diff --git a/descqagen/app_mag_func_test/data/README.md b/descqagen/app_mag_func_test/data/README.md
new file mode 100644
index 00000000..823a2f2c
--- /dev/null
+++ b/descqagen/app_mag_func_test/data/README.md
@@ -0,0 +1 @@
+This directory stores downloaded data products
\ No newline at end of file
diff --git a/descqagen/app_mag_func_test/data/__init__.py b/descqagen/app_mag_func_test/data/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/descqagen/app_mag_func_test/data/download_data.py b/descqagen/app_mag_func_test/data/download_data.py
new file mode 100644
index 00000000..fa5a40eb
--- /dev/null
+++ b/descqagen/app_mag_func_test/data/download_data.py
@@ -0,0 +1,57 @@
+"""
+run sql queries on HSC database
+"""
+
+from __future__ import print_function, division
+import sys
+sys.path.append("..")
+from defaults import PROJECT_DIRECTORY, DATA_DIRECTORY
+
+
+def main():
+    """
+    run scripted SQL download.
+
+    to download the HSC data for this project, in the temrinal window type:
+        $user python download_data.py data
+
+    to download the HSC randoms for this project, in the temrinal window type:
+        $user python download_data.py randoms
+
+    Before running this script, edit the hsc_credentials.txt file in the sql directory.
+    """
+
+    if sys.argv[1] == 'data':
+        sql_file = PROJECT_DIRECTORY + 'sql/hsc_data.sql'
+        savename = DATA_DIRECTORY + 'hsc_data.csv'
+    elif sys.argv[1] == 'randoms':
+        sql_file = PROJECT_DIRECTORY + 'sql/hsc_randoms.sql'
+        savename = DATA_DIRECTORY + 'hsc_randoms.csv'
+    else:
+        print("specify either 'data' or 'randoms' as a positional argument.")
+        sys.exit()
+
+    # read in username and password for HSC account
+    user_info_file = PROJECT_DIRECTORY + 'sql/hsc_credentials.txt'
+    f = open(user_info_file, 'r')
+    username = f.readline().strip()
+    password = f.readline().strip()
+    f.close()
+    if (username == 'username') | (password == 'password'):
+        print("Please alter the file: {0}, to be your username and password".format(user_info_file))
+        print("It is possible your username and password are 'username' and 'password'. Shame on you.")
+
+    # open sql query string
+    f = open(sql_file, 'r')
+    sql = f.read()
+    f.close()
+
+    # run query script
+    import subprocess
+    exec_script = PROJECT_DIRECTORY+'sql/hscReleaseQuery.py'
+    process = subprocess.call(['python', exec_script, '--user', username, '--password', password, sql_file])
+
+
+
+if __name__ == "__main__":
+    main()
diff --git a/descqagen/app_mag_func_test/data/hsc_data_types.py b/descqagen/app_mag_func_test/data/hsc_data_types.py
new file mode 100644
index 00000000..ff897c36
--- /dev/null
+++ b/descqagen/app_mag_func_test/data/hsc_data_types.py
@@ -0,0 +1,39 @@
+cols = {'object_id':(0,'i8'),
+        'parent_id':(1,'i8'),
+        'ira':(2,'f4'),
+        'idec':(3,'f4'),
+        'imag_cmodel':(4,'f4'),
+        'imag_cmodel_err':(5,'f4'),
+        'iflux_cmodel_flags':(6,'bool_'),
+        'iflux_cmodel':(7,'f4'),
+        'iflux_cmodel_err':(8,'f4'),
+        'merge_measurement_i':(9,'f4'),
+        'a_g':(10,'f4'),
+        'a_r':(11,'f4'),
+        'a_i':(12,'f4'),
+        'a_z':(13,'f4'),
+        'a_y':(14,'f4'),
+        'rmag_forced_cmodel':(15,'f4'),
+        'rmag_forced_cmodel_err':(16,'f4'),
+        'rflux_forced_cmodel':(17,'f4'),
+        'rflux_forced_cmodel_err':(18,'f4'),
+        'rflux_forced_cmodel_flags':(19,'bool_'),
+        'imag_forced_cmodel':(20,'f4'),
+        'imag_forced_cmodel_err':(21,'f4'),
+        'iflux_forced_cmodel':(22,'f4'),
+        'iflux_forced_cmodel_err':(23,'f4'),
+        'iflux_forced_cmodel_flags':(24,'bool_'),
+        'gmag_forced_cmodel':(25,'f4'),
+        'zmag_forced_cmodel':(26,'f4'),
+        'ymag_forced_cmodel':(27,'f4'),
+        'tract':(28,'f4'),
+        'patch':(29,'f4'),
+        'gcountinputs':(30,'f4'),
+        'rcountinputs':(31,'f4'),
+        'icountinputs':(32,'f4'),
+        'zcountinputs':(33,'f4'),
+        'ycountinputs':(34,'f4'),
+        'iflags_pixel_bright_object_center':(35,'bool_'),
+        'iflags_pixel_bright_object_any':(36,'bool_'),
+        'iblendedness_flags':(37,'bool_'),
+        'iblendedness_abs_flux':(38,'f4')}
\ No newline at end of file
diff --git a/descqagen/app_mag_func_test/data/hsc_randoms_types.py b/descqagen/app_mag_func_test/data/hsc_randoms_types.py
new file mode 100644
index 00000000..e8d6353c
--- /dev/null
+++ b/descqagen/app_mag_func_test/data/hsc_randoms_types.py
@@ -0,0 +1,13 @@
+cols = {'object_id':(0,'i8'),
+        'ra':(1,'f4'),
+        'dec':(2,'f4'),
+        'tract':(3,'f4'),
+        'patch':(4,'f4'),
+        'gcountinputs':(5,'f4'),
+        'rcountinputs':(6,'f4'),
+        'icountinputs':(7,'f4'),
+        'zcountinputs':(8,'f4'),
+        'ycountinputs':(9,'f4'),
+        'iflags_pixel_bright_object_center':(10,'bool_'),
+        'iflags_pixel_bright_object_any':(11,'bool_')
+        }
\ No newline at end of file
diff --git a/descqagen/app_mag_func_test/defaults.py b/descqagen/app_mag_func_test/defaults.py
new file mode 100644
index 00000000..93dfcba0
--- /dev/null
+++ b/descqagen/app_mag_func_test/defaults.py
@@ -0,0 +1,4 @@
+import os
+
+PROJECT_DIRECTORY = os.path.dirname(os.path.abspath(__file__))+'/'
+DATA_DIRECTORY = PROJECT_DIRECTORY + 'data/'
diff --git a/descqagen/app_mag_func_test/notebooks/HSC_app_mag_func.ipynb b/descqagen/app_mag_func_test/notebooks/HSC_app_mag_func.ipynb
new file mode 100644
index 00000000..5c206939
--- /dev/null
+++ b/descqagen/app_mag_func_test/notebooks/HSC_app_mag_func.ipynb
@@ -0,0 +1,1874 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function, division\n",
+    "#add project directory to python path\n",
+    "import sys\n",
+    "sys.path.append(\"..\")\n",
+    "from defaults import PROJECT_DIRECTORY, DATA_DIRECTORY\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load catalogs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_csv\n",
+    "from utils import get_ordered_list\n",
+    "\n",
+    "#set location of data files\n",
+    "deep_file = DATA_DIRECTORY + 'hsc_data.csv' # COSMOS, DEEP2-3, ELAIS-N1, and XMM-LSS fields\n",
+    "rand_file = DATA_DIRECTORY + 'hsc_randoms.csv'\n",
+    "\n",
+    "#get column info\n",
+    "from data.hsc_data_types import cols as data_cols\n",
+    "from data.hsc_randoms_types import cols as ran_cols\n",
+    "\n",
+    "#read in column info for the data file\n",
+    "data_dtypes = get_ordered_list(data_cols)\n",
+    "data_names = [x for x,y in data_dtypes]\n",
+    "\n",
+    "#read in column info for the randoms file\n",
+    "ran_dtypes =  get_ordered_list(ran_cols)\n",
+    "ran_names = [x for x,y in ran_dtypes]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (19,24) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  interactivity=interactivity, compiler=compiler, result=result)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#load data file\n",
+    "hsc_data = read_csv(deep_file, dtype=data_dtypes, engine='c', header=0, names=data_names) #note that there are some missing values and mixed types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "object_id                              int64\n",
+       "parent_id                              int64\n",
+       "ira                                  float32\n",
+       "idec                                 float32\n",
+       "imag_cmodel                          float32\n",
+       "imag_cmodel_err                      float32\n",
+       "iflux_cmodel_flags                      bool\n",
+       "iflux_cmodel                         float32\n",
+       "iflux_cmodel_err                     float32\n",
+       "merge_measurement_i                  float32\n",
+       "a_g                                  float32\n",
+       "a_r                                  float32\n",
+       "a_i                                  float32\n",
+       "a_z                                  float32\n",
+       "a_y                                  float32\n",
+       "rmag_forced_cmodel                   float32\n",
+       "rmag_forced_cmodel_err               float32\n",
+       "rflux_forced_cmodel                  float32\n",
+       "rflux_forced_cmodel_err              float32\n",
+       "rflux_forced_cmodel_flags             object\n",
+       "imag_forced_cmodel                   float32\n",
+       "imag_forced_cmodel_err               float32\n",
+       "iflux_forced_cmodel                  float32\n",
+       "iflux_forced_cmodel_err              float32\n",
+       "iflux_forced_cmodel_flags             object\n",
+       "gmag_forced_cmodel                   float32\n",
+       "zmag_forced_cmodel                   float32\n",
+       "ymag_forced_cmodel                   float32\n",
+       "tract                                float32\n",
+       "patch                                float32\n",
+       "gcountinputs                         float32\n",
+       "rcountinputs                         float32\n",
+       "icountinputs                         float32\n",
+       "zcountinputs                         float32\n",
+       "ycountinputs                         float32\n",
+       "iflags_pixel_bright_object_center       bool\n",
+       "iflags_pixel_bright_object_any          bool\n",
+       "iblendedness_flags                      bool\n",
+       "iblendedness_abs_flux                float32\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hsc_data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#load random file\n",
+    "hsc_randoms = read_csv(rand_file, dtype=ran_dtypes, engine='c', header=0, names=ran_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "object_id                              int64\n",
+       "ra                                   float32\n",
+       "dec                                  float32\n",
+       "tract                                float32\n",
+       "patch                                float32\n",
+       "gcountinputs                         float32\n",
+       "rcountinputs                         float32\n",
+       "icountinputs                         float32\n",
+       "zcountinputs                         float32\n",
+       "ycountinputs                         float32\n",
+       "iflags_pixel_bright_object_center       bool\n",
+       "iflags_pixel_bright_object_any          bool\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hsc_randoms.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of galaxies:  10294602\n",
+      "number of randoms:  10564959\n"
+     ]
+    }
+   ],
+   "source": [
+    "#check the number of galaxies\n",
+    "print(\"number of galaxies: \", len(hsc_data))\n",
+    "print(\"number of randoms: \", len(hsc_randoms))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# make cuts on data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of galaxies:  9172467\n",
+      "number of randoms:  9385922\n"
+     ]
+    }
+   ],
+   "source": [
+    "#make some further cuts on the data\n",
+    "bright_mask = (hsc_data['iflags_pixel_bright_object_center']==False)\n",
+    "ran_bright_bmask = (hsc_randoms['iflags_pixel_bright_object_center']==False)\n",
+    "nan_mask = (np.isnan(hsc_data['imag_forced_cmodel'])==False)\n",
+    "inf_mask = (np.isinf(hsc_data['imag_forced_cmodel'])==False)\n",
+    "\n",
+    "#note that in the SQL script, meas.iclassification_extendedness != 0\n",
+    "\n",
+    "#make uber masks\n",
+    "data_mask = (bright_mask & nan_mask & inf_mask)\n",
+    "randoms_mask = (ran_bright_bmask)\n",
+    "\n",
+    "#remove masked galaxies and randoms from tables\n",
+    "hsc_data = hsc_data[data_mask]\n",
+    "hsc_randoms = hsc_randoms[randoms_mask]\n",
+    "\n",
+    "#check number of galaxies in sample\n",
+    "print(\"number of galaxies: \", len(hsc_data))\n",
+    "print(\"number of randoms: \", len(hsc_randoms))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# define and plot fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define fields\n",
+    "Nfields=4\n",
+    "field_bounding_boxes = [[34,38,-6.5,-3], #min_ra, max_ra, min_dec, max_dec\n",
+    "                        [148,152,0,4],\n",
+    "                        [240,246,53,57],\n",
+    "                        [350,354,-2,1.5]]\n",
+    "\n",
+    "#define some masks for each field\n",
+    "\n",
+    "#convert to record arrays\n",
+    "fdata = hsc_data.to_records()\n",
+    "frdata = hsc_randoms.to_records()\n",
+    "\n",
+    "data_field_mask_1 = (fdata['ira'] > field_bounding_boxes[0][0]) &\\\n",
+    "                    (fdata['ira'] < field_bounding_boxes[0][1]) &\\\n",
+    "                    (fdata['idec'] > field_bounding_boxes[0][2]) &\\\n",
+    "                    (fdata['idec'] < field_bounding_boxes[0][3]) \n",
+    "data_field_mask_2 = (fdata['ira'] > field_bounding_boxes[1][0]) &\\\n",
+    "                    (fdata['ira'] < field_bounding_boxes[1][1]) &\\\n",
+    "                    (fdata['idec'] > field_bounding_boxes[1][2]) &\\\n",
+    "                    (fdata['idec'] < field_bounding_boxes[1][3]) \n",
+    "data_field_mask_3 = (fdata['ira'] > field_bounding_boxes[2][0]) &\\\n",
+    "                    (fdata['ira'] < field_bounding_boxes[2][1]) &\\\n",
+    "                    (fdata['idec'] > field_bounding_boxes[2][2]) &\\\n",
+    "                    (fdata['idec'] < field_bounding_boxes[2][3]) \n",
+    "data_field_mask_4 = (fdata['ira'] > field_bounding_boxes[3][0]) &\\\n",
+    "                    (fdata['ira'] < field_bounding_boxes[3][1]) &\\\n",
+    "                    (fdata['idec'] > field_bounding_boxes[3][2]) &\\\n",
