQfuFqthpINHXfr9fpRcSj8ZwABmvb+NYBkt9sFB5+oPhQDePqo/wA2aPAEMIL/3O/3A4gAMomwFgq3Gunw5QFthUIh6AyotvB8+OPO1QZEMQeaZ3W7XTUajSg2RTt/v99rOBxqt9tpNBodOUzUD2BE4t/bg6MIM1QsMgOsuTsVFNU6xYaeC67fzrzPz8+P9gTgeHJyopOTk8iIsKZ0PZYU3Y+dmgOIlhTRcFSX3Bkk65HvRzCbzcJZYCycCbInHu1/P+fSncLRaKTZbKbT09OoCaG2gWZzAGRqUsgI0L0XZSNAdr1eV7vd1uXlpd5+++04p+VyWRcXFyoUCtE/otFoqNFoxHip52g0Gjo/P9dqtYr6E6L2RPjZG2o0JB1RlaiJIOuGQhLZJORGcci4F9kNOh1Tp8G7wB6R5cJBZK3oEzGZTEJ1aDKZaLlcRq8Jrx1Iluwe2mv7HZllWUp1JUuW7LXaV+0A7Pf7PyPpz0hSlmW/X9L/ab/f/9Esy/4fkv4F3aoc/EuS/s6XuxcgbD6fR3TYC2ExooIAHL4nHaK1RBhdd19SADKn2aDtTzTbudmAy81mE5FpLz4G0EH98IgnY3HVGCKtqLkQDeXrPHebsTtfG6DqnWJ9/h7N9QgvQPe9ffM9vFPVxWVNHWS5g5UHyXkVJPbGVWm8XqFSqaher78UMeZnnjF4vzOT5+n7GPk568jausSoX88+eM0CY/L18ywHDglZpjynP5+Juau2gMJrHAyyDTTzcgUpKEo4yDi9ZGlcRYqzl1ea4v1wBSnff3/PaEaWX2PWgPtQJM55dm6/Z24o/sZx4p75WhZJ0Yxss9kErc77IbizlyzZfbTX+TsyWbJkyV63fT36APywpL+eZdmfl/QPJP0nX+4DqOc4BxqAIt1G/mjWRUQasIQEJNFQwJFzubMsC6AFwKvVarq4uNBms9FwOIwMgEeH0Z2Hi415ISLPh3sO8GYOABuioTyvVquFJnuxWDziNPM5AOX5+XkUTpIlITMwHA5DBYio/F38+cePH6vZbGo6narf72uz2cQ9vauudJCE9HmQ3ajVaqFG5KCSzq7wvlGW4ToUh5y7vlqtor4iHwGWDtSf0WgUPHF+DoWH7wN03bIsC1Wc4XCo7XYbe9zv96NrMOtNdHs8Hks6di4lBcin1wF9Knq9ns7Pz8Nhyt5TuWEOnBPPpuBkoRLFGWJuo9FI0+lUy+VST548OSp8Rp7V60xooOVOIF2WGSfnulgsRpfpdrsde0jtAXOAi+/vnn+W7Br7xbuH00UjNBwRd9KohcgXHvOeom7V7/f14sULNRqNWHd6fKQMQLIPqb3y78hkyZIle932WhyA/X7/9yX9/ff+/TlJ/8SrfB4wSzQwH231aD2gRzpwhT1qDfUDEOtFsK6bXigU1Ol0tFgsdHNzcwRAPVqNI0Axpau+SIeGV4vF4ojLDyBmfgBAmmDhEGAuh5mXAaWAeDgcHtFsmDcZE49U54uSK5WKWq1WAELpUGyKhr8Dfq+JILPRaDTUbrc1m82imNT52mRT2CcaqfF8fuaAm467jMc/y56S7eF5zsn3MecNGhPF4GQkSqVSOGw4WZ7RgLbjhbCcG8ApVBaKshuNRnyec8Q5xIHy2hCi5PV6XZVKRaPRSPP5PApwcfI4p+6ouaIOzq3f05/hVC3G570Q/Ay6Ae75rGcoKED3Ghf/uTuwPI8sG8/Nd+pmT72xHNQhnAjoU3n6UrJk992+1t+RyZIlS/a67V50AiaahyKMFwV7YSjgDEDK5wDN0BukAz3DO6Lmi07JOnB/QA81AUT3eRbAzukZRHpxXvjbu8d6sWWxWAy1I+nlhmIOxB1MwRWH293r9Y7oKhhAlOg1vHZANso2ZEhQX6HZGUDTi4oBjvRFgKpFJsT3kboJutYCzC8vLyXdAr9er6fFYhHPOT8/jzqCWq2m8/PzUBLyImtvAOV0muFwqBcvXgRvnn2hNgIFGs4QThNdiFkjb+xFF1ui99yDDA7nq1gshlNFTcZisdBgMIjzwHOpXWEOOGDucHId4/GiaM6MdHCafS0A5JzvzWajy8vLl+hX/n54lo3Mghdrs2c4MA7mcR7IvvGu0eWZzBDPB9y70hTvFhkMDKe/Xq/r0aNHRxm/drsd58MLzZMlS5YsWbJkX5ndGwdgv99H9BOeMLx3QP1sNtNisQiw79xzj7Z7NBdQQnSZaC10Go8kupKLq5uQFeD7rlhDNBKA5BQkri2VSlEUnKdDOB+bbAb3YuxerAt1h0i8K7hICrAKiHRJS6hM3JsCYQpMT09PtVwuI7KNsUbz+fwoU+H1DESLAXkAwF6vpyw7NN6CivTixQuNx2O12211u11Np1NtNptoBEbkmeZZvsfOaS8UCprNZrq5uVGv14v5EvnH8YE+wppxtqCWQHXhnvzMlWiIuHMOO52OSqVSPMcLZSeTSYBV6QDSOa8UAfM1683fXuQKACfLg5PgoN/pT+7gDofDcGKYm79zrCORdpxkd8iQGMVh4P5Qcfr9fki0QmdbLBbh6PqZ4Z1k/pxXnBnMa21OTk6ijgD6EPdMdQDJkiVLlizZq9u9cAAAFXnagQPs7XYb0WHpwMv24lYvinUqDF1aUTpBbcelH6WDkpB3XHWA3mq1grvsgK1QKOj09DSex725P/Miu8FzpIPa0HK5PJKr9KJgih4BRSihSIpIqvPV4V47OMIhyKvZ4ODs97d672RAALH7/f5ovRx4UiPBmB20AhoB2/V6XfV6PeoC6CJMLwfpEOEeDAbhsHixbrfbjY7QdNP1LrZQRKQDX585cx6IuDebzdhXagFarVZQm/L0HwfLroTk53S9XgcYxvgcjhpODc4hz3DngH1kv3kGzyVyTsZiPp9rtVqF8+lFySg5Qdshi+Kymz5fd3hdDpS58Pzdbnekw18ul4/Wo9VqRT8AzNWIvFife3iGB+qXU4Wg411fX4cQQMoAJEuWLFmyZK9u98IBAJgQIQekeTQSrnW329Vms4niUooOvYBYOjgIKOfQmMr59XmuPzKFUESkg2qKdBu1PT09DXlHSUFpODk5OVKfoSmSZyiYy2KxOCoSHY/HAdo868BckEOVFOMbDodqNptRkOmADqeCtcU5YqzuLKAqQxQdis5ms9H19bV2u11kLDxa7bURjJf5uhNFZJeaCJwdJE2fP3+ufr+vZrMZMqxQSjwiXyqV1G631Wg0IksB5Yf9d7oYxaReF8F6I53ZbDaDe99qtaKQFwcN0IkjxX5whqBEdbvdkLuEp0/EHCCPA+AODRkAz1jxOZeY9boCV0dyR4wxuwNAtgnFJaeaufMmKbJNXnfj9R84ln6m/B1krzebjZrNZtSt4KTVarWgfHl9SV6liXEOh8N4xzBoRKxxsmTJkiVLluyrs3vhAEiKyD8RauflkxHAHETkKUAYYAnA6VF3b6oEFQRg5gW9AG+yBh59hK4CDQL6kBfvViqVAFr5YkkKNCUFYIJqA0iDTsO9PJLNOvBcv3c+KupZEi9kZs09gu4FwPC3PavBc/LcfygePI/PwBMHYNbr9ajxAMg1m80A5ER+HRwyfmoPAJ3QeQaDgSaTSYBO1tZBNUpPXh/B/FFTyrJM0+k0vgefHhoP+wFoPzs7OyqopZ7C6xNc896pYYBZVJ2gGLnjSgExaz0ej48yON7zAOqNg3QKt1lLb+LltCreI4qWyRgA0qFzUfAOMOezrkDkXH/GgUOyWCyO6gyKxeKRI8z7znuAshP/L7iKkmczkiVLlixZsmSvZvfCAfBiXknBHXa5RP87H8WWDoooXjxcLBZ1fX2t0WikarUa9yRSSfTaC2cB/g4Ge72e6vW6xuOxXrx4EZSPcrmsbrer1WqlFy9ehEMhHVROeI6Dd5wE1IB6vV5ESaHIEOWn4ReRUeYMmMNZAaQReZdelrEE2OMkuNNTqVTU7Xbj60KhoG63G9d5DYI3gIITP5lMIlrs4KxWq730tXRL84E3z/q22+0A7u6k4ATxDObG+ozHY43H45AqJUMEUPSGYNQJ4AwsFouI5I/H4ziLRJqbzWbQzkajUTS5yrJMjx8/1snJiV68eKHhcHik4JN3LFizbrcbhbKr1SooQ97kbbvdRoE2xrW8H14bwrozNsZBkTM1Ck5jQhGLOhCcYhwPAD/F2vV6Pc769fW1siwLGVucbAyny88Ye0lmgoweGSzeE2plarWaGo2GZrOZZrNZZKFwtmj+lhyAZMmSJUuW7NXtXjgALpsovdzcy6kLgB3n9bvlteCRv3TZTUAl0XPAkBffotAiKa5FwccVWyaTSURPubd0AFjScV+DPJUDYy6AUhwiVwSC1+1UDQxwNJ1Oj6QxnSoCsHeVIsAuKkA4IET5+bxLYQLuoP5QUMy92Uuvy2AtuRfOklNayPa4w8H6u+Rnfn3cyQBYc18v2uZ8AHad4w4VijXlrHDOvCAdZ5R1chlOIutklbgPa+Y0N+blDq1Tf7w4HNUdHFRJca59nXyPWVfeMRxAz/x4bQDP4x6VSiU6PDN3rwngOuoMeD5zYK6cERxIdwyh3fm7z7nk/SR75FKmPqdkyZIle932O3/n79R3fdd3ve/PR6ORfuEXfiH6xiRL9mGze+MAABD42oErkW3AY7VaDQ1/orYYwBMKQ6/Xk6Qo4B2NRrq+vg6OuQMtotQ0rQL0ER3udDo6Pz+PZlGLxUJPnz49otV4JkNSgGTm5UWYgDTA0uXl5ZEEKiAeStRgMNB4PI65AH4Y+2Kx0NXVlYrFot54442QMZUURcZQSnq9nrrdriaTifr9vlqtls7Ozo7UVRwQs6YA0NVqFaDZI/8OMCUdUYUkRQFvXrYVsIdDAtBzGg9j7/f7UUhMx9hisajlcqmbm5u4DhBbLpdD4vTp06cRYWa8jUYj7sX9ULGBquSUKORJqV/wfdztdrE/3kEamgw0Hi9S96JvJDRdjYr5dLtdzedzfelLX9J+v1ev1wt1I5eDzUvn+nmiWJci8VarpSzLolgXZxdKFtkVnEkcEc9CUf/CuUBe12sCyEzhTDitiWJ0d0T6/b4mk4l6vZ5OTk6iXwTn4a7Gb8mSJUv2uuyf/+f/ef3wD//w+wYZPvvZz+qP/tE/mhyAZB9auxcOAGA/X8DpIAnzSKvz3zH49fmIPPxlQEYesOYNwInaCsDDOcqSQts/T0VgbF4gm89wOC/b/8b4jI8ZEIdD5MDZHShvCuVKPUTrKdBkHmQEiAo7DYfMgyvWeOFqfv08Suxz9doNvkfmgrn4PFzRCZDtEWUHpYwZQO1UEZ7l5iozOCM84/2M9SQzkt9zL8Z2Pr6kWFu/Nm/5ffZCYqfDuVPlqkysgzsYPNMdAl8nrvH6FX/HsuxWwckLlj0rRWTeqVlIe3q9gtc2OLWJNc3vz12FwqwvTp30csYvWbJkyb5ay7JMH//4x/XkyRN9x3d8R9R/3WUXFxf6nu/5Hp2cnOgzn/lMKPMlS/ZhsXvjAKxWq1CKefvttzUajdRut4P7Doj2gkIHiwAgV7WBVuOa8rvdLqLnSD4CQFD/8UJXL3DlmRgOgUc0uR+a+yjXwHmHm418I8o8RMwBfy7LeXFxoXq9HkWyZ2dnuri40Gg00osXL0JeUVLwoim49BqJUqmkTqejTqej09PTyI4UCgdp0XK5rNPT05gv2Q+aUnmkm/XIA71isah2u31UfEoGAgeEezNnLyLF6SHKC9UEcNztdlWv1+P5SHhC5YFLDohkPfyMENn+tm/7Nj148EDT6TSi0d49F+C52+1ijTudzlG9iatW1et1rVaraIQGGCfCntff95oHaDZkVaARsX7IpZ6cnMSakYnabDbx/Ls6//L9wWAgSbH3rtzkzs90Oo3MxxtvvBH0MvbLs14UMlN7AD/faxQo/j07O4vn8M551scb9/V6vdg7aEPUDXgQIFmyZMlehxWLRf3xP/7H9cf+2B/7QPAvSW+88Yb+4l/8i3rnnXf0x//4H9cv/uIvfoNGmSzZ67F74QB46l86RN/5Ix03+JL0EsXE6UFEMr1w1YtxMe7tVBqcBNcn98gu5oW1d5nTW/IAliyFAyE+43/TsMyjq8zP5ROJEkPPuIsbTVEnDghFljhM8/k86gwoCqUY2XnXOEjMyXXyfUxep+Dr6LUI3izNn+F7wTp4NJy98wi2y2nmo8meXfC6A6LZPi4cMs/O5CP3ec6+R7B93X2sboB8SUc1JETS88/wLItnUHy9WEecVq+bYCxOp8pnANyJY228FgBqGPvp76bvFefQZWM5H1zL1zw3f1b9XXOqHJ/Lv5vJkiVL9josyzJ1u109fvz4y15bKpV0cXERwgvJkn3Y7F44AKVSSaenpwE2T09P1el0XuJQO7BYLBZqNBp68uSJttutvvjFL2qz2ej8/DxAsoOH1WoV/GvugfMAiIYSQyEpINALiCWFMgkKQk4jAgCdn59HtHw4HAa3HHOpU8Bjs9lUoVAITvsbb7wRzxuPxyqXyzo5OdFms9HTp0+VZVlkM+DEP3nyRFmWqd/vHwHHk5MTtVotdbtdNZtNnZ6e6smTJ+r1enrw4EFErcfjsT7/+c+r3+/r85//fCjkeFEu93VgRhSXzAc0k9PT0wCn+/0+lHTOz89DAaZYLGowGBx1rYXmU61WYy2vrq6CB+4UE/ZjuVyGkwe1CIrUcDjUbrcLvj8AF7Wl0Wik0WgUGSfO2Hq91nQ6VbFY1MXFRYBszwCs1+uoiSCCTZaHzIcrRJVKJT18+FDlclnD4TCUiDabjdrt9lHkybMkSM56ke1+v48MEXUP8/n8qK6FOXphLlkEZEv5LIB8Pp+HshTFwDc3NzEWMjHUVxQKBY3HYy2Xy6N+DRRcsy9kQDhLnjXYbrdqNBoqlW47Z6OS1W63o1Mz55Q6luQAJEuWLFmyZK9u98IBkA6RU+dBE0UHbLq5Kkze8tFSovpOA5IOkXYcBZ4H8Gcsbh7x9u85FYaoKY4BP8tHNl2lJx/5JUIOuIc+A10ICVPu4ff0cXvE17nlyI42Gg212+2gYOz3e9Xr9ZCN9Mi/R4Wlg+NE5Bxg6lr5zg/3ax2ke/2DO27cP89597V3ShJAk3XyqDF0Ht9vzorTxLzvBGPK15oAyvNnwM+xnzOvZ/F9wtHxfWFMfl64t1OC2CueeVeWwTMwTpfjWa505XU4+YyXZxGI/rMevgb83JWj7qqv8YwJ5ypf/+DKWfm9R5Up8f+TJUuW7LeeFQoFnZ6efmBmZTKZBNU22Vdn98IBWK/Xurq6Co12eNFe9NdqtTSfzzUej1UqlUKDfDQaabfbqdfrRUQXzXcANGA8DzBdgUU60CS47uzsTLVaTTc3N8EPp3C11+tpvV7H84miPnr0KDjckkJlhei4N4LyglpJMcbhcBjgBmCbVzpCdWUwGISqy2az0dXVVcwLvjTzo3su2Q+Uarzokyg5kVzpAA7henc6HdXr9aOoLMo1ZEo8MwCPnqwHNCXGS9dknzP7tN/v9ezZsxi/0308iwLXvVKpqNFo6OTk5Kh+AwPwttttlcvlUJKSFNmEwWAQ58IVhXAUbm5utNls9PDhw6BRod2PLKdLc5ZKpYiSA9bZo4985CN68OBBfO/q6kpvv/22Wq1WnGnPELEP0L0KhdtGXNPpNO7pjhrvAxQv5o1TRR8CwHar1YoCc+7HeavX68qyLOotyGiRbQLAj8djDQaDeF+oTfDzwBqR0aFPAvMiCzYcDnVzcxNOJZkQagM+qGg7WbJkyZJ9+Oz09FT//r//7+t3/a7f9b7X/O2//bf1Yz/2Yy9RfpN95XYvHID9fn+k5+/ZAOk4sg04hFJCp16XFQQoAOL4TD5K6xH7uwzevHOiGQ/AyKPPHsV0FRrXrr9LGYXx8W/WIs+J9zEwHygm/GyxWIRKEOviEqV3RY29VsKdJOYC2IYW5UWlmGcaiDwzZu8J4BkVork08XKw7dFrVKG8DoQx5aPR3AcKDs6gm9eY7HYHbX7mCvUFB9LPHvvDWvIzdyydGoX5Pvh1SGTyGRw6pwy5epJnBnBsoWl5toWz504tc3KlJKhbfM9pY35O2bt85sIzL55B83V3xSbPEvjeouLE+kANYm7uxOWpc8mSJUuW7MNvhcKtLPfZ2Zl+7+/9vfon/8l/8n2v/exnPxvS2PP5/Bs4yt86di8cAKL0dBSFcw7gAAi5cox32nXQAzAhCu/UDmQfq9VqdJD1gkn4/ICgd955R5JiXOi7A2jhkzu3GtBKt9nlchnjJksgKbqzAka9KBirVqsqFosRqYcX7UAZWgg68F5QDH9d0hFHnDmgTMQfvn727JkGg4Fubm40m81iLQFh8/k81lVSRG0xB7iAaMAh4LHdbqvZbAafm31rNpvqdDoBNHe7XVCgnCLjgNCLyLkXzgp7D4h+9OhR6PxTvOUqRI1G44hrD6/fi5SfPHkSSjdOV8N5YEzujJCxonbk/Pw8ukvT7RknodVqRZSe6P5ms4lOwKwhjgOa/pPJJM4pc2AcRNQ9CyPdRloajYYajcYRjcjpXO6QeNEuZ86fhUNdrVZjDk4B4xzxnnlDMs4sWQGyKg8fPow+ALzzKfKfLFmyZL+17MmTJ/rhH/5h/fbf/tv1nd/5nR947R/4A39AP/VTP6X//r//7/WX/tJfujPYl+yD7d44AIAwp7zkKTIAkru4y/moJUAQ8+g317gEIfQRgByAijE5DxvHwoso3QN1zXLnx+dpP0RNHdi6AdhwNMgiAOL4Oc4SRaeuKAOX36PSXAttiPGPx2NNp9NoOEZjp3wdBPKL+boN9syj4USp8/MCWDv452u+R9QYcO5ULeef52spWBPPanAd4Jg6ijyNiOwJRcAY9DLpFvC6lr3vnyv5+NqQSaKjNI22cE4oNCez4UXKnCnGw5o6jU267Z8xnU5f4k1y3j1bwb9rtdqR0+FOtnP1ed/cASBz5cpU/o5wbqmr8BoXHGHeH+ZDVsgzeDjT/v+Dr3uyZMmSvQ5zSfAPYgdgHmBK9rVbp9PRP/PP/DP6Hb/jd3zZaz/2sY/pYx/72BE2SPZqdi9WrVgsRiGq6657ZJXoKmo4bDqRS6dYSIqoPD+r1+s6Pz8PnXfu74DIlVPgfHukEdDJPZ2WgQF2UJ2p1WpHUqVexOrFn9AhXI1oPp/HPRxg8dzpdKrRaBQRXYClR9z5DwxQNhqN4nlOMZnP57q+vtZisdD19XX0ZViv18H9dyeLZzhX3qkfrKvTTdhb1gl1He9HgFIQY+eerLsXiuK8uSSly756A6l6vR6ccvovAMi9AZk7Mjgfk8kk1ivLsqhDwchaueoQ+0bknw7FqFuxrt1uV6enpwGAWT/uUS6XdXZ2FpkAHEeny0hSrVZTp9OJSPp8Pg/VJ6L4rCWR+/1+r36/H9mpWq0W2S6c28ViEfUF1BzghDp1STo0thuNRqHSRabBKVt8jYOz2+3C+WMs0Kzy/0cgT+tzT5YsWbLXYbvdTn/tr/01/c//8/+s7//+79f3f//3v++1T58+1V/5K39Fv/mbv6nf/M3f/AaOMlmy12P3wgEoFAoR/QS0AdCJEAIYAKOSQsXGC31xHohk1ut1VSoVNZtNtVotTSYTDQaDyCR4tPouLjfmUU/vZgrlxzn62+02CkUfPXoUICivnOLPB/Q493w4HB5JTOY5zxQhEz32yCrz8Uh1lt1KnPb7/Vg7GqvNZjNdX18fUaNwXmg2dRfH3b9GlpJ18ih1Xr0H4FcoFKKxE1F7AB7m/RuIOJdKpeCHS4dOu5wN3wvOl3QrqTqfz8NhYn29tsDVa9wBwNn04lMi0x6dZh7Qt6BblctlXVxcqFarBSg/PT2NBm9kctygS2GeISMCD81IUhTRQwVqNBpBmaFhnHPxkQflnOAA1Ot1VavVyDxQpMuaMLa82hFOxmg0OnLCPevgmSuP5uMA+P3ZQ6hZOMY4hMmSJUv2umy32+nTn/60Pv3pT+vJkyf6g3/wD74v3bDf7+unf/qn9dnPfvYbPMpkyV6P3QsHAOUb/8XvhX5QB2q1WiiA1Gq1oA05Px3VF6evlMvlUKvxgkIAqtcZQP3gufV6/aiAlSivF8A6BQlQRuEjc6BwmSZcnuUA3OOU8Nmzs7P4HhFjHCBqGsggeCGxpCPuP9cAViXp8vIyIvBEerk32Qa450SP3byI1Kki1ETQRZZ5o4LD2vre552DarUa93DZyeFwqEKhEJFuIu/OPW80GgFu30+OtFAohFOBA4SVy2V1u11NJhONx+Ojug3PPuTXggh5tVo9apoF4JYOlC7n4nMGXYUJR20wGMTcvFDXu+V6mppzwTW8Q5xx9oFo/H6/V6/Xi8wHjgD9F5CKRfXHaV2eMeOdIkvS6/XUarVifTwrw3MYGx2j89c1Gg3V63VNJpOoe0GqlgJvzxAkS5Ys2eu0v/f3/t4HCg1cXl7q6urqGziiZMler90LB2Cz2WgwGBwBHGgSRFKRWCQKC8UFoOdSldBLlstlRMdpSuQ0BABNrVaLSCfAE7lLj6BDO/FnuqqOA3AvfsSZgM8MdYIIOBFhQCoRbqLW0+k0pDan0+lLawOwdzCJIwTQ8uuQX9xut2o2m+r1epEFcGqQF/vmi5Q9IwJnksJUnAkcI6Q5KfKE0sN/rl43gJO03++j6JPibAA5zgfg3Ytt6Z7soJ4zli+ahnbl/PRKpaJOp3MERuG4A7zzqkA4CBQ2I7mJA+Dc9zyNybNWAGkcgMvLy6C5OWgGgHPGcLQ8Ms4zWCvG3Gg0jjIAqGmxHmTMuCdrylmGZuacfc4u70K321W1WtVisdBisYgicz8/nP1utxtOBGeNcTUaDU2n05A9pSi+UqmEY5qvm0mWLFmy12E/93M/p5/7uZ/7Zg8jWbKvm90LBwAQDC2DAtV86g1w7MWsTv1xa7fb0akUmgdgEVUXQIjzrp1igiOCCgrXO+j3bsIOpChmdWdDUoBvaBpe2ApIJTLvkWauA6ziULij4RQqrken3mk6PAf6Bg4GYLVSqcQcXC7T6TUu2+pZC8bVarVeUpABzLpKz10FxKwzakDdbjfWm+fmu8y6pCXm2QOMnwPA846kq9BAkXJeP9FzxsOeSgdHDWeBdXJVKpwfsiz0mACQ4+ThwOBIkJnwAmiyUIyfdXP1LIrC4dY7Dc2pO15D4fUOXuyNk4L0rteueDaL61zNC5oX94dSBrWo1WrFXrEGZL3ydTZ+3lPxXbJkyZIlS/bqdm8cgFarFYDv6dOnmkwm4RRIClALJcf16B2kAgouLi7UbDY1GAyCRuCA2yPQTlXByZAO0plIeea5zNB5AJIAXvjzDuC9+JVMg0elcR4kHQE55kjhM9QSlHIA2gBLr48AeFUqlaijICoLx5t7UlyJIgyUKxwF6bbHAgAZAMfa0+gJB+Lk5ESLxULvvPNOgEnWlWLT1WoVa4iT5tmE8/NzVSoVnZ2dxbPg8FPjQdQYJ4JzwdhRkGFdWSdkMck4sCcUmu/3+yg6pbcEa0skOl9vwD263a7Oz89DlUdSROMB96zbaDTSdrtVu92ODswUKjt1iIg49QRkhPL0KZwx1pqC53xGxMG/z4m19fcCByMvv+lOL5kel0NlfyqVShT5e+M6r814/Pix6vV6FP9Sd8I595oLd/iTA5AsWbJkyZK9ut0LBwDOPrxjIsVQIwDqq9Uq5DYBJU63cFDPfQHYDuAA3l5w6VQKwJFr6iPZ6DKcrqLD/V3v36lCREeJ2npmgPsAqADrzIHn1Wo19Xq9I5lMv9aj6A7epIM6EQCaNfSsgTtSfMY5535/l02VboEmGRMiwBTBspY4YawVUWjWzNfS15ZOvRSoenGzF5V6USxr6/vL33m5UDIJRKjzYDnvyOEAsg6eHeKezJN9Yt8bjYYkhQPltB+yQs7Z50ys1+sjNSLqRqB15TM2nB2e6xko1hXjvOD8EYUHgEPJ8jPJ+fDvs468G5414nrWk0weYx2NRuGskD1DmQhn2mttPOuRLFmyZMk+/DYajfTf/rf/rT73uc/pe77ne3R2dva+137uc5/TP/yH/1C/8Au/kOrBvkq7Fw7AZrPR1dVVRASJQBMFBOhMJpOQsXR+PAApy7JomuVFtXD8neZBRFXSUaTVC0jJGBCB7/f70SUWSoekuCdR9P1+HwAVINjr9SIqjbk8KBQKLwZ1h4Rofq/Xi7nxXAdzzDnvpEyn06NmX15Ay3OQG2VPoKZ0u93g7wPwcNKazaZGo5Gurq5CdpKXsVgs6vz8PIDucDiMZ5OhAGhCH8rTeCSFpCp0llarFdQRr4OA248z56Afh4zotstcUvvgFCDPfABIAd+u3uQA1AvFOaeA58lkomq1GpKno9Ho6LN8jaPnzixAno7JZ2dncb7o4bDZbFSv16MIG4dvsVgExcbPFk6HS7lCM6rX60dZOFSIcJDIiEwmE0mKcz2ZTLTb7dRqtY56KTBPziROhVPo3nrrLe33e52cnEQ2iTNPt8fxeHx0Xt35TZYsWbJkH25799139Wf+zJ/RkydP9J/9Z//ZBzoAf+/v/T39qT/1p46y1sleze6FAwAQIWJIVB9AxPedaiIdovbOyYfaAn8ZoMzPXb6S6Kp7j4BFlymEUpFXXHEQ4hmGu2oXPBI/n88DBAF4oci4Uo9HXH2O/jffB4w6rYhx5rMKTp8AUGHOdc+DZt+v/Nrnv85zyn0dfN0B01zj+8HPXH6S66D6oAjDzx385zMhd4F2n5OP2dWV8vcFNLuUKd/j3KFA5fUmAHIcDT8fztHnnFBP4Fkp9srPsteHeIYGh43CcJc65fM8E2cQihn779fgNEKj4rl5OVBX8fK19j3w8+J7z1llvF6wzP3INLC2yZIlS5bsw2+wQfr9vn7xF3/xpWy826/92q8FhTbZV2f3wgEoFm8bJKHiMxwOtVqt9PjxY52cnAQ9gGZey+UyIqz0AQAQXlxcqFKpRKSbw0FkXDrWqHeaAiCu0+lov99HFBQ5RPjVFM56zYHThbgPX2NEhl+8eKHT01O1Wi0tl0tdX1+rVqup2+2+FIFnXoDEvOqQz2Wz2ej6+jooS9JBDpToORFmuPFE8r1QlCzH6emp9vu9fuM3fkPz+Vzn5+ehEgPgAzB2Op2gOOHMrNdr3dzcREEta0fkH4Udz6ZwT5yYarWqR48eqVgs6q233jqqn7i4uFCv14smWXzegSYg1ouBvZA2D94BwS5Di0NKJgQlKp7Js1Cqurm50c3NTRSTA55Xq1Uo+3jRNM/07tL0q4Auw1mHGsTeI4tZq9V0eXmp4XCoR48e6fHjx0cFzThKOHO8N95crFQqRRM27z8AdWs0GoUDy7vGu5FlWXSWrtfrqtfrkSnAUcsrIbEu3v+ALAV1KTQIw5Ehq1AqldTr9V5ytpMlS5Ys2Yfb+v2+/tyf+3NHKox5m06nCfx/jXYvHIC8Eox/34Gca5E7RUM6VgZxnrCD/ny00Dnt/jyPWHskE7AtHbT9yRT49V4c65F61FLcoIlA/2EtJIU8KD8HqDFOzxTgzMDpBzC56gvRdCK2ADLPDniBMc9kTs4Hh0Z1l5qO/2GvoNFA0ZF0pAvv+8lL746UpChsZd75QlDnhHuknHXwaP1dwNFBtWcQMCRIcTLyNRf+TNaZ9cUB9Sh3/jxx/7xDyvO4nuyBy7P6eFgLqHL+bt2VEeH7jEM6rnvIr1E+w4L5urqzyfnwdfR3zp0z1sYzBNRYcNahMblTnyxZsmTJfmvYbrfT9fX1N3sYv+Utuw8qGq1Wa//d3/3docwC177T6YRmfrF42wjI9eU9eg+gAXS3Wq2g8lA/4GouDk6c0y0p1GgcwEsHcMLf1Wo1Or3Clwe40DyJz/X7/eBM73Y7NZtNtdvtiPiuVitdXV1FpLlcLkctBODw6dOn0S/BteS9CBn+Og2WWJ9utxsRap6HpCMcfKLxRFbJCNDV+OLiQvV6XV/60pd0dXWlj3zkI/rIRz4Saz+bzXR5eflSVqVUKgVvnQZko9FIs9nsaB6shdO8HLg2m02VSqXgvHsxKn8cHFar1eAQsj9EzylsHgwGQckiMzGfz9Vut/XkyZOjvZUOKkdOJcK54bl5etl6vVa/31eWZbq4uFCpVIrslEfxcQL3+1s9fnoKkEVxB4ZzioNGFoz+FXTNxWEgE4C5wywpng/NB4eJHg5kiADgUIDy71ypVArVrVarpXa7rfl8Hl2byZrwnlA/4HUvnAWUlrwonAZpZCiKxaJ+6Zd+6Zf2+/0nv9b/h5Ilu6+WZdk3/xd1smTJPqx25+/Ie5EBkF6OPBN9dkDlgNYj+x7FlxRRVwCKR7yd/pGPinqE1rnUgM08gPIMA9r5HvXOc84Ba06hkRSKJ9Ihms29WQciwCjzcD8+A3iijsAjqtzP18E55PmaAwAm9AzUmXAQuA+1EV7fwHoxd/YSgOoRZNbOo+Iu+8i8oBnl6SOcj3whLvP16DTzy3/f6zM8o3CXY8wYyVjkz69Hr9lT59f7ufB943OuAoWTks/keMrTKU2Yvzc4VMw779D6Zz1rhQOXd7K5N59jTOw3Z59zytfQljxTxTjyznc+c8O59zPrZzdZsmTJ7pv1er2g4d5ly+UyAjvJkn2z7F5kAJrN5v53/a7fpSdPnqhSqegLX/iCBoNB8M2d707xX6vVisgqQKtQKOjhw4eqVCoaj8darVY6OTlRq9WKRmDj8Th48vQQgJ+MshB8bM8EFAqFUKxxnj+g+/T09IjuQUSTRlw0/8K8k+l+v4+Iv6RQU/F573a76Glwfn6ui4sLDYdDXV1dqVKphMrQ+fm5drtd/OcCAMPpAMBSIOpGxPf09FTb7VZXV1fabrc6Pz9XrVYLhSWKMFutVqwtuvUOClerla6vrwPEZlkWlBTmzZoCWt3xaTab2u/3octPnwYcB8A/+0OkG4UnB9tcO5lMgqeOs+KUHM4Ye+xF0DyfqDWZCD9jy+UygD0AnMJ2zoo7D9B4OJ9w3snWMJ71eq3RaBSfQ7KT6Dnc+OVyqWazqWazeQSs80XkAPF+vx8qVXlnjgJ1MjWsGb04ULiiEOvs7Ez1el3T6TT2dLfbqdfr6eLiIt4/xu/GOL23AXvOHy+oJmAgSf/Vf/VfpQxAst/SljIAHx4rFov6t/6tf0s/8AM/8L7X/PIv/7L+3J/7c4nmkuwbZfc7A+AABRD9QdquroHvUVMi7IAX51oD4O6qM5BejgYDvjySn4+Gel8AgCjOgqsPQT3x+Tq4kxSqO66T77x2xgJ9CUoTVCenz1BA7KCZDArgjrEDUt05oGCUTAjPgIoDFcb16D06z5yYD2uOA+AgOB/Jd4UcXy8cKG84xX3R2ifrgDPHfgEemaPXHXgmhHl5FJyoPnuJcU7zdSR+dsmYOAee8+NnzeU5WRMyBx5593PqEX4/04yLqPxd2RCvGfFoP3vjZw8pUS9c5h6sGw6Mj5ViXhxH1oP98j3FIXCHbb/fx17zXvv483NKlixZsm+m1et1NRoNfdd3fZe+93u/932vKxRu5ZwJsKRMQLJvht0LBwAAf3NzE7/se73eS6AkTykhogqXHe1+gC5a5R5RXK1WAZQvLi6OxgEQIvLvkX4oGUSxG41GgDRJury8lHQoFs7TLRaLhabTqVqtVkS2N5tNAOLNZqOnT58e0UhQfSHS+uDBg1ChKZfLarfbevToUQDZ+XyuX//1X5ekqG9wByjLsqNmapKOAJ2rsQAwicLiKAD6i8WiGo2GarWaLi4uVK1WNR6P9fz58yMVF4Bwu90+kiZlrff7fWRfOp2OFouFhsNh7F2+NsPPBGtMfwFoSigr5dVnHEzP53PNZrOYA2eEceFEALKlg8wmxdw4NsvlUpPJJM4nSjknJyd68uSJ5vO5fuM3fkOSojYFcI46UKfT0dnZ2ZGDRh8K1JUooD4/Pw8VIrIVSOQiLTsej1Wv10Npajgcxrlg353mBsffnVivy/D1d+BPRg5H06lo7CvZGDJ3q9VKw+HwyHEiy1OtVo+yVTzHHebpdBo1AMkJSJYs2X2wer2uH/qhH9I/9U/9U/rdv/t3f+C1n/jEJ/TjP/7j+vVf/3X9h//hf6i33377GzTKZMkOdi8cAOkgt+i0GqepAK68sJeoc15BxcElUWGPrEKJAQzxee8bALDJq5xICulDoqyAVa8BIPKLESX1mgb+1Gq1KJQEBPl8AUDNZvMogl6tVtVqtWKum81tQ7VSqaQHDx68VNOAc7FarV5yUPJUEelYhck57FAyAHfNZlPdblfFYlFXV1exph75B9ixBh6FZ540wOLzAF5Xw/GoNesIPabZbEZEnP2jNsPn6Q6iP5Pz4bUQULOgAgHunc6FI4iDxH5n2a08Kl9Lh2ZYfM4LylutVkhp5j/jij+cj36/H5QfzzJw1ryDM9Kq7Dtz9NoX36/8GWC8nt3wbBhg3hWFyBzhQPC1v2tkW5B3dUfC63z4e7VaaTKZRP1JcgCSJUt2H6xUKul7vud7PpD6g52dnen7vu/79Nt+22/Tf/Qf/UffgNElS/ayfU0OQJZlPUk/Kel/IWkv6f8g6R9J+mlJ3ybpC5L+0H6/73+Z+xwBUO8eKilURwCJrpZCNqDb7UaE38EfoKHZbKrRaAQfW5Kur68ja4AR2XbpRKc0zGazaIYE6AJMSjpS/5EUkXD+ns/nR5F5IpqSdH5+frQuACEH77vdLjok1+v16JJ6dXWl3W4XhUdEz1kf6D6z2Sx6KqC6JN06Naj/eBZDUkSGkSDt9XrRMRbQXS6X1Ww2dXZ2pn6/r3fffTci25Iisu1qM4vFIvoDzGYzPX/+XIVCIbrITiYTFYtFnZ2dRWSadfD1aLfbarfbWiwW0XEXKhT1FPD4UUEiI0EPAcY+HA7V7/c1n881n8/jvJE92W630UfhLlqPU1xKpZLefvvtI1BPZgKw3W63dXJyotVqpRcvXsQ9vNCb7tJ8n72FC99oNFSv1zUajSI6DqcfyhYdglGiog8ADhJrSnYD55sui6y/KysNh8NQX3K6G04C78disdC77757lAGYz+dHzgSOXr6rY61WU71ej3cl3xcjFQMnu8/2un5HJkuWLNnrtq81A/Djkn52v9//C1mWVSQ1JP1ZSf/dfr//kSzL/rSkPy3ph7/cjTwqiUQl0f589DzPhyaqC4CRDlQLwFepVIrC4WKxGHKCgHnpAA5PTk5Uq9UCDLkjAGUDSkyr1Tri0wMYAUWMBwlFIsjSgdcOaH348KEKhUIUkvpz+bPdbjWbzTQajYIOlK8jkHTUAAogD51ouVyqXq/HtbvdLgpnnY/vfGwAIVzsVqsV0X+iyABrQBwFssViMdYaRSGyMzhxNMlqNpt6+PBh7KXzv9l77s1YcQCurq50c3MTGR6Kflerlfr929+vT548Ua1Wiz1+5513NJ/P1ev11Ov1Qo4V8Fuv13VychIOgKQA1owJ587rTCgIv7q6innu9/sA4Nyr2+2q1Wrp6upKo9EopDPZF94F31vOK04ajcCQz+12u0GPAeTX6/WgofkcnBLljiycfs47Tl6xWIxGebxbzA1H3p1mHIDJZKJWqxWOCJQ9Vwpyuhu0Jhyq/HmRDlmWZMnusb2235HJkiVL9jrtq3YAsizrSvpfSvpBSdrv9ytJqyzL/jlJv/+9y/5TSX9fX+Y/t2KxqF6vFwoteU4zqiIuoQkYgrpAwapHa5GlBHQ6vWOzudXMlw5gCBWU8XgcQC1v9CWAGuGRf0mRgYC/T8YgDwyZixcW39zcxNikWykx6g6IlgL84YG/ePEiIvOspXQb4WU8ALgsy0LJhUiySz9Kx1Fs7sn1ZFUodII/79FZABy0Eq8vIAI9m81eyvLU63U9fPhQksJBOjs7izXGeQL0eh0IWZFGo6Hf+Tt/59G1XmBLtmW5XMYccQz3+310nGb94dDTj4G5UlfC/QGmZIgAuO4QofbQ6XRUKBRCpcnVbur1urbbrUaj0dFZJQJOjwfvCMz6ctZxYvOKT9CXeF6/3w8HgOi+F6p7Ubc3j1utVnrrrbdiXSnUZZxebI3zxnsMRS/LMp2cnIRjLym6CUuHbACZvul0Gg4q7/xdhfXJkt0ne52/I5MlS5bsddvXkgH4mKRLSf+3LMt+j6RfkvRDkh7u9/un713zTNLDuz6cZdmnJH1KugWrvV4vWjsDONvtdijOSIemUoASV5sBUHY6nYg6bjYbnZ2dhVwiEUdJEeF1yg9dbl1Bx+UKsywLio0XJbpk5XQ6VZZlOj09VbVajXFRoIqDAFgDlAHQGFupVNLJyUlQZvb7va6uro5kQAeDQRQfe10ENBqPljNvQNRsNgt5R+oMdrtdFOM6N9wjyVAyoJ00m82IgMPbd26/rxVNtZxy4uOq1+uazWa6urpSuVyOIm1AO2Cx3W4HB7xYLGo0Gmk4HOrx48f67b/9t2s8Huudd96J+XixLrKxnU4nnB8cAKhVSL96jQPc9c1mE83DAO6AdXcElstlZCbYU84PYwZAO71tNpuF8+mFs2SsCoVCOIacSa91wQHw7+dVpbbbbTyj1+sdddn27I8rThGlp1g9yw6Nv/gDnQunk/Wl/gNHBAeADAn75NQonA7OVqfTiXFwxr2APVmye2iv7XdksmTJkr1u+1ocgJKk3yfpX9/v9/9jlmU/rttUZth+v9+/n37xfr//CUk/IUm9Xm8PUAYQQoUhgukFgYBJAIqkKIaFlgGggWqQBwtOHwCIEaUE/KBuwjj8uYyRPzgkgFQH9FBcAPYAV5eK5H5kPpgv4JdrcUD4PlkPOO+MEyAFAKbIGvCEgwGo5ftkGaRDJN55+5VKRdPpVIPBIHj8qDCR7QDc0wfAC4HZQ5eodClKAKs7XvyMyDeUFUDmZrMJ9Zubm5vIMJAdcgDMOtXrddVqtSO9etaSgmbA9Hw+j3PkxbheBO6FyUSn9/t9UJ+oryDSDfWG6wHHhUIhHFaXJ3U1LJw9KGqcJ5dqhZvP3Lk3mROn0Tjdi3VgTRgzjgDnEGeCM+3vFO8D71a+IByH12sAPNPghfxkQejx4IXGnKdkye6pvbbfke93TbJkyZJ9tfa1OABvS3p7v9//j+99/Td0+5/b8yzLHu/3+6dZlj2W9OLL3QhaBSAB6oHz9/N0GaKLHr0ELADm0bKHn+3UAiKmDkbywKtWq4XaDwCErAEgB4lD1GQAwNCLiLCPRiONRiOdnp6q3W4fSYgSlSeq/ejRI0nSu+++ewS2Hzx4oE6no/V6rcFgcFSU2u12tdlsQl6Re6N0xPWswdnZmc7OzjQejzUcDsPp2e1um4hBl6FQtVarReTZ+fyussNaEwlfr9d69uyZ9vu93nzzzQDf0HMkBYCGPoRD4QW2OFVnZ2eqVCp68eJFcNklRYH3drvV06dPwyEB+Do473Q6Qc3hM5w7HAkoP8yNrIWDUtbKn+XjYZ8uLy9VqVR0cXFxVGfhcpw4APP5XJ1OJwqnpduOkfmMFOcdhwunwKPmUJmIxnvROtf62t7lYPNMzmW73T7KUnmxLs6jOw0uOcu5w1FDwhQnjp8xN6cHsQ4UGuM4IXWbLNk9tdf2OzJZsmTJXrd91Q7Afr9/lmXZW1mWfWK/3/8jSX9A0q+99+dfkvQj7/39d76Ce0XaP0/t4Rc8AI4aAOnQLIhiSaLc2+02tPKdL05kFh4/EXSPiAIWHTxKB+lPDM60c6P5PnOSDlxqnAXoGS7R6fQdHBKPcALcAZ1egAqwIosBXcifLemI8869fP3cyXIQl6dMsabr9TrqALw4GQUdntXtdgO4AZa9wJu1IgLuUfS8LCU/o5jUI/M4ZvDGubbX60VmgHFIiuJsMjOcAahajIkz42eMKLkDVM4B++idkSn09rX34mwcHuoz8g3Z2EvqXuim65F7xssfouScE2+85Xx/xsz4eZZn4nBQBoPBUXaJmhkKyn28fJa15FyxhvycGhfWEieCn/PH/6/g8/7vZMnum73O35HJ7r9tNhv9D//D/6D9fq/v/u7v1sc+9rH3vfbq6kq/8Au/oN/8zd+M/8+TJftG29eqAvSvS/ov3lM3+Jyk/72kgqSfybLsX5b0RUl/6Cu92cnJiQqFgq6vrwNswptfLpehkOLcZiKJREOfPXum9Xqt7/zO7wwevsuGNhoNnZ2dBUh1vrMXFLsaCaoqqK0APOA9SzpSV4HGIB0Ad6vVioi504wA4thms1G/31epdNvoqNvtRrRXUkhnEvEHwEKP4Hl5bjQgzTnb3qyLuTFmKDcUYcJBXy6XGo1GKpfLOj091enpaUSYp9Opnj9/HuATmVLpVjKSXgfr9TrWUzr0gBgOh0dKSg6iJYVz9/jxYzWbTb3zzju6vr7WYrHQeDyO+gSKcFutlj760Y8GePWI9Xg81nQ6PSp+Rt6Tuoabm5uQR81nqRjzer1Wp9MJ/juUodFodKSyw55yRrrdbgBf5EaRyFwul5H5QWmKCPl6vda7776r7XYb5xvH0Ivlud4B+GKx0PPnzyVJp6enEd33s0ImjbOEgzGbzXR9fR2OYbVa1cnJSaxXsViMvfAzR9YAx4/3hHPKmiDH6s3GcCCoUcEpQh0qP/Zkye6hvdbfkcnur83nc/2lv/SX9B//x/+x/uJf/Isf6AB89rOf1b/5b/6bevfdd4/+z0yW7BtpX5MDsN/vf1nSJ+/40R94xftEFJl/exSbv4lYApA8UghobDQakT0g4ujKQpIiiusdYvm+pKPouht1ART1Sof6ASRK75obAM2jtU6zgPMOLcN1zj2i646K1xdwL9aC+/NzN6LcSJoSKXegjbE+zj1nXADg6XQaXWrJqDAez2IwTp+jc9w9GuzdaCUFn577OkecnxMh974GfJ+CVQe8vv6sW74hXJ4ixrn0OgpXIuI+vn6uksR1/Jvz4xHtPPedc+nrwT09o+DGufd1JjuCA+t7RAbJ61s4734OcApwcr1mwzMGvq7MaT6fH62RZ39cRYqfMzfPWvhZSg5Asg+Dva7fkck+HAaF9Vd/9Vf18z//8+973T/8h/9Q/X4/AnvfaDs/P9cnPvGJ96VQbjYb/aN/9I9CvS7Zb027N52AiWI7l9hBh4PT2WymL37xi6pUKnr8+HGotez3t91yoW4AUNF5JxpNcyinBgHisyzT+fm5CoVCRHyhQHA9VKXRaKR33nlHko6AGNcA2pxiAchZLpdHUqO1Wk2np6cBblBbyTdFon6BRmDtdjsyAfkaiSzLokYB0AyX++rqSv1+/4j+RJGw10BIh06vqO/0+/0A/t7QCToTxa9e2InKD9Fc1obPlstlnZ2dhZIQlBjWEmC+Wq1ChQdFmXa7HZF/L5aGliJJ3/7t367dbqfRaKTlcnmkew+4Bzhz32q1quVyqadPn4bDyP4CpjkbnA8cQ8b/8OFD7Xa3DdoAzKvVKuorcMLIPrijBdBnX6ANPX78OM4SDgl7Tu3MbDaL7FS1Wo31Pz8/jywXjnG5XNZwONRisYgzJulInUhS7At9AJiDvxvVajVqd3CkxuOxnj17FnUkfk8cCFSR2DeyDIPBQIPBQCcnJzo/Pw9aXqVS0enpaaoBSJYs2b2y7Xarn/zJn9R/+V/+l+97jfem+WbY93zP9+jHfuzHQlQkb5PJRP/Gv/Fv6Gd/9me/wSNL9o20e+EAOAj1qKSko6irpIiGQ7fg8x755XvSoVjYVU5wEDx66JFcIpXc0yOW3APA65QJH79f6zx//z7PzVN3/Hn5aLA7ETgt/nwH104zYj3JRvB55p1fc+bCz73o1edN1N+v5d44cu68+bzccSCCz309qu6SoZ794H44TTzXC2JxvMh6ACBxmJx+5evhe+UqPNIhq8K64XRAPWL8XhPgtDDmkV8b9tEdEvaMdeD5OFpc47Sd/DnyuZAVwbF0h8+zQL7evj/UZrD27PNms1Gz2Qy1KD8Dnr1yJ5lzw3x9P/NZGqe7YXdl6ZIlS5bsm23D4VDD4fCbPYyX7OTkRI8fP9Z3fMd36MmTJxEkyttkMtF3fud36ktf+pKePn36TXVWkn397F44AJVKRW+88YbeeecdLZfLkF+EF+zKPfCiafg0Ho+PpBkBML1eL3jtNLzyKDnKOfv9Prjm1Wo1+Nqr1UrdbveomJjoLdzvLMuCH84fgBWUE3jzgCcHOkR9UZ3h514UDX0C0EtNBCCWfzNG5C+J5ML79yjucDjUZrMJOg57cHZ2Fmu8Xq81Ho9VKBR0cnISc1mtVmq1Wjo5OYlItjtZd9FRUP3xgmsizk7ngK+OZny/33+poDnLMk0mExUKhdDohycOIGw0Gnr48GGA+WKxGMpLXlDt54GxU7dA4S77CgXFqUvsE9+bz+e6ubmJjJSkiJI7ZcmdSYAuZ56IOJkIisc3m00UEqOmhDmljf0m4k+RNoXlKOdQA8NcPHvhNCQci/1+H1mWq6srlUolvfHGGyoUCnr69KlWq5VOTk7U6XRi/BSGFwqFaFyH8wK4JxPBulBYPhqNNJvNIuswHo81Ho/DuSN7lD9vyZIlS5bsbvu+7/s+/dk/+2fV6/WORE3y1mg09G//2/+2/pV/5V/Rn//zf14/8zM/8w0cZbJvlN0LByAfjc8DynyRbrVaVbPZDDArHUfeuZ6otkeHXctdOkSiee5dWQGPNAL6vJsvUen8GLjeI/j5+/vffN67zOYzId7MzJ0Fv6f/AezyXOf+Mx5fX8bs68V974pK55VYnNstKeobqJXw7rUeAXf6kmc23o83zjp4tJ71wvFzhZt2u63tdhvN2QCPqAe5g5GPVLMH+XPp0W32DcDKGL0excH0XcDV18XXmUi7r5N/nii5106wLoBtl1bNZ7vuql/wn5FVIovifH+vOfFIv2cH8pkJ33uyB3yfNeYanuP0Lq51pypZsmTJkn2w9Xo9/Y7f8Tteqg3MW6FQ0JtvvqmHDx/q5OTkGzS6ZN9ouxcOwGq1iu6vUFwA+kh2wrWGn40aiPPoAeREwuF50ywKigYA0EEVEd5isaiHDx++VJAJqOGaXq93xGP3SCbKOczNx+dgcblcBh/dOd7eLKper8d4J5OJptNp9CcAVKE6A9AFrPsch8NhRGUBqqvVKtRnlsul3nrrraN98cZo+/0+/iNYLBbRLIw5UpNAwyvWrd/va7c7KOj0er3ovEz2BDUnTzMSrXZz8Cgd+gkwTxxD5DThvKO6s9lsdHl5GecH0OwybPP5XPP5PD7PGbxLchLgm6chIU/qCj7UYuTvgRITRpapULhVjqpUKqrX60cF8oyRiL3XM9BNeDqdBr8Tzr9nLwDg4/FYWZYd1SJ4gS9nhYyZO6Q439x3MpmE2g8OEnUGXOO0Lup+drtdzBUHkffeaT9kOFAWevbsWZIBTZYsWbJkyb4KuxcOADQcfvkToafY1iPkDmC8kyh/e6Gsc8XzBrCRjrnEFJ0CaPI8cUlBQ/AMQb5vQV7D3SPxnk1wzXo+79FbIt84MWQeuC9/vHYCgOvrBLC8i3/OvXFEcMQYL+Mkqo7SAePDscJ586gsz6QwFPCPbCYgHjCIeRQ/b/kIsUexUf8BLDYajSiCXa1WcaaIjHtfCeYJDY1n4QDl61Hc/PwAnqXDueRnbq6Ak8++uBIO+1Aul8PZ9HE4j54z4rx5f088qs71zIs5OP+eMbl8rTfZ89oD6Ev5OebXEXOOv/fr8Pmzt3mqWD6TlSxZsmTJkiX7yu3eOAB02pWkdrutZrN5BHCcSgMferu97d5LhLNYLB7x6SVpNBppsVio1+sFxx3gQJS21+tJuo3Gch+PihKFBFwCRFArIQpMJkF6Gey5Y7JcLpVl2REvXTpIKF5dXR19BseH5+cpEZJ0cXERIDHLstDwv7q6Cq36Xq8XAMyLh3EYBoPBkQPAs4geLxaLoIGg5kMdAJJm3tnV6xpGo1HMXbqtOaDLMoXEeRlQ1oV1xXlweVLoNuv1OmomCoWC2u22yuWyptNpRKVRpuH57JE7M4ydfgwe+efZzWZThcJtb4bFYhH1Hs1m84iehAPiNKU8JYz7kmmAi++Fw2RGcB7JxNADA6eGLFGv19OTJ080m800GAzCAWC9SqVSZAdms1k4d5xPHEacclc7cjoPzgYODOeRdwPnin4ei8UiMkI4Hdybs0Z2g7VaLBaaTCZqNBpqt9uRLdzv92q1Wl/pfzHJkiVLlixZMrN74wA4EOx2uwFo7uK+A1AowASkeiQVw7Gg6IVoOs2+oJrs9/ugqQBciY7iGBAV9ig6NAzpOJPgEX3+9ugwwMp5zNyXCG9e6SXPP+czdDfGoCoR1Z/NZup0OqGj7s8CFBNR5Wc4I4yf6CyRZ7IGRJFd/aZSqajT6QT4A5QzbtbBlWBYMz7jCjJ5x4o9pygXB82dGgA+mSCPjruTIenO++NU4qAVi8VYY5wPP4def+BRa+9F4GeT9XfOvvMyfY3JzADSOYf0wiA6TzM8JFW9ADvP58eZmc/nsZbVajWcCubmWYi8QpfXSngNh/ceoLiZ8+i0Or82z+v3r3FKOD+MudVqJRWgZMmSJUuW7Kuwe+EAALq8yy7Rdy+y5BroB0QficBLt1x3lGmIlrbbbZVKpVCxIeLbbrcDBAFgPLLuSjoAV6LsgC7ACZ144c0DDHE43JFg7ICwk5MTbTab4FTXarXgQQO2iGADwoiaAgZRSCFCT2QcFSNoVijf4FgRed7tdhFZznPvXaJVOkR4cX6azWb0WMjXVbCnZHSc1rNarQKsk1Vxc9Aq6WhvPDNCXUeWHVSZ2A/OB+cJgJtv4Ma5ANBWKhWdn59ruz10/B2NRpG5YZ2ppcDhgNZE9oJzk6eq5KUtcT4xMhFOf+FckJlgDs1m82jPxuOxPv/5z4dD53UMfE/SUWEvuvuuiNXpdKIGgTOYr4Xwgm3eF+hcZGzoqizppag9e5zPUvjZo8Nzs9mM3h6sbXIAkiVLlixZsle3e+MAOA+byCUOgFMiaM6E3KYX6Uo6ohFRFFoqlTSdTo/oIBSsAoydD+3RSYCRgxToKwBLl8ME9HjhItx4wCKZB+7VbreDHgH4yrLspUJIV5Xh3h6tznPZpYMTIynWLS/LyVoBzgCTd/GrAcwuOYkDQJE2QJsMCXPxaLWkmAuODM2y8gBzuVyGkwUgZu/JmEDFoWYBoEsGYDAYHM0Hxw1HbLFYHEnO0jDN6Tqz2SzOJ1mMvNNIpBvnJF8/wtj9rOEcOgB2R4ozxDl0eUy+DzhGprbf7x8VJ1M4S8EwTiROhtPL+Jo9JhvHPJH2hL7DmlSrVVUqFY3H46DX4VSMx+NoBObqQZxFz2bgsJLhYwwugev/VyRLlixZsq+P5VkVyX7r2L1wAFDNQQXHC1C9gJUiSLjkRPElRVTdqRxEJv//7f17jK17etcHft+1Vq37rVZV7V37nNN9TrfHt441nkQtxuMM4DSRYi6KBwgxTDTDxdiMxIgARgSDBDNokBInSiAKENnxBUYe3IRpjB3jsYFhZAkJQ9sY4tht9+XY3eecveu67vfLO3/U/vzW9313nT6X3n127dO/RyrVrqq13vd3e/f6Ps/zfb4PIBCuM8osgDT48s71T5LkiZoADJ4yAIv7YSi1rNfrEC3m2oBDB3iMrVwuB4ALyHZFH+fGcy3em6dqjMfjjCMAIAZQcW20913elA7BROuhaXg0HUlNePc4NqgDEc0nE+DUFSkbffZ9AuhTaA1I9PVi/o1GIwDDRqMRsj2uZMP7fE9xUDhHrHmz2QzrQB8C7rfb7QJ4lRRoQWj+1+v1cM6gC5XLZTUaDW2324xiTqlU0tHRkZIkCZ2J2Vv4/J75YOw8B9S/dDqd0J9hPB5nKDo4Z3x5gS7PFc4EmY+Dg4NwTnB8/Rn1M14oFNRutzP0O858vgC+VqsFOtt0Og2UI6e7cTZ4dqAO4VDvdjudn59nnlEvGo4WLVq0aLfbv/fv/Xv6lm/5Fn30ox99W1nT1WqlT3ziE/qlX/olffKTn3wPRhjtWdidcAD40Cc6zBeRViLngHaAL8We7iw4vcS15IkUQm0AyCL3SAGlR+CRznQwhaY9NQFQXzCiktPpNEMN8UixR7lxPFzdx9fBufoAO5wgqFLuLDAGQCK0EV7fbrcDOCWy6w7JZrPRYDDIqNB40SfRavYNgEwE/erqKjQV4zWSMrx+nIK8wg/zIcLsHH0H89Br2u12ppW5073ySkEeeV8ul5pOp4Hjz9/dmdlsNrq+vg4892KxqF6vl1F5wqmhIdlkMsnw9WkqNp/PdXl5qd1uF8Bwr9cL44TSkqY3zbaQfkUO1J0O1hnKllPbWq1WoPoQPecscfbyilqcU8+s8JyQ1fH3ejSItT4/Pw+ZATJSeTWjer2u2Wymq6ur4KiSfUuSJMiA0oQPB4AMx2g0CsXxzC1PF4oWLVq0aE/at3zLt+gv/sW/+LZfv1wu9fGPf1w/9mM/9uUbVLRnbnfi05NItCvrFItFTSaTjGoLgFjaR0UB7XRcBWC5owC4JqIMz5mINwAIoOdqQ94tFscAQAOAkhSyFwCsZrMZItdESJ3iUSwWNZvNQjdgd1IAW0TR8/KhdL71pkkO4B0s8z64+NzPo8D+eugiRMGhi3gzKnfQKDpFbpMiahRgAHhcG2DL9eh0670FoKN492Le7xkXePDtdlutVkutVivIi0InqtVqgb8PeMbB4DX5FCeOCtkBzoZz3YliF4vFTPdk9pCI/Xw+D46X79FoNFKhUAiF717AjEPm++9OGnQqqF8U8AK+ndrEPnNOiZq70g7PFY7pdrvN3INnzJ8lnk8v9uX3UJgYtz+LnGUcK4/85zNb7DVOOTUZnEnmFC1atGjRslYoFPSbf/Nv1kc+8hF99KMffVvvmc1m+umf/mm9+uqrevXVV7/MI4z2rO1OOABIPAJsKVylsRAAx1VM0CKH2w3QhLcMeEXtBxWg1WoVwBfAG4Nq4KDF+d28BsnHxWKhwWAQpEQ9GtnpdLTb3TTCImKOg1KtVgPgxbHAALUujSjtnQAHW163AEB0yVRXl/FCUrjUzqf2SHiv19Nms9HV1VXIquTVjryw+fDwUKenpwGQwVuHiiTdgFaoUNRprNdrnZycqN1ua7FY6PLyUtVqVb1eT9VqVe12O3MNnB7WBOfqwYMH+sAHPhDoQPV6PRQ0M09sMpmo3+/r9PQ0SMg6T5/MCOfE9e4dwDpffjQaZbIWrvoDnx5VnuFwqPV6HaLZTq/C4ZRuKDg4Yr63qN9wThaLhY6Pj1Wr1UINgCsiQdG5urpSpVIJcrGAeld8chpcs9kMBdDj8TicM86lJF1dXYV9cVoZjgpOkBf6utwq68hZ8NofpHJZ01qtpqOjozCm+XyuR48e3VqnEi1atGhf6VYoFPTt3/7t+s7v/M63LZYwHA711/7aX9M/+2f/7In6tWjvP7sTDoCD29u4+170iQNA1NLpHtJe3x4Q5EWVZAkAu/kD7jQVL6BkXETPXY7To/DSXv4TQOOyia4K5Ko38KI9gwD1iewI0V2uCV/bwS3ZCaKvjMev7RKnKOcAZn2dAXsu00i0nOtB9XA5TK8pcAoTe0J2gI69UKRYD65LMWs+Q+H7wN50u10dHh6q2WwGmg4OElF45kWxMXuE+g8A1bMdTh/zOgkKqb2hGHQvHAC+Y35m8pkmSZlzylxRMmLt2U/mBl+fiLlncfgi69Tr9TKRfiLvcPAB0mRk2Ecfn58nSRkHkmt55sEdYubNezl7FBK74+0ZA8yzI4wVKl+0aNGiRcuaqye+XeOzIAZWvjLszjgAHkX0Qk3vEEz0D1DsTZkkhYg78oiSAi3k+vo6REFR14H3zLWIxANCiNxDK+ELEOXSjFKWTkHnW+hCRHGd1wwF5Pz8PMhOOjhE9ebo6Ei1Wk2Xl5eBdnFwcBA0/qH4OEXG5TJxGHA+qGMYDAaBMsU1nBLFtXBA2Bf42YA0OPWSQnQa8OYNyWgkRiS62+1m6EHNZjOjcsMccHKgffV6vcB3L5fLevnll/XBD35QL774or76q79as9lMjx49Co7JaDQKAPPw8FDVajXISc5mMy2Xy7CW/AdIxBlw6l/D4VBJkuj4+DjUDaBNn3emON95BwDHiog355SMxXQ61WAwCOsi7Z1Lsl+cMXcCvZAY56bRaOjFF1/UarXS2dmZCoWCjo6OVCgUdHl5GdShNpuNPvjBD+rk5CQ4Pf4hghwoIJzngz0cDAaazWZqNBrhmYE25+d6s9loPB6HNeQ8cp7ItHh2i6ZunuV78OCBCoWCfvEXf/FL+v8nWrRo0aJF+0qzO+EAYHCMASNOYyF6DkiFJ+90Ao/gOn3Ao4QO9HES5vN5hlvuEo351JkrrZCBkPaAOx+R9AwC43YuPWAQdR+PPhPVddlQj0YzP2gvXgRMVoKxOnWGOVB34fQq1sDnSJTfgSvX9mvmo+aYR6+5B8W+TrUCTHtzLTecF4An2vCuHgWti66x4/E4zAVwzl7A8XeA6mo3eTlS1pC/e2dcLz7GaeB9zM8j6p5R8Nfh3JEJye8jr/UaEYrIvXCW9eK7O6k4Rjwr7gR7Bo69uW1POdtck/v4/Z0q5nN1hzRfpM04eCY4P57pg0rEOKNFixYtWrRo78zujAOw3W41Go1CkS6qP8gAwp/vdDoaj8d69OhR4Iu71n69Xg9RaopQoZJAbzg6OtJ6vQ4KKIPBQJJCsyrAWh6wocoC4PaIpnPAnXZTq9VUqVRCpJmsRq1WU7vdDnQWoqJE4D36SgQ0ryQEH5w5bTYb9ft9SXu+vGcciLQul0uVy2Xdu3cv9DRYLpdhHQD5gGmAFhQX5s3+wDNnTQHFrJu0B32DwSBQl6rVqiaTiabTqe7fv69XXnlF0+lUk8kkrB3ZFGlf/Pzw4UMtl0sdHR3p5OQkUHoYG42xuHaSJEHT/4033giZpNlsFjTmWTNXaMLRkBSu64CWKH2v19Pp6WmoN4HDDqhHYcprMm6jz6zX66AohBMM1YmiaHcEpRvwf3l5qfl8rgcPHoR5cmYYs0fXyciUSqXgQNGVOk1TDYfDjOIS8yDLAThvNBqZIvdmsxmyFdQ+0BgNqhHn6+TkJKhV8ey5gpG0z5zgFPMM8v8EjdmiRYsWLVq0aO/M7oQD4BHtfKTRiyp5rYMQotnOvX6zqD1gyiOLgBuuRZTRizj5GbDv9A4fL3/3iDLjAox7BN1f6xxpr2/wglKP5AIw89kFxoNDhPPksqNeR0GmAboIa+71CLyX8bgije+h8+V5j8txulMDb/42JSJfW66RV23CiWDcUJGgSEGvms1mwfkCzLJP1FhgeelQjzjzez8XgPt8nYNnDvLzz88zv86e5fGiWs/q+Jjz//Zzyrnj2n6W/bzyGmoGXHKWs+jrwjW82Dz/TPjzCqUuX5TumQX+7s8O96C3gj9LzPPtFrdFixYtWrRo0fZ2JxwA6QYoEE2GX+wUBpoZwTXv9XqSFLq3wp9H6ccbHEl7Hj/AEPDoRYtw3bnvaDQKILFcLmswGGg0Gqnb7QYlHaLTACCkLGnSBNhtt9vhGkSAr6+vQx2BpBA9xcgyME4474PBQJeXl6EB1nK51BtvvBHoQtKenpN3WFjr24DrYrEIBaNO94FrzrXyhcVeGM3cyETQLIrmYfV6Pajv7Ha7oHtfLpc1HA7DPZIkCfQdlGKcoy9JZ2dnSpJEL7/8cljn8XgcouXj8Vif/vSnNRqN9Oqrr2o+n4eo/mw203g8Dg3dyMQAOOkU7SpERLEd4KPDz1mhbwTR73q9HsbsDiXzqtVqKpVKmkwmWiwWoZnWcrkM9RnsKfdlLbkWqllkMGazmSaTSSisdllUCt0B9ldXV9psNup0OqpUKmEcp6enOjo60nA4DFF6aV+vs91uQ6aAWgieOS+yn0wmIfvAOIjqkzmjDoKMBLS0crmsi4sLXV5eqtFohEZs1CK4MlW0aNGiRYsW7e3bnXEAJD0RIfSIoEcLPUqe5xYDTPMGz1l6kqefv69z3F0JCJDpVBwirR7RlPbRWMyj+zgzAFnvD8A9AKVEv/MZhduiuc479/XiPvloqfPT+fL6ifz3/HsZl9Nm8mpF/npfU/+Zefj+5tWYPPPA/lIsS4SfdQB4TyYTzWaz8BrApnPc81H//LkgE+M8fj8fnn0h+u8R8bzaj1/7NvMsTt48sp6/Fuckz9l3B9AzD/naGD8z7KXfy2sfnNOfz2rksw7+Gr/Om2VE8vNzGhXj8rFEixYtWrQnLU1TDQYDvfbaa+p0Omq1Wm/62s3mpvHlo0ePwmdptPe/3RkHYLfbhWJXIoFuRFbr9bqWy6WGw2Gm+BLgA0gGZAIO0YZHOz1N0xDxRUGn1WqpUCiEiDfRaxRnsEKhELTRiZpzjWazmaF0QDuCL+4RUgf8ksI9eADzUenFYhG45CcnJ5IU5oEkIo2n4JhTt4C0Y55eRR0ETgyRawdpjAunxiVOoRpRSM19cLactgIYdelUwDkUKWkfaacomuh2u90OdR0A7ouLi8D5B1BSK+Kgkaj4ZDIJa0O3WxqRoffPWFnrRqOh9XqtR48eabfb6fT0NHM/nDn47jgEXlTsa0idC30sOAvVajWMEzDu3aaHw2HGMfRaALIf4/E41MbwN3oKLBYLXV9fS9r3O+BDYTabhaZiZNEePnyo7XYblHyYD5kZel2cnZ2FhmzUlJRKpYwkr9N+UPjCWSuXyyFjhjqWU7S63W7g/KNGtNlsNBwOYxFwtGjRot1i2+1W3//936+f+Imf0Hd8x3foD//hP/ymr33ttdf05//8n9enP/1p/eqv/up7OMpoz9LuhAMAkMrzxokMuqa/838BoXleuhehEmnFKXClG74TZSbSTNSS39GhGGDrEV/nr0OPyNcPOK3Jx5iP5jM3+P35+gCiodAjAFPUN7g6knPJHazno9eufuTr6+uT78Ls0XlqJ3wO/N6jvx4Z5nVeh3BbTYGfDfYD+gn3Xa/Xgc7jVCbn6/t+Qf/CWWRfkaH0GhK0/Yni80U2ySPcbkmShDF43YNH0H0dN5tNkLNlDn5dLzYHNPu59poSHCAv4Hbalhu0tyRJQndsL/6mGZcX47IuvofIqEKzY76eJWKcvh9e54CTxzmlrkNSkDRlPF4LEh2AaNGiRXvS0jTVZz/7WX32s5/Vb/ktvyXQk2+z8/Nz/fzP/7x+7dd+7T0cYbRnbXfCAYC3zIe7N4Qiio3CCtFxFEP6/X5Qw3GFFt67WCyCcoikwK/mmnCxb5N/RIcf4MRr8rUJkoJqEY2NiHDmMwdOJ8IZ4Xc0rOI+g8EgA5C9kJdsCes0GAwy4ApA12w2Q+SVugqXaXQwz9ivr6+VpmloUNbtdoMKkYM3FGS88RRrxT3h9fMe5txut0Mkl9d7ZJ+oMHtMcW+5XA4qUUSLHz16pKurK9VqtVBH8frrrz9R35AH6qwlkWYi17VaTavVKkTimRMcdBwsL44lyl0ul4Oij3STzfGCdboLe5fnQqEQzre076Xg0p7Idm42G52fn4dnoFAoZOQ/mUe1Ws3I6tJ5uF6vZ4qgLy8vM84186FjNcCb36Pgs91udX19rc3mpocBZ4R9nE6n4XnlbFALcHBwEDJY3sCMvZcUzoKfLbIKPMvUV0SLFi1atDe3v//3/75+5Vd+5U3/Ph6P9cYbb7yHI4p2F+xOOADO83Vw6hFTaCdpmoYiTyK6gB4vbgX8eIMhb5yUV8Oh2Naj306jgT7jlB0iyFzTOfQeyXVDkhNA53xx73wq7bul+lpwf8CYg3ssz/vHwWG++bUnGo4DRfSapmsUZyNx6e/zrIlnGJyT7iDPMzVE0rkme8a4KRY+Pz8PTo33iUD3HxoTFCFem6/JyHPgnffue3PbWeKMedbD15trcx7yakH8fjqdarvdBnoZxlmBMsZ4PKLOvehoTfaCc4ATAJjHkWRPOQtQ46DRrNfr0JTNFYdYB+eEUoSLI71er9Xr9TK9Ndhziq6hBa3Xa00mk0zvBgq7mRtrzjVc+Yrr8+x4E75o0aJFi3a7fepTn9KnPvWpZz2MaHfM7oQDUCwWQ2TSI6aSMlF5uMw4AHCkpb0iCNz8VqsVlGCIhk8mkwDsisViiBYDXAExRO+9ENkLJFFRcSoIIHc6nQbwBFCczWYajUaaTqchOgsYdxUgb4AFPcNpM/DX0cOX9oAI1RciseisDwaDUC9RLBZDBLrZbAbaCZF/p9pQ+0BdQ6FQCHUIrBc8ecYh7bvUstbwtxeLRQY0z+dzjUajEDV2vj73x+nDEYCnD6DkvbwnTW807L3RFRr/NB3jte4Y8HrUdbgvVBvWOk85cRqLZ1O81sEpLZIC5x7ngKyDFxgXCoXgpFIj4XZ0dKRisRj22tV3iLC7k+jODGAep+Hw8DBE/F1Vh3oJP/NcD0fZi8ryTgLPKI41XaAPDw9VLBZ1cXERaGfuVPv6kSEajUahzgUpV56d6ABEixYtWrRo79zuhAMAtcNlFl39BmBKcygHza7s41zver2uVqsV+OkUOWJ+DUkZsEiUFoDpUXePUAOeiRB70W2321WpVAqglQZXrrZDtJbuxq7Dz7UdwDlQciUdouKAYgqe0zTV9fV1+Ju0L/bESZEUosL5zAYAk3Xp9/uZbAGg3DMZAHeaRnW73YzDAMA9OzvTYDBQu91Wu92WpAwA9tfjMEEnwgHifhRKA5g9U0QknK6/DswdPLL+ninh+jgMZHk4C7yeiHa+wNrpVtQk0DyLyLfXc/gZw3m8rdC81WoFipQ7M0iIesaA58jnyJp69ol1IROHJKlH4FkXxnN4eKhSqRSc7vzckXwdjUbhPfV6PTTfw8l0pSSyT0iBIrXLe3EofOzRokWLFi1atHdmd+oT1KOgBwcHockTwB8ONOBL2jc4IsJMlPbq6krj8ThER4mQAkqcEw+X3AuLAY7cA6UbVFaIngOqPDpcKBQ0GAwCAEySRM1mMwA4lwxFEUZSiIQDgnBYms1miM46XciLb3u9XqDp8Ht3InA8UBZKkiSj0Q7YPDg4CNcaj8dKkkRHR0cBcHszNbIZ0j5TA9D29WGNAJfSjTLNvXv3wjwBqUSPmSeAmGg5YH6xWIQ1dcfPaV3O1fcCcEmZnhNemO01CN55V1KoSWCN2Vv4+3mpWArCpb1zk5evJOvj1CjmzTqh6MOZhwKV7/JbqVQyNSdOQ3PH0Z0sz3Kh8LRcLkO0nqyPZwGYg3eZLhQK4VmjfoBanlqtFubg+5Hfa8ZMhoHXQUWi8y/cfyhh0aJFixYtWrR3ZnfGAfDoJxFoOMbQc4h6w0WHr0wRLJKFhUJB19fXGbUVrFKp6OTkRJPJJCMr6JkGAKRTOmjIBChutVoBMOEAkKWAPy/dUD4qlUqIdDIW5gagJcIpKRQue00AdKX8uGiw1Ov1tNlsNB6Pw/uZW6FQCNF7gBnODGtPPUWj0QhRagpHkWiEcw6A9KJbACoUKOghTquhVmK326nb7aper6vf74fmaBTGegdadwDI9pDV4D3MlzWBfuJFxx7JT9M0KNZMp9Mn6ifyEWbW0mVivUD30aNHmkwmGQlLzzhI+27UXifBtV2xibXl52q1qvv372uzuZE8JSPgykScg3q9rkajEYqQvRYEwwHgy5vVMZ7pdKrj4+PQCIyaA+Yg7cE/0qeSwv7XarVMUz2avc1mM/X7/fCckAHjbPl6+HjJAI1GI9XrdR0dHWmxWKjf7z8xv2jRokWLFi3aW9udcAAAelBN8lHsPD2GyCXUHn8vABXnIM8tXq/XGo/HIWJ6cHAQtPu9M7CkDMDiek7Lmc/ngcpBVJ37+zW8mNfH4bxpgK43kkIO1IuApX0hLU4C6+B686yVF7lC30B3n2i+67MT+fc5EQGG/kP0Fm4/wN6LN/O8dSLHXtPgUXFAO3sl7eUmGbt3E240Gmq32yEavF6vAyBnrZibnzGAOfQi1pziX3otAEgdRBNtBqRCPaJrsNOKnMbi10HBhrXlzHkk3CPz0k13ac9M5alKKPXgmHh2iSJreipwPjg3RNs5Y+VyOShHcRapt/H9d9lOzwjA+3en0s27cZNVW61WGUoSDjmOF92uGQOUo1gDEC1atGjRor07uzMOALx9ACjRaECZF5EC2lerVYgo814AONcl4g73fbVa6fr6OgANmkZJN9x/V6IBJEKDkG6oI4CjyWSi119/XdVqVS+++GIAcQ50B4NBiPCT1SBCC20EsOm654VCIVB0JAWHAGAFqIOuId3QRY6PjwN48ui5pHDN2WymbrerbrerSqWiRqMRQOBsNtPrr78u6Uaqs1QqZVRn+IJSQwR9MplkVJH6/b4kZWo2iAi7UhJzq1QqarfbwUEDrHs2YTAYaLPZ6MUXXwxF3q6u02g01Ov1ggPidR1QutxR4TuFxq1WKzg/7jjkaTQo6FA422q1QqEy2QPAN4CbrBBzuHfvnsrlcmhUx1rndfFXq5XOzs5CMbarGeEAcKZcNpO9oHEeTl+r1QrSqlCycPZwCr3RGPUCGK8hA+AAnKJ1HGQcJXcwms2mFouFxuOxttttaG5G5obzwrrwfE4mk5D5o37Anb1o0aJFixYt2tu3O+MAeIEt4ILvzmMnoshrUQ8CiEAh8mZJXpzpUWGuh8a885sBJc5Nz3O0C4WbLrEORIjeAsoBNDgSfm1ez1idXkEBLRkAIraAKd7jGulEXt2pAOQxxkajEehGpVJJ6/U6ACqiyw7qHAB7cSra9Ci8zGazsF+Aasbp4wOoMn4cgtuUgvxceFGuK/eQRfDusxQhO9WFdfA6BJw09gYnEaeJ+3gmCmDPvbxQ1s8h6+W1Kmmahg7FXlvAOjgXnveTkfLsAE4N73c1IqccJUkSqGb5veT3XIPzkY+os2Z+bYqNcba9VwCOFueWNfZsD3U9PEfMiayI7zfZLZyb/Bj9mYwWLVq0aNGivT27Mw6A866bzWZQRknTm4ZT5XJZl5eXeu2114ISDEokaZqGiCLgs9VqZegwq9Uq8OFLpVKIxpJdKJVK6nQ6GeUWaQ9QPGLNeEulku7du5eZB9Fxz0hst9vAzSdyCcgpl8vhtRgg98GDB4G/D6XGwS5OAyAZ8ESk1ek7AFi08gFOV1dXevToUSjmJFp/Wx2Eq95Uq1VNp9Og9T6dTlWv1zMSlNQJuJzp1dVV0IJnL1gjHBGAn9NZJIVItI9JUkbOkmzE1dVVBlB7TwfWEf46GYThcJh5jfeF4NpOGRuNRloul4FK5hx/MjVkT4hwf+ADH1CSJLq4uAhcfklPFMPinOJkulEr4pF/1gFVKrJWZK5wppgvWTbOC46s1+Fw/heLRchmrFYr9fv94GSSIXIaD/cni8HZYd2ZL2eyUCiEgm5vAHZwcBBqVchMMBecL3fuo0WLFi1atGhvz74kByBJkj8l6Y9KSiX9z5L+sKQHkn5U0pGkn5f0f0rTdPWmF9EejBHpBHTxQQ8IhBrhBZAeBXbOOxFJXi/tCyAxB/eSMoWqHiV2Sg+UhreSIHSOOdfx93iWwpVrWA9JockZYNqpPA7QHLTxXigwrB+AOU+ZADR6USZ0ENaZnxm3900AsJOpoWDT6VDOScc5coUeHydOk2vnY16A7PQd7sc6OL2HTICfE6eI8XrWguyEO3vcw+s0GEeeB+91HJxXP3sUh3tdCT9T8A6Fhsi7O2OeaeJMkBFwJ9XPLP/O1xzkaVj56D/3yO8j584LjfmbP8N+TS8g9iZjrsDkmQnvocD583Xl/4yYAYh2l+1pfUa+X61YLOprv/ZrdXx8/KavOT8/16c//emo+BUt2lO2d+0AJEnyoqQ/IekjaZrOkyT5u5J+v6TfIem/SdP0R5Mk+e8lfYekv/lFB1EqBb14j6DC+XVutf9H4RFOpDIBJVdXV9put+p2u6GRVblczlBBXDFGuim29IJWsgiAWdczz0cjXY2EjMRutwsAkYLQ2Wym+XyeidaORiMVCoUngPHl5WVQzKnVaiHiCUiG2w0IK5fLarVa2u12Ojs7C9F6rydwRwvO9Vd91VcFdRnUbhw0owqUV2jpdDqq1+thzR6fixC5ho6UpmnQ8D86OlK1Wg2KSjgdzLlSqQT5SXcAC4WC2u12UP2BsoOjIyl8d8rS1dVVxkFst9uhMJdrTCYTNZvNIA/ryjpE8b1wFYetVqsFYMq5Yo0onj08PAwAfrFY6NOf/rRWq1WgYOEYTSYTTafTMI7NZhNoVe604QhwfZwQnhecRhwPAHS73dZisdDZ2ZkkheeNPfOMEr/jOvn9x5GlCRyytTwTKF+5/K6k4CCUy+WQOXMlH+hok8kkOJPdblcnJyehmZ6kcIapXYkW7S7a0/yMfL9arVbTn/kzf0bf+q3f+qav+cmf/En9yT/5JzWdTt/DkUWL9v63L5UCVJJUS5JkLaku6aGkj0n6Pz7++9+S9H/TW/znRpTVm3/xwe60BaKRLu+I5Qs1icR6lJF/83uPBHMvSZlIqEeApWyk1K/lr/fCW66HY+PX5meP6L8VoLmN8uDXYY552pK/37MGRFSdY881+HL6Bk3D8mvm2Y08mPR1yEfwJYVmWFCofP+JDHsWgYi2g/G8E8D789Qqr5XAycivpWdXWKP8vvn1PDLFGYaTn1/3vOUVp/wejNWj6K4GlB83Z49rekbIs1qMPb8/vs6e1fA9kJQ5A14wzTz8PLp5VN/H7Zk73yPG5MXKPqZo0Z4Deyqfke83K5VKeumll3Tv3j29/PLLevDgwZu+9uWXX9bXf/3XZyjA0aJF+9LtXTsAaZq+niTJfyXp85Lmkn5GN+nMQZqmPKGvSXrxra613W7V7/dDwSyFqpKC7N94PFa329X9+/czxZHtdjuoiSDJ6GoqAA6A7Gq1CpF0oqREkaGY8Pv5fK7pdBqcC+QeiXgTNSfCLylkKvjyxltQMJziVC6XdXJyIklPUF663a6kvZwnvG6i94BMONySguQo76UGAMoFIIyx0/EVVaTNZqOrqytJ+27Dl5eXWq/XOj09DfdJkptGYmdnZ6pWq2HdoTVVKpUMYKNfAPxzCm5RIqrX66EWAHUn9tmLZakRqdVqury8DLrzdImFcy4pRJFxhJIkCa+Dv8/v2G+XtyS6nqeQUYMBVWk+n2s+nweevDuFcNrJVNEQi5+73W5QuvLoujebI/tAvwRkOpHQ9KyFKwFx9pfLpc7Pz1UsFkM2LO9wAL4p8HXHA6BPBL/T6Wi32+n6+lqSdHp6qkqlon6/H7J2nkXxbAXP5nw+D8+k1120Wq1MZ+jpdJrpE+HOwWQyiRSgaHfWnuZn5PvNDg8P9Vf+yl/RRz/6Ub3wwgtf9LXf9E3fpB/5kR/Rz/3cz+m7v/u7dXFx8R6NMlq097d9KRSgQ0nfJulDkgaS/kdJb57He/L93yXpu6R9B1wHGq6+g5qKAyvAZV45xa4f3u9/92i+Rzk9Ap7Xqs9H9T1Sepvxd49WElnO0yxwHrzo1XnR/nq/r9dFuJIOvPF8hNivh/F+ouj5OeeBpfcCwIEB5OEsEF3Pmxce+/gZlysneaSfn6W96s1tUXv/zv0obPb3Mk/W2xvMefTcI96enXEOuq+jn1cyHJ5lYL7stVOkXOWH8eFM5bNdHnn3s3zb/np0HSnafD0Jr/OsDnNyR8Hvy/7z5ePP9yHw/cvX2/ja8no/y77veVoT5+7NnsFo0Z61Pc3PyPebHRwc6IMf/KC+5mu+5i1f22q11Gq19OjRo7esvYsWLdrbty/lafr3Jb2apumFJCVJ8glJ/66kbpIkpccRjpckvX7bm9M0/T5J3ydJzWYz7fV6IcJKlB7+MtFjVEE8GgmAPj4+DhFDovJOI5AUIt8UhsLDPz4+DqAD3jgqJkR1C4WCptOpHj58mIlGEhHPOyRkGfiZ7IIX/wJ2vDhSutG794JKNO/pY+BcbAde2Ha7Deoz1DPwd/jTrCe22+0C153mWkRlu92uttttyBa02201m82g3b9YLHR+fh5qCty8uVuapur1eiGCPp/PQ2R4s9kETrmkMO/1eq3Ly0ttNpsQvaeDcalU0tHRUXjParXSbDYL+4rC0m6303A4DGvA2ler1bC2s9ksk+1B3pTz5fUpXId/s/esM/clGk7WIO8oSArZAaLmGO8Zj8d64403wny3260ePXqUcQ5brZbK5bJqtVrILiwWC1WrVdXr9UwGCB49a+CSu2TUmNd2uw11Fk714szyXPIa5kENiBegu4wte10ul3V6eqpSqaTJZBJ6Y6CwRcE4heVkE3AAJpNJLAyMdpftqX1GJkkSU13RokV7qvaltNH8vKRvSpKkntyE9X6bpF+W9E8l/UePX/MHJf2Dt7oQih6AEI96EiF1nfd8BBywBNj06GE+eulSoVy/UqkEOgcAhQglYB/pTkAd72Xczm+HcuJRc4/qO/DPZwMYM1F3ovP51/scbstI8F4i3fnsAfd0frdzzD3CTaE0ToCvnVOfkKXEQfF7unqOr2k+Is/aEvHNR8Y5G3S29QZjrBtz8HXjvX4PVz3Kv8ZrT/ycEt33QvJ85Jxr5+sPPCvD+Px83LY/AHocqDRNgyPmRdkAZpwVdzT8bLkD6ecj/zz4mc6Pj/dxjl05yzN0PhfP7EHzcic7/+wz13xtio/Fn9No0e6gPbXPyGjRokV72val1AD8XJIkf0/SL0jaSPpXuolW/KSkH02S5P/x+Hc/8Daupc1mk9EiB4hDXeh2uwGIDodDfeELX1CtVguKNd4118EX9AeADxFxnA5JQbGHSDORU/oPjMfjENEnIkn004ErYCVJkkAtyb8G0OrUHwfL8PfT9EZJiDUAOMPhRiaTKH2emgIYowbAi1s94jubzTQej1WpVNRsNrXdbnV5eakkSUI0GB47gBgw75x7V8EhcutqSYzj/Pw8jIU95gvuOVQV5nb//n2laVYpB1WlUqkU1G8AvoXCvnFavrCUa3Lu+v2+hsNhWF8Hnuwx63nbvyUFBxJOO92M2Xt3ALm21x3Q44HXp2ka1KI2m03g3HNuyUg5uEYXn+el3W6HNfN7eCaKjMBut9NkMlGpVNLx8bFKpVJwMtI0VbPZDHPDccExyWeZGF+n09F0OtV4PA41G1D5UK9aLpe6uLgIz4zTkLg255nnhgyd99GIFu0u2tP8jIwWLVq0p21fEqEuTdO/JOkv5X79OUm/6Z1eC8oLEXWi7ev1OtAbAJQAcuchA0492glQIULpVIbboqKDwUBpmoaoMmOBpkFUmt97dB1w71mA24oonTPt4Nn/Dm0FSgpfRFwByQBF5npbTYQXTGMAQBR3oBOhrDOfz1Uo3HQiLpVKYRxEir0WgIhxtVoNWYB8BJ6fJYVr0UzMsw+sQ563TlEohbtQXrzAOs/h92i0O2aSMjUPRNZRIuIsuoIOjiXAnwg4Z4kMAiDdHdfbKFpkTnBccfC4t68xZ57si98v3+SLDAz0LHd83bny2gAcFhw6Iuo4YNzf9wbjTHgWhGekUqlkGt81m02t12tNp9OMcwYViFoN5/3zPEsKTrM3APM9ihbtLtrT/IyMFi1atKdpd6Kihmg8VIfZbBaAf7VaDXSE9XodVEpQT5H2HXjz4F9SiBI7Zx76SaPR0GazCR1giXgDruE0My7GQo0Cr93tdprNZpliUToT93q9oMQC0PPiSuhLdNOVFBpzHR4eSlKI9BNJRRmFKDXdb6U9FQSlmE6nE9YhSZIwF8+WEGEejUYBRJfL5aAVf319HeZfKpW0Wq2C+g+/g68P4AVME8llDwGRgDeixDhy3nSMaxFNbzQaajQaQWc+TdPQWIuzAPAHmI5GIxWLxaCoA9gFRBPld+oJa0B9R76AmMi48/AB2NRYkGGBtkaUPE/hwomB5sJ7Z7NZULby54S1Zi8B97zXMy5E4/kZpSd39jwrxllDFavZbAZHEWecM4OhRsV7cVyh93D+RqNReB3n0886FCKeOWpvcATYoyTZd7m+rdA9WrRo0aJFi/bWdmccAEAGkUdAADKOXjyYpmloCoUBBvIOACDMC4ahGtFci0hqo9HIFA7nixi9eHU4HIbMBMDFufM4AO12OxPV9SJKVGFoUOaRWOmm0dbBwYEuLy8D571WqwVaB/KbLjOJPCURbag4AOuzszPN5/NMQWyj0dB8PtdgMAjXoPgaDvhyuVSz2VS1WlW/3w+UqzS9kUfF4WA/mR/RWnje+UhyvnBX2jsPOFfMLR/5hwfvWSOkRKV9gW2hUAhdi4la4xjyHcfS6TqMw6lTnI3NZhOcGhwqIux+JnEAGBPXgN6DI4JTWqvVVKvVdHFx8UQTM65Xq9UyKkw4PTgBkoIDDc2MswvgJoM1m81CBo0zulwuA93OKVn+vLI/PidJ4VlYLBbq9XpqNpuBZoaDRNYIqpyk0NwNR6Ber6tWq4UMDdF/f29UBIkWLVq0aNHend2JT1CizwAvOq6iJuJRSgd30l53H5Dvha1cC0pLu90OVB8Ao3PoidoSnZ3NZuE+AFykCuv1egBszsUmStputwOQdgPkEDnG2dlutyESj6HKA0hfr9e6vr7Wdrvv8MvrodMwf8Dc2dmZ0jQNwIsiXmg5Dh5xVlg71GY8k0AWAyBeq9WCk7bdbkNXY6fTsDb8DI1Duokgo42POVWLPSeL4vUW+VoPKSsJShSf8UKbcTDNnvEaIvDUYEh7/X8/r3z3glQv1Ga8KDqx51BZqOWYTCZaLBZBQ59OwOv1OowdoIvzxHmmSzWFuJxb9ob9wPFk/i4HWqvVgtyo06/YQ8bmNDiMObdarbCWKFCR+cEBxJn2ZzNN0wDwifx7NgQlptlsFvoi+BpHByBatOfTRqORfvAHf1A/+7M/q9/ze36Pvu7rvu5NX/vLv/zL+sQnPqHPfOYz4f/kaNGifel2Jz5Bt9utxuPxE/QIgBcSjURsAVySAk8ZIEQUnqgrIKLdbgfKz2AwyHCKJYUoMcWRgJfFYhH46owV4EQ0FsC0Xq9DE62jo6MnnAPmhmMDUFytVoEukaY3UqYAKWkf+R4Oh0HesdFohHUAWAK0iMgioblYLDINugBYruoCtcSj9ufn55IUIv8UbhK5pViadSdDA8B1MI4xRqgr9+7d0+HhYeCvQ0Fxjj0Al/uwlsiRct08LYf98X4A1CRwZqgdwVmjsHwymejhw4ehJsO57z42V/KBs+4Fx9fX15l5kO2ikJlszqNHjzQYDNTtdtXpdFSv14OTwNoyR3cAnNaEE0DkfzqdajgcZrIrrmzkBek8N5PJJDSrOzg40Hg8zszBi4BxasjG4XgOh8MgCzocDkM2wR2pvCoQReLQ3FxVCqfFMyFOI4sWLdrzZZPJRD/0Qz+kdrutj3zkI1/UAfiVX/kVfe/3fm8E/9GiPWW7Ew5AsVgMevLQFVyjvF6vq1wuBwURjCily1ECJFz20wuB4RBTqAggdXoIhapQaSQFYEWxKxkJQIzTf7z41iUNAVEARFfsARQDIL0AdTQaBfCDk+KFs6504+oyFEpCv/E1dTDtwApg50WoXgztgIsoLfQs6ihwSHC2JIWCYpw4QDX0Gxw1zkM+ulssFsO+QZ8hk8D4AY15qU8cQh83DoA7eQ6ooS2RMQLoOhj3rAXOhlOyqDXwcbD+RLzJJG232+AYcB9p7zC5VCffXQKTWhLO2nA4zNS9lMvlDFWNc8o8cAz4mTqc5XIZ+kKwhtyfPaTZGWcDp9CzF+5kMF72IJ9t833ygnwvXPYC+GjRoj2ftlqt9FM/9VOh18lt9ou/+IsZ9bZo0aI9HbsTDkCpVNLh4aGurq60Wq3U6XRUqVRCZLNer6tSqejy8lLD4TAAfwACgHGz2QT6AQAEsMJ/INyLqCSgFidhu90Gfvvp6akajYYGg4Emk4lqtVqgZeCs5CUh8xrl+W6lHo0lekyBJdFamiNxncFgoNVqFaLlHrnHwQCAPnjwQMvlUp/97GdD4S7893z/AQAV64cKDBF41rhWq6lSqQRA6WMHtDO3o6OjcO3FYqEvfOEL2mw2QZmGDES321Wz2VS/3w8UE1ed8ag9WY7Dw0OVy2X1+/3gNCD/SXEy+8J78oW8OAuoOW02G43HYzUajUC94RrUd5yfnwengfe4o0Q0v9lsqtvtqt/v6/r6Ojib0r6eAAoMmZKjo6PgBDSbTdXr9dBEzOVXOVc4IJ69ojidtb28vNTl5WWgaJERcPUdp0