Feature/krenn1/smart sample 2

.flake8

@@ -0,0 +1,2 @@
+[flake8]
+exclude = .git,__pycache__,venv

README.md

@@ -1,3 +1,8 @@
+# WIP
+1. add a threshold
+2. test with additional functions
+3. continue test driven development of best_candidate...
+
 # scisample
 
 `scisample` is a Python 3 package that implements a number of parameter
@@ -25,7 +30,7 @@ are written in  the YAML markup language.
  1. `pytest tests`
  1. `pytest --cov=scisample tests/`
 
-# Community 
+# Community
 
 `scisample` is an open source project. Questions, discussion, and
 contributions are welcome. Contributions can be anything from new
@@ -40,7 +45,7 @@ branch on the scisample repository.
 Your PR must pass `scisamples`'s unit tests and documentation tests, and
 must pass most `flake8` and `pylint` tests. We enforce these guidelines
 with our CI process. Please see [CONTRIBUTING.md](./CONTRIBUTING.md) for
-more information. 
+more information.
 
 # Code of Conduct
 

bin/pgen_scisample.py

@@ -24,12 +24,15 @@ def get_custom_generator(env, **kwargs):
     except ValueError:
         raise Exception("this pgen code requires SAMPLE_DICTIONARY " +
                         "to be defined in the yaml specification")
-
     return new_sampler(SAMPLE_DICTIONARY).maestro_pgen
 
+
 def main():
+    """ main """
     print("This script needs to be used by maestrowf.")
-    print("Please visit https://github.com/LLNL/maestrowf for more information.")
+    print(
+        "Please visit https://github.com/LLNL/maestrowf for more information.")
+
 
 if __name__ == "__main__":
     # execute only if run as a script

examples/codepy_simple/codepy_config_random.yaml

@@ -1,3 +1,4 @@
+
 # run this with codepy run . -c codepy_config_random.yaml
 
 setup:

examples/python_api/chat_gpt_code.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Value (loc): 5\n",
+      "Value (iloc): 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Create a sample DataFrame\n",
+    "df = pd.DataFrame({\n",
+    "    'A': [1, 2, 3],\n",
+    "    'B': [4, 5, 6],\n",
+    "    'C': [7, 8, 9]\n",
+    "})\n",
+    "\n",
+    "# Get value by row and column label using loc\n",
+    "value_loc = df.loc[1, 'B']\n",
+    "print(\"Value (loc):\", value_loc)\n",
+    "\n",
+    "# Get value by row and column index using iloc\n",
+    "value_iloc = df.iloc[1, 1]\n",
+    "print(\"Value (iloc):\", value_iloc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_scisample",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/python_api/minimization_test_functions.txt

@@ -0,0 +1,77 @@
+
+https://en.wikipedia.org/wiki/Test_functions_for_optimization
+
+2D functions to test minimization routines on
+
+ChatGPT
+Certainly! Here are some commonly used 2D functions to test minimization routines:
+
+Booth's Function:
+python
+Copy code
+def booth(x):
+    """ Booth's function """
+    return (x[0] + 2*x[1] - 7)**2 + (2*x[0] + x[1] - 5)**2
+Himmelblau's Function:
+python
+Copy code
+def himmelblau(x):
+    """ Himmelblau's function """
+    return (x[0]**2 + x[1] - 11)**2 + (x[0] + x[1]**2 - 7)**2
+Beale's Function:
+python
+Copy code
+def beale(x):
+    """ Beale's function """
+    return (1.5 - x[0] + x[0]*x[1])**2 + (2.25 - x[0] + x[0]*x[1]**2)**2 + \
+           (2.625 - x[0] + x[0]*x[1]**3)**2
+Goldstein-Price Function:
+python
+Copy code
+def goldstein_price(x):
+    """ Goldstein-Price function """
+    return (1 + (x[0] + x[1] + 1)**2 *
+            (19 - 14*x[0] + 3*x[0]**2 - 14*x[1] + 6*x[0]*x[1] + 3*x[1]**2)) * \
+           (30 + (2*x[0] - 3*x[1])**2 *
+            (18 - 32*x[0] + 12*x[0]**2 + 48*x[1] - 36*x[0]*x[1] + 27*x[1]**2))
+These functions provide different optimization challenges such as multiple local minima, narrow valleys, and non-convex shapes. They can be used to evaluate the performance and behavior of minimization routines on 2D problems.
+
+Feel free to choose any of these functions or explore other 2D benchmark functions based on your specific needs.
+
+
+
+
+
+User
+which of these have global minima
+ChatGPT
+Among the functions mentioned:
+
+Booth's Function: It has a single global minimum at (1, 3), with a function value of 0.
+
+Himmelblau's Function: It has four symmetric global minima, each located at one of the four corners of the square: (3, 2), (-2.805118, 3.131312), (-3.779310, -3.283186), and (3.584428, -1.848126), with a function value of 0.
+
+Beale's Function: It has a global minimum at (3, 0.5), with a function value of 0.
+
+Goldstein-Price Function: It has a global minimum at (0, -1), with a function value of 3.
+
+These functions exhibit different global minima and can be used to test the ability of minimization routines to find the optimal solutions.
+
+
+
+
+
+User
+which have local minima
+ChatGPT
+Among the functions mentioned:
+
+Booth's Function: It does not have any local minima other than the global minimum at (1, 3).
+
+Himmelblau's Function: It has multiple local minima in addition to the global minima. These local minima are located in various regions of the function's landscape.
+
+Beale's Function: It does not have any local minima other than the global minimum at (3, 0.5).
+
+Goldstein-Price Function: It has multiple local minima in addition to the global minimum. These local minima are located in various regions of the function's landscape.
+
+Himmelblau's Function and Goldstein-Price Function are known for having multiple local minima, which can make the optimization process more challenging as the minimization routine needs to avoid getting trapped in these local minima and find the global minimum instead.