+    "                    (fdata['idec'] < field_bounding_boxes[3][3]) \n",
+    "\n",
+    "ran_field_mask_1 = (frdata['ra'] > field_bounding_boxes[0][0]) &\\\n",
+    "                    (frdata['ra'] < field_bounding_boxes[0][1]) &\\\n",
+    "                    (frdata['dec'] > field_bounding_boxes[0][2]) &\\\n",
+    "                    (frdata['dec'] < field_bounding_boxes[0][3]) \n",
+    "ran_field_mask_2 = (frdata['ra'] > field_bounding_boxes[1][0]) &\\\n",
+    "                    (frdata['ra'] < field_bounding_boxes[1][1]) &\\\n",
+    "                    (frdata['dec'] > field_bounding_boxes[1][2]) &\\\n",
+    "                    (frdata['dec'] < field_bounding_boxes[1][3]) \n",
+    "ran_field_mask_3 = (frdata['ra'] > field_bounding_boxes[2][0]) &\\\n",
+    "                    (frdata['ra'] < field_bounding_boxes[2][1]) &\\\n",
+    "                    (frdata['dec'] > field_bounding_boxes[2][2]) &\\\n",
+    "                    (frdata['dec'] < field_bounding_boxes[2][3]) \n",
+    "ran_field_mask_4 = (frdata['ra'] > field_bounding_boxes[3][0]) &\\\n",
+    "                    (frdata['ra'] < field_bounding_boxes[3][1]) &\\\n",
+    "                    (frdata['dec'] > field_bounding_boxes[3][2]) &\\\n",
+    "                    (frdata['dec'] < field_bounding_boxes[3][3]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 475.2x950.4 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot the spatial distribution of galaxies and randoms in fields as a sanity check\n",
+    "\n",
+    "#subsample to make plotting easier\n",
+    "subsamp = np.random.choice(len(fdata), 50000)\n",
+    "rsubsamp = np.random.choice(len(frdata), 50000)\n",
+    "\n",
+    "subsamp = np.in1d(np.arange(0,len(fdata)),subsamp)\n",
+    "rsubsamp = np.in1d(np.arange(0,len(frdata)),rsubsamp)\n",
+    "\n",
+    "#plot each field\n",
+    "fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(3.3*2,3.3*4))\n",
+    "for i in range(0,Nfields):\n",
+    "    ax1,ax2 = (axes[i,0], axes[i,1])\n",
+    "    if i==0:\n",
+    "        ax1.set_title('HSC galaxies')\n",
+    "        ax2.set_title('HSC randoms')\n",
+    "    min_ra = field_bounding_boxes[i][0]\n",
+    "    max_ra = field_bounding_boxes[i][1]\n",
+    "    min_dec = field_bounding_boxes[i][2]\n",
+    "    max_dec = field_bounding_boxes[i][3]\n",
+    "    ax1.scatter(fdata['ira'][subsamp], fdata['idec'][subsamp], s=1)\n",
+    "    ax1.set_xlabel('RA')\n",
+    "    ax1.set_ylabel('Dec')\n",
+    "    ax1.set_xlim([min_ra, max_ra])\n",
+    "    ax1.set_ylim([min_dec, max_dec])\n",
+    "    ax2.scatter(frdata['ra'][rsubsamp], frdata['dec'][rsubsamp], s=1)\n",
+    "    ax2.set_xlabel('RA')\n",
+    "    ax2.set_xlim([min_ra, max_ra])\n",
+    "    ax2.set_ylim([min_dec, max_dec])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Caclulate area of survey"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total area in square arcmin, square degrees:  93859.22 26.0720055556\n",
+      "Area of deep field 1 in square arcmin, deg:  18314.15 5.08726388889\n",
+      "Area of deep field 2 in square arcmin, deg:  28104.28 7.80674444444\n",
+      "Area of deep field 3 in square arcmin, deg:  23422.92 6.50636666667\n",
+      "Area of deep field 4 in square arcmin, deg:  24017.87 6.67163055556\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate the effective area of the survey\n",
+    "# There are 100 random points per square arcmin.\n",
+    "\n",
+    "full_area_sq_arcmin = float(len(hsc_randoms))/100.0\n",
+    "full_area_sq_deg = full_area_sq_arcmin/3600.0\n",
+    "\n",
+    "print('Total area in square arcmin, square degrees: ', full_area_sq_arcmin, full_area_sq_deg)\n",
+    "\n",
+    "# calculate area of individual fields\n",
+    "# from table 3 in Hiroaki + 2017, deep area=26 deg^2 for public release 1\n",
+    "\n",
+    "area_sq_arcmin = float(len(hsc_randoms[ran_field_mask_1]))/100.0\n",
+    "area_sq_deg = area_sq_arcmin/3600.0\n",
+    "print('Area of deep field 1 in square arcmin, deg: ', area_sq_arcmin, area_sq_deg)\n",
+    "area_sq_arcmin = float(len(hsc_randoms[ran_field_mask_2]))/100.0\n",
+    "area_sq_deg = area_sq_arcmin/3600.0\n",
+    "print('Area of deep field 2 in square arcmin, deg: ', area_sq_arcmin, area_sq_deg)\n",
+    "area_sq_arcmin = float(len(hsc_randoms[ran_field_mask_3]))/100.0\n",
+    "area_sq_deg = area_sq_arcmin/3600.0\n",
+    "print('Area of deep field 3 in square arcmin, deg: ', area_sq_arcmin, area_sq_deg)\n",
+    "area_sq_arcmin = float(len(hsc_randoms[ran_field_mask_4]))/100.0\n",
+    "area_sq_deg = area_sq_arcmin/3600.0\n",
+    "print('Area of deep field 4 in square arcmin, deg: ', area_sq_arcmin, area_sq_deg)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# calculate apparent magnitude functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mag_bins = np.arange(17.0,27.0,0.1) #for differential mag function\n",
+    "mag_bin_centers = (mag_bins[:-1]+mag_bins[1:])/2.0\n",
+    "\n",
+    "#g-band\n",
+    "g_mag = hsc_data['gmag_forced_cmodel'] - hsc_data['a_g'] #correct for dust\n",
+    "g_mag = g_mag[np.isfinite(g_mag)]\n",
+    "g_mag = np.sort(g_mag)\n",
+    "#cumulative number density\n",
+    "N_g = np.cumsum(np.ones(len(g_mag)))\n",
+    "n_g = N_g/full_area_sq_deg\n",
+    "#differential number density per mag\n",
+    "dn_g = np.histogram(g_mag, bins = mag_bins)[0]/full_area_sq_deg\n",
+    "dn_dmag_g = dn_g/np.diff(mag_bins)\n",
+    "\n",
+    "#r-band\n",
+    "r_mag = hsc_data['rmag_forced_cmodel'] - hsc_data['a_r'] #correct for dust\n",
+    "r_mag = r_mag[np.isfinite(r_mag)]\n",
+    "r_mag = np.sort(r_mag)\n",
+    "N_r = np.cumsum(np.ones(len(r_mag)))\n",
+    "#cumulative number density\n",
+    "n_r = N_r/full_area_sq_deg\n",
+    "#differential number density per mag\n",
+    "dn_r = np.histogram(r_mag, bins = mag_bins)[0]/full_area_sq_deg\n",
+    "dn_dmag_r = dn_r/np.diff(mag_bins)\n",
+    "\n",
+    "#i-band\n",
+    "i_mag = hsc_data['imag_forced_cmodel'] - hsc_data['a_i'] #correct for dust\n",
+    "i_mag = i_mag[np.isfinite(i_mag)]\n",
+    "i_mag = np.sort(i_mag)\n",
+    "#cumulative number density\n",
+    "N_i = np.cumsum(np.ones(len(i_mag)))\n",
+    "n_i = N_i/full_area_sq_deg\n",
+    "#differential number density per mag\n",
+    "dn_i = np.histogram(i_mag, bins = mag_bins)[0]/full_area_sq_deg\n",
+    "dn_dmag_i = dn_i/np.diff(mag_bins)\n",
+    "\n",
+    "#z-band\n",
+    "z_mag = hsc_data['zmag_forced_cmodel'] - hsc_data['a_z'] #correct for dust\n",
+    "z_mag = z_mag[np.isfinite(z_mag)]\n",
+    "z_mag = np.sort(z_mag)\n",
+    "#cumulative number density\n",
+    "N_z = np.cumsum(np.ones(len(z_mag)))\n",
+    "n_z = N_z/full_area_sq_deg\n",
+    "#differential number density per mag\n",
+    "dn_z = np.histogram(z_mag, bins = mag_bins)[0]/full_area_sq_deg\n",
+    "dn_dmag_z = dn_z/np.diff(mag_bins)\n",
+    "\n",
+    "#y-band\n",
+    "y_mag = hsc_data['ymag_forced_cmodel'] - hsc_data['a_y'] #correct for dust\n",
+    "y_mag = y_mag[np.isfinite(y_mag)]\n",
+    "y_mag = np.sort(y_mag)\n",
+    "#cumulative number density\n",
+    "N_y = np.cumsum(np.ones(len(y_mag)))\n",
+    "n_y = N_y/full_area_sq_deg\n",
+    "#differential number density per mag\n",
+    "dn_y = np.histogram(y_mag, bins = mag_bins)[0]/full_area_sq_deg\n",
+    "dn_dmag_y = dn_y/np.diff(mag_bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot cumulative mag functions\n",
+    "colors = plt.cm.jet(np.linspace(0,1,5))  \n",
+    "plt.figure(figsize=(3.3,3.3))\n",
+    "plt.plot([26.5, 26.5],[0,10**8], '--', color='grey')\n",
+    "l1, = plt.plot(g_mag, n_g, '-', color=colors[0])\n",
+    "l2, = plt.plot(r_mag, n_r, '-', color=colors[1])\n",
+    "l3, = plt.plot(i_mag, n_i, '-', color=colors[2])\n",
+    "l4, = plt.plot(z_mag, n_z, '-', color=colors[3])\n",
+    "l5, = plt.plot(y_mag, n_y, '-', color=colors[4])\n",
+    "plt.yscale('log')\n",
+    "plt.xlim([16,30])\n",
+    "plt.ylim([10,10**6.5])\n",
+    "plt.xlabel('magnitude')\n",
+    "plt.ylabel(r'$n(<m)$')\n",
+    "plt.legend([l1, l2, l3, l4, l5],['g-band','r-band','i-band','z-band','y-band'],\n",
+    "           frameon=False, fontsize=10, loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# digitized i-band app mag func from HSC data release paper, fig. 16\n",
+    "x = np.array([19.246851,19.75063,20.254408,20.758186,21.24937,21.765743,22.244333,22.735516,23.264484,\\\n",
+    "              23.755667,24.246851,24.75063,25.254408,25.758186,26.24937,26.740555,27.231737,27.748112])\n",
+    "y = np.array([1363.0817,2016.9734,3156.3826,4670.549,7173.8726,11018.929,15417.234,23242.803,35700.496,\\\n",
+    "              53821.555,78168.32,117845.445,155908.11,187891.02,239471.84,253259.44,155908.11,63663.395])\n",
+    "\n",
+    "#plot differential mag functions\n",
+    "colors = plt.cm.jet(np.linspace(0,1,5))  \n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)\n",
+    "plt.plot([26.5, 26.5],[0,10**8], '--', color='grey')\n",
+    "l1, = plt.plot(mag_bin_centers, dn_dmag_g, '-', color=colors[0])\n",
+    "l2, = plt.plot(mag_bin_centers, dn_dmag_r, '-', color=colors[1])\n",
+    "l3, = plt.plot(mag_bin_centers, dn_dmag_i, '-', color=colors[2])\n",
+    "l4, = plt.plot(mag_bin_centers, dn_dmag_z, '-', color=colors[3])\n",
+    "l5, = plt.plot(mag_bin_centers, dn_dmag_y, '-', color=colors[4])\n",
+    "p1, = plt.plot(x, y, '+', color=colors[2])\n",
+    "plt.yscale('log')\n",
+    "plt.xlim([16,28])\n",
+    "plt.ylim([10,10**6.5])\n",
+    "plt.xlabel('magnitude')\n",
+    "plt.ylabel(r'$\\mathrm{d}n/\\mathrm{d}{\\rm mag}$')\n",
+    "plt.legend([l1, l2, l3, l4, l5],['g-band','r-band','i-band','z-band','y-band'],\n",
+    "           frameon=False, fontsize=10, loc='upper left')\n",
+    "#plt.legend([l3, p1],['i-band','Hiroaki+ 2017'],\n",
+    "#           frameon=False, fontsize=10, loc='lower right')\n",
+    "plt.show()\n",
+    "\n",
+    "fig.savefig('/Users/duncan/Desktop/diff_app_mag_func.png', dpi=250)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#examine difference between Aihara+ 2017 and our measurement\n",
+    "\n",
+    "from scipy.interpolate import interp1d\n",
+    "f = interp1d(x,np.log10(y),fill_value='extrapolate')\n",
+    "err = (10**f(mag_bin_centers) - dn_dmag_i)/(dn_dmag_i)\n",
+    "\n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)\n",
+    "plt.plot(mag_bin_centers, err*0.0, '--', color=colors[2])\n",
+    "plt.plot(mag_bin_centers, err, 'o', color=colors[2], ms=4)\n",
+    "plt.ylim([-0.5,0.5])\n",
+    "plt.xlabel('i magnitude')\n",
+    "plt.ylabel('$\\Delta\\phi/\\phi_0$')\n",
+    "\n",
+    "fig.savefig('/Users/duncan/Desktop/err_plot.png', dpi=250)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# fit power law to faint end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calculate number density at at fixed points\n",
+    "\n",
+    "m_bins = np.arange(17.0,30.0,0.2)\n",
+    "\n",
+    "inds = np.searchsorted(g_mag, m_bins)\n",
+    "n_sample_g = n_g[inds]\n",
+    "\n",
+    "inds = np.searchsorted(r_mag, m_bins)\n",
+    "n_sample_r = n_r[inds]\n",
+    "\n",
+    "inds = np.searchsorted(i_mag, m_bins)\n",
+    "n_sample_i = n_i[inds]\n",
+    "\n",
+    "inds = np.searchsorted(z_mag, m_bins)\n",
+    "n_sample_z = n_z[inds]\n",
+    "\n",
+    "inds = np.searchsorted(y_mag, m_bins)\n",
+    "n_sample_y = n_y[inds]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fit power law to faint end slope\n",
+    "\n",
+    "#define power-law model\n",
+    "def power_law(m, A=0.39, B=5.0, n0=0.01):\n",
+    "    return n0*(10.0**(A*(m-B)))\n",
+    "\n",
+    "#fit power law to data points\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "#define range over which to fit power law for each band\n",
+    "g_mask = (m_bins>23) & (m_bins<25)\n",
+    "popt_g, pcov_g = curve_fit(power_law, m_bins[g_mask], n_sample_g[g_mask])\n",
+    "r_mask = (m_bins>23) & (m_bins<25)\n",
+    "popt_r, pcov_r = curve_fit(power_law, m_bins[r_mask], n_sample_r[r_mask])\n",
+    "i_mask = (m_bins>23) & (m_bins<25)\n",
+    "popt_i, pcov_i = curve_fit(power_law, m_bins[i_mask], n_sample_i[i_mask])\n",
+    "z_mask = (m_bins>23) & (m_bins<25)\n",
+    "popt_z, pcov_z = curve_fit(power_law, m_bins[z_mask], n_sample_z[z_mask])\n",
+    "y_mask = (m_bins>23) & (m_bins<25)\n",
+    "popt_y, pcov_y = curve_fit(power_law, m_bins[y_mask], n_sample_y[y_mask])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot power law fits\n",
+    "m_sample = np.linspace(15,30,100)\n",
+    "\n",
+    "colors = plt.cm.jet(np.linspace(0,1,5))  \n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)\n",
+    "\n",
+    "plt.fill_between([23,25],[0.1,0.1],[10**9,10**9],color='grey', alpha=0.25)\n",
+    "\n",
+    "#data\n",
+    "p1, = plt.plot(m_bins, n_sample_g, '-', color=colors[0])\n",
+    "p2, = plt.plot(m_bins, n_sample_r, '-', color=colors[1])\n",
+    "p3, = plt.plot(m_bins, n_sample_i, '-', color=colors[2])\n",
+    "p4, = plt.plot(m_bins, n_sample_z, '-', color=colors[3])\n",
+    "p5, = plt.plot(m_bins, n_sample_y, '-', color=colors[4])\n",
+    "#fits\n",
+    "plt.plot(m_sample, power_law(m_sample, *popt_g),':', color=colors[0])\n",
+    "plt.plot(m_sample, power_law(m_sample, *popt_r),':', color=colors[1])\n",
+    "plt.plot(m_sample, power_law(m_sample, *popt_i),':', color=colors[2])\n",
+    "plt.plot(m_sample, power_law(m_sample, *popt_z),':', color=colors[3])\n",
+    "plt.plot(m_sample, power_law(m_sample, *popt_y),':', color=colors[4])\n",
+    "\n",
+    "plt.yscale('log')\n",
+    "plt.xlim([16,30])\n",
+    "plt.ylim([10**2,10**8])\n",
+    "plt.xlabel('magnitude')\n",