ylaarBYBCyS+VyWaPRSKvVSvV6PWSmkPFk7DgqRPuh77RaLXW73UyGC8qXF8pzDrbbrS4uLjIyoFDZWBtp7wzhrNDsLlq0aM+nLRYL/cAP/MAXzeR58CFatGhPz+6EA0ChJ7QQoqeYR3ObzWam6BXtccANwBGKDrQVQKmkELH2rsLuABA1JcqPk7BcLoM2f77RE7KOFKEC2rwo2VWK/P2ASygfLqvo0XAAFkAJ7rTPDdDIPKlrwCHKdxHGAfHmUABvXuORWZwGrx1wUMlridTWarVMEadnOJxrznV8rH4tvw/rheMEzYfX8prbKCJOM8MpxNmE6uIZpCRJMmAWZR2oaTgt7hB5ZB5KGco6+ZoQio8ZizuE1G40Gg1tt9tMbYS07+TMWvLl1CeXWWX92QM/a2SWOJde4Mt5p+aAMeTVkTg/3iNC2te94Gy40tZqtQrOJo6Nj5caDa7nYICMR7Ro0Z5fi+A+WrRnY3fCAdhsNrq4uAiFgwBu1HoAKQcHB3rxxRd1dXWli4uLEB0F+PCaSqWie/fuqV6vB261c+4lhYg7tBrAdPq48VGpVFK/3w9RUaQZqR8gE+CAC+119M43m03IZgC4cAQYJ/KglUpFh4eHgb5BRJXIsKRAhWIeTisCDPX7/Sc05MkASDfFV4PBIDQ0Q4VH2neBPT4+fqIWgiwDEV1kHgFuLtdIFiVJknAtALfLfHKNvIQo2Q/oXryfYl3GORgMQtak3W4H+o/z8KW90yLtQTPrznsnk4nOz88zNCnoSl/1VV8lSRoOh5KkXq+nUqmkwWAQrgGtBwfAKS6LxSJQoXq9Xrh+kiS6uLgIrwcguwNRr9f1wgsvaLFY6PXXXw9/I7tQLBaD0wd4R8LVC3eJmvMal4UlQ+M1ETiBODXNZjNkn+DoX11dabfbhedlPB6HjAzUPZS2RqNROI8uQzoej4Ojw7p4dos1bTab6vV6mUxIp9OJNQDRokWLFi3au7A74QCgwEJ0z4EtQAEDZLq+P+DJuehEIiVlIuS8H6ADYCJiTQ0CMo/OxZey/H0H9EQymQ+vQ97RqUGMiTFL+ywIDhBOjfOoua9r1hOlB7BzDW+shKwjkV0HYdzfo78ALMbnkWMfO9f3AlyXAmXdPKPBv10Wk32j2JOxs8Y4DV4n4cWgruTDPP3vt0lG5rMafhZ9D51qQ/Q+z5f39ZKUOb9EzH1dPPLNOc1naryOxBt5Oe0KjX+P2LMXmGdG+O7ZKC8y9nE7sHbHxDMjrpiVP6tQpHifr7ErO3GuOHc4eP56p6v5evtzHS1atGjRokV7+3YnHIBicd+MarvdBklEHAMveAXgnp6eSlKIYBINhLtcLpdDRJgCSN7v3XOdaw/3X7pxMHq9nqrVqs7OzjQYDAJH26k71Wo1qP+QgYDbjMpKsVjUaDTKcLoPDw8zSjmr1UqXl5dBZpSxEa3FkSBqCwhst9uhCHqxWATuea/XU6FQ0OXlZZCXrFQqocjUeeDQnqhNgA4EjcR13l1vHqfF9xG5UjIhl5eXkhScDqL68/lcy+VS7XZbrVZLw+FQ19fXarfbOj4+zlCg6AQM/ardboc+BygtzWYzHRwchOZpkjLKPL7Wu92+WRUAlLljlUolqCINBgMVizeN2ZIkCY3R8hQjd0goLKcPA2tOhPv6+lqr1So4svD1MeobptOpzs7OMlH7i4uLcP1qtRpqEFhzitR5T75mRNpz7MkoUWdCZgjHEuO5dEew2Wxqt7uR8MVxrtfrgc7Hs8jvfW28YJj58vx4VokMFs4Gz4+0d8ajRYsWLVq0aO/M7oQDQOSQyJ5LNnpWAHP+uhd9YkQ0ibI6BcSjhkQ8KTb2yD2RSEB4nmqQj3B7nQIAGfAMSHFVIlfwgUrEHD167fdmjB41JuLt88n/29VTvA6BtfSmWESnuZ+rBOWVb1ypJj92qDaeUfDXAyRZCyL9roDE3J1zDgD0eo58tNj3B3PQ6bx4uOh+P/beX++OmfciyGdkbsuY3DYuz5r463xsvI4mcDgAPBf8nX31zAT3dAfXzyUOgr8O584LcXFYWMO8+TjymQiu7c5s/qz6Wvna+Fpx1j0z5BS1aNGiRYsWLdo7szvhAEjKFLfeu3dP0o1W8GQyCcCTCDYdbheLhc7Pz0MEnkhuuVwOijDw5gFc9XpdvV5Ps9ksdNgFDHU6nSCliZPhHH8izRRm0ujJx0/mgvvmqTUoyaRpmml+xj0cCFPIjB46fQGwWq2mbrebibZyH6LWSFOyllwbzjXr4YotqPJcX19L2ndIpjgbZRkAGoo17NPBwYFOTk4y+vR5LX0i7oDTVqul+/fvS8oWl6ZpGrIrXu/h6+mSrtPpNOxPobCX9ISvDhDNN54COFPfISlTS8F9cBId0HoHa/jq3W43dLd2ChTzIjPkNR2c+dFolCn+pes06+eN0zabja6urjIODlFzZGspeCebsl6v9fDhQ+12O927dy+oR5VKJQ2HQ02n06DYlK+r2Gw2oRg5T4vjPaPRKKyD9zLg+V0sFuH+OCY4oWSPWPfJZKLhcKgXXnhB9+7dCzQx9icWEEaLFi1atGjv3O6EA5CP1joHGPDkUUkirw6snVNeKpVCZkDKKtc4cKPA2CkF/nWb/JhHJ2+LPDu3GcuDP492OgefsfkYPMrM/T0yze8AsPl18wJgnKB8/wK47VzPVYb4GeDtdRXsAUDQ1wvnB3UZn6e/1vn8rLk7AJ4pYcxe95GvR6DQ1V/v18AA9/m9yUepGTOZCB8v9wT84/BwlnBg/B6+Dp5d4XVeu+J74VkHz0Qwhvy5y2canPdPFok1oQjXx8ze8uXn2dfW5+W1FzgMRO3zZ9ylVHndF8sA5Hn/fhajRYsWLVq0aO/M7oQDAMAk8gcAQGaxXC4HgEBEFw420VB4/SijSAocaxRjLi4ugv5/vV7Xiy++GLj3DnABP2jN0xSJqCqAuVKp6PT0NFOcShR7Pp8H7XMAj9MjGo1GiGgOh8MAZshiUFgM9QQgTcR5uVxqNBppMpmEa0o3nOrdbqfJZJIBjdVqNUSdAYBIU9ZqNS2XS/X7/QyIb7VaTwBA52VT7yAp8M9R0iFr0O12tdvddBVeLpfq9Xqhp0O/39e9e/d0//59jcdjfe5zn1O1Ws3w+N35ADyyH2SDcOKIHlNfAMCU9gW8RLRZD+bg857P56rVajo6OgprudlsQmdkItbUEfg5hUOfd6qgNkGNqlarGceEngKujT+dTsPeeaE5jgiZD54N5sJajMfjEDFHJYqMGhkY1gMngHFQv1CtVtVqtbRYLDQajUKmzB01zi38/W63G84acyebdnl5qfl8HrII0HuoO4DWhs3n85DFuLq6Cgpb3uwtWrRo0aJFi/bO7M44AET5PJrtUdvbuNPSXv6SiLdHLT3aD1caq1QqqlarGZ409/EoukdJXRnGI5wAUOdpQ4nhOpIyqkVeoOkKKHnOP4WjTiXKj8951nSVZV25vlN2cACch02BNObqQtwPQJeP9nqGAcAJGG00GpkItrR3iFgj1p+uvp7JcMsr5HCtQqEQwDEOHD/Dj88X+XI9B6fcm3GylmQ5KECHmpWv6eCanrVxwO57l88gcFbySk9e6JpXyHJKUpIkgUbDvBkzToJLjOKgcE3X4Cdj4DUovt6oWpEtyGeFoCuRveCcuKKRZ2e8kNezLaybO/RkSNinWAMQLVq0aNGivXO7Ew4AajHdbleVSiWovaDysl6vM4DaFWvg3OMIoLTjYHg2m4XeAJICmB+Px5IUHAGi7B7Rhy8P8HE+eJqmIQpMJJ6mY2QtHMTni0pfe+21DIAD4ACO6CXAukBr8mJlFI78voA5B2atVkvlclmTyUTz+TxDuxiPxyEqDGh0MM+9nJbEGpGNATSzVvzMOnY6naA1v9vtQr1FkiR69OiRkiTRCy+8ENaWdWJ/pD3gdZ393W6nVqsV9Oa9TsLpQtQgVKvVkDEiA+Jyr5LCXqC2w5miH4Kfi3K5rE6nI+kmYzWfz0MkfbVahSg+mRnAL/x1j3ZztqEw5c8ae+/OMICeugbWhywHZ5rv3pyLuTmYRolnuVzq+vo6OJZczwtw89Kh1IjgXLN/nqHBQaPOgroX6mfYNz+7KFe1Wq3wbLvTFC1atGjRokV7Z3YnHAAA2eHhYYYm4wWaUDloUkRhL5xvgCVAw6OOZAboIsx78tx/HIHZbJahAjEmj/4Dcolso5FOQyuKbD0K751b5/N5KMptt9sZTjpOgCsclUqlACoBdm7cx6P0GFHZarUaqBeMz7u0Mk+u5cpGrKHvGe91NSNX8WE9cFR8rDgKFDc3m021Wq0nzoUry/CddWUfyuWyWq1WiGCzjh559z4IOIX5CLLXmUDBwcFhn5mTZwJwDGmAxnlYLpdBnpTuwZzHvNJOHuDn+fT8zmtAPGvlaj3M3x02mmzRVI4iaDIFrrRFhojC4XymwWtSfBycF4rEvUYlX3PBfuCEerbE95DMBufV1zBfOxQtWrRo0aJFe3uW3IUUer1eT7/2a782RD8B4vP5PIAtSU+AI88EEDEErE0mk8ATd8UZ+NaVSkX1ej28lgh4nvog7YG/F2g6ZWO73Ybo5mKxUKFQ0NHRkQ4ODnR1daXpdKp2ux067zptB2qPAx+cHXT4Adhw2+mwmnd88lHZNwNJRMIrlUqGj81c0czPU6c8Q+JgbjabaTQaqVqtqt1uB5CGE/N4j2/VbJ/P51osFqrVasFBm8/nYYybzSajM++yndQhQLvBqeKsuNPltCjm6VkXp1MBnMng4BACPHkvTgzjcsqY09kA6F647g6Q3585ccZwHlarVVBloucF9+CsA7yZg2dAmDP7wDMxHo8D758ovPdHwHjWOHNeA+CvdVoYa+0UM541in/pquznifc4dcxrUXBGcKp+5md+5ufTNP3oE4crWrT3iSVJ8uw/qKNFi/a82q2fkXciAwCImkwmStMbqc5msxnAIcWORFs9Ok30EGoFvxuPx5pMJgEM5aOm0h68z2azAGiIknotQK1W08HBQWgiRuTb1XqcJ+7jAJS6/CRFpjSHQr4TIIQT0e12VS6XdX19HahG3NclRgFly+VSw+FQ0o10p9NBJpOJVqtVcEScW+2gtVQq6fDwMGRIttttoGTxHn7voMyjv5IC15uCTe9XICkAd5wVl3b0wlVJwUFhvIBczgSF2mQTnK5E5iFfQNvr9VSv13V2dhai9l6QmlcX4rxgzptn7shcTiaTIMMKDc2LkRkXalVkuHiNU4woiuU1XuuB8+LN7SSF4mbAvI+f7AsF3tQ1OCefcTB3pyqxT/58sB84fZyPPEXHMw4u6Yq5E4GDgyNAQT5OTP490aJFixYtWrS3b3fCAfBoYpqmGZDtf3eaCqDeaSqSQjTz5OREJycnAZwDSJ2OgsMB4OP30DcwFIA8qg74cWC52+0CpQFgX6/XA3WB+XA/lIy8kBlgnaapBoNBmGOhUAgUKadAQJMBGDrNKU3ToCSEM+E0JuoG6vV6Jgr9+uuvB+61pMDxZywAPKgsu90uKL941mCz2ajZbAY6zWQyCdFyQDrA1tWV+O7RcY+ms//OB4ei1Gg0Akh0yg/XkfaysDha3JuMAXtN9iKvEsQcHMASPWd9pCezDtIeUFPwzHw5l1Ch5vN5cLwYr6+LF9ISmWdcPh/PZrFX6/Vab7zxRmZOLqnrxcd8x4lkLagr4BwzN9bXC5dRCCKjQcfmfHEvjiD75l2MvQCfv6M0FS1atGjRokV7Z3YnHAApG4WmcBJA4RKZqNkAwAFXgBwoBg8ePAjRWDIJRN7r9XqQb3QKBxHny8vLAGAB0R655X4OsKFNUNQIZQRe+3g81nw+zwDM5XIZ5CiJ5HpDp8FgoO12G5yIw8NDdTodDYdDjUajQLWBFnFwcKBGo6HNZqPhcJhxZF544YXQNIx74QBQaL1YLLRarfTGG2+oWCyG5lBOb3IKENei8RWAkd+n19j2SQAAaC1JREFUaRoKN9944w3N53N1Op0wZ8ZPwzbqGlgPxs/Z8LVJ0zTjlOE4EcH3mgCPnnNu2B9JmUg7UpZkbnAQcHaSJAnFzBQHuxoOTgvn0uk0fm8cAOoHcBIPDw/V7XZ1fn6uR48eBaqWZ0nYU7IveYlX5uDXxWGazWZaLpc6Pz/Xdrtv6uYN83CUeT/ZueFwmMk4sV/sN/N0qpbTd5xadXJyou12q36/r9VqlZFn5TUUuOOQ5DNIOHvRokWLFi1atHdmd+LTE7oL0XRoNICBxWIRFE5u41hLe2qCN5RyKgJdbwHwACUp2zRL2kegXaHFo9AubwhIcQ4+v4fbD28c7XcAE5FiornQYnAq8kWUdJotFotqtVoBIOOsSFlJVWnfxXez2QTVI9ZxsVgEh8qLlY+Pj5+ICPt4AeGsBZHz7Xar0WiUeS2c/GazGaLyRMuZI5kD1tB5/QBKj7QzBi/GXq1WGR18OjQzDr8m6+lykkStGQN7wPn0+7lSD0XfHol2UJov7PWOvrzWr4ki1mq1Clx/dPm9gFdSAP1em8K+QO3hHnnZUu7r0qGr1UrFYjE4sTw/FNa7U1AsFjWbzTJ8fM8KcQY8Q8Y58OfKKVqMnTWnE/Fmc9N7gnuQIZzNZjEDEC1atGjRor0LuxMOwMHBgY6PjwPQvLy81Gw2U71eV6PR0MXFRYaOQaEowA+AXSwWdXh4mImwA4zQQsect0/RKYW1TpUpFAqaTCYZbj9GpJnoOWDJC0QB54Cw2WwW+PxEcpfL5RMOgiuhAIqgrNRqNR0eHmo0GgWOvc/LAVen0wkR7el0GiK+y+UygEQyFgCt09NTpWmaic6y1oBHQC5rRJT46uoqUEQAyEly0yCtXC6r3+8HzjoZERwDMhJQoohkexQfmUnvi+B0nkLhpicAjgj3+NCHPhS08CmodmoQoJw9c8nKvJIO3zebTZChzWvZ+7glhTNKZiJPvaH49/LyUtfX12o0Gur1epnzSVO16+tr7XY38qeeiXB+PUpYkjLRfZwFb2bHmsH1r9VqQSaV1zs9inWkKPnBgweqVqvh/nwtl8vg7OJY9Ho9SXu6EPU11OEwRs4FNKhisahOp6PpdKrRaJRpTBYtWrRo0aJFe2d2JxwAABmRaJcEdKAk7SOERC69cDBP73BQRwQWcASwc9Uhb0LE+4na5jMOmCvkuGKO8+AdxFPMSjTWo+meXZD2kWQvsqS7sCRNp9NAl4LqwXU9ki0pA/Rd+QaHCkDmKkSsHdmMPOgHhONU8EXUXlLGyXHZVe7v1BD2zusvKFrlNWRKqGEgos1rPNLOmeF9Xnwq6daCX0mZSD8Rb0mZXhScLSLrvs7sq3P/XXXIFXy4DmeO87Hb7YL6EPtDBgfw7jQsnhN3ABmrO6hIl7rOPmvvzh3nBUeVugr+jhPhFCzWjjUlC+dFz14ojMQnTq2D+bwD7/Qqd95jEXC0aNGiRYv2zu1OOACbzUZXV1dqNpsBpAIeiFIDwuDjt9vt8PNtaiMOgAB0xWIxcLsPDg7UbDZVLpeDYg7RZEAVlJJarRYacTkA5/6lUilo+ff7/YyyD2MA3Ht2gH9L+14ITnHx4uRisRgKaafTqcrlskajkfr9vmq1mo6OjiTtnR8yFhRuQqeiqZpH9XFIAKbQdrwwWto7IqwtEfp6vR4aXUHx6PV6AaSvVit94Qtf0GKxCCpEg8FA0+k08Nex5XKpwWCQqc1oNpsZOhX7O51Og4Rop9PJFJ+iFIOiE+B/Mplou92q1+sFtRyi1dCrAKWbzY0sZ61W02az0dnZWVAc4lw0m82gQOQStRggn6wG9/ezSh0Fr6lWq2EdXOr1+vpalUpFL730kkqlkkajUXAy3Qni/ErKONTlcllHR0cZ6pMXP7N+hUIhNOriealUKup2u5L2TcTq9XpmP6ihaDQaGQWnSqWidrsdnmnUoUqlUqgdwTF1Kh3KVWmaarFYhPVoNBrBUbwLMsbRokWLFi3a82Z3wgEApPiHOdxnosSulMN7XLIQmoZHPSkMdfqG03vy9QNEhz0q6gWd1A0AqKS9AwIH3v/m13YOtlOCPFLNPVxJSNrXNcARz2cVeB/jgSbjvHmvX/Cx8eVR5HyWA2NdiORz7dVqpcFgkGnARjaF9xAt9m7KOB5eKJt32twhZI5eUIv52rNPnA/WivVnDQCeHoUnM+S/94yMO0r8DQfCpWoB7TiCXD9vrAOFyMvlMiPf6VksHCIAPGeJ8d02R18ff48X7uJk+GvZD7+O8/R9n1h3zo/XtrBGfOGgehfpYrEY5u0ZFvbb5W7Z+9v+z4gWLVq0aNGivT27Ew6AtAcoDiCJjrbbbR0dHWk2mwWde1RshsOhCoWbxltOUyE6enx8rHa7HbjL/N0juZgXrHrxsUsiOp1ktVoF6hKgOc/HR8EHp2MwGGg4HKrRaIQshkdGi8Wijo+PlSSJLi8vA7AkKkxhJfQQdw48en58fKxSqRQ07imYxCEiQlsul0NDMFReAHXUXbRarQwNx52Q7Xar6+trjUYj1et1nZycBIqOtKduHR4ear1eP9EAa7vdajgcZtYWh6HZbCpNU43HY+12u6D6AigG5FMDwHiq1ao6nU5YDy/EdTCZp4xRu+DRa8AqHHwyCbweidPRaKSTkxM1m81MZqDf72fOBeuLwc8nG/Hw4UNdXl5mHCCoVa4ola/JKBQKoRbA3+s0OaQ8qZEge8XcqtVqaL7nThZny5us4TR4PQPgfTqdhmfMawJYS7ItknR+fq5CoZA56zw3kgI9CKfKMyx5ZztatGjRokWL9vbsTjgArhpCVA/KBwZgc0UWwCVAwLnI0j4q7hFKvybX4nfOcffr+8/wogFI+de5qovfn/H471wnnvElSRIi6a6+4/eQntRcdwqTA8y8TKLXTPj7fS98vG75+Un7Lq9EhT0TkVdt8usj9fhm5vvkABZuuq+rXze/12Q3HMz6+/OR6fyX77NnihiX15c4OMVBIsvha8BrfF44cz4nrpfn9GNkN/LZEK/RYMz+jHhmhrUGnPPlNDWvU/F18OcH5z2fNWD87jT537xngq+Tg/t8hszXO9YARIsWLVq0aO/c7oQDAHceDnatVguKOB7hdZ5zrVYLXHZA3HK51HQ61W63U7vdDvxvFGcAHET1iejy8+npqYrFokajkXa7m+ZWqKwAiviZ98B1BxhRT0DRJWozqLzsdjfyprPZTOfn5yHa6QXGRFqZs3f/dWpSuVwO9QmuJgPXvFQq6fj4OHQxJsvg1I88ePTf09UXMFapVEJGY7u96RBMETKRbHj/RL69gBaVGYBpq9UK4JP5+dqi7MP6DIfDDH0KgJ0fP5SkYrGoo6OjTIZG2vdwcJWa1WoVGsOx9sPhMGQ1PHMwHo+1XC5D74JutxvWJkkSTadTXV1dhTltNpsQFScbRJ3BdDrNdJVer9dBKcgzMd7lmAwV6lRE7ylqdoB8cHAQJDOpdzk4OND9+/fD7xkfmajtdqtut6tWq/UEKPfiec4BWQWcBs6CS3z2+/2gMER2h3oT9h96naTwXJOhYmysoWeqokWLFi1atGjvzO6EA8CHON898ondxmsGkHnU37nIAD0iua6o4pxyAA7AhvtJyrzG7+9Nlxi7g2fnrvuYXG3HFYPcXDEIxwD+PPcmEs848mvga8k65aPA+WyIR3Q9QuyZmTzozkdpoaXAF/eIOFFuSZm1zmctMJ9D/nf+s0ep82uYL8p1vjxj8vf7GZF0K8jMR7Hz++F9GTxTIu0dotui+/kxc7a9+JkxeRaDvXLHiN97xihf/yEpnAtXE/Lnh/vn61p4tm6zvCyqK2v5c+LZJgf0Xjjvhed5zn8+YxItWrRo0aJFe3t2JxyAg4MDnZycqNfr6eDgQJPJJKNuQiEwkVq49x6RdD4yAHS1WqlarWa6ogIwXAcfgEM9Adr63BfdfpoREeUE+K3X61CbAH0HME3E1+UtKfpstVqqVCohEg5oJnI6nU5DxNsdkVqtFjrsAtzG43FQ+CkUCup0OiqVShoOh0qSRK1WS41GIzSTGgwGGo1GStM0rB2RdiLwrVZLxWIx3GMwGGgymYRMiANd6SbKPxgMMqpD7lDRZyAvy0ltAGuDMgwZCOg0KNMgZem1B3QPBvgCDsfjceDRFwqF4KBcX19rtVrphRde0NHRUYYKlaY3SjzUaFDnAeh1UE90frlcqtVqqdVqZbI0cN2d7uSAmiJwzimZMNSRvDjXHaUkSdTpdNRsNnV6ehrOw26309XVlQaDger1eqgboK6BzIwXpedBvjtmi8VCk8kkzL9arerw8FC73U5nZ2ehRscdD848xn7hxHAWJIU1lvZ0IdacXiDNZjPUjJCtcPneaNGiRYsWLdo7szvhACRJEqQkoavQFApzkJKPSPrrABooitzGlZeyevfw7V2j3aOVOBNE0qElEHn1sQGe+fIIOJF1ougAaa7B+ACLjMe52tCgqtVqAEWAab8HUW2i91zHuf8O/li72yRUMVeO8ci48+iZPw6Sv98juIzZI9yME1DsUWJoItBacIry9RXOr/eoOfQrrkUhNYCeIlMvRschQvrVVYA8u+RRa88IEFnn9w5wPcrOtV3JyvfRJVC9hgQqDJQ5ZDQ5b54V4j1eJHyb5WtUvBaCfYHW5L0kcMCot/CsCtk1Vy3idXn1Ls9seMbjtjXLn89o0aJFixYt2tuzO+EAlEoldTqdwNWGy05UlO65DnKdLpGmaegKenx8HID1ZrNRp9NRvV4PkWbpJhoKpcalRGlaRO0AUXUAt1Mflstl0MAHIDGXQqEQIr9eTAkIgvNdr9e12WzU7/fD3wHhmBf/Em0mck6UnroG1Hec895utzMOy3g8DoAW7XyM4uZ2u61CoRCyCYBuwDE1FfDR6Y9ATUS1Wg19CeCYMw949zgHjUYjRP8Bz4B1vuCDM1ZUcNbrtQ4ODoKyDJkRrxkh20CGhn1stVqBL4+OP/dhPxlno9EIjgB/85oMxrZYLPTw4cNMEbhnNhxcTyaTTE0L9QfUnqzXa11dXYUMEfr6u90uKFyRBUM9h94KknR8fBx6GEwmE11fX6tQKITsivPtC4WCer2eCoV91+vlcqnLy0s1m0194AMfCMpB1CRQE8L6kilYr9fhmXMngOwXdLXJZPJFHRycmcvLy7DuOEU4G3nqXLRo0aJFixbt7dmdcAAA/P6hzgd9rVYLtA1XB3GOPsW2gFWKBqEJAPgB5YAWAIhz8XEMoP4gK+nRc6KTeWUi1yr3aCjvZazMEaBL8bMrtrAGPk6nzTAPCoCZb74BVL7RFkCtVqsFig1zJ8rO6329HLxyD4AkhaM4RNVqNRR5IqXJ2pFFYE39fgB2egY48HP6y2KxCNKcpVIpOADMFXUcNzIhgH6XhHXHkjXmjADinV7G3rDfgOnNZqPRaBTmD7DlbHgkG4eUMdCYjTlAH+LM4IQSkffsCT+zt81mM9yfs0FDLs+EeSbD7+tfzWZTrVZL8/k8I+/J/rsKEPsAVYvnM58hYc15TvK1Kfwd5y9/3nj2vGN1tGjRokWLFu3t21s6AEmS/KCk3yXpPE3Tb3j8u56kj0t6RdKvS/qP0zTtJzef3n9N0u+QNJP0h9I0/YW3ugda8DSxIgpLpNoBhIOocrmsw8NDFYtF3bt3L4C34XCoVqsVIvnz+Vzj8TjozdPBFp15QOP19fXNojwG1vlsA+B0tVqFKOhsNpN0o3MP53+324XsgV+vVCqF3282mzA/9O0ZL/Qlj74D3OCYA6SIrKLAA33Iedj0AUAXH5WhcrmcAZS8fjabhbnAm/diWy9yXi6XGRWfWq2mYrGofr8fgC5rwL6ypmmahswOUXWUX9DXx9ErlUqBa44KD/vIF/QxL7heLpeBL+/KRpIy3ZpRkMoX9LIePgeyGFDH+Jt34M0DX6c+SQqqUDhCrAevqVarOjk5CT/jZHmRMp2zidjTVIvnA0CepqlOT08lKSgroVaFc4wj3Gw21Wg0NJ1OA0WKDNXh4WG4rtNxOIvMhfvghLrzxtxeeeUVSfuGfXzH2aXrMGcGZ8PpVXmKU7Roz8Lei8/IaNGiRXva9nYyAD8s6b+T9Lftd39O0j9J0/Q/T5Lkzz3++T+T9NslffXjr/+tpL/5+PsXNcAeYJIPe0ArHGdJIcoN7YImVUSNf+M3fkOLxUKHh4eq1+tBKhQaEZQMwKq0p+3QSKnX64Xobd4B4PdEx1erlQ4ODgIYQwUGUANoYYy8D1DnNQVehOuRf+6b12anGBonQdqr31CszO8AdJPJRNPpNAC/zWYT6i2IegMkuQZRWDIBvJb3kn0gmo9D504DwNTrB5wPznsdwALmvTkU+4cD4BF61h6QzVyQZ+V17BtryRotFosAzJkn15D2hbTz+TzsH1mLvNpOXuUGsOpqQO5ksfZcgzGv1+sgbesF6wDpg4ODQIlifbHlcqnxeKxaraajoyOtVitdXl6G7BrOMEaBuXeippC6Vqup3W5rtVppPB6HObKfPhfWF4fF1YmgPeHc9Pv94BD72W40GqH5HXvlykJ5taZo0Z6h/bC+zJ+R0aJFi/a07S0dgDRNfzZJkldyv/42Sd/y+N9/S9L/Tzf/uX2bpL+d3qCef54kSTdJkgdpmj58i3tos9mEQkyMD33oBYD2arUaQCEgzIFfsVgM0XUARLlczmj2r1YrXVxcBIUYB7jQKgBA0C92u13QbZcUsg2lUimARIBOu90O9QH0FahWq0EliIi5tJdJhNYB8AU48Rq0/PP0ncViodFolAFh3IdxSTdOAQ6WO1T5hlyASKctbbdbNRoNVSqVANgA8J55oGsvTkWn0wlUKV4LcPM54uhA/eE8eHScqDLgGLBO7wBAKhmPPKXMC5+n02mgXnE2Op1OAJYUvLqzB32IdfQsDLUidEJGwtNBuztzOIzX19ehS7MX56bpXvceFZ18Lwz2AAfSMx+cBzrq0hEZehzPB+9xSpSfT6c5UQzPetCPwCli/m/6Z/B7xkafBhwsMhF+5twR8cJqKHDsW7Roz9rei8/IaNGiRXva9m5rAO7bf1iPJN1//O8XJX3BXvfa49898Z9bkiTfJem7JAVeb71eDyCQKCggDhDfarVCVBjgD7/baUKAZYC8S2ciAYpkZbfbDcDSJRABizQwInpOVBfONzQMrwWg2PLhw4eBSuHKLDRl8iJk53JvNptMIy5pL41IYSqOAwWjBwcHunfvXgBWLkOJwhLqMfDBJT0RvWYNPVqfpqkqlYoajYZGo1GGMw9Y3Gw2obiV/YJbzjU8y0ARr0tGOvULTjqZIW8OBSjG+ULestPpZOoHMIAsxaWsZaPRCJkkqC80EeP17K20zxZ5RH40Gmk8Huv09FTHx8cZLX3nx7uCEXSmN954Q9fX12q1WhmnjmtwTrwY2p3R7XarTqcTHDMK4kejkZrNpjqdTmhqBoWMqL6P0w3AD62qXC4Hys1yuQyyqMyBbEbeyOAB4PN1CwB/7oeTRh2PF7VDo+Pc8N6oAhTtjtpT/YyMFi1atKdtX3IRcJqmaZIk77gbT5qm3yfp+ySp1WqlzWZTtVpNSZJoMBgE3jm0DSgiAC8AWZ4XjRMAOHTQS4SciCnAE1DabDYzwI33OCh14Ak9g9dzbyLMRNe5PhQkgAvA0pt5uZMAxcOpFM1mMwOaiJzixMxmswzFwwuAyXJ4NNypLnyxthShemaGgmxpHymmFgJnyiVVUdihRoH5sQ/uiOHw8XqyB9zTZU95LeeEdSYyD2hm74mm46ARpWe/ANcuvUpWg94J7DPRaCL8zWYzRKvpAEwtiN/TMz30J0CRyK/hThevdyoaDhXRdkBwXumKvaZbsV+DzAD3IavAWafQerVahXNLfQdrh8Pryl1eAO81Di4p6ufUlbVw1N1JxGHDMV0sFuG9dEyOFu0u29P4jHw3748WLVq0L2bv1gE4I22ZJMkDSeePf/+6pA/Y6156/LsvakTMiTReXFxoOBwGuk+9XletVstQcwCuDigAZwA4gDIAF0UfZEGdcw9dhYi6UyvIGDQajSAf2W63QzbAufCAPsB2t9tVpVLR2dmZBoOBjo6OVKvVtF6vAz8dQAeA4xoo6gyHw0wtBI6Bc6+r1arm87kePXoUKBRE+qV9UzG41RSOekTbKT0U6BaLRXU6nZClgVoj7YuiJ5OJhsOhut2ujo6OQgSeyDzOFcAuTVPV63U1Gg29/vrrury8DMo5i8VC4/FYjUZDJycnmRoE9hiwP5vNQoEu2SOAMQ2mWEuyQZwJ9tYB63Q6zdBNqBvgvc5hxwGR9vUW5+fnevToUYi85+VccfJ2u5tmXdK+FoXx5KPkjJ398Wg5Bc7sF2AdyVCAOg7AYrHQ2dmZkiTR8fFxpu4AR4CzDPAm4t9sNkMWiewT12d9cE7JArl0qVPGKEbHoXVHBuWv4XCYcULY2/l8rtFoFORRowMQ7Y7aU/2MjBYtWrSnbe/WAfhxSX9Q0n/++Ps/sN//X5Mk+VHdFDYN3w63EX43kpFePCvtFU0AKS7H6CAYygQceGg0rnYDnUTa9xDgO9cDaLl0I9kCtNf5N9dzhRzMxwjQ8SwGEXeXIZUU1IAAdV6PAJ2EtfDob5qmIUrrVCZf5+l0mpFM9WJc5otD4jKLTg+CKgWdBaoOKjyuWsMcoZNQQ0GmATUmz8bg4ACgAY8YUX7ez9g4G8zJaxO8SJZ9opMsDiXX5rUoNkGj8jVFxWmz2QS6CtQsL0qmFoAzl98XB+CMnXHi4OLkuLHG3l2as+J1NMyJc0r2yZ0av5+rCXEdsihOK/Oz4DUKOGv57Afni/Wi7wBKSpxlqFLT6TR05mZvcMw4f/63aNHumD3Vz8ho0aJFe9r2dmRA/45uipmOkyR5TdJf0s1/an83SZLvkPQbkv7jxy//h7qRN/uMbiTO/vDbGcR2u9X19XWg/hBhBTwAdJxyQPQa8Ah/GoBzcXGh2WymXq8XmkNBzYH7TVEw4MYlJwE9rjyClKhr/dP5lqgohYlEqx+voR48eKBqtarBYKDhcJjJFLimfalUCpKiNO0C2OEoOBe8Xq9novVEzTGix4Dkq6srzWYzHR8fq9frBaoFgO/g4ECdTkfSPmvgmRfWmLUhwtxoNLTZbELEn7nhHDUaDdVqtTAnQHe73c5w7WkARyE39CJoPx55JwOB8whApWmV90DAMaHwl7VDutN7CnA9Gl/xd7T1Wf/Ly0tNp1N1u90QHT8+Ps7QcsjU5IthnbLmNRiuhMO9aOKFvj7gG2cO2VanDLFW7BvPS6/X03a71WAwCPNiT0ulUqhnODw8DNkcnEqcoeFwGArgK5WKvuZrvkaNRkOvvfaaxuOx6vV6KDZmHfiOGha1Ev1+P+PgjcdjrVYrnZ2d6fr6Wu12O2QWKHRmrBH8R7sL9l58RkaLFi3a07a3owL0B97kT7/tltemkv74uxmIFwA6bcdVbFxxxCUuHfC69KLXCUgK1wXguySnR2Dd0SAb4JKcXIuoJpxvxiztJSOd2kME1OsFfJyAIDIgFFF6JoJrEqmGquH3ckDp/Gmi3pLCeKHM+P1Zc97n1CZfz7xjICmzN15U7b0NAIc4Nr73XrfBdWu1WgDi7IfvBXx97kdWgawD+8E1GKdryvt9AeuAataAvWN+yL/6/DgTvMblZJmj7yVn2usw2CtXTvKMC3PA2eR9rAnXwwFDOQcHz7NovJa/VSqVQLnJF9lDs/J6DnfG/Tlz58OfSfbIpXxdAcnv57UGZDH8POczbtGiPQt7rz4jo31l2ssvv6yXX375Tf8+nU71y7/8y6FOMVq0t2t3phMwHH+oCdJNFB3gTAQU2oakDIgHlBCdrVQqoaZgNpuFiC8gc7FY6OHDm8yrA+BCoaDDw0OVSqXQnKxarYbrA/gAMQDHbrcbIpqSdP/+fVUqFV1fX2symWixWATuPYWmADuuRYMugPHDhw+1XC51enqqVqsVgJJTkKhDAECORqOwrtCFdruder1e4N1zDQqL0Vsfj8fhut4HIK8W4xQeABxA26UZAfHb7Vbn5zcU2F6vF6Q6r6+vM04ZdQtkE5BYvX//vna7nT7/+c+HWgyALoCUAnLPSLAGSZKEIunLy0utVqvMmcIxqlQqQR2HPXYa02QyCbQjxlUul/Xw4UNdX19nmpL5WfO1I0OFk0Xxu/fB4FyhdMVZpjkW2Y6joyMVi0UNBoNQOO0qWtRm8HOpVNJkMglnjufOHaZisah2u631eq3ZbBY4991uN/TH6PV6WiwWur6+Dg30ULriOavX6+H59TnRIZrnhGJwMnhvvPGGhsOh6vW6Dg8PgxO3Xq91fn4ezjTOjmfookWLFu39Zr/v9/0+/ek//aff9O+/9mu/pj/6R/+oPvOZz7yHo4r2frA74QB4dLBQKARaBh/wDiSJfnq0HSAqZaOheYqAc56JlrvhABB1JVoLrcIj1c4Zvw2EMF7u5/KePmcoMq7Q4gWnvI55ElF2TjxzzWcWfG3y0WXWGL66R2kBrLeN4bZ5+jjI0ECr8n2SFCL03M+58h6Fhqbj+0jE2dfcI+SsuY/flXMAwdzXnRscPMbDfJijF5g7gM6/Nn8N5u7v9T1hTr7OfsZwcJzy4tfxM+5ULx8La+HX9fOd32t/v1/bFZh4Nils9mfJ6XRQoDgL/iyTKeAZ9zPBOeD67kQxJy+YjxYtWrT3kz148EC9Xk8f/vCH9eDBgzd93Xw+19d//derUCjotddeu7VmLFq02+xOOACoxqCdf319rd1upwcPHqjVaunq6kpXV1fabDZBPQSeOAACagv8ZHjCAKX5fJ6JfqLsI+3BD/QZQAUqPKjNQMEAmMGHJkrptJSrq6sQva1UKkEOk2yCg3jGMxqNVCwWA5+82WwG6oa0L5BdLBaaz+ch2soaAm4BxdQkFAqFoK6DwgvFnu12W71eT5IyxaNekJkHWczXC6vH47EODg4C/5sIPAAXMMffuZevtcttSjfZgtVqpUePHmk2m4X6AcZAZ2D2eblchv0gMwLnv9fr6eDgQK1WKyOz+ejRI/X7fZ2enur09DSsI4bjVigUwjXyDglSnp6NaDQaYTwUZ1MrIilw8Pv9vvr9fqC7cMZQdtrtdhklHdaK/WGOXphL7UOj0VCz2czUqrDGvJ5i6eFwKOmGg0+Wxc8WdTXSXv613W4HYO4F4+xTu90O6kM0iENaNu/kebaNRnflcjnUJHhGBBrTxcXFF3VOo0WLFu15tFKppD/2x/6Yvv3bv13Hx8df9LUvvfSS/upf/at67bXX9Kf+1J/SL/zCL7xHo4z2vNudcACkPcggqpjnPTsdgyihpExEk5896ukG7cVVRYiEEg32aPmbKfpI+wJXMgNeK+CRaBpuueqPfzmlB3MOvUdu83PC4fCshuvTS3tqFN2F4YMDXvl3fj3yEeL8+PKOARHdfMbA19cj6vkiWQA96+SRdPbX99nX0MfO34hWwxlHHccpV15HAb/cz4fPge++Lq52Ayjmbx7RZn6eKXLOu0fDiX7zWn5GCSu/L7dlxHycfnby/Qk4M3mVKT9bfPm+cf88Jc0zDvkMmGc88s+513fg2Ptee0bO62Wg+0WLFi3a+8mSJNHp6am+7uu+7i1fWy6X9eEPfzgET6JFe7t2JxwAONhEpaUbYDgajYKmOEWcUDEcCDj4p2B0NBppvV6H4lCitCiZVCoVffCDHwzAiywE0VKUgnBA4Ek3m00Nh0NdXl4G8J8ke1lFQBzccgxgs91uQ/TV+xKQsZD2NQkAYO4xmUw0Go1ULpd1cnIiKduADP78er3Wa6+9Jukmil6r1QJvH4cCHv1ms+9m7AotvsasCwawBjxLUrvdDgAP7reDNiK9qOswt5OTE52cnKhSqWR487yX68Mxp/st41gsFqrVakEpBv19HB7m1Wq1QjZEUuDcFwqFoAb0xhtvZPoy4FDRPbff76tQKOj4+DjIlLIOTr+ZTCYhU0Xkv1qtBvUdzwyRCXD5Uzjv8/k8dKF2mhbz5ztRfdaAjBX7QVM1gLYXNLvTK0mNRiP0fcABp+6Gs02/ApSVcJaJ2pOVGQwG+sIXvhB6PKDO5LQfT1d7kTy0ImoScNwYE89apABFixYtWrRo79zuhAMgKURDPQoJaAWE87d8ZN4j8BShwvHHOchHP6Fj5KPZAK/1eh0AGePDEeB+nonw6KiDWI+OAlicZy0po3rD773IFvMoKSAMgEyU1EFRXvueyGua7ju/Qh/hnj7XvFqSm0d43YkCwAGuyUAwNpSNPLsBLYooOXvIekKboXEbRdJE8RkDtQf5Jmce+WftGb/XK0DRykeVcZRYa7IJ3tnWDXCM0+f3xmHg7Hl2AfPofp6jz/19vwDUUI9wwjD2gf3xM5bn2CdJkuke7VkE5sHe5ets/OzjIFGUDo2JL9beuf35DJE7RhgOAPeJDkC0aNGiRYv2zu1OOAD0ASAyDuAHLFAQ6E3CJAVOM+o7aZqGyDbUEsAQYBKtdklBQnMwGKhQ2Kv/OK1mt9sFcCrdRMMrlYpeeumljHIP16a3QL/fz/Q0gNJBRoJILhShJEnUarW02WwCXxqut3cihkMPB3w0GgVwvdls9Nprr6lYLGbqKS4vLzMgHPBULBZD11oAdZqmOj8/V6lUUqfTCY4Z3HjqKQDYNNri2vzMvrrzAD2JtUYBh7mdnJwEp2m9XocOyK6chLOUJIkGg0HQ6peUcUQ8m4KDR62C01BQHnIKEaA+TW8kQSeTScgGJEmi6+vrTNYJR4S/o9rD35bLZYY/n5eVPTg40Hw+D9mMer0e6hY4D6wJjoqk4ESQodlut6EvALUPAHE6D0s3zgRni+fE19rPvKsgScrIdOKIIoFKtq7T6ahWq6lSqej09DTURfhzhXNKynqxWGi9XofXYd4Ubj6fZ5xZd46jRYsWLVq0aG/f7oQDAKih8RPFh86hhl+e52cTDXTqAK+XlPk9zkStVgtRWkB0oVBQt9vN8N8ZmwP4zWYTil296ymRTOgaFxcXWi6XmcJarslciKjyNyLgNNOiARMZCS+eJCLske7VaqXBYKCDgwPdu3dPBwcHQSKy2WyGtfI1hHsPNWq5XGo2m4XIvHfklRSitkRnHYR59N6zEABK389qtap2ux3oU0mSBDAI4Fyv10GtyPsFAABXq5Wm06mm02loCJfPHgDKuSbNzZxu45QjuPY+foAn68c5hbaV57sD8HHyOGOSglPlNQ7FYvGJWgAkb1GOwjnjHLsDQXaCcXEuWS+cEH9e+I4ziQQs4D6fsWIt+M7aSvvuyTjU3iiOQm0cN75zLnC8/BlyZwwn5rZI/231KdGiRYsWLVq0t7Y74QAQsc5zmSeTSdBsd2BCNHSxWIToPUBGykpuwi1Hr7zdbgc6hHQD2D/4wQ+GcUB5Wa1WAWxLWQlRwA5RX6dnAKScXuHFn4C5crmsRqMRuhNXKpWg9c/r2+12pncA2uxYmqZBVx1w1mg0giIOYwRw12q1EB0m2ttqtcJ4meNtBZfw3QGGnU7nCZDGXJzmBMgl80EUez6f6/r6OijWTCYTnZ2dqdFo6OjoKNBH0MbHQeAs7Ha70OUZ3Xki4Kyhz8mpL1Cf5vO5arVa4Ocz7lLppiPwbDYLSjtO1UFJiCg/TgpOiRcvO3j1AmH2kX3DGdntdsEBo8sx9DYcY9bYC7jd4eC1nm1gL3FyOKfw/Z06l6ZpKIp2ChUZHNaFTARRexwNlJpYg81mo+l0Gs6O0+Ly9DP2CccMZ4zMRqFQCBmH26hp0aJFixYtWrS3tjvjADSbzQC0ut1uoMYg5+hKNQBSpDNLpZJeeOGFADChCq1WKzUajSDJOJ1OQ1GnpADQcDwmk0kAPg48nTcOeAHoESUFgAHs8go5+QhzuVwO4B6ZSSKqgM1ms6l6va6Li4vwXopb0zQNzg1zlfaNplyWUVIo5pzP5wHEEZWGDpKXEHWHysFmt9vN3NeValg3HCAoVYBIj4qzLqVSSfP5POPMUcjK2jAH1me324WsBvcgU+KZIiL/8/k8UF+KxWIAsZwz72jMXgLsMdbmNtqQc+k5J4zNuzNjKNh41Nuj9QcHB8HpIwvFOXUVJ8blZ4315rp8Z344Gb4eZEEYF88RjiNOHr93apTTe3h2XNWI8wM1yseMY+/ONZkBxoozi4QoToE7ddGiRYsWLVq0t293wgFwiUzpJoqOiokXAOapDEQXPUNAJgCgQtHpdrtVo9EIWuKAVa4h7YEV4Gm9XmsymWS4zq5+U6lU1Gw2g5IMY5QUxu2ACJBNtJQurWQk6JaLg8L9cJDcWYAOROQYWhM1CTgTFM6SRWBNAd+VSiXjzKDv7g6Jd/dljrPZLIBQQO9isdBkMsk4WdR10CjK+fXuVOHMAQBxVMjIbLfb0OOBM8D7GQNzYp2JhFP0jPqO0444HwBWQLekAFihgHlvCelJWVDmx757sS7ng3G7M8RrvKjXi3/Zt6urq0yhLdx/L44HiPM7zg/F2JxP72jNOMgeOM0K54jiePaEc8KzWCrddEYme4OTIO3rY8iW5IvGcZa4DmeOjAjPCGvoTkV0AKJFi/Z+s+12q5/4iZ/QG2+8oY997GP6rb/1t77pay8uLvTxj39cn/vc5/T5z3/+PRxltOfd7oQDIGV13flghwcNKCEiDHAFLHjEkoZM0+k0o+hTr9eDrCdKMtPpVJIyoFraq6oA3rmXRyIBX0gf1mo17Xa7ULBZr9dVKpV0fX0dALZ/ucwj9JXz83MdHBzogx/8oA4ODjQej0P0uNFoaDabaT6fB6BOZBgHAKDoWvaskdcCAKT422q1yhQHuzoSYI89Yq1dsQkghwNQq9V07949SQqOAqDawb8DVdZ2PB6Hgm7ugfNCkyov0HbgDAiW9ko9KAhVKpUM8KbYdTKZhCLkWq0WGsZVq1V1u11JClQfHKG8OpMbYNodAFcyIqLv1DKXEfVsCevN+nFejo6OwvnAGQKoe+0DjirZJqdlNRqNAPI5M9R1OGVN2lPeoNzwGq7B8wmd6OLiIjhrq9UqNPbyTB6OOPtJ8T5joh7GawMwKG5QoaJFixbt/WS73U4/+ZM/qX/4D/+hisXiF3UALi8v9df/+l/Xpz71qfdwhNHeD3YnHIDtdhu69LoyCkCT6KBTHgBSRBA98ispFIQC2rxbL6AW9RvXM/fCROg00p53DZB0sJSXagSUOLXBI7eeDdjtdqEImQjobDYLa0BXW4AcevYu4+lRZJRboNh4BBXHgawJFCQyJAB+qFK1Wi1TQCspE0Umsux/R9+eSLSrObGf2+1WvV4vZCdw1m6TDgWsAyYd5OeN+7iD4HUIeTqXdBMJB3w6Bcuj8L7Hfhagz5AJ4ZoAYpeNzUfmGSPOFnQjz8RwJjinOJJcg/X3Zmpe+MzveX6gL0Gzkm5UrTgrPDc+d/bfzzD3wdngGeQ55Jpcz+shOC9k+Lg28+dn79nA/jWbzYySk2cMokWLFu39Zmma6l/8i3+hv/E3/sabvoZu9tHevX34wx/Wxz72sYxoi9t6vdY//sf/WL/+67/+3g7sy2x34tNzu91qNBplmiZVq9UQtQZUwu0GcDkozEepkaRsNptBgcZBaKVSCbUBq9UqfEkKyiWdTkeVSkWXl5caj8chkindHAhXjwEAeTTXlYQc2DnAgvteLpd1enqqYrGo8XisYrGoBw8eqFwuh9oEaBBEq6fTqYbDYeCZs3YUwxKVl26KPavVqqbTadCoRzN+Op2GiCuFmayfKw4xHxywer2uWq2m5XIZqEf5pmblcjnDVycjcHJyok6no+FwqOFwqPF4rKurqwCamYs7Sjgqrq+PxKfTgfw7Dbna7XZYJ3cAKCImAk5xtr/GVXEoTvaaC6LjAGQoLj4HzohLkvq+kN1xCg7Xp4HdYDAI54m1JfvjmQPvTwD4R0q33W7r4OAg0HcePnyo2WwWHABXRQL8e6ExjqP/naJcb9bGeXVntVarqdvtajab6eLiIsjg4jS4c0RmgfF0Oh212+0g/ZokiZrNZlQCihYt2vvafuqnfko//dM//aZ/dypptHdn3/iN36jv/d7vDdTlvI1GI/2RP/JHogPw5TA49IANV8oBgKLG4gfdo39EIaHMeBEhUWdALMDWf+fXAqRBHyoUCgFsMAZAGNxmwItzwT2CDCgEtLkj4P0CpD23HANASsqAJAdneU61g0S++33Rl89nP9xJYZ4uoYqij3PfKVDltVzfqVoAf2g4i8VCw+FQ0+k0AFCnlHjk2bM8zr9nf/NrRuQ8z89nDv47V+nxvWOenB8fhxe4ejaCKD01I5JCQS+RBc+eeH0DdQq+d5xRnBnmw/r7ungGJ08nQnIVh6RSqajX60mS+v2+1uu1Go2GyuVyphu3n1tqI9wZhL5zWxTezxFnm/nS80C6ycAUi0VNJpOg2sX5cUlfIv/b7VbdbjdSf6JFi/YVYZ4pjfZ07cMf/rC+8Ru/Ud/8zd/8RA8at1qtpm/+5m9WoVDQv/pX/+p94wjcCQeA5lyPHj3SarUKWuGtVku1Wk3X19fq9/uBWw23G2BOVNbpPMg7ohPPQ9RsNtVqtQJYg8rj9QQ4INfX19putzo9PdW9e/c0mUxCsyP0zqGruEwptBCPHHtEGD40xa9cg3lLe9UWB3IULqMQA1jzrqqeESkUCkGxhvoHHKh+vx/WNM+lR+KTtXSpykqlElSAhsNhyNzQmZj3zGYzFYtFdTod7XY7XV9fB73+g4MDjUYjjcfjkHlpNps6Pj7O1IDguFGr4fry7JWkDNDHeQB0UgRMwTVZHd6DZj57RX0IGRLGwBfrwJc7AOyHN+Aist3r9TKcdwDxcDjUarXS8fGx2u12oGSRCfC+DBR9U3iOI0itiDsBrFe321W73dYLL7wgSaEHwoc+9CFJ0tXVVXgu0jTV5z//eV1fX4eGZDjH0+lUk8kkowxFRuL09DTQxSSFzJLTiKR9w7/pdKrRaKSDgwN94AMfUKlU0tXVlebzuVqtlprNpgaDQaCEQREcj8dqtVp65ZVXtFqt9OjRo4xKU7Ro0aJFi/Z27WMf+5i+93u/N+DFN7NaraY/8Sf+hL7zO79T3/3d360f+qEfeg9H+eWzO+EASHveNZFQOv8CiDzSDbh1aUMMIOg0HC9oBaw74AX85zXZ+R2UGrjocKmJkudlM6V9Ws754yjYwN92WghjB9QB5gFzPibWiXXz4kqnofi4AIess1NoPFIMTUbaK9n4HHAIoFLdJpMJaON3nmXxegnGC9/bC4q9roP9Ye/zdKu8AwcA94g4xtzZSx+Lj0dS6MhMRNwzDETkfT5kDYjcMwdoMX4PxoUDRHE3Tqkr5fAdZw+nzufE3pDlyDsonDnodThRlUpF1Wo1OF1OJWL8cPfz/0H6mru6k8/d1wcHPk3TMAfOZLVazWTuvMYAGhlr42sULVq0aNGivRs7ODgIwjFvZXxu5lURn2e7Ew4A4ADuda/XU7Va1aNHjzQYDDJqInR8hevuQM7BrBcQEzElotrv9zPShM1mM8hM7nY7nZyc6ODgIHCZZ7OZrq+vA5Cs1+u6f/9+iEzCb/a5SDeAkcj7ZDLRfD4P/PFms6nDw8MA+DlYRLxdlYXag3a7rXq9HnjQgDocFKg4HpXmYM/n86AiU61WVa/XQ9EpwLDdbmu5XOr8/DxDdzk5OQljg4tOky6nEFGk7JF4nBhUkQD4UD1wOpjDer3WdDpVsVgMDyZzgtbD61AHopDXnQ0i/0hXMlc46b/xG7+hwWCgo6OjDO+PGoDBYKBXX301ZBPK5bIODw9DdsedChw7ajOgujC/zWajX//1X1epdCOVWSqVMjUaFM1SA8FXu90Ohb/T6VQPHz4MgLpSqQQnlLOHSlaz2QwUH87jdDpVvV7XyclJ6H2w2+3U7XZDBH42m2XUisiEUOzMtbx+QVJwYrz5Hk4YWS32gOwGkX+c0BdffDE4RDjZpdKNIlW73Q7SwLPZTI8ePcoUWEeLFi1atGjR3pndKQcA4Oi8a352iodHz4ne4pURvYTG41FM1xXnvkSSnddPtPzNFGc8o+B1C3Dgb+NmS3uqCmAKigO/9wg4IChN0xAdlZ7kvHt9gPPm88otHu32zIMXrMK9dm678+WdyuH1E35d5sb+4QAQ/YcC40o77IPvJxx3z/jwdVvGxdfCx+R7xDpwpryPgZ8rr93gO3vJPTzq7mNgH/mb17TwO68jyUcT/Npki1gDr2vJ9z1gPb3Pgb/PMy6+n8w7nxHyYnY/c07RYv8Yr2cNPGPEl9fveJ0HjibnkufH99XrLvLXiBYtWrRo0aK9M7sTDgCRdKL7l5eXWq1WarfbOj091Xw+D5xyAJ6nbEqlko6PjwMtgsjvYrHQYDDQdDoNKiIOEIk40r0XMHZ+fh4ADtFxIuaAUgeWgNXdbhcKFQEnrs7D/Ch6vLq6UqPR0P3790PRMR1WAYi8t1AoBKkvxgRQgutP47PNZhP42sfHx4FOxd9RAfIIKk2ziE5zb1d6wYmCQkX/A8y573zNZjMlSaJ79+6pWq3q7OwsKDu1222NRqPQAZhIO3MjE3B0dBTqJNI0DRFmp7Q43aVQKIRaCIDnYDAI9DLWirqF8Xisw8NDdTodLRYLzWYzHRwc6JVXXsmAbSLdrBMZCMYkKdPoarVaBYlXirDZV4pe79+/r2azqX6/r8lkona7rU6no9FopM997nNqNps6OTlRoVDQvXv3AsCmvoB7UW/RaDR0eXmpz3zmM3rw4IE+/OEPZxwbd9y4lp99ipK9CzNzoRHd1dWVpL1aFlkFshpePO69Kfy5oJ+HU4S8gJkxkjU4ODgI6+ByvtGiRYsWLVq0d253wgGQ9jxspwtIe031fLTP/+1RYa7hSjlQV2h4BVjjtdwLeUPn+nNf5xu7NCbXACh7Qapz4Xmfq/8AljBXfPHiXDjVvB7ajEd+AUUAOC8M9uvB33cOeV5RB6oOgBpHA1UmB5FkVXz/vK7Bo+b5+gRf/zyn3WsB8mfE990j2m4+Jj8TzN8j134fzp/XmLCeZDU8K+F0FV9PouhOS/IMDdek3sXrWjw67mfK1yU/L+acz0JxTQqI88bffR1ZOwfl+QyNnxvfN685cTDPHPxZkfZZFs641454xsEpP/mMVLRo0aJFixbtndmdcABKpVLQB0/Tm66grVZLi8VCZ2dnkpQBlQBeaS+V+MYbbyhJErXb7QDQi8Wi7t27pwcPHoToJRF1gA0c72KxGPjPkgLQPjg40Gw2CxxqVIeQMQSEAk4ODw/DNdI01WQyyXTBdUlSxn59fa003UudAvYAO4BkuO84JOVyWUdHRwFcUffA+nhBq0ur0oyJSDw/o+riYBn+eJreqDK1Wq0ndN5dw58sR6vVClkEoribzSZcg8JYOO6APeeFwzWn14E7OajZUMBKFgfHCgNowvNnr9vtdlgDugyzT+z/crnMcOz57hQwxuRr3Gw21el0QkM3B80+tmKxqPl8LklPjKNUKukDH/hAyCKRGeIeZGyQ7lytVnr11VeDotArr7yiWq2myWSio6MjvfLKK5kCW+ZKYzkkYenEvNvdNDrzpm5kXHq9ngqFQqj36Pf72mw2Ojw8DBQkisnJmLRarSDlmXeoPRORl+adTqe6urpSq9UKZ519/mKqDdGiRYsWLVq0N7c74QDA0ZcUdOTL5XJGEtG57lKW4w44lhSoLYAuQKLLSeazB7ep4njdgKQQ6XUpTUCRc/upFAfIOGjHnAMvKQBdjz7nX+/ZDFdZcdoTcysUChmpSzccC4oteZ9zu/mdc/0BZb5OfFGMXalUArBEVtJ520S8oRmxjvl9dQ6/pEAjIbrsnHoHjl4L4fOSFLI+TnkqFouhoRdOnXPRfXy37cmbZTSgQqGw4zx6j8w7TYbfU/BOgy8yDx5Nd06+j3U2m2kymajb7apWq4X5oboD8CdCzzr4s0dHZnfEuBegHWcX55L3UADsdRC81xWzWDP2x+fhzjvP0XK5DMXVTo1zxyFatGjRokWL9vbtTjgAFMQSjYZrXSrddAAFrECXWK/XOj8/z0RBoc4AomazWWjihZMAICdy22q1An9eUkbLHwBEpJEMAYAORZ1WqxXuhzY/tQUOqhuNRphfuVxWs9lUu90OykCMY7fbhZ4GzLvRaAT6EPQO+hHwOkDxZDIJCjrw3Yliw+XfbDZqNBqB8z4YDDLSmUS1cYC8QRccd2oi6LFAI7fxeBy612KFQiGozQyHw1DP4Q7WfD7XeDwOwG673YZr4ESwHswV8I5CEoo6XiMxn8+VJEmIVk+n00zRdr1eD9kAl14lawF1huJyrx+ZzWYZsIzjikY9WRQyNWl6o9lPvwCnb7E3tVpNnU5Hm81Gl5eXoWO1U44mk0nIGDkgbrfbISp+cXERFJ/K5bLOzs5Ur9fV6/W03W51cXERqFf5wlvm55QiB/FkW6S9854kN+pMzIuMDs735eVlJmqfz1BBXYN2xZrStIy+DNPpVOfn5yqVSkGVKVq0aNGiRYv2zuzOOABE/SQFvXOixYBcAA3RfApJAfQAROd6e1EivwcU5hWFXHUlr67igNipSEQ8x+NxRjnFI9yuJkORq0skDgaDcJ889575eJ0DDgbA36PI0JwAvHklGN5DhBmahkfGmS8OENFkL2ouFouBcrTb7UJxMIWjHpGXFJyf4XCo5XIZnCrGh9Pn6w9YzEfz81x3nLM0TUPRMdFjwCv7hnzpcrkMjlmtVgtnDpqWOxhkNYhsc15ZO7IzODXL5TJkm9h75gItiPHhoHI2yd44zQxaG9kPP9dkzDhbAOrJZJKpaZlOpyHSjvOCtC4ZAVcK8gg+mRycNdbFG5bh+FAkz/NBsfRisQgOvFPcyFTls0F+XX/W6BxdKpVCMXa0aNGiRYsW7Z3Znfj05IP+7Ows6Km7IoxLCDrFxEFJo9EIYE+Sjo+PJSlDGwCYArzgI/M6qDgU2RLNdKlIwBEgjW6kOBW8169JhsDngfoQYIp6AVfD8euMx2PVajW1Wi2NRiONRqPM2qEZD8e90+lkFH2cNoQzQJbF6xZwMAqFQugEOxwOtdlsgh6804gYP79Hf5+6Bign0D6gauE4sMar1SpE2wHLOEA4aDhVAD9XXlosFhkqCk4C5wMFJRxJ9p0I+Wq1eqLINy+/CtA/Pz8Paw0dxmlC7An0FUA7DorTeDyCT1bl8vIyZCmob0ApC+dP2lNxGBtzIRPCtdfrtSaTiVarlabTaeaMcTZms5lGo1E4D4B2nEucMr9vt9tVoVAI6j/5M0tdA3vPurK31Hmwbq6S5A441CVqT9rt9hNUsGjRokWLFi3a27c74wAcHBxoNBppMpmEzqNE/QBMRFwlhSg6fH8AOSC3Xq/r4OBAw+EwgE5XOQFQYM6/Bxh5BN7vT8ZhvV6r3+8/AeIAyLxHUihc9foGgJ1HgIm4Q9vBUVkul2q1Wmo2m5pMJiHSzzq1Wq0wNubPe10ZiMZk0D4KhX0jNObrqkiMncg+6w0Ay6vU1Ot1LRaL0EyM/ZWyuu04VV7fcHBwEObkzlI+ewEdZDgchui2q/jgMDAmaDPMyZV2nIbkTp6DVY+GUyw9m82CtGneyASwpxT3OvCHUsTvmLdnHrjWcrnUer1+QmI2H/3GaSES78o67CGR+A9/+MOqVCrhDOI0ULeAg3hbDwPfa2RfPYPnWanxeKxKpZJpxsd+kzXJ130A/Dk7xeJN52akbXEycSKjRYsWLVq0d2N5dcGn/fq7bHfCASACeXx8rG63G0BqvV5XvV7XdDoNEV7AEVFBp3wUCoXASSfKi4qJg3GABxFXOPiAd6LgvBcg5QWXUJROT0+1XC4z1yDSjXSmR0+J+uMAVKtVdbvdQMsoFAoh8gxIohMwdIlaraZut5splDw7OwtgGmUW7rfb7TX7AWcATgf+rCsgmTlQ+8D8uYdTOQDRrG2329V6vQ6a8d1uV+VyOaggOcAmEozSEh2S6XaMI8CeQo/BIWFduL/TacggOUhF+QnQCY+/0+kEwMl1vPgVilKz2cxw15vNZnBUyQTlC81xnIh4dzqd0HX44OAgZCAA3F5gTA8EVyGi0Jl9IIvidQecD84W1yoWi0FdC0rU1dVV6EDNuWC/6RvhDgyZlyRJgnIRc+a+OLJe9Mv5ccoP9QPuMLKG/noyBLzu/dSSPVq0aNGivbf2L//lv9Rf/st/Wd/wDd+g3/27f/ebfqasVit94hOf0C/90i/pk5/85Hs8yi+f3QkHYLe7kWOkmReRxnq9rkajodlsFgACRagAToANBaNOnQCYwvEmUkwkvlarBcciSRL1er1AByGr4BkIwCggq1ar6fDwMDQdo5CXqC1SoowLIAY1YrFYqNfrBclMjw4DipHfxBmBZtTpdAJIGo1GOj8/D9F8fy9ADtA6mUw0m80COPP7AtaIsCLLSjaBcQPmvIiVqD2Uj263GzIBrEOj0QgFzmR3HKTCNe/1esFRcVoPXHfWEieGzAFgkXFKykSznVoC3YizVy6XVa/XM8XOOGSo8QCQG42G6vV6cOZwJllb57pjlUolsx+dTifjcNL0TFLo0+DOFVkQd0S9qzJjns/nwbny902n09CMbrfbBbpdv9/XcrkMGSpXm/IsljtF0o3TjoPm9S7Snp5Xq9XUaDTCc+t0Kmo3cGAoIHbHDJoZe09NA9fCMYoWLVq0aNHeqX3yk5/UJz/5Sf3e3/t79Tt/5+98UwdguVzq4x//uH7sx37svR3gl9nuhANApJYoKKAIxRgvsHW1FWkvIwgwASjzN0ASHXIBEgANdyZwDogwAiIBGdPpNKjsODc5SRIdHh4GUCYpOCxkJgBXFMMCihqNRoiKM2ZXWZEUKD+1Wi0AWgAZ1yU67mDev7sTk49sswdcczgchnmzPmQa8pFZePysGxFu5kg0ebVahWJPgKXLn1JQ6gWqg8EgZGIA60SCoYXMZrOgkpOPLEvK9GvgfACgPerOujmgBLTjODJesjxQZ/Jnmbntdnt1JNKGnB2n5nBvCtw5p+ydZztcMhNqF3Q4ztZ4PA7n1EE9Ov+SAg2M9cAB4D4AcupJXIXJATp7Sv8DzidODH/nOfSiXummx4Nn53jmvJ8AZ4QamXyNSLRo0aJFi/Zu7bOf/ay+//u/P9Mk1m2xWOjVV199j0f15bc74QAUi0U1m031+32t1+vALYa/7tQCIptslOuau3IMYBH6EJF+b2QEmALoEXUGWACKAJyDwUAXFxchmi0pFCyfnp5qvV7rtddey/D7PRIMnabRaGSoLYAjQOFkMgmRZqLDaZpminUBy0S2PVPBOgDaAJ/MD3O6Ew7AcrkMko1HR0eBNuJ1A7cpKwGmp9Np4Ic7bcX550R2vUgapy5NU41GI81ms5DVePHFF8P9oUSVSiWNx2MNBoMACtk7MgmSAmjkzBB1p34Afj3rwj3IskCP8et2Oh1Vq9WM7CXmxamoVFGDQOaD87perzUYDLRcLtXpdDLngrOOA1IulwPtCyrMYDDIOLUnJyfqdrv63Oc+p4uLC0l7mgwOT7/f18HBgV555RVVq9XgoFxdXQXAX6/XNZ/PNZvNAiWLRmSclbx6EQ5At9tVv98PxcbUCPBe6GXI+VJE32w2w/nDcSYLUiwWNZ1ONRgM1Gg0MmsaawCiRYsWLdqXYv/6X/9r/dk/+2e/6GscO71f7E44AK4G4zQBlzn0SKBHvvMAwBV/pH12AbqLd8aliBDw7HKPDm6cyuG0IgpMPRqOY+JcbqfaOI2IeRL15O8uEYo5yHRngShpq9UKai+85rZ1dvMGU07laTabkvYOAlQYIuVefAmfHn6/F28y9/zvWM/83vqe+/3zNQougSkpgFNfH/+307ag0CCj6mfO+0YQYffuwFyLzBQOHtF1lH6Yt9N2+D1RcQA5zmW1Wg2g2KlKfi6gG3nGApnN7Xabaf7le+7SoRgOCGvJ2BaLha6vr0PWgbPs//n5M+q1MuwNTuNutwvdi3kGoSxRi0BnbY/4s3/57Ao0Ns/S+bMZLVq0aNGivVMjuPmVZnfCAdhsNhoOh6FrLxQCgA60BCK8zkXGAOxQfYiAE6GHz4+ayGKxUL/fV7lcVq/X0263C82b2u12RmZS2vPjKdw8PDwMxcuAf6LDAGiUeqQ9eOT3AE2UUqQ9p55oKMWVXhAqZbv2wlE/OjrSZDLRYDAI98uvsd8D0ApApCEWUXvngl9dXQXZS3cE2C9vKjYcDgPlR1LgbQOCGRvcbnT42Rc4/9BnkP/0+grANg7WZDLReDwOEqLUF+AkkGnwMTAXnB72kvlAhXKgyVp9/vOfV5qmOj4+DupLrCVZAjIgjIGofqfTCXu72WzU7XaD8g7OAE4FZ4b1cCeHMZHNYD2hzLmjTJM1gHepVAqOxmAw0Hq9VrlcVqvV0ng81tnZWUaC1yVFOUPsoSS1Wq3grNFNudVqqd/v6/r6OtQeuFNOBs0zWZyXYrGohw8f6vr6OjiI9Xpdx8fHmUxEbAIWLVq0aNGivTt7y/BZkiQ/mCTJeZIkv2S/+y+TJPlUkiT/JkmSv58kSdf+9j1JknwmSZJfTZLkP3i7AyHq6dQcaCGAL6KHDiD9/YAv12Yn0ukce6cL5Ys1PRpOBBbQCF2IqDTAzqkwNJnKR62hdEjKvEdSiPjD4Xag75QLjPE4jYioK8pJHsm+jSbh2vyALKLW/N2j826eIWEf8vfwNQGs4YgB0uHDcy9AsPdqyCvqOMUGpSiakkl7Xf/bahy4Dn8DDHNOcCw8WyPt+wLkqWg+Ps6lZxnyjo+vi0vc4kCgc+9OLs+FtM8qeQTc54fxDPCVV5Hi+eFennGBJuWZsvx55vziAHpWhvPJ/vGMsX70jmCPGYfPw+Vicbr9HHrWJlq0Z23v1WdktGjRoj1NezsZgB+W9N9J+tv2u38k6XvSNN0kSfJfSPoeSf9ZkiQfkfT7Jf1bkl6Q9I+TJPmaNE2/KHkK4JQ81mX3gl4oKKj9IJkItxs1HOgnRFjh7/MdIO2qIi+88EIAQw6U6/V6UH7B6ZD24HK324VGXHmqxvn5eaboE6AI4EUxyCVC2+12AEte3Bk2yRwSj8Rut9tMV+Nms6mXX345ZAh8/oBY57LX6/UwbhwHePyFQkHtdluSgoY982QsZC+IzrvMKhFxMgdHR0eZ4muyOTgpjUZDx8fHms/n6vf7ASS7VKjrzC+XS/V6PTUaDX3hC1/Q9fW12u22Tk5Owv29XsFpRoVCISgr8TsaYVEYm197nJkk2fdYIJPCOMfjcUaFhznm9fihbFFX4mcU3rs7PVCJKA7n7xSvU/eCY9VoNIKjBccfVaaLi4uQcXN6FU7YycmJDg4ONBgM1O/3Q30Hqj6A9nK5rAcPHqhUKukLX/iC5vN56MbM2SuXy7p3715whmazmfr9vmq1WlAjev3117XZbHTv3r3gjLO2BwcHajab6nQ6GeoZ9Dh3HKJFe4b2w/oyf0ZGixYt2tO2twyjpWn6s5Kuc7/7mTRNCQn+c0kvPf73t0n60TRNl2mavirpM5J+01vdA/BBVI8Pf4AD4EvaR5/5N+ZdevN8ZYAaBbNEcD3CD9DnGk538Ci585/5u6QA5j1T4VF27pN3GPLzzdcb8F4v5mWORJqlPU+bcSCv6df0KL07PHnA6YWdgHSPyvq++fjYBy/g9Ptyz3y2x/fA9yH/Xl+D/Po5D90Luj3aD92HjIurP+UjzR7VdzqaZ4Pyluf459eKffIv31Nv4OX34zW+pvn7sgcud+r1D/n6C67rMqxv1ljLKWj5s5Lfjy+2x76mXmzvWb98Vsuzf+y///u28UaL9l7ae/EZGS1atGhP255GDcAfkfTxx/9+UTf/2WGvPf7dFzWi14BJ5ASvr69DVJXIKVFCL5SEc5ymqS4vL0NRIZz8Wq2m8Xis6+vr0ERrs9mEpmEefUVu0Bs6odwzHo81Go0ydB5AX6vVCsDF6RIoGjEHOPY0MYMfTRYDKxQKIXoLbQJ+NLrt1AtsNhudn5/r4OBAR0dHIUtBtJoIN70JkiTRcrnUarUKvRakmyJY1HrgnmNEtHkdFBC43USJm82m0jQNTdaI5qPoxNyIEANIicDTQIrsBa8vFAoZKpfXAlQqFb388ssqlUpB9WkwGITxEL0mg8K+lEqlwI+vVqt6+eWXn6DzuBypU5Zc4QkQ2mw2dXh4mCkwdydF2he28r1arYb5emG3nwMcuwcPHmi73YbIvK/lbrfT9fV1WPtarab5fK6rq6tAr4KWg1PslLbLy0vNZrPQiA9FICRYXX2IPaPehLVxx5xiXhxIHAiyXTxfZPA4C9TUkCHbbDahRoYCaO9xES3ac2Bf8mdktGjRoj1t+5IcgCRJ/oKkjaQfeRfv/S5J3yXpCR4+UUlpH+XNR/7zkdC88gtZBb+W65g79Yf7eKEnNBmAjY/HI5Z8ebQ/P5Y8p9zH4uPhtR7ZzEfdAT4OxKBDAEz5TuGpm0dUmY+P0Q3w5lkLH6OU7TPA71h7runRWiLqbqw51BKoUL7/+Xt71T6cd8Z8W6G4Z5N8rX0tcQA9u8J7fF6+3/n6hHzmwh0ErukZLbfb9przxt/9POfvT1QdOVWPst9Wp+H1C/msj//d7816kzHwdcnvl+8v13JZUkkhC8PrvGia3/kaRNAf7Xmyp/UZGS1atGhP2961A5AkyR+S9Lsk/bZ0j3hfl/QBe9lLj3/3hKVp+n2Svk+SGo1GSjQcjj8gnm6xtVotREOJbEt7YPPo0SNJ0vHxcVD8QT/8+vo6ZAUkhQg/KiN0EXbQvN1uMx15JWWaNnkEfLPZ6OzsTLvdvmkVQA8FHc9qDIdD1ev1oJ4CmKU2wJVrdrtdKOCEB04kHE754/XUarXS5eWl0vRGO15SUDSCf+/giqj65eVl4I1LCuo7RMJd4caLc6V9H4ZC4aaZ1Hw+D2u5Xq91cXEROP5kQvKykg7IJYVC6Pl8ngH6gED0+5GFdJANJ58sT7/fD1x3sgZOJWEuUGcAwJVKRa1WKxRWS/t6DvaXfQC4esSdAtxGo6HFYqE33nhDSZKEbtcUwq7X63DevRibzI/Xbrz++uuhfoEzylqjfCXtlXTIfHgtCIpKzWYz9BKgRqLb7QbngQLr4XCoR48eqdFo6PT0NNR7bLfboDbUaDRULBZDl2uMfXTHpNvthvdKCn0iULxib7yOwLtMk9Xh7EWLdlftaX5GJknypK5ztGjRon0J9q4cgCRJvlXSn5X0W9M09ba1Py7p/5UkyX+tmwKnr5b0L97qevnIN6ASgOa8Y+fw83ooF5ICYAOkeTMxoo3r9foJXfQ8p9t/zvOjvTDYQbK0B1/YarUKVB+AKg2xvDaA73lVIaKnnsVwLrxH3Z0D7gW50j5q61FdSZnIuzsGPre85esKyGAA0jDWOZ818Lnmz4G0L3p1Og5jzRcQe92FR8w9So0DsNvtO/S6lCmv9Qg4Zw7AzxpSK5DP2jA+nEWUiFxliAyRd5KG98/fWFvn50Pzmc1mgdbGGnnE3V/P/sHvz4+V9XU5Vn/OPJsF3cx5/Z6h4XnMR+tv29t8rYvXRvCMeQ2HP2uMC4fA6yOiRbtL9rQ/I6NFixbtadtbOgBJkvwdSd8i6ThJktck/SXdKBpUJP2jxx/O/zxN0/9Lmqb/S5Ikf1fSL+sm7fnH07ehblAul3VycqKHDx9quVzq8PBQ1Wo1UHGcorJarQJlyGk8REUBLDRqGgwGmkwm6vV66na7mkwm6vf7kvbRceQYiUZ6VNnBoLSnH8Gx/uxnP6tCoRAAFK8dj8eBu++OSrPZDJKfaOR75FN6kk7jVBcAXpLcyJi2Wi0tl8ugvw/QPD4+DtQOAL7TprzOoVarabfbhWj9crkMNQhe78BakF1xEEdEnHG2Wq0A4tbrtcbjcdiHRqMRMhkU48LFd+AHUMbZm06nYS3q9XqQmJxOpwEcu+pStVpVt9vNOJd0V8YWi4VWq1UGnFPXQAbCQf3BwYF6vV7oKTGfz8M+l8vlsO5E7c/Pz5Ukie7duxdqIfKAPE3TJ9a0VqsFHf71eh3GRo0K5wLHYLVahXGxV6x9pVJRr9cLzcJwoPJZB5wF3gut7OjoKGQtWNdisajj4+PwjHE2cGwd2POe5LESEE4ntSmSQpbPO0PjfPG8u1PG3KJFe9b2XnxGRosWLdrTtrd0ANI0/QO3/PoHvsjr/4qkv/JOBgHYXK1Wmk6ngcqQu24APB45J6LpkXei7F7UCtBFa90LRAHURBZRcvEoukcaATrT6VT9fl+VSkXtdjsUSwK6KVYkis21AUkAW5f29Pk6Xz8/Bgo4KeB1wIX2Ow2nuLZHmKHiAMBZM+giKBNBB/GoOsWhgNh8jYCkQIVqNptaLpehkPbo6CjT8ZbmXe4E+XXzKjXL5TJkeBy0uvPBegA8PTORl9mEiuN76w7PZrMJDbhwOLwGgX0GaONE8v7pdBpkUHe7m2ZeOBxu+cg53aaJjHs2BAeN6L33L3AlnfV6HWRJKQKGXgMA9xoPP6e+B8zJC5SJxG+3W11cXGg+n6vVaoVnwClSnCUyVK5yhHMFHS7fjdsdAWpDbnteokV7VvZefEZGixYt2tO2O9EJGPDZ6XQCV93/BhBAu36322k6nWq73Wo6nWYaM3lxJxQPAHGeZiHd0FQmk0kGWEkKqidEZmk2BeiTbkDK/fv3A8CCqkEU3jMCDrAAnPnC2nyxaK/X08HBgabTaUYiElnL5XKp1157TcViUY1GI/DQJQWONVFz5k7nXdbI1wLAj849CjHQm1BWYq1YD+Yv7Wkys9ksZCmIhjugBkzSuRZAirOAnr07Nuzz4eFhyCLA8e92u5n1JHov3QBXIs1kavr9fqgj6HQ6ms/nISPCNTAcAmg9w+EwQy8jm7PZbHR5eRnODs4nZ8WpLd6Rl1qOxWKhZrMZsieDwSDj+HJmfDw8E1JWcQqgTQ3Lo0ePMrUSGOeWyDzngWyS7y2OVL5WBUeS2hvPbjB/VKTYZ699caMfhfev4FxyppmDFxBHixYtWrRo0d6+3RkHYL1eB1qH00A8SgnIoGsqEX4i09Ke28x7ofMAZvPqLVAooD4A3Ik48n5XS3EHoNfrBXDt3HIpq5yS7+7r481z45lDp9NRuVzOdE9lfJVKJdCZ6vW67t+/H6K+OAJQLBgXVJN6va7lchmcBSxfk+BzIrNAUadnOXAM/Bqr1SoUcZdKpUzXWa8TWC6XIcIu7R2yer2uw8PDcD/2jQLWTqcTnId2ux2KWqk7cODpkqLsyXw+12g0UrfbVa/X0/X19a2RefbW7w+9DNAMaO33+xqNRpmshtdxcH4A1oxNUihOphgbGhvj9YLl/HeK592Zy9diLBaLkLlhHF7PMBqNQq0KYB0Azv05B2SPWFOKpclusc/5ugr2nog+c3JzJ9MpSkjf+u/JfkWLFi1atGjR3pndGQdgMpk8IddJtJMv1EPQYAegOoeZ6/F+gBCvBbzwOtRPHJQDSgA9qJvglOAgAMSdA++KQ9CSAHyMgwixRzCpZ3CwTuSfSC9Rd7IYUJEATWRE0jRVrVYLKjSupjSZTILG++npqYbDoa6ursJaAzj5Tn0DUWsHxKwhSi0e0XUqB/PzuUIzgf7FfNwhIuLLetCZGG3+Wq2W6ebM2kkK1B9Aaq/XC7UZ4/FYtVotRMzpj8B5QAefPef+gFd6LZRKpfC79XodwLPTmFgb1gzVKC8+9jUol8thzGS7XBKT+fsesCc4QKXSTXdpslXUUzilhqwBjg+RfoA955ezBfWLewHqfY7QkFarVdh/5uUFvjwHkkLNAs8K73cnwo1sCa+9reA4WrRo0aJFi/bF7U44ANvtVpPJ5AkFHQAp4J4aAYpw85x0j05jTp8gMu4UBQfJvJfXADAGg4H6/b5qtVrgQwPgcQC4F3KG9+/fz4A8aU/twYHw97qSDBHa2WymQqEQMgGj0Sj83a+BM0JmJEkStVotVatVdTqdAFThpK9WKz148EAnJydhTd1JgMfNHnjHXP9dvq6BDIVnQVh7B8Xu5BQKhZBpqVQqAYC6swf95/T0VJVKJTSIe/DggbrdbtgfHBDOx2q10nA4VLFY1MnJiZIk0ec//3ktFgt98IMfVKfTyUhkMjdkUD1yTeS7WCzqpZdeUqvVUpIkIbPh0qHL5TIUJec7M5OpwJliP9vtthqNhqbTqebzuarVqhqNRmgYl6eJOTWOyD/AG+BOpoJ943UHBwdqt9vabrd69dVXNZlMdHp6qmazGYrmeS64B1mYYrGoer0eMjXMhcg/mSWkb5vNZqjdcOeO88XZotB3Pp9nVLq8BobzBFUr9gSIFi1atGjR3p3dCQfAaQ55jXqKcQEOqIkAICWFwkbn/nOd25wKnAf+BtAgep7n6MMb98JTB40u3UgHXIAlUVMHwHCma7WaFotF0FMH6OQ53iiy8DtJgeIDPxsefafTyVA74L6zJqzZYrHQaDQK2QCAIQ4A14Q2RKTVI64AeM88cB8UZPz1rr7jikQYYJvz4OvrtC6+UA1yagjnCHpSvnCV3gE4WABN1IBms1lwJCmcpcCXLrbUGHg/BuYNoIe6xdrjzOEIuAPgUp9cg6i8S2GyllDVKPT2ol+UlZw6J+2zatDtRqORdrtdiMAT6ccRwxF2R9szIWSZ2EueAY/KuxPlSks4fawLdS08W9ClWKP5fB5ek68xiBYtWrRo0aK9c7tzDgBgx/XuUS1pNps6PDwM/GsHAQAMgA7XabfbqtfrQSqSyD3Rc661XC51fX0d6EWAFUnqdDoBKEk3EpoUqnIfQDN0oslkotlspnq9HiLbngnw6CiypNwX4AN9wgsiK5VKiDBXq9XA56ep2cnJSQCwFE46kAaszedzXV5ehoyKR6ur1WpoKoZ0qctU4ngA9pzyISnD9XZQB3iWlMkysI8UYTvgZNx5icrN5qbJF/uQ18ynbgKwjGPV6XQCNQgHyHnqq9UqRODb7bbSdK+Dz9oOh0NNJpOwp3xxL8CrpEw2gyJs1trBOcDci2x5NvwZ4axLN/Qa1grwv1wuw9mW9jUQ7pxtNpvgAFSr1SA5yriIuo9Go/A71sadk16vl3Gu+bc7xIyNNWTMZDdo6ibdOLfUE7iTNxgMVK1WQ60D9RXUt0SLFi1atGjR3pndCQcA8AQ4ALB4EyOAC1FpgD6SnXkwyTVqtVoA51zPo+F+Le/4ynfAI9KhDmy4j/P5AcLdbjcDePPUpMViocFgEOg3/hq/r3Pziaz6dVw6ERBM7UOensG83bEi0k0GgHs4JYjosMtnegaG1/v1MV6Xj2QTefcoNXNmzz0T4Ko00I9cccnlJx2IEmH3eg4HxJ45wJHgTPF3rrtYLMI1qQXx83ubeQG7z5v5IeHpZ9z5/awt50PaK/fg8EKv8QyPFy3zOl8bp3Bxbrk+TgVrQHaN+VJvgYNAv4g3uzegnSyOK0GRRcoXQ2OegWLd3AmMFi1atGjRor1zuzMOwHK51IMHD1Sv14NWOlSA6XQaorZXV1cBdKAz7xFHIro0R3LQSoGlUzOgTiRJotPT0wwlCADINYm4O/iAQoO0Jk2aPvShD6ler+v8/DwU3rrMIlmEYrGYKb4sFAqB5gBAoruxdy+m9gFtdOeab7dbDYdDpWmqF198UdVqVaPRKCjLABh3u53a7XYokHW6TrFYVKvVClFiB9lIVubVceCAS3vZS/YRUEdkGp44fHXPLsDxJsvANaUbQNhoNFSv18OciBznqViuhLPZ3Ojpn52dabVaqd1uZ3TliTC76g0ZCaL5w+EwrHuSJCGa7z0M8jKi7Gez2VSaprq6utJud9N0DhlUIuvUpDhtLe/4FQoFdbvdUPNBBgkJUeRucUgdLLtcLnMik+E1EGS22LPpdCpJoVEXzxASrC+99FIoWGbMZA6I2lcqlZDd2G63Oj8/D88YDivr4OeqVCqFTBn3JUOSP7PRokWLFi1atLdnd8IBACRJ+2iqR2k9I0A02qP+mGukAwygo3iGAJDhkVjnYBNJJyKfrzXg+kQ0KaT1cUCBAeB4RNSju0SgfQ1oXMZ8nOKR55u7PKhH4T2azhg8Qs71Xedf2vc/YB08M+C1Fr4uDtacvuGF1Iwvvxa8P7+PXnPg+8x4Aaw4Psw33x2W8wCvnWi1R6zzY8fhcNqRG8AYLrzTzjzSznUpFs5ngPJjhObFz6wLwN1Vo7xAmbHnG9F5RgBaGcCf9eJ5wgHJy5PyWsbhmSx33PLF7L4f/h6v/XDHJp+9YJ05M8yBa+IgRgcgWrRo0aJFe+d2JxyAg4MDnZycBG4yBa1Ezb0Y9/T0NCiBADKkPSBvNBoZp2G9Xms4HAa1HHjW3FdSiMDDxe/1ehneM+bShESPl8tlpnnV0dGRSqWSLi8vM1rt0CAkZSLFXPfg4EC9Xk+r1Uqf/exntdls9ODBgzA2SSEDwD0lBaDOtdH2B+g5tQOlHjIWtVpN4/E4RGPJchwfH2eiyIDmy8vLUMzKuNfrtRqNhjqdTqboE445TgkA1OUepX3xqRuAnkg0DgiA8/z8XIPBQC+88IKOjo6CM0FjMOoY2Fu4/wBgHDbPLJTL5aCCc3l5qUqlok6nE5SjkPDkPl7nwDyRqHQJ2GKxqOVyqbOzM0kKkf+rqyttNhudnp6GRmQoCZH5YK8ajUYm80MEHKei2+0+0TxvMBiEnyl+5lppmgaHFaoa9RBE7F0+1AuX2XdJeuGFFwL9DSfHi329QJvaGgqXcSi9+zT7ValUQoaIezUaDR0dHenq6irTXTkfBIgWLVq0aNGivbXdifCZR7qdX+8KK4BmLyqVlIlEcy2nT7j6jrSvEcgbPOm8ApBf282Bh2cteC/gOB8lv81cGpFx5CUQnRKUr4sgg+DqK06HYX4eiffIel7u0ufsUXq/N/Nmf7jumwGy2xSBfCxvZv46vqBt+byYt2c9/P18EUm/LevgZ8p56HkpTZ+3O6B+zny8vsaY38P3wt/D3/idn3s/C8zL1zF/7j0r4XvqGaF8ZsbHk/+br6VH8t28xsZrLfyeXOu2+XINMgF+piMFKFq0aNGiRXv3lrwZwH1PB5EkF5Kmki6f9Vjeph3r+Rjr8zJO6fkZ6/MyTun5GeuXOs6X0zQ9eVqDiRbtrtlz9hn5vPy/Iz0/Y31exik9P2N9XsYpfZk+I++EAyBJSZJ8Mk3Tjz7rcbwde17G+ryMU3p+xvq8jFN6fsb6vIwzWrRnac/Lc/K8jFN6fsb6vIxTen7G+ryMU/ryjTXmz6NFixYtWrRo0aJF+wqy6ABEixYtWrRo0aJFi/YVZHfJAfi+Zz2Ad2DPy1ifl3FKz89Yn5dxSs/PWJ+XcUaL9izteXlOnpdxSs/PWJ+XcUrPz1ifl3FKX6ax3pkagGjRokWLFi1atGjRon357S5lAKJFixYtWrRo0aJFi/ZltjvhACRJ8q1JkvxqkiSfSZLkzz3r8WBJknwgSZJ/miTJLydJ8r8kSfKfPv59L0mSf5Qkyacffz981mOVpCRJikmS/KskSf6nxz9/KEmSn3u8rh9PkqT8Vtd4LyxJkm6SJH8vSZJPJUnyK0mS/O/u8Jr+qcd7/0tJkvydJEmqd2FdkyT5wSRJzpMk+SX73a1rmNzYf/t4vP8mSZJ/5w6M9b98vP//JkmSv58kSdf+9j2Px/qrSZL8B+/lWKNFu2t2Vz8fpfgZ+eWy5+Uz8q5+Pj4e23PxGfksPx+fuQOQJElR0l+X9NslfUTSH0iS5CPPdlTBNpK+O03Tj0j6Jkl//PHY/pykf5Km6VdL+iePf74L9p9K+hX7+b+Q9N+kafq/ktSX9B3PZFRP2l+T9P9J0/TrJH2jbsZ859Y0SZIXJf0JSR9N0/QbJBUl/X7djXX9YUnfmvvdm63hb5f01Y+/vkvS33yPxoj9sJ4c6z+S9A1pmv6vJf2apO+RpMfP1++X9G89fs/fePx/RLRoX3F2xz8fpfgZ+eWyO/8Zecc/H6Xn5zPyh/WMPh+fuQMg6TdJ+kyapp9L03Ql6UclfdszHpMkKU3Th2ma/sLjf4918xC+qJvx/a3HL/tbkv4Pz2SAZkmSvCTpd0r6Hx7/nEj6mKS/9/gld2WcHUm/RdIPSFKapqs0TQe6g2v62EqSakmSlCTVJT3UHVjXNE1/VtJ17tdvtobfJulvpzf2zyV1kyR58J4MVLePNU3Tn0nTlNbI/1zSSzbWH03TdJmm6auSPqOb/yOiRftKtDv7+SjFz8gvhz1nn5F38vNRen4+I5/l5+NdcABelPQF+/m1x7+7U5YkySuS/m1JPyfpfpqmDx//6ZGk+89qXGZ/VdKflbR7/PORpIEdoruyrh+SdCHphx6nYv+HJEkauoNrmqbp65L+K0mf181/bENJP6+7ua7Sm6/hXX/G/oikn3r877s+1mjR3kt7bp6H+Bn51Oy5+Ix8Dj8fpefzM/LL9vl4FxyAO29JkjQl/b8l/ck0TUf+t/RGRumZSiklSfK7JJ2nafrzz3Icb9NKkv4dSX8zTdN/Wzft7TOpzLuwppL0mB/4bbr5D/kFSQ09maq7k3ZX1vCtLEmSv6AbGsGPPOuxRIsW7d1Z/Ix8qvZcfEY+z5+P0t1Yw7eyL/fn411wAF6X9AH7+aXHv7sTliTJgW7+Y/uRNE0/8fjXZ6SHHn8/f1bje2z/rqT/MEmSX9dNivhjuuEQdh+n5qS7s66vSXotTdOfe/zz39PNf3Z3bU0l6d+X9Gqaphdpmq4lfUI3a30X11V68zW8k89YkiR/SNLvkvSfpHs94js51mjRnpHd+echfkY+dXtePiOft89H6Tn6jHwvPh/vggPwLyV99ePK8bJuChx+/BmPSVLgCP6ApF9J0/S/tj/9uKQ/+Pjff1DSP3ivx+aWpun3pGn6Upqmr+hm/f6/aZr+J5L+qaT/6PHLnvk4JSlN00eSvpAkydc+/tVvk/TLumNr+tg+L+mbkiSpPz4LjPXOretje7M1/HFJ/+fHSgffJGloadBnYkmSfKtu0vH/YZqmM/vTj0v6/UmSVJIk+ZBuirL+xbMYY7Rod8Du7OejFD8jvxz2HH1GPm+fj9Jz8hn5nn0+pmn6zL8k/Q7dVDp/VtJfeNbjsXH973WTIvo3kn7x8dfv0A138J9I+rSkfyyp96zHamP+Fkn/0+N/f/jx4fiMpP9RUuVZj+/xuP43kj75eF1/TNLhXV1TSf93SZ+S9EuS/p+SKndhXSX9Hd3wLte6iRh9x5utoaREN0oin5X0P+tGteFZj/UzuuEy8lz99/b6v/B4rL8q6bc/6zMQv+LXs/y6q5+Pj8cWPyO/PGN8Lj4j7+rn4+OxPRefkc/y8zF2Ao4WLVq0aNGiRYsW7SvI7gIFKFq0aNGiRYsWLVq0aO+RRQcgWrRo0aJFixYtWrSvIIsOQLRo0aJFixYtWrRoX0EWHYBo0aJFixYtWrRo0b6CLDoA0aJFixYtWrRo0aJ9BVl0AKJFixYtWrRo0aJF+wqy6ABEixYtWrRo0aJFi/YVZNEBiBYtWrRo0aJFixbtK8j+/xRc8ynRKhrrAAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.999175150221156 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.19157586234558 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.901291583246511 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "NUMBER_OF_IMAGES = 4\n",
- "\n",
- "for _ in range(NUMBER_OF_IMAGES):\n",
- " plt.figure(figsize=(15, 5))\n",
- " dataset.update()\n",
- " image_of_particle = dataset.resolve(skip_augmentations=True)\n",
- " particle_label = get_label(image_of_particle)\n",
- " plt.subplot(1, 2, 1)\n",
- " plt.imshow(image_of_particle[..., 0], cmap=\"gray\")\n",
