L-Nafaryus/anisotropy
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anisotropy/playground/analytics.ipynb

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New: failed analytics
2021-10-06 00:58:37 +05:00
{
"cells": [
{
"cell_type": "code",
Mod: -
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"execution_count": 2,
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"id": "cbaf1c39-e423-47a3-a3ea-e78a528bc4c1",
"metadata": {},
"outputs": [],
"source": [
"from anisotropy.core.database import Database, Structure\n",
"import pandas\n",
"from pandas import DataFrame, Series\n",
"import matplotlib.pyplot as plt\n",
"import seaborn\n",
"import numpy\n",
"import warnings\n",
"\n",
"# ignore some warnings, especially from seaborn\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
Mod: -
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"execution_count": 3,
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"id": "09b1bf83-6b42-4144-81a0-b57c661181b1",
"metadata": {},
"outputs": [],
"source": [
"db = Database(\"anisotropy\", \"woPrismaticLayer\")\n",
"db.setup()"
]
},
{
"cell_type": "code",
Mod: -
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"execution_count": 4,
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"id": "f341fbd4-797b-4d5d-8393-0f7cf2e67883",
"metadata": {},
"outputs": [],
"source": [
"res = db.search([])\n",
"df = DataFrame(res)\n",
"\n",
"df_prep = df[[\n",
" col for col in df.columns \n",
" if not isinstance(df[col][0], str) \n",
" and not isinstance(df[col][0], numpy.bool_)\n",
" and not isinstance(df[col][0], dict)\n",
" and not isinstance(df[col][0], list)\n",
" and not df[col][0] is None\n",
" and not col[-3: ] == \"_id\"\n",
"]]"
]
},
{
"cell_type": "code",
Mod: -
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"execution_count": 5,
New: failed analytics
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"id": "c2dbce30-d80a-4b78-b2a6-cf9c2075f5b5",
"metadata": {},
"outputs": [],
"source": [
"df_prep = df_prep.assign(direction = df[\"direction\"].astype(\"str\"))\n",
"df_prep = df_prep.assign(type = df[\"type\"])"
]
},
{
"cell_type": "code",
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
"execution_count": 5,
New: failed analytics
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"id": "d182461e-1e42-4e0b-8dd2-ba6425654ee7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
"<seaborn.axisgrid.FacetGrid at 0x7f7cb8333af0>"
New: failed analytics
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]
},
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
"execution_count": 5,
New: failed analytics
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1184x360 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"seaborn.set_style('whitegrid')\n",
"seaborn.lmplot(\n",
" x = \"theta\", y = \"flowRate\", \n",
" hue = \"direction\", col = \"type\", \n",
" data = df_prep, order = 3, \n",
" sharex = False, sharey = False, ci = None)"
]
},
{
"cell_type": "code",
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
"execution_count": 6,
New: failed analytics
2021-10-06 00:58:37 +05:00
"id": "7b7092aa-de0e-430c-9ed5-07483b278d45",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>direction</th>\n",
" <th>theta</th>\n",
" <th>meshStatus</th>\n",
" <th>flowStatus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>bodyCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.01</td>\n",
" <td>Done</td>\n",
" <td>Failed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>bodyCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.13</td>\n",
