From 3810aa8001fe557d7d02e7790658dae4dc7639b2 Mon Sep 17 00:00:00 2001
From: Jeff Lantz <jcl393@cornell.edu>
Date: Wed, 1 Mar 2023 17:41:32 -0500
Subject: [PATCH 1/6] Added hooks for conda env, auto generate pdf, and
 .gitattribute for nbstripout

---
 .gitattributes         |   3 +
 README.md              |   8 ++
 spider-algorithm.ipynb | 240 ++++++++++++++++++++++-------------------
 3 files changed, 143 insertions(+), 108 deletions(-)
 create mode 100644 .gitattributes

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..f24d933
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,3 @@
+*.ipynb filter=nbstripout
+*.zpln filter=nbstripout
+*.ipynb diff=ipynb
diff --git a/README.md b/README.md
index 492e99f..6926b54 100644
--- a/README.md
+++ b/README.md
@@ -3,3 +3,11 @@
 This directory contains an ipython notebook contianing Python code for the Spider Algorithm. Information on how it works and references are contained inside notebook.
 
 This project was done by Jeffrey Lantz and Riley Guyett as part of their senior theses with Professor Hubbard in Spring 2023.
+
+## Development Note:
+The python environment is managed via (mini)conda, which can be installed [here](https://docs.conda.io/projects/conda/en/stable/user-guide/install/index.html). The environment is managed in env.yaml, and should be automatically managed via git hooks. However, on initial install, one may need to run `conda env create -f env.yml`
+
+Additionally, as working with Jupyter notebooks in version control can be difficult, we have included `nbstripout` in the conda environment. This may need to be manually initialized with `nbstripout --install --attributes .gitattributes`. This makes `git diff` show fewer non-code related changes, at the expense of not showing output data. To counteract this, a pdf of the code is automatically generated and included with commits so a sample output is visible from the repository.
+An alternative still being considered is to include hooks to do a 'clean run' of the notebook and export the code to a .py, but it's not clear that this is a better workflow pattern.
+
+In order to run the Jupyter Notebook, simply call `jupyter lab` from the root directory of the repository, then select `spider-algorithm.ipynb` from the file explorer on the left. Jupyterlab supports a few nice features over jupyter notebook, accessible under `Settings > Advanced Settings Editor`. Here you can change the theme to dark under `Theme > Selected Theme`, and enable automatically formatting the code on saving the notebook under `Jupyterlab Code Formatter > Auto format config`.
diff --git a/spider-algorithm.ipynb b/spider-algorithm.ipynb
index be0217a..4e5c2da 100644
--- a/spider-algorithm.ipynb
+++ b/spider-algorithm.ipynb
@@ -2,13 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import math\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "from matplotlib.lines import Line2D"
    ]
   },
@@ -27,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,122 +37,156 @@
     "    ## It has legs that go off to infinity (or |z| = exp(2). Close enough)\n",
     "    ## The legs land at points in C and do not cross.\n",
     "    ## Spiders may be equivalent under a certain homotopy condition, which we currently ignore\n",
-    "    \n",
-    "    \n",
+    "\n",
     "    ## A spider may be is defined by a rational angle\n",
     "    ## In this case, it has legs going radially with endpoints at exp(2^j 2pi i theta)\n",
-    "    def __init__(self, theta=0, endpoints=np.empty(0), legs=np.empty((0,0))):\n",
-    "        if theta == 0: # If we are given the endpoints & legs\n",
-    "            self.endpoints = endpoints # Give the endpoints\n",
-    "            self.legs = legs # Give the legs\n",
-    "        else: # If we are given the starting angle\n",
-    "            angles = np.array([theta, theta*2 % 1]) # Initialize an array of angles\n",
-    "            while not any(np.abs(angles[-1]*2 % 1 - angles[:-1]) < math.pow(10,-6)): # While the angles in the array differ by at least 10^-6\n",
-    "                angles = np.append(angles, angles[-1]*2 % 1) # Add double the last angle to the array\n",
-    "            self.endpoints = np.exp(2 * np.pi * 1j * angles) # The endpoints are points on the unit circle of those angles\n",
-    "            self.legs = np.kron(np.linspace(1, 3, 100), self.endpoints[:, np.newaxis]) # Legs are straight lines perpindicular to the circle\n",
-    "        self.divline = np.linspace(-1,1,100)*np.sqrt(self.endpoints[0]) # Constructs the appropriate line dividing the space in two halves\n",
-    "        self.knead = [] # Initialize the kneading sequence\n",
-    "        for point in self.endpoints: # For each endpoint\n",
-    "            if round((point / np.sqrt(self.endpoints[0])).imag,7) > 0: # If the endpoint is in region A\n",
-    "                self.knead.append('A') # Give it a kneading value of 'A'\n",
-    "            elif round((point / np.sqrt(self.endpoints[0])).imag,7) < 0: # If the endpoint is in region B\n",
-    "                self.knead.append('B') # Give it a kneading value of 'B'\n",
-    "            elif round((point / np.sqrt(self.endpoints[0])).real,7) == -1: # If the endpoint hits the cc boundary of A\n",
-    "                self.knead.append('*1') # Give it a kneading value of '*1'\n",
-    "            elif round((point / np.sqrt(self.endpoints[0])).real,7) == 1: # If the endpoint hits the C boundary of A\n",
-    "                self.knead.append('*2') # Give it a kneading value of '*2'\n",
-    "        \n",
-    "    def graph(self,draw_circ=False): # To graph the spider\n",
-    "        plt.figure(figsize=(10,10)) # Makes a 10x10 grid\n",
-    "        for leg in self.legs: # For each leg\n",
-    "            plt.plot(leg.real, leg.imag, 'black') # Plot the leg in black\n",
+    "    def __init__(self, theta=0, endpoints=np.empty(0), legs=np.empty((0, 0))):\n",
+    "        if theta == 0:  # If we are given the endpoints & legs\n",