- " plt.subplot(1, 2, 2)\n",
- " plt.imshow(particle_label[..., 0] * 1.0, cmap=\"gray\")\n",
- " plt.show()\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3. Defining the network\n",
- "\n",
- "The network used is a U-Net, with a the pixel error as loss."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:17.356091Z",
- "iopub.status.busy": "2022-06-30T10:46:17.356091Z",
- "iopub.status.idle": "2022-06-30T10:46:17.859090Z",
- "shell.execute_reply": "2022-06-30T10:46:17.859090Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"model\"\n",
- "__________________________________________________________________________________________________\n",
- " Layer (type) Output Shape Param # Connected to \n",
- "==================================================================================================\n",
- " input_1 (InputLayer) [(None, None, None, 0 [] \n",
- " 1)] \n",
- " \n",
- " conv2d (Conv2D) (None, None, None, 160 ['input_1[0][0]'] \n",
- " 16) \n",
- " \n",
- " activation (Activation) (None, None, None, 0 ['conv2d[0][0]'] \n",
- " 16) \n",
- " \n",
- " max_pooling2d (MaxPooling2D) (None, None, None, 0 ['activation[0][0]'] \n",
- " 16) \n",
- " \n",
- " conv2d_1 (Conv2D) (None, None, None, 4640 ['max_pooling2d[0][0]'] \n",
- " 32) \n",
- " \n",
- " activation_1 (Activation) (None, None, None, 0 ['conv2d_1[0][0]'] \n",
- " 32) \n",
- " \n",
- " max_pooling2d_1 (MaxPooling2D) (None, None, None, 0 ['activation_1[0][0]'] \n",
- " 32) \n",
- " \n",
- " conv2d_2 (Conv2D) (None, None, None, 18496 ['max_pooling2d_1[0][0]'] \n",
- " 64) \n",
- " \n",
- " activation_2 (Activation) (None, None, None, 0 ['conv2d_2[0][0]'] \n",
- " 64) \n",
- " \n",
- " max_pooling2d_2 (MaxPooling2D) (None, None, None, 0 ['activation_2[0][0]'] \n",
- " 64) \n",
- " \n",
- " conv2d_3 (Conv2D) (None, None, None, 73856 ['max_pooling2d_2[0][0]'] \n",
- " 128) \n",
- " \n",
- " activation_3 (Activation) (None, None, None, 0 ['conv2d_3[0][0]'] \n",
- " 128) \n",
- " \n",
- " conv2d_4 (Conv2D) (None, None, None, 147584 ['activation_3[0][0]'] \n",
- " 128) \n",
- " \n",
- " activation_4 (Activation) (None, None, None, 0 ['conv2d_4[0][0]'] \n",
- " 128) \n",
- " \n",
- " conv2d_transpose (Conv2DTransp (None, None, None, 32832 ['activation_4[0][0]'] \n",
- " ose) 64) \n",
- " \n",
- " concatenate (Concatenate) (None, None, None, 0 ['conv2d_transpose[0][0]', \n",
- " 128) 'activation_2[0][0]'] \n",
- " \n",
- " conv2d_5 (Conv2D) (None, None, None, 73792 ['concatenate[0][0]'] \n",
- " 64) \n",
- " \n",
- " activation_5 (Activation) (None, None, None, 0 ['conv2d_5[0][0]'] \n",
- " 64) \n",
- " \n",
- " conv2d_transpose_1 (Conv2DTran (None, None, None, 8224 ['activation_5[0][0]'] \n",
- " spose) 32) \n",
- " \n",
- " concatenate_1 (Concatenate) (None, None, None, 0 ['conv2d_transpose_1[0][0]', \n",
- " 64) 'activation_1[0][0]'] \n",
- " \n",
- " conv2d_6 (Conv2D) (None, None, None, 18464 ['concatenate_1[0][0]'] \n",
- " 32) \n",
- " \n",
- " activation_6 (Activation) (None, None, None, 0 ['conv2d_6[0][0]'] \n",
- " 32) \n",
- " \n",
- " conv2d_transpose_2 (Conv2DTran (None, None, None, 2064 ['activation_6[0][0]'] \n",
- " spose) 16) \n",
- " \n",
- " concatenate_2 (Concatenate) (None, None, None, 0 ['conv2d_transpose_2[0][0]', \n",
- " 32) 'activation[0][0]'] \n",
- " \n",
- " conv2d_7 (Conv2D) (None, None, None, 4624 ['concatenate_2[0][0]'] \n",
- " 16) \n",
- " \n",
- " activation_7 (Activation) (None, None, None, 0 ['conv2d_7[0][0]'] \n",
- " 16) \n",
- " \n",
- " conv2d_8 (Conv2D) (None, None, None, 2320 ['activation_7[0][0]'] \n",
- " 16) \n",
- " \n",
- " activation_8 (Activation) (None, None, None, 0 ['conv2d_8[0][0]'] \n",
- " 16) \n",
- " \n",
- " conv2d_9 (Conv2D) (None, None, None, 2320 ['activation_8[0][0]'] \n",
- " 16) \n",
- " \n",
- " activation_9 (Activation) (None, None, None, 0 ['conv2d_9[0][0]'] \n",
- " 16) \n",
- " \n",
- " conv2d_10 (Conv2D) (None, None, None, 145 ['activation_9[0][0]'] \n",
- " 1) \n",
- " \n",
- "==================================================================================================\n",
- "Total params: 389,521\n",
- "Trainable params: 389,521\n",
- "Non-trainable params: 0\n",
- "__________________________________________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "import tensorflow.keras.backend as K\n",
- "import tensorflow.keras.optimizers as optimizers\n",
- "\n",
- "loss = dt.losses.flatten(\n",
- " dt.losses.weighted_crossentropy((10, 1))\n",
- ")\n",
- "metric = dt.losses.flatten(\n",
- " dt.losses.weighted_crossentropy((1, 1))\n",
- ")\n",
- "model = dt.models.UNet(\n",
- " (None, None, 1), \n",
- " conv_layers_dimensions=[16, 32, 64],\n",
- " base_conv_layers_dimensions=[128, 128], \n",
- " loss=loss,\n",
- " metrics=[metric],\n",
- " output_activation=\"sigmoid\"\n",
- ")\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 4. Training the network"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The network is trained similarly to example 2."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:46:17.881090Z",
- "iopub.status.busy": "2022-06-30T10:46:17.881090Z",
- "iopub.status.idle": "2022-06-30T10:48:45.745591Z",
- "shell.execute_reply": "2022-06-30T10:48:45.745120Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.253959005002361 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.734783463053265 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.731273448157776 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.547640352308868 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.342153535857395 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.3436082587228 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.038753528194913 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.050852060759805 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.851243026551137 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.640758504229094 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.718925229308404 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.93370064131247 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.657160096559998 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.386908011872354 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.62510174225979 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.287139512205844 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.10669490222566 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.601565886639083 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.616621242755285 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.421490691920038 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.487531287580605 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.451126672697416 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.80832342661923 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.962856865812686 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.355409226953494 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.93135068438388 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.696287213416554 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.11763437449825 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.307955278254065 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.955816036592426 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.487845244924616 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.799816963522037 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.36087005828847 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.081551199664496 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.720076832566841 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.36048008816563 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.526857872651034 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.295337716114783 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.380730953584859 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.035373269769348 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.429648906073162 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.306070629989852 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.4906657424851 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.441917974009206 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.816746102964325 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.523429274030509 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.970790996330056 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.928316280013089 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.14228578842545 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.35164548766119 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.049637315326233 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.911092785289535 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.199507840266836 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.394859654232917 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.014718670943425 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.494879416113395 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.248993843775516 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.38253007472153 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.909354276622684 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.422097983940278 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.909344858181942 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.807308642981159 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.494767490467318 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.206987495704814 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.418324355860815 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.366077677906933 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.363476879445614 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.00078824803306 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.16932396501133 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.90664196946962 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.027776127566977 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.334072054080735 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.3703427055809 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.103057981978832 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.724547717979133 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.124997865026364 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.03460896580603 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.77825003594341 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.254116067382984 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.530839181699044 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.899959408466906 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.225568859945225 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.372847156930673 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.524196299666322 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.761120984532944 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.911590355741335 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.78234923673136 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.666256685210097 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.31690438383922 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.579326035899522 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.916151392820057 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.553837076005838 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.911057581521312 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.093356990412731 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.301809400237085 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.285898477344821 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.960188448943883 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.986714218932363 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.853516112591443 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.751265877851758 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 0 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.477586392197614 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.890960278289551 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.701938534349093 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.918002834805867 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.923991457272006 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.938249732551666 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.5320409347003 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.358434955553982 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.501545906707491 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 6 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.277828755485421 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.02052598790636 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.712635532253994 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.14593317445339 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.163921428553735 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.88450897943275 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 13 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.513808131477187 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.399072380018689 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.025199578679963 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.593353891264004 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.445215382011703 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.267719044962398 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 19 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.21637031381851 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.86484311910791 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.508880681127891 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.519402891174497 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.454379406962023 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.97385779632901 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.085667183541691 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 26 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.506005478517253 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.693295729884817 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.086355262404755 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.97406147605277 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.861516036221065 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.46744702812734 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 32 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.75659573901833 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.94515159875257 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.985628635237864 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.51983555228206 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.80007867554679 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.383438350618881 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 39 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.40623650994123 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.64814702757904 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.667798463262011 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.062579185870177 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.917591705847821 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.385774988192452 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.040359450580725 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 46 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.956955929798955 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.103882344226333 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.118211936046697 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.142072791806877 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.687253514452209 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.842484499133949 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 53 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.612419609302954 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.854200048424211 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.022501486206881 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.23472624518779 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.750894515900926 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.00263949188824 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.940686975149308 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.131607744938986 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.400661130109398 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 59 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.726033474992025 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.766433438931653 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.89198331333959 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.167390559126535 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.500176443212926 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.259173505772441 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.02399464830234 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 66 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.99988893963048 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.487833440888402 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.466082667312685 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.166027883683444 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.31922999368762 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.879573538989607 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 73 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.80245633908039 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.248827128177663 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.01402584762375 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.858888966540984 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.571821030466632 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.36701265777184 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 79 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.315506395567132 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.360079170539276 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.543839816792534 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.511480705025425 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.316366485574044 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.748396969101263 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 85 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.597525293965289 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.327605742860804 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.01336977931544 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.834394694830076 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.221100399351583 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 90 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.956054440210558 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.864054805778796 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.722488835310793 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.485039979191123 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.287760459495743 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.073553167738156 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.096431790559816 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 98 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.254432390735648 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.456473654563634 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.376731226612094 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.518918176402384 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.448967460971737 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.040714038667339 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 104 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.466300267090983 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.23482293938892 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.220122767424975 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.393742022677324 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.044251018057322 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.772853049210513 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.696672354668186 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.825706975972523 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.680173082662915 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 111 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.741614348860104 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.415319087292797 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.027431097990299 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.414935263464685 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.876742278506354 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.581597302474668 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 118 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.089937600675423 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.377438351843221 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.225139270849407 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.605779536712813 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.60709684161853 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.695202961161996 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.695146041976809 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 124 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.621166447762533 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.251805418715927 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.723153620943378 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.532512673643822 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.508194216211045 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.704988519395473 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 131 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.087664517517691 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.829721521702897 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.380786968029836 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.030297497334443 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.85456787070704 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.985680700465705 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 138 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.873515332063356 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.771605721057382 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.474814093944046 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.167837337605402 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.741148846048304 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 144 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.288469529331119 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.772906876141416 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.92508658988142 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.18229014601998 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.387301452606511 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.320592358558752 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.095789872140145 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.646652010079205 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.709537189797564 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 151 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.849435037604152 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.543247914571246 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.107727283051867 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.904006590149613 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.367516775194389 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.092664448372066 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 157 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.001509733697803 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.376150150735066 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.04983569484728 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.355737924524425 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.96366849385949 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.129675608853585 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 163 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.842088815531131 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.733453110048767 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.223062992815812 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.567774236193333 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.957211087953988 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.754900354248512 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 170 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.186401842678155 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.474829112492763 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.096352165747849 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.020364693966318 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.251254767551409 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.858751677449838 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.569166783886585 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.992827024269646 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.920442105642845 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 177 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.174221151426941 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.747071610226046 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.555586473838643 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.48017170529847 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.864044031354434 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.433136449959726 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 184 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.73364000055167 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.222507330745465 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.757976032738114 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.771711262378279 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.777264865673011 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.45880464348733 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 190 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.187757253375091 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.384433673031744 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.323748001453918 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.252066048173289 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.485428709442154 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.350749488920876 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 197 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.147129552339411 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.020606681216524 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.944279691281462 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.25197365497267 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.29591496651475 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.751984163002584 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 203 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.883806660588512 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.477412165530877 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.162646285529789 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.326557490327552 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.917984101020824 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.077733015948265 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.296156149976433 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.11608039557948 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.68996468582902 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 210 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.024787585789134 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.715151118479938 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.270992492994425 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.61326127467004 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.807151001235379 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.054855881578677 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 216 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.65423715184265 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.185484800624792 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.893699946847278 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.931554256421357 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.511355678834104 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.751441906908335 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 223 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.99117516011123 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.12902426764026 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.38420191919992 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.016205018483051 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.77768573471446 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.736572374549448 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 229 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.487766267179452 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.879377232769718 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.308455003391733 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.422727475715005 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.655157354025619 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.816685901370787 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 235 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.508219451693448 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.054578389005101 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.441774505329645 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.741282889870668 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.532900141783033 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.748955799782323 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 242 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.889982401701797 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.79076159767987 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.354494078663791 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.23360771717413 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.782948523445485 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.189816380423988 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.489539256770884 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 250 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.99754082946141 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.902431480010012 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.72104864491189 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.389461907345705 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.227746642990917 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.398851955132287 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.055841826722627 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.720441922527911 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.864818968272724 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 256 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.931400927534852 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.532699003605837 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.047892926380287 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.224316475489934 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.454147492555105 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.049707681017733 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 263 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.048579483362248 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.852015467462122 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.804959637651812 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.982756069360784 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.894912562150207 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.967025121155174 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 270 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.345011400713366 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.440145769384873 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.61574618617285 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.373817771437906 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.046958642202155 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.286825043491588 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 276 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.99196764563655 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.970705052758271 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.709198992881236 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.435083199036697 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.52238874990552 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.746072517717256 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 282 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.86771665157438 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.863532636475806 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.076239753796438 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.62420717026031 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.582690504825278 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.2568820456759 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 288 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.88595044927412 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.22881099188715 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.904423198473445 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.36227952310661 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.87707990573298 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 294 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.993051141773153 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.301959876349448 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.05624080229511 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.651700266177395 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.810256996599794 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.250756436234 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.743501974613089 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.632154470391894 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 299 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.345226541274304 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.405501556656102 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.32704136570617 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.830606997730444 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.270982257786395 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.365326416259963 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 306 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.906920006993623 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.575501431289695 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.784670786161708 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.15862146437477 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.226561475911337 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.091393736085607 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 313 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.263188491384485 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.874607916331875 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.327674332543118 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.098233694414688 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.6008365395535 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 319 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.556332941344422 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.221788470532157 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.627488555355308 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.438958052290857 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.93412506230054 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.568915956454589 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.489664627015635 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.906476188123586 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.412310988187388 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 325 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.992130320807938 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.071957399883047 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.750982151312845 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.372709213729426 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.555012714344043 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.479592308139633 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 332 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.097121031167134 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.897510168541015 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.02866685296312 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.324159620549022 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.29656393480539 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.103345487296826 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 338 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.880618602079265 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.953130945912388 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.190631250821946 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.738684211916148 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.08579248667978 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.256090778212592 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 344 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.186549408181659 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.336557472433299 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.50048833731016 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.972947335347362 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.949003981489778 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.671717900607893 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 350 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.69215141877689 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.898169229159695 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.937886571054584 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.691099471106455 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.649143821292531 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.39580726294339 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 356 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.758342300118455 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.800580970915622 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.272254853867274 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.474570303016352 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.364161624694104 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.18775561212636 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 363 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.4615552587368 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.299252824401243 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.799239707031074 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.822158619427812 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.922248397472575 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.258973273842999 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 370 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.124030418549765 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.694463044332167 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.301109932850153 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.796008798479981 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.77864462865676 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.491478823025757 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 375 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.867946338492743 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.404243310092134 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.020753250413122 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.29342407244352 