" <td>Done</td>\n",
" <td>Failed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>bodyCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.14</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>bodyCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.15</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>bodyCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.17</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.13</td>\n",
" <td>Done</td>\n",
" <td>Failed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.14</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.15</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.16</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.17</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.11</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.12</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.13</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.14</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.15</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.16</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>bodyCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.17</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>faceCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.06</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>155</th>\n",
" <td>faceCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.09</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>faceCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.10</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>157</th>\n",
" <td>faceCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.11</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>faceCentered</td>\n",
" <td>[0.0, 0.0, 1.0]</td>\n",
" <td>0.12</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.03</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.09</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.10</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.11</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.12</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.09</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.10</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.11</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>faceCentered</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.12</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.03</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.05</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.06</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.09</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.10</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.11</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.12</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.13</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.14</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.15</td>\n",
" <td>Failed</td>\n",
" <td>Idle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.27</td>\n",
" <td>Done</td>\n",
" <td>Failed</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.28</td>\n",
" <td>Done</td>\n",
" <td>Failed</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type direction theta meshStatus flowStatus\n",
"101 bodyCentered [0.0, 0.0, 1.0] 0.01 Done Failed\n",
"113 bodyCentered [0.0, 0.0, 1.0] 0.13 Done Failed\n",
"114 bodyCentered [0.0, 0.0, 1.0] 0.14 Failed Idle\n",