+    "plt.ylabel(r'$n(<m)$')\n",
+    "plt.legend([p1, p2, p3, p4, p5],['g-band','r-band','i-band','z-band','y-band'], frameon=False,\n",
+    "           fontsize=10, numpoints=1, loc='upper left')\n",
+    "plt.show()\n",
+    "\n",
+    "fig.savefig('/Users/duncan/Desktop/hsc_app_mag_func.pdf',dpi=250)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot implied completeness \n",
+    "\n",
+    "c_g = (n_sample_g)/(power_law(m_bins, *popt_g))\n",
+    "c_r = (n_sample_r)/(power_law(m_bins, *popt_r))\n",
+    "c_i = (n_sample_i)/(power_law(m_bins, *popt_i))\n",
+    "c_z = (n_sample_z)/(power_law(m_bins, *popt_z))\n",
+    "c_y = (n_sample_y)/(power_law(m_bins, *popt_y))\n",
+    "\n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)\n",
+    "plt.fill_between([23,25],[0,0],[2,2],color='grey', alpha=0.25)\n",
+    "p1, = plt.plot(m_bins, c_g, color=colors[0])\n",
+    "p2, = plt.plot(m_bins, c_r, color=colors[1])\n",
+    "p3, = plt.plot(m_bins, c_i, color=colors[2])\n",
+    "p4, = plt.plot(m_bins, c_z, color=colors[3])\n",
+    "p5, = plt.plot(m_bins, c_y, color=colors[4])\n",
+    "plt.xlim([20,30])\n",
+    "plt.ylim([0.01,1.1])\n",
+    "plt.yscale('log')\n",
+    "plt.xlabel('magnitude')\n",
+    "plt.ylabel(r'$C(<m)$')\n",
+    "plt.legend([p1, p2, p3, p4, p5],['g-band','r-band','i-band','z-band','y-band'], frameon=False,\n",
+    "           fontsize=10, numpoints=1, loc='lower left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot linear difference ratio\n",
+    "\n",
+    "c_g = (n_sample_g-power_law(m_bins, *popt_g))/(power_law(m_bins, *popt_g))\n",
+    "c_r = (n_sample_r-power_law(m_bins, *popt_r))/(power_law(m_bins, *popt_r))\n",
+    "c_i = (n_sample_i-power_law(m_bins, *popt_i))/(power_law(m_bins, *popt_i))\n",
+    "c_z = (n_sample_z-power_law(m_bins, *popt_z))/(power_law(m_bins, *popt_z))\n",
+    "c_y = (n_sample_y-power_law(m_bins, *popt_y))/(power_law(m_bins, *popt_y))\n",
+    "\n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)\n",
+    "plt.fill_between([23,25],[-2,-2],[2,2],color='grey', alpha=0.25)\n",
+    "plt.plot([20,30],[0,0],'--', color='black')\n",
+    "p1, = plt.plot(m_bins, c_g, color=colors[0])\n",
+    "p2, = plt.plot(m_bins, c_r, color=colors[1])\n",
+    "p3, = plt.plot(m_bins, c_i, color=colors[2])\n",
+    "p4, = plt.plot(m_bins, c_z, color=colors[3])\n",
+    "p5, = plt.plot(m_bins, c_y, color=colors[4])\n",
+    "plt.xlim([20,30])\n",
+    "plt.ylim([-1,1])\n",
+    "#plt.yscale('log')\n",
+    "plt.xlabel('magnitude')\n",
+    "plt.ylabel(r'$\\Delta N/N$')\n",
+    "plt.legend([p1, p2, p3, p4, p5],['g-band','r-band','i-band','z-band','y-band'], frameon=False,\n",
+    "           fontsize=10, numpoints=1, loc='upper right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Define jackknife samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#find ra and dec values that split fields into 4 equal parts\n",
+    "\n",
+    "class quad_split(object):\n",
+    "    \"\"\"\n",
+    "    splits a plane into 4 equal number quadrants\n",
+    "    \"\"\"\n",
+    "    def __init__(self,ra,dec,max_iter=100):    \n",
+    "        \n",
+    "        self.ra = ra\n",
+    "        self.dec = dec\n",
+    "        \n",
+    "        self.min_ra = np.min(ra)\n",
+    "        self.max_ra = np.max(ra)\n",
+    "        \n",
+    "        self.min_dec = np.min(dec)\n",
+    "        self.max_dec = np.max(dec)\n",
+    "        \n",
+    "        self.max_iter=max_iter\n",
+    "        \n",
+    "        self.find_ra_boundary()\n",
+    "        self.find_dec_boundary()\n",
+    "        \n",
+    "    def find_ra_boundary(self):\n",
+    "        \n",
+    "        self.ra_split = (self.min_ra+self.max_ra)/2.0\n",
+    "        da = (self.max_ra-self.min_ra)/2.0\n",
+    "        \n",
+    "        df = 0.5\n",
+    "        f0 = 0.5\n",
+    "        \n",
+    "        i=0\n",
+    "        while df>0.01:\n",
+    "            i+=1\n",
+    "            N_above = 1.0*np.sum(self.ra > self.ra_split)\n",
+    "            N_below = 1.0*np.sum(self.ra <= self.ra_split)\n",
+    "            \n",
+    "            if N_above>N_below:\n",
+    "                self.ra_split = self.ra_split+da\n",
+    "                da = da/2.0\n",
+    "            elif  N_above<N_below:\n",
+    "                self.ra_split = self.ra_split-da\n",
+    "                da = da/2.0\n",
+    "            else:\n",
+    "                break\n",
+    "            \n",
+    "            if i>self.max_iter:\n",
+    "                break\n",
+    "            \n",
+    "        self.ra_iter=i     \n",
+    "    \n",
+    "    def find_dec_boundary(self):\n",
+    "        \n",
+    "        self.dec_split = (self.min_dec+self.max_dec)/2.0\n",
+    "        da = (self.max_dec-self.min_dec)/2.0\n",
+    "        \n",
+    "        df = 0.5\n",
+    "        f0 = 0.5\n",
+    "        i=0\n",
+    "        while df>0.01:\n",
+    "            i+=1\n",
+    "            N_above = 1.0*np.sum(self.dec > self.dec_split)\n",
+    "            N_below = 1.0*np.sum(self.dec <= self.dec_split)\n",
+    "            \n",
+    "            if N_above>N_below:\n",
+    "                self.dec_split = self.dec_split+da\n",
+    "                da = da/2.0\n",
+    "            elif  N_above<N_below:\n",
+    "                self.dec_split = self.dec_split-da\n",
+    "                da = da/2.0\n",
+    "            else:\n",
+    "                break\n",
+    "            \n",
+    "            if i>self.max_iter:\n",
+    "                break\n",
+    "            \n",
+    "        self.dec_iter=i  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#use randoms to split each field into 4 equal area sub-samples\n",
+    "s1 = quad_split(frdata['ra'][ran_field_mask_1], frdata['dec'][ran_field_mask_1])\n",
+    "s2 = quad_split(frdata['ra'][ran_field_mask_2], frdata['dec'][ran_field_mask_2])\n",
+    "s3 = quad_split(frdata['ra'][ran_field_mask_3], frdata['dec'][ran_field_mask_3])\n",
+    "s4 = quad_split(frdata['ra'][ran_field_mask_4], frdata['dec'][ran_field_mask_4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#store the results\n",
+    "ra_splits = [s1.ra_split,s2.ra_split,s3.ra_split,s4.ra_split]\n",
+    "dec_splits = [s1.dec_split,s2.dec_split,s3.dec_split,s4.dec_split]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define masks for each data jackknife sample\n",
+    "mask_11 = (fdata['ira']<s1.ra_split) &\\\n",
+    "         (fdata['idec']<=s1.dec_split)&\\\n",
+    "          data_field_mask_1\n",
+    "\n",
+    "mask_12 = (fdata['ira']>s1.ra_split) &\\\n",
+    "         (fdata['idec']>=s1.dec_split)&\\\n",
+    "          data_field_mask_1\n",
+    "\n",
+    "mask_13 = (fdata['ira']<=s1.ra_split) &\\\n",
+    "         (fdata['idec']>s1.dec_split)&\\\n",
+    "          data_field_mask_1\n",
+    "\n",
+    "mask_14 = (fdata['ira']>=s1.ra_split) &\\\n",
+    "         (fdata['idec']<s1.dec_split)&\\\n",
+    "          data_field_mask_1\n",
+    "\n",
+    "mask_21 = (fdata['ira']<s2.ra_split) &\\\n",
+    "         (fdata['idec']<=s2.dec_split)&\\\n",
+    "          data_field_mask_2\n",
+    "\n",
+    "mask_22 = (fdata['ira']>s2.ra_split) &\\\n",
+    "         (fdata['idec']>=s2.dec_split)&\\\n",
+    "          data_field_mask_2\n",
+    "\n",
+    "mask_23 = (fdata['ira']<=s2.ra_split) &\\\n",
+    "         (fdata['idec']>s2.dec_split)&\\\n",
+    "          data_field_mask_2\n",
+    "\n",
+    "mask_24 = (fdata['ira']>=s2.ra_split) &\\\n",
+    "         (fdata['idec']<s2.dec_split)&\\\n",
+    "          data_field_mask_2\n",
+    "\n",
+    "mask_31 = (fdata['ira']<s3.ra_split) &\\\n",
+    "         (fdata['idec']<=s3.dec_split)&\\\n",
+    "          data_field_mask_3\n",
+    "\n",
+    "mask_32 = (fdata['ira']>s3.ra_split) &\\\n",
+    "         (fdata['idec']>=s3.dec_split)&\\\n",
+    "          data_field_mask_3\n",
+    "\n",
+    "mask_33 = (fdata['ira']<=s3.ra_split) &\\\n",
+    "         (fdata['idec']>s3.dec_split)&\\\n",
+    "          data_field_mask_3\n",
+    "\n",
+    "mask_34 = (fdata['ira']>=s3.ra_split) &\\\n",
+    "         (fdata['idec']<s3.dec_split)&\\\n",
+    "          data_field_mask_3\n",
+    "\n",
+    "mask_41 = (fdata['ira']<s4.ra_split) &\\\n",
+    "         (fdata['idec']<=s4.dec_split)&\\\n",
+    "          data_field_mask_4\n",
+    "\n",
+    "mask_42 = (fdata['ira']>s4.ra_split) &\\\n",
+    "         (fdata['idec']>=s4.dec_split)&\\\n",
+    "          data_field_mask_4\n",
+    "\n",
+    "mask_43 = (fdata['ira']<=s4.ra_split) &\\\n",
+    "         (fdata['idec']>s4.dec_split)&\\\n",
+    "          data_field_mask_4\n",
+    "\n",
+    "mask_44 = (fdata['ira']>=s4.ra_split) &\\\n",
+    "         (fdata['idec']<s4.dec_split)&\\\n",
+    "          data_field_mask_4\n",
+    "\n",
+    "j_masks = [mask_11, mask_12, mask_13, mask_14,\n",
+    "         mask_21, mask_22, mask_23, mask_24,\n",
+    "         mask_31, mask_32, mask_33, mask_34,\n",
+    "         mask_41, mask_42, mask_43, mask_44]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define masks for each random jackknife sample\n",
+    "\n",
+    "ran_mask_11 = (frdata['ra']<s1.ra_split) &\\\n",
+    "         (frdata['dec']<=s1.dec_split)&\\\n",
+    "          ran_field_mask_1\n",
+    "\n",
+    "ran_mask_12 = (frdata['ra']>s1.ra_split) &\\\n",
+    "         (frdata['dec']>=s1.dec_split)&\\\n",
+    "          ran_field_mask_1\n",
+    "\n",
+    "ran_mask_13 = (frdata['ra']<=s1.ra_split) &\\\n",
+    "         (frdata['dec']>s1.dec_split)&\\\n",
+    "          ran_field_mask_1\n",
+    "\n",
+    "ran_mask_14 = (frdata['ra']>=s1.ra_split) &\\\n",
+    "         (frdata['dec']<s1.dec_split)&\\\n",
+    "          ran_field_mask_1\n",
+    "\n",
+    "ran_mask_21 = (frdata['ra']<s2.ra_split) &\\\n",
+    "         (frdata['dec']<=s2.dec_split)&\\\n",
+    "          ran_field_mask_2\n",
+    "\n",
+    "ran_mask_22 = (frdata['ra']>s2.ra_split) &\\\n",
+    "         (frdata['dec']>=s2.dec_split)&\\\n",
+    "          ran_field_mask_2\n",
+    "\n",
+    "ran_mask_23 = (frdata['ra']<=s2.ra_split) &\\\n",
+    "         (frdata['dec']>s2.dec_split)&\\\n",
+    "          ran_field_mask_2\n",
+    "\n",
+    "ran_mask_24 = (frdata['ra']>=s2.ra_split) &\\\n",
+    "         (frdata['dec']<s2.dec_split)&\\\n",
+    "          ran_field_mask_2\n",
+    "\n",
+    "ran_mask_31 = (frdata['ra']<s3.ra_split) &\\\n",
+    "         (frdata['dec']<=s3.dec_split)&\\\n",
+    "          ran_field_mask_3\n",
+    "\n",
+    "ran_mask_32 = (frdata['ra']>s3.ra_split) &\\\n",
+    "         (frdata['dec']>=s3.dec_split)&\\\n",
+    "          ran_field_mask_3\n",
+    "\n",
+    "ran_mask_33 = (frdata['ra']<=s3.ra_split) &\\\n",
+    "         (frdata['dec']>s3.dec_split)&\\\n",
+    "          ran_field_mask_3\n",
+    "\n",
+    "ran_mask_34 = (frdata['ra']>=s3.ra_split) &\\\n",
+    "         (frdata['dec']<s3.dec_split)&\\\n",
+    "          ran_field_mask_3\n",
+    "\n",
+    "ran_mask_41 = (frdata['ra']<s4.ra_split) &\\\n",
+    "         (frdata['dec']<=s4.dec_split)&\\\n",
+    "          ran_field_mask_4\n",
+    "\n",
+    "ran_mask_42 = (frdata['ra']>s4.ra_split) &\\\n",
+    "         (frdata['dec']>=s4.dec_split)&\\\n",
+    "          ran_field_mask_4\n",
+    "\n",
+    "ran_mask_43 = (frdata['ra']<=s4.ra_split) &\\\n",
+    "         (frdata['dec']>s4.dec_split)&\\\n",
+    "          ran_field_mask_4\n",
+    "\n",
+    "ran_mask_44 = (frdata['ra']>=s4.ra_split) &\\\n",
+    "         (frdata['dec']<s4.dec_split)&\\\n",
+    "          ran_field_mask_4\n",
+    "\n",
+    "j_ran_masks = [ran_mask_11, ran_mask_12, ran_mask_13, ran_mask_14,\n",
+    "         ran_mask_21, ran_mask_22, ran_mask_23, ran_mask_24,\n",
+    "         ran_mask_31, ran_mask_32, ran_mask_33, ran_mask_34,\n",
+    "         ran_mask_41, ran_mask_42, ran_mask_43, ran_mask_44]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 475.2x475.2 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(3.3*2,3.3*2))\n",
+    "axes= axes.flatten()\n",
+    "\n",
+    "for i in range(0,Nfields):\n",
+    "    ax1 = axes[i]\n",
+    "    min_ra = field_bounding_boxes[i][0]\n",
+    "    max_ra = field_bounding_boxes[i][1]\n",
+    "    min_dec = field_bounding_boxes[i][2]\n",
+    "    max_dec = field_bounding_boxes[i][3]\n",
+    "    for j in range(0,4):\n",
+    "        mask = j_masks[i*4+j]\n",
+    "        ax1.scatter(fdata['ira'][subsamp & mask], fdata['idec'][subsamp & mask], s=1)\n",
+    "    ax1.set_xlabel('RA')\n",
+    "    ax1.set_ylabel('Dec')\n",
+    "    ax1.set_xlim([min_ra, max_ra])\n",
+    "    ax1.set_ylim([min_dec, max_dec])\n",
+    "    #plot boundaries\n",
+    "    ax1.plot([ra_splits[i],ra_splits[i]],[-90,90], '--', color='black')\n",
+    "    ax1.plot([0,360],[dec_splits[i],dec_splits[i]], '--', color='black')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# calculate jackknife errors on magnitude functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 8803869\n",
+      "1 8771012\n",
+      "2 8770910\n",
+      "3 8786923\n",
+      "4 8500753\n",
+      "5 8451261\n",
+      "6 8514267\n",
+      "7 8538199\n",
+      "8 8553383\n",
+      "9 8517189\n",
+      "10 8576370\n",
+      "11 8524660\n",
+      "12 8463799\n",
+      "13 8410345\n",
+      "14 8695322\n",
+      "15 8708743\n"
+     ]
+    }
+   ],
+   "source": [
+    "Nr = len(hsc_randoms)\n",
+    "Nj = len(j_masks)\n",
+    "m_bins = np.arange(12,30,0.1)\n",
+    "\n",
+    "#create arrays to store jackknife calcs\n",
+    "n_sample_gs = np.zeros((Nj,180))\n",
+    "n_sample_rs = np.zeros((Nj,180))\n",