+    "            self.endpoints = endpoints  # Give the endpoints\n",
+    "            self.legs = legs  # Give the legs\n",
+    "        else:  # If we are given the starting angle\n",
+    "            angles = np.array([theta, theta * 2 % 1])  # Initialize an array of angles\n",
+    "            while not any(\n",
+    "                np.abs(angles[-1] * 2 % 1 - angles[:-1]) < math.pow(10, -6)\n",
+    "            ):  # While the angles in the array differ by at least 10^-6\n",
+    "                angles = np.append(\n",
+    "                    angles, angles[-1] * 2 % 1\n",
+    "                )  # Add double the last angle to the array\n",
+    "            self.endpoints = np.exp(\n",
+    "                2 * np.pi * 1j * angles\n",
+    "            )  # The endpoints are points on the unit circle of those angles\n",
+    "            self.legs = np.kron(\n",
+    "                np.linspace(1, 3, 100), self.endpoints[:, np.newaxis]\n",
+    "            )  # Legs are straight lines perpindicular to the circle\n",
+    "        self.divline = np.linspace(-1, 1, 100) * np.sqrt(\n",
+    "            self.endpoints[0]\n",
+    "        )  # Constructs the appropriate line dividing the space in two halves\n",
+    "        self.knead = []  # Initialize the kneading sequence\n",
+    "        for point in self.endpoints:  # For each endpoint\n",
+    "            if (\n",
+    "                round((point / np.sqrt(self.endpoints[0])).imag, 7) > 0\n",
+    "            ):  # If the endpoint is in region A\n",
+    "                self.knead.append(\"A\")  # Give it a kneading value of 'A'\n",
+    "            elif (\n",
+    "                round((point / np.sqrt(self.endpoints[0])).imag, 7) < 0\n",
+    "            ):  # If the endpoint is in region B\n",
+    "                self.knead.append(\"B\")  # Give it a kneading value of 'B'\n",
+    "            elif (\n",
+    "                round((point / np.sqrt(self.endpoints[0])).real, 7) == -1\n",
+    "            ):  # If the endpoint hits the cc boundary of A\n",
+    "                self.knead.append(\"*1\")  # Give it a kneading value of '*1'\n",
+    "            elif (\n",
+    "                round((point / np.sqrt(self.endpoints[0])).real, 7) == 1\n",
+    "            ):  # If the endpoint hits the C boundary of A\n",
+    "                self.knead.append(\"*2\")  # Give it a kneading value of '*2'\n",
+    "\n",
+    "    def graph(self, draw_circ=False):  # To graph the spider\n",
+    "        plt.figure(figsize=(10, 10))  # Makes a 10x10 grid\n",
+    "        for leg in self.legs:  # For each leg\n",
+    "            plt.plot(leg.real, leg.imag, \"black\")  # Plot the leg in black\n",
     "        if draw_circ == True:\n",
-    "            plt.plot(np.exp(np.linspace(0, 2*np.pi, 100) * 1j).real, np.exp(np.linspace(0, 2*np.pi, 100) * 1j).imag, 'black') # Plot the unit circle in black\n",
-    "        limit=5 # Set limits for axis\n",
-    "        plt.plot(self.divline.real,self.divline.imag,'red') # Plots the dividing line in red\n",
-    "        plt.scatter(self.endpoints.real,self.endpoints.imag, s=20, marker='o') # Plots the endpoints of the spider in blue\n",
-    "        i = 0 # Intializing an index\n",
-    "        while i < len(self.endpoints): # While this is a valid index for an endpoint\n",
-    "            plt.text(1.1*self.legs[i][99].real, 1.1*self.legs[i][99].imag,'z_'+str(i+1)) # Label the endpoints\n",
-    "            i += 1 # Increase the index\n",
-    "        plt.xlim((-limit,limit)) # Plot real axis\n",
-    "        plt.ylim((-limit,limit)) # Plot imaginary axis\n",
-    "        plt.ylabel('Imaginary') # Label the imaginary axis\n",
-    "        plt.show() # Show the plot\n",
-    "    \n",
+    "            plt.plot(\n",
+    "                np.exp(np.linspace(0, 2 * np.pi, 100) * 1j).real,\n",
+    "                np.exp(np.linspace(0, 2 * np.pi, 100) * 1j).imag,\n",
+    "                \"black\",\n",
+    "            )  # Plot the unit circle in black\n",
+    "        limit = 5  # Set limits for axis\n",
+    "        plt.plot(\n",
+    "            self.divline.real, self.divline.imag, \"red\"\n",
+    "        )  # Plots the dividing line in red\n",
+    "        plt.scatter(\n",
+    "            self.endpoints.real, self.endpoints.imag, s=20, marker=\"o\"\n",
+    "        )  # Plots the endpoints of the spider in blue\n",
+    "        i = 0  # Intializing an index\n",
+    "        while i < len(self.endpoints):  # While this is a valid index for an endpoint\n",
+    "            plt.text(\n",
+    "                1.1 * self.legs[i][99].real,\n",
+    "                1.1 * self.legs[i][99].imag,\n",
+    "                \"z_\" + str(i + 1),\n",
+    "            )  # Label the endpoints\n",
+    "            i += 1  # Increase the index\n",
+    "        plt.xlim((-limit, limit))  # Plot real axis\n",
+    "        plt.ylim((-limit, limit))  # Plot imaginary axis\n",
+    "        plt.ylabel(\"Imaginary\")  # Label the imaginary axis\n",
+    "        plt.show()  # Show the plot\n",
+    "\n",
     "    def update(self):\n",
-    "        z_2 = self.endpoints[1] # Select z_2 so we don't have to keep calling it\n",
-    "        i = 0 # Initialize an index\n",
-    "        new_endpoints= np.empty(0) # Initialize an array for future endpoints\n",
-    "        while i <len(self.endpoints)-1: # While i is an index of an endpoint except for the last endpoint\n",
-    "            if self.knead[i] == 'A' or self.knead[i] == '*1':\n",
-    "                new_endpoints= np.append(new_endpoints, 2*np.sqrt(self.endpoints[i+1]/z_2)-2) # Compute where the corresponding new endpoint is\n",
-    "            if self.knead[i] == 'B' or self.knead[i] == '*2':\n",
-    "                new_endpoints= np.append(new_endpoints, -2*np.sqrt(self.endpoints[i+1]/z_2)-2) # Compute where the corresponding new endpoint is\n",