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.572923599367597 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.175895914956259 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 382 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.021950950875414 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.572938087494231 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.033078321270166 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.977116837980484 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.256677997553055 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.439123236247625 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.468897140717884 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 388 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.053370979764267 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.696449443374398 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.537820738531769 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.537147074087633 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.101205533338227 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.594420630828518 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 395 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.429666027577762 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.361192927584433 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.553102454931008 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.453778066013472 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.000301614193013 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.710188031501279 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.781095506159014 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.194508585857081 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.762553690790682 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 401 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.456247159332083 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.47683963070764 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.695184008549244 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.824854760495537 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.405278952318337 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.403103197102403 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.759367316180551 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 409 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.809702990801311 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.776612650505811 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.21348643145598 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.208647193280306 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.20084937349531 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.43071220001941 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 415 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.987250616032417 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.247183701078278 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.038891545802908 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.164059140094803 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.749894565229843 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.13645281094964 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 422 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.989159329409643 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.955499226979715 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.779751936387328 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.70666857249589 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.617503609414285 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.795588146414117 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 428 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.646451020481537 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.956839635229633 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.074688175147374 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.257506241585428 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.010607335245975 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.100062080187705 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 434 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.926594905238938 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.127504629297466 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.012689950079379 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.448694415027528 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.650212446917713 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.2785891572301 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.880393549087144 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.126998531936431 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.028367236388739 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 441 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.349954209473536 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.16176145866222 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.440805678271097 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 447 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.855537477242395 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.192771429542987 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.688706152383947 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.845179318347785 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.78989735227931 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.361148894394601 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 452 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.795665781466322 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.451968847004053 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.28931971260134 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.571031698468953 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.179817941819627 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.451221867880285 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.89295180209334 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.199344460314231 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.552242974415975 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 459 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.00800411449742 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.371958958734023 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.199878867729987 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.656357052982454 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.842353042147561 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.107600791972498 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 465 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.916929280659202 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.59025109774706 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.046189884433197 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.4787826933282 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.371967529676516 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.881758515780922 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 472 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.256141946887215 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.10209706434588 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.028901462453675 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.832801415335576 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.080516728518527 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.008130018837239 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.07643549252377 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 479 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.672669437720451 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.849892849298417 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.639031054212928 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.474954295158296 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.026198823159717 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 485 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.353791685892366 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.785020835611222 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.870320603339119 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.619772525566031 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.452232386340711 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.504213340183794 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 492 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.85806345531285 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.188472637366099 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.077864093245724 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.205120074059472 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.511155839171597 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.665722802542142 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 497 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.531523489647137 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.379556959450742 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.964199330460598 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.18322082050331 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.255043723705887 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.278198111732827 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 503 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.927320824993298 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.715584959076834 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.320086951163768 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.133602806645296 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.653109200920035 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.044616780287175 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 510 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.12704300647039 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.246827221397929 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.54036768571151 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.108278719680543 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.214651077412075 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.813199721778041 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.7301329305642 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.644656778184382 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 516 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.191473169524421 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.477872448254384 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.419417671682496 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.302651228120794 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.952754351565407 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.255323730930158 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 523 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.043386579061886 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.715064228612444 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.427962014893913 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.190653903863955 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.887000456330107 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.980696838004793 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 529 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.415386406170178 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.056425721525338 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.764314345487254 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.811965257317167 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.320359000697659 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.679923602363994 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.394653084460055 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.767599131068605 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.981769066574989 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 536 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.167339446635376 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.606314193187593 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.263980841882095 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.59775451234485 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.277662863210383 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.076263252977956 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 542 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.437507943099043 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.949409733034559 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.035895230534617 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.767785442574944 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.387667079656733 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.282503284306813 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 548 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.873804179424516 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.255947161893186 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.122025840781092 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.018649877617372 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.551793069197785 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.504446828465465 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 555 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.331799150045052 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.846648842781073 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.749036680078676 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.4684319333177 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.113748513715862 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.701018499516383 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 562 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.516828861948255 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.419154320123887 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.597170349913949 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.091620747732392 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.050793361810658 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.265610720881742 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 569 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.410021396707382 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.792296810122792 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.374753829404858 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.21606608651243 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.926536488048253 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.227776875353706 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.846037914583546 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.077213292847254 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.963256293997022 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 575 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.940133102420603 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.249640927014001 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.366746878721846 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.110946272337447 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.000612478154643 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.055231637446713 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 581 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.32404780918583 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.404874132717211 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.458661737757335 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.62091957872073 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.732807103614686 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.278796932312009 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 588 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.714328970149538 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.910934032749141 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.401216036875718 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.2373338011858 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.04331738547501 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.474530723232508 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 594 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.494467640906176 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.352031046821356 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.30818903184422 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.573495377973867 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.56876066367509 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.72009673272683 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 602 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.940317896189672 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.261055885932366 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.564668268226061 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.781805719427645 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.583431802101678 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.930291370212682 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.962545591639506 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.88885025577468 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.75996294879696 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 608 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.520730187692585 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.786833942707423 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.217222265784597 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.02933221466308 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.306733854441458 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.378581774095466 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 614 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.331342329395133 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.084675496019397 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.79815461072996 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.483421413871497 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.452902845056217 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.7424375865869 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 620 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.812433901907074 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.759825305977092 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.138581767226455 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.399972583670232 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.565934086998535 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.148657278373875 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 627 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.714643514896782 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.632872803116669 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.441898174462981 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.829609108475758 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.250252568710858 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.12966869040513 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 633 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.6096540188107 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.373098189385633 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.982175655975803 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.36230577950165 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.487756062687978 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.193532367710842 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.372639265423519 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 640 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.077285864713078 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.420524216144795 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.87087041212998 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.251129709842353 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.044022836360043 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.660390907663428 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 647 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.307854848711216 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.787051556118488 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.021587778357947 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.870925782042377 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.039800792046488 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.58009996534494 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 652 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.455898686606488 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.073144207182656 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.296449176919552 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.815700487206112 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.418699390922225 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.760933987332642 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 659 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.453388585936832 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.682930534120633 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.079741528192065 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.314580608434854 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.152376326785566 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.468655485677305 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.06769505857583 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 665 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.431391888395753 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.53914137163052 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.927579298299735 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.881513851325886 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.406630788123167 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.038469810328113 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 672 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.967351914423206 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.395348913800378 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.845093898451955 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.254393068103777 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.011986547450423 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.217513603235442 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 678 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.961292374987066 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.28042417406442 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.810304014430887 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.641631487836491 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.838025726084664 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.071694867888501 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.475887432684466 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.627645233550156 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.303407297832004 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 685 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.818131267785077 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.293351239877314 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.335728623379225 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.497042218607561 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.026461153449642 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.360249538113765 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 691 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.106752820733108 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.606682173385263 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.715263123402458 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.637129692280793 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.142041953322558 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.961669064272723 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 698 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.520564707524185 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.007305036316074 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.484357389003678 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.561250974785516 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.62254450976923 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.261463454493471 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 704 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.257479961757955 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.946680869436952 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.777762083277132 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.10934954951323 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.96168633704061 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.372161553764723 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.459990344122888 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 710 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.658423587305206 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.489269010973596 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.933035339085059 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.865250073666058 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.32902040312692 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.226246417505424 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.98348672190022 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 717 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.226296685812402 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.024242161179657 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.793039858979176 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.789678166401075 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.298359775028533 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.178578050152305 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.806863179980407 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 724 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.476289577676635 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.923802019734946 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.24097073227604 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.346119144186764 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.922887955663445 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.639189292322147 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 731 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.952278243273676 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.049497667481466 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.156119025120738 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.266732889426418 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.464926938986682 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.919088484075296 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.409462311643907 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 737 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.171738842925318 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.42955484415334 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.099292158570732 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.766247964354802 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.742583625103894 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.35082306366818 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 744 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.502325432160898 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.753603885538338 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.53326944355496 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.437750580657472 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.070050864425001 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.736527684029044 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 750 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.651873496429605 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.977726704214458 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.11257363466261 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.164822379933646 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.374327534836386 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.272682463948483 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 757 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.271533575292151 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.351637507147577 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.22674428088213 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.549729962519118 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.145165386683066 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.831631367547587 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 763 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.332000646702516 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.640801399790663 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.337726763082905 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.210333888430897 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.923237926822043 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.70679883996544 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 769 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.445721051569302 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.472897898562099 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.465376846919199 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.22718623710893 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.136539978473957 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.618480617330489 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 776 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.081520260935681 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.224559403039857 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.836841959364845 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.894021113737825 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.907400728785063 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.331519110232923 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.79748356629004 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.60393534079683 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 782 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.670093140751018 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.903294590169061 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.12576342755786 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.502572026769574 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.323606864479125 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.159547252023566 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 788 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.023079014779857 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.96395848883946 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.03026357984292 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.309826924323948 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.555214832754459 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.074584671931351 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 795 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.988851572478787 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.31303349886727 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.673876493577762 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.61692964896693 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.857048912808796 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.771042738812085 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 801 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.445573237340238 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.910201478682682 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.143541446221324 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.019125458813042 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.722005750923646 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.095297534617817 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 807 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.099386610363618 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.641953594824296 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.541818778348944 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.543499434294057 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.782856121611573 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.96600442613779 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 814 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.184135064831182 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.034922326905559 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.197974562893325 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.990840220944639 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.444921010021602 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.996714578348161 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.381670143410767 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 821 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.262947605388245 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.400843218661182 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.713882100954727 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.457693053510114 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.277962722529894 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.023971025039966 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.21200571059483 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.58527116353237 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.578721780893595 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 827 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.583358322258746 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.390303566200933 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.599179841939847 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.410224905946865 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 832 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.830955538831033 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.662950719071926 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.22094490524966 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.337010604350649 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.130115866407829 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.888069053097077 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 837 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.529484880675424 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.454914905099965 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.174181027241929 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.932012990220448 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.053403655548689 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.50196619756769 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 844 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.954074154091215 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.73242810576042 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.559476255515282 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.949296106718203 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.151240630999649 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.0994705350779 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 850 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.439798606008537 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.5779974237618 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.806295104978123 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.574185632400471 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.277170889131883 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.664713291578751 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 856 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.926693823142106 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.919026130890852 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.290847363600484 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.343450214180837 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.315160239834768 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.372447732949254 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 863 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.224691155236865 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.241279996175994 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.119767439479503 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.514943970426353 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.826316654764781 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.969820341479089 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 869 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.388282053000278 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.729393262449904 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.4054736522231 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.178687988807685 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.245574864304295 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.508708705799622 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 875 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.234302324164135 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.784475673494576 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.471335543327026 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.61399996478035 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.411871577355237 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.09419013907471 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.799122975793079 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 882 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.98176874876778 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.759417005369976 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.679170649627675 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.411680422611475 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.141756479697525 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.432018517879365 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 888 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.291332577029124 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.115163941838977 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.603500944470044 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.516406567525449 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.824661362639613 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.133865446441913 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 894 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.99605213488065 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.313965253866451 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.020373167688346 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.245434166899207 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.657974666776312 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.930894765142527 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 900 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.356222437890194 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.991994321368853 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.46367158430774 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.008508523811823 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.185953182663788 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.196907998262807 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.506577224183177 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.959573993407368 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.585191126450814 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 907 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.467803633701498 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.049393917966 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.593530780032129 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 913 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.945764150661521 