+    "n_sample_is = np.zeros((Nj,180))\n",
+    "n_sample_zs = np.zeros((Nj,180))\n",
+    "n_sample_ys = np.zeros((Nj,180))\n",
+    "\n",
+    "popt_gs = np.zeros((Nj,3))\n",
+    "popt_rs = np.zeros((Nj,3))\n",
+    "popt_is = np.zeros((Nj,3))\n",
+    "popt_zs = np.zeros((Nj,3))\n",
+    "popt_ys = np.zeros((Nj,3))\n",
+    "\n",
+    "#loop through samples\n",
+    "for i in range(0,len(j_masks)):\n",
+    "    \n",
+    "    #remove only the sub-field\n",
+    "    d_mask = ~j_masks[i]\n",
+    "    r_mask = ~j_ran_masks[i]\n",
+    "    \n",
+    "    N = np.sum(d_mask)\n",
+    "    print(i, N)\n",
+    "    \n",
+    "    #calculate the area of each jackknife sample\n",
+    "    Nsub = np.sum(r_mask)\n",
+    "    f = 1.0*Nsub/Nr \n",
+    "    sub_area_sq_deg = f*full_area_sq_deg\n",
+    "    \n",
+    "    #calculate cumulative apparent magnitude function\n",
+    "    \n",
+    "    g_mag = hsc_data['gmag_forced_cmodel'][d_mask] - hsc_data['a_g'][d_mask] #correct for dust\n",
+    "    g_mag = np.sort(g_mag)\n",
+    "    N_g = np.cumsum(np.ones(len(g_mag)))\n",
+    "    n_g = N_g/(sub_area_sq_deg) #cumulative number density\n",
+    "    \n",
+    "    r_mag = hsc_data['rmag_forced_cmodel'][d_mask] - hsc_data['a_r'][d_mask] #correct for dust\n",
+    "    r_mag = np.sort(r_mag)\n",
+    "    N_r = np.cumsum(np.ones(len(r_mag)))\n",
+    "    n_r = N_r/(sub_area_sq_deg) #cumulative number density\n",
+    "    \n",
+    "    i_mag = hsc_data['imag_forced_cmodel'][d_mask] - hsc_data['a_i'][d_mask] #correct for dust\n",
+    "    i_mag = np.sort(i_mag)\n",
+    "    N_i = np.cumsum(np.ones(len(i_mag)))\n",
+    "    n_i = N_i/(sub_area_sq_deg) #cumulative number density\n",
+    "    \n",
+    "    z_mag = hsc_data['zmag_forced_cmodel'][d_mask] - hsc_data['a_z'][d_mask] #correct for dust\n",
+    "    z_mag = np.sort(z_mag)\n",
+    "    N_z = np.cumsum(np.ones(len(z_mag)))\n",
+    "    n_z = N_z/(sub_area_sq_deg) #cumulative number density\n",
+    "\n",
+    "    y_mag = hsc_data['ymag_forced_cmodel'][d_mask] - hsc_data['a_r'][d_mask] #correct for dust\n",
+    "    y_mag = np.sort(y_mag)\n",
+    "    N_y = np.cumsum(np.ones(len(y_mag)))\n",
+    "    n_y = N_y/(sub_area_sq_deg) #cumulative number density\n",
+    "\n",
+    "    inds = np.searchsorted(g_mag, m_bins)\n",
+    "    n_sample_g = n_g[inds]\n",
+    "    \n",
+    "    inds = np.searchsorted(r_mag, m_bins)\n",
+    "    n_sample_r = n_r[inds]\n",
+    "    \n",
+    "    inds = np.searchsorted(i_mag, m_bins)\n",
+    "    n_sample_i = n_i[inds]\n",
+    "    \n",
+    "    inds = np.searchsorted(z_mag, m_bins)\n",
+    "    n_sample_z = n_z[inds]\n",
+    "\n",
+    "    inds = np.searchsorted(y_mag, m_bins)\n",
+    "    n_sample_y = n_y[inds]\n",
+    "    \n",
+    "    #define range over which to fit power law for each band\n",
+    "    g_mask = (m_bins>23) & (m_bins<25)\n",
+    "    popt_g, pcov_g = curve_fit(power_law, m_bins[g_mask], n_sample_g[g_mask])\n",
+    "    r_mask = (m_bins>23) & (m_bins<25)\n",
+    "    popt_r, pcov_r = curve_fit(power_law, m_bins[r_mask], n_sample_r[r_mask])\n",
+    "    i_mask = (m_bins>23) & (m_bins<25)\n",
+    "    popt_i, pcov_i = curve_fit(power_law, m_bins[i_mask], n_sample_i[i_mask])\n",
+    "    z_mask = (m_bins>23) & (m_bins<25)\n",
+    "    popt_z, pcov_z = curve_fit(power_law, m_bins[z_mask], n_sample_z[z_mask])\n",
+    "    y_mask = (m_bins>23) & (m_bins<25)\n",
+    "    popt_y, pcov_y = curve_fit(power_law, m_bins[y_mask], n_sample_y[y_mask])\n",
+    "    \n",
+    "    #store results\n",
+    "    n_sample_gs[i,:] = n_sample_g\n",
+    "    n_sample_rs[i,:] = n_sample_r\n",
+    "    n_sample_is[i,:] = n_sample_i\n",
+    "    n_sample_zs[i,:] = n_sample_z\n",
+    "    n_sample_ys[i,:] = n_sample_y\n",
+    "    \n",
+    "    popt_gs[i,:] = popt_g\n",
+    "    popt_rs[i,:] = popt_r\n",
+    "    popt_is[i,:] = popt_i\n",
+    "    popt_zs[i,:] = popt_z\n",
+    "    popt_ys[i,:] = popt_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m_sample = m_bins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calculate errors\n",
+    "g_cov = np.matrix(np.cov(n_sample_gs.T, bias=True))*(16-1.0)\n",
+    "r_cov = np.matrix(np.cov(n_sample_rs.T, bias=True))*(16-1.0)\n",
+    "i_cov = np.matrix(np.cov(n_sample_is.T, bias=True))*(16-1.0)\n",
+    "z_cov = np.matrix(np.cov(n_sample_zs.T, bias=True))*(16-1.0)\n",
+    "y_cov = np.matrix(np.cov(n_sample_ys.T, bias=True))*(16-1.0)\n",
+    "\n",
+    "g_err = np.sqrt(np.diagonal(g_cov))\n",
+    "r_err = np.sqrt(np.diagonal(r_cov))\n",
+    "i_err = np.sqrt(np.diagonal(i_cov))\n",
+    "z_err = np.sqrt(np.diagonal(z_cov))\n",
+    "y_err = np.sqrt(np.diagonal(y_cov))\n",
+    "\n",
+    "n_sample_g = np.mean(n_sample_gs, axis=0)\n",
+    "n_sample_r = np.mean(n_sample_rs, axis=0)\n",
+    "n_sample_i = np.mean(n_sample_is, axis=0)\n",
+    "n_sample_z = np.mean(n_sample_zs, axis=0)\n",
+    "n_sample_y = np.mean(n_sample_ys, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calculate poisson errors\n",
+    "g_err_p = (np.sqrt(n_sample_g*full_area_sq_deg))/(full_area_sq_deg)\n",
+    "r_err_p = (np.sqrt(n_sample_r*full_area_sq_deg))/(full_area_sq_deg)\n",
+    "i_err_p = (np.sqrt(n_sample_i*full_area_sq_deg))/(full_area_sq_deg)\n",
+    "z_err_p = (np.sqrt(n_sample_z*full_area_sq_deg))/(full_area_sq_deg)\n",
+    "y_err_p = (np.sqrt(n_sample_y*full_area_sq_deg))/(full_area_sq_deg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot power law fits\n",
+    "colors = plt.cm.jet(np.linspace(0,1,5))  \n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)\n",
+    "\n",
+    "plt.fill_between([23,25],[0.1,0.1],[10**9,10**9],color='grey', alpha=0.25)\n",
+    "\n",
+    "#data\n",
+    "p1 = plt.fill_between(m_bins, n_sample_g-g_err, n_sample_g+g_err, color=colors[0])\n",
+    "p2 = plt.fill_between(m_bins, n_sample_r-r_err, n_sample_r+r_err, color=colors[1])\n",
+    "p3 = plt.fill_between(m_bins, n_sample_i-i_err, n_sample_i+i_err, color=colors[2])\n",
+    "p4 = plt.fill_between(m_bins, n_sample_z-z_err, n_sample_z+z_err, color=colors[3])\n",
+    "p5 = plt.fill_between(m_bins, n_sample_y-y_err, n_sample_y+y_err, color=colors[4])\n",
+    "\n",
+    "plt.yscale('log')\n",
+    "plt.xlim([16,30])\n",
+    "plt.ylim([10**2,10**8])\n",
+    "plt.xlabel('magnitude')\n",
+    "plt.ylabel(r'$n(<m)$')\n",
+    "plt.legend([p1, p2, p3, p4, p5],['g-band','r-band','i-band','z-band','y-band'], frameon=False,\n",
+    "           fontsize=10, numpoints=1, loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#calulate errors on the extrapolations\n",
+    "#m_sample = np.arange(12,30,0.1)\n",
+    "\n",
+    "ne_samples_g = 1.0*np.zeros((Nj,len(m_sample)))\n",
+    "ne_samples_r = 1.0*np.zeros((Nj,len(m_sample)))\n",
+    "ne_samples_i = 1.0*np.zeros((Nj,len(m_sample)))\n",
+    "ne_samples_z = 1.0*np.zeros((Nj,len(m_sample)))\n",
+    "ne_samples_y = 1.0*np.zeros((Nj,len(m_sample)))\n",
+    "\n",
+    "for i in range(0,Nj):\n",
+    "    ne_samples_g[i,:] = power_law(m_sample, *popt_gs[i])\n",
+    "    ne_samples_r[i,:] = power_law(m_sample, *popt_rs[i])\n",
+    "    ne_samples_i[i,:] = power_law(m_sample, *popt_is[i])\n",
+    "    ne_samples_z[i,:] = power_law(m_sample, *popt_zs[i])\n",
+    "    ne_samples_y[i,:] = power_law(m_sample, *popt_ys[i])\n",
+    "\n",
+    "g_cov_e = np.matrix(np.cov(ne_samples_g.T, bias=True))*(16-1.0)\n",
+    "g_err_e = np.sqrt(np.diagonal(g_cov_e))\n",
+    "\n",
+    "r_cov_e = np.matrix(np.cov(ne_samples_r.T, bias=True))*(16-1.0)\n",
+    "r_err_e = np.sqrt(np.diagonal(r_cov_e))\n",
+    "\n",
+    "i_cov_e = np.matrix(np.cov(ne_samples_i.T, bias=True))*(16-1.0)\n",
+    "i_err_e = np.sqrt(np.diagonal(i_cov_e))\n",
+    "\n",
+    "z_cov_e = np.matrix(np.cov(ne_samples_y.T, bias=True))*(16-1.0)\n",
+    "z_err_e = np.sqrt(np.diagonal(z_cov_e))\n",
+    "\n",
+    "y_cov_e = np.matrix(np.cov(ne_samples_z.T, bias=True))*(16-1.0)\n",
+    "y_err_e = np.sqrt(np.diagonal(y_cov_e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#make master that uses extrap above mag=25\n",
+    "mask = (m_sample<25)\n",
+    "\n",
+    "n_master_sample_g = np.mean(ne_samples_g, axis=0)\n",
+    "n_master_sample_g[mask] = n_sample_g[mask]\n",
+    "g_master_err = g_err_e\n",
+    "g_master_err[mask] = g_err[mask]\n",
+    "\n",
+    "n_master_sample_r = np.mean(ne_samples_r, axis=0)\n",
+    "n_master_sample_r[mask] = n_sample_r[mask]\n",
+    "r_master_err = r_err_e\n",
+    "r_master_err[mask] = r_err[mask]\n",
+    "\n",
+    "n_master_sample_i = np.mean(ne_samples_i, axis=0)\n",
+    "n_master_sample_i[mask] = n_sample_i[mask]\n",
+    "i_master_err = i_err_e\n",
+    "i_master_err[mask] = i_err[mask]\n",
+    "\n",
+    "n_master_sample_z = np.mean(ne_samples_z, axis=0)\n",
+    "n_master_sample_z[mask] = n_sample_z[mask]\n",
+    "z_master_err = z_err_e\n",
+    "z_master_err[mask] = z_err[mask]\n",
+    "\n",
+    "n_master_sample_y = np.mean(ne_samples_y, axis=0)\n",
+    "n_master_sample_y[mask] = n_sample_y[mask]\n",
+    "y_master_err = y_err_e\n",
+    "y_master_err[mask] = y_err[mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = plt.cm.jet(np.linspace(0,1,5))  \n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)\n",
+    "\n",
+    "plt.fill_between([23,25],[0.1,0.1],[10**9,10**9],color='grey', alpha=0.25)\n",
+    "\n",
+    "#data\n",
+    "p1 = plt.fill_between(m_sample, n_master_sample_g-g_master_err, n_master_sample_g+g_master_err, color=colors[0])\n",
+    "p2 = plt.fill_between(m_sample, n_master_sample_r-r_master_err, n_master_sample_r+r_master_err, color=colors[1])\n",
+    "p3 = plt.fill_between(m_sample, n_master_sample_i-i_master_err, n_master_sample_i+i_master_err, color=colors[2])\n",
+    "p4 = plt.fill_between(m_sample, n_master_sample_z-z_master_err, n_master_sample_z+z_master_err, color=colors[3])\n",
+    "p5 = plt.fill_between(m_sample, n_master_sample_y-y_master_err, n_master_sample_y+y_master_err, color=colors[4])\n",
+    "\n",
+    "plt.yscale('log')\n",
+    "plt.xlim([16,30])\n",
+    "plt.ylim([10**2,10**8])\n",
+    "plt.xlabel('magnitude')\n",
+    "plt.ylabel(r'$n(<m)$')\n",
+    "plt.legend([p1, p2, p3, p4, p5],['g-band','r-band','i-band','z-band','y-band'], frameon=False,\n",
+    "           fontsize=10, numpoints=1, loc='upper left')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12.0 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.1 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.2 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.299999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.399999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.499999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.599999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.699999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "12.799999999999997 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
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+      "13.099999999999996 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
+      "13.199999999999996 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.03961617137073147\n",
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+      "23.29999999999996 12500.039031653867 274.9121149249121 12500.039031653867 274.9121149249121 21.896185544432374\n",
+      "23.39999999999996 13932.939925335433 297.1271857871068 13932.939925335433 297.1271857871068 23.117143147878142\n",
+      "23.499999999999957 15540.045512012082 312.6987205322009 15540.045512012082 312.6987205322009 24.41399904178312\n",
+      "23.59999999999996 17374.71401897952 329.6510564711127 17374.71401897952 329.6510564711127 25.81496927596862\n",
+      "23.69999999999996 19442.979183441614 347.0746460088884 19442.979183441614 347.0746460088884 27.30826984147687\n",
+      "23.799999999999958 21769.88442773067 367.3364135816244 21769.88442773067 367.3364135816244 28.89620713216925\n",
+      "23.899999999999956 24417.24873083249 392.61892975850077 24417.24873083249 392.61892975850077 30.602798562155634\n",
+      "23.999999999999957 27414.573996110455 419.4312074924456 27414.573996110455 419.4312074924456 32.42675802758524\n",
+      "24.09999999999996 30746.661295641945 450.867041850094 30746.661295641945 450.867041850094 34.340907034903175\n",
+      "24.199999999999957 34516.89224609716 480.5119242912762 34516.89224609716 480.5119242912762 36.385523134425455\n",
+      "24.299999999999955 38681.76098236364 511.144536286096 38681.76098236364 511.144536286096 38.51819243361398\n",
+      "24.399999999999956 43305.627049763505 547.2981443226015 43305.627049763505 547.2981443226015 40.7553798953023\n",
+      "24.499999999999957 48396.430141397774 594.3339855920533 48396.430141397774 594.3339855920533 43.084339955168375\n",
+      "24.599999999999955 53987.17728881499 636.2179890592914 53987.17728881499 636.2179890592914 45.50489226829923\n",
+      "24.699999999999953 60033.032300678744 686.4080256724433 60033.032300678744 686.4080256724433 47.985267500294576\n",
+      "24.799999999999955 66578.71169418699 745.041221025572 66578.71169418699 745.041221025572 50.53362737500332\n",
+      "24.899999999999956 73605.70671352455 812.5393398460343 73605.70671352455 812.5393398460343 53.13351208966371\n",
+      "24.999999999999954 81154.62379591222 888.7090032048432 81154.62379591222 888.7090032048432 55.79167683183481\n",
+      "25.09999999999995 91985.63807174515 940.594675169624 89169.55758548336 961.4297086695711 58.481847767380515\n",
+      "25.199999999999953 102631.71339976453 1021.5138413917365 97705.64546994599 1044.279590864873 61.217079827641975\n",