-    "            i += 1 # Increase the index\n",
-    "        new_endpoints= np.append(new_endpoints, 2*np.sqrt(2*self.endpoints[len(self.endpoints)-1]/z_2)-2) # Add the last future endpoint\n",
-    "        new_divline = np.empty(0) # Initialize a new dividing line\n",
-    "        for point in self.divline: # For each point in the current dividing line\n",
-    "            new_divline= np.append(new_divline, 2*np.sqrt(point/z_2)-2) # Compute where that point goes under p^-1 and append to the new dividing line\n",
-    "        new_legs = np.empty(100) # Initialize new set of legs\n",
+    "        z_2 = self.endpoints[1]  # Select z_2 so we don't have to keep calling it\n",
+    "        i = 0  # Initialize an index\n",
+    "        new_endpoints = np.empty(0)  # Initialize an array for future endpoints\n",
+    "        while (\n",
+    "            i < len(self.endpoints) - 1\n",
+    "        ):  # While i is an index of an endpoint except for the last endpoint\n",
+    "            if self.knead[i] == \"A\" or self.knead[i] == \"*1\":\n",
+    "                new_endpoints = np.append(\n",
+    "                    new_endpoints, 2 * np.sqrt(self.endpoints[i + 1] / z_2) - 2\n",
+    "                )  # Compute where the corresponding new endpoint is\n",
+    "            if self.knead[i] == \"B\" or self.knead[i] == \"*2\":\n",
+    "                new_endpoints = np.append(\n",
+    "                    new_endpoints, -2 * np.sqrt(self.endpoints[i + 1] / z_2) - 2\n",
+    "                )  # Compute where the corresponding new endpoint is\n",
+    "            i += 1  # Increase the index\n",
+    "        new_endpoints = np.append(\n",
+    "            new_endpoints,\n",
+    "            2 * np.sqrt(2 * self.endpoints[len(self.endpoints) - 1] / z_2) - 2,\n",
+    "        )  # Add the last future endpoint\n",
+    "        new_divline = np.empty(0)  # Initialize a new dividing line\n",
+    "        for point in self.divline:  # For each point in the current dividing line\n",
+    "            new_divline = np.append(\n",
+    "                new_divline, 2 * np.sqrt(point / z_2) - 2\n",
+    "            )  # Compute where that point goes under p^-1 and append to the new dividing line\n",
+    "        new_legs = np.empty(100)  # Initialize new set of legs\n",
     "        i = 0\n",
     "        while i < len(self.endpoints):\n",
-    "            new_leg = np.empty(0) # Initialize a new leg\n",
+    "            new_leg = np.empty(0)  # Initialize a new leg\n",
     "            for point in self.legs[i]:\n",
-    "                if self.knead[i] == 'A' or self.knead[i] == '*1':\n",
-    "                    new_leg= np.append(new_leg, 2*np.sqrt(point/z_2)-2) # Add a new point to the new leg under p^-1\n",
-    "                if self.knead[i] == 'B' or self.knead[i] == '*2':\n",
-    "                    new_leg= np.append(new_leg, -2*np.sqrt(point/z_2)-2) # Add a new point to the new leg under p^-1\n",
-    "            new_legs = np.vstack((new_legs,[new_leg])) # Combine the new legs into a list of new legs\n",
+    "                if self.knead[i] == \"A\" or self.knead[i] == \"*1\":\n",
+    "                    new_leg = np.append(\n",
+    "                        new_leg, 2 * np.sqrt(point / z_2) - 2\n",
+    "                    )  # Add a new point to the new leg under p^-1\n",
+    "                if self.knead[i] == \"B\" or self.knead[i] == \"*2\":\n",
+    "                    new_leg = np.append(\n",
+    "                        new_leg, -2 * np.sqrt(point / z_2) - 2\n",
+    "                    )  # Add a new point to the new leg under p^-1\n",
+    "            new_legs = np.vstack(\n",
+    "                (new_legs, [new_leg])\n",
+    "            )  # Combine the new legs into a list of new legs\n",
     "            i += 1\n",
-    "        self.endpoints = new_endpoints # Change the endpoints of the spider to the new endpoints\n",
-    "        self.legs = new_legs # Change the legs of the spider to the new legs\n",
-    "        self.divline = new_divline # Change the dividing line of the spider to the new dividing line\n",
-    "        \n",
-    "        \n",
+    "        self.endpoints = (\n",
+    "            new_endpoints  # Change the endpoints of the spider to the new endpoints\n",
+    "        )\n",
+    "        self.legs = new_legs  # Change the legs of the spider to the new legs\n",
+    "        self.divline = new_divline  # Change the dividing line of the spider to the new dividing line\n",
+    "\n",
     "    def listpts(self):\n",
-    "        print(\"knead\", self.knead) # Prints the kneading sequence\n",
-    "        print(\"endpoints:\", self.endpoints) # Prints the list of endpoints\n",
-    "        print(\"new endpoints:\",self.update()) # Prints the list of future endpoints\n",
-    "        print(\"legs:\", self.legs) # Prints the legs"
+    "        print(\"knead\", self.knead)  # Prints the kneading sequence\n",
+    "        print(\"endpoints:\", self.endpoints)  # Prints the list of endpoints\n",
+    "        print(\"new endpoints:\", self.update())  # Prints the list of future endpoints\n",
+    "        print(\"legs:\", self.legs)  # Prints the legs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "favorite = Spider(theta=9/56)"
+    "favorite = Spider(theta=9 / 56)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "favorite.graph(True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "favorite.update()"
@@ -159,20 +194,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "favorite.graph()"
    ]
@@ -187,7 +211,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -201,9 +225,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.16"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 0d857d5a9b36d91e6e556f9670c71fcea73ace49 Mon Sep 17 00:00:00 2001
From: Jeff Lantz <jcl393@cornell.edu>
Date: Wed, 1 Mar 2023 17:42:59 -0500
Subject: [PATCH 2/6] add conda env file, fix commit hook to include .pdf, not
 .py