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.574322490971259 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.844525506255053 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.131917058044955 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.842492442200179 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.020228943997395 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 919 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.156317149462694 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.409109027179532 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.171874471779608 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.33553711794633 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.687102386764096 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.685227139296885 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.919648026295555 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.952761981161249 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.135478690679708 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 925 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.812863749630056 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.485269727168822 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.200544633139407 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.64176146364672 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.980054761313996 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.09532878948313 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 932 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.105981059523772 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.046865970985156 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.692539122569798 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.224318805591338 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.927273028751337 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.39138204457144 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 939 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.194010189818385 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.877549548259648 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.525465924035561 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.01723133825565 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.378035384284516 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.061395597717762 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 945 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.378483558205025 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.374143887247515 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.86525329882511 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.95869956779 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.117477932624082 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.195850527529622 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 951 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.57317856559477 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.733400830388828 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.714153461272362 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.492100222169611 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.398830540246067 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.565986346118331 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 958 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.524176861864404 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.462455862187708 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.770221716941162 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.49850633440875 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.422706617047856 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.905451408006092 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.363752391310127 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.576913794987139 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.801604980445497 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 964 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.926501870707659 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.720141466098845 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.39716211201841 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.919588195935779 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.562839482447352 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.270004802018523 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 970 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.27581388920694 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.437800847178503 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.181364110599127 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.218940354781324 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.543451127243785 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.050017674349348 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 977 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.932155244075409 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.024738255172362 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.95530298491218 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.262730855426607 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.290178892001446 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.945943700266865 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 983 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.456443166765466 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.326165035740509 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.027122966855751 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.422027131499751 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.769019924546843 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.702245352391834 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 989 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.595497638291187 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.660315188622075 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.076262451314385 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.783042689318945 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.893496812324292 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.285093191232356 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 997 / 1000.0 samples before starting training\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.037986328992863 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.013510435567783 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.357944790121186 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.514935095245685 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.761151047775327 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.74926047245495 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating 1003 / 1000.0 samples before starting training\n",
- "Epoch 1/10\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.963002971190651 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.788975274544393 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.364220909182448 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.40035118526174 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.526587630306235 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.976798415598587 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.35405890757129 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.888007843221699 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.517268614402804 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.546535729494543 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.367514667208486 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.322958816018174 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.349176605802352 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.4158071044173 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.472298722724627 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.141510334306133 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.096811268864592 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.43624077606933 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.471295667017916 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.150294037539568 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.456690177720564 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.06095417732202 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.285016538368366 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.488482869449154 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.02375177401094 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.402672562351631 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.60502974914207 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.426685742389175 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.83315207881126 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.154181565736039 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.364653854497037 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.98304216868116 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.957330858790499 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.362595946120075 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.742593190219182 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.121607770812181 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.366568023356983 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.573499653515459 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.535015524824447 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.167052193884054 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.103357759866256 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.439780032025165 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.461877711191855 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.114847574655919 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.963734299280397 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.272201357309699 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.6035557539862 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.925686773589396 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.690412413652208 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.485820135995345 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.074196922057867 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.944667885946481 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.895408117563958 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.117213200301784 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.195261043483884 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.95375219674525 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.868428917564065 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.754096522560344 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.69963315117572 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.57398838474951 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.252969556998679 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.931248321467335 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.307038953617681 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.943007057836045 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.686767303845858 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.85513815896075 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.45712974435677 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.14850573233856 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.593506219346958 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.243682967536513 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.938275252904248 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.148788994213735 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.905813576227201 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.432836051308438 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.07800948543985 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.002789153618856 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.508577961368344 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.79212705790192 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.672970256669439 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.004220162840628 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.30504412341848 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0703 - nd_unet_crossentropy: 0.2576"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.261191144386835 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.776285750344549 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.55088693867637 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "37/62 [================>.............] - ETA: 0s - loss: 0.0632 - nd_unet_crossentropy: 0.1813"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.762339952541344 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.571043818913683 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.586355569561492 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "61/62 [============================>.] - ETA: 0s - loss: 0.0558 - nd_unet_crossentropy: 0.1473"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.522112053175361 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.692703965118355 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.593954633194155 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.561178340515436 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.128656139926944 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.99338649795216 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.381349347810131 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.57192625051387 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 8s 21ms/step - loss: 0.0554 - nd_unet_crossentropy: 0.1457 - val_loss: 0.0369 - val_nd_unet_crossentropy: 0.0375\n",
- "Epoch 2/10\n",
- "18/62 [=======>......................] - ETA: 0s - loss: 0.0270 - nd_unet_crossentropy: 0.0578"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.593624437883927 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.15971339521704 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "33/62 [==============>...............] - ETA: 0s - loss: 0.0231 - nd_unet_crossentropy: 0.0539"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.485391060678321 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.586932428576766 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.88429576240988 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "58/62 [===========================>..] - ETA: 0s - loss: 0.0202 - nd_unet_crossentropy: 0.0483"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.687454332741021 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.830274103388266 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0197 - nd_unet_crossentropy: 0.0472 - val_loss: 0.0128 - val_nd_unet_crossentropy: 0.0383\n",
- "Epoch 3/10\n",
- " 7/62 [==>...........................] - ETA: 0s - loss: 0.0141 - nd_unet_crossentropy: 0.0335"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.912826455098994 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.27288224858442 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "25/62 [===========>..................] - ETA: 0s - loss: 0.0146 - nd_unet_crossentropy: 0.0360"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.650257842312556 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.231560852705815 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.662510012693625 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "51/62 [=======================>......] - ETA: 0s - loss: 0.0133 - nd_unet_crossentropy: 0.0336"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.399990065718008 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.77868926883232 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.712184054413495 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0129 - nd_unet_crossentropy: 0.0327 - val_loss: 0.0106 - val_nd_unet_crossentropy: 0.0356\n",
- "Epoch 4/10\n",
- " 6/62 [=>............................] - ETA: 0s - loss: 0.0107 - nd_unet_crossentropy: 0.0289"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.66076749104932 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.231474022103583 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.143316921246942 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "28/62 [============>.................] - ETA: 0s - loss: 0.0109 - nd_unet_crossentropy: 0.0277"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.683133637199381 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.381960301175603 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "51/62 [=======================>......] - ETA: 0s - loss: 0.0104 - nd_unet_crossentropy: 0.0266"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.289188126675755 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.455070122136853 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.354246208035809 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0102 - nd_unet_crossentropy: 0.0261 - val_loss: 0.0097 - val_nd_unet_crossentropy: 0.0323\n",
- "Epoch 5/10\n",
- " 7/62 [==>...........................] - ETA: 0s - loss: 0.0106 - nd_unet_crossentropy: 0.0282"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.53892164164666 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.746874014842058 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.98717079462898 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "31/62 [==============>...............] - ETA: 0s - loss: 0.0098 - nd_unet_crossentropy: 0.0254"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.84295637661421 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.572563129255107 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.196415670867465 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "51/62 [=======================>......] - ETA: 0s - loss: 0.0099 - nd_unet_crossentropy: 0.0257"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.36322807415414 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.126697302977918 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0099 - nd_unet_crossentropy: 0.0256 - val_loss: 0.0101 - val_nd_unet_crossentropy: 0.0353\n",
- "Epoch 6/10\n",
- " 5/62 [=>............................] - ETA: 0s - loss: 0.0102 - nd_unet_crossentropy: 0.0244"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.269777363857795 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.430530058533565 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.002961895828893 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "25/62 [===========>..................] - ETA: 0s - loss: 0.0089 - nd_unet_crossentropy: 0.0228"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.710750882155999 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.19301731512573 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "44/62 [====================>.........] - ETA: 0s - loss: 0.0087 - nd_unet_crossentropy: 0.0225"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.535488614793394 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.765631326339753 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "59/62 [===========================>..] - ETA: 0s - loss: 0.0088 - nd_unet_crossentropy: 0.0227"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.779954707252097 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.142949302143887 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0089 - nd_unet_crossentropy: 0.0227 - val_loss: 0.0087 - val_nd_unet_crossentropy: 0.0185\n",
- "Epoch 7/10\n",
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0080 - nd_unet_crossentropy: 0.0221"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.66214602918198 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.872077577359933 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "27/62 [============>.................] - ETA: 0s - loss: 0.0082 - nd_unet_crossentropy: 0.0217"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.075358658818981 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.088182112612682 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "46/62 [=====================>........] - ETA: 0s - loss: 0.0085 - nd_unet_crossentropy: 0.0220"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.247541177739802 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.199723221018477 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0085 - nd_unet_crossentropy: 0.0219 - val_loss: 0.0082 - val_nd_unet_crossentropy: 0.0231\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.283392785401103 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.865195295751452 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 8/10\n",
- "12/62 [====>.........................] - ETA: 0s - loss: 0.0079 - nd_unet_crossentropy: 0.0217"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.295181155489633 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.43508454210347 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.092795035738916 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "37/62 [================>.............] - ETA: 0s - loss: 0.0086 - nd_unet_crossentropy: 0.0222"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.271212863870131 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.50061111588664 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.968095220560675 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "60/62 [============================>.] - ETA: 0s - loss: 0.0086 - nd_unet_crossentropy: 0.0221"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.458378451909818 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.195144425320091 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0085 - nd_unet_crossentropy: 0.0223 - val_loss: 0.0086 - val_nd_unet_crossentropy: 0.0257\n",
- "Epoch 9/10\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0085 - nd_unet_crossentropy: 0.0210"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.532885548274503 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.784092155204338 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.82667821736054 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "36/62 [================>.............] - ETA: 0s - loss: 0.0081 - nd_unet_crossentropy: 0.0209"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.765659271306028 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.01522280775324 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "52/62 [========================>.....] - ETA: 0s - loss: 0.0082 - nd_unet_crossentropy: 0.0214"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.468163414057052 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.650652597507932 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0083 - nd_unet_crossentropy: 0.0215 - val_loss: 0.0079 - val_nd_unet_crossentropy: 0.0179\n",
- "Epoch 10/10\n",
- " 6/62 [=>............................] - ETA: 0s - loss: 0.0078 - nd_unet_crossentropy: 0.0210"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.732542264906101 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.185048450948454 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.260972709139097 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "27/62 [============>.................] - ETA: 0s - loss: 0.0078 - nd_unet_crossentropy: 0.0205"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.973797148766067 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.796465068270512 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.460631112268842 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "54/62 [=========================>....] - ETA: 0s - loss: 0.0077 - nd_unet_crossentropy: 0.0204"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.060773939453803 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.096677814333885 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.650931385513744 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 11ms/step - loss: 0.0077 - nd_unet_crossentropy: 0.0202 - val_loss: 0.0079 - val_nd_unet_crossentropy: 0.0189\n",
- "Epoch 1/60\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.63856299203052 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.606868814490168 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.400293084813246 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.736674066047371 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.208135803502259 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.4077005797387 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 6/62 [=>............................] - ETA: 0s - loss: 0.0242 "
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.79298483509271 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.42946766756899 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0178"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.74765876625218 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.008385313325807 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.160731357819897 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "41/62 [==================>...........] - ETA: 0s - loss: 0.0145"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.342097930202083 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.785976541840176 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.614416032531107 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "61/62 [============================>.] - ETA: 0s - loss: 0.0131"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.817339584820852 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.393886733606653 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.759479468543267 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 2s 15ms/step - loss: 0.0131 - val_loss: 0.0100\n",
- "Epoch 2/60\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0099"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.59529863784659 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.646351404307161 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.354381494361773 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "35/62 [===============>..............] - ETA: 0s - loss: 0.0100"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.704637796630635 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.656342324503319 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "55/62 [=========================>....] - ETA: 0s - loss: 0.0098"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.217514581726316 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.692418631433783 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.644572456843093 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0098 - val_loss: 0.0095\n",
- "Epoch 3/60\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0097"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.917152504359454 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.859758084658964 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.480601766001474 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "32/62 [==============>...............] - ETA: 0s - loss: 0.0097"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.430061368606587 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.90307735915014 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.1269531402949 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "55/62 [=========================>....] - ETA: 0s - loss: 0.0095"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.584110403856688 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.045839324865364 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.852007062517703 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0095 - val_loss: 0.0096\n",
- "Epoch 4/60\n",
- "15/62 [======>.......................] - ETA: 0s - loss: 0.0094"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.685335315852686 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.43036482264176 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.024752475944426 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "35/62 [===============>..............] - ETA: 0s - loss: 0.0095"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.892291730900544 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.319329147833866 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.428787183155254 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "60/62 [============================>.] - ETA: 0s - loss: 0.0094"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.37465662754467 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.23359543199071 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.591060297400437 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0095 - val_loss: 0.0099\n",
- "Epoch 5/60\n",
- "20/62 [========>.....................] - ETA: 0s - loss: 0.0093"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.644315682427221 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.56117487850044 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.676501622485265 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "47/62 [=====================>........] - ETA: 0s - loss: 0.0092"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.720537469925066 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.030529547960644 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.349303024054501 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0093 - val_loss: 0.0092\n",
- "Epoch 6/60\n",
- " 1/62 [..............................] - ETA: 1s - loss: 0.0081"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.594850058710291 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.120309791096107 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.393107583771487 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "27/62 [============>.................] - ETA: 0s - loss: 0.0088"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.01483810440939 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.453505539868097 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.922233113936208 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "45/62 [====================>.........] - ETA: 0s - loss: 0.0089"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.364720352365103 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.578749871539086 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.322354666541706 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "59/62 [===========================>..] - ETA: 0s - loss: 0.0090"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.542034530159945 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.93903884738917 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.06150261970487 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0089 - val_loss: 0.0103\n",
- "Epoch 7/60\n",
- "16/62 [======>.......................] - ETA: 0s - loss: 0.0089"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.405139467815296 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.039724278148302 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.00611775732753 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "41/62 [==================>...........] - ETA: 0s - loss: 0.0087"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.331301541040574 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.522678960431344 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.434663668447985 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0087 - val_loss: 0.0090\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.399274003424107 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.896009118923834 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.42509728412413 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 8/60\n",
- "19/62 [========>.....................] - ETA: 0s - loss: 0.0084"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.698816875416263 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.92268460789849 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.806949366290933 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "40/62 [==================>...........] - ETA: 0s - loss: 0.0082"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.878289456370256 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.778183238370714 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "53/62 [========================>.....] - ETA: 0s - loss: 0.0082"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.532514128547435 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.774075723015871 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 13ms/step - loss: 0.0082 - val_loss: 0.0089\n",
- "Epoch 9/60\n",
- " 7/62 [==>...........................] - ETA: 0s - loss: 0.0078"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.279360263232551 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.188939589296748 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.547279379416752 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "35/62 [===============>..............] - ETA: 0s - loss: 0.0084"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.438920181351877 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.319450338763938 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.052102667281387 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - ETA: 0s - loss: 0.0083"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.115856197395981 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.907049800729244 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.099247623479366 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0083 - val_loss: 0.0088\n",
- "Epoch 10/60\n",
- "15/62 [======>.......................] - ETA: 0s - loss: 0.0079"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.281030817179055 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.745794614270935 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.27908446738568 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "36/62 [================>.............] - ETA: 0s - loss: 0.0083"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.09787110634091 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.193471711711114 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "56/62 [==========================>...] - ETA: 0s - loss: 0.0087"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.942869786949 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.39483108416565 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0087 - val_loss: 0.0092\n",
- "Epoch 11/60\n",
- " 7/62 [==>...........................] - ETA: 0s - loss: 0.0084"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.147818394844235 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.383800341350286 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.62794934339431 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "32/62 [==============>...............] - ETA: 0s - loss: 0.0086"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.908847687329223 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.543719881532551 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "52/62 [========================>.....] - ETA: 0s - loss: 0.0084"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.905827426804056 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.319814043423643 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.483895411566522 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 11ms/step - loss: 0.0084 - val_loss: 0.0096\n",
- "Epoch 12/60\n",
- " 6/62 [=>............................] - ETA: 0s - loss: 0.0089"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.409102963088372 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.132972036017756 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "26/62 [===========>..................] - ETA: 0s - loss: 0.0081"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.883030038807751 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.820469390933573 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.179051526670479 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "55/62 [=========================>....] - ETA: 0s - loss: 0.0080"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.949662452753033 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.754648891541164 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0080 - val_loss: 0.0091\n",
- "Epoch 13/60\n",
- " 5/62 [=>............................] - ETA: 0s - loss: 0.0079"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.631919604637272 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.46658114146153 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "22/62 [=========>....................] - ETA: 0s - loss: 0.0074"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.638265646642223 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.895018350063776 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "42/62 [===================>..........] - ETA: 0s - loss: 0.0073"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.062607155834279 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.027882180232194 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.388658430856355 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0076 - val_loss: 0.0094\n",
- "Epoch 14/60\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.51966584009099 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.034339375122162 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.157987652749222 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0072"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.799143819962932 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.977826844673793 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.189449978348808 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "47/62 [=====================>........] - ETA: 0s - loss: 0.0077"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.069052307692905 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.531461175676853 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.808025970967163 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0077 - val_loss: 0.0089\n",
- "Epoch 15/60\n",
- " 1/62 [..............................] - ETA: 1s - loss: 0.0067"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.83920550570229 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.99699832965771 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.91866519001638 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "22/62 [=========>....................] - ETA: 0s - loss: 0.0071"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.50439025857199 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.699599102917944 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.901062022131445 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "46/62 [=====================>........] - ETA: 0s - loss: 0.0072"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.350459820108805 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.11189288623855 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.968834116348667 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0073 - val_loss: 0.0092\n",
- "Epoch 16/60\n",
- " 6/62 [=>............................] - ETA: 0s - loss: 0.0070"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.048137934271136 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.184061359215296 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.636724169034704 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "28/62 [============>.................] - ETA: 0s - loss: 0.0073"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.053354599179144 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.124087778771537 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.768985455985321 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "52/62 [========================>.....] - ETA: 0s - loss: 0.0076"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.087148411180737 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.97367084139097 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.07938733727555 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0076 - val_loss: 0.0088\n",
- "Epoch 17/60\n",
- " 7/62 [==>...........................] - ETA: 0s - loss: 0.0070"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.977603239320997 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.651355338927946 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.557445827407488 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "34/62 [===============>..............] - ETA: 0s - loss: 0.0074"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.116991487312996 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.173139946095036 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.043546278414388 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "60/62 [============================>.] - ETA: 0s - loss: 0.0073"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.443066626554561 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.498949768512233 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.737411970841483 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0073 - val_loss: 0.0087\n",
- "Epoch 18/60\n",