+      "25.299999999999955 114509.92996118411 1114.0265670358745 106708.15043130025 1130.3270611459768 63.97518879316361\n",
+      "25.399999999999952 127762.89214606768 1220.7948771391495 116158.8651448366 1220.9344454176912 66.74810803381392\n",
+      "25.49999999999995 142549.70901074854 1345.0365579587121 126063.07223852357 1320.1291784473867 69.53552268017289\n",
+      "25.59999999999995 159047.90449193068 1490.5838303071775 136357.01674690685 1421.8961634502339 72.31885234319427\n",
+      "25.699999999999953 177455.54870554208 1661.9372089411786 147043.4562510525 1551.0121026260122 75.09925541229632\n",
+      "25.79999999999995 197993.63591838395 1864.3174829937775 158075.4751460712 1691.0137913645328 77.86549126094965\n",
+      "25.89999999999995 220908.7377421431 2103.7214867579405 169397.83079460458 1857.5968405024562 80.60587604469283\n",
+      "25.99999999999995 246475.96340363828 2386.9889729302763 180881.25524749144 2056.487368978595 83.29320273210378\n",
+      "26.09999999999995 275002.26263190643 2721.887929820276 192582.4679811777 2269.8290415772453 85.94510648107064\n",
+      "26.19999999999995 306830.11081618187 3117.2242906572437 204332.3453717039 2525.4543836204734 88.52814006282316\n",
+      "26.299999999999947 342341.6206783385 3582.9799224106005 215976.10878994048 2827.918141644533 91.01555802736196\n",
+      "26.39999999999995 381963.1298240714 4130.480924792228 227376.33899161604 3203.567360100201 93.38678294980623\n",
+      "26.49999999999995 426170.3192504938 4772.5971906004515 238448.8284361862 3648.0504892040794 95.6335718053043\n",
+      "26.599999999999948 475493.9242624725 5523.974010455352 249028.4371477493 4131.9433828726105 97.73210468789337\n",
+      "26.699999999999946 530526.1063624666 6401.297058902083 258922.3893568018 4643.633831907108 99.6546534071563\n",
+      "26.799999999999947 591927.5626142654 7423.593070060574 268015.10296980955 5165.466891266067 101.38936751699673\n",
+      "26.89999999999995 660435.4578351487 8612.569637326951 276287.5644265738 5693.011684910942 102.94220084597386\n",
+      "26.999999999999947 736872.274849884 9992.998697489891 283703.7330451003 6228.86409905555 104.31464998991578\n",
+      "27.099999999999945 822155.6890622033 11593.14932810253 290210.61930405966 6736.320329770565 105.50412256496357\n",
+      "27.199999999999946 917309.5858973996 13445.27650609826 295782.600907009 7152.657806879051 106.51213541564358\n",
+      "27.299999999999947 1023476.3533910236 15586.17348354942 300461.08705706627 7457.9409423809375 107.35119842537092\n",
+      "27.399999999999945 1141930.597508126 18057.796479656074 304315.1051730639 7636.801477083071 108.03750221252984\n",
+      "27.499999999999943 1274094.4448588474 20907.971510042822 307540.65924494446 7747.816256346503 108.60855872609676\n",
+      "27.599999999999945 1421554.6165344904 24191.194412599707 310322.58779382345 7837.538641892777 109.09867449185002\n",
+      "27.699999999999946 1586081.4780524175 27969.536515987384 312716.974395885 7915.566169526424 109.51875743241335\n",
+      "27.799999999999944 1769650.2941234605 32313.669960657724 314821.9672596451 7980.68901673412 109.88674121590537\n",
+      "27.89999999999994 1974464.943426704 37304.02845041219 316645.0586079367 8035.482093740082 110.20445153887495\n",
+      "27.999999999999943 2202984.378111604 43032.12121276792 318240.6891717565 8083.794853063203 110.48177249100578\n",
+      "28.099999999999945 2457952.1457008473 49602.02020842971 319630.05953876564 8125.450273046256 110.72267966225907\n",
+      "28.199999999999942 2742429.327834868 57132.0431863025 320865.43736611237 8161.656928730787 110.9364462112323\n",
+      "28.29999999999994 3059831.291321921 65756.65806731884 321956.9413476346 8184.248600568967 111.12497503985601\n",
+      "28.39999999999994 3413968.6927284487 75628.63739847676 322932.27354405756 8205.506933324586 111.29316803723658\n",
+      "28.499999999999943 3809093.2288131784 86921.49529417523 323791.3225732668 8219.810779405341 111.44109811173512\n",
+      "28.59999999999994 4249948.682087598 99832.24342684713 324572.31661127554 8231.392123630623 111.57541674184655\n",
+      "28.69999999999994 4741827.874359375 114584.50730189886 325272.2770671229 8242.507375045301 111.69566162242144\n",
+      "28.79999999999994 5290636.212047438 131432.04931884535 325911.1388928261 8250.009054113296 111.80529760340337\n",
+      "28.89999999999994 5902962.586199231 150662.75105612687 326493.70303667325 8254.375923315025 111.90517862792872\n",
+      "28.99999999999994 6586158.478442628 172603.11390396467 327034.6894339479 8257.983475475134 111.99785135314957\n",
+      "29.099999999999937 7348426.222626562 197623.3447045524 327532.44864427083 8259.302271981629 112.0830514364239\n",
+      "29.19999999999994 8198917.481829335 226143.10154472123 327982.93346064177 8260.445836393572 112.16010389875994\n",
+      "29.29999999999994 9147843.123061148 258637.98440545602 328409.48699616187 8261.71803611733 112.23301431411683\n",
+      "29.399999999999938 10206595.808830597 295646.86613770504 328805.17018039734 8263.118882861303 112.30060576604359\n",
+      "29.499999999999936 11387886.777425967 337780.17135726375 329179.52291291236 8262.91576018004 112.36451607425478\n",
+      "29.599999999999937 12705898.454114199 385729.224501062 329531.21564865846 8264.515152822518 112.42452471972577\n",
+      "29.69999999999994 14176454.725531181 440276.80365822185 329860.6612578207 8264.661496295299 112.48070834036783\n",
+      "29.799999999999937 15817210.921604043 502309.0540907803 330171.8159361629 8263.02249263116 112.53374687009367\n",
+      "29.899999999999935 17647865.78596026 572828.9348390244 330466.75283644634 8263.258511016087 112.58399789712054\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(0,len(m_sample)):\n",
+    "    print(m_sample[i], n_master_sample_g[i], g_master_err[i], n_sample_g[i], g_err[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.table import Table\n",
+    "\n",
+    "#g\n",
+    "x = m_sample\n",
+    "y = n_master_sample_g\n",
+    "z = g_master_err\n",
+    "yy = n_sample_g\n",
+    "zz = g_err\n",
+    "pp = np.mean(ne_samples_g, axis=0)\n",
+    "names = ['mag','n(<mag)','err','data','data_err', 'power_law']\n",
+    "dat_g = Table([x,y,z,yy,zz,pp], names=names)\n",
+    "dat_g.write('./hsc_g_n.dat', format='ascii', overwrite=True)\n",
+    "\n",
+    "#r\n",
+    "x = m_sample\n",
+    "y = n_master_sample_r\n",
+    "z = r_master_err\n",
+    "yy = n_sample_r\n",
+    "zz = r_err\n",
+    "pp = np.mean(ne_samples_r, axis=0)\n",
+    "names = ['mag','n(<mag)','err','data','data_err', 'power_law']\n",
+    "dat_r = Table([x,y,z,yy,zz,pp], names=names)\n",
+    "dat_r.write('./hsc_r_n.dat', format='ascii', overwrite=True)\n",
+    "\n",
+    "#i\n",
+    "x = m_sample\n",
+    "y = n_master_sample_i\n",
+    "z = i_master_err\n",
+    "yy = n_sample_i\n",
+    "zz = i_err\n",
+    "pp = np.mean(ne_samples_i, axis=0)\n",
+    "names = ['mag','n(<mag)','err','data','data_err', 'power_law']\n",
+    "dat_i = Table([x,y,z,yy,zz,pp], names=names)\n",
+    "dat_i.write('./hsc_i_n.dat', format='ascii', overwrite=True)\n",
+    "\n",
+    "#z\n",
+    "x = m_sample\n",
+    "y = n_master_sample_z\n",
+    "z = z_master_err\n",
+    "yy = n_sample_z\n",
+    "zz = z_err\n",
+    "pp = np.mean(ne_samples_z, axis=0)\n",
+    "names = ['mag','n(<mag)','err','data','data_err', 'power_law']\n",
+    "dat_z = Table([x,y,z,yy,zz,pp], names=names)\n",
+    "dat_z.write('./hsc_z_n.dat', format='ascii', overwrite=True)\n",
+    "\n",
+    "#y\n",
+    "x = m_sample\n",
+    "y = n_master_sample_y\n",
+    "z = y_master_err\n",
+    "yy = n_sample_y\n",
+    "zz = y_err\n",
+    "pp = np.mean(ne_samples_y, axis=0)\n",
+    "names = ['mag','n(<mag)','err','data','data_err', 'power_law']\n",
+    "dat_y = Table([x,y,z,yy,zz,pp], names=names)\n",
+    "dat_y.write('./hsc_y_n.dat', format='ascii', overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       mag               n(<mag)        ...      power_law      \n",
+      "------------------ -------------------- ... --------------------\n",
+      "              12.0 0.040918475359524524 ... 0.054111670334843896\n",
+      "              12.1 0.040918475359524524 ...  0.06037407045080632\n",
+      "              12.2 0.040918475359524524 ...  0.06736122708032367\n",
+      "12.299999999999999 0.040918475359524524 ...  0.07515701760907537\n",
+      "12.399999999999999 0.040918475359524524 ...  0.08385502673697023\n",
+      "12.499999999999998 0.040918475359524524 ...  0.09355966993069752\n",
+      "12.599999999999998 0.040918475359524524 ...  0.10438744689677536\n",
+      "12.699999999999998 0.040918475359524524 ...  0.11646834012280607\n",
+      "12.799999999999997 0.040918475359524524 ...  0.12994737527617264\n",
+      "12.899999999999997 0.040918475359524524 ...  0.14498636219249575\n",
+      "               ...                  ... ...                  ...\n",
+      " 28.89999999999994    5902962.586199231 ...    5902962.586199231\n",
+      " 28.99999999999994    6586158.478442628 ...    6586158.478442628\n",
+      "29.099999999999937    7348426.222626562 ...    7348426.222626562\n",
+      " 29.19999999999994    8198917.481829335 ...    8198917.481829335\n",
+      " 29.29999999999994    9147843.123061148 ...    9147843.123061148\n",
+      "29.399999999999938   10206595.808830597 ...   10206595.808830597\n",
+      "29.499999999999936   11387886.777425967 ...   11387886.777425967\n",
+      "29.599999999999937   12705898.454114199 ...   12705898.454114199\n",
+      " 29.69999999999994   14176454.725531181 ...   14176454.725531181\n",
+      "29.799999999999937   15817210.921604043 ...   15817210.921604043\n",
+      "29.899999999999935    17647865.78596026 ...    17647865.78596026\n",
+      "Length = 180 rows\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dat_g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6HCx/8j42Lfmas86GXzzYk17JF2DensuBs89xWpil83GKojFCaOy55tTVGKbS8kK8Di1+MB5t4uawlPKfp1Vp057fqoSo8ppqnDx6lFXP/pa1WXMRQnL3b9ux+ZsqDwt0umLwN8avrKRjbyLLazfMvfw5Wl6IsYBDSnnstCpq/vNblRDBM3nU6dLakkdVV1SQ+9q/WPnX5zh5/BQXD4KfNjT+fongUPTF9CzaWuuct0CcKhCCmIEDKdy40fOGBsoPb9lC3IQJJE+b1vgGOqto+cDgUcAoIcRRYB1aeoszbqY0WLSm2dKtH37Ionvvorz0lLOsThEKOBgziJ6HNjoL1p1/O0t/HuQRuSoEPP54PNOmBXe9+HTxhxBtUsqXwLms0R8IHSGuegoO+7k5PWJhxCt1ns7MzHQuW2RlZdVydTvT3NzKjhxhzlXDKPxxW/0XCii4/UZmDF4KUtKxpIjyrt1gXS4s/RR+bjvC88YfQuwmhIiVUuZLKdcD6/1QZ6vGcEEbP358rQV9cLm5gZbX1MhPaiQjjouLIy0tDYvFQnp6unPT06ysLMxms4ebW3R0dMim3F90//388NZb9V7TJ7kXuz9+mLy8ISz7dJy2tiAlrF1H+dJPnde1VQEa+EOIcUB3IcQzuLxqZvmhXv9Qj+UKFBaLBZvN5tyo9ExzcyvasYN3x47h8I8/+TwfPwK6P2/muS5TWLb0Jvib0Bf3NAGiC3DQoBi2bDnMhAlxbVaABn7pmqLFEO4EbRHfD3W2ejIyMsjJycFqtZ4xbm4nDh1i7rXXUrhpU61zl43qxO23lLN24JUsuXo0ubmJmgVEQk2Nq/upM3Fi27V+vjhtIerdUffjgHrStAbS09Mxm83OpQrvgOC25ub2yYQJ2LN9h5PGj4Aez5tZcvVovpdC64I+P85pATtvWM/JDz5xXt/Wu6B10eb3vlAEDllTw/9GjWLnF1/4PG90QT0tIM4xoLsFhLZlBdVGpYoWoeD773n7hhs4cfCgR3n8CLjxTsG6gVey2FuAUEuEbXUc2GJC1D1q1nkv5AshHmnJyRolxJbl40ceYf3s2UScLSl1+8vHj4AxqbB2kJklI0cjJR4iNJ1VgeN4O4+xYFuygN602IK+lHK5EOL3gNOdTQhxIXC0uXUqQhcppdYNXb4coJYIezxvZsrI0URX96SmptC1HAGMji7C9ptpUFXjvKcti7A5nO5kzU6viIuUlvQzba20Nn9T6513sslru/GY3nD4IMQN1ydjRo6m77FEfjNtHGWVxlWSHo5NLJviGbKqRFib08pZowcFp4DT5/SMX8xvDK0lrWLVqVNkxcXVEmH8CIh710xV9TOs+eoZllw9mp83JzLhFU8RRmz+nsNzXSIMDxdKhHXgj3XE9boILcoaBodALFvsXrWKd2+6ifISz/hv53LEyNFagYS8vESWLnVNyIQLSbv89ZQudC1LKAHWjz/WEZcLIWYAM/zQHr+z7KmnKMj3r69pr9hYRr9St8eO1WolPT2dvLw8bDabz25oqPqbfvLoo9hn1Z5r856M6UlPDtYUkus2FgS4/bxSlk56lZMnq5xlSoQN45csblLKx/xRT1vBSH8YFRWFyWTyORYMRX/Td5KT2b50qUeZCHONA6foVjBBJpI5Zxyr93jef2P3Ij589L9UV7tmsZUIG0dzk0d1bUz8YWOvCyT1Wa5AkpqaitVqdXYbQ9nftPTgQWYPH87RHTs8yt3XBI2u6Imdidz8v3G4LxgJoPfeH/l0iudYUomw8TTXIibo63e+XSpwplo8CtR5TVsmJSWFUaNGOf1MQ9HfVErJ92+9xUcPPoC7sty7oVONsSBQvCOR195xHwvCBLMk7/k3WLNmn6s8XLS5BfpA0ywh6uPCSCHE02jxh8af0chrmgcsaEymt7bMqFGj6hRKsP1NF957LxvefrtWufuaoDsHfkxk1vsuEU6Mh9dGS2JjZ7BhgysLmrKCzUO5uAUAdyGFWrhSTXU1b44cyd6vv/Yodx8LGt3QISQyqmoccTNhk1vStInx8K/rqhg0aDrbt7v8N5QIXQQjVYb7w2PRQqJ2+bPe1obNZnPuZRFKHPzhB96yWCgr9ExFOGhUF2695aTHWHAIiVx8ZBznvQmFZa5rJ8bDiyPKueii6ezd6xr+KxGeHv5IHvU0kIC2y28OEK18TUOLxY89Rl52tmdmXXysCeokyEQO2McxaSlUud0yMR5OWj/izTc9l4OUCGvT4tEXQogrpZTrhRCRgLHR6PLTqrRpz1dCrIf5t9/Ojwtrp5mtS4SDKhNJnz6OXV6j+4nxUDJvIe+848r2pCZl6iYYQrwWKApW5jYlRN9UnjzJG8OGcfD7751lvvxDQeuGjmUcyfNg6XbPesIFTIiDQ2++j9W6xVmurGD9BGOMGNo5a85A9q5Zw+yrhiOrqp1l8SNg6OO96Xa0oNZY8JqKcQx6w3NCBjQrOG0MjB07jyVLXBnYlAj9T8jnrBFCZEkpJ/izzrZK1alTvPHLX1Kw3tP33uiGvurVDR1CIr32j+Pc/0FJuatcAI/rIrzuurl88cUu5zklwsAQtJw1QgizlNLudpwCFKPtnZFpXKOXKRpgwfjxbNb9VN3xHgsKBAkM4caacQx/E9zW4QGXFZRSMnSo50K9EmHgCMqOwUIIC5CB1q01BIeU0iaEMOnHDpQIG6SmqopZw4ZxIDe31jlvERpjwZ1Hoe9sKDjhutYYC04bA+XlVTz22BIlwhYkKELUBeeWOJ1UtKUP0ARoQdtZqggwCSFMUkpHCzcz5Cnavp1pl11GTaUzCNDpngauSAnQRJgsx3Hd2/CFV5/FsIIAhw+X0afPy1RW1hAT04WiopNqZrQFCIoQfRCFx44GdNfFGqWfU7ghpeQti4VdXtnT4kdAn+eHMmXktR7lQ0hkWNk4LpoFDq8+hrsIN28u5PLLX3dGTxQVnaSq6i8B+x4KF6EiRJ9IKYvRNrlR6Hz08MPkv/lmnYvzi9xEaHRFx70LN3ltO+E+IQPw6afbGDNmnsc1EybEBeIrKHxwWqky/EgxYMT6RKF56TQaIYTHa+rUqf5uX0jw7s03kz97doMeMgLBEBKxVI3jihnwiZsIw4VmBWv+4pqUGTFidi0RqjFhyxIqFnE+2kanACa0JZFG09YX9KvKy5mVmOixOB8/ApJTtYVb77HgWMaRaoWbNnvW494NBXj88cVkZeV561qJMAgExSLqSxXx+jvGMoY+m1rsvqxxpnN461b+ERFRS4RDH+9NjRC1RDhGjmP4bHi/ARE+8sjHzJjhKUIhlAiDhQqDClGklOTPmcPHDz3kUV6Xj+gQEok9No6hb8C+465y77EgwJ13Wpk/37VRjBKf/wlqGJTCP9SXwMnX4vxYxjH6bfjMa4HH2woC3HDD//j8c9eFSoShgRJiiPH++PFs8fKQqStodyzjKK2AS2fCj27TW+6L8wbV1TXEx2eTn+/aq0KJMHRQQgwRpJTMHj6cvd9+61HuK4GTIcK1++CXs6HaK2bQ2wo+/LAWQ+jeg1ciDC3UGDEEOFVcTJbZTPFOl8uLdx5RgyEkNugn6k5q6gLed5u5UTGELYMaI7YyDqxfz5ykJCqOudJO1CfChR+NY9z3HknXAN8ivP76/5GTo8aDrQElxCAhpSSjWzefKe29RWhMysyeP45FWz3r8TUrWlMjSUjIxm4vcJYpEYY2SohBoKq8nIxu3ag6edJZZkzIeIvQcNa+6k34Zq+rDl8WEODYsXKuuOJ1drnlulAiDH3UGLGFKT14kH+fd16tiIkb7wxj3cDYWhMyx8vhiizY6easXZcIt28vYsiQmRw9esp1rRJhUFBbd4cwB9avJzs+HmpcG3bGj4Bzn/8lC0cmOcsMEW4vgiGzwE1XdYrw5pvf5eOPf/IoUyIMHmqyJkTZ/MEHLPDKc2os0PsS4U3vwmKviAlfIpRScvXVb7LabUcYNTPa+lBCDDCypoYXIiOpLC31KK/LSyZZjuOXb8K3buNBXwv0AJWV1cTGzmDz5sPOMmUFWydKiAGkvkkZX14yldVw6QzY6uYlU9+kzKBB09mzR2XbbgsoIQaI8mPHeCkmhuqKCmdZQ5Myl70Oblns6xThvn3HuPzyGRQVuQSuRNi6UZM1AeBEYSEv9+mDrHLtmhs/Ai54fgTvjxzhLDNEeLAUBs2Aw157TPgSoXfkBCgRhiJqsibIHD9wgOkDB9YS4cDnk5k98gpnmSHCbUcgbiYcdxnORs+MtqZJmaSkOQCsWPGAZ/lc7X3F/a6y2WiRJw/xiGcli5K091tXeBTPSdLKH1jhWd6aCJVUGW2CRfffz7/69OHUUddWZYbT9uyRV9CZzs40FmMZxx0L4OJpjRPh6NFve4hw4sR4qqr+0ipEGEzsdjv9+/dnwoQJFBcXY7PZ6Natm3NvyczMTKxWK1ar1VlmYJyz2WzYbLZa5/2JEqKfWHjvvfzw1lvOYxHmcldbN/BKAE5xiuf4G2MZR8r7sMC1lYQzl4wvEV5zzRw++8y1rXZr7Ipu23aElSt3M2nSEs/yI7ByN0xa6io7wmF2sZPFfOxZSfE22L8SVk7yKD6ybRu7V65kySTPctA2hDWbzUyYMIGoqCgsFgsmk4m0tDRsNhtms5mUlBRSUlLY4bZ1+YQJE5znLBYL0dHRHuf9jRKiH1h0//0eu+/Gj4C//Ke2u1oCQwC4bT588KPr/onxUPWsbxEOG/YGK1bsdl3bCkUIsH+/tnyTlZXnWa6v6rgXH0dLMbCOtZ6VlO3X3jd5boNeul8rz/PaHr0hiouLsdtdWVkmTNB2dnA4HOTm5npsMms2m4mLC1xWOyXE0+TYvn0eltCwgjUizGdSp6924+G4XZcVBHjxxdV8951rQbG1ihCgT58IoHaKRr0Y9+KzORtw/cflpEsf7X2g51YoEX208rgJTdsiJSUlhfnz5xMXF0dmZiZmsxnQurO+tlxPS0trUv1NQU3WnAalBQW8csEFzmNDhBsGXYV15NXOckOEa/bCSH1ywlfUhDuRkS9w7FgFkZEdKS2taDWTMnUxYEB3BgzoXus7DOiuvdz/HbrTg+70YCzjPCuJGqC9Rk7zKO4+YADdBwwgeZpnuTvGLs6gWUKDvLw8bDYbWVlZTJgwgawmWlV/oSxiMzlVXKzNjlZrW5+JMEhOFewbNM6nCLcUwrDZrvvDRN0ijIp6kWPHtBmc0tIKNSnjBywWi3MsGBWlJY83hGmxWFiwYIHz2Gw2Oz+7496N9TdKiM2guqKC6YMGcbLItUtA3HCoEZA9chAd6eQxO3r/h9pivftqZ11JtC2Wtyhx2yNNZdsOHDabzcM6Gt1Rk8lEfHw8Npsrva77dYFAdU2biJSSxY89xvF9rjwVzmBefXa0gnKe428APPwxvPWD6/66/EZBW6JYvtyVLqM1jwlDBbvdjt1uZ/78+ZhMJnJzc3E4HGRnZ2MymZxiKyoqck7WAGRlZZGZmYnD4XAK1H3yxt8oITaRFyMjqTh+nC7RkZwsLvEZzGtMMlRWw2y3Dc3rm5hZtWp3q1+iCEXMZrPHsoPFYuGo2zpvfUyePDlQzaqFcnFrAv+74QYcn3+uPTdMW6KQCNYOqp1hDeCsF6BMj/+tT4Rbtx7m0kunIaWWbfvxx5UIWztNdXFTY8RGcvCHH5wiBG1MCCAFPkV4zVyXCMPrmZg5cqSMIUNmOlMdhoUJJcIzENU1bQRGukMDYwOYGqFFUoAWT2iI8OufwW0Nvs6JmZoayb33LnLOkIKanDlTUUJsACklM2JjvZYpoGTQ//HyyPOd1xnjwgPHYcQcrayhtcKoqBc5fryC3r0jOHToRKtfK1Q0HyXEBsh9/XVKdrvMW9xwkCKc/4zsD2h9T6NLOnEJvO7mqlXfWuEtt7zLcd3b+9ChE2pn3jMcNUash8Nbt7LUzZFYW6YQbBuYRKUuQvcuqbsIjWUKXxw4cJyPPnJFUqjuqEJZxDqorqxk0b33Oo+NLmmNgLdHJnIWEZRxwtklvf19170T46CuHmZVVQ0XXPCKVqeaIVXoKCHWQWb37lQcP07Xc3tx/ECB1iXFFdJUxgnnon1lNSzUoynCRd0iBEhMnEkbuYbyAAAcAUlEQVRlpZZOUc2QKgyUEH2w6P77qTiuheIcP1DAX/4DIFg3KI4lI68HPCMDerykvQvq7o6CtmjvngZfdUkVBkqIPnAPa3KtF4bxiS5C93Hh/uNgrD7UNzlTVlbJ2LHvaverLqnCi5AVohDCBEQBFsAqpaztDh8A5o0d6/wcb4kk+Q6QlaXO9ULwtIYXvaa3l/qt4TPPLOfYMc2ZW3VJFd4ETYhCCLOU0u52nAIUA2YpZab+bhVCAKQAgUsYolNTVcW2JVoqBxEGybeUQO9kto59icVoEfju1vDLnXBSzxFVnzXs0SOTI0dOEh3dmZKSU6pLqqhFUIQohLAAGUCcfmwGkFLahBAmXaTG/tUWwOq7Jv+SP1eL2hVhYcQN1yZU5M/LeJfa1rCqBq7Xs2PUZw1raiRHjmj5R0tKTqn1QoVPgrKOKKW0AUVuRalo1hDAgSY+Q7C2luiWVp06xSd6KoS4EeEkpwIICgfehtQjCd2t4fxNUGX4h9ZjDbt3dxlyZQkVdREqC/pReAqzuy7CdGCC3m0NKOumT3fu0pS3SluslyKMrJGXOa8xrGGNhCc+1crqs4anTlVRXKxt5RQersaFiroJWhiUECJHSjlK/5wFZEkp7boAR0kp0xtZT+uO41K0WVpjpu9iIFr/HAUcqefaWgQ7pvK9jXDXQsh++n36d+7OtVwHQFraJyxa9COFBU9CWHttBkhxRqBPMjaaUPllzAeM/HUmwFbPtSHHdf1g2d1wV/gdThECPPKImayssciwDpoIq8vZ+9XnvDNmDBUnTgSxxYpQIyhC1Md88cbYz1jG0Lulxe7LGq2BmLPghosgooN2XIW2pjFkyLncdtul2v+ONVUw/0qqv32Owz/+SPGuXcFrsCLkUKky/MSmQ9o+92UXv0cZZTzAQ9TUSDZtOkRERAf69esGW/8HEedR3XM44e3bB7vJigCiUmUEiVfXwn0fQj9pYgAXA9rYNT5+Jq+/nqtddMm9cG4S4e3bU1NdzQ/vvEON265RijMXZRH9xE9HoEM4XBjlWb506TYuuaQ7/fvrc1GyBuwZbPtuN/N+lcXt773HoNTUlm+wIqA01SIqIQaAaqop4ADn0tf3BUtuho6R7Ax/kAuTkpo8w6YIfZQQg8iy7Vo0xoVXLucrVvB70ik/EsaKFbsYM2YAnTvr48Kqk9Cus/O+Y/v2gZR07VuHcBWtDjVGDCLvbIB/rIZYriSVu+hEJ1av/pmUlAWsX++KQ3SK8MQBarZ/wtxrrmHRffcFp9GtHJvNxqhRo/xan81mw2q1BjzNvgdSyqC80CIqLMDkOs6b9WtSGqhHhgqFJ6SsqPIsKy4+Kb/7bo88daqy9g3LUqV8o6fctuQjeXDjxpZpZBskJSXFb3WlpaVJKaVcsGCBXLBggbPc/XNj0H+XjdZDsNYRndEWQLFx7MUEqUVgmOo4H3L06ALtw7XP1VSzhm8piNxFYmJfOnb04cQ0/GW4eTkXjRlHz4EDAdiRk0ONnrpRETyMnaNA24AmJycnoM8LVtfUZ7SFgb7QvwNASpkpT3OBfzazmM2s06mi0czbAI8v0SI11rCGn9jK998XMH36utoXR5wL3Qdpn/fksH/td7x9/fWsq2efv6aSlJTEnDlzAKisrCQpKYm39d2Ny8rKSEpKYv78+QCUlJSQlJTEwoULATh8+DBJSUl88sknABQUFNR+gBdWq5W4uDhsNhvZ2dkeOyplZ2djt9vJzs4mOzubuLg45x73drsdq9XK+PHjAW3/eqMOo9zXPve+nm+1WsnOzvZ4bn3PMrDZbOTm5mK1WklPT3c+Kzc311keKIIlxFrRFl7nE9AiMMxCiAZ3AhFCeLymTp3q87qWEKTjKKzdD5VVYTzIw4xlHEuWbOPJJz+ltLTC901FW+DjG+gT/iUp779P/GOPBbSNgSQlJQWTyYTFYiEtLc25w1JmZiYmkwmz2ezcXSk6Ohqz2YzFYiEqKgqTycSCBQtIT093lu/YsYOUlBTsdjspKSn1bgxjt9txOBykpKR4bDialpZW57PcsVgsmEwmUlJSSHVbUnIvDxSh4vTtiyNSj8YQQqRIV6BwLWSIzJoCPDMC/qzvU9pR34L6nkcv4Z7HBxBh+MB5E30p3LgQzh/NwLhOAFScOMHXmZmMeOYZ2nXs2Oz2rFixwvm5ffv2HsddunTxOI6MjPQ47tGjh8dxr169mvx8k8mEw+Fg3bp1zh+yyWQiKysLs9mM3W5n1KhRWK1W59bZDofDub99//79AZznQNvrvqioiISEBA9hms1mioqKsNlsREdH405dz6oLYzPTliJYFrGhaIsdaF1W9PcEfz34AAeYzSyS5kLSXH/V6iJMn7CurtHfqWZRzNus7Las/htNt0C7TtrSRt6LbF+6mFV//zt7v/vO/40MMO6zjUVFRZhMJhISEpy78DocDhISEkhNTSU9PZ2UlBSPrdMSEhKc1vOOO+6oVX9WVhYLFiyoZR2zs7NxOBzOfQzd21HXs5pKoHYNDpYQfUZbCCGM/4ZsXud9DLAawaIk7dXC2BzQ59+wowjCCecGRtM993LS0j5p2HrvWgzfPcNlw3ryqx9/5MKRIwGoKi+v/74QoqioyDkWzMjIALS9Bu12OzabDbvdzuTJkzGbzU7L1L9/f+Lj453XGssIubm5znt8icAot9vtmEwmpyU1m80e49O6nuWO8RyHw0FWVhY5OTlOMZtMJqxWq7Nb7W+CGRichmbtTFLKbL0sT0oZ53a+SD9f5whdCCFldiRvZJYigfjfXsVTXVaQXwCLOycR0R7sj/+f8/oDHKA3vXlr7iMArLi/dp1JSXO0cyseaNZ323sMfvs5PDcSLo3RyubMyeePf1zOd+se5IK+0fVXULQZovXMANUV7LN/z/xbb+WODz6gb2Jis9rUkowfP77W+OtMo6kL+kEbIxri8yqLq+98Y0n5exS3SuCJWEorYfXPrhClzlGw4xDkF0Bs04c8jaJvV3jfa1x/zz2D6XdfMUvC5pHGY7Sr75/eEGHhelh6C51/8W96xcbSLUD/G/sTd6sSKOvRFmlTnjWFe6tZ/e/8hi9sIQ6d0KwjQPv24fQK68W5nEu1bOQ6YfuzIepioi8fwd2LF3NWTAxSSr755z85VVISuIafBsZMpxJh02hTQnTnLEoZHJbP4LB8Jn/zkse5mJ4HuO2uwC5jVFTDJdPgr1+5lW3oxp8HF/D9usPO4OF6iboIbs6BzjEgJXz9e/bnzMP2hz+wOYBrWoqWp8lCFELECiEu9H9T/Evh3moWvFra7Pvz8wucY8Xm0CEc/jkK/nSVq+y88yI5++wOVIaXk80M1rKm8RWe2Ac/zePcnod4/IcfuPKhhwA4uGEDlSdPNruditCgUWNEIcTTaEsIR4ActKWHlnFVCTDGEoavSRvQJm7y8wuIje3V5Mmbh6/0PI6K6sQ33zxMFVXsowfRtfwY6iGiL9y1CTpGEiPC4OBaqqrb8c7om+g7dCh3fPBBk9qmCC0aaxFtUso7gD+gpfLcGbgm+Z/zS/bwxMqXapXnF2gvIGDriiWn4N5F8MlWV1nVKei3bgQXcREAm9jIUY42XFmnbloSKilh5eO0++oBbnv7ba565hkAKsvKKNyyxf9fQhFwGivEbkKIWClliZTyAynl8oC2KkA8sfIlpr71d9KWzPY5Tswv8L8Yu7SHTYWwy21u5YknlnLNNXMpKjpJBRUs5mOW0wSnYiFgXA6MmseF11xDn9jBsGkm37yUyYzLL+fY3r3+/RKKgNNYIcYBdwoh3hdCzBdCPBLIRgWaPkcO1CnGumjumLF9OKx9BJ5wbSBFevpVfPjhnURHd6YDHZjAREaj5ew/znEKOdRwxZ2iXQ7jOz+CFWkk3DKYG//7X2eAcd7Mmez84osmt1nR8jR2HdGGluZwJ4AQol/gmuRfFrxaSpiQjHi+cdfnF0DpiLthQAFs8/wRG2Js6lixnf7f3Xd7tR2FEy6K5qKLXIv6Ubj8GnP4jC1s5vek05FG+pj2T4Hbv6FLr2HEXwFsmE5NeSnfZM6k77Bh9Lv2WgDKjx2jY9euTWq7omXwaRGFEP2EEM4tkKSU6w0R6setYoxYuLeawr2+1+zcreLlN9S2itUxPcm/cnSt8uZaxspquPdDmOyWOvmf//yGP/zBM5fy9YzmNlKcIszhc35iK/UiBPQa5jo+sIqwgpU8vmED17/8Mhz+nmLHT7wUE8PG995rctsVgcenEHWhCSHEo0KIR9xF2Ro5tLcG23N7apUbYgS4/IZZTVpbTEqaQ1TUi40WZftw+OROWOSWsG3XrmJ27y7x8D+NIIJL0TxrKqhgA9+zn32A5kD+A99TRln9D7v+XRhtpV2nTpzVtT1YhxK+4RWGPPGE5iJ3/Gf2fPstS594grLDhxv9nRWBo1G+pnpX1AJIwCGlPO2Bh4+NSZt03u06p6/pob3VnH1eJADH95S4XQORF2p5YjpWn+Ku33mGI+2NvIDXRj5N56gDABQcOIe3Zt5H5EnN4beks9Z1DC88RMSCecTqvnH5+Z6Bso1d4qiRkPE13HdZJX26tas3i1sNNVRTTXva8zO7mUU2d3AngxhMMUexYyeOOCKpI2ynugL2fA5d+2vhVkc2wXuDyC16hC9f+4hfb91AB3GcPZsPIcLCODcxUWWV8wMBz+LmJcoiKeXCpjXRmSrDJLUdgdOAXOm5e3C9573qapIQS3adpOd5wkOMeyMvAKBvyW72d+9N1pgHATi8rwcLp93mFKJB5MliYtcvqyVEwEOkdQlz/QEYOhtmJMODsVBYeIL//nctU6YkERZWvygPsJ/u9KATndjCZt5jHhP5FefQi+1sYzWruJXbiCSKMsqopoqziCDM6PyUHYKf3ob+46nq0JN2+5bCp7fx9oJESg6WMGldDpw8xIHd1XS7aACdWjgur63QoukUhRCRQFxTLaQQIgPIkdoOwRa8rF5D573qki+4MhPSVRfcsV2e3iZ1lTflnhrdUkRd0Ik9Z/el49aftfKaMCorNGEX9tC6lTGHN3vUVdjjMiJKi+hcsls77nkZPQ5t5lTkBXwTfQP2/T14uPx1ojlKeLggIqIDvWK1EUFBfj69YmN5wC1I16CCCtrRjjDC+JEtrOBLHuIROtCBVXxFDp/xJ/5CRzqyjjXYySONxxEI1mNn1/HV3LyrPeW9buGjNUvoUrmUm/Yt4tXMvlSbLmT/3ddyU69z6Rd1CbvDI/gov4KHxsUy8LzOSCkbtp6VZWz98H3y5lpJ+vvf6HNFqx7lNJqWsIixaDOou5rYNvc66t0PsSn7JQoh5FSvsqi+4RT7mKSpq7w596i6ml5Xyf5qel7bm8KdHTlYEk3YihuZsno6016MpOBET7qsupY/fpnFf/9xNgUVfYn8ejhP58zitefP4kBNP875Op5ffz6X/0ztxIHwSzjv68E8vuxt/vNsBw50Hki/Fb8g7bN3+c+f2rE/6koGfN6Ph3Pe5z9/FOyLSeTSJX25f/kHvDa5hr3nDufyD8/hni8+5LWnK/m537XELejGnV9+zGu/K2f3xdeTOC+C25YvZtrkCn4eOJrhczqSnLOMGc+Usys2mZHZYVy/7DNm/qWSnXE3YZlWyQVf7ecjx5959dUb/R8G1dpc3Or68dRV3px7VF3Nq+ug7QDxI6D7rGjeFuGsrBpCaXQVx8J7UvRdBCurEintXsGx0j4c+/ZsVlYmUtq9nOOnzuPU2q6sqEiktEcZxysvYOeaSFZUDOVETCmlXIBjXTdWVAzleM/jlLbry468aL4sH8bxnsWc6NKX7eu7s+LUUI6fc5QTXc5l2/fd+PLUMI6dc4SyTr3ZtuFsvjw5jGPnFFLWqRc/bezCipPa+RMde/Pjlg5ElA+lpOcRytqdw49bwulSMZSSmCJOtothy0+CouLmLQ81drLmSinler0ramyd1mzvGq+uZwpewb8NnfeqS1nEVlhX/AgYkwrfXXwVy0aNJIEhjGWcz2e1RgIVGGy4uOUD/vAung8YuQo8UmVIKYvrOl8XU3xM1nToVMesaefGTdYUHOjFx69pPwz3yZrwwkNcpS/05x+ve7KG/s2P8D96EqI6ae2++I6v2N0vlkPPdSWyEwz59UbWdR9ESTp07Qixkzbw/TmDKf0DnNUBegz/iJjbL+P7lwfQIRxmvZnPyKvOY8CAJjiYB4gZN5zHwc8197vcVVrZmNTVJG77mrUDr2TxSNqUGJtCY4UYh5be8Bm02dIcKWWzu6b62C/ex8aky9Emf+o6f1pYppxHX32yxBtj+aLwUG/e+sd9VFfXEIkr+ZCxdAH4DO1vTnRGXXRzm3z66j9mDhw4TmQnrcszuKaA2Koyzmqv+czF1exlLMWc1WEEACuzh3H++ZF00BMdP/Jg6EyOPPbZHpZMmkTu9OmAS4zJqZLEjXbW8BzPXr2GRJF4xgkyaC5ugUyV0Vj2d+9NdvJDnCzuDUDhod4sfPcRwJV/tK5lCvCv+OqiV68IevWKcB6/8ZpHLmbeeP1Gj+OBA3sGtD2nS7KePHnd9OkINDHmfQ1xw2EMdgSw+GpAnFnWsVFClFKuNz7rQcGTgccD0yT/07NvGCOmnOdRZojQ4IfPHqEOvfkk0AJsyxhizMvK0rYXqHHrqqJ1fs40MTY6Ql8I0VUIMQPNOtZONhmCxPQNJ6ZveL3XFB7qzQ+f1Q4mCS88ROz62rlIW8IKngkkT5vGX6qqSJg4EWO6MHcVLJ0PQzbaGfvVMtbINUzhzyzm46C2tSVo0CIKIboCmcB1QLqU8jEhxOcBb5mfGP/rCNq3r9C9NTW8raE7sb2AN+dp3VGvsaASof8xrKP3uHEMdiSwdORo1sg1bd461msRhRAvAnnA51LKAW7ubKGT474JvDbyafZ37+08do0JNWJ71Z0yQ4kwcCRPm0b8xInOY3fLOHX6C5p1rFkbxBYGnoYsYg5alu16lw9aE4Yl9BahIrj4sowSGJsqGbJRs45PXzCEly5sm1axXiEai/ZCiOv0xXyblPJYi7TMj/wceR7TRz5dq9y951mXJVS0HN4zqnmrwO42owrP8TS0STE2dtbUXZBRuDaQafU0JEDVHW1ZGp5RfY7FF7a98WKT8ppKKZdLKT8AXgxQe/xGTN9wxv86ouEL60CNCYNHvTOqm9a3ydnUZmX61sUY0pwggg01sWyoiSXzl57dUjU+bB0kT5tWS4yfvidJ/moZEtmmJnBCeaPSJhPTN5z438Qy51UordD88iLaw1Xnu645AFxyPjgCtAGNwr/48sSJe9bO1DvXs3bglW1mAqfNCDH1qQiqpZb5OP+P2rZsD5DUrLpUlzS08J5R1SZxJHHD7dz+fNuYwGkbQny0GGM0GAuscJ7QPtlDN3RS0UjcJ3FkdTVSn8SJf7ZtiDGYG5U2lDzKjL5rsJSyzq2PhBCyoe8w202IxkalD6HGiK0V9wgO0GIbuz9v5vDIKSEzm9rUeMSgbMumiwwppQ0oNo69mKAL0FTH+WahRNj6SZ42jbJByc7j3FVw5Fk7PVY+12pnU4O1P2IqOIP9HGhR/050a7kDQEqZ2VA8ohDC4zV16tQANFkRSmRsWOxTjFOn/Z3kaTezc+WYILau6QRrjBgFFLkde4ePJ4DTclrqy2sK0NjutbKEbYuMDYtJHzyWLhuXAN6xjZ+ykzH0G7k0yK1sHKG8Y/ARwxLqFrLZPMQjSoRtFMMy1qANx4xJnL/+CjY/+2mrsYwBs4h6YmBvHMa4EJebXBRadjh3duCymA40C6n2qlb4JGPDYgDSB4+l88YlGDMkuauAZz/lwtQwxKDHYeS0oLWxIYIya6p3OeOllNlCiMlozuR2I3mUEMIEpEgpM/XzjrpmThsza6o4c/AWo0HcCBhzp2D3wNEt0l1t0Uzfp4NuMR1oqRKz9bI8I2+Nfr6IelIp6tcpISo8mDRpCbsWz2HIz747UfEj4LLnbwyoIFuNEP2FEqLCF5MmLWHX9OdIYB0Ibb9573D2+BGQ/PzEgHRZlRAVCp1Jk5YwfXouQsCN529kyO7aFjKmNxRqm4D5VZhKiAqFDyZNWsL0XTGMKZzLkHXTG7w+flQ0ybeUwMAJzRJmq/CsUWicaY4Hwfy+06YlM/HCQpau68nUMWsoSLkRGS7ockFnn9fn5hTx3KRqnkuaznNCOF9Lbh0ckPYpixhE9P81g92MFiNUvq97l1WOuRESEnhsYzK9rJ+6LhLUmSItum84RV77e3h3a1XXtBURKj/MliIUv68hSsbcCEMSGH3jJ8TFr6X8oS+JmPtNk+pyH29OBSXE1kIo/jADSah/X3dLOXBgDBvPj3eKMz5hDf1uy2bvx4eJ7FP3LlkGU1FCbDWE+g/T37S27+u0lgZjboSEeDoeO0p5ZLRToGG/WobIWs9ZfTtRtlvbXXoqZ6AQg90GhcIXZ5QQFYq2gFq+UChCACVEhSIEUEJUKEIAJUSFIgRQQmxBhBBp+ivDrSxFCGHR4y7bFHV83wzjXPBa5n/0v6GluX9bJcQWQghhQQuAzkbLTGdpZDa7Vomv76ufShNC7ECLRW0T6H+3Ufrf0SyEMDf1b6uE2HKYcGWrc+jH9Waza+X4+r4A46WU/fUfaJtASmmXUqbrhyY911KT/rZtI9N3K8DIQqBjBuajbc9RXza7Vksd3xc0iwF1JJZuzehd0An6YUOZCj1QFrGF0bsoOQ3lam0reH9fPU+tDeju1l1tE+j/sUzQ9xBtEkqILY97ntaGstm1BZzfV5+8MFJjHsHVXW3VuI8J0bqhaTTxb6uE2IIIIdLcfpQWtO6a8WM0AW1m3AQ+v68D13fsD+TWdW8rw4Kn6Bw08W+rhNhC6D/EDCHEDiHEUdAG+W7nittSd7We73uHsaVCG/q+xsxwCmibJjX1b6ucvhWKEEBZRIUiBFBCVChCACVEhSIEUEJUKEIAJUSFB0IIkxBiwWncn9EWHdgDjRKiwgMppQN41Dhuxt6U8xu+ROGNEqLCA31LPJP+OQoY1cQqihu+ROGNEmIrQw+fyjHi3Nxi3rIMH0fDlcyI+RNCROnXGvek+ajHPUzHiKmLB+INq2jco3/O8Iq9m6wvXqd4l7W12MNAoITYytAdpqP1dyuQqn/OQxONGS0Ux4orEiAezbvDBiRIKbN91aPX70C3avo55yaxXqFLWcYHXWh2/bxNL8twK+sfiH+LtoQSYuvEPbzG4V6mu1LZdetklBnisADpddTTWHx1PeOoHehrAqL0/xh2NOM5ZxRKiK2TOsdhunUyuYnPEINNSmnTLV6D9fio1zvC3D1yIo/akRTr0KypHXi/sc85U1GBwa0M3aoZYTfGZyPavwhY4HbejhaGZBVC5AkhHGiW6wW07qp3PVFogjILIUy6aB36GNHolq7T2xAFWPTrsvXxIGhBwKOA8WhpMYyohDYVWeJvlNP3GYC+rpctpSzWRTvBLbWDIgRQQjwDcLNgxfq7o6GwHEXLooSoUIQAarJGoQgBlBAVihBACVGhCAGUEBWKEEAJUaEIAZQQFYoQ4P8BfMTGr1VL4VEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 237.6x237.6 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = plt.cm.jet(np.linspace(0,1,5))  \n",
+    "fig = plt.figure(figsize=(3.3,3.3))\n",
+    "\n",
+    "#upper panel\n",
+    "rect = 0.2,0.4,0.7,0.55\n",
+    "ax = fig.add_axes(rect)\n",
+    "\n",
+    "#ax.fill_between([23,25],[0.1,0.1],[10**9,10**9],color='grey', alpha=0.25)\n",
+    "\n",
+    "#data\n",
+    "p1, = ax.plot(dat_g['mag'], dat_g['n(<mag)'], color=colors[0])\n",
+    "p11 = ax.errorbar(dat_g['mag'], dat_g['data'],yerr=dat_g['data_err'], fmt='o', ms=2, color=colors[0])\n",
+    "p2, = ax.plot(dat_r['mag'], dat_r['n(<mag)'], color=colors[1])\n",
+    "p22 = ax.errorbar(dat_r['mag'], dat_r['data'],yerr=dat_r['data_err'], fmt='o', ms=2, color=colors[1])\n",
+    "p3, = ax.plot(dat_i['mag'], dat_i['n(<mag)'], color=colors[2])\n",
+    "p33 = ax.errorbar(dat_i['mag'], dat_i['data'],yerr=dat_i['data_err'], fmt='o', ms=2, color=colors[2])\n",
+    "p4, = ax.plot(dat_z['mag'], dat_z['n(<mag)'], color=colors[3])\n",
+    "p44 = ax.errorbar(dat_z['mag'], dat_z['data'],yerr=dat_z['data_err'], fmt='o', ms=2, color=colors[3])\n",
+    "p5, = ax.plot(dat_y['mag'], dat_y['n(<mag)'], color=colors[4])\n",
+    "p55 = ax.errorbar(dat_y['mag'], dat_y['data'],yerr=dat_y['data_err'], fmt='o', ms=2, color=colors[4])\n",
+    "\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xlim([16,30])\n",
+    "ax.set_ylim([10**2,10**8])\n",
+    "\n",
+    "ax.set_ylabel(r'$n(<m)$')\n",
+    "ax.xaxis.set_visible(False)\n",
+    "\n",
+    "p01 = ax.errorbar([25],[10**3.0], yerr=[500], fmt='o', ms=2, color=colors[0])\n",