---
 env.yml              | 192 +++++++++++++++++++++++++++++++++++++++++++
 spider-algorithm.pdf | Bin 0 -> 78400 bytes
 2 files changed, 192 insertions(+)
 create mode 100644 env.yml
 create mode 100644 spider-algorithm.pdf

diff --git a/env.yml b/env.yml
new file mode 100644
index 0000000..d7c73ab
--- /dev/null
+++ b/env.yml
@@ -0,0 +1,192 @@
+name: spideralgorithm
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - anyio=3.5.0=py38h06a4308_0
+  - argon2-cffi=21.3.0=pyhd3eb1b0_0
+  - argon2-cffi-bindings=21.2.0=py38h7f8727e_0
+  - astroid=2.14.2=py38h06a4308_0
+  - asttokens=2.0.5=pyhd3eb1b0_0
+  - attrs=22.1.0=py38h06a4308_0
+  - babel=2.11.0=py38h06a4308_0
+  - backcall=0.2.0=pyhd3eb1b0_0
+  - beautifulsoup4=4.11.1=py38h06a4308_0
+  - black=22.6.0=py38h06a4308_0
+  - blas=1.0=mkl
+  - bleach=4.1.0=pyhd3eb1b0_0
+  - brotli=1.0.9=h5eee18b_7
+  - brotli-bin=1.0.9=h5eee18b_7
+  - brotlipy=0.7.0=py38h27cfd23_1003
+  - ca-certificates=2023.01.10=h06a4308_0
+  - certifi=2022.12.7=py38h06a4308_0
+  - cffi=1.15.1=py38h5eee18b_3
+  - charset-normalizer=2.0.4=pyhd3eb1b0_0
+  - click=8.0.4=py38h06a4308_0
+  - colorama=0.4.6=py38h06a4308_0
+  - comm=0.1.2=py38h06a4308_0
+  - contourpy=1.0.5=py38hdb19cb5_0
+  - cryptography=39.0.1=py38h9ce1e76_0
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - dbus=1.13.18=hb2f20db_0
+  - debugpy=1.5.1=py38h295c915_0
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - defusedxml=0.7.1=pyhd3eb1b0_0
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diff --git a/spider-algorithm.pdf b/spider-algorithm.pdf
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From f03106f44b270b4a11f7629bc0cb46cd6533a1a7 Mon Sep 17 00:00:00 2001
From: Jeff Lantz <jcl393@cornell.edu>
Date: Wed, 1 Mar 2023 18:15:22 -0500
Subject: [PATCH 3/6] test 3

---
 spider-algorithm.pdf | Bin 78400 -> 78394 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/spider-algorithm.pdf b/spider-algorithm.pdf
index 1e8c633e5fb4057104aabba5eb0960e4f825cff7..2c6cc1e4222f9d7dfb4a634d93c2d55dd51b2ef6 100644
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From e25d4d5098907dd2e38150d77660af7e65173c8d Mon Sep 17 00:00:00 2001
From: Jeff Lantz <jcl393@cornell.edu>
Date: Wed, 1 Mar 2023 18:30:51 -0500
Subject: [PATCH 4/6] test 5

---
 .gitignore           |   1 +
 spider-algorithm.pdf | Bin 78394 -> 78401 bytes
 2 files changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 176af7e..57555f7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 .ipynb_checkpoints/
+.commit
 
diff --git a/spider-algorithm.pdf b/spider-algorithm.pdf
index 2c6cc1e4222f9d7dfb4a634d93c2d55dd51b2ef6..266f811e8c2337e6a89d2e28ef47ffa2c944aa1e 100644
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From 62c005b54a5198e1ce276af15fd40304558eed8e Mon Sep 17 00:00:00 2001
From: Jeff Lantz <jcl393@cornell.edu>
Date: Wed, 1 Mar 2023 18:53:05 -0500
Subject: [PATCH 5/6] Wrap up hook dev

---
 README.md            |   5 ++--
 env.yml              |  60 -------------------------------------------
 spider-algorithm.pdf | Bin 78401 -> 78397 bytes
 3 files changed, 2 insertions(+), 63 deletions(-)

diff --git a/README.md b/README.md
index 6926b54..6b7ace4 100644
--- a/README.md
+++ b/README.md
@@ -4,10 +4,9 @@ This directory contains an ipython notebook contianing Python code for the Spide
 
 This project was done by Jeffrey Lantz and Riley Guyett as part of their senior theses with Professor Hubbard in Spring 2023.
 