- "16/62 [======>.......................] - ETA: 0s - loss: 0.0068"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.441139880291189 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.316521257720789 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.37975871890996 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "38/62 [=================>............] - ETA: 0s - loss: 0.0069"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.503138790722053 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.176206475279546 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "57/62 [==========================>...] - ETA: 0s - loss: 0.0069"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.825613748489054 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.348990104374039 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0069 - val_loss: 0.0089\n",
- "Epoch 19/60\n",
- " 5/62 [=>............................] - ETA: 0s - loss: 0.0064"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.630410725356445 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.366846309094512 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.264412918460497 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "31/62 [==============>...............] - ETA: 0s - loss: 0.0068"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.176083667179947 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.939588514793307 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.833944786836875 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "50/62 [=======================>......] - ETA: 0s - loss: 0.0068"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.568364881219793 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.788748266297441 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.311825611627302 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0068 - val_loss: 0.0093\n",
- "Epoch 20/60\n",
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0060"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.235505999364499 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.233505169874306 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "28/62 [============>.................] - ETA: 0s - loss: 0.0064"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.008423129931948 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.251278727946836 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.378159301543127 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "49/62 [======================>.......] - ETA: 0s - loss: 0.0066"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.29432061376206 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.584510842950777 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 13ms/step - loss: 0.0066 - val_loss: 0.0090\n",
- "Epoch 21/60\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.71640005894004 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.659373711401699 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.513473010552655 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "22/62 [=========>....................] - ETA: 0s - loss: 0.0065"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.030689999806315 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.950956970298439 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.385445551419199 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "48/62 [======================>.......] - ETA: 0s - loss: 0.0065"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.867158298347638 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.625312190119569 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0064 - val_loss: 0.0091\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.748533262275187 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.453985739424983 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 22/60\n",
- "16/62 [======>.......................] - ETA: 0s - loss: 0.0067"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.575041943520606 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.110478909941772 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "32/62 [==============>...............] - ETA: 0s - loss: 0.0064"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.143509145058829 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.621053287583672 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.507772229000535 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "54/62 [=========================>....] - ETA: 0s - loss: 0.0065"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.55054158853958 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.213189640575399 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.583020133417806 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0065 - val_loss: 0.0091\n",
- "Epoch 23/60\n",
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0063"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.414177862145225 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.05153342022366 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.30003300279976 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "36/62 [================>.............] - ETA: 0s - loss: 0.0064"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.12909556148876 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.905238547045373 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.762466172367406 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "55/62 [=========================>....] - ETA: 0s - loss: 0.0065"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.903455197341756 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.089654328770603 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.531866485344981 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0066 - val_loss: 0.0095\n",
- "Epoch 24/60\n",
- "15/62 [======>.......................] - ETA: 0s - loss: 0.0063"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.2205601452943 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.968115391074214 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.589948715739034 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "41/62 [==================>...........] - ETA: 0s - loss: 0.0063"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.031175062699774 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.816377244901753 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.509466327427152 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "61/62 [============================>.] - ETA: 0s - loss: 0.0063"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.480041050159652 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.090014371071137 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0062 - val_loss: 0.0092\n",
- "Epoch 25/60\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0064"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.663574484771036 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.577754250428793 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.883526097121361 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "42/62 [===================>..........] - ETA: 0s - loss: 0.0062"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.89193078053767 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.762428276954083 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.048545333111097 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - ETA: 0s - loss: 0.0062"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.289908845207812 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.934052050643714 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.718195064682178 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 11ms/step - loss: 0.0062 - val_loss: 0.0096\n",
- "Epoch 26/60\n",
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0061"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.955864783885206 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.752749732058028 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "29/62 [=============>................] - ETA: 0s - loss: 0.0058"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.33283659354393 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.215895158452907 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.451512084434341 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "54/62 [=========================>....] - ETA: 0s - loss: 0.0059"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.808026431829248 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.401759569452354 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.284183717354265 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 13ms/step - loss: 0.0059 - val_loss: 0.0094\n",
- "Epoch 27/60\n",
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0058"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.834500066920308 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.96447189551217 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.294847088793974 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "37/62 [================>.............] - ETA: 0s - loss: 0.0057"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.974554359625778 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.03260583670967 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.381760154638991 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "61/62 [============================>.] - ETA: 0s - loss: 0.0059"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.04464392936966 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.86763023814552 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0059 - val_loss: 0.0096\n",
- "Epoch 28/60\n",
- "16/62 [======>.......................] - ETA: 0s - loss: 0.0054"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.036960403486853 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.825934248950242 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.757387338771125 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "36/62 [================>.............] - ETA: 0s - loss: 0.0055"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.79337633741488 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.02644991253205 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.661402426958535 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "58/62 [===========================>..] - ETA: 0s - loss: 0.0059"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.745145648243467 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.327886222627829 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.50577804330757 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 11ms/step - loss: 0.0059 - val_loss: 0.0097\n",
- "Epoch 29/60\n",
- "17/62 [=======>......................] - ETA: 0s - loss: 0.0058"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.319334224009411 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.567999909371897 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.696402870820577 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "43/62 [===================>..........] - ETA: 0s - loss: 0.0058"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.224943803510763 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.474191249642113 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.748712320277036 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0062 - val_loss: 0.0098\n",
- "Epoch 30/60\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.576143883143704 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.444192875602619 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0054"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.076744352143523 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.75390026675709 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.521428046483406 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "46/62 [=====================>........] - ETA: 0s - loss: 0.0056"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.61240340117218 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.546168789116935 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.51195697268198 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0056 - val_loss: 0.0100\n",
- "Epoch 31/60\n",
- " 5/62 [=>............................] - ETA: 0s - loss: 0.0053"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.613177700320822 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.103702989429738 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "20/62 [========>.....................] - ETA: 0s - loss: 0.0051"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.980494469326816 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.626702055017667 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "38/62 [=================>............] - ETA: 0s - loss: 0.0051"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.855667114750684 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.140947336890765 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "56/62 [==========================>...] - ETA: 0s - loss: 0.0051"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.739980790709012 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.52750823158772 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.09670655895967 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0051 - val_loss: 0.0094\n",
- "Epoch 32/60\n",
- "12/62 [====>.........................] - ETA: 0s - loss: 0.0049"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.92812612613478 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.823170432153194 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.449995154054157 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "38/62 [=================>............] - ETA: 0s - loss: 0.0054"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.75548812967048 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.319111395852893 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.460266977064187 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - ETA: 0s - loss: 0.0053"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.45063086563772 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.989733852896979 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0053 - val_loss: 0.0098\n",
- "Epoch 33/60\n",
- "14/62 [=====>........................] - ETA: 0s - loss: 0.0050"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.353090106846789 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.854246071516954 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.93162630103648 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "38/62 [=================>............] - ETA: 0s - loss: 0.0050"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.42280946167115 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.735161609402802 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.394834327846098 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "60/62 [============================>.] - ETA: 0s - loss: 0.0050"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.927680963181537 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.584444573182777 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 13ms/step - loss: 0.0051 - val_loss: 0.0098\n",
- "Epoch 34/60\n",
- " 9/62 [===>..........................] - ETA: 0s - loss: 0.0050"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.113953748765022 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.094303344144022 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "26/62 [===========>..................] - ETA: 0s - loss: 0.0049"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.243766041789556 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.117714259317049 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.055632950701426 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "52/62 [========================>.....] - ETA: 0s - loss: 0.0049"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.485725846111114 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.911116614013405 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 13ms/step - loss: 0.0051 - val_loss: 0.0097\n",
- "Epoch 35/60\n",
- " 1/62 [..............................] - ETA: 2s - loss: 0.0043"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.107488785266087 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.999432466141783 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.661384707923473 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "28/62 [============>.................] - ETA: 0s - loss: 0.0052"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.197211935385921 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.603173880447752 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.653975701133566 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "48/62 [======================>.......] - ETA: 0s - loss: 0.0051"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.450043876333122 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.813350868227044 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.875308916790699 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0051 - val_loss: 0.0100\n",
- "Epoch 36/60\n",
- " 7/62 [==>...........................] - ETA: 0s - loss: 0.0045"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.458535160091813 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.677698244244464 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "22/62 [=========>....................] - ETA: 0s - loss: 0.0044"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.131018480535163 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.92466895713238 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.791972050133001 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "41/62 [==================>...........] - ETA: 0s - loss: 0.0047"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.708595128427007 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.19944523156557 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "58/62 [===========================>..] - ETA: 0s - loss: 0.0048"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.315580175222005 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.221197609967582 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.256366222884814 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 13ms/step - loss: 0.0048 - val_loss: 0.0101\n",
- "Epoch 37/60\n",
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0047"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.979600301033763 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.053075646720291 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.908648423019853 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "35/62 [===============>..............] - ETA: 0s - loss: 0.0046"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.453305683191257 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.92297068444784 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "56/62 [==========================>...] - ETA: 0s - loss: 0.0047"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.337467774609042 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.483609605421494 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.621386056248141 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0047 - val_loss: 0.0099\n",
- "Epoch 38/60\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0046"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.770187227895894 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.494416874965834 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.618084964660493 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "36/62 [================>.............] - ETA: 0s - loss: 0.0046"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.592899481667242 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.379630121580977 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "52/62 [========================>.....] - ETA: 0s - loss: 0.0046"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.151820842272114 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.58083619333188 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0047 - val_loss: 0.0108\n",
- "Epoch 39/60\n",
- " 1/62 [..............................] - ETA: 2s - loss: 0.0053"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.602737173285 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.899726465826456 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.015997166169623 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "31/62 [==============>...............] - ETA: 0s - loss: 0.0047"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.196769786279214 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.707283506069183 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.530287936049229 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "51/62 [=======================>......] - ETA: 0s - loss: 0.0047"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.928319448497874 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.657178574472422 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0047 - val_loss: 0.0097\n",
- "Epoch 40/60\n",
- " 1/62 [..............................] - ETA: 2s - loss: 0.0043"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.436758969026425 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.153092381325962 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.732079349299529 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "27/62 [============>.................] - ETA: 0s - loss: 0.0044"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.055958567147893 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.74254707853009 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.516373909451456 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "50/62 [=======================>......] - ETA: 0s - loss: 0.0047"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.148990527865868 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.871266705864551 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.737041775099728 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0047 - val_loss: 0.0102\n",
- "Epoch 41/60\n",
- " 1/62 [..............................] - ETA: 1s - loss: 0.0048"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.220464252204117 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.016071918335449 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "19/62 [========>.....................] - ETA: 0s - loss: 0.0045"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.460497169231736 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.102215672668306 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.017162197613198 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "45/62 [====================>.........] - ETA: 0s - loss: 0.0044"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.839743778760607 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.197804861528237 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.701273807561353 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 13ms/step - loss: 0.0043 - val_loss: 0.0101\n",
- "Epoch 42/60\n",
- " 1/62 [..............................] - ETA: 1s - loss: 0.0042"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.250526150537183 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.842814905498972 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.47787859837668 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "23/62 [==========>...................] - ETA: 0s - loss: 0.0042"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.110713128130248 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.807223724282291 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "43/62 [===================>..........] - ETA: 0s - loss: 0.0042"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.679200095324358 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.725060421363889 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "58/62 [===========================>..] - ETA: 0s - loss: 0.0043"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.750046283669803 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.119195831207936 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.74994581168631 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0043 - val_loss: 0.0105\n",
- "Epoch 43/60\n",
- "16/62 [======>.......................] - ETA: 0s - loss: 0.0043"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.821211296393384 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.658561003033512 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.984496145253797 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "37/62 [================>.............] - ETA: 0s - loss: 0.0042"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.75730890015589 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.593108246883073 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.032446478096738 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - ETA: 0s - loss: 0.0042"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.207403565895497 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.092981880418982 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.689711776174171 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0042 - val_loss: 0.0106\n",
- "Epoch 44/60\n",
- "16/62 [======>.......................] - ETA: 0s - loss: 0.0037"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.343762562766884 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.868396746471578 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "36/62 [================>.............] - ETA: 0s - loss: 0.0037"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.85444754365372 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.288763072934731 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "56/62 [==========================>...] - ETA: 0s - loss: 0.0038"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.945314172048258 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.176866309669592 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.361424767882596 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0040 - val_loss: 0.0118\n",
- "Epoch 45/60\n",
- "12/62 [====>.........................] - ETA: 0s - loss: 0.0051"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.480180438166595 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.464489220078697 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.975487873277332 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "38/62 [=================>............] - ETA: 0s - loss: 0.0045"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.987102253410992 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.85739794761443 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.403771531943192 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "59/62 [===========================>..] - ETA: 0s - loss: 0.0043"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.781267287240459 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.8616971211051 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0044 - val_loss: 0.0106\n",
- "Epoch 46/60\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0041"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.381532411086333 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.424597276114314 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.51683361566465 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "34/62 [===============>..............] - ETA: 0s - loss: 0.0051"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.308560012447536 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.669670238176174 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "48/62 [======================>.......] - ETA: 0s - loss: 0.0053"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.83144135545702 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.733569664565676 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "59/62 [===========================>..] - ETA: 0s - loss: 0.0054"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.816713505856288 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.239818901900586 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 15ms/step - loss: 0.0054 - val_loss: 0.0107\n",
- "Epoch 47/60\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0046"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.931769391074672 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.571664168478666 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.6667604911477 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "32/62 [==============>...............] - ETA: 0s - loss: 0.0041"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.416059841315654 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.14948424549569 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "44/62 [====================>.........] - ETA: 0s - loss: 0.0041"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.260245353691264 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.969875288179562 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 14ms/step - loss: 0.0041 - val_loss: 0.0103\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.116489154301949 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.750420951436347 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 48/60\n",
- "17/62 [=======>......................] - ETA: 0s - loss: 0.0038"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.539914488769762 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.237458652303756 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.849876973823198 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "33/62 [==============>...............] - ETA: 0s - loss: 0.0038"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.949432643888949 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.474053384017424 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "52/62 [========================>.....] - ETA: 0s - loss: 0.0038"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.102832640461802 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.39401879298408 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.076939387815298 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0037 - val_loss: 0.0108\n",
- "Epoch 49/60\n",
- "10/62 [===>..........................] - ETA: 0s - loss: 0.0033"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.760686092910296 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.326098999942952 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.470689255774293 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "30/62 [=============>................] - ETA: 0s - loss: 0.0036"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.546459438179829 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.357433963612493 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.700415321277799 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "55/62 [=========================>....] - ETA: 0s - loss: 0.0036"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.643536583020456 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.100181980273927 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.327240938756514 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0036 - val_loss: 0.0106\n",
- "Epoch 50/60\n",
- " 1/62 [..............................] - ETA: 1s - loss: 0.0028"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.757939454600235 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.389017326585051 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0037"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.048410182486533 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.92075032238194 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.086628735265425 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "46/62 [=====================>........] - ETA: 0s - loss: 0.0038"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.14448529358877 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.58978239672943 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.607322954629275 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0038 - val_loss: 0.0106\n",
- "Epoch 51/60\n",
- " 5/62 [=>............................] - ETA: 0s - loss: 0.0032"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.367032404081172 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.378095310719484 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.48151490245657 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "30/62 [=============>................] - ETA: 0s - loss: 0.0036"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.33365614238952 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.442968604908732 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "47/62 [=====================>........] - ETA: 0s - loss: 0.0035"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.653293889411032 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.64817308880992 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0035 - val_loss: 0.0113\n",
- "Epoch 52/60\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.388138514474402 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.93066483379912 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.835717404573945 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "22/62 [=========>....................] - ETA: 0s - loss: 0.0034"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.544365052376106 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.63272643802485 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.395183145294729 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "48/62 [======================>.......] - ETA: 0s - loss: 0.0038"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.905497383870244 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.49357495107761 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0038 - val_loss: 0.0108\n",
- "Epoch 53/60\n",
- " 1/62 [..............................] - ETA: 1s - loss: 0.0037"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.463031123847898 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.580348324018962 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.401053556719553 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0036"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.44497042860235 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.61836728722393 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "41/62 [==================>...........] - ETA: 0s - loss: 0.0035"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.518108952908854 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.856214633856878 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.623711610077613 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - ETA: 0s - loss: 0.0034"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.226393579035776 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.655314853605445 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.536479212829075 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0034 - val_loss: 0.0112\n",
- "Epoch 54/60\n",
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0034"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.680779574797887 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.020219498472633 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "37/62 [================>.............] - ETA: 0s - loss: 0.0032"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.18764587429295 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.140241917823369 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.592376009964543 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0032 - val_loss: 0.0108\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.183614014811873 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.783673038259824 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.296356636314208 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 55/60\n",
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.406848209276227 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.27852818730495 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.306192986501923 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "41/62 [==================>...........] - ETA: 0s - loss: 0.0031"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.682700750954035 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.786180028110204 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.700973583998918 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0031 - val_loss: 0.0114\n",
- "Epoch 56/60\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.131036728122055 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.54800196971511 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "17/62 [=======>......................] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.927971934714268 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.740336177793676 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.354167520089936 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "39/62 [=================>............] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.203837541849733 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.671575392348288 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.118552154353162 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0035 - val_loss: 0.0109\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.30110205139768 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.499778248154236 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.485893327979205 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 57/60\n",
- "21/62 [=========>....................] - ETA: 0s - loss: 0.0034"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.16131853476091 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.989128582348108 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.154950260029569 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "47/62 [=====================>........] - ETA: 0s - loss: 0.0032"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.016375935829505 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.034234613636166 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.908121365292345 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0033 - val_loss: 0.0114\n",
- "Epoch 58/60\n",
- " 1/62 [..............................] - ETA: 1s - loss: 0.0033"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.97834092290269 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.075389121499377 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "20/62 [========>.....................] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.121834637451968 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.298596680013418 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.960985828909244 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "40/62 [==================>...........] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.77319875443838 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.722283466785093 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.295956852911484 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "60/62 [============================>.] - ETA: 0s - loss: 0.0029"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.012808001840476 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.137538863600945 should be at most 0.5\n",
- "To fix, set magnification to 25.0, and downsample the resulting image with dt.AveragePooling((25.0, 25.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.127894905432232 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0029 - val_loss: 0.0120\n",
- "Epoch 59/60\n",
- "22/62 [=========>....................] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.40639756216661 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.16887073557265 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "37/62 [================>.............] - ETA: 0s - loss: 0.0029"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.566625918678742 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.505329535143211 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "57/62 [==========================>...] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.382216009213577 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.976957216146179 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.305209279935326 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0029 - val_loss: 0.0121\n",
- "Epoch 60/60\n",
- "11/62 [====>.........................] - ETA: 0s - loss: 0.0031"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.96911522008514 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.955841104994178 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.848096365766171 should be at most 0.5\n",
- "To fix, set magnification to 28.0, and downsample the resulting image with dt.AveragePooling((28.0, 28.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "36/62 [================>.............] - ETA: 0s - loss: 0.0030"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.377268688112023 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 12.702204126979304 should be at most 0.5\n",
- "To fix, set magnification to 26.0, and downsample the resulting image with dt.AveragePooling((26.0, 26.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.397866699403137 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "61/62 [============================>.] - ETA: 0s - loss: 0.0029"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.613168665682043 should be at most 0.5\n",
- "To fix, set magnification to 30.0, and downsample the resulting image with dt.AveragePooling((30.0, 30.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.295808499631743 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n",