+    "p01 = ax.errorbar([25.5],[10**3.0], yerr=[500], fmt='o', ms=2, color=colors[1])\n",
+    "p01 = ax.errorbar([26.0],[10**3.0], yerr=[500], fmt='o', ms=2, color=colors[2])\n",
+    "p01 = ax.errorbar([26.5],[10**3.0], yerr=[500], fmt='o', ms=2, color=colors[3])\n",
+    "p01 = ax.errorbar([27.0],[10**3.0], yerr=[500], fmt='o', ms=2, color=colors[4])\n",
+    "ax.text(27.5,10**2.7,'HSC', fontsize=10)\n",
+    "\n",
+    "ax.legend([p1, p2, p3, p4, p5],['g-band','r-band','i-band','z-band','y-band'], frameon=False,\n",
+    "           fontsize=10, numpoints=1, loc='upper left', labelspacing=0.2, title='extrapolated')\n",
+    "\n",
+    "\n",
+    "# lower panel\n",
+    "rect = 0.2,0.125,0.7,0.275\n",
+    "ax = fig.add_axes(rect)\n",
+    "ax.plot(dat_g['mag'],(dat_g['n(<mag)']-dat_g['n(<mag)'])/dat_g['n(<mag)'],'-',color=colors[0])\n",
+    "\n",
+    "#ax.fill_between([23,25],[-1,-1],[1,1],color='grey', alpha=0.25)\n",
+    "\n",
+    "delta = (dat_g['data']-dat_g['n(<mag)'])/dat_g['n(<mag)']\n",
+    "err = dat_g['data_err']/dat_g['n(<mag)']\n",
+    "ax.errorbar(dat_g['mag'], delta, yerr=err, fmt='o', ms=2, color=colors[0])\n",
+    "delta_2 = (dat_g['power_law']-dat_g['n(<mag)'])/dat_g['n(<mag)']\n",
+    "ax.plot(dat_g['mag'], delta_2, ':', color=colors[0])\n",
+    "\n",
+    "delta = (dat_r['data']-dat_r['n(<mag)'])/dat_r['n(<mag)']\n",
+    "err = dat_r['data_err']/dat_r['n(<mag)']\n",
+    "ax.errorbar(dat_r['mag'], delta, yerr=err, fmt='o', ms=2, color=colors[1])\n",
+    "delta_2 = (dat_r['power_law']-dat_r['n(<mag)'])/dat_r['n(<mag)']\n",
+    "ax.plot(dat_r['mag'], delta_2, ':', color=colors[1])\n",
+    "\n",
+    "delta = (dat_i['data']-dat_i['n(<mag)'])/dat_i['n(<mag)']\n",
+    "err = dat_i['data_err']/dat_i['n(<mag)']\n",
+    "ax.errorbar(dat_i['mag'], delta, yerr=err, fmt='o', ms=2, color=colors[2])\n",
+    "delta_2 = (dat_i['power_law']-dat_i['n(<mag)'])/dat_i['n(<mag)']\n",
+    "ax.plot(dat_i['mag'], delta_2, ':', color=colors[2])\n",
+    "\n",
+    "delta = (dat_z['data']-dat_z['n(<mag)'])/dat_z['n(<mag)']\n",
+    "err = dat_z['data_err']/dat_z['n(<mag)']\n",
+    "ax.errorbar(dat_z['mag'], delta, yerr=err, fmt='o', ms=2, color=colors[3])\n",
+    "delta_2 = (dat_z['power_law']-dat_z['n(<mag)'])/dat_z['n(<mag)']\n",
+    "ax.plot(dat_z['mag'], delta_2, ':', color=colors[3])\n",
+    "\n",
+    "delta = (dat_y['data']-dat_y['n(<mag)'])/dat_y['n(<mag)']\n",
+    "err = dat_y['data_err']/dat_y['n(<mag)']\n",
+    "ax.errorbar(dat_y['mag'], delta, yerr=err, fmt='o', ms=2, color=colors[4])\n",
+    "delta_2 = (dat_y['power_law']-dat_y['n(<mag)'])/dat_y['n(<mag)']\n",
+    "ax.plot(dat_y['mag'], delta_2, ':', color=colors[4])\n",
+    "\n",
+    "ax.set_ylim([-1,1])\n",
+    "ax.set_yticks([-0.6,0.0,0.6])\n",
+    "ax.set_xlim([16,30])\n",
+    "\n",
+    "p0, = ax.plot([10,30],[-2,-2], ':', color='black')\n",
+    "ax.legend([p0],['power-law fit'], frameon=False,\n",
+    "          fontsize=10, numpoints=1, loc='upper right')\n",
+    "\n",
+    "ax.set_ylabel(r'$\\Delta n/n$')\n",
+    "ax.set_xlabel('magnitude')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "fig.savefig('./app_mag_func.pdf', dpi=250)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/descqagen/app_mag_func_test/notebooks/app_mag_func.pdf b/descqagen/app_mag_func_test/notebooks/app_mag_func.pdf
new file mode 100644
index 00000000..6cb2e3c7
Binary files /dev/null and b/descqagen/app_mag_func_test/notebooks/app_mag_func.pdf differ
diff --git a/descqagen/app_mag_func_test/notebooks/hsc_g_n.dat b/descqagen/app_mag_func_test/notebooks/hsc_g_n.dat
new file mode 100644
index 00000000..934d1102
--- /dev/null
+++ b/descqagen/app_mag_func_test/notebooks/hsc_g_n.dat
@@ -0,0 +1,181 @@
+mag n(<mag) err data data_err power_law
+12.0 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.054111670334843896
+12.1 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.06037407045080632
+12.2 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.06736122708032367
+12.299999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.07515701760907537
+12.399999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.08385502673697023
+12.499999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.09355966993069752
+12.599999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.10438744689677536
+12.699999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.11646834012280607
+12.799999999999997 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.12994737527617264
+12.899999999999997 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.14498636219249575
+12.999999999999996 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.16176583735412914
+13.099999999999996 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.18048723117784454
+13.199999999999996 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.20137528612967348
+13.299999999999995 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.2246807546960304
+13.399999999999995 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.2506834095999052
+13.499999999999995 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.27969540239939156
+13.599999999999994 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.3120650107881316
+13.699999999999994 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.3481808195836109
+13.799999999999994 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.38847638559564
+13.899999999999993 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.433435442376334
+13.999999999999993 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.4835977073341637
+14.099999999999993 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.5395653609260321
+14.199999999999992 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.6020102757096182
+14.299999999999992 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.671682082040316
+14.399999999999991 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.7494171672410125
+14.499999999999991 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.83614871627933
+14.59999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 0.9329179144902597
+14.69999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 1.040886446832538
+14.79999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 1.161350443732038
+14.89999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 1.2957560409318276
+14.99999999999999 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 1.4457167401447624
+15.099999999999989 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 1.613032778923279
+15.199999999999989 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 1.799712742281807
+15.299999999999988 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 2.007997675519716
+15.399999999999988 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 2.2403879877197763
+15.499999999999988 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 2.499673468899527
+15.599999999999987 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 2.7889667811726238
+15.699999999999987 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 3.1117408259831736
+15.799999999999986 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 3.471870436008759
+15.899999999999986 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 3.8736788922461147
+15.999999999999986 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 4.321989824720352
+16.099999999999987 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 4.822185119889904
+16.199999999999985 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 5.3802695299305086
+16.299999999999983 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 6.002942759539035
+16.399999999999984 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 6.697679895666381
+16.499999999999986 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 7.472821145747344
+16.599999999999984 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 8.337671961745698
+16.69999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 9.302614752016218
+16.799999999999983 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 10.379233522099305
+16.899999999999984 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 11.580452940778905
+16.999999999999982 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 12.920693500912583
+17.09999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 14.416044637763948
+17.19999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 16.08445788315164
+17.299999999999983 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 17.945962374263658
+17.39999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 20.02290530436018
+17.49999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 22.340220202020465
+17.59999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 24.925726259679063
+17.69999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 27.810462304951646
+17.79999999999998 0.040918475359524524 0.001941783743063023 0.040918475359524524 0.001941783743063023 31.029059424147977
+17.899999999999977 0.07923920595405316 0.03785291502304254 0.07923920595405316 0.03785291502304254 34.620156711399034
+17.99999999999998 0.15589377095954615 0.05879853735898376 0.15589377095954615 0.05879853735898376 38.62686513456123
+18.09999999999998 0.1942546312076348 0.06504134272931483 0.1942546312076348 0.06504134272931483 43.09728508671764
+18.199999999999978 0.5777249569112916 0.1935241631016001 0.5777249569112916 0.1935241631016001 48.085083836609286
+18.299999999999976 0.9997123054877289 0.23547351878886613 0.9997123054877289 0.23547351878886613 53.65013981043395
+18.399999999999977 1.6133620654734766 0.33074112878343775 1.6133620654734766 0.33074112878343775 59.85926143978228
+18.49999999999998 2.725644485181711 0.492291051392411 2.725644485181711 0.492291051392411 66.78698920567349
+18.599999999999977 4.911577409253215 0.7625846168793434 4.911577409253215 0.7625846168793434 74.51649050745175
+18.699999999999974 8.670355716817085 1.2125873621009526 8.670355716817085 1.2125873621009526 83.14055809969709
+18.799999999999976 13.810154007830608 1.8211673553665104 13.810154007830608 1.8211673553665104 92.76272408367608
+18.899999999999977 20.483718645493727 2.334159020219612 20.483718645493727 2.334159020219612 103.49850282713355
+18.999999999999975 29.65067457378818 3.0739474014332604 29.65067457378818 3.0739474014332604 115.47677773406073
+19.099999999999973 40.46755295863446 3.8177900333070056 40.46755295863446 3.8177900333070056 128.8413485130469
+19.199999999999974 53.08675184357003 4.433669546208722 53.08675184357003 4.433669546208722 143.75265751966907
+19.299999999999976 70.57661716092811 5.283757381609058 70.57661716092811 5.283757381609058 160.38971589823154
+19.399999999999974 90.17773138412713 6.232159811278109 90.17773138412713 6.232159811278109 178.9522526468421
+19.49999999999997 114.07382213496106 7.288568304988056 114.07382213496106 7.288568304988056 199.66311240609417
+19.599999999999973 141.42224940215354 8.406182359884461 141.42224940215354 8.406182359884461 222.7709307576549
+19.699999999999974 171.37905846337404 9.50720363425758 171.37905846337404 9.50720363425758 248.5531191506889
+19.799999999999972 206.09261190737473 10.413697576911735 206.09261190737473 10.413697576911735 277.31919529126833
+19.89999999999997 244.1435133205854 12.037185181605008 244.1435133205854 12.037185181605008 309.41449897735095
+19.99999999999997 287.40984552044955 13.884350008708063 287.40984552044955 13.884350008708063 345.22433798935367
+20.099999999999973 341.60718514683117 15.91775513051573 341.60718514683117 15.91775513051573 385.17861380937285
+20.19999999999997 398.722376252176 18.03908729494971 398.722376252176 18.03908729494971 429.756982702673
+20.29999999999997 463.27887560851 20.59028838809614 463.27887560851 20.59028838809614 479.4946141223405
+20.39999999999997 532.8588075539398 23.317495484348083 532.8588075539398 23.317495484348083 534.9886155691397
+20.49999999999997 609.3840394295809 25.941221437411627 609.3840394295809 25.941221437411627 596.9052010397568
+20.59999999999997 697.4123034800951 28.238025869452706 697.4123034800951 28.238025869452706 665.9876891237599
+20.699999999999967 795.6448137955607 31.053547712284317 795.6448137955607 31.053547712284317 743.0654267699541
+20.79999999999997 899.0562906028807 35.556663194925584 899.0562906028807 35.556663194925584 829.0637458559402
+20.89999999999997 1013.4732855563909 39.21741124210428 1013.4732855563909 39.21741124210428 925.0150720940442
+20.999999999999968 1138.2501057909349 42.98201038809185 1138.2501057909349 42.98201038809185 1032.0713196412075
+21.099999999999966 1273.4604000183508 46.096189423153405 1273.4604000183508 46.096189423153405 1151.517720215992
+21.199999999999967 1424.9690771178134 51.029296780271345 1424.9690771178134 51.029296780271345 1284.7882527478373
+21.29999999999997 1588.7899706611836 55.770407921455885 1588.7899706611836 55.770407921455885 1433.4828587990996
+21.399999999999967 1772.6373122552748 62.30598531860059 1772.6373122552748 62.30598531860059 1599.3866504395273
+21.499999999999964 1972.5528338532067 68.61114483098147 1972.5528338532067 68.61114483098147 1784.4913411734074
+21.599999999999966 2190.576268598635 76.57036111937276 2190.576268598635 76.57036111937276 1991.0191572085844
+21.699999999999967 2428.1968641136564 84.59231874658681 2428.1968641136564 84.59231874658681 2221.4495161345735
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+21.899999999999963 2984.7141430695924 100.41158076765251 2984.7141430695924 100.41158076765251 2765.4035332606354
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diff --git a/descqagen/app_mag_func_test/notebooks/hsc_i_n.dat b/descqagen/app_mag_func_test/notebooks/hsc_i_n.dat