"115 bodyCentered [0.0, 0.0, 1.0] 0.15 Failed Idle\n",
"117 bodyCentered [0.0, 0.0, 1.0] 0.17 Failed Idle\n",
"96 bodyCentered [1.0, 0.0, 0.0] 0.13 Done Failed\n",
"97 bodyCentered [1.0, 0.0, 0.0] 0.14 Failed Idle\n",
"98 bodyCentered [1.0, 0.0, 0.0] 0.15 Failed Idle\n",
"99 bodyCentered [1.0, 0.0, 0.0] 0.16 Failed Idle\n",
"100 bodyCentered [1.0, 0.0, 0.0] 0.17 Failed Idle\n",
"128 bodyCentered [1.0, 1.0, 1.0] 0.11 Failed Idle\n",
"129 bodyCentered [1.0, 1.0, 1.0] 0.12 Failed Idle\n",
"130 bodyCentered [1.0, 1.0, 1.0] 0.13 Failed Idle\n",
"131 bodyCentered [1.0, 1.0, 1.0] 0.14 Failed Idle\n",
"132 bodyCentered [1.0, 1.0, 1.0] 0.15 Failed Idle\n",
"133 bodyCentered [1.0, 1.0, 1.0] 0.16 Failed Idle\n",
"134 bodyCentered [1.0, 1.0, 1.0] 0.17 Failed Idle\n",
"152 faceCentered [0.0, 0.0, 1.0] 0.06 Failed Idle\n",
"155 faceCentered [0.0, 0.0, 1.0] 0.09 Failed Idle\n",
"156 faceCentered [0.0, 0.0, 1.0] 0.10 Failed Idle\n",
"157 faceCentered [0.0, 0.0, 1.0] 0.11 Failed Idle\n",
"158 faceCentered [0.0, 0.0, 1.0] 0.12 Failed Idle\n",
"137 faceCentered [1.0, 0.0, 0.0] 0.03 Failed Idle\n",
"143 faceCentered [1.0, 0.0, 0.0] 0.09 Failed Idle\n",
"144 faceCentered [1.0, 0.0, 0.0] 0.10 Failed Idle\n",
"145 faceCentered [1.0, 0.0, 0.0] 0.11 Failed Idle\n",
"146 faceCentered [1.0, 0.0, 0.0] 0.12 Failed Idle\n",
"167 faceCentered [1.0, 1.0, 1.0] 0.09 Failed Idle\n",
"168 faceCentered [1.0, 1.0, 1.0] 0.10 Failed Idle\n",
"169 faceCentered [1.0, 1.0, 1.0] 0.11 Failed Idle\n",
"170 faceCentered [1.0, 1.0, 1.0] 0.12 Failed Idle\n",
"58 simple [1.0, 1.0, 1.0] 0.03 Failed Idle\n",
"60 simple [1.0, 1.0, 1.0] 0.05 Failed Idle\n",
"61 simple [1.0, 1.0, 1.0] 0.06 Failed Idle\n",
"64 simple [1.0, 1.0, 1.0] 0.09 Failed Idle\n",
"65 simple [1.0, 1.0, 1.0] 0.10 Failed Idle\n",
"66 simple [1.0, 1.0, 1.0] 0.11 Failed Idle\n",
"67 simple [1.0, 1.0, 1.0] 0.12 Failed Idle\n",
"68 simple [1.0, 1.0, 1.0] 0.13 Failed Idle\n",
"69 simple [1.0, 1.0, 1.0] 0.14 Failed Idle\n",
"70 simple [1.0, 1.0, 1.0] 0.15 Failed Idle\n",
"82 simple [1.0, 1.0, 1.0] 0.27 Done Failed\n",
"83 simple [1.0, 1.0, 1.0] 0.28 Done Failed"
]
},
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
"execution_count": 6,
New: failed analytics
2021-10-06 00:58:37 +05:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"failed = df[[\"type\", \"direction\", \"theta\", \"meshStatus\", \"flowStatus\"]][\n",
" (df[\"meshStatus\"] == \"Failed\") | (df[\"flowStatus\"] == \"Failed\")\n",
"].assign(\n",
" direction = df[\"direction\"].astype(\"str\")\n",
").sort_values(\n",
" by = [\"type\", \"direction\", \"theta\"]\n",
")\n",
"failed"
]
},
{
"cell_type": "code",
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
"execution_count": 12,
New: failed analytics
2021-10-06 00:58:37 +05:00
"id": "1a25020c-5bcc-4e46-985f-cfe55c30d117",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='theta', ylabel='chordalError'>"
]
},
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
"execution_count": 12,
New: failed analytics
2021-10-06 00:58:37 +05:00
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"simple3 = df.assign(direction = df[\"direction\"].astype(\"str\"))\n",
"simple3 = simple3[\n",
" (simple3[\"type\"] == \"simple\") & (simple3[\"direction\"] == \"[1.0, 1.0, 1.0]\")\n",
"]\n",
"\n",
"seaborn.scatterplot(data = simple3, x = \"theta\", y = \"chordalError\", hue = \"meshStatus\")\n",
"seaborn.lineplot(data = simple3, x = \"theta\", y = \"minSize\", color = \"grey\", label = \"minSize\")\n",