-## Development Note:
+## Developer Notes
 The python environment is managed via (mini)conda, which can be installed [here](https://docs.conda.io/projects/conda/en/stable/user-guide/install/index.html). The environment is managed in env.yaml, and should be automatically managed via git hooks. However, on initial install, one may need to run `conda env create -f env.yml`
 
-Additionally, as working with Jupyter notebooks in version control can be difficult, we have included `nbstripout` in the conda environment. This may need to be manually initialized with `nbstripout --install --attributes .gitattributes`. This makes `git diff` show fewer non-code related changes, at the expense of not showing output data. To counteract this, a pdf of the code is automatically generated and included with commits so a sample output is visible from the repository.
-An alternative still being considered is to include hooks to do a 'clean run' of the notebook and export the code to a .py, but it's not clear that this is a better workflow pattern.
+Additionally, as working with Jupyter notebooks in version control can be difficult, we have included `nbstripout` in the conda environment. This may need to be manually initialized with `nbstripout --install --attributes .gitattributes`. This makes `git diff` show fewer non-code related changes, at the expense of not showing output data. I tried to include automatically generated outputs, but it wasn't working; for now, if we choose to include sample outputs in the repo, they will be added manually.
 
 In order to run the Jupyter Notebook, simply call `jupyter lab` from the root directory of the repository, then select `spider-algorithm.ipynb` from the file explorer on the left. Jupyterlab supports a few nice features over jupyter notebook, accessible under `Settings > Advanced Settings Editor`. Here you can change the theme to dark under `Theme > Selected Theme`, and enable automatically formatting the code on saving the notebook under `Jupyterlab Code Formatter > Auto format config`.
diff --git a/env.yml b/env.yml
index d7c73ab..316efd4 100644
--- a/env.yml
+++ b/env.yml
@@ -15,10 +15,7 @@ dependencies:
   - backcall=0.2.0=pyhd3eb1b0_0
   - beautifulsoup4=4.11.1=py38h06a4308_0
   - black=22.6.0=py38h06a4308_0
-  - blas=1.0=mkl
   - bleach=4.1.0=pyhd3eb1b0_0
-  - brotli=1.0.9=h5eee18b_7
-  - brotli-bin=1.0.9=h5eee18b_7
   - brotlipy=0.7.0=py38h27cfd23_1003
   - ca-certificates=2023.01.10=h06a4308_0
   - certifi=2022.12.7=py38h06a4308_0
@@ -27,37 +24,24 @@ dependencies:
   - click=8.0.4=py38h06a4308_0
   - colorama=0.4.6=py38h06a4308_0
   - comm=0.1.2=py38h06a4308_0
-  - contourpy=1.0.5=py38hdb19cb5_0
   - cryptography=39.0.1=py38h9ce1e76_0
-  - cycler=0.11.0=pyhd3eb1b0_0
-  - dbus=1.13.18=hb2f20db_0
   - debugpy=1.5.1=py38h295c915_0
   - decorator=5.1.1=pyhd3eb1b0_0
   - defusedxml=0.7.1=pyhd3eb1b0_0
   - dill=0.3.6=py38h06a4308_0
   - entrypoints=0.4=py38h06a4308_0
   - executing=0.8.3=pyhd3eb1b0_0
-  - expat=2.4.9=h6a678d5_0
   - flit-core=3.6.0=pyhd3eb1b0_0
-  - fontconfig=2.14.1=h52c9d5c_1
-  - fonttools=4.25.0=pyhd3eb1b0_0
-  - freetype=2.12.1=h4a9f257_0
-  - giflib=5.2.1=h5eee18b_3
-  - glib=2.69.1=he621ea3_2
-  - gst-plugins-base=1.14.1=h6a678d5_1
-  - gstreamer=1.14.1=h5eee18b_1
   - icu=58.2=he6710b0_3
   - idna=3.4=py38h06a4308_0
   - importlib-metadata=4.11.3=py38h06a4308_0
   - importlib_resources=5.2.0=pyhd3eb1b0_1
-  - intel-openmp=2021.4.0=h06a4308_3561
   - ipykernel=6.19.2=py38hb070fc8_0
   - ipython=8.10.0=py38h06a4308_0
   - ipython_genutils=0.2.0=pyhd3eb1b0_1
   - isort=5.9.3=pyhd3eb1b0_0
   - jedi=0.18.1=py38h06a4308_1
   - jinja2=3.1.2=py38h06a4308_0
-  - jpeg=9e=h7f8727e_0
   - json5=0.9.6=pyhd3eb1b0_0
   - jsonschema=4.17.3=py38h06a4308_0
   - jupyter_client=7.4.9=py38h06a4308_0
@@ -67,48 +51,20 @@ dependencies:
   - jupyterlab_code_formatter=1.5.3=pyhd8ed1ab_0
   - jupyterlab_pygments=0.1.2=py_0
   - jupyterlab_server=2.19.0=py38h06a4308_0
-  - kiwisolver=1.4.4=py38h6a678d5_0
-  - krb5=1.19.4=h568e23c_0