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.285283952205333 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "62/62 [==============================] - 1s 12ms/step - loss: 0.0029 - val_loss: 0.0123\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 13.30973113690197 should be at most 0.5\n",
- "To fix, set magnification to 27.0, and downsample the resulting image with dt.AveragePooling((27.0, 27.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- }
- ],
- "source": [
- "TRAIN_MODEL = True\n",
- "\n",
- "validation_set_size = 100\n",
- "\n",
- "validation_set = [dataset.update().resolve() for _ in range(validation_set_size)]\n",
- "validation_labels = [get_label(image) for image in validation_set]\n",
- "\n",
- "if TRAIN_MODEL:\n",
- " generator = dt.generators.ContinuousGenerator(\n",
- " dataset & (dataset >> get_label),\n",
- " batch_size=16,\n",
- " min_data_size=1e3,\n",
- " max_data_size=1e4,\n",
- " )\n",
- "\n",
- " with generator:\n",
- "\n",
- " # Train 5 epochs with weighted loss\n",
- " h = model.fit(generator,\n",
- " epochs=10,\n",
- " validation_data=(\n",
- " np.array(validation_set),\n",
- " np.array(validation_labels)\n",
- " ))\n",
- "\n",
- " model.compile(loss=metric, optimizer=\"adam\")\n",
- "\n",
- " # Train 30 epochs with unweighted loss\n",
- " h2 = model.fit(generator,\n",
- " epochs=60,\n",
- " validation_data=(\n",
- " np.array(validation_set),\n",
- " np.array(validation_labels)\n",
- " ))\n",
- " \n",
- "else:\n",
- " model_path = datasets.load_model(\"QuantumDots\")\n",
- " model.load_weights(model_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 5. Evaluating the network"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 Prediction visualization\n",
- "\n",
- "We show a few images, with the ground truth and network prediction."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:48:45.748590Z",
- "iopub.status.busy": "2022-06-30T10:48:45.748590Z",
- "iopub.status.idle": "2022-06-30T10:48:47.170091Z",
- "shell.execute_reply": "2022-06-30T10:48:47.169590Z"
- },
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.702994989478627 should be at most 0.5\n",
- "To fix, set magnification to 32.0, and downsample the resulting image with dt.AveragePooling((32.0, 32.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1/1 [==============================] - 0s 214ms/step\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1/1 [==============================] - 0s 12ms/step\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.16021299482758 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1/1 [==============================] - 0s 14ms/step\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 15.020121640482753 should be at most 0.5\n",
- "To fix, set magnification to 31.0, and downsample the resulting image with dt.AveragePooling((31.0, 31.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1/1 [==============================] - 0s 14ms/step\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\gu\\deeptrack\\deeptrack-2.0\\deeptrack\\optics.py:207: UserWarning: Likely bad optical parameters. NA / wavelength * resolution * magnification = 14.399039625281212 should be at most 0.5\n",
- "To fix, set magnification to 29.0, and downsample the resulting image with dt.AveragePooling((29.0, 29.0, 1))\n",
- "\n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "image/png": 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cr2EYME0TpVIJR0dH2N7ehmma0hgRI0aMGDFi3AxOxBMkam7udDoIgkCUFEVR0Ol0UKlURE3KZrPSkcfOvWq1ikKhAABwXVdKeaZpSlt9tOU/m82iUCggk8lgMBhIeYkRBCRfNNRTzWLcADvIWJ6jmV5VVayursp7qX4UCgUpP06nU0wmE1kbIwMSiYSoW+y6Y2ZWKpUSwzkAiYgg6cnn8+IfY2mzWCxKifTw8BAAhDRcuXJlwTvGaIlsNiuddIlEQvZH1arf78MwDMznc3S7XRweHsKyLCFeQRAsKD80snc6HTleuVxGs9mUUNf5fI5yuSxZYezWZJmPxI2lO553r9fDwcGBqGO2bUuWGDOuqEqSRLZaLUwmE+kiPTg4kHIxy7D05MWIESNGjBivFyeiFMjyFB/ebNOnF4jt9ZT46K8CrneWkQBRQYoa3XkMPqxJyJjkTWWHZMkwDFFCop4eRgiwZEeiR48QM7UymcwCQSJZYz4VlSceH4DsazgcCtkhwWHGUzKZhK7r4gUi0eK+AAjBSafTyGazSCQS6Pf7cr48d2ZPUfGjqhQEgXijSOhIktjlFyWh0bIZjfu6rouaNJlM5HqSBNLLxiYEHo8lxMlkgsFgIOGszPniNSapZhmXvjpN08S/xetJssksL8/z0G635XNTFEXiJsbjMVzXlWsZI0aMGDFivF6cCMUKuG7OplqgKAp0XYdhGAthkSQ11WpVjOZ8KDebTSnlsHwUBIEQtP39fQkLzWaz4jnSNG2BSJVKJfHfAMcPZ8/zFjrWSOBYWqMnimW0TqeDYrEohMbzPOTzeQRBIMqI53niPQIgxIIkhCSDyevj8VhSwkl8SNyYNp5KpeRYLG/u7+8jn8/LPnge7Eikx4r7Iflh9xHLryyfeZ4n2VtLS0tSOiXZy2QyMAxDyrKapsGyLFGgMpmMRDbwHPk6EmYAcv24Ht/3JSGdnaBUsqhskdzxPTT5A8dNBePxGIeHh+KryufzuHDhgnjDGBsRI8ZJxnA4RKPRQLVafcnPGo2G/L8rRoz7BSfpnjgxcQsbGxvSdUdlJGp0JjFikjZN1lR85vM5CoUC2u22lK+A694bKhgrKysy7oU5TXxIs3w4n8/F7/TQQw9JBxmJAMtQVFPYLUcSSNWGM/T4vsceewyKosD3fXieh42NDTz//PPQNE2ytCqVinQFZrNZ1Ot1FAoF6W5j3hVwff7ffD7H6uqqnBMACQnlNVxZWUGn01kwxZdKJfEsMRaC42sYIUHTuqIo2N/flzKbruuwLEviDEhuXdcVrxTPmx44lm1N04RlWXAcB+12G61WC4VCAWfPnpW4iWazKcQpmvpu27Z8tpZlod1uS5lR13UAELO8pml48cUXkc/nkUwmhRCTfBWLRayurqLb7YqfbD6fY2dnB5PJJG4tj3Giwf/f3dgBValURJW9nYjjFmKcdJyUe+JEKFZUXTjihQSHJTE+CKma2LYtZRuW8hKJBLa2tiR/aTgcygxAxh0sLS3BNE0Zj2IYBnq9nqg8JET0TCmKgqOjI/FkqaoKwzBQLBZFIWGZirEFLN0BkNBNKm2Koki3IctzVLkAYGlpCZlMBvV6Xd6vaRo8z4NpmjAMA6PRCKdOnVoY53Pq1CnxlXF//X5fylytVguapsF1XfEnkWTQc0S/GdPrOYCZMxdJKFkG5NzGYrEohCoMQ6yvr6PdbmM0GiGbzSKfz0t5k6pjKpVCo9EAcJw7sry8jGKxKMoVcGxu39vbE99cs9lcGA4d7bi0LEv8b+xSdBwH3W4XKysrYtBnttXh4SEqlYrkYBWLRVG5gOuxGTFinGQ4joOHHnroJdu73e5dWE2MGHcfJ+WeOBHECsCCb4hBjswhojISNRkriiL+meFwKAQgGhrKsiKHGzO3ikSBfikSJXqtaFanL8uyrIW5ds1mUx7WLDfmcjnJgAKuD0nmNgZh0vvEMTq2bcs4lclkgiAIpDMvOtePSlTU70VVi2ocr1upVILrushms0in05IcT/M+PUedTkfKfFQLAQghot8t2iXX7/dRKpXE2M51sbxIE3+z2cRoNJLjUiUEjtPSc7mc5JNFRxGxtDqbzWQG4mw2E29Zr9eDaZoSVQFABiobhiHBqQBEdYt+F8IwlO8DP6eXyyuLcedhWRb+5b/8lwvb5vM5fvzHfzwuz74M5vM5Ll26dLeXEeMNxMvdE8Sv/dqv4cUXX7zDKzrZOCn3xIkgVix90eeTzWZhWZZ066mqCtd1hTAx94hRAyQUJAY0utMcrmmaBHECEBM3ACFy2WxWDNpR8/tkMkGxWJTy42QykYRwkjhFUVAoFBYUGVVVpUQVjWjg35mJVSqVYNs2UqkU6vU6ms0mcrmcqDckeCwxsjuRxCqZTMpcQ3YD5vN5iXrgbEXGDZBUApD90eROQsEgTfrBgOtkYzQaSUOB53lCMHnO0eHZzNji/L3osOxqtSqfL4kPVaNogjuJm2EYCIIAjuPIWl3XlZE76XRayC3JGcvK0WPP53PpHqRPLUrmok0SMe4cKpUKvvqrvxo/+qM/urB9Npvh05/+NP76r/9apgTEiHE/4JXuCeLg4ACTyQRXrly5wyuL8Vo4EcSK8+Nms5nM/GOwZ7QMBUBULCpB9AqxzMOk82gu1Xw+F+WFRCSTyWB1dRW9Xk/2z1Et1Wp1YaZeu90WZYNEhA9jhlKm02kxynMUjeu6WFtbQ6FQQL/fl3E0NGzn83mcO3cOq6uryGazuHTpEnZ3d1Gv1+G6rpTbqDwBWDCOT6dT9Pt96fCrVqvQdR3dbleGQRuGgVwuh0QigXq9LteM748ayTnwmInsjEfg7MLo9eVYHgCSkq9pGhzHEZ8Wr1eUDAGQOI1utytjfqggsuS3traGer0O27bl+nLUEEkqjfQcDWSappR22ZE5mUzEkE8iWqlUZJ0sX1K1YsdhjDuLb/3Wb8VHP/rRl2xPJpP4kz/5E3zjN34jPvOZz9yFlcWIcXfwSvcE8Yu/+It44okn8P73v//OLSrGl4UTQazY3aaqKjY2NjCfz+VhTP8UcByoSQM3S2A0sXe7XTSbTdi2LQqM67riuaKBPZ1Oo1qtYjKZ4OjoCNlsFp7nwTAMGfxLNSg60LlYLCIMQ/i+j7Nnz+Lg4EC6GDudjjzMaYJfXl6WwEoOlabhPZVKYWVlBe985zuRSCTw+OOPw7IsnD9/Hr1eD91uF61WC67r4uzZs7h48aLEODAjivEDABZmEvZ6PXieh1KpJApdtVrFc889J0OfSfyoEEZHBhWLRSE4hUIB3W5XyoXD4RCnT58Wckq1kBldvFZra2vodDpwXRee52FtbQ3ZbBaO40jplXllDIK1bRuVSgW5XE5KlQCERNMbtrW1JXMZE4kELMtCv99Hr9eTLC8SRpY+i8UiGo0GgiCApmm4fPmy/J3XkfMDLcta6EyMESNGjBgxXg9OBLGiJwaAPJwVRZFOrXK5DACiVNBE7nmelOL44KX/aDweo1KpYHl5WQzj+XwenudJ2Y8EqlQqifqTSqVwdHQkyebj8RjVahX1el2Ijeu6otZEyR+VH6pdiqJIejrjBIDj4amcN5jP57G1tQXDMGAYBs6cOYNmswngWJE5OjqSjC2ay7kfzgEsFAoSKUBliEGZqVQKzWYT6XR6wetkmqaU8QzDQK1Ww87OjgxjDoIAqqqiUqmg0+mI+b7dbssQ6nw+L2VcgmoXPyd+tlSWMpkMut2uzCckMaZqyNE9R0dHEhQahqGohKdPnxYSS9LFaI5KpYJWqyWjkfh3lkFJQB3HEV8dcOwbI5miGhcjRowYMWLcDE4EsaIHh+nfVBzojYl2qkX9VHwNPUMcfRPtHqOqwwBSBlYSPBbjHfg+4Lr3y7IsNBoNIQN8PUuLjCsgSeAoHf6bEQu2bUu6ua7r2N3dRRAEokDRR8YMKZbMoj4kluqoEEXT2EmqSHRYvoyWLXlN2bnIrkqWCLlPKlRUuTh65ujoCMCiyhgdsMx0eRIUDsymb4nnQrLE9Y3HYyG2/CyjIa/0PpHEsiTL+ASa07l/Xif6xbLZrPzhdy06oDo6GDouBd7f+Jt/829iY2NjYduLL76Iv/qrv7pLK4oR4+5jZWUFX//1X7+wzfd9/N7v/d5dWtHJxYkgViQwjABgR6Cu69LaT2WKXhiSkWjStmEYL0nw3t7eFkLW7XblQQtgIfIgOv5mZWVFgiI5wJn+oeFwiLe+9a3SnZZIJNDpdKTsxP2wLMiOviAIUC6XRSUDgK2tLXieB8dxkE6n0Ww2xfNkGAYcx0EqlUK/35dwTYZj5vN56cxjGQu43p3H/aiqKl2BJEgkDryevBaMbRgOh/B9H0EQoFarCYm1LAvb29uwbVuue7ValZT5bDaLXC6HZrMpJJWEsVaryexH5n3l83kJ5uz3+2i32zAMA5ZlYWVlRfxkVCITiQQODg5ktI1hGJjNZtKM0Ol0hBRS5eNYH352o9EIiURCvFj0mzWbTSFlMbG68wiCQEJ1bwSz6e4EisUifviHfxjf/u3fvrD9ox/9KD784Q/HUQYx7hgYhwMcfy+jlYF2u40wDMW3+0Yjl8vh67/+6/Fbv/VbC9uvXr2K//E//gc6nU48CiyCExEQappmuLm5KUnqtm3DsiwZbcPSTqVSEQXk6OgIlUpFymx7e3vycxq2r169upBgPh6P5aFOb9Hy8jJ0XcdwOITrunAcB9lsVsap0FfF3CR28zF8M5FIwLZt1Go19Pt9dLtduK4rSeP0fDFvid6jyWSCSqWClZUVUeqCIEAQBGi329A0DaurqxiPx/I/c3rPlpaWRH2jp8n3fVG3VFXF3t4eTp8+Ddu2MRqNcOXKFbk5x+MxGo0GVlZWhIAUCgXpbHRdF/V6Hfv7+zBNU0qKyWQS7XYbuVxOoitM08R4PIbjOEgmk3j00UfR6/XQarVkXuHOzg40TRPyzPeR+Kmqiq2tLdkHhznTQ0eFL5FI4Nlnn0Umk0E+n8dsNsPq6qpch+l0ir29PfHKRUuz1WoVmqZhNBpB0zR0u11RrFqtlpwnAJw/fx7D4TAOQ7zDWFtbw+7u7sI2duWyvPxG4+jo6GWTmwHg0qVLOHv27B1Zx0lEHBB650HV/sqVK9jc3ARw/Z7wff+O2RY+/vGP473vfe/L/ozNRgcHB3dkLScJJzog9Mb5bv1+f2H48GQyEZ8VX8cwzeFwiH6/L+Wx0WgEx3GkUy46WuXw8HDBnzMej9FqtURJAiAEIDpDjyoYvVz9fh+5XA4ARG2pVCqSkcVUcqphJFv8jYNKHIlku91Gv9+Xrjumi3PgM4/DWAGmpWezWZRKJZmBxzJlNpvF2toaut2ulO4YtTCbzaRjkGVO/kxVVQn3NE0TZ8+exeXLl+Uabm9vS2p7tAwKYOF6sZMxmvHFEFV6xGazmTQW+L6P+XyOzc1NCVll1yeDPPf395FKpVAsFoWMcXA3TfCpVEoyx9gVynwq3/clToFRGCShiUQChUJBQl5ZJo1xZ1Gv1/HYY48tbONcyzuFV4vaiGM4YtxpkDi9613vkl/8eE/cSVEkvi9eH26aWCmKsg7g4wCWAIQAPhaG4S8pilIE8NsANgFcBfAdYRi+qn5OlYK5TfTm8OFpmiYajcZCmSs6V1BVVaysrAjZocrFf9PDBRynmff7fYRhKCndVJ9Iinq9njxwaXwGIP4bkolocGa3210o2VFBYZL8fD5HLpeD7/uS30RiSA8UFSceg+fBqIBr111IHmf7kXCR5I3HY+TzeYmSYI4VA1FJnEi0aLxnUCh9auzC5IONag+9SFQPoyn5JL6Mt2DeFQdI67q+MESZpT4qWPTPkZQSVCajnjt+d5hDxs+D15++OXYY0mdH9YPnG1UjAdy0pH0774n7EdPpFM8///zdXkaM24j4nrg9OAmhlzG+fNxKxPQUwD8Lw/BRAF8D4J8qivIogA8B+KMwDM8C+KNr/371RURGwzCPiKNgMpkMNE2TMEsqTjRIA8cqU7lchq7r8oCPkjU+XBleSRN0Pp9HLpcTAhSGITKZDIIgEGLEIMloDhIf4tG08V6vJ/tgt1p0Vl8YhmKijqazM/vKtm0hLPRkcQ1Ul2iQj4absuORShRzrQiSFv6csxNZEqU/KZFIiJE+DEOMx2MhmCxT8prSO8bPgSSGJIr75fqZ4E6fVrQDj+SJCh9VNxJbEkE2BNCzxiHZHJ3DzzWapM61A9eHfCeTSekmJdHiOdEUfwseq9t2T8SIcY8gvidi3He4acUqDMNDAIfX/t5XFOU8gFUA3wLgHdde9hsAPgfgn7/avqi0aJomZaOtrS1kMhkMBgMcHR1B0zRUq1XM53McHR3BdV3JLaJPZnV1VUhMKpVCu90WYkJVh34lKjQ0B7K8xZIi1RU+fEkASA5YBmMw59WrV1GtVsVH1O12F0ib53loNBrS9m+aphjqWb7a2tqSGIZ0Oo3V1VXs7e0JwSQhI/Ei4RsOhzg4OJCUeXqgSBJ4juy6o5eK6o/jODg6OpLz7vf76HQ6SKfT2NzcxGAwgOd5kvlEpQs4JiyNRgPlclnS7UmSAUiJkeskKTs8PES5XF645vS9sfRHRfHadwwAZLRNKpUSQzzLfRzSzDIiTe+FQkFS5G3blu8A97O+vi7zIIGbV6xu5z0R4+7glT776FikGF8+4nvi3kC0M/tG8P/ZMa7jtpjXFUXZBPCnAM4B2AnD0L62XQHQ5b9veM8HAHwAAFKp1Fe+9a1vlQchc45YIjMMQzxTLDOl02mUy2VRiEgyWHobDoeickQzoDzPQ7FYlH+zdMXYAnqAgiBAIpFAuVzG6dOn8YUvfEHKahyMfG3tUFUV+/v70HVd5thFs5qoNrVaLeTzeSnn0esDXM/v2tzcxN7enpQO9/b2UKvVoGmaqHdHR0fyP3nGTzDEE4Dkf/ELrygKTNMEcD2C4dKlSzhz5szC3MFisShhnFR02u22lC1t20ar1ZIuQMMwYJomOp2OdNPxukfLua1WS0bWzGYzUa1IGBOJBJrNpmRrTSYTLC0twXVdIbfD4RCdTgcrKysoFApIp9PY2tpa6FDsdrsYDAZSUtQ0DbVaDa1WC5VKRTK0+J2JNkdYliUdoxcuXIDjOLdkHLjVewLAV97K8WPcHPL5PD72sY/hO77jOxa2//Iv/zJ+7Md+DK7r3qWV3X3cqnk9vifevNB1Hd/8zd+M3/7t317YfvXqVTz55JNwHOe+JFevdE/cMrFSFMUE8CcAfiYMw/+iKEoveoMoitINw7DwavswDCM8ffq0BH82Gg1RHRgbkE6n4bquBGEyNZtqFOfv0bvD0ls0NJSt+FS5omZ5EpXhcIjV1VVRV8IwlIBQprHTfO55nhCzqFJGVarf70NVVSE+nINH8kfViMSQw5hZfjRNU0b80CMFQFSYaNeb53mi0kTLpiQcp0+fxng8lnIbCVDU45RIJKRpgMOoHcdZCO8cjUYLpInlWHbtNRoN6bBj+ZFEhnELtm2jXC5Lejs/C5YnWVItl8tSEgWOk/d1XYdt29A0DZ1ORyIX+LmzFMnPIZvNSsyGrusoFAo4OjpaGE3E6AV+X5555hn4vn/TD5HbcU/cjx1QJwVve9vbXpJjdfHiRTz99NN3aUUnA7dCrOJ74s2PWq2Gt7/97QvbPM/Df/2v//Uureju4w3pClQUJQ3gPwP4zTAM/8u1zUeKoiyHYXioKMoygMaXsTgxcSeTSRmuTHWFnWjs0IsmtZPUMPiS5AaAkJloKS+acwVAyA4VHz7cGbVAUzrfy/gFkgqW5Kiw8e8kByQ7k8lE5u4xMDN6jlwjB0zTw1UqlQAcG8d938dkMhGjt6ZpmE6nC/MOqfJFx/2QSGUyGYxGIwwGA8ndIiEkoeG6o54k+tR4vdlJGN0vfVn8/HRdRxAEkmrOjLHxeCyDkRnWOZvNJMyVHivHcZDJZOD7vozbieaW8XvDz537o2TNJgiORmI0Bgk0g2FZYuV3i+XIu31PxLh7+OIXv4gvfvGLd3sZ9wzie+LeQL1ex3/+z//5bi/jTYGbNq9fk29/DcD5MAx/IfKj3wPwvmt/fx+A332tfVF1ocmcc96YR8SHHhWtaEBoNH082jXIYEqSLpbRoiSLRE5RFDF2r6ysSLkok8nIz6l4cY4eZ/JNp1NRsEzTRLFYRLFYRLfbXTDbk1SxI5H5Vvl8HsViEaZpSoefruuyVg6EphmcSl70PI6OjuTvzGbyPE8M46VSCZ1OB5qmSScfAPEUDYdDtFotGV3j+77EP0TN9lTpTNMUXxjzvlqtluSY8Ly5b0YuMJ8snU6LMT6XywkRbLfbEgrK+X0koiRkNLAzWJXXgcRzNpuJYjaZTOA4jsRfRMf7DAYDmWXIzsaod+1u3xMxYtwLeLPcE/z/E//EEQIxbgU3XQpUFOXrAPwZgGcA8Ff8DwP4PIBPATgFYBvHbbSdV9tXPp8PH3rooYWSk+/7UhKs1WoSfUCCYVmWvI6eomq1ikajIWSHSkyhUJBwTpqqSVLm87mk2Oq6jgceeEAe8OwoY/nK8zz0ej2Uy2UpT5I4pVIp9Ho9ITYsTw4GA1Gmer0eqtWqlM1830e1WpUsLqo/rutKujg9P6VSCblcDhcvXpTBxyQDLHsxpuDq1asolUqSyTUajSQ2guQEOP4NhJ2RmUwGV65cEQM+OwGZME+yMh6PUSgURFVsNI5/0aRK12w2Ua1WhSxxZiOJ5WQyQS6Xk/IbOwSZRcbOScdx0O/3sbS0BMuykMlk0Ov1AFzvJiQxpJI4nU5h27bkWiWTSZRKJfi+D8dxAACVSgWNRkPmKzKTbDqdirp1LVfsdf+f9XbeE3HZI8ZJw82UAt8s98QzzzyDRx99FMDxL5CVSkX+fxMjxivhDfNY3Q6oqhp+xVd8hRi4B4MB8vm8jCGhesWAtMlkgqOjIywtLckDFQAODw+lTAUc138LhYJ4qqIqDgDJrWo0GmI87/V6WF9fl9BOptwyMiC8NuNuPp+jXC4jmUxib28PuVxOzPSe56HdbsvIFpIbdgAySsJ1XYlPiI5SsW0bwPFIg3w+DwCirrDzj+Z5kkvbtoUsTiYTIRSKokjHXrfblbwmllijc/ZSqRQ8zxOFJ+rbYvr96dOnFyIscrkcBoOB+LdYlqtUKnLOHI5M0snrzyiGwWCARx55BLu7u1K+pVrGrkCWPHmMVCqFcrksnYC8RvwOWZaFYrEoZH1rawu+76NcLouCybIiYzHYGfrCCy8gCIJ7LmXatm388R//8cK2+XyOd7zjHXdsNEaMNy/uxeR13hNvectbFoKBn332WfzQD/0Q/uiP/uh2HzLGPYQTnbzOgEf6dAaDAZaXl6EoCoIgAHBcUqLnJ5o3RZ/MeDzGysqK5DIBx6U+GsKp0vC97FKLBlkqiiKZVDweE8MzmYwMb2bpiQbwbDYrXWs027NbjX8mk4mMrhkOh6J2cb0kuOxSY4nTdV0YhiHkR9M08W7RQ8WxN9wHTdjRc+bIG17raNAoABkOHb3WuVxugXiZpgnf94XUqaqKfr+PfD4vuVK8Zuww5MzFqG+J62HeVDTmIBoMy5BXmup5rVmW5Rq4nqiHio0OJMU0qjOLS7k2ZJoNDbZtL5D0ew3nzp3Dd3/3d+OJJ55Y2B6GIT70oQ/hP/yH/4AXXnjh7iwuRoy7gFe6J/gz/sIWI8brxYkgVlHTNxUPlqdonvZ9XyIXFEWBpmnyb5KLM2fO4ODgQLriqA7Ri0XzNlO8AUiZjkpHPp/H9va2hJZms1k0m015qJPsUe1gp2C/35eOuGq1ikKhgEajIQSAXYAsmTGYMmq8p/meSlG0XAlgQemKho0mEomFzjeW+5igPhqNRDGiJ4rKFkkMy2v8u+M4qFQq6Pf70o1oWRa63S663a4ofN1uF9VqVdbKUTVUDWlAB66PPiCpYSCsZVnyGs4FHI/HErdAg7lpmtJcQAM7S4STyQT5fF6+P/1+H61WS9Q9ZnSxcSAa5JrNZlEoFNBut4UcnkQ8+OCDovYR29vbX9bIl6/4iq/Ahz700gxGRVHw4Q9/GH/1V38VE6sYbzq8EfdEjBi3ihNBrMIwxOHhoXTOveUtb4GmadJllsvlsLq6igsXLsDzPJk1N5/PpUSWSqXw/PPPC5maz+d4/PHHRTEhqaBPinPlDMOQrsTBYIDt7e2FsuNwOMTKyoo8dA3DkKgHtvgz+4pp8I7jyEiZQqEAXdfh+774vvhQj3YNAscqCwkZzfCmaaLf70uy/P7+vpAyy7KgaRoMw8DW1pb407hu4PjBORgM0Ol0YJqmrLHRaKBYLCKfz8tMPwaIkpgdHBxIR91sNsNgMBDvARW75eVluTYA5BgXL16UDC+WHNltqKoqyuUyPM8TL1symUS1WpW1ZrNZDAYDGarMmAX+m+um54yGeF4Tdm12Oh1RD3u9npw3SRYVqm63K40D0a7TkwJFUfCHf/iHL4kBePe7331ftzvHuH8R3xMxTipOBLGazWYwDENGxuTzeeTzeWxsbIjiQRWkXq8Lcdna2hLyRQP3YDCArusol8toNpvSts+RJ6VSSR6iNGmT7KTTaVSrVQng1HUdpVIJiqKgVqsBOFbXSCbYxUdvFsuZvu+LWkI1jGW6aI7VcDgUgjEYDKRbsFarCRnRdR1LS0ticOd5UMFjGZTmdY532draknR33/eltGoYBnK5HE6fPo3d3V0hmTTnc85goVBAp9NBs9nEbDaDrutYXV2VFHyWZOkNi6o8e3t7KJVKMvLG930hayzB8vpHM8YYvkgFk8nsLPceHh7CNE14nodmsynfE3ZJTiYT2QcVNXr2SOA0TRMCz9DQra0t7O3twTAMUcROElZWVvD000+jUHhpzM+nPvUp/Pqv/zp+8Ad/8C6sLEaMu4P4nohxknEiiFUqlZKRIrVaTR66xWIRhUIBvu+j2WwuzAbc3d2V+AU+3JlqHlUcoqU313WlfAhgodWeiM6xAyDqEEtthmFgd3cXwPX5c57nIQxDlEoliSQIggDValVyo0qlkvh+kskkCoWCdLbRg2RZlnQ4kliNx2PpWqTiQ4LAc5hMJrK/TqcjZIRrpIpEZSrqXWK0AUHFh9d0dXVV0uGjcxhJZqkWsUw6m82EVGmaJtETUR8USSTLhvl8fqHMG52l2O/3JeKhWq1iMpnIoGma1HkOs9lM1k6FLAgC6aBkA0KtVkMQBGg2m6JcciwOVbaTBHY3vhwsy5JU/Rgx7hfE90SMk4wT8wRhpxk7xZiRRF8NFR8+9NjFRLNz1PQMQEI/qWRQ8eExovlHNMxzHApJC5ULkheW/Xzfx2g0koc3YwKiY1oYx0BQvaL6wj9RxYfKS7RTMzrGh2VNkiSeK5UgjuhhbAP9V9HwUp43rx/Xz047rokeBSpkURWHr2PXXnSGVNQEz6BOdvhFM7FIflKplPjpSKzYxMBhzvz8aFxndyFLuBzGTN8c902yxTWTADKnhp87AJlRedJI1e3CwcHBK5ZHPvvZz+Lw8PAOryhGjLuL+J6I8UbhRChWLAUy6ZxGZXbv8QFrWRaGwyHa7TYMw5COQcMwFnw0wLHXh2ZqEhUSCJrOaQDng3s6naJcLkNVVTSbTSlHPfXUU1hdXZWHsW3bCxEDqVQKlUoF7XZbSAyzmHRdl/LSysqKeMJc1xWFit2JvA5UgVieNAwDzWYTrusinU6LoZ+GfNM00Wq1JMGcKfW6rkv459ramuyPMwBZqgOOTeNMNueaLMuC67oy8ocjfrLZrISwstRIUsQ5goyYcBxHMriY9M7PBrg+a1HTNDiOs2D2Z/QDR/f0ej0x4eu6LsOuWZpkh6VpmtIMwFmFTKJneZQ+uvl8Lj4sYLGz8F7Cn/zJn+DZZ5/FlStXFsjjbDbDd37nd0rOV4wY9wvieyLGG4UTQaySySTK5bKoNoZhiPmZGUiNRkMe2pVKBc899xyq1aooMUEQCGECjpUTdrX5vo/pdIq1tTU8/fTTMAxDjOvJZBKdTkfet7W1hc3NTbiui+FwCM/z8MADD6Df74s3iA9hqiyqqsK2bezu7mI0GsmDHgAajYbkZdFHlEqlUKvVJFuK5IHlTqpNqVQK29vbMpx4Pp9D0zTk8/mFAcLMygIgAaFra2uiiq2trUnHH9U3prHzXBqNBlzXFdJhGAZc14XrulhfX0cymcSXvvQllEolyZpiQrxt2+JVK5VKODg4ECne931cunQJlUpFFD+W30h26vU6dnd3USqVoOu6rIeKH7Ov6vW6EEBFUXB4eIhqtSr5UyTHqqpiOBxKrhUAIZWVSgXb29uiwlHJPHPmDFqt1j09ZLfdbqNYLL5ke7QUHCPG/YT4nojxRuBEBIRms9mQbbMsDamqijNnzsA0TbzwwgtSCqPpezgcotfryWv7/T42NjYWBhhT3dA0TUanbG9vy9icYrGIvb09aJompTpGIDD1nXENmUwGnufB931sbGzIUGaSK6oxJESdTgdra2vys8lkgna7LQGfpmkKQSHxsyxLlCsAC3PvqJBZliVz73K5nKTN81pYloV2u41CoYBWqyWkbjAYoFgsSimT5wtArqWiKMjn80I4WFrlGCFFUbC9vb1A8KIRDdH5i6qqisdpPp/LWJtMJoNWq4V0Oo18Pi/zBgFIjARzu6LqY7lcRrfbRS6XWxg9s7+/L4SMJVWa09k52ev1JNCUjRD83EiMc7mcpL1fvHgRo9HoxIQhZjIZvPWtb8Xv/d7vYXl5eeF1P/ADP4Df//3fx9WrV+/0EmPcZzhJAaHxPRHjJOBEB4QCkOgClpAYIKnrOhzHkYc9/5CERDvuSFBYUmTJkBEEVItIVjh/jmU3PuBZ4qOqQZM2gzepXFElK5fLopaRVHB9UYM3E+ABSEQEwfOmOZzesHQ6jclkIl1uUVN9NM8parpnVlU0zR1Y9B65riuRBCSaVKp4Xjx3esoMwxC1DIB4uaI5VRwczc+CKuSN58qyIz1lXEvUZ1YqleSaUHHiedD4bhjGwnuiI4WYAcbxOgyHpd+K12M+n8t6eR4nCePxGF/84hfxb//tv31JF9Qf/MEfxA+QGPcd4nsixknGiSBW0QcZCQbHz9CYTkWEfxKJhKRl01vkOI4YnNkByPRw+qhIckiayuWyGOaphrF8lEwmhQTQPG0YBo6OjiSok6oVlS6+z7Zt1Ot18VsxeykMwwUPUxAE4k9yHAe2bS+Y0gl2ru3u7so5cmQMANnGDrrJZCLnSqN7dFC17/ti7mf5j2NxSIaotJFIDQYDmQPI3C8Sq6g5Xdd1+fy4/yjxoypJ8gZAjkuD/XA4xMbGhjQc+L6PMAxlzBBJEUfURIdSZ7NZBEEgQ7jX19dl/1Tt+J1iUCrJF6/RScTP/uzP3u0lxIhxohDfEzFOIk4EsaJalM1mpfV+ZWUFzWYTzWYTxWJRvFVsk+dw5XQ6jcFggMuXLyOfzwupCIJAMpvolXr44YfRbrdRr9clZZ3ZWCQ4lUpFfDaMDIg+hDlnz7ZtCZkkmXFdF5PJRBQ2djcyhmAymWB5eVnm+/V6PQRBIF4lzr5LJpNIp9OSFL+6uipxASzR8bc0draZpindjPSVMYCUZCGZTELXdbmeh4eHWF1dhaqq4mnq9XrIZDJYX1/HZDIRZS06RohjgoIgQKPRkLl8yWQSh4eHuHjxInRdx2QyQavVgmEYC+GowLHilsvlEAQBWq0WUqkUNjY2xCvVbrfRbreFhE4mEywtLeHw8FAUwWjpVdd1ZLNZpNNpuK4r504DfzRJn92fLAcygiLaaRkjRowYMWLcDE6ExyqTyYSPP/64qCSJREJymZhhxRZ5ggQgOg+PpTLug4N1x+OxdPMRUR8Rx74Mh0M4joPTp0+j0WggDEOsra2JP4ip7RwQzTDNc+fO4fnnn5d2faon6XR6Yb+Mj+BQ6V6vh+3tbaytraFarcL3fXQ6HTHpk3iwDMaxOvROUX1iWjg7EpeXl/GFL3xBUs4NwxCjO31kQRCIipRKpRaGXjO6odlsYnNzU67PYDDAW97yFvGXkUyyC5BEdHd3V0pviqKg1+thaWlJuglzuRz6/T6y2SxGo5EkuZN0JpNJtFot8YZR2WK3H0t5wHEchOu6Qr40TZOIidlsBsuykE6nYVkWZrMZtre3US6XhbQyvJTnPZvNsLW1daI8VjFinAScJI9VjBgnASfaY8XSC5UlmouZYZTL5cRrE53XB0DKTyRKVDCiagZ9RDRDR1UJ7jsIAozHYwm3pELUarWQzWbRbrcBQMbpcOZgKpXC0dERRqMRMpmMlOionM3nc0mTZ1ccIxcYlQBACARJA71YuVwOrVZL9sPz4TlQRWOmFABRlVjSo3+Nx2PcAa8ZM6GSyeSCAV3TNHQ6HRQKBRQKBVQqFYl5YEmTihXJFUNX6WUCjud50YDP8z48PMT6+rrEbDAhn92O7NaM5mft7OxgeXlZVCae+3w+lzItSSPLiq7rolgswnVdCRB1HEf8bqqqSoI9fVontRQYI0aMGDFOPk4MsWJJKwgCJJNJURA4MoZEgKU4Eg96mLLZrJjOo/P4uH8O/iV54IOYD1Ear6NhlvQXkahFy3Ms102nUxn7QhM1By4zYZwluE6nI8efzWbI5XLy76h5moSEhIGqGvfN2XpcM19LMuM4zsI8RSp4DDAFsHBtAIifLeqJsm1buix5zQeDwQKRjTYdRDPCWGYDID63KMGl4Z2fjWmaQtaopgVBIJ1+/E7QTxctr0YN8iSSiqKI14pqHTtEOTeSylbUaB+9pjFi3Osol8v4G3/jbyxsGwwG+PM///O7tKIYMe4ubsc9cSKIFZUFqiF8WLPdP5VKYXV1FYeHh0KwaELmQ7xcLsP3fezv72M8HovKRN8VlSUOaFZVFdVqFXt7e6JckJzpui6kJ5PJoNfroVqtyria0WiEUqmEwWAg4ZnRRG+WljzPkwc8Z/pRWZrNZuj1eqJqsQQ2Go2EsPm+D9M0xVtGlYamcE3TZAQNw0VTqRSuXr2KfD4vWVwAFgJUSWAYOcH18rUkJsViUbxMzWYTR0dHqFarWF5elgaD9fV1+RyIaO5WOp3GwcEBarUaCoUCstksXnzxxQWlrtfrYWNjA0dHRzLwmWGk4/FY/F3r6+sLgaLZbFbCPIfDIZrNpniqONbC9314nidEWFEUPPzww9jc3ESv18Ply5eh6zr29vakUeGkdQXGuHcQHTdF8BeSO41MJoOv+7qvw+/8zu8sbL969Soee+wxsSHEiPFG4l68J275V3NFUZKKonxJUZTfv/bv04qifF5RlEuKovy2oiiZ19rHeDzG0tIS1tfXUSgUYNu2pKx3u12Mx2MJDC0UCrAsCwCkXFev13Hp0iV0Oh0pIzqOs1DaAoBms7mg0Hieh263u9AlWCgUpOxHs3YYhmJw59pKpRIymYyU+aiqdbtddLtdmKYpJutUKoUrV65IJhZwHGBqmibW19eluzE6OocGdRI/+pyYSl+tVqGqKvb393HhwgVomobRaITDw0PYto1GowFVVbGysoKlpSVcvXoVnU5HCAvVK5YgWfajUlUsFqWsR6LFNHyWTum7YobUYDCQ618oFCQdvVKp4NSpU+KpKpVKYtRvNpuYTCY4OjoS1SiqlhWLRaysrMhQbnrm6FlzXVeIMRVLy7IkH6tcLksZ88yZM/hbf+tv4au+6qug6zqq1Sq+8iu/ErVaDZZlSXnwVsMBb8c9EePexHvf+170er2FP//tv/23u7KWX/7lX8Z/+k//6SXbNzc30e12X5IPdSuI74kYr4R78Z64HTWPHwRwPvLv/wvAL4Zh+CCALoDve60dMBG83+9Liz+T1lmq0XUdruvKaBeWvgqFAsrlspSAqFSw1NZut8VbxUyrfD4vysajjz66wJbptaKPiCSJieAkG9vb20ilUlheXhZiRIZNHw+N06lUCtVqVdZIxjscDrG9vY0wDFGpVJDJZHDq1Cm5HhxhU6/XZbyO53mo1WpCYMrlMtLpNC5fvixjeFiqBI4VoXa7jc3NTei6jlKphFOnTsmIGXq6hsMhzpw5I8rcwcGBXJdsNgvLssQnVq/X4fs+8vm8KG70mx0cHEiWVaVSEVJDpYneLxJL27aF0HDOIEn1I488IuOAxuOxhLayE3Nra0vWn8vlsLa2hsFgsFCK5OvT6bQQ21OnTuFrv/Zr8da3vhWlUgmapuHBBx+UmIjbMNLmlu+JGPcePvKRj+DDH/6wZOfxzxNPPIHPfe5z0tV7p3BjvlwU0cag24T4nojxEtyr98QtlQIVRVkD8E0AfgbAB5Xjo34DgH947SW/AeAnAfzKl7k/WXi0zKcoijxgAUBVVQwGg4XcKLbL06RdrVbF50SVqlQqSamH3iPO1aMhmt4sPpCZcM4HLn1LVHLY+Ra94PRzVatVjMdj8Y3xwa0oiswbpBGeeUoM3QzDUMbysBw2mUygqqrMxKPCw5R1+qjG47Ekq/PcqZRFrxXJF7smSVapTuVyOVH0+D4Go3Lg8Ww2EzM9XzMYDJDL5aDruvxhN6KqqjBNE57nAbge9kkfFa/jbDZb8LMx/Z3HTSQSQuaYU8b5iPxMo1lbJGej0UjmBPLceXwm4jebzS/3Fni57/BtvSfuJB588EF84zd+48K2TqeD//gf/+NdWtG9hXPnzuHBBx98yXbTNPH2t79d7sd7DfE9EeOVcK/eE7fqsfoIgP8TQO7av0sAemEYsv62B2D1y1rItQczTclsq2f9lR1tNKvTb0SCwAcux9CsrKyg0+kgk8kIAWKYJEtrNE4XCgWoqioja3zfF6LBcSds5yey2ayUv7hP4Hq6OX1BNIPPZjMxsKuqilKpJKoax91QqaPC4/s+arWaxDDQc9Xr9SRbajqdolarod/vi4E8CAIpbXGIc6fTkY5Cjs1JpVILBKnb7cocxVqtthBUSjJqGIZkS7H7jyVAxjtER9PQj0XfVSaTkfXw2kU/Z/qqUqmUKJgMYM1ms7KN8wb5WfGzz+Vyck25TpIsqoCMzWi1WnJd+/0+0uk0SqUStre3b+GWuH33xJ1EpVLBu971LvzSL/3SwvZLly7hz/7sz7C7u3tPDqeOcUfwEcT3RIz7CDdNrBRF+WYAjTAM/5eiKO+4ifd/AMAHACwoExwbA0CUpPl8jt3dXfk359dNp1NRZvjQJNlwXRedTkcGbLKrr91uI5FIwDAMlEol2LYt3YCFQgHNZlN8SHw4d7tdUbjoEcpmswCOH+aNRkPIAcmapmnY29sTRYtmcmI8HiOZTMrYltlshnw+j93dXSGSYRjiwoULYvqmEpbJZGBZlgRwTiYTlMtl6Rg0DAMHBwdSZpvNZuI5I+GheZ4G+Hw+L2nxDAXt9Xoyc7Hf76PX6+H06dMSmVAul9Hv9yXUEzjuqOC+6aXa39+XlPlutysEi/MbdV3H0tIStra2hCTRUE6Vi3MiWY6kYhltcgCOh6pyjFAikUCtVpMAUsZ3fOpTn8JwOEQ+n4dlWWg0Gjg6OpKy4M3idt4Tdxq/8iu/gm/7tm97yfYHH3wQW1tbqFaraLVad2FlMd7MiO+JGPcjbkWxejuAf6AoyjcCUAFYAH4JgK0oSurabyNrAPZf7s1hGH4MwMcAIJ1Oh57nIZ/PI5vNiml5MBgIGVlbWxOPzY3qBn9roBcqOgaHZIIz9+jHWV5exsMPP4zTp08DOJ4zWK/XcfnyZRQKBRweHmIymUg3Hbvj+v0+FEWB53nSociSFJO90+k0ut2uZFvx577vw7IsKY+RKEUzqGzbln1wlM7e3p6cS7lcxuHhoYSfLi8vS8mLoaLT6RRra2tCUnK5HMrlMhzHketPcsmIhd3dXUmf5ygflvZSqRRKpRLW19clZ4zDpdnxSHWOBr9Wq4WdnR2MRiNUq1Xs7++LOqTr+kKpznVdyZti12M+nxelj4RsY2NDoh1IuJiiDxz7yVZWVkTVYqyFpmmwbRvpdBpXrlxBv9+Xrk92W5IsM5bhbt8TcRhijDcaH/zgB/FHf/RH+PjHP76wfWdnB1/3dV+Her1+Ow4T3xMx3jT44Ac/iP/+3/87fuM3fmNh++u9J26aWIVh+GMAfgwArv0m8iNhGH63oij/CcC3A/gkgPcB+N0vayHXSluMM6A3JpPJwPM8kHjRk8PkdSpEHGFCwsN2frbu07/EQEo+1Gn2dl0X/X4fDzzwAF544QUZV0MiR+8P85WYqUTixgBPJn5rmiZp5iQjLM+NRiPs7+9LBANwrNoxwoH7jxK1a9dZktWZ8cWg1BtznKjosazqOI50FrLUxvOi/8gwDEmuZ0mUShBfy3RzmvJZjlUUBZlMBqurqzJ7EICUMYHjUTtBEIgCReM71SSqTCzvcsg2y5kkPPSLcSwRz5Hkjr4rKpw02TNSol6v48EHH5TRR7yujK24WWJ1u++JGPcWPvKRj+Dw8BDvec97FrZfvXoVP/uzPyszOe8UOp0OPve5z+Gf/JN/srDdcRzs7u7elmPE90SMV8NJvCf++I//+JbviTcix+qfA/ikoij/GsCXAPzaa72BCgRwHIMQhqGUi6gi0M/DjKdobZvEig9++nJYvqOiQfIFHJfwOp0OOp2OeJxc112YQRfdH4/JMTXRNUTXTxJHlYQKDDsWM5kMhsMh+v0+DMMAANkPXxfNk7oxbBM4NuG32+0FjxnPWVEUMWTTbM81cI03hmyGYShdhvSEsTuTJnKWWKOJ9gwKJaFkiZKEleVUlhZ935fPlQQuOjswGpAaNalHj8XPOxr0SuUwOsYIwMLr6cWiL47kkmVfRk5EDfS3Ea/7nohx7+H3f//3MZvNsLa2trD9+eefx8c+9rG7sqbd3V189KMfvRuHju+JGPfsPXFbiFUYhp8D8Llrf78C4Ktf1yKuteIzMZ3kh62XHAi8s7MD4NjHQ+8MlRsA0s3GMEgA8pBnNhKT3DOZDNrtNp577jnYto1+v49+vy+p5f1+X2IQ6vW6KFc0rXN8C0nQuXPnxJDNUEqGYjLOoFgsirJSqVSEDDFV/Pz58xLySQWvXq9L8CiJCgB0u135QnqeJx6qTCaDTqezoNyQMNIAzoHNDORk3tXe3p7419LpNHzfx2w2EwO/qqrY2dmB4ziYTCawLAvdblfm85GoMG4COC7PptNp7O/vL6TpD4dDIa4sQXJMUCKRgOM4MkKH76ERnV2M5XIZzWZThigDx0SsWCwKEez1ekKCGdZaKBREGQ3DUD6v0WiEXC53WwYx3+o9cadB/xnvG4IqZ4zbg8985jP4zGc+c7eXcVcQ3xMxXg734j1xIoYwa5oWbmxsyBeZD9dcLgdFUXB0dCRGZCpYABZKdFR0Go2GkIGLFy/i7NmzYtbe2dkRM7uqqiiXy9KtxlBIRVHEC0TFhsSPagZVDkYeUP2Iqmjtdhurq6uwLAvz+Rz1eh1hGOL06dNIJpOS2M6SFYcpU7UiPM8TlQiAeIIKhQJmsxlarRbW1tYwnU7FjO84DrLZrIyOmU6nEkMwHA5lnAyDQnkNGRiqqqqUQhkAytR2ElcSPR4LgHTVjUYj8Zglk0nYtr1wTtHyIUf2kNR0u130ej35fDjGh/P8qL6Nx2PJ9ur1ekgkElhbW8OVK1eEVFLpK5fLompFlUUA0m3aarWkkeHzn/88xuPxfTVwNp1O49u+7dvwiU98YmH7Cy+8gCeeeCJ+kMS474Ywx/dEjNfCK90TJ4JY6boenj59WvxJJC0sc/HhF+0KpJIDYCHDikSIfqtSqSQdbRwszG40epRIyujXqlQqkgA7HA6xvLws2UYsY3HuHI/DTjmSFMMw4DiOkIdsNovBYCDjdQCgXq+LCRsAer0earWaKDPsdqQvKZlMotfrQdf1BZWGafTRsh+JHv1Jg8Fg4ZrT9E9vk2mamE6nch66riOVSqHZbGJtbQ2maYrCxc+CxJBhn8lkEo1GA7ZtS3mQ3qZSqSQm9clkgkKhIOSGZniSXN/35ZqoqirKE0urAMQof+rUKSGptm1L9yiJ33w+Ry6Xg+/7SCaTqNVq2N7elkYJNiJES6Hnz5/HcDi8rx4iwHF7+blz5xa2DQYDfP7zn7/TS4lxAnG/ESsgvidivDpe6Z44EbMCWeaJltpSqZR4maKlMZaROBCYQY/9fh/A9YgGAGJsBiCvIwnjOBwqRiwXsSRFbxXN4SQKzLaiasW8Kqor3E+hUJChy3wfyQ9JzXg8Rj6fF5JCEsF1MPOJw5y5jesh6eTwZZYhDcMQAsLSGQBR4MIwlK44z/PkGkUDWXndWX6kn4nXlh4pKltRFYyEj9eP50rljwSa66LSF/VsMfOL+4x2KE4mE4zHY+RyuYU8MkYokERG9wdAiF50XiOvO+cnArhvZwU2m0388R//8d1eRowYJwbxPRHjZnA7RtrcMmazmSgZLA3lcjkhWY7jiErEh7WmaUI4GNHAxHR2nKXTaSkTlUolAMcGcRInAOIdIgHhwOFerydKFEfLJBIJTCYT5HI5iQvwfR/z+RzLy8uS6B0dkcPuuX6/LyoMS2wcpMwHOQcaAxDjPmMZopES9DHZto18Pg9N02TtyWQSlUpF0tLH47F40egn6vV6MAxD3s80d5Yjo+bxVCoF13XRbrcl0JREip/ZbDZDo9HA4eEhNE2TMM5er4d2uy2dnSwLRmMNqJg6jrOQ6M4By/SxURlktyAjORhDQWJVLBYXSqtMkGfEg+/7MgaIBG1paUmIXjQENkaMGDFixHi9OBGKFZUqTdOkdKdpmgRy+r6PXC4n6eKVSgU7OzsolUpCFhh5EB2yzEHCnudB13WUy2UEQSCdZI1GQ2IBqGIxgTsMQxiGgXK5LFlaLH01Gg2k02nxWE2nU+zv74s/yTRNXL16VYz4DLA8ODgAAIle4IOcqovruhgMBrAsS8qM29vbUs6jF8m2bbk2UU9WNptdUKuKxSKCIMDW1hbOnTuHw8NDUccODw9FrWHQKGMcoh4mDncmuaSqRhN8u91GqVRCsVgUpYrkj3MZq9UqDg8P0Ww2MR6Poes6DMMQUqtpGprN5kKMwu7uLpaWljCZTITE0VTPSAeOHWKifqPRwN7eHvL5PLrdLg4PD1EqlUQ1ZBhpvV6HZVlCuHzfl6wxerFixIgRI0aMm8GJ8FhlMpnwgQceAIAFRchxHPi+Lz6larUqcQf7+/uifCiKgmKxKOoRiQsAuK4r/pler4cgCOSBmk6nsbW1JUbw+XyOlZUVMZqTSFBxofdoMBggm83CcRxMp1MYhiGZT+y8cxxHFJfocGGul8oL1TMOMqbqxdJXq9WScmEymZQE96g6xy4/AEKyWOoMwxC6riOfz2Nvb0+8a+PxGMViUeYUMsjT932JrgAg3Zc8f8uyhKgwF4rHM00TGxsbktROgkLiQtIbBAFSqRQKhYKM2WFZl2XZRqOBzc1NUQZ7vZ6oa47jwHVdmSFI0PdGssvjR7PAqKYx9LRQKGBnZweapkng7OXLl+8783qMGK+F+9FjFSPGq+FEe6zoi+IwXNM0RSViCY4DjBnBQEM7S0os7ZimKWnifD19WUxHTyaTYrq2LEs8W1GPE8M5DcMQ8zgf+owNINhBGB0Knc1mpWuEIZUkG+l0WjKTov6iRCKxQJBmsxlM0xTPE83lPBe+p9VqoVqtCqnjekjgJpMJms2mnBOP5zgO8vm8nCPfS4+baZqi3LFTkF4q7gOAKHLJZBLtdhv9fl9yx6h4qaoq5UoqbyRaJDv87EhQWeYDIIoS38eE/ahPiyOOoub/VCqFdrstpIxknK/nOkkUmYcVI0aMGDFi3AxOxBOEI1QURRHyQ3JDZYndeCQ16XQalmVJ2YwPQ84OjBIrAOIhYicYAHnIMzWcXqXo8GEqKny97/syp45gJlN0jAqJBs+HpIB5SizJ0Q9EIzoAMZhHM6WiwabRuAkatNklSD8W104zPs39jCFguZPkkdeMRIZrYwmN15oBrdEyZ9RX5nmeXB+uQdM0+TlJMFUvkpxoWRU4nhXI7lCeD5U2RVEk74vXG4B44KgAApBOP5ZbqUJGSTKv7Y3BszFixIgRI8brxYkgVmEYwrIsScxuNBoAAMuypFzU7XZlBiDHs5BE8IFMVYLkQFEU5HI5MXjzAU4jvGVZ8DwP6+vrkpGVSCRg2zZGoxE6nQ48z5M1NRoNyYaiyZsPdsYmUBWZTCai8gDA/v4+NE3D0tISqtUqTNOE53miIHEfrVZL0uBN08R8PpdhwTRgM0qBXqeHHnpIRsdQ6dvY2BByyTUdHh5KeOhgMMDDDz8MAFLqY9gnlaD9/X1pGPA8D51OB0tLS0LUaNofjUaSt1Uul4VE+b6Po6MjIackryRXTGJnQClnMfb7fWQyGeTzeSFGw+FQzOfsFuV8Q5YvV1ZWMBqNYBgGFEWRTKz19XVks1lRwEiwOUKJjQT5fB6FQuEOfetjxIgRI8a9iBPhscpms+E73/lODIdD7O/vo1AoiH+JHWjr6+vQdV0e8tPpFMViUToHDw4OkEgksLKyIgbzQqEg7f58oFqWJd1nNEBHk7lN0xSTPJUgPtA5T45ZVIlEQsbvkCiwpDibzSRYE4B4vHK5nKhQ9Imxsy6dTqPRaCyU/86dO4dWqyWjcqioMTG9WCxK5yNH1NCPxDgCx3FE3QKOFULLsrC1tSVqFQBcvnwZjzzyCDRNk1E0VK7YeVev11GtVhdiHBiVET0GTeqFQgFhGGJnZ0fKuEEQ4MyZMzg4OEC73UYQBNjc3EShUEC/34fv+5JRdnBwgPl8jlqtBsuypDuRHi0a4sfjMUajkeRXMTuM8RNUQX3fR6FQQKVSwWAwQLPZRCaTQb1eF3Lbbrfh+37sJ4kRI4LYYxUjxiJOtMcqk8mg2+2i3++LiTlKiGq1mhALlp/a7bbEKjDUk1ECHBvDchcJUL1eF48Os5XoLYoOMAYgPh7DMCRBnDlPtm2j1WqJN4wlKD7weVzg+nBpmtuZxN5qtRZmIdJ7debMmYUZhP1+XxQaqm1MWU+n0wuKC8tyXDP9aQAWCBlJWbVaBXCs7CmKImZxdgUy14mfQy6XwwMPPCDxF9HAVqpKAKSzkwnonudhdXVV9lsoFPDcc8+J4hiGIRqNhmRJDYdD8bsxo2s6nWJvb09InK7rogoS0fIsuzNZuqSRX9M0eJ4ncwNZsqRni6OOYsSIESNGjJvBiSgFAtcDJ5PJpJSKDMOAZVkyWJjlP87+AyCdcQzqJEkhmSJ5oWE8OlOOcQIsH9I3RHUjOm+PqsdkMhHCwWNRAaIZnUobER2sTFCR4rmQAJDAkRC5riskgF4wlrrG47GU+mjUZoI5fVtUm3h96XuiCZ0/p5+L+40OvabXajgcigrG68jrC0CuF8ts9DtFDebMiaJXKpPJCIlyHEcaCUgCuT+SX16X2Wy2oKjRrK7rupR0+flH86kYpEqfHnPEuA6uPUaMGDFixLgZnAjFiqU4ZjfxYUrP1fb2NgCISZop5iQBNLRHR63QaM4BxoqiYHV1Fd1ud2E2Hk3S7LZjMKXv+0KGfN+XEMl+vy8Eg8RK0zSsrq7CdV2JIAiCQEp69HbN53MJCjVNE3t7e0JMMpkMHMdBt9uVcyQhIEGi4mUYBhqNhniLqDyRfDKCgH6mqKGbZn3HcSQ+Iurz4ucxm81QqVREKRyNRmJK57mQ1DCJnrEXJHlU3tLpNPr9vihF9GIBx/46errot8rlcrAsS0qcTGJnbhbLt4xN4LWh4uS6rowrYsAp/XlsIqDvjvthGj4HM8eIESNGjBg3gxNBrJLJ5AIpajQa6HQ6oipNJhNUq1X0ej2Uy2Vsbm7i6aefRrlcRi6XA3A9zZwKTzSOgKNMNE2ThzxLWYPBAKVSCYZhIJlMol6vS1mOqgkN9PRP0dzOB/psNsNzzz234PGp1WoIgkBUH4Zm9no9UWBY0iyVSjBNU/5cunQJ8/kcpVJJ5h0yb4qlxHQ6Ddu2haAxNoJKUL1el3wrvnZ5eRmDwUCubaFQECP6dDrFysoKWq2WEJl6vS5KEVUxElASNapsJGXRiAlVVWEYhrx2OByKh2p9fR29Xg8ApJxnWZZ07F2+fFk+V+ZsVSqVhe8NS6oM+zQMQ0gSP5vRaCTXTFEU2LYtni+GxlJR6/V6cfJ6jBgxYsS4JZwI87qmaeETTzyxoB4sLy/D93156GezWRmeDEC8VQSTy6NdZABEDaGZenl5WYYrs0RGpFIp5PN5tFotKTuyLEUSQZ8XO9dM04Rt2wteKABYWlpCq9USn0+32xWSRGO8bdvY2dmR8pyiKDK3DoCMp9nf31+YeUdDeFQxo5rEUuD+/r6YsW3bFlM8zyHa/UbCV61WhRxyZmC0y67X60m6PJW2crksjQb8/Kg+smyaz+dRKpUwm80QBIEoSgAk/oB/p1I3n88XIhe4f8ZFkPT2er2FWAvgWGFMJBKS5u/7vqifnuehVqvh6tWrAADbtlGpVLC7uyvE9qmnnkK/34+NujFiRBCb12PEWMSJNq9HhwszmJIPSxINkg5FUaTFnuQqlUrBtm0puZEYEHwNTfF8X5Q8sPwYBIGoTvRg8cHseR6CIIBt2xICyjEoQRBI1+JkMlnwQtE0zSHSDOrkqB2qa0xj53oZbsmMK6p3vV5PZu6RvNDoTWJZqVQktoFZTUyAJ8Gi0hT1IjGjivEVnMHI68QxORz4zM5Kfl7FYhGHh4dC6qgI0bzPz3QwGIiaxs+G5xElaDxPBoiyDMhSL0f4sGTKjCzgWNHK5XJi8KeKRfM+j9vtdsVbxeHab2b8vb/39/COd7xjYduXvvQlfPKTn3zJaxVFwU/8xE9IMC3xkY98BIeHh2/kMmPEiBHjnsSJIFYEH3x8WPLhT1N6FDSQ8z0MomRJiuoFcKz8MCqByeI0x0dJFlPMGZZJczPVKXb4UQ1h91+32xXjO+MGbiQrNFTTiJ5Op0VFY8mSKekkN8lkEgcHB6hWq7JfADIuB4BkUjH8kuVUxglQSTNNUwzpqqqi0+kI2QMg5x81ukePyWtdLBbR6XQkLZ0khsZvprUPh0OYponl5WXs7+9LbEU0loGkit4vlgYByHUg4WQUBrfPZjPx0EUbFqiA0W/Gz4zkmeoeiTRHJ1UqFSGMb+aQ0HPnzuG7vuu78N73vndh+2c/+1mcP38ef/3Xfy3bNE3DY489hg9+8INSUieeeuop/Mmf/InMt4wR482MtbU1LC0tLWxrt9uiXMeIcTtxS6VARVFsAL8K4ByAEMD/AeBFAL8NYBPAVQDfEYZh99X2k06nw42NDSEGHERMEsTgRz4cmQm1srIio2FmsxmKxSIajQb6/b4QpminGEMvaZDmrDyWpubzuRAZeoRyuRx2d3cXSFOn01koO+3v7+Mrv/IrRe1ioKlpmqL4cCDxYDCQkNHHH38cnueh1WrJPD2C/rLpdIpKpSIlMippNKTPZjNomiZryuVyWF9fh23bePHFF9HpdJDNZrG2tgbP89Dr9eB5npi3SaQ8z5MMMCpza2trMhBbURR4nodEIiHlv729PUm9LxQKyGazaLfbaDQayGazKJVKWF5ehuu6EoUxHo+Rz+cXwlpnsxk2Nzdx+fJluebMoiI5dRxHCDDJWL/fF0LA/TEIdjAYIAgCGIYhCfvz+Ryu66JQKMDzPJlBmU6nsbGxgcFgAMdx8Nxzz2E4HN5U2eN23RM3W/a4cOECzp49+7I/4+gj3vNPPPEEvvSlL73ivv71v/7X+PEf//GbWUaMexA3Wwq82/dEIpHAz//8z+Of/bN/trD9E5/4BN773vfKL+MxYrxevFGlwF8C8NkwDL9dUZQMAB3AhwH8URiGP6coyocAfAjAP3+1nUSJAgCYpgnLspBIJMSTw5IblSRGHxiGIenmpmlC13U4joODgwNcunQJq6urYjDvdruo1Wqi1HQ6HXQ6HRiGIW381Wp1YWRNs9mUEqFpmtINyCHJVNgGg4F4vqJrXFtbw3Q6xfb2tpSqaNSnlwsADMPA0tISer2ejJ8xDAOapi3Mt6PHq1arATgmeQcHBygWi+ID6/f7ErSay+XQarWwtbWFcrksilK32xWlCIAMiWY+F+cH0p/GtR8eHsp5apqGnZ0dvOUtb0Gz2cRkMsHKyoqU34IgwJUrV7C6uirlNzYAsMTr+z4cx8FgMMDb3vY29Ho9tFotSXOP+txIiJgxls/nsb+/j3w+D9M0pZzYarUwm81EkWLG2XQ6le9KtCQcnR0I4FZnBd6WeyJGjHsId/We+OIXv4jHHnvsJdu//du/HV/zNV+DBx544E1f/o9xsnDTxEpRlDyAvw3g/QAQhuEYwFhRlG8B8I5rL/sNAJ/Da9ww9ObQn8TIApZumNUUzbFiR1y5XEa1WoVlWeI/osnatm3xjnBOHzOvWDIbjUbi1VFVVVrzWc5yXReVSkU8PdPpFPV6HZubmwAgni5mOLHzbT6fI5vNotvtYjKZIJFIoNPpCLnJ5/OyJsuyAByX+OhNYtksDEP0+33oui4jePhzHnt1dXXBF2ZZFprNJur1OlRVRalUkhJXEAQIgmBhNiFwXC7t9XpiGGccQ9T7REI4HA6l07JYLKJQKIj3qtlsIpfLLZRSeb3pqeI1ppeOipnneZhMJgjDUMqWPCbn+kXHADGqgd+XqELJDkld1xEEgZSWWVqsVqsL0Ri5XA6j0eiWugJv5z0RI8a9gJNwT/CXrhtBhT9GjNuNW1GsTgNoAvj/KYryOID/BeAHASyFYUjXax3A0su9WVGUDwD4AAAp7wDXU77ZGUafDICFNHOWiJjmTWM2t1F14YOa4Mw5eoJoZiZJoVpBDxCVDZYCuS+SC3p7SDT4oKbRnmpILpdDEAQArvvCqAiRKLmuuzAGh+Quenx2D1K9I1EYj8cyrJq+JybGs2zpeZ6Y4xn3wDE4UVM8De0kt1wPFR8qVlSf2MHH19ATRbJHk36UlHF9/JzpdaJqxLiJaJAqfWvR60pTPr1XJE/0rTFJnspc9LqRQPOzvrFL9CZw2+6JGDHuEcT3RIz7DrdS80gB+BsAfiUMwycB+DiWcwXhMQt5WY01DMOPhWH4tjAM38YHLlWFYrGI2WwGx3HgOA4Mw5AHfJRMtNtt7O3t4fLly3j66afheR6azaZ4bxhzwJTx6IOT5nW22PM3GsuyhBxQJRqPxzIjUNd1VCoVdLtduK4rpEdRFAkVTSaTEpRJZYZ+o2QyieFwCN/3pQwWBIEQilwuJ2thWW82m0lGF3A9HoGlsmiYaTTBnWNaptMper0eOp2OnItlWdA0TZQ6EiAa0zmKhqOF5vM5HMdBPp9HNpuVwM5oSXUwGEj5ktEJ/X4frVZLSppM1Z/P5+h2u2i1WuKpYwwGCRGHJ5PYUdkk+SThZtmVpUXOeiwWiwCOVcVyuYzl5WXpeIxmfpXLZUwmE/lORGM87tY9cbML4DW8EePxGJ1OZ2HbdDpFp9N52TIIy7MxYtwi7vo9ESPGncZNm9cVRakB+H/DMNy89u+/heMb5kEA7wjD8FBRlGUAnwvD8OFX25dlWeHGxoaoF8PhUEiPoihSXuIDeH19HaZp4uDgAPl8XsjP5cuXRcFwXRe2bSOVSqHX6yEIAgmYZKyB53kIw1C8O71eb2EkC3A96JIqGX1CAHB0dCRrYmmRr2OHmu/7GAwG8DwPwHVysLS0hP39fQnr1HUdS0tL4n26dk1hGIYMUU4mk3BdF5ZlodPpCCErlUoSTWCaJur1OgCIMZsRFo1GA57nYTabiSm/VqtBVVU4jiPvYSyEaZqSb0V/1cbGhmSLAcckr1wuYzAYYDQaIZvNLihEVMK4P1VVRWGKlmmBY08ZR+c4joNCoYBCoYDZbIZmsykzB9PptBjfLcvCwcEBJpMJSqWSqGiO46DdbmMymUjpVVEUKe12u13xjp05cwbnz59HPp+Hqqp49tlnb8q8fjvviZs16iqKgp//+Z/Hj/zIjyxs/63f+i285z3veQmJSiaTaLVaMvSaePLJJ/HUU0/dzBJi3KO4GfP6SbgnXk9DR4wYrwevdE/calfgnwH4R2EYvqgoyk8CMK79qB0xJRbDMPw/X20/2Ww2fOihh2Qe397enhAcqkwsXZHUMBmcxmWWmGj85sPV933JnKKqEY0lIAniv+v1unSXMTOLyg/DKlkmY8J3Pp8XRY1m7xszl8IwlG43+ptYAuPrVFWVIE8AkrTO9/OzKhaL0mHHsh6HLnMWXrValdgEnifnKjKjiu9jzMDa2pqUWNlRR08aySoA1Go1JJNJBEGAXq8nPgV27fF4LDMGQbCQk5TNZoXImaaJYrGIo6MjzGYzKas6joOVlRWJkjBNExcvXsTGxobkXAVBIOfMMFCuMZfLQdM0NJtN6VyczWYYjUYol8uo1+uSL2aapow6SqVSuHLlCnzfv9kOqNtyT9xKGGKtVpORQUSv18Pe3t7Lvv6RRx55SbTGpUuXXlb5inH/4ha6Au/qPfHggw/iX/yLf4H3v//9C9t///d/Hz/6oz+KF1544WZ2GyPGG9YV+AMAfvNap8cVAN+L4/LipxRF+T4A2wC+48vZEf1HVDUAiFeKihE78fg/fF3XF4gBy170+JBMRDOToqNYSGY8zxOCFU335hro9eKDl74n+nNolua+6duK+rVYWuN4Fdd1pVvO8zwJPKUqxjWSwJGEkbABkAHSPMfojEUawEkeuE/6yRg7wPVzP5xLyI66KFnktWUH5Gg0QiaTwWw2k+Pz7yy5kgzyM2HDAAnccDiU8iOVLkZuUL1ibhW/FxxyTUJM0kwSyWwwXiPgekgsSXY+nxejOs383O8t5ljdtnviZlGv10W1/HIQP1hivMG4q/fEpUuX8Ju/+ZvY2tpa2P7MM8/E3/0YbwhuiViFYfgUgJerfb/z9eyHBmMSlWg5ioSCRnE+jNlxRhWGHXskBCRI7CpLJBILXYVUnabTqRjUqWhFk9DZkk+li8SKoaU0jLPExjDS4XAoZAaABGCy+5E/ZzmPpKxcLkNVVTlfzuajMqUoigRcMsF9NpvJsVVVhWVZODo6kjRyZkNF101fFr1SyWQSzWZTUvAZZ0FyxmvIa06zv23bC7MZSc6A68O1mTGVTCaFqLHjcDweS8AqPT0kazS5U8Fisj3LkJy5yONxndFz5DFo4ifhYjOB7/tChqlo3Qqxul33RIwY9wpOwj3xh3/4h/jDP/zDO3W4GPc5TkTyeiKREI8HSZaiKKhUKjLWhQ9WKio0ftu2jVqtJkOY+eCcTCZwHAe1Wk068La2tlCpVFAqlRZm7ZFsRbOSSMI4wJglxlQqBcdxcOrUKVGZOPi5WCxiOBzKiJRoCW8+n6NWq0HXdRlDc/78eYkHoJcqOs9vMpng6OgIhUJBQk5Z2iwUCphMJtjd3cXjjz+Op556aqGcyLUDxwSHae30c3F2H03rAFAul+VcmLNFEzjLd1T4gGNiU6lUJJ2b13Q+n6NYLArBbLfbACDDr4vFosRhUMEbj8cyoHk2m2FjYwO2bQtxGwwGYtqnMZ8ZYtx/uVxGEARotVpCmkka+dml02lsb28veIo0TZOE/TgsMEaMGDFi3ApOxBDmXC4X0qdEIzaVD03TUCgUJL2bZKXf70NVVRSLRckgolFdURQJ8iwUCuh2u+h0OigUCpK8bhiGxDNQlWIJiuXI4XCIvb092LYNwzi2BTB4kp19UcM6tw8GA6ysrEh3WiaTgaZpYpimKjebzWDbtoy32d3dlWtC8rO/vy/kYDqdotvtYnl5Wczbg8EA4/FYSCjXyPImYwd0XZcB18lkEqurq6jX66LudDodnD59GqPRSLr8bNuW65DJZLCysoJWqyUqFP+wBAtAyrbs7uP+gyCQ8iRjHYDr0RP1eh3r6+ui1vV6PXS7XeRyOelczOVycF1Xyr/tdhuPP/64KJnR0qnv+zLMG4CUWDOZDLa3t1GpVDCZTOD7PvL5PIDr8Q9Xr17FaDSKB87GiBHBzXqsbhfieyLGScMbYl6/XVBVNVxaWkIul0OtVpOkcvp/crkc6vW6mL/5MKS6wFITzdbX9rnwkPc8D67rSkmMSlXU5Mt9s3wY9TnRuA4ckx7XdcWIrmmadLaRKJimKWNYSLzY0RfNhopmaHHQc5QQsUQ5GAzk3KKZXxx5w468dDq9YJqnR43Bonwv18Ysr2QyiU6ng/X1dQwGA9TrdczncxiGIX4py7JEFaQxnSpZNK2c13AymaDf76NSqSxkYtHfxVIjzfTlchm+7wt5ymazcjx+3hw2zcBVhqtS9VpeXhYV0XEcaJqGYrEox0kkEtjd3ZUxQYziIKn0fR8XL1686ZE2twvxQyTGSUNMrGLEWMQbZV6/LVAUBbquCykAFsmDoijo9/vinwKOs4tGo5GUnjjgmKAxOZlMCgFiRxhDIunziQZyshTHNXCgL5UyronH5HrokWJZib4wEqSo14h+L5qzqXrxnOnN8jxPCApwfVBy9PjRwEyeQ3R//ENfE8+VRnGWDJlmzvmDNJszNoIqGL1mJDkMTeV6aGpn+jo/Ix6H44ui7+N1Z8gnP1+SJipQURKXSqVg27aUjUky+bNoNAaVRKpo0eHPNOMzJPVWktdjXEcymcTf//t//yWZYJ/97GelUSVGjBgx7kWcCGKVTqdRLpfR6/WwtbWFTCYjKgPVCpbBOJiXgZdsl2dXGEGPFdUnZjdRHTIMA+VyWcp/wDFxYb4TS2/RuIJo2nd0REJUAaNCRaIS7fKbz+doNpsyqoUqEv1CzWYTg8EA2WwWk8kEnU4HmUwG7XYb+Xxe/F/5fH4hwXx/f188XbPZDLquQ9M0lMtlIavsXOQ6eH2jhHZlZQWdTkeGWJfLZSiKAk3TJMqCc/to9GYcxGw2k6wqxkVks1kUCoUFczuPRQLDGA3miDG/jO+xbRuu66LRaCCZTIo3jNeBxIzXmjEKAGQkEkkguxqr1ap4t0juut2unHeMW0MqlUKlUsGnPvUp8fkR586dw8WLF2814T5GjBgxTixORCkwl8uFa2trSKVSEn5J43EqlZKhvIVCQRLDDw8PZRxMKpVCp9NBqVRaeMhzTEwikVjIw6LpuVAoyIOYKkaj0UC1WkUymVxQlDiyBYAoQKZpYjab4eDgAEtLSygWiwjDUObxpVIp8WFxMDFjIBKJBBqNBmzbxsrKCgzDwPb2Nnq9nsQKsPWfXqBEIoHHHnsMR0dHsg52MrZaLSlBkkzW63WMRiMYhoFMJoPRaATTNKFpmuRlMdeK5cDovL3xeIzDw0OJOqjVavjSl74kMxg5R5Dkil2ChmFgf38fs9lMCCRnD9Jb5nmezGBst9tCvNglyZIwU/Hn8zl2d3dx7tw5Mf/Tj8dyKnAc+MfybhAE6Pf7YpAnwWLQKtWtfr+PpaUlUdzOnz9/0zlWtwtv5rLHu971Lnz2s5992WHW8/kc3//9349//+///V1YWYxbQVwKjBFjESe6FDidTiXQstPpYGlpCdlsFo1GA41GA7PZDEtLSxgOh2i1WgCOPTEcuzEej2HbNpLJpMQecJQMYw10XZdZdMx6ogIUjTWIeq5YChwOh6KsZLNZlEol9Ho96XJbWlrC6uqqKF+GYSAIAhnqy7gAlvrY+fbwww/j8PAQV69eBXCsfK2uroqJmq8PwxDlclkGJLO8yJ9xDh+3U+lKJpOwLAu2bUuYJ1U1NghEZylOp1PJfaI5n/6o8XiMZrOJr/iKr5DhzktLS7hw4QI0TROz+Gg0Woh6GA6HkmjOklulUpEOQl4v27YxGAxkHE6hUMD58+cXUvgZ7MlS5Xg8xvb2tiiKDHHd29uDYRgwDAPz+VySxalqUmkzTRPZbBb7+/vo9/solUoL5dIYr46Pf/zjOHfu3MK2n/qpn0IQBC9LqgAIeY0R417F93zP9+CHfuiHFrb9z//5P/FP/+k/vTsLinHHcSKIVXQWHFUednqxbMfOM3quqFREB++SGND7RAWH+UmKoqBQKAA49hg1m82F0h5JSq/XWyAqqqpKd2KlUkGxWJQ2/8FgIOpYPp+X1PJCoYBOpyMZWFRK6Pli92DUJE9VKerfivqlmAxPQzsJYzQ+gesdDoeiTnG0DuMqWLrMZDJSTiN5jPrLNE1bKKuxLMgAUqaV07wfDUnVNG0h1DW8NkAZgFxffiaqqqLVakkuFcuFzDPjMOnRaCSkCoCY06lWUV3kQ53KJTPSmG/GzkMOrrZtG47jiEIYP/hfHZlMBj/8wz+Mv/N3/g7W1tYWfvY93/M9kqofI8b9hve97334zu/8Tjz55JML23O5HH7kR34Ev/ALvxD/4nYf4FaGMN82sMOMpICDe1m64yy+aKwBAy5JBEhOqNDQp0RCQFJmWZYYluv1unT80ffEchRVGqpWhmGgWq1ifX0dKysrWF1dxdraGqrVKrLZrIxtoReLg6NZrmOZjdsURcHR0dECgUun0/A87yVkCoCQMZZFB4OBzNujMkWvFocUm6aJXC4nRIOzF1mmJBlkrhfJK4kqM7eiqfOTyURm+tXrdTkXHoNp+STDNLNHVT8SK5bwUqmUlBxJmnu9nsReUJ1iWZVEld2FHHhNozzfxwYB/mE5kISRqhw7SJmCfxLK4ycZmUwGP/MzP/MSUgUA3/qt34rv/d7vvQurihHj7uP7v//78e53v/sl2x988EH81E/9lPxSGOPexolQrBipEB2KTNJE4sFyD03pLOWQWEW/sCxJ3ejNGo/H4jvKZrMol8totVqyX3aYMaWb5admsym+IgAoFArSneZ5npACmukzmQzOnz8v6g5VGODY95PL5bC6uorz588LaWSW1Pr6upAyksGDgwNYloVcLgfTNLG6uoqdnR20222EYQjLstDtdsVLtbOzA8uyhDQyyqHRaMg4mjNnzkjJLpooT1KSTqexv78vXisGsp46dUqUJTYNRPO3BoMB2u32whzEpaUlAJCssTNnzkguGc3i5XJZsq3YHUilkOvjCCOWHVmmBCCxGJqmwbIsUeL29/elRDydToUssmtxOByK34wp7bGBPUaMGDFi3CxOBLEaj8dwXVcSx2ezmeQWBUGAer2OpaUlMUSzS5AP01QqhbNnz+Kv/uqvpFwEHJOtYrGIUqmEbDYrA3mp8nDQcLvdlhwqdr1R6bl69aoESzJfanNzE3/2Z3+GixcvotFooFgsyr4ALAyvbbVamEwmC4OSqV6xa4/qlKqqC6ntLKmUSiUpT62trb3EBM/hupzBp6oqqtUqut0ums0mRqMRNE0T4sXXdjodmSXI855OpzLgGYAMOq5Wq1hdXYXneeIBC4IAzWYT6+vrohZRQSKxKhaLcBxH1MPpdCrhrtGuPjYUMI6Bo3uoPLqui4ceegjD4RDNZhOdTkc6FTla6PTp0+h0Ojg4OMBwOESxWMTy8vJLhmFTiYyOEqKax5JhjJvH5z73Obz//e/HxYsXX9IV+OSTT8bz2WLEiHFP40QQKxIEeqYURZH5eAAkLqFWq2E4HEqpjqXDRCIhCeXsJqNqwq60RqMhnhrGAVCtyWQyMjKGKtRgMEA6nYau6xLvwId6IpHAxYsXZfxLEARQFEUM6iRmVF7Y4n94eCglKJKFarUqPiB6swAshHvy78DxWBiSBqpcjJKg6jIYDHBwcABd12FZlsxC9H1/YaYfO/TCMEQul0Ov1xPjMb1RzJtiQ0C0TJZMJkX5IUEkyYlmaLFUyPOpVqtwXReTyUTMzCyDkuzwOxGdL9hqtUS5AyCzAPnwZomR36XBYIBCoSCKGkvJHM7N2YeapkkJlt+/u43Tp0/jp3/6pwEAnU4HP/iDP3iXV/TlYzweY29vD+973/teUvq4cOHCwi8eMWJ8uXgz3xMx7i+cCGKVTqdRqVTEZM2MJio7fHBaloV0Oi1xBsyKYqcXVQ/OuqPC5Ps+fN+Xgb3stiMhYkAmR9yQyND3BBw/tKOkhGQFwMKQZBIDHpf+HcYiRM3s0fWTZHC8zY0GdhKrTqcj/qVofhbPi8Qqql7lcjmJY2D5K+qhYgwDfUhUz2gaB6572IDrYajRgdMsX5JMUhmLNibQY8X3MRoDwIK/i2Gd0TDUaKQEvzO8Puzk4+dGT5bneahWq1JW5J/omtg9yfJiNAvtbqJYLOI973kPgGPV87Of/Sw+97nPnYhwzdlshk9/+tP423/7b8s4IOKZZ57BX/7lX2I2m+ETn/jEXVphjHsRJ/meIP7iL/4ClmXhkUceWdjearXwp3/6p7F/8z7Biah5ZDIZPPbYYzh37hweeOABWJYlJIjJ2QCkTEPy4Lou2u02ut0ukskkWq0W6vU6Go0Gut2udNlxaHGtVpOMJz7AOVCYkQv9fl9KU8yr8jwPvu8Lodvd3ZVSHDvfhsMhCoUCksmk+LgGgwGq1SqWl5dxcHCAfr8v79M0DY8++qgYvFOpFMrlMjzPk+gHqluc8UcljnEOk8kExWJRCE0QBPLapaUlyXHiKKBisShxDL7vwzAMKTl2u10pz9G/xpgEmv9JeElC2KnHGIVsNiszGqOhnCQ7/Bx3dnYWxhExEkPTNOTzeYmHACBNBIqiwPM81Ot1tFotBEEgqhaVtdXVVVHIWF7m2nRdR6FQgGEY6HQ64tnTdV0GfrOD9CQoVlGUy2V8+tOfFq/a3UYQBPgH/+Af4KmnnpLvK//8m3/zb/DjP/7jd3uJMe5xnLR7gvjgBz+Ij370oy+5L/7iL/4C3/Zt33ZifnGL8cbiRASEbm5uhu95z3vkIfwHf/AHC7EL7NYDroc+MkmcasX+/j4eeOCBhUG80+lUohHCMMTBwQFqtZocN5U6Hv4LXC9HMneJviN2rbVaLSlh0OfEhzCJWLvdBnBdfWG+FufTsWzGKAPTNCUck+cWBIEEhE4mE1iWJSU8er2ofpHA5HI5yW7K5XLiO6Lqp2maqDbR9HgmzLOEyiR4kjmqZOx05Fpp9h+Px8jn8xK6yteYpilZVY7jwHEc8bnR5M/B0PP5XEJKaZwHgGazKWoTSRivE//ntLa2JqojcEzSWI5lE0HUP5fJZGTeIdWw8XiM4XCItbU19Ho9KaMGQXBX2dXb3va28Itf/OLCttOnT0vm2UnAy5FQlpZj3Hu42wGhb4Z7AoBUEKKIzpqNce/gRAeEZrNZnD17Fvv7+zJ8meWqVCoFz/NkGwBomibRCSxvVatVIQAMB93f3xdFJgxDVKtVpFIpdLtd8RVFS11UjkajERqNBrLZLDY3N/Hiiy9KGjtLlUwtJ3H42q/9WmSzWXQ6HfR6PQDHpnOawJeXl7G/vw/DMISwtNttyaHiGiqVivh9dF0XZYbXolAowHEcKYUuLS0JcaHZn36oG4kfj0EiUyqVpOQ2HA7FMxYddhzNqFJVVeb3UT2sVCrY2toCAFkfDfBRn1U0T4qKGP1iPG/geomPDQGMhaBfzjAMWQ8zrvgaKlcMAmXw7OHhIfL5vAyajibNM+usXq9Lp2NMDL48xA+KGDFeimj4cYz7EyeCWEWzhqIz8EgM2AbPsh5VDaaIA9f9MmyXn06nUsriPhOJBPr9vvydnWw0sNNITT8QjezMbQIgZnnXdWUbFRyulf6kKKHh+m7MdyKpAY5jJ2h2j3amkXiFYSjS8nw+RzablVwq+oZYdtM0Tc6VnXU3DirmPgDAdV0JzuR5MnaCql308yJ830epVJLSnW3baDQakn/Fz5AdflTbWLKj74q/5bFrj6VZEk+SZqbn35gYT6Uymuw9n88RBAEsy0IYhpL9xVBSkjEej164uCswRowYMWLcLG7pCaIoyg8rivKcoijPKoryCUVRVEVRTiuK8nlFUS4pivLbiqJkXms/TEH3PE9IEZUW4DqZiYJqBckRyRLLd4PBQMpUfGAzVBI4JiB8yEaVFZrYbduGruvo9/sol8tioCYJYGmQg4a73a6U7IrFohjhWcKbTqeShcVjkEj1+324risdjFwfACGSqdTx4ONutytEjH6n0Wi00GUXVfp6vZ5coyjB5KBjdskxmZ1kleW9aCcdj8ky4HA4RL1eR6VSQS6Xk47LaGcd/04lLDpKB8BL3jOdTjEYDBaaA/gadmW22230+31Jfo/6y6i2cY2e58nAZc/zJLGeBJfElud6qx6r23VPRDGbzXD58uU4XyvGmxJvxD0RI8ZJxk0TK0VRVgH8fwG8LQzDcwCSAL4TwP8F4BfDMHwQQBfA973WvgaDAba3t9FsNmUIca/XExM3H3zFYhGmaWIymaDdbqPZbOLo6Eiyn3Z2dtDv9wEcq0h7e3u4fPky2u22PGw3NjakdX80GuEtb3kLVFWF4zhoNBoS6dBoNOA4DmzbRr1eRzqdhmVZMAwDR0dHsG1bSBZw3PVBk/tgMIBt2wtysOd5ME0TzWYTe3t7cBwH6+vrUpZiyz+zrVRVlfIeox4ymQzK5TLW1taEDB0cHEgpLJlMiirmuq74uMbjMSzLWujUS6VSOH36tIyNKRQK0nU3nU7RaDREzaGiE4ahBJ5aloVqtQrg2A/lOA48z0Or1UKz2ZRZjCxjFotFmTdIgkiyd3R0JAoaiV+xWMSTTz4JDudutVoolUool8vI5/OSMl8qlQBAUvdZUgyCAOPxGMvLyxiPxygUCtjY2MDa2hoqlQra7TZ831+4LlEz/N2+J6I4ODjAgw8+iP39/ZtaV4wYdwtv1D0RI8ZJxq2WAlMANEVRJgB0AIcAvgHAP7z2898A8JMAfuXVdjIajfCFL3xByjjT6RSrq6toNptwXRe1Wg07OztiYp/NZshkMpLlxFBRdnlFk9o5JgU4NoOrqorl5WUhbRziSzKTTqcxGo2k2y4IAslrSiQSEt1A4sSH+/PPPy9+LaoowLF/jAb70WiEhx9+WIjhc889BwCiSA2HQ8liYnkxmrOVSCSESAEQI3oulxNjPMt9KysrODw8RCqVQqVSwZUrV8TEz4wnDqaeTCZSwuNaDMOQtTPWQdM01Ot12LaN+XwO13WxsrKC/f19STufzWZybTqdDjqdDs6cOSNp+YVCAaPRSEq0wLEi1el0UKlUpMPy8PBQSppUzXjteW1LpRLq9TqOjo4wGo1kWDNjJHq9Hi5fvixREkygTyaTWF5eRr/fx/7+vnjhqOydhHvi6aefxurqKoDYyxTjTY/bfk8QR0dHt3utMWLcMm6aWIVhuK8oyv8NYAdAAOAPAPwvAL0wDPl02gOw+nLvVxTlAwA+ABw/WJnVFCVBzHWaz+col8vy0KNiouu6hEpms1npfgMgA4YHg4GUfC5durQw5oUPrOi8viAIpCTGrjFN0+C6rpTnmPxOEuJ5HpaWloTg0QgfhiF6vZ54pqKG7ugQ4qj3ifvkNRgMBqIiAZByFQ3azOxyHGehe44GdcYrAMfKIGciZrNZOI4jZND3fTH389gccsxjsGRHzxn9bMyCYlnVMAxRxfi5kbQwY4rXHMDCNWcJjwQrGjbquq6oWiztKooC27bFc8VjUV1jyCvJIU3/NPlzZE6n05HS8c2a12/nPQEcK1UxYryZEd8TMe5H3EopsADgWwCcBrACwADw0umTr4AwDD8WhuHbwjB8G9O1o8nXHMBsmqZ0hJEEsJOLBmu2sjKviQ/RaDcdH+jsQiNYeiJp8n1ffEbclkgkxPdDQjWZTKSrjLMHDcMQo3VU/RoMBrJGKmUAhCRwPSSX0cRyho1GQ0V5zgAkUNTzPDiOI1ELvV5PTN2MSeB6aVj3fV8M5iRMJDtc73g8ltcwmiE6+oVjeoDrMQgkpBzyTG8XSRNJJdcT7RyMlgK5fu43Si75en4euVxOBin7vi+m/Uwms0CU6KtzXRej0Qi6rgtRIzG8WfP67bwnbmoBMWKcMMT3RIz7EbdSCvz/ANgKw7AJAIqi/BcAbwdgK4qSuvbbyBqA1zSGcIQNiQUVoXw+j3Q6LWblaEkkSgTm87kMMJ5MJvA8T0p4NHizfMRyYDabxXA4hOM4KJfL0qXX7/cxGo1QKpVEFfJ9H8D17kWSQA5tTqVSODo6wtLSkjyUC4WC+HwGg4GY6QmOXQEgJJHGdF3XJTCT5JKlKq673W4jkUigUCiI6Z/XqFQqydy94XCIfr+PfD6PfD4vJIwje5gan06n4TiOkKXJZIKVlZUFpc5xHCwvL2N3d1dmDEZT4nld2u22lPCAY6WM5VZd1yXCATgmSfl8foHk2baN06dP45lnnhHFjteJDQFUyVqtllw/XdfR7Xale5RdmiyJptNpMcJHSwie5yGXy4kCd+N8u9eB23ZPxIhxjyC+J2Lcd7gVYrUD4GsURdFxLPG+E8AXAfwxgG8H8EkA7wPwu6+1I5KfXC6HYrGIbrcr5Td6pdg1F31YR9vkU6kU9vf3JfwROM6JYjIvO8ZIJjRNw+OPP47t7W0xUkdn2lEhIvmJlg1rtRqOjo7E3xUEAXzfx97eHnK5HPL5PLa3t8VPlEodD4lm5yNwXKqs1WqihrXbbeTzeTz00EMyZmYymeChhx7C9vY2crkcyuUyer0eqtUqKpWK5Dk9+uijODw8FFIIAIZhyLWwbVuiGaKjadi5OJlMMBwOsb6+LoGpTL+naZ5KHBPUfd+H67rQdR3FYnGhG/PUqVNotVpiumf2FOMO+BlT1VMUBaVSSToGh8MhLl68iHK5LJ83xwjR20Ulj2GtvK6MhiDR5Ofp+z40TcPa2po0HwDH/j7LstBqtRY6E28St+2eiBHjHkF8T8S473BLyeuKovwUgP8dwBTAlwD8IxzXyj8JoHht23vCMBy92n40TQtrtRoMw0ChUEAYhjg8PFwwZV++fBlra2vysJ/P5wvt5zRNd7tdyVRilxfjAvgaEpLhcCimbZbgjo6OYJqmRCbMZjMJGKUCYlmWdLexLMkMJcYyzGYzlMtl6apLJpPo9/uylkwmIyNuSG4URZESGL1KVL2o0lDhodrWarWkO4/HZSk0GpfQ6XTEmxXNpGJZkEQuGjlAbxi9byyhkbiGYSgRFDyH6L8BSHbYysqKeMbG4zFWVlaE0NIzR+O+cm2gtWVZ0qXHEurKyopEMnDkEL1klmXBtm288MILCMMQhmHIebIEahgGDMNAt9uVtbCcS+Xu4OAAg8HgpjIXbtc9oShKnFIa40ThZpPX43sixr2KNyR5PQzDnwDwEzdsvgLgq1/PfhRFkbIeW/BZuqE3ioSKOUWDwUDKcFQZ6Huir0ZVVRnNwmgEqjNBEGAwGMhgZ6onBwcH0gnHshADNokgCERpASAPZ5b36N0Bro9hofJG0kJiGM2rYmfgZDKRkToEt5dKJSkDcixMv9+X68drEfWW0UgezfsiSaPfK5obdmNpj+fAa8mgTwCiStGLReM6fWAMIeXfgesZXQwLZTmRx4oGmtKbxQ5IErYbmw3YoDAajUSN4s+p1vFY7Likt4rXg5/DreB23RMxYtwriO+JGPcbTkTENIlVOp1GEARotVqwbVuGKLNcw8DLyWQiyedRgzMf/IZhQNO0hWDR+Xwu7fpUP6KdZ3w9PU2MPmDoJx/kNKDzddlsVjxEzHqKjlgBIMQtn88LweF7qCipqgrbthfUJCooAKQ8OJ/Psbu7K0nmS0tLL+lupNmfihsztEgoHMeB7/syW6/b7Yr3bDKZCKlhCZbXt9PpQNd1SUtnplj0vNlsQOM75zUOBgPp4LQsC/1+X86dqegkZAxdZbQGZx5ms1kZgwNcbzwwTVOOe3R0hGq1imq1Kt8pHoPkr9VqiULIz55/SLBixIgRI0aMm8GJGWnD+XpUNQzDQKPRQL/fx/LyMnq9npSxVFXFI488gjAMsbe3h3a7Ddu2kc/nRUVKJBKo1+vy4E2lUnAcB4ZhSNt/JpNBr9eDrusLSeokXdzX/v4+Njc3pfTU6XSEDFJRYWmK63j22WfFEE2V5aGHHpLy2Wg0QhAEkv3EVPFcLickETgeNVMul2Ga5kJkAxWvyWQiqen5fB6ZTAZbW1sSnTCbzdDv90WpYaI6VRx2W5LARQkhS5aMvahWqzIommQRAIrFIsIwxGQyQafTwXA4lFwrVVVRLBbx4osvCkElWaZRv1gs4tKlSzJCiKQNgCS2R0cMMbm+3W6jXC7LOVL1Ysgs1xzt/uOsRw7Hppk/CAJUKhVomnYHvvExYsSIEeNexS15rG4XNE0Lv/Zrv1ZMxnt7ewsPdYZZkqCoqoparSbddtPpFKZpii+LxCMao0DQcA0cl9dqtZoMNWZEg23bomqFYYjNzU20220MBgOJdRiPx1KqjJaoSLRYkqLvaTQayUM+nU4jl8vJOBp2Qf7lX/4lHn300YUyY6PRQLFYlPWnUins7u4il8vJbMR+v4/19XXxXSmKglqtJpELmUwGOzs7OHv2rJAQdsdx6LDjOACwMKA4CIKFwM39/X3p6iPJ7fV6om6xM6/RaEgoKkf5MEOK5npVVRdKg9wXgzyZIB8da6Ncm4tomiZyuRyCIMDR0ZH4smazGYrFojQzkDj5vi9jhY6OjlAul4UI81zCMBTStbOzg9FodPNzbW4DYj9JjJOGm/VY3S7E90SMk4Y3xGN1u6AoipSm2P4PXM8rGo/HUBQF+Xxe1KHhcAjP86Bpmpi5qaIkk0kpZwEQLw3LRWz9tywLrusKqQIgoZ4sbaVSKdTrdSEEDCxlWY0P42heUzKZlFIWS07sPGMMAVUbZkiNx2PxSYVhKCWqo6MjeJ6HUqkk3iGavGkYV1VVSB/LdLxm8/lcohWCIFgIXeVxo0Go0c+EpIPeLCp/JOOct8h8Ks7rYymV15QhqTT7k7hG87sYBUHTfJQQAZB0eABy7WhGj66H1yXqxdI0TXxgmUxG1EBiPp9LBMSt5FjFiBEjRowYJ+YJwvZ9lnpo9Abwki61+XyOIAhEUaJiQaWCD8fBYCAPYT5s+dBUFAW6rov5mkpJMpkUksJyYbPZlI45KjE0ZDNGIJp9xMHJ9BrRe0S/FgDxiDHnimVIANJZCFwfNs3zoxGfPyMBYtp4dDwOS37T6VRKYQCkrEbSShISDcgk2eCgaBI/EjKayW+MJiB5I2GLNgKw05EELuony2QyQiapKNK/xWtCkzk9WcPhELlcTrxRUf9av9+XEijXw2vCtbOcys8/StBixIgRI0aMm8GJUKyio13G4zFqtRrm87kEWQKQ4cdhGAohAoBOp4PBYID19fUFFQQ4LqNZliWZVBzWzBLdwcGBkAr6jyqVikQLABCVIzpahREBvu8jmUyiUqmIp4nlrm63K2SEpnjON2QJ65FHHsH58+elm49kQVVVMfGzLMbSJLsf2YloWRZSqZQkxtNjNJ1OUS6XxcDNQFVeC9M0JReLQ5p5bB4jkUigUqnAcRy4rotSqYS9vT2Uy2UAx2VDmsMrlQpUVUW73RZDfC6XE1VQ13XJ02JXo67rQhyZUk8PGn1cLFuWSiUh0jSa93o9LC8vYzgcYjAYyOfBeAw2JxiGIURvNBrBNE30+30hVTwXxnichPJ4jBgxYsR4c+JEeKxUVQ2ffPJJDIdDdLtd8QuRaMxmM1QqlYU4hhuVDPp4AEh5KpPJLCgqHMvCzi/6nzhrLwgCPPDAA0in07h69SqGwyHK5TI8z4NlWQAg3ioSJY6PmUwmogRx28rKisztOzg4wPLyMoDrYaO6rkuqPACcOnVKOulIEKvVqpTz6Ccj+QEgpvxTp07JuTM7ionxNNTPZjO4rgvXdaUDMBq/4Ps+xuOxjIhJpVJYXl5Go9FAu93GZDJBoVCQDj52JR4cHIhSVa1W5TgkT1S2qPhRZSMBZMmUpVwSVN/30Ww2kclksLGxgWaziUqlIteDxGk2m0HXdSwtLeHpp59GpVJZOE6z2UQYhjI/kmvl94cZZCRZR0dHN51jdbsQ+0linDTEHqsYMRZxoj1WwGLSdzqdFlM0fTwsETJigWngvV5PsowY3En/DUkjiUp0nApLYDTI02DOlPNoCz8DOAGI4jIajcTPRSWLhmhd1+U9VNSoQkU9PPQ1kVi0Wi0YhiF+s2w2C9d15YHPjki+l0nmq6urUjZjWCZfzw7DTCazUGYbjUYLMQ0sgbJMNxgMYNs2Op2OELRCoSBjdPg5sKuOwZuj0Ug8aizlkjTx3xwtw/XRw0XDPMtylmXJ+QIQVYyxDaZpykif4XCI3d1drK2twXEcOSa9cVEMBgPpsmS5MjoUmunzMWLEiBEjxuvFiSBWLINFTeEsCQEQXw0f8HwARsMkOXqFmUqz2WzhIU3yQ1/TcDgU8ziPwagDPpTp06JPh8ft9/tCiOjTKRQKYpBmjlSU/JRKJcnZYscj98uON66J5CiVSsmxGLDJLrnojD6eFz1SXAePxTmI7JwDjskFoyeY5t7tdmWeIWci0pfEHKxoHANLnAxzJbnUdX0hnZ2Ei9f+Ri/ceDwWL1d0IDI7//iZKooiJTxee5aK2bDASAheG54LAPHmRck018PrECNGjBgxYtwKToRLV1EUNJtNyWhipxt9TiQxvu/LuBoSIAZ1DgYDdDodeQCzrOg4zoLiQ3WDqhV9SSwl0bM0nU4la6nVagnBGo1GEppJc3p0cPJ0OpVj0rOTTqdRLBalvEdVK6rYAMdDhlmmYqlxOByiVCrJwGNmLrG0ZVmWkBYOOeY6WYozTROj0UjeOxqN4Pu+dCHS+1UoFGDbNmzbhmmaQmhM04RlWdIZ2O/3JdGcsxFphB8Oh6hWq1haWoJt2zAMA6dPnxbiFCW7UViWJc0DJGtUz0i4XNdFo9EQMjQcDrG9vS1+LE3TYBiGKF28Hjemx+dyOSGIUaWPnw+/dzFixIgRI8brxYnwWOm6HnJYsmEY8mDe29tDvV6XBzKN0owOuPZeiRsAIOUdDiDO5XJSshuPx2i32wCuj5BZX1+H7/tChqjY0M9EYtFqtWQQMv1OUTWIBI+5Vp1OR5QrduS1220hFBz4zPmB6XRaCEM0x2p5eVmCMTk8mgqQ53lwXRcAFmYJAsclyGq1Ck3TpJTKJgEAkrPleZ74xqjscI0kNlSVer0ebNsWkgkA5XJZ1MLxeIydnR3oui6hphx/c+HCBSF5JDDdbhfJZBK2baPf7yOXy6FUKiGdTuOFF17AeDxGuVyWMvH+/v6CqmSaJra2tmCapqhZiURCrhnT84vFIkzTlHOkR49eu2effRYrKyvI5/MIwxDPPPMMxuNx7CeJESOC2GMVI8YiXumeOBHEStO0cHV1VRQQqhoApH2eIZpM5W61WrAsS0qIS0tLODw8FIWICo+maTLGhuoFiRZN5qurq3AcB51OR5SWZrMp3i3TNGWci23bmEwmQtCAYyLy4IMPwvM8NJtNtNttFAoFANeN9JPJBIZhiOla0zTZJ/OXGKVg2zbm8zlc15VSJz1K7P6jr+nUqVO4fPkyAAhR6Ha7KJfLYpbf29uTtebzeei6ju3tbXzVV30V9vf3Jak8qp5Foxp4rRzHERUoWr4sFAo4ODhAo9FAGIZ49NFHhciSEJMIA5BQVJJX4LisGP0uMuyTChW9YVSfmAXGOIvod6VQKEj8BUkfOyzPnDmDIAhEHVVVFZ1ORwgjAOzu7sLzvPghEiNGBDGxihFjESfevM6hwzQ/M/6AhCKaQcQ5edEOOiaH0+8T9UTxAUpFiB4sDixmCZHt//1+X+byARDPE5WzG0M4TdNEs9kUDxP9SYwRoPeLKhlVo6WlJWSzWfFVpdNp8Ysx8ym6T469qVaropZRpSHZ4Jp930e320UYhhKIyU5AepAuX768EOEQLYFR5TJNU85H0zQhW9FBzRySzZDPRqMB27Yld8xxHJRKJelUZPekZVlSPiwUCtIdCUDKlSTZ9GVNp1OZ8UdyRzWOsRS8noqiSAmW6fGO4wiBJOGlyZ3fF5L6GDFixIgR4/XiRHisAIiZmBELzBOiinOjT4ZjSAAIseLrmdVErxHN1zwO/VX8N1v2mffU7/cXiBCPzXwl/oxrpPrEfVA5oplc13XkcjlRqph8ns/nkcvlhCBEu/g4rxCAhJIGQSAlSJ4//VbRbkOeR7fbRbvdRhAEEsNAn1Y2m8XOzo4Y6qMGb/rLHMcR4sPcLI6V4Ro53JozGdmZ6XmeqElUpaKfRzQDiyZ7qna87lFvXHQ8EXPGGJjKz5pgJAeJdDKZhGEYkj1GYsjkfGZpsWEimoUWI0aMGDFivB6cCMUqDEM0m015ANOAzREtzJCiSZ0dbfQoeZ4nJMTzPCFSR0dHUFUVhUJBTNbMaiKSySSq1So8z5NSVzKZhGmaUFVV5tVRoeKgYQCisGiaBtM0kc/nRdFRVRXValXIQbVaRavVEtO167oL5UyWykjYmCw/nU6Rz+cX8rqYBp9MJlEqlXBwcIDZbLZAUorFopjuSehY7vI8Dw888AB6vR6q1SpyuRx6vZ6kyAPXvWrR4dckLLPZTNLOmao+Go2QyWSwvLwswa5cb7lcxs7ODgBIfAKHNQMQJS5K4DY2NsRoTuLECA2SZ5ZeSRrDMBQFMJvNQtd1UbCY1wVAvk8kWWw+6Ha7C3EVMWLEiBEjxuvFiSBWAPDwww8jCALU63UsLy8LgWLbveu6UBQFhmEAAM6fPy9qFkttURI2mUyk1MbXMHgSgBAQqh66rkNRFMmSUlUVqqrC9320Wi2JDdB1HUEQyLBfdqyR0FFVyufzSKfTQjz4c5I6GrJJKFjOY5kymUzKoGR6jKjiTKdTnDp1CqPRCPv7+0KebNtGsViUETvRSIeVlRV0Oh15neM4ePDBB+E4Dur1OnzfR7lclkHKuq6jWCyK8jWbzXD69GlsbW2JghQEAdrtNkqlkuRCtdttUdRouvd9XxLUs9ksarUaDg4OZCYi87JGo9GCOT8IAskb498ZxZFKpVAsFmHb9kKXIUuA/P7QF2fbtiiFyWQSruvK39kAwZw0NgTEiBEjRowYrxevSawURfl/AHwzgEYYhueubSsC+G0AmwCuAviOMAy7yrFJ55cAfCOAAYD3h2H4V691jEQigcPDQymlURECjv0uh4eHQig4T299fR2j0UjM31SPqHpF592RBLiui9XVVRweHkpJj2VGxiMUCgWJUyAhUlUVrVZLBjtbloWDgwMhO71eTyICCoUCNE1Du91eyJTiujlImiobjw9cT2IvlUqSCbWzswNFUVAoFORc8/k8HMeRcptlWTIEen9/X4iFpmniMdre3haiRkLIOAmW5Q4PD2WcEH1eUZLo+z4syxJCypIjOxrZBckAToaIrq6uot/vS7TFlStXYFmWdF2GYYiVlRVR5cIwRKPRkOR7qoU0wquqKn4vkiCWEBOJhDQsBEEghIlKGecH0tQenZvI79dr5VndiXsixpsDmUwGn/rUpxZmhQLA+9//fjSbzbu0qjuP+J6IEeM6vhzF6tcB/DsAH49s+xCAPwrD8OcURfnQtX//cwB/D8DZa3/+NwC/cu2/rwr6dgBI9xe38+FMPxFwPdcqlUrJgGaGZAKQ4EeqW9xH1KcVDcAEIL4pKlL04EQfttE/0e65aPAot02nUyk1zWYz+L6PXC4nhmng2M9ULBbF+3Rj0Gcmk0EQBDLjj6NbSIg4dJmKF3O1AIj6Q3Wp0+kIyaRBPpvNip+JJu7ZbCbr4/gfDrnm30l2aPTn9Yt+VlH/G/1X0cHNJFn8nKnW8dyowlFx03VdFCg+xKhi8ZrT2xb9nLh/+uaoKNIAz/dQCQOux028Cn4db/A9EePko1ar4Zu+6ZvwTd/0TS8h49/93d+Nz3zmM3jxxRfv0uruOH4d8T1x36NWq+Fd73rXwjbP8/A7v/M7d2lFdwevSazCMPxTRVE2b9j8LQDece3vvwHgczi+Yb4FwMfD4yfT/6soiq0oynIYhoevdgw+5Gh2Nk1TlB4+dElW+BqOaaHnh6Qkm81iMBig3++jVqsJGVMUBbquw3VdeRjP53P0+31RjkzTlBRydruxrd+2bSkZcWgxy4vlchn1el38SFSGXNcV8uc4DqrVKg4ODgBA4iPW19eF+LA0ReO6pmlQVRXlchmTyQT9fl+8UyyDstxIIsXROblcDoqiiCcpn89D0zQZbk01h1lVo9EI6+vrotapqiqRB+12G7PZDOvr6xiPx3jwwQeRTCbR6/Wk5BadZ0jSxXJrt9sVLxlVRjYYkMSwNNfv96WUaVkWut0uUqkUSqWSqIXsDKXxPpoOT6URuG7iZ6fiaDSSQFXOQgQgqiNJenS+5N26J2KcfDz22GP41V/91Zf92S/+4i9iMpncN8Qqvidi5HI5vP3tb8fHP/7xhe17e3v48z//c7Tb7fumMehmPVZLkZugDmDp2t9XAexGXrd3bdur3jBhGMpImMFggFKphEajseA7YiI32+tJQDgShsnsTPDu9/uwbRutVmthAHCv1wOAhXTvSqUipnEAErUQjS5YWlqS4E96sDzPg+d5QmgYEEoPEI9L4sbuQKam7+3tYTgcwrZtlEol7O/vo1AoiNrFEmA+n0e9Xke32xXvE4M/XdfFk08+ie3t7QVSYds2LMvCcDjE1taWlEHZAcihy4x1KJVKsCxLhk4HQQDLspBMJlGpVIQsftd3fZd4yhqNBhqNBgqFAiaTyQIhIlEkaTFNUwJWOeonk8lI9yEJHgDxhrmuKyZ5vuf5559HsVhEuVwGcDw/8PLly9IAwCYGeudarRZc1xWvlWma6Ha7AK6rlvS+6bouafR3+56IEeMeQHxP3Ef4hV/4Bfyjf/SPXrJ9bW0NjUYDp06dwu7u7su8897DLZvXwzAMbya4TVGUDwD4AHB9FiDLMkzpZplKVVX0ej14nicKFhUbKlvj8RgPP/wwdnd3JYDy6tWr4mHiQ5QPWZaZqDyxzZ6GapaZstksTp06JYSO6gzJlOd52N3dRa1Wk7EtHCAdBAFs24amaaKwcEDxdDpFtVqF4zgIggCapsGyLCGNHO9iWZZ4uFZWVgBAugp57XZ2dkQd4nt7vZ6Qzmq1CsMw0Ol0UCqVsLy8jE6ng8lkIuW9VqslxFZVVViWJZlXpmlibW0Nf/fv/l3k83msrKwIQa3VaqjX65IjRYWKMx9JtqiERYkWS4MktdFyHq8zAPGDsXQaVSzb7bYMgKZ6x0BTdgKSGE4mE+zs7ODRRx/FlStXRM1st9tCQqMlwZvF7bgnYpxs/Kt/9a/wvve9724v402D+J649/Fao8Dup1FhN0usjijdKoqyDKBxbfs+gPXI69aubXsJwjD8GICPAYBhGCG9QZlMBvl8fqGs43ke8vm8ZA2xXEMixEwj5lsxD6tUKonaxRBIKiMMF+WDPJpJNZlMkMvlhIxFAyNVVUWxWBTTdyqVksG/ruuKFyqbzcpraOxut9sy3oYeJ2ZY8XXD4VC6GJlszutAEsKSHv1LNIvzmlSrVQCQ6AT6zWjAn81mC3lSqVRKEu8ZNRDtZKTvi6ODoiTuxvDTIAhQLpclpJNp8yRdwPVxQiw56rqOfr8v425IophyTxUulUrBsizxv7muK/tlWZZlPH4vOLaHBJIlWpZRwzBcyLvi9+Bu3xNxyvTJxvLyMtbX11/7hfc34nsixn2Jmw3s+T0A/HXtfQB+N7L9e5RjfA0A58utm9PwzIchu7X44GOUgmma8lBnJxjHrPi+D03ThByx5Z++J+B6wjtwPZSUpmuSDI694Xw5dgySeEUHNSeTSayuropS5TiOEDgqYSRx7NSjf4keLhrP+YfnBkAULpIxDleOEgD+l3lSLD1Gt9GozUBPnivPm2SN1zZ6zlTYmObuOA5c15X9MICU4aPsYOQ4IBIilueYRwVASpFRIzmzpPL5vJjcqVzy+imKIuVSEiteMw6wzmazsG1b1EdGNDiOI4pn9HMmObtJYnXb74kYb148++yz91VX4Csgvidi3Jf4cuIWPoFjA2JZUZQ9AD8B4OcAfEpRlO8DsA3gO669/NM4bqG9hOM22u/9chZB87miKDJ2hKpCLpeTcpNlWaLQDIdDLC0tCXEZDAaoVqsS5lmpVHB4eIjTp08DwMJIHF3XZZzK4eEhisUikskkZrMZWq0W+v2++LdIvKKk7eDgQJLcSVCKxaIMRR6NRlhbW5NsJ6a527YtSg6jGJjZBEDKXDR3MwBzc3NTSAbTwqmyFAoFFAoF+L4vsQij0QimacoaaQzf3NwUYz87ETOZjERa0CBOlYgjcBiieXh4CNM0ceHCBXQ6HVy4cAHNZhONRgOtVkuOy+N5nifRGaurqzg6OkK320Uul0Or1ZIwVdd1Ydu2DKnmmCAqf+xKpD8NOFY26VnjfhzHQb/fR6VSkfMCjgd7F4tFMfQHQYBGoyHfu/l8LsO1qc7d7Xsixpsb3/RN3yShuPcD4nsiRrSK8nI/Owlzie8UTsQQZtM0wyeeeEIe4BygPBqNJFV7bW0N9XpdyMi5c+fw4osvotPpiF/J87yFstnS0pJ0qtEgr6oqms0m0uk0Njc3ceXKFXm4hmEoo2foOWJnGfOfAODg4ADZbFb+JJNJ8RVRqer1eqhUKpjNZuj3+9jb20Mul1vwcT388MMS8JlKpbC2tobhcCgEh6GovV5PvD8svVE1G41GaLVa0DRNSEmtVsPW1hZyuRwymYyUCqni0Zs0nU5RKpXEHD4cDuF5HorFouwjn89LAKiiKFhaWpKh1FeuXJFOD3qblpeXUa/XAVyf0RgEgcxwpJr0/PPPS/wDFSkOpTZNU64RZwRSxWu321Janc1mkqbPc8vn8zg4OICqqsjn8+j1eguG9Ol0ina7jbNnz0ogbaVSAXDcFhyGIfb29uIhzDFeFb/6q7+K7/u+73vFn29sbNxzxCoewhzj1aBpGt71rnfhd3/3dxe27+3t4dy5c2KVuZdwoocwK4oivhoSmfl8LqSl0+kIaWKJ5+rVq/Jvqi+MU6APidlH9CuRdPEhy64zdrmxSy6fz0vZaWlpCX/9138tEQyMICAZo8LBfZCEUQVj2nc6nUapVFrIvzo6OhLfD3Bc9ltfX5f5fxw1w/E+TClvtVpi2B+Px0KqiE6nI781sMzJZHn+W9M0Mf5HZ+3VajVpCuDaqRD2+30hd4xOODg4ENN5Op1GvV4XtYjbqdixFEjCdWPe1Nra2sLw5K2tLenApKLJXCsa2KPlWprVWdIDIKGi/DcDTxlsyqYDXofXilqIEQMA/t2/+3fY3d3FT/7kTy5sb7Va+MAHPhCXAWPcd/j/t/e1MW6dZ3bnJYdz+c3L4ddQ8yXJmsBWKydulEV/FGiCwG12s0GCBEi2P9wtUiApkB/517WxP/IzKAo0QLNBgSDdZDdBd9EU2CQossDaCxhGgMTbNFBWcjKWZI00XxySd4aX5OU3h3d/zJxnXkqWLTsjkdK8BxhYojjk+17yzT05z3nO0+l08POf/xyf/exn73m8Xq9PaFWTwVQQK9/3xc+jKws0GOtdbiQ09XpdcpD0IEmCj0UikTGPDQBJQacZnsSCKpmen0XPEQAZBMwOM96EqbiQaA0GA2QyGfT7fVGI9CBTlpparZaoKb7vS5mOKeUsV5KYsdOOgZ7sZORMPPqzlFIyKw+AKHico+d53tj153pINgBImZAhoAz2pAKoh6cCEDIzGAwkyoLXLZ1Ow3VdIYwcoszPiyZ3ZpDRY8X3pOFez50iAY9EIkLG+D3Ss89mZ2fF56V76PSZgOwCJU5L1orB+8eVK1cwHA5x8eLFsccdxzl1YYgGBkS1WjXff0wJsTo4OECn0xFPDEeukEzEYjHUajUUCgUZs8JgSD3rit4e4JA0MfRyMBhIqY7qBRUeKlU0rDMmgSbwSqUiHYKcY+c4DhzHEdM0c6H4+1wD18ewTHbZETrhokl8bW1NCAEVMF3hKZfLkjXF2Aca93n90um0dFICEL8T/Uy1Wk06D2ngDgaD2NvbQ71eF6WQa+d1zeVyQvRIQM+fPy9KH/1LvB403J89exalUkkiJwDINRkOh6I86WqR67qyZ53okLSFQiFJrSchZXI7X4fPWVpawt7eHgaDAeLxOLa2tpBKpdButyX6gWVAo1oZPCiuXbuGL3zhC5NehoGBwZRhKjxW4XDYX1hYEALB8pHeOcauM3aReZ6HWq2GfD6PdDqNarU6pqCwtEhywJs8S12cIbe3tye/GwqFJHiSvia9HMb18UZPc3qtVhPjNgApvXHoMckUwzMjkQhCoRC2trbQarXEnM3XZ+faW2+9hV6vh9nZWWSzWSQSCXieh2effRbXr19Hv99HPp8XcsLX8TwPtm0jFovJ3+v1OmKx2FikABPlfd9HqVSSTC/mWWWzWZTLZVHV2u02Go0GMpmMlFY5VgeAdOiRhHa7Xcm74ufEciEVJJKtZDIJ27blNdkkQOWJnyPVJRKhfD4vnY6u646VWxlxwa5DRjRcvnwZruuKYkl1jMrd7du30e12jZ/EwECD8VgZGIxj6j1WHBXTaDSwvLyMcrksIZm8AcZiMRwcHGBvbw+pVAq+70sYJ83ZVCbC4bD4c9jpRh+Rnr5uWRYymcyYj4gjWWimX1paEtLEaIe1tTXMzs7Ctm3kcjns7u5KqZAEiZEDwWAQu7u7kktFVatUKkknXywWQzqdxv7+vszJW1lZkTR5Rh0kEglJWadqR7UpGo3K69m2jVarJfuyLEtmBDLSgmQTgJTLWDpTSqFcLo+FdxaLRRlyDEDiFebn5yXEczgcIpFISEYUSXKv10O5XJZr0+12pSFAj2CgZ4qqJMurgUBAlDGWNiORiGRZWZaFbDYr5USSahK6drstgbC3b99GIpGQWYosq7I0+HZdLQYGBgYGBg+CqSBWvFmTKACQshY9VCQl9EIFg0GkUin0+320Wi0AkLKhXgpqtVoSxMkSI8kISRdvxCw90dfFkTexWExIGQDpcmPUAzORmMPEmzQJyGAwQCQSEUWG5vxMJiP7Z5mOhIleLqag67MLGUfADCmllAycJiHxPE86Kkl0PM+T68r1s9RIEz99bhw3Q2WPBCQajYoCR3LDz4elQK6JwaMAxFzOkT/0Q83MzMh14Wesl0B15dKyLImA0H1v+ixJpqfTZ8b353NJ9IBDYsjUewCyDwMDAwMDg/eLqSBWJDm8GfMGTxIQCoVkkDFDOUejkbTTc9QNSQTJErsNg8Gg5Dqx+4tkiAnjNHvrBml27AUCAcmaogeLZTUSLhIrGqOp8rAkyVl57XZbgjXz+bz4l3q9HhqNxlg0QL/fF2JJUsh1B4NBKYkBh0OdSWoikYik0FuWJcSBStjdShDXqhNShq9Go1HZV6PRkOvB3+X1JtFhqjtJDAkzya/v+6jX64hGo0Lc2KXHcTftdluM9zTo8xonEgkhs1TiqHaxtAgcm+ljsRhc15XPBICQSBL1u/1dBgYGBgYG7xdTQazY9cZOL5rH6YWiH4hlJMuy0Gg0kEwmAUAiEmgW502dZTx2sM3NzWF3d1dIG31VVEyYkUW/UTgcRqvVQrfbla4xmraXl5fFt1Or1aTln4bw3d1dhMNh2LYtZU6W60ajEarVKuLxOGzblgwpAIhGo8jlcuh2u9jf3xdTOmf81Wo1mcHHEik7H3kdhsMhbNsWlYkeMAAST0Fvl57oblkWRqORlENXVlbQbDblM3EcR8qSVMPm5+fHRue4roszZ87IdbMsC/V6HcFgUNQjx3FkbZ7nwXEcydziZ8wcLRJpKlc0z8/OziKZTEqHYq/XQ7PZxMzMDHK53Fjpl+OFSFLr9Tp835dkduCQTJKwsTPSwMDAwMDgvWIqzOuRSMS/fPmymJ3j8TiCwaAoO/qgZN5EeePUfUyDwQCpVEoS1Pf395HNZkWpOHfuHO7cuQMAUj7i61Fx2tnZwfz8PEqlEvr9PjKZzD2ZS7xhU+2h6qETGJrg6d+hAkMS1W630el0ZLiybduo1WqyZ/40m01kMhkpecXjcTSbTTGtVyoVWJYlaxqNRtjf35fRP8yusiwLrVYL0WhUjOFck96NSYIWiUQQj8eFBLGERz8Tzeuu6+LChQuSCt/r9VCtVsUsz4HPLENaloVisYirV68ilUrBtm1Eo1H89re/xcLCApLJJEKhEJrNJoDDMNaDgwOk02kx61erVezv7+PZZ5+F67pCSkOhEOLx+FgkRCKRkPmL7HBkTpYe7aGXYG/cuGECQg0M7oIxrxsYjGOqzesHBwfY398XEzcNzbxZs7xG47dSSkpJVIiY/cTuvbm5OUkEp4JVq9XkdYDjOXUkLbOzs8hkMigUCmJkByCvQw8VcJhBxZIklRmWB9vtNqrVKhKJhJS44vE4KpXKWBxDOp2W0mIwGJTRNHw9mrHT6bTM7mPWlh5A2Gg0pGTH66l7jrrdLp5++mkx/w+HQ6RSKUlcZ5Cp7kcCjsf1EIyAoEeNRHdzc3OMnI1GIylpNptNHBwcoFgsihLJJHyWGF3XlTBXvk8gEJB98TPI5/MSnBoOh1Gv12U9kUgExWIRpVJJlE/P88aM9jS000fF99nd3TXlwIeMXC6Hb37zm2OPDQYDfPGLX5Qh2wYGBgZPAqaCWBG6cZgKAm+SerDjzMyMeIdYJtLHz3COHLvPSJrq9Tq63a4M7yXx4ev6vo+5uTkha3pKu64iRaNR8YHRh0XTO3BYLmT4Jw3X9C3p0Icg0xvFeAZ6lugd4x6oTJEc0jPF66J39THYk76sQCAgIZ30aXHvvPb6mvj6vPGxpMbZfCS5nAfI1yOR9X1fcq1I3hjGqnu3PM9DLBaT8iS7BKk0cb8s41FRc10XwWBwLHNLv7bRaFSCZgHI2vTQUZJRXmODk8fZs2fx/PPP35P5NBgM8OMf/xivvvoqHMeZ0OoMDAwMThZT0VceDAaRzWYRDocl18h1XfHyjEYjpNNpMU4zGT2bzUrZj8pMu91GvV5HtVrFaDTC3NyckKVWq4VGoyGKCfOwGEPAXCdmJNH8zdynbreLRqMhPiDg0BP1zDPPyDBgdu0xryqfz2N+fn6MtLGkqPt+AKBUKonaFggEsLW1Bdd1hbhw9Io+P49lLe7tbvUsEolgYWFBDPgkFyQWNKn3+33xUw0GAykJAhibvwgcm9uZG8b/cqA0rwMAKduWy2U0Gg1RtrLZrDQq0NzP9YXDYXke19LpdFCtVuE4jjQstFotZLNZ5HI5ycean58HANi2jZWVFQCHURJ8XiKRQCAQQLPZRKvVkogHvq/+3gYng0996lP49re/fc/joVAIP/zhD3Hp0qUJrMrAwMDgvYNRSu+Eqfi/6KPRCDs7O/JnmqtZxmHpi8oUTd2c3wdASltUnnhz7Pf7kmE0HA6xvLyMZrOJTqeDSqWCbDaLCxcuoNfroVarYXt7W8gLx6W0Wi10Oh0kk0kxwBeLRZTLZdTrdbiui42NDZlLGI/HEY1GkUgk4Pu+dB1yeDNLg8BxOrtSCvV6XchdOBxGPB6XTjwSxm63i9XVVXS7XVGVGDjKsiQA8Z4x8oDrarVaQjwLhQIcx0EgEMDq6iqCwSC2trbkdd98800UCgVRdUgw9a5Cqnr6LD+Gg/Z6PVkPFbJQKCTJ8CyLkjAmk0kMBgPs7u4im81icXFRCCBwmJKfz+flWrCbcjgcylpY6uQIJNu2hRTyutOwz9wzxl6Q2JHoGhgYGBgY6PjGN76BCxcu4Pnnn7/vc6aCWDE7iTft1dVVXL9+XW6UhUIB29vbsG1but2oAFHh8jwP0WhUficYDCKTyUhwZzwex8WLF7Gzs4NsNit+rEajIQOdWaICjktJwWAQ29vbWFpakviEZDKJ9fV1MV5XKhXJmgIggaQsTyqlJMiSpTGGdDIpnF2HnAfIFHP+DjsYlVKoVqsIh8PSCWfbtpAv+tCy2ayYuc+ePYvr16+Lx6rT6WBxcRGtVgvpdFoGXTOAM5FISNmNpVZ9XA5LjrOzs9jf30c+n5f8KaabU51j910ymZQ5iCwtMg6DERSFQgHAYU5YuVzG+fPnhRQHAgFks1lsbW0hHA4jn8/j9u3bODg4kCiHubm5MTJIMlWtVsdmFNq2jWQyOUbCFhcXpdFhbW3tkX7/DR4dvv71r9/zP4jf/e538a1vfWtCK5puvPTSS/jc5z6HF154YdJLMXhIMGfiwfHTn/4UH/nIR2BZFp555pn7Pm8qiBUA6TSjIpVOp9HpdDAYDKRkA0A8SFQrqADRtA5gbCAvS3b8Xb4P1YtarSb/TjLDjj1GGTCIlL6qYDAoxIIkT+88AyDERy+9UUEiAWJCPIlKMpmUZHT6lbg/qj8kjSQnoVBISBsAKa8ppZBKpZDL5YQUsjOPBIrZT1SOut0uksmkKFBU1fTgUOAwD4vxGOys5DVlWZHXi9eKKiLXpitYAIQ8AhAy1mq1pOzI66qPKIrFYmPDu1m+pWpJnx6ztLh3KpJ3B6QyCsOEhD6Z+OpXv4pPfvKT95Qem80mer0evvOd70xoZdOJr3zlK/jMZz6DD3/4w+9a+jB4PGHOxHvDpUuXpOP/nc7EVBArpdSYb2drawsf+MAHEAwGxWPEm7PeueV5HtLptAwbpvF6MBhIRlIymZQbLXOOWBqioZvhm8Bx51ur1RJ/kG3bcBwHwWBQEs7pg+JNvVarwbZtiV8IhUKo1+vSeeb7PlKpFCzLkn0yboFmfADY29sTI7XexUYVjB1/fA4JSDqdlv3Q4D43N4fl5WWcP38eS0tLWFtbk9IoTd1MeydJpJkdgJTRdDJJLxdTzfkaLD1StWOavT6XkNeKYaw6sdIzqvje7XZb9tXpdKQzk92IJKIkp91uF+l0WgZwk/DNz88jGAwKmWWCPZU4NhfoRniDk0OtVsP6+jrOnTs39rjv+7h586aotQ8TSil87WtfQzqdvuffPvrRj2J+ft7cRO7Ciy++iMXFxUkvw+AhwZyJh4epIVau60oHnWVZqNVq6Pf7Yx1pVFosy5JEddd14TgOBoMBisWikAIa4Tc3NxGLxRCNRrGxsSEda4FAAK7rihpF8kVVSM/SovJycHCAdruNTCYD13XF18PHHMcR5ebmzZvo9/uYm5tDLBYbU2TC4TAWFxexsbEhxnSCJI0qCktntm1jYWFB4gxYQtzd3UUul4PneWg2m+j3+0I2C4UCnn76aXz84x/Hr371K1GYOAbo5s2bmJmZQTKZlEBUesYASPlubm5uLA6B18XzPBSLRZnXGI1GpQTIEh79UKlUSt6Do2QYxUAlcXZ2Vq4zzfPcE/8eCoUQiUQkeDQejyOXyyEQCEgEBYky0/lDoZB8n9hkQJLGFPZarYZ0Oo35+XkJnjU4GfzgBz/Az372M6yvr4893uv1cOnSJcl+MzAwMHgS8K7/11wp9edKqYpS6pr22H9VSq0ppf5RKfU3Silb+7eXlFI3lVJvKqX+7YMsgqUhAGJaZ7o2jdDLy8vwfV+iEubn56UTjYZkkoRUKiXt+IVCQTKRnnrqKQnrzGQyyOfzWFhYwMrKChYWFsQ/xBIT5wxS+SDxIsmjgsXOQZrIGWfAP7MkRd8Vy1HPPfeclBSpuuRyORSLRWSzWUlm582eBnXP83Djxg2sra2hUqmgVCohFoshlUohFovJdZuZmUG9Xsevf/1rLC0tSZfi2bNnhehR4dra2pIE/FQqhbm5OdRqNckIq1QqACBjgVhqDAQCKBaLEl1Rq9Vk1h99TTS40/TOuYgsb9Lg3m63xxSkhYUFCWjlfECWTenbYkwD1UE2PLD5oFar4Te/+Y1420h4qZQxcoFKJr9Hkz4TTxo2NjZQKBTGfpaXlw2pekJgzoSBwTEeRLH6HoA/A/CX2mMvA3jJ9/2hUuq/AHgJwJ8opS4C+CMA/wzAGQCvKKU+4Pv+O/av84bJ3CoqI/TV0DeTTqfFE8PWfRqQgcPSIG+KNKxTpWGKOUtu7CYbDAZjcQUkGnoeluu6yGQy6PV68DxPVCbdq8WUbwDiMdK7GkejkZQKSdC2t7elnMVSF/1D9DnpN3r+mUOOOdiZ5IWDhweDAUqlkpA63/exu7uL9fV1lEollMtlWZMeucC4ChK9aDQq14vX07Kssdl+eulQ/zvnHIZCIczNzUmcAxPbWZ9mZARLslQsPc9DrVaTgc3MJdNLmSyPssTIPDESu+FwiF6vJ+VE3/eRyWSkE5Pm9lwuJ2vjyKJJn4knDaPRSMi5wROJ78GcCQMDAA+gWPm+/xqA/bse+zvf92l2+gUAFuI/DeCvfd/v+b6/DuAmgN9710UceVrY6s4bG0ttSil0Oh2JIGBQJjvg+Lu86TLhmwGRwOEN3PM8KQMyukHPtqLHh8Zr/eYNQAgHc7CAY8M7k8Z1RUX3hbEEyXIXE8cty5IUc3qH+MMbP9UVPciUe7IsS1LDdcO54zgol8vY2trC9evXcfXqVVy/fh23b99GuVweuy6MYtDfA4D4wbgneqL0vC7+OwkscBymCkAyvxhxwJgIXgu+HgD5d4a6souQpVmWhvnZ84dkiO+pD1zmAG5+NslkUp7LtQWDQSm58rvzTngUZ8Lg5PHKK69gd3f3nsdv3bqF1157bQIrmm68+uqr2NzcfKDnmjPxeMKcifeGBz0TJ+HS/SKAvz368wIA/V23jh67B0qpLymlfqmU+iVvlvTj6ONsSHio6rAMZVkWms0mAoHAWNQBU887nY6UzVhG0k3aAOR3XNeVqAV25B0cHKDf72N/fx/tdhvb29sSDko1hj+j0QjLy8sSOQBAgih5k4/FYhgOh2PGcRquqUTZti3dfSyTxeNxpFIpUXg6nY7kPtHYHw6HRcHSzd6tVguVSgXXrl3D7du3USqVhKj0+31R5GzbRrFYFC8VCSE7BXn9mbNFYzjVrmKxiFQqJfshMaYSyc+MRIzvoRvwB4MBNjY2cOPGDWxubkpXJWcnRqNRVKtVIWYkvoyBGA6HMpOQSfLhcBjZbFaIHv1cNK/bto18Pg/HcaRZgcG0kz4Tv+sCDMbh+z4+//nP4+WXX5ZZkvz5/ve/jy9/+cuTXuLU4YUXXsCPfvQjaUT5HWHOxJTBnIn3jgc9Ew80hFkpdRbA//V9/5/f9fifArgM4LO+7/tKqT8D8Avf939w9O//E8Df+r7/f97p9cPhsH/58mUxLScSCWxtbUk+E5UEDtYdDAZwHAcf+tCHAByHbAKQMlSv10MoFBKTOwkESQHLXlRhWBpqNpvI5XKiaLB8x5JgpVKR8hIA8RFxiDNLhTMzM1hdXZXEcZqoOWan0WjAdV1p3aQ6RTLF5PabN28ikUhIAGqlUhEVj0Rlbm4OOzs7SCQSsG1bDOhMIXccB5cuXZKsKpKTbDYrJCgWi2F9fV0iDkjkqCwx6JRzBlutFvb29pDP50UR6vV62NnZQS6Xk07PmZkZbG5uolgsivG+0WiMRR2QCKqjgdFH3wmkUqmxeYxUpNrtNnq9HhKJhGRccZ1UIqkSJhIJKS/PzMzgzJkzeP3114X4AkA+n8fm5iby+TzS6TRef/11NBqNd8xceNhnQpmBsw8Fb9f1yfR/g3uhj8wajUbmTDyBMGfiveFBzsT77gpUSv0HAH8I4OP+MTvbBrCkPW3x6LF3BKMQ6L9xHEdKa1RmSIx0v9He3h4ikchYlx9b/Jn1pCtFvV5PCA5LfvQ8MZdJH9XCrsN6vY50Oi038ZWVFSFTBwcHcF0X1WoVxWJR1JhisYharYZIJAKlFGq1GvL5vChYMzMzKBaLUsajiZqKV7vdhuu6Mm+P5KHX62FxcVGex2HU7GRzXVd8RyypBYNB3Lp1S8gEyRglYGZrMThTVwcLhcKY4qOHm8ZiMbiuKwrc7OwsFhYW5PdDoRCSySRWVlYwOzt7TymQeVFKKSwsLKBer6PRaEjpdTAYSGMAFbZwOIx0Oi2qZb1el/Jgv99HqVTCysqKkFQqf0xTdxwHuVwOwHHJstFoSOo7ieukz4TBwwG/wwYPht/1epkzMf0wZ+K94UGu1/siVkqpTwD4zwD+te/7be2ffgLgfyml/hsOTYmrAP7h3V6PpOHotcVYTnM4yQ7Lef1+f8yXQ0WJadtUkdjpxRwmfSgzcOyZoqdKKTXW9TYcDmX0Sq/XEzLFDkZ6wPTSIFUaBoxyHdwfiQXDP/WbuK68sRSnKzEkdsxjAiDvzX3RQE7jPENO9ddk958+b1DPB6OpnV4q3S/Fz4hEhUSN15BBm3xtmsn1jCv+Dq+5Tpw4e5A+qF6vN/Y58bOkQgYcNwdEIhEZ36NfV6qculeNg7RZvtU7AR9Exb0bJ30mDAwed5gzYXBa8a7ESin1VwA+CiCrlNoC8DUcdndYAF4+Uh1+4fv+f/J9/w2l1P8G8BsAQwBfeZBOD5ZsWG4CDmMXGOJJ9YE3YM/zhOCwpBePx0X9IblKJBLY3d2VFv5MJoM7d+6IYZmz5Di8uN/vy6BnEiZ9KDBztgjP88QDxnIZiRFLmSRXVJSorLD0qBvqqUyRINEPxFIZvUFMi6ealUwm0Wq1EIvFxoZDMzGde9JH3uhZXcznYlfiYDAQ7xpLjlwPSQeJDwNTqVINh0MUCgUkk0kJBnUcB+l0eoyo6UGto9EIe3t7MoibY25YAmRJtlKpjDUVkKSyNMrmBvrz+N0iGeX163a7sG0bAERxZDjog8wJfBRnwsDgcYI5EwYGx3ggj9XDhmVZ/sc+9jG0Wi2sr6+j1+shFovJTV73NPEm3+12USgUpBZM/06z2YTv+5idnRUvEHOSOONPN1rT40NyQ4LAUlM4HMbe3p609HNO3ebmpow/CQaD4qNixEOlUhFlJBAIoFaroVgswnEcef35+XmJeIhGo0ilUrh69Sqee+45xONxuK6L7e3tMdWuXq8jk8mgWCyi2WzijTfeQCqVEqJAdYmp7SQboVAIt27dElWOqe+9Xk/KkACEfITDYSSTSTH2z8zMIJPJYG1tTczySiksLS2JqX84HGJzcxOpVEqiJwaDARYWFlAul8cGHFuWJXIqfVQkm1x/JpMZGy3EkioVt8XFRVSrVSGznP1IzxcAVCoVrK6uotlswnVdeJ4nqfnsgPQ8D4VCQb4rOzs76PV6E51rY/wkBtMG3/fNmTAw0HC/MzEVxCoajfof/OAHhUgBQL1ex/z8PGKxGLa2tiQ4lArW7u6uKELsTiOpYGlxNBrJcGKGfSqlxtLUqebo5j12EeoDfvkeJB0c98KbeSKREAWGpTFmQZGwkXzwJx6PS/glhyw7joNsNiu+p0qlAtu2hYhw5A19WvSiAZASYSQSkS5HvncoFBojnbZtyzVotVoolUp4+umn4bouQqEQYrEYms2mEDga/ZvNpiiCLJFyn5xNuL+/j0AgAM/zJBU/nU7LPhzHQTweF1UyEAhIOj0JW6fTQSqVkpE7VCgZ9AlgbCwRS5rValWen0gkhESyvAwckkeOOqLqFY/HEYvFoJTClStX0Ol0zE3EwECDIVYGBuOYamIViUT8ixcvAoDc7JiNxJyjcrkszycx0QcY0xzNPKZ2uy3lL6ogJFv6sF/gOIuKHhya29nZtrGxMdblxrBPRgEw7bxarUoHIgkOPUGpVAp37tyRMh9fh1lUJCIkGrwO/N2DgwPxFJEsHV078RuRZOpmcr1bUv+xLAuRSESCSYfDIRKJBILBoPik6IFiBx+fF4lEZDg2uyr5WZAksozZbrdlZqMec8FrGQqF5LPjTEUmuEejUSHBetYW96DnhvG74jiOeNNIxOLxOOr1upRsqcDpZJrdjpZl4cqVK2i1WuYmYmCgwRArA4Nx3O9MTM20WXqP6P+h18bzPLkJk3BQcQCOPTQse9E7xaG69PDo78MhwFSL+Bw9E4lEiooIO9ioWrFsdHBwIO+lm9L5fHq1mGxOwsY103hN1YU+McYgkHRxbbZtC4nhulge5Q9VPypLJDQ6EaFhXZ+NSFWKfqrZ2VlRe/TOSZrMqQrxupMQ0c8UCAREZaSK1+12x1RD5liRAOsxEiSPACQElKVE/ZoBx2ORZmZm5LMFxjs49M+Q15cEVP+sDAwMDAwM3i+mYggzU9FnZmbEp6SPntGjBZivdO7cOWxsbEjHl+d5yGQyuHXrFizLwlNPPQXXdXH27Fk4jgPHceSGats2otGolB6ZIg5AcpfOnDmDUCiEnZ0dZLPZsSiHfD6P7e1tRCIRBINBaeGn+ZndZgAkKb1UKkmJzfd9tNtt5PN5uK4rBnjOxePvRqNReJ4nShbn+G1ubsLzPFHHAMhYlv39ffGa6YSUa+EYnEQigRs3bsiaaJ4nqRoMBjLPj4RrZWUFtVoNrutKSVRXsvQORJYh9S4+fR31el3IG2MTqDjGYjFks1ns7+8jlUphMBhIOGgkEpFSaLvdRjabHRvxQzWO61lZWcFbb70lMyWpArJLlLMG2Q1KomlgYGBgYPB+MBWlQKVUFUALgDPptUwQWZj9T8v+V3zfz01yAeZMAJiu78QkME37N2diOjBN34lJYJr2f98zMRXECgCUUr/0ff/ypNcxKZj9n+79vx1O+zUx+z/d+387nPZrYvb/eOx/ajxWBgYGBgYGBgaPOwyxMjAwMDAwMDA4IUwTsfr2pBcwYZj9G9yN035NzP4N7sZpvyZm/48BpsZjZWBgYGBgYGDwuGOaFCsDAwMDAwMDg8cahlgZGBgYGBgYGJwQJk6slFKfUEq9qZS6qZR6cdLreRRQSt1WSl1VSl1RSv3y6LE5pdTLSqkbR/9NT3qdJwml1J8rpSpKqWvaY2+7Z3WI/370nfhHpdS/mNzKHz3MmTBnwpyJcZgzYc7E43QmJkqslFJBAN8C8PsALgL4d0qpi5Nc0yPEx3zf/5CWyfEigL/3fX8VwN8f/f1JwvcAfOKux+63598HsHr08yUA/+MRrXHiMGfCnAmYMzEGcybMmcBjdiYmrVj9HoCbvu/f8n2/D+CvAXx6wmuaFD4N4C+O/vwXAD4zuaWcPHzffw3A/l0P32/Pnwbwl/4hfgHAVkoVH8lCJw9zJo5hzoQ5E4A5EzrMmXgMzsSkidUCgE3t71tHjz3p8AH8nVLq/yulvnT0WMH3/dLRn3cBFCaztEeK++35tH4vgNO7d3MmDmHOxL04rXs3Z+IQj92ZmIohzKcQ/8r3/W2lVB7Ay0qpNf0ffd/3lVKnKgfjNO7ZYAzmTNyF07hngzGYM3EXHpc9T1qx2gawpP198eixJxq+728f/bcC4G9wKHWXKWMe/bcyuRU+Mtxvz6fye3GEU7l3cyYE5kzci1O5d3MmBI/dmZg0sfp/AFaVUueUUrMA/gjATya8pocKpVRMKZXgnwH8GwDXcLjvPz562h8D+PFkVvhIcb89/wTAvz/q+viXAOqaFPykw5wJcybMmRiHORPmTDxeZ8L3/Yn+APgDANcBvAXgTye9nkew3/MAfn308wb3DCCDw46HGwBeATA36bWe8L7/CkAJwACHtfD/eL89A1A47AJ6C8BVAJcnvf5HfK3MmfDNmTBnYuxamTPhmzPxuJwJM9LGwMDAwMDAwOCEMOlSoIGBgYGBgYHBEwNDrAwMDAwMDAwMTgiGWBkYGBgYGBgYnBAMsTIwMDAwMDAwOCEYYmVgYGBgYGBgcEIwxMrAwMDAwMDA4IRgiJWBgYGBgYGBwQnhnwAPhDfPrC+gUwAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "NUMBER_OF_IMAGES = 4\n",
- "\n",
- "\n",
- "for _ in range(NUMBER_OF_IMAGES):\n",
- " plt.figure(figsize=(10, 10))\n",
- " dataset.update()\n",
- " image_of_particle = dataset.resolve(skip_augmentations=True)\n",
- " \n",
- " predicted_mask = model.predict(np.array([image_of_particle]))\n",
- " particle_label = get_label(image_of_particle)\n",
- " plt.subplot(1, 3, 1)\n",
- " plt.imshow(image_of_particle[..., 0], cmap=\"gray\")\n",
- " plt.subplot(1, 3, 2)\n",
- " plt.imshow(particle_label[..., 0] * 1.0, cmap=\"gray\")\n",
- " plt.subplot(1, 3, 3)\n",
- " plt.imshow(predicted_mask[0, ..., 0] > 0.5, cmap=\"gray\")\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 Prediction vs actual\n",
- "\n",
- "We play a video of quantum dots, as tracked by the trained network."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:48:47.172590Z",
- "iopub.status.busy": "2022-06-30T10:48:47.172590Z",
- "iopub.status.idle": "2022-06-30T10:48:49.656592Z",
- "shell.execute_reply": "2022-06-30T10:48:49.656099Z"
- }
- },
- "outputs": [],
- "source": [
- "import skimage.io\n",
- "\n",
- "IMAGES_TO_PLAY=64\n",
- "\n",
- "images = skimage.io.imread(\"./datasets/QuantumDots/Qdots.tif\")\n",
- "images = np.expand_dims(images[:IMAGES_TO_PLAY], axis=-1)\n",
- "images = dt.NormalizeMinMax(0, 1).resolve(list(images))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-06-30T10:48:49.659090Z",
- "iopub.status.busy": "2022-06-30T10:48:49.659090Z",
- "iopub.status.idle": "2022-06-30T10:49:15.886571Z",
- "shell.execute_reply": "2022-06-30T10:49:15.886079Z"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import IPython\n",
- "to_predict_on = [images[i] / 3 + images[i + 1] / 3 + images[i-1] / 3 for i in range(1, len(images) - 1)]\n",
- "predictions = model.predict(np.array(to_predict_on), batch_size=1)\n",
- "\n",
- "for prediction, image in zip(predictions, images[1:-1]):\n",
- " plt.figure(figsize=(14, 7))\n",
- " mask = prediction[:,:,0] > 0.99\n",
- " \n",
- " cs = np.array(skimage.measure.regionprops(skimage.measure.label(mask)))\n",
- " \n",
- " cs = np.array([c[\"Centroid\"] for c in cs])\n",
- " \n",
- " plt.subplot(1,2,1)\n",
- " plt.imshow(image[..., 0], vmax=0.1, cmap=\"gray\") \n",
- " plt.axis(\"off\")\n",
- " plt.axis([0, 1200, 0, 1200])\n",
- " \n",
- " plt.subplot(1,2,2)\n",
- " plt.imshow(image[..., 0], vmax=0.1, cmap=\"gray\")\n",
- " plt.scatter(cs[:, 1], cs[:, 0], 100, facecolors=\"none\", edgecolors=\"w\")\n",
- " plt.axis(\"off\")\n",
- " plt.axis([0, 1200, 0, 1200])\n",
- " \n",
- " IPython.display.clear_output(wait=True)\n",
- " plt.show()\n",
- " plt.pause(0.1)\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tutorials/2-examples/DTEx213_multi_particle_tracking.ipynb b/tutorials/2-examples/DTEx213_multi_particle_tracking.ipynb
new file mode 100644
index 000000000..e26b30887
--- /dev/null
+++ b/tutorials/2-examples/DTEx213_multi_particle_tracking.ipynb
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+{
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+ "iopub.status.busy": "2022-06-30T10:46:03.888292Z",
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+ "shell.execute_reply": "2022-06-30T10:46:07.409651Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "[notice] A new release of pip is available: 25.0.1 -> 25.2\n",
+ "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+ ]
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "# !pip install deeplay deeptrack # Uncomment if running on Colab/Kaggle."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Detecting Quantum Dots\n",
+ "This tutorial provides an example on how to set up a simulation pipeline using `DeepTrack2` to train a U-Net for quantum dots localizations in experimental images.\n",
+ "\n",
+ "The dataset is acquired from a fluorescence microscope imaging quantum dots inside membrane proteins of living cells. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Setup\n",
+ "\n",
+ "Imports the objects needed for this example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:46:07.433650Z",
+ "iopub.status.busy": "2022-06-30T10:46:07.433151Z",
+ "iopub.status.idle": "2022-06-30T10:46:16.462590Z",
+ "shell.execute_reply": "2022-06-30T10:46:16.462098Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import random\n",
+ "import zipfile\n",
+ "\n",
+ "import deeplay as dl\n",
+ "import deeptrack as dt\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import skimage as sk\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Loading the Dataset\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def download_file_from_google_drive(\n",
+ " file_id: str,\n",
+ " root,\n",
+ "):\n",
+ " try:\n",
+ " import gdown\n",
+ " except ModuleNotFoundError:\n",
+ " raise RuntimeError(\n",
+ " \"To download files from GDrive, 'gdown' is required. You can install it with 'pip install gdown'.\"\n",
+ " )\n",
+ " root = os.path.expanduser(root)\n",
+ " filename = file_id + \".zip\"\n",
+ " fpath = os.fspath(os.path.join(root, filename))\n",
+ " os.makedirs(root, exist_ok=True)\n",
+ " gdown.download(id=file_id, output=fpath, quiet=False)\n",
+ "\n",
+ " return fpath"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get the dataset id and folder name.\n",
+ "dataset_id = \"1naaoxIaAU1F_rBaI-I1pB1K4Sp6pq_Jv\"\n",
+ "folder_name = \"QuantumDots\"\n",
+ "model_id = \"131H6xdyC5gyTMQcnzb1ozztBH2nelWLg\"\n",
+ "# Create the datasets folder if it doesn't exist.\n",
+ "if not os.path.exists(\"datasets\"):\n",
+ " os.mkdir(\"datasets\")\n",
+ "\n",
+ "dataset_path = f\"datasets/{folder_name}\"\n",
+ "\n",
+ "# Skip if dataset already exists and has content.\n",
+ "if os.path.exists(dataset_path) and os.listdir(dataset_path):\n",
+ " print(\"Dataset already downloaded.\")\n",
+ "else:\n",
+ " # Make dataset folder if missing.\n",
+ " if not os.path.exists(dataset_path):\n",
+ " os.mkdir(dataset_path)\n",
+ "\n",
+ " # Download zip file.\n",
+ " print(f\"Downloading {folder_name}...\")\n",
+ " fpath = download_file_from_google_drive(dataset_id, \"datasets\")\n",
+ "\n",
+ " # Extract zip file.\n",
+ " print(f\"Extracting {folder_name}...\")\n",
+ " with zipfile.ZipFile(fpath, \"r\") as zip_ref:\n",
+ " zip_ref.extractall(\"datasets\")\n",
+ "\n",
+ " # Delete zip file.\n",
+ " os.remove(fpath)\n",
+ "\n",
+ " # Fix nested folder case.\n",
+ " nested = f\"{dataset_path}/{folder_name}\"\n",
+ " if os.path.isdir(nested) and not os.listdir(dataset_path):\n",
+ " os.rename(nested, dataset_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.1 Defining the training set\n",
+ "\n",
+ "The training set consists of simulated 128 by 128 pixel images, containing multiple particles each. The particles are simulated as point scatterers. Their position in the camera plane is constrained to be within the image, and is sampled with a normal distribution with standard deviation of 5 pixel units in along the axis normal to the camera plane. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:46:16.465590Z",
+ "iopub.status.busy": "2022-06-30T10:46:16.465090Z",
+ "iopub.status.idle": "2022-06-30T10:46:16.468590Z",
+ "shell.execute_reply": "2022-06-30T10:46:16.468590Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "IMAGE_SIZE = 128\n",
+ "particle = dt.PointParticle(\n",
+ " position=lambda: np.random.rand(2) * IMAGE_SIZE,\n",
+ " z=lambda: np.random.randn() * 5,\n",
+ " intensity=lambda: 1 + np.random.rand() * 9,\n",
+ " position_unit=\"pixel\",\n",
+ ")\n",
+ "\n",
+ "number_of_particles = lambda: np.random.randint(10, 20)\n",
+ "particles = particle ^ number_of_particles"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The particles are imaged using a fluorescence microscope with NA between 0.6 and 0.8, illuminating laser wavelength of 500 nm, and a magnification of 10."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:46:16.471091Z",
+ "iopub.status.busy": "2022-06-30T10:46:16.471091Z",
+ "iopub.status.idle": "2022-06-30T10:46:16.474590Z",
+ "shell.execute_reply": "2022-06-30T10:46:16.474098Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "optics = dt.Fluorescence(\n",
+ " NA=lambda: 0.6 + np.random.rand() * 0.2,\n",
+ " wavelength=500e-9,\n",
+ " resolution=1e-6,\n",
+ " magnification=10,\n",
+ " output_region=(0, 0, IMAGE_SIZE, IMAGE_SIZE),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The noise is simulated as poisson noise with a signal to noise ratio between 4 and 7. The image is finally normalized by rescaling it to be contained between two random numbers within (0, 1)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:46:16.477090Z",
+ "iopub.status.busy": "2022-06-30T10:46:16.476590Z",
+ "iopub.status.idle": "2022-06-30T10:46:16.480091Z",
+ "shell.execute_reply": "2022-06-30T10:46:16.479652Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "normalization = dt.NormalizeMinMax(\n",
+ " min=lambda: np.random.rand() * 0.4,\n",
+ " max=lambda min: min + 0.1 + np.random.rand() * 0.5,\n",
+ ")\n",
+ "noise = dt.Poisson(\n",
+ " snr=lambda: 4 + np.random.rand() * 3,\n",
+ " background=normalization.min\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now define how these objects combine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:46:16.482590Z",
+ "iopub.status.busy": "2022-06-30T10:46:16.482091Z",
+ "iopub.status.idle": "2022-06-30T10:46:16.485090Z",
+ "shell.execute_reply": "2022-06-30T10:46:16.484597Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "imaged_particle = optics(particles)\n",
+ "simulated_particles = (\n",
+ " dt.Value(imaged_particle)\n",
+ " >> normalization\n",
+ " >> noise\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 Defining the training labels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:46:16.487590Z",
+ "iopub.status.busy": "2022-06-30T10:46:16.487090Z",
+ "iopub.status.idle": "2022-06-30T10:46:16.490590Z",
+ "shell.execute_reply": "2022-06-30T10:46:16.490590Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "get_masks = (particles\n",
+ " >> dt.SampleToMasks(\n",
+ " lambda: lambda particle: particle > 0,\n",
+ " output_region=optics.output_region,\n",
+ " merge_method=\"or\"\n",
+ " )\n",
+ " >> dt.AsType(\"int\")\n",
+ " >> dt.OneHot(num_classes=2)\n",
+ ")\n",
+ "\n",
+ "pipeline = (simulated_particles & get_masks) >> dt.MoveAxis(2, 0) >> dt.pytorch.ToTensor(dtype=torch.float)\n",
+ "pipeline.store_properties()\n",
+ "\n",
+ "# Call pipeline to obtain simulated image with labels.\n",
+ "image, labels = pipeline.update()()\n",
+ "positions = np.array(image.get_property(\"position\", get_one=False))\n",
+ "\n",
+ "plt.subplot(1, 2, 1), plt.imshow(image[0, ...], cmap=\"gray\")\n",
+ "plt.title(\"Original Image\")\n",
+ "plt.subplot(1, 2, 2), plt.imshow(labels[1], cmap=\"gray\")\n",
+ "plt.title(\"Mask\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.3 Visualizing the Training Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "NUMBER_OF_IMAGES = 4\n",
+ "\n",
+ "for _ in range(NUMBER_OF_IMAGES):\n",
+ " plt.figure(figsize=(15, 5))\n",
+ " simulated_particles.update()\n",
+ " image_of_particle, labels = pipeline()\n",
+ " plt.subplot(1, 2, 1)\n",
+ " plt.imshow(image_of_particle[0, ...], cmap=\"gray\")\n",
+ " plt.subplot(1, 2, 2)\n",
+ " plt.imshow(labels[1] * 1.0, cmap=\"gray\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Defining the network\n",
+ "\n",
+ "The network model used is a U-Net, implemented with `deeplay`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 189,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:46:17.356091Z",
+ "iopub.status.busy": "2022-06-30T10:46:17.356091Z",
+ "iopub.status.idle": "2022-06-30T10:46:17.859090Z",
+ "shell.execute_reply": "2022-06-30T10:46:17.859090Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Regressor(\n",
+ " (loss): CrossEntropyLoss()\n",
+ " (optimizer): Adam[Adam](lr=0.001)\n",
+ " (train_metrics): MetricCollection,\n",
+ " prefix=train\n",
+ " )\n",
+ " (val_metrics): MetricCollection,\n",
+ " prefix=val\n",
+ " )\n",
+ " (test_metrics): MetricCollection,\n",
+ " prefix=test\n",
+ " )\n",
+ " (model): UNet2d(\n",
+ " (encoder): ConvolutionalEncoder2d(\n",
+ " (blocks): LayerList(\n",
+ " (0): Conv2dBlock(\n",
+ " (layer): LazyConv2d(0, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (activation): ReLU()\n",
+ " )\n",
+ " (1): Conv2dBlock(\n",
+ " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " (layer): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (activation): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (postprocess): Identity()\n",
+ " )\n",
+ " (bottleneck): ConvolutionalNeuralNetwork(\n",
+ " (blocks): LayerList(\n",
+ " (0): Conv2dBlock(\n",
+ " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " (layer): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (activation): ReLU()\n",
+ " (upsample): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " (decoder): ConvolutionalDecoder2d(\n",
+ " (blocks): LayerList(\n",
+ " (0): Conv2dBlock(\n",
+ " (layer): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (activation): ReLU()\n",
+ " (upsample): ConvTranspose2d(32, 32, kernel_size=(2, 2), stride=(2, 2))\n",
+ " )\n",
+ " (1): Conv2dBlock(\n",
+ " (layer): Conv2d(64, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+ " (activation): Identity()\n",
+ " )\n",
+ " )\n",
+ " (preprocess): Identity()\n",
+ " )\n",
+ " (skip): Cat()\n",
+ " )\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = dl.UNet2d(\n",
+ " in_channels=None,\n",
+ " encoder_channels=[32, 64],\n",
+ " out_channels=2,\n",
+ ")\n",
+ "unet_regressor = dl.Regressor(\n",
+ " model=model,\n",
+ " loss = torch.nn.CrossEntropyLoss(weight=torch.tensor([1, 10])),\n",
+ " optimizer=dl.Adam(lr=1e-3),\n",
+ ").build()\n",
+ "\n",
+ "print(unet_regressor)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Training the network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 190,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO: \n",
+ " | Name | Type | Params | Mode \n",
+ "-----------------------------------------------------------\n",
+ "0 | loss | CrossEntropyLoss | 0 | train\n",
+ "1 | train_metrics | MetricCollection | 0 | train\n",
+ "2 | val_metrics | MetricCollection | 0 | train\n",
+ "3 | test_metrics | MetricCollection | 0 | train\n",
+ "4 | model | UNet2d | 114 K | train\n",
+ "5 | optimizer | Adam | 0 | train\n",
+ "-----------------------------------------------------------\n",
+ "114 K Trainable params\n",
+ "0 Non-trainable params\n",
+ "114 K Total params\n",
+ "0.456 Total estimated model params size (MB)\n",
+ "34 Modules in train mode\n",
+ "0 Modules in eval mode\n",
+ "INFO:lightning.pytorch.callbacks.model_summary:\n",
+ " | Name | Type | Params | Mode \n",
+ "-----------------------------------------------------------\n",
+ "0 | loss | CrossEntropyLoss | 0 | train\n",
+ "1 | train_metrics | MetricCollection | 0 | train\n",
+ "2 | val_metrics | MetricCollection | 0 | train\n",
+ "3 | test_metrics | MetricCollection | 0 | train\n",
+ "4 | model | UNet2d | 114 K | train\n",
+ "5 | optimizer | Adam | 0 | train\n",
+ "-----------------------------------------------------------\n",
+ "114 K Trainable params\n",
+ "0 Non-trainable params\n",
+ "114 K Total params\n",
+ "0.456 Total estimated model params size (MB)\n",
+ "34 Modules in train mode\n",
+ "0 Modules in eval mode\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "37c6039c98b54a13ad9b5495056a6b0c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Training: | | 0/? [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(,\n",
+ " array([[]], dtype=object))"
+ ]
+ },
+ "execution_count": 190,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dataset = dt.pytorch.Dataset(\n",
+ " pipeline,\n",
+ " length=128,\n",
+ " replace=.1\n",
+ ")\n",
+ "dataloader = dl.DataLoader(\n",
+ " dataset,\n",
+ " batch_size=4\n",
+ ")\n",
+ "unet_trainer = dl.Trainer(max_epochs=100, accelerator=\"auto\")\n",
+ "unet_trainer.fit(unet_regressor, dataloader,)\n",
+ "unet_trainer.history.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Evaluating the network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.1 Prediction visualization\n",
+ "\n",
+ "We show a few images, with the ground truth and network prediction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 191,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:48:45.748590Z",
+ "iopub.status.busy": "2022-06-30T10:48:45.748590Z",
+ "iopub.status.idle": "2022-06-30T10:48:47.170091Z",
+ "shell.execute_reply": "2022-06-30T10:48:47.169590Z"
+ },
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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+ ""
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "NUMBER_OF_IMAGES = 4\n",
+ "\n",
+ "\n",
+ "for _ in range(NUMBER_OF_IMAGES):\n",
+ " plt.figure(figsize=(10, 10))\n",
+ " pipeline.update()\n",
+ " image_of_particles, labels = pipeline()\n",
+ " \n",
+ " predicted_mask = model(image_of_particles.unsqueeze(0))\n",
+ " plt.subplot(1, 2, 1)\n",
+ " plt.imshow(image_of_particles.squeeze(0), cmap=\"gray\")\n",
+ " plt.subplot(1, 2, 2)\n",
+ " plt.imshow(predicted_mask[0, 1, ...] > 0.5, cmap=\"gray\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 Experimental Data\n",
+ "Sample random images from the quantum dots dataset, and overlay detections."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 192,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-06-30T10:48:47.172590Z",
+ "iopub.status.busy": "2022-06-30T10:48:47.172590Z",
+ "iopub.status.idle": "2022-06-30T10:48:49.656592Z",
+ "shell.execute_reply": "2022-06-30T10:48:49.656099Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "IMAGES_TO_PLAY = 64\n",
+ "\n",
+ "# Load data.\n",
+ "images = sk.io.imread(\"./datasets/QuantumDots/Qdots.tif\")\n",
+ "images = np.expand_dims(images[:IMAGES_TO_PLAY], axis=0)\n",
+ "images = dt.NormalizeMinMax(0, 1).resolve(list(images))\n",
+ "\n",
+ "# Helper function to obtain detections.\n",
+ "def mask_to_positions(mask):\n",
+ " \"\"\"Convert binary mask to detections.\"\"\"\n",
+ " labels = sk.measure.label(mask)\n",
+ " props = sk.measure.regionprops(labels)\n",
+ " return np.array([prop.centroid for prop in props])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 198,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "num_samples = 4\n",
+ "indices = random.sample(range(len(images)), num_samples)\n",
+ "\n",
+ "for idx in indices:\n",
+ " \n",
+ " # Contrast scaling for plotting.\n",
+ " vmin, vmax = np.percentile(images[idx], [50, 99])\n",
+ " \n",
+ " # Convert image to tensor\n",
+ " image = torch.from_numpy(images[idx].astype(float)).unsqueeze(0).unsqueeze(0).to(torch.float32)\n",
+ " \n",
+ " prediction = model(image).detach()\n",
+ " pred_mask = torch.nn.functional.softmax(prediction, dim=1)\n",
+ " positions = mask_to_positions(pred_mask[0, 1, ...] > 0.001)\n",
+ "\n",
+ " plt.figure(figsize=(6, 6))\n",
+ " plt.imshow(image[0, 0, ...], vmin=vmin, vmax=vmax, cmap=\"gray\")\n",
+ " \n",
+ " if positions.shape[0] > 0:\n",
+ " plt.scatter(\n",
+ " positions[:, 1], positions[:, 0],\n",
+ " s=100, facecolors=\"none\",\n",
+ " edgecolors=(0.0039, 0.45, 0.70)\n",
+ " )\n",
+ " \n",
+ " plt.title(f\"Image {idx}: found {len(positions[:, 1])} detections.\")\n",
+ " plt.axis(\"off\")\n",
+ " plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
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+}