add files

random/.ipynb_checkpoints/syndata_color-checkpoint.ipynb

@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glob import glob \n",
+    "import numpy as np\n",
+    "\n",
+    "training_data_loc = \"../data/training\"\n",
+    "evaluation_data_loc = \"../data/evaluation\"\n",
+    "\n",
+    "training_files= glob(training_data_loc+\"/*\")\n",
+    "evaluation_files = glob(evaluation_data_loc+\"/*\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'../data/training/a85d4709.json'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_files[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(400, 400)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(training_files),len(evaluation_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## function to read in from json file\n",
+    "import json\n",
+    "def read_json(file):\n",
+    "    with open(file) as f:\n",
+    "        data = json.load(f)\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_json = [read_json(file) for file in training_files]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['train', 'test'])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_json[0].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'input': [[0, 0, 5], [5, 0, 0], [0, 5, 0]],\n",
+       "  'output': [[3, 3, 3], [2, 2, 2], [4, 4, 4]]}]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_json[0][\"test\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lk = [np.array(k).shape for i in training_json for j in i[\"train\"] for k in j.values()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xi = []\n",
+    "for k in range(1,30):\n",
+    "    xi.append(len([i for i in lk if i[0] <= k])/len(lk))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff1095f59f0>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(xi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python311",
+   "language": "python",
+   "name": "python311"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}