   - lazy-object-proxy=1.6.0=py38h27cfd23_0
-  - lcms2=2.12=h3be6417_0
   - ld_impl_linux-64=2.38=h1181459_1
-  - lerc=3.0=h295c915_0
-  - libbrotlicommon=1.0.9=h5eee18b_7
-  - libbrotlidec=1.0.9=h5eee18b_7
-  - libbrotlienc=1.0.9=h5eee18b_7
-  - libclang=10.0.1=default_hb85057a_2
-  - libdeflate=1.17=h5eee18b_0
-  - libedit=3.1.20221030=h5eee18b_0
-  - libevent=2.1.12=h8f2d780_0
   - libffi=3.4.2=h6a678d5_6
   - libgcc-ng=11.2.0=h1234567_1
   - libgomp=11.2.0=h1234567_1
-  - libllvm10=10.0.1=hbcb73fb_5
-  - libpng=1.6.37=hbc83047_0
-  - libpq=12.9=h16c4e8d_3
   - libsodium=1.0.18=h7b6447c_0
   - libstdcxx-ng=11.2.0=h1234567_1
-  - libtiff=4.5.0=h6a678d5_2
-  - libuuid=1.41.5=h5eee18b_0
-  - libwebp=1.2.4=h11a3e52_1
-  - libwebp-base=1.2.4=h5eee18b_1
-  - libxcb=1.15=h7f8727e_0
-  - libxkbcommon=1.0.1=hfa300c1_0
   - libxml2=2.9.14=h74e7548_0
   - libxslt=1.1.35=h4e12654_0
   - lxml=4.9.1=py38h1edc446_0
-  - lz4-c=1.9.4=h6a678d5_0
   - markupsafe=2.1.1=py38h7f8727e_0
-  - matplotlib=3.7.0=py38h06a4308_0
-  - matplotlib-base=3.7.0=py38h417a72b_0
   - matplotlib-inline=0.1.6=py38h06a4308_0
   - mccabe=0.7.0=pyhd3eb1b0_0
   - mistune=0.8.4=py38h7b6447c_1000
-  - mkl=2021.4.0=h06a4308_640
-  - mkl-service=2.4.0=py38h7f8727e_0
-  - mkl_fft=1.3.1=py38hd3c417c_0
-  - mkl_random=1.2.2=py38h51133e4_0
-  - munkres=1.1.4=py_0
   - mypy_extensions=0.4.3=py38h06a4308_1
   - nbclassic=0.5.2=py38h06a4308_0
   - nbclient=0.5.13=py38h06a4308_0
@@ -119,23 +75,16 @@ dependencies:
   - nest-asyncio=1.5.6=py38h06a4308_0
   - notebook=6.5.2=py38h06a4308_0
   - notebook-shim=0.2.2=py38h06a4308_0
-  - nspr=4.33=h295c915_0
-  - nss=3.74=h0370c37_0
-  - numpy=1.23.5=py38h14f4228_0
-  - numpy-base=1.23.5=py38h31eccc5_0
   - openssl=1.1.1t=h7f8727e_0
   - packaging=22.0=py38h06a4308_0
   - pandocfilters=1.5.0=pyhd3eb1b0_0
   - parso=0.8.3=pyhd3eb1b0_0
   - pathspec=0.10.3=py38h06a4308_0
-  - pcre=8.45=h295c915_0
   - pexpect=4.8.0=pyhd3eb1b0_3
   - pickleshare=0.7.5=pyhd3eb1b0_1003
-  - pillow=9.4.0=py38h6a678d5_0
   - pip=22.3.1=py38h06a4308_0
   - pkgutil-resolve-name=1.3.10=py38h06a4308_0
   - platformdirs=2.5.2=py38h06a4308_0
-  - ply=3.11=py38_0
   - prometheus_client=0.14.1=py38h06a4308_0
   - prompt-toolkit=3.0.36=py38h06a4308_0
   - psutil=5.9.0=py38h5eee18b_0
@@ -145,9 +94,6 @@ dependencies:
   - pygments=2.11.2=pyhd3eb1b0_0
   - pylint=2.16.2=py38h06a4308_0
   - pyopenssl=23.0.0=py38h06a4308_0
-  - pyparsing=3.0.9=py38h06a4308_0
-  - pyqt=5.15.7=py38h6a678d5_1
-  - pyqt5-sip=12.11.0=py38h6a678d5_1
   - pyrsistent=0.18.0=py38heee7806_0
   - pysocks=1.7.1=py38h06a4308_0
   - python=3.8.16=h7a1cb2a_2
@@ -155,14 +101,10 @@ dependencies:
   - python-fastjsonschema=2.16.2=py38h06a4308_0
   - pytz=2022.7=py38h06a4308_0
   - pyzmq=23.2.0=py38h6a678d5_0
-  - qt-main=5.15.2=h327a75a_7
-  - qt-webengine=5.15.9=hd2b0992_4
-  - qtwebkit=5.212=h4eab89a_4
   - readline=8.2=h5eee18b_0
   - requests=2.28.1=py38h06a4308_0
   - send2trash=1.8.0=pyhd3eb1b0_1
   - setuptools=65.6.3=py38h06a4308_0
-  - sip=6.6.2=py38h6a678d5_0
   - six=1.16.0=pyhd3eb1b0_1
   - sniffio=1.2.0=py38h06a4308_1
   - soupsieve=2.3.2.post1=py38h06a4308_0
@@ -171,7 +113,6 @@ dependencies:
   - terminado=0.17.1=py38h06a4308_0
   - tinycss2=1.2.1=py38h06a4308_0
   - tk=8.6.12=h1ccaba5_0
-  - toml=0.10.2=pyhd3eb1b0_0
   - tomli=2.0.1=py38h06a4308_0
   - tomlkit=0.11.1=py38h06a4308_0
   - tornado=6.2=py38h5eee18b_0
@@ -188,5 +129,4 @@ dependencies:
   - zeromq=4.3.4=h2531618_0
   - zipp=3.11.0=py38h06a4308_0
   - zlib=1.2.13=h5eee18b_0
-  - zstd=1.5.2=ha4553b6_0
 prefix: /home/jeff/anaconda3/envs/spideralgorithm
diff --git a/spider-algorithm.pdf b/spider-algorithm.pdf
index 266f811e8c2337e6a89d2e28ef47ffa2c944aa1e..f1c8b0d855f444b1722a9009be1346b3fd89103f 100644
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From 093cd32828af20b1fa07e30e62745f9515a11811 Mon Sep 17 00:00:00 2001
From: Jeff Lantz <jcl393@cornell.edu>
Date: Wed, 1 Mar 2023 18:54:53 -0500
Subject: [PATCH 6/6] Updating conda environment