new file mode 100644
index 00000000..32e4f81b
--- /dev/null
+++ b/descqagen/app_mag_func_test/notebooks/hsc_i_n.dat
@@ -0,0 +1,181 @@
+mag n(<mag) err data data_err power_law
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diff --git a/descqagen/app_mag_func_test/notebooks/hsc_r_n.dat b/descqagen/app_mag_func_test/notebooks/hsc_r_n.dat
new file mode 100644
index 00000000..b5b4cd39
--- /dev/null
+++ b/descqagen/app_mag_func_test/notebooks/hsc_r_n.dat
@@ -0,0 +1,181 @@
+mag n(<mag) err data data_err power_law
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diff --git a/descqagen/app_mag_func_test/notebooks/hsc_y_n.dat b/descqagen/app_mag_func_test/notebooks/hsc_y_n.dat
new file mode 100644
index 00000000..5807820a
--- /dev/null
+++ b/descqagen/app_mag_func_test/notebooks/hsc_y_n.dat
@@ -0,0 +1,181 @@
+mag n(<mag) err data data_err power_law
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diff --git a/descqagen/app_mag_func_test/notebooks/hsc_z_n.dat b/descqagen/app_mag_func_test/notebooks/hsc_z_n.dat
new file mode 100644
index 00000000..e9b28553
--- /dev/null
+++ b/descqagen/app_mag_func_test/notebooks/hsc_z_n.dat
@@ -0,0 +1,181 @@
+mag n(<mag) err data data_err power_law
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+28.39999999999994 2087890.6638937541 71360.91391365058 329720.4572155854 7705.213120010015 2087890.6638937541
+28.499999999999943 2246148.821479351 77147.16639335753 330138.23365694296 7715.541354158705 2246148.821479351
+28.59999999999994 2416402.6783721815 83412.0466425131 330521.8719802471 7724.8923626566075 2416402.6783721815
+28.69999999999994 2599561.490513878 90195.22608974509 330882.4542768567 7734.149649316991 2599561.490513878
+28.79999999999994 2796603.4342533015 97539.63938705689 331225.5443775202 7743.201392373129 2796603.4342533015
+28.89999999999994 3008580.83043628 105491.75018225703 331549.26602224325 7749.925268240647 3008580.83043628
+28.99999999999994 3236625.764476576 114101.83833925515 331858.6385015768 7756.64252152131 3236625.764476576
+29.099999999999937 3481956.1324231047 123424.3103221542 332151.3199631269 7764.859851578111 3481956.1324231047
+29.19999999999994 3745882.1453135638 133518.03459504232 332421.2917344906 7772.634399296493 3745882.1453135638
+29.29999999999994 4029813.326552353 144446.70403666617 332678.2970682565 7778.943525276498 4029813.326552353
+29.399999999999938 4335266.039683649 156279.22752736873 332915.9313331995 7786.03919282613 4335266.039683649
+29.499999999999936 4663871.586763249 169090.15303681922 333148.00484136125 7791.9582208976235 4663871.586763249
+29.599999999999937 5017384.920580125 182960.12472561127 333366.99446372007 7799.53275393513 5017384.920580125
+29.69999999999994 5397694.017257147 197976.37677249344 333565.0392485696 7805.275832384497 5397694.017257147
+29.799999999999937 5806829.959287293 214233.26685413226 333759.5172986611 7811.227402272191 5806829.959287293
+29.899999999999935 6246977.78285599 231832.85243525027 333940.1852813484 7816.772715295188 6246977.78285599
diff --git a/descqagen/app_mag_func_test/sql/README.md b/descqagen/app_mag_func_test/sql/README.md
new file mode 100644
index 00000000..24c393af
--- /dev/null
+++ b/descqagen/app_mag_func_test/sql/README.md
@@ -0,0 +1,5 @@
+# SQL scripts
+
+This directory contains SQL scripts to download HSC survey data
+
+The scripts download data for the COSMOS, DEEP2-3, ELAIS-N1, and XMM-LSS fields.
\ No newline at end of file
diff --git a/descqagen/app_mag_func_test/sql/__init__.py b/descqagen/app_mag_func_test/sql/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/descqagen/app_mag_func_test/sql/hscReleaseQuery.py b/descqagen/app_mag_func_test/sql/hscReleaseQuery.py
new file mode 100644
index 00000000..05451b0a
--- /dev/null
+++ b/descqagen/app_mag_func_test/sql/hscReleaseQuery.py
@@ -0,0 +1,192 @@
+"""
+python module to query HSC CAS server
+
+This is heavily modified code borrowed from: https://hsc-gitlab.mtk.nao.ac.jp/snippets/17
+"""
+
+import json
+import urllib2
+import time
+import sys
+
+#store query server information
+version = 20170216.1
+release_version = 'pdr1'
+api_url = 'https://hsc-release.mtk.nao.ac.jp/datasearch/api/catalog_jobs/'
+
+
+__all__ = ['submitJob', 'deleteJob', 'jobStatus', 'jobCancel', 'download']
+__author__ = ['Duncan Campbell']
+
+
+def main():
+    """
+    Run SQL qury on HSC CAS database.
+
+    This script can be run in the terminal as:
+        user$ hsc_query.py test.sql test_out.sql
+    """
+
+    # Read in the user's creditionals.
+    user_info_file = './hsc_credentials.txt'
+    f = open(user_info_file, 'r')
+    username = f.readline().strip()
+    password = f.readline().strip()
+    f.close()
+    if (username == 'username') | (password == 'password'):
+        print("Please alter the file: {0}, to be your username and password".format(user_info_file))
+
+    credential = {'account_name': username, 'password': password}
+
+    if len(sys.argv) == 3:
+        sql_file = sys.argv[1]
+        savename = sys.argv[2]
+    else:
+        print("main takes two positional arguments: path/to/file/containing/sql/query and output file.")
+
+    # Read in the sql query string
+    f = open(sql_file, 'r')
+    sql = f.read()
+    f.close()
+
+    # Submit the job
+    job = submitJob(sql, credential)
+    print('running job: ', job['id'])
+
+    #block the script until the job is done running
+    blockUntilJobFinishes(credential, job['id'])
+
+    out_file = open(savename, "w")
+    download(credential, job['id'], out_file)
+
+    #delete job after downloading
+    deleteJob(credential, job['id'])
+
+
+
+def httpJsonPost(url, data):
+    """
+    """
+    data['clientVersion'] = version
+    postData = json.dumps(data)
+    return httpPost(url, postData, {'Content-type': 'application/json'})
+
+
+def httpPost(url, postData, headers):
+    """
+    """
+    req = urllib2.Request(url, postData, headers)
+    res = urllib2.urlopen(req)
+    return res
+
+
+class QueryError(Exception):
+    pass
+
+
+def submitJob(sql, credential, out_format='csv.gz'):
+    """
+    Submit a job to the HSC CAS server.
+
+    Paramaters
+    ==========
+    credential : dict
+        dictionary contraing username and password
+
+    sql : string
+        sql query
+
+    out_format : string
+        file format
+
+    Returns
+    =======
+    job
+    """
+    url = api_url + 'submit'
+    catalog_job = {
+        'sql'                     : sql,
+        'out_format'              : out_format,
+        'include_metainfo_to_body': True,
+        'release_version'         : release_version,
+    }
+    postData = {'credential': credential, 'catalog_job': catalog_job, 'nomail': True, 'skip_syntax_check': True}
+    res = httpJsonPost(url, postData)
+    job = json.load(res)
+    return job
+
+
+def jobStatus(credential, job_id):
+    """
+    get status of job
+    """
+    url = api_url + 'status'
+    postData = {'credential': credential, 'id': job_id}
+    res = httpJsonPost(url, postData)
+    job = json.load(res)
+    return job
+
+
+def jobCancel(credential, job_id):
+    """
+    cancel job
+    """
+    url = api_url + 'cancel'
+    postData = {'credential': credential, 'id': job_id}
+    httpJsonPost(url, postData)
+
+
+def blockUntilJobFinishes(credential, job_id, max_time=60.0*60.0):
+    """
+    run until job finishes
+    """
+
+    interval = 10.0 # wait 10 seconds before checking again
+
+    start = time.time()
+    while True:
+        time.sleep(interval)
+
+        job = jobStatus(credential, job_id)
+        print(job)
+        if job['status'] == 'error':
+            raise QueryError('query error: ' + job['error'])
+        if job['status'] == 'done':
+            break
+
+        dt = time.time()-start
+
+        if dt>max_time:
+            print("job time maxed out.")
+            jobCancel(credential, job_id)
+            deleteJob(credential, job_id)
+            sys.exit()
+
+
+def download(credential, job_id, out):
+    """
+    download the reuslt of a job and save to output file
+    """
+    url = api_url + 'download'
+    postData = {'credential': credential, 'id': job_id}
+    res = httpJsonPost(url, postData)
+    bufSize = 64 * 1<<10 # 64k
+    while True:
+        buf = res.read(bufSize)
+        out.write(buf)
+        if len(buf) < bufSize:
+            break
+
+
+def deleteJob(credential, job_id):
+    """
+    delete a jopb from the CAS server
+    """
+    url = api_url + 'delete'
+    postData = {'credential': credential, 'id': job_id}
+    httpJsonPost(url, postData)
+
+
+
+if __name__ == '__main__':
+    main()
diff --git a/descqagen/app_mag_func_test/sql/hsc_credentials.txt b/descqagen/app_mag_func_test/sql/hsc_credentials.txt
new file mode 100644
index 00000000..e9919964
--- /dev/null
+++ b/descqagen/app_mag_func_test/sql/hsc_credentials.txt
@@ -0,0 +1,2 @@
+username
+password
\ No newline at end of file
diff --git a/descqagen/app_mag_func_test/sql/hsc_data.sql b/descqagen/app_mag_func_test/sql/hsc_data.sql
new file mode 100644
index 00000000..ad5c804d
--- /dev/null
+++ b/descqagen/app_mag_func_test/sql/hsc_data.sql
@@ -0,0 +1,81 @@
+SELECT
+  meas.object_id
+, meas.parent_id
+, meas.ira
+, meas.idec
+, meas.icmodel_mag             as imag_cmodel
+, meas.icmodel_mag_err         as imag_cmodel_err
+, meas.icmodel_flux_flags      as iflux_cmodel_flags
+, meas.icmodel_flux            as iflux_cmodel
+, meas.icmodel_flux_err        as iflux_cmodel_err
+, forced.merge_measurement_i
+, forced.a_g
+, forced.a_r
+, forced.a_i
+, forced.a_z
+, forced.a_y
+, forced.rcmodel_mag        as rmag_forced_cmodel
+, forced.rcmodel_mag_err    as rmag_forced_cmodel_err
+, forced.rcmodel_flux       as rflux_forced_cmodel
+, forced.rcmodel_flux_err   as rflux_forced_cmodel_err
+, forced.rcmodel_flux_flags as rflux_forced_cmodel_flags
+, forced.icmodel_mag        as imag_forced_cmodel
+, forced.icmodel_mag_err    as imag_forced_cmodel_err
+, forced.icmodel_flux       as iflux_forced_cmodel
+, forced.icmodel_flux_err   as iflux_forced_cmodel_err
+, forced.icmodel_flux_flags as iflux_forced_cmodel_flags
+, forced.gcmodel_mag        as gmag_forced_cmodel
+, forced.zcmodel_mag        as zmag_forced_cmodel
+, forced.ycmodel_mag        as ymag_forced_cmodel
+, meas.tract
+, meas.patch
+, meas.gcountinputs
+, meas.rcountinputs
+, meas.icountinputs
+, meas.zcountinputs
+, meas.ycountinputs
+, meas.iflags_pixel_bright_object_center
+, meas.iflags_pixel_bright_object_any
+, meas.iblendedness_flags
+, meas.iblendedness_abs_flux
+
+FROM
+pdr1_deep.meas as meas
+LEFT JOIN pdr1_deep.forced as forced using (object_id)
+WHERE
+-- Please uncomment to get a field you want
+
+-- AEGIS
+-- s16a_wide2.search_aegis(meas.patch_id)           AND
+
+-- HECTOMAP
+-- s16a_wide2.search_hectomap(meas.patch_id)        AND
+
+-- GAMA09H
+-- s16a_wide2.search_gama09h(meas.patch_id)         AND
+
+-- WIDE12H
+-- s16a_wide2.search_wide12h(meas.patch_id)         AND
+
+-- GAMA15H
+-- s16a_wide2.search_gama15h(meas.patch_id)         AND
+
+-- VVDS
+-- s16a_wide2.search_vvds(meas.patch_id)            AND
+
+-- XMM
+-- s16a_wide2.search_xmm(meas.patch_id)             AND
+
+ NOT meas.ideblend_skipped                  AND
+ NOT meas.iflags_badcentroid                AND
+ NOT meas.iflags_pixel_edge                 AND
+ NOT meas.iflags_pixel_interpolated_center  AND
+ NOT meas.iflags_pixel_saturated_center     AND
+ NOT meas.iflags_pixel_cr_center            AND
+ NOT meas.iflags_pixel_bad                  AND
+ NOT meas.iflags_pixel_suspect_center       AND
+ NOT meas.iflags_pixel_clipped_any          AND
+ meas.idetect_is_primary             	    AND
+ meas.iclassification_extendedness != 0     
+ ORDER BY meas.object_id
+;
\ No newline at end of file
diff --git a/descqagen/app_mag_func_test/sql/hsc_randoms.sql b/descqagen/app_mag_func_test/sql/hsc_randoms.sql
new file mode 100644
index 00000000..44f7c779
--- /dev/null
+++ b/descqagen/app_mag_func_test/sql/hsc_randoms.sql
@@ -0,0 +1,26 @@
+SELECT
+  random.object_id
+, random.ra
+, random.dec
+, random.tract
+, random.patch
+, random.gcountinputs
+, random.rcountinputs
+, random.icountinputs
+, random.zcountinputs
+, random.ycountinputs
+, random.iflags_pixel_bright_object_center
+, random.iflags_pixel_bright_object_any
+
+FROM
+pdr1_deep.random as random
+WHERE
+ NOT random.iflags_pixel_edge                 AND
+ NOT random.iflags_pixel_interpolated_center  AND
+ NOT random.iflags_pixel_saturated_center     AND
+ NOT random.iflags_pixel_cr_center            AND
+ NOT random.iflags_pixel_bad                  AND
+ NOT random.iflags_pixel_suspect_center       AND
+ random.idetect_is_primary             	    
+ ORDER BY random.object_id
+;
\ No newline at end of file
diff --git a/descqagen/app_mag_func_test/utils.py b/descqagen/app_mag_func_test/utils.py
new file mode 100644
index 00000000..0e00c165
--- /dev/null
+++ b/descqagen/app_mag_func_test/utils.py
@@ -0,0 +1,24 @@
+
+import numpy as np
+
+__all__ = ['get_ordered_list']
+
+__author__=['Ducnan Campbell']
+
+def get_ordered_list(data_cols):
+    """
+    return an ordered list from column dtypes dict
+    """
+    names = np.array(data_cols.keys())
+    order = np.zeros(len(names)).astype('int')
+    column_types = []
+    for i in range(0,len(names)):
+        order[i] = data_cols[names[i]][0]
+        column_types.append(data_cols[names[i]][1])
+    sort_inds = np.argsort(order)
+    column_types = np.array(column_types)
+    names = names[sort_inds]
+    order = order[sort_inds]
+    column_types = column_types[sort_inds]
+    data_dtypes = [(x,np.dtype(y).type) for x, y in zip(names, column_types)]
+    return data_dtypes
\ No newline at end of file