---
 env.yml | 60 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/env.yml b/env.yml
index 316efd4..d7c73ab 100644
--- a/env.yml
+++ b/env.yml
@@ -15,7 +15,10 @@ dependencies:
   - backcall=0.2.0=pyhd3eb1b0_0
   - beautifulsoup4=4.11.1=py38h06a4308_0
   - black=22.6.0=py38h06a4308_0
+  - blas=1.0=mkl
   - bleach=4.1.0=pyhd3eb1b0_0
+  - brotli=1.0.9=h5eee18b_7
+  - brotli-bin=1.0.9=h5eee18b_7
   - brotlipy=0.7.0=py38h27cfd23_1003
   - ca-certificates=2023.01.10=h06a4308_0
   - certifi=2022.12.7=py38h06a4308_0
@@ -24,24 +27,37 @@ dependencies:
   - click=8.0.4=py38h06a4308_0
   - colorama=0.4.6=py38h06a4308_0
   - comm=0.1.2=py38h06a4308_0
+  - contourpy=1.0.5=py38hdb19cb5_0
   - cryptography=39.0.1=py38h9ce1e76_0
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - dbus=1.13.18=hb2f20db_0
   - debugpy=1.5.1=py38h295c915_0
   - decorator=5.1.1=pyhd3eb1b0_0
   - defusedxml=0.7.1=pyhd3eb1b0_0
   - dill=0.3.6=py38h06a4308_0
   - entrypoints=0.4=py38h06a4308_0
   - executing=0.8.3=pyhd3eb1b0_0
+  - expat=2.4.9=h6a678d5_0
   - flit-core=3.6.0=pyhd3eb1b0_0
+  - fontconfig=2.14.1=h52c9d5c_1
+  - fonttools=4.25.0=pyhd3eb1b0_0
+  - freetype=2.12.1=h4a9f257_0
+  - giflib=5.2.1=h5eee18b_3
+  - glib=2.69.1=he621ea3_2
+  - gst-plugins-base=1.14.1=h6a678d5_1
+  - gstreamer=1.14.1=h5eee18b_1
   - icu=58.2=he6710b0_3
   - idna=3.4=py38h06a4308_0
   - importlib-metadata=4.11.3=py38h06a4308_0
   - importlib_resources=5.2.0=pyhd3eb1b0_1
+  - intel-openmp=2021.4.0=h06a4308_3561
   - ipykernel=6.19.2=py38hb070fc8_0
   - ipython=8.10.0=py38h06a4308_0
   - ipython_genutils=0.2.0=pyhd3eb1b0_1
   - isort=5.9.3=pyhd3eb1b0_0
   - jedi=0.18.1=py38h06a4308_1
   - jinja2=3.1.2=py38h06a4308_0
+  - jpeg=9e=h7f8727e_0
   - json5=0.9.6=pyhd3eb1b0_0
   - jsonschema=4.17.3=py38h06a4308_0
   - jupyter_client=7.4.9=py38h06a4308_0
@@ -51,20 +67,48 @@ dependencies:
   - jupyterlab_code_formatter=1.5.3=pyhd8ed1ab_0
   - jupyterlab_pygments=0.1.2=py_0
   - jupyterlab_server=2.19.0=py38h06a4308_0
+  - kiwisolver=1.4.4=py38h6a678d5_0
+  - krb5=1.19.4=h568e23c_0
   - lazy-object-proxy=1.6.0=py38h27cfd23_0
+  - lcms2=2.12=h3be6417_0
   - ld_impl_linux-64=2.38=h1181459_1
+  - lerc=3.0=h295c915_0
+  - libbrotlicommon=1.0.9=h5eee18b_7
+  - libbrotlidec=1.0.9=h5eee18b_7
+  - libbrotlienc=1.0.9=h5eee18b_7
+  - libclang=10.0.1=default_hb85057a_2
+  - libdeflate=1.17=h5eee18b_0
+  - libedit=3.1.20221030=h5eee18b_0
+  - libevent=2.1.12=h8f2d780_0
   - libffi=3.4.2=h6a678d5_6
   - libgcc-ng=11.2.0=h1234567_1
   - libgomp=11.2.0=h1234567_1
+  - libllvm10=10.0.1=hbcb73fb_5
+  - libpng=1.6.37=hbc83047_0
+  - libpq=12.9=h16c4e8d_3
   - libsodium=1.0.18=h7b6447c_0
   - libstdcxx-ng=11.2.0=h1234567_1
+  - libtiff=4.5.0=h6a678d5_2
+  - libuuid=1.41.5=h5eee18b_0
+  - libwebp=1.2.4=h11a3e52_1
+  - libwebp-base=1.2.4=h5eee18b_1
+  - libxcb=1.15=h7f8727e_0
+  - libxkbcommon=1.0.1=hfa300c1_0
   - libxml2=2.9.14=h74e7548_0
   - libxslt=1.1.35=h4e12654_0
   - lxml=4.9.1=py38h1edc446_0
+  - lz4-c=1.9.4=h6a678d5_0
   - markupsafe=2.1.1=py38h7f8727e_0
+  - matplotlib=3.7.0=py38h06a4308_0
+  - matplotlib-base=3.7.0=py38h417a72b_0
   - matplotlib-inline=0.1.6=py38h06a4308_0
   - mccabe=0.7.0=pyhd3eb1b0_0
   - mistune=0.8.4=py38h7b6447c_1000
+  - mkl=2021.4.0=h06a4308_640
+  - mkl-service=2.4.0=py38h7f8727e_0
+  - mkl_fft=1.3.1=py38hd3c417c_0
+  - mkl_random=1.2.2=py38h51133e4_0
+  - munkres=1.1.4=py_0
   - mypy_extensions=0.4.3=py38h06a4308_1
   - nbclassic=0.5.2=py38h06a4308_0
   - nbclient=0.5.13=py38h06a4308_0
@@ -75,16 +119,23 @@ dependencies:
   - nest-asyncio=1.5.6=py38h06a4308_0
   - notebook=6.5.2=py38h06a4308_0
   - notebook-shim=0.2.2=py38h06a4308_0
+  - nspr=4.33=h295c915_0
+  - nss=3.74=h0370c37_0
+  - numpy=1.23.5=py38h14f4228_0
+  - numpy-base=1.23.5=py38h31eccc5_0
   - openssl=1.1.1t=h7f8727e_0
   - packaging=22.0=py38h06a4308_0
   - pandocfilters=1.5.0=pyhd3eb1b0_0
   - parso=0.8.3=pyhd3eb1b0_0
   - pathspec=0.10.3=py38h06a4308_0
+  - pcre=8.45=h295c915_0
   - pexpect=4.8.0=pyhd3eb1b0_3
   - pickleshare=0.7.5=pyhd3eb1b0_1003
+  - pillow=9.4.0=py38h6a678d5_0
   - pip=22.3.1=py38h06a4308_0
   - pkgutil-resolve-name=1.3.10=py38h06a4308_0
   - platformdirs=2.5.2=py38h06a4308_0
+  - ply=3.11=py38_0
   - prometheus_client=0.14.1=py38h06a4308_0
   - prompt-toolkit=3.0.36=py38h06a4308_0
   - psutil=5.9.0=py38h5eee18b_0
@@ -94,6 +145,9 @@ dependencies:
   - pygments=2.11.2=pyhd3eb1b0_0
   - pylint=2.16.2=py38h06a4308_0
   - pyopenssl=23.0.0=py38h06a4308_0
+  - pyparsing=3.0.9=py38h06a4308_0
+  - pyqt=5.15.7=py38h6a678d5_1
+  - pyqt5-sip=12.11.0=py38h6a678d5_1
   - pyrsistent=0.18.0=py38heee7806_0
   - pysocks=1.7.1=py38h06a4308_0
   - python=3.8.16=h7a1cb2a_2
@@ -101,10 +155,14 @@ dependencies:
   - python-fastjsonschema=2.16.2=py38h06a4308_0
   - pytz=2022.7=py38h06a4308_0
   - pyzmq=23.2.0=py38h6a678d5_0
+  - qt-main=5.15.2=h327a75a_7
+  - qt-webengine=5.15.9=hd2b0992_4
+  - qtwebkit=5.212=h4eab89a_4
   - readline=8.2=h5eee18b_0
   - requests=2.28.1=py38h06a4308_0
   - send2trash=1.8.0=pyhd3eb1b0_1
   - setuptools=65.6.3=py38h06a4308_0
+  - sip=6.6.2=py38h6a678d5_0
   - six=1.16.0=pyhd3eb1b0_1
   - sniffio=1.2.0=py38h06a4308_1
   - soupsieve=2.3.2.post1=py38h06a4308_0
@@ -113,6 +171,7 @@ dependencies:
   - terminado=0.17.1=py38h06a4308_0
   - tinycss2=1.2.1=py38h06a4308_0
   - tk=8.6.12=h1ccaba5_0
+  - toml=0.10.2=pyhd3eb1b0_0
   - tomli=2.0.1=py38h06a4308_0
   - tomlkit=0.11.1=py38h06a4308_0
   - tornado=6.2=py38h5eee18b_0
@@ -129,4 +188,5 @@ dependencies:
   - zeromq=4.3.4=h2531618_0
   - zipp=3.11.0=py38h06a4308_0
   - zlib=1.2.13=h5eee18b_0
+  - zstd=1.5.2=ha4553b6_0
 prefix: /home/jeff/anaconda3/envs/spideralgorithm