"seaborn.lineplot(data = simple3, x = \"theta\", y = \"maxSize\", color = \"black\", label = \"maxSize\")"
]
},
Mod: improved geometry construction by adding flexibility New: StructureGeometry class Fix: fixed invalid shapes (external edges on shapes, etc) New: issues list
2021-10-06 22:15:05 +05:00
{
"cell_type": "code",
"execution_count": 13,
"id": "78e0c05f-3161-404a-9ed7-cd9ebc981a43",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='theta', ylabel='fillets'>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"seaborn.scatterplot(data = simple3.round({ \"fillets\": 2 }), x = \"theta\", y = \"fillets\", hue = \"meshStatus\")"
]
},
New: failed analytics
2021-10-06 00:58:37 +05:00
{
"cell_type": "code",
"execution_count": 42,
"id": "7d6de026-fe25-497f-a872-66b249e0e979",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='theta', ylabel='chordalError'>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA3VklEQVR4nO3deVxU9f4/8NfsDPs+oAyIQoqCYeWKSYKIiWtqWlqWkdWjxe71ZnX7RWVfl7K6od30mmU3NVvMcsG0JLtmpZlLRJK5jQwIw77Peub8/qA5Mc7CIA7LnPfz8ZjHnHPmc858PjPwec3ZBSzLsiCEEMJbwu6uACGEkO5FQUAIITxHQUAIITxHQUAIITxHQUAIITwn7u4KdNTp06chk8m4cb1ebzXuaTy9fYDnt5Ha1/t5Qhv1ej2Sk5PtvtbrgkAmkyEhIYEbLyoqshr3NJ7ePsDz20jt6/08oY1FRUUOX6NNQ4QQwnMUBIQQwnMUBIQQwnMUBIQQwnMUBIQQwnMUBIQQwnMUBIQQwnO97jwCQgjhA5ZlodFocPHiRVy8eBGVlZVYuHAhgoODr/t7URAQQkg30ev1UKlUXGd/4cIFq+eWlhaurEQiwZgxYzBy5MjrXg8KAkIIcROGYVBSUgKVSoVLly5xD8t4aWkp2t4bTC6Xo3///hgwYAAmTJjADffv3x/9+vWDl5eXW+pJQUAIIdfIZDKhtLQUly9f5h4qlYrr6IuLi2EymbjyAoEAUVFR6NevH9LS0hAbG2vV2UdEREAgEHR5OygICCHEgebmZqjVanz//fc4cuSIVYd/+fJllJaWgmEYq3kUCgViY2MxYsQIzJ07F7GxsejXrx9iY2MRHR0NqVTaTa1xjIKAEMJLRqMRpaWlUKvVKC4uhlqt5h6W8ZqaGqt5RCIRoqKiEBMTg9TUVMTExCA6OhoxMTHcsFwu76YWXTsKAkKIx9HpdCgtLUVJSQlKSkrsDpeVlVltnweAoKAgKJVKREdHY8yYMVAqlVAqlWBZFqmpqejTpw/EYs/rNj2vRYQQj8UwDCorK3HlyhWbR2lpKdfJV1dX28zr7++PqKgoREVFYciQIVyHb+nslUolfH197b5vUVERoqOj3d28bkNBQAjpdkajERUVFSgrK0N5eTnKy8tRVlaGsrIyq86+vLzcZpu8QCBAeHg4IiMjER0djdGjR3Mdft++fblnPz+/bmpdz0dBQAhxC4ZhUF1djYqKCmg0Gu7RtqO3DFdVVdlspgGA4OBg9O3bF3369EFiYiL69Olj81AoFJBIJN3QQs9BQUAIcVlzczMqKipQWVnJPQoLC7mzYNt2+pWVlTCbzTbLkEqliIiIQGRkJPr374+UlBRERERw0yzDCoWi198esrdwaxAcPnwYK1asgNlsxpw5c7B48WKr11euXIljx44BaN25U11djZ9//tmdVSKE/IlhGNTU1KC6uhpVVVWoqqrihtt29JWVlVznr9Vq7S5LLpdDoVBAoVCgX79+GDlyJMLDw7lpbR+BgYHdcqw8ccxtQcAwDJYvX47NmzdDoVBg9uzZSEtLQ1xcHFfmn//8Jze8ZcsWnDlzxl3VIcRjsSyLpqYm1NTUcI/q6mqbYUsnb3mura21uzkGaO3Yw8LCEB4ejrCwMAwePBhhYWHcwzI9LCwMNTU1uPnmm6lz78XcFgQFBQWIiYmBUqkEAGRlZSE/P98qCNrKy8vD448/7q7qENKjWTrzuro61NbWcs/2Hm1fs3T2RqPR4bK9vb0RHByM0NBQhISEIDo6GqGhody4ZbjtNG9vb5frrtfrKQR6ObcFgUajQUREBDeuUChQUFBgt6zlkK9Ro0a1u1y9Xo+ioiJuXKfTWY17Gk9vH9D728gwDJqbm9HU1MQ9t33U1dVBq9WisbERTU1NaGhosHq2TLe3Pd1CIBDAz88P/v7+3EOpVCIxMREBAQEICAhAYGCgzbO/v3+HtrNb6twRvf37c4Wnt7FH7CzOy8tDZmYmRCJRu2VlMhkSEhK48aKiIqtxT+Pp7QO6to0sy0Kv16O5udmq825ubkZjYyPXKbd9djStoaEBDQ0NaG5ubvd9hUIh/P39uU46ICAAkZGRNp14QEAAgoKCbB7+/v4QCnvm7UPob7R3cBZkbgsChUKB8vJyblyj0UChUNgtu2/fPuTk5LirKqSXMBqN0Ol00Gq1Th8tLS0uPSwd/NUd/tXHoTvj7e0NPz8/+Pr6ws/PD35+fggLC8OAAQOsfp07e5SVleHmm2/usR05IW4LgqSkJKhUKqjVaigUCuTl5eH111+3KXfhwgU0NDRg2LBh7qoKQesvYbPZDJPJBKPRyD0bjUYYDAaXhg0GA/R6vUvPOp0Oer3e6bNluLm5GXq9vkMddFsSiQTe3t52H8HBwfDx8YGPjw98fX2dDl/d4fv4+Li0ltqexsZGCgHSo7ktCMRiMXJycpCdnQ2GYTBr1izEx8cjNzcXiYmJSE9PB9C6NjB58mS372xSq9V44403YDAYAIB7v7bva29a26MqLMP2nq9+mM1mp+MMw8BsNnOPq8ct0xobGyGTycAwDPcwmUx2hy3jlo7e8rCMu5NAIIBMJoNMJoNUKoWXlxe8vLwgk8msngMDA7lhy/SWlhb07dsXcrmce3h5eVmNt53m4+PDdfRyuZxOJiKkk9y6jyA1NRWpqalW05YsWWI13lVHChUXF+OTTz6BwWCw6cjbDrd9diUs2j5bHkKh0Om4QCCASCSCSCSCUCjkHlePC4VCGI1GrsMUiUQQi8XcvPbGJRIJxGIxxGKx1bCjcYlEAqlU6tKwpZO3PLcd7syFuDxh+yshvVmP2FncFVJSUlBaWtrd1egw6iQJIe5GGy4JIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTnKAgIIYTn3BoEhw8fRmZmJjIyMrBx40a7Zfbt24fJkycjKysLS5cudWd1CCGE2OG2m9czDIPly5dj8+bNUCgUmD17NtLS0hAXF8eVUalU2LhxI7Zv346AgABUV1e7qzqEEEIccNsaQUFBAWJiYqBUKiGVSpGVlYX8/HyrMp988gnmz5+PgIAAAEBISIi7qkMIIcQBt60RaDQaREREcOMKhQIFBQVWZVQqFQBg3rx5MJvNeOyxxzBu3Diny9Xr9SgqKuLGdTqd1bin8fT2AZ7fRmpf7+fpbXRbELiCYRhcvnwZW7ZsQXl5ORYsWIA9e/bA39/f4TwymQwJCQnceFFRkdW4p/H09gGe30ZqX+/nCW10FmRu2zSkUChQXl7OjWs0GigUCpsyaWlpkEgkUCqV6NevH7eWQAghpGu4LQiSkpKgUqmgVqthMBiQl5eHtLQ0qzITJkzATz/9BACoqamBSqWCUql0V5UIIYTY4bZNQ2KxGDk5OcjOzgbDMJg1axbi4+ORm5uLxMREpKen49Zbb8X333+PyZMnQyQSYdmyZQgKCnJXlQghhNjh1n0EqampSE1NtZq2ZMkSblggEODZZ5/Fs88+685qEEIIcYLOLCaEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ6jICCEEJ5zaxAcPnwYmZmZyMjIwMaNG21e37lzJ0aNGoXp06dj+vTp+PTTT91ZHUIIIXaI3bVghmGwfPlybN68GQqFArNnz0ZaWhri4uKsyk2ePBk5OTnuqgYhhJB2uG2NoKCgADExMVAqlZBKpcjKykJ+fr673o4QQsg1ajcIGIbB+++/3+EFazQaREREcOMKhQIajcam3FdffYWpU6fiiSeeQFlZWYffhxBCSOe0u2lIJBJh7969uO+++677m48fPx5TpkyBVCrFRx99hKeffhoffPCB03n0ej2Kioq
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"simple1 = df.assign(direction = df[\"direction\"].astype(\"str\"))\n",
"simple1 = simple1[\n",
" (simple1[\"type\"] == \"simple\") & (simple1[\"direction\"] == \"[1.0, 0.0, 0.0]\")\n",
"]\n",
"\n",
"seaborn.scatterplot(data = simple1, x = \"theta\", y = \"chordalError\", hue = \"meshStatus\")\n",
"seaborn.lineplot(data = simple1, x = \"theta\", y = \"minSize\", color = \"grey\", label = \"minSize\")\n",
"seaborn.lineplot(data = simple1, x = \"theta\", y = \"maxSize\", color = \"black\", label = \"maxSize\")"
]
},
{
"cell_type": "code",
Mod: -
2021-10-10 21:46:54 +05:00
"execution_count": 8,
"id": "e994037e-e266-444a-9ee3-80cccdf8386f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='theta', ylabel='fillets'>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fc = df.assign(direction = df[\"direction\"].astype(\"str\"))\n",
"fc = fc[\n",
" (fc[\"type\"] == \"faceCentered\") & (fc[\"direction\"] == \"[1.0, 1.0, 1.0]\")\n",
"]\n",
"seaborn.set_style('whitegrid')\n",
"seaborn.scatterplot(data = fc, x = \"theta\", y = \"fillets\", hue = \"meshStatus\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b541a36e-f3b2-4b50-bfc9-ec8958666053",
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"metadata": {},
"outputs": [],
"source": [
"general: list = db.loadGeneral()\n",
Mod: -
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"dfe: list = [] #DataFrame()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2d93a051-acab-448b-a7fd-5fe281cc323f",
"metadata": {},
"outputs": [],
"source": [
"def pandasify(listOfDicts):\n",
" dfe: list = []\n",
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" \n",
Mod: -
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" for entry in listOfDicts:\n",
" structure: dict = entry[\"structure\"]\n",
" extended: dict = db.load(structure[\"type\"], structure[\"direction\"], structure[\"theta\"])\n",
" fields: list = extended.keys()\n",
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"\n",
Mod: -
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" for key in fields:\n",
" extended[key] = pandas.json_normalize(extended[key])\n",
"\n",
" dfe.append(extended)\n",
"\n",
" dfe: DataFrame = DataFrame(dfe)\n",
" dfe: Series = Series(\n",
" [ pandas.concat(dfe[field].to_list(), ignore_index = True) for field in dfe.keys() ],\n",
" dfe.keys()\n",
" )\n",
" \n",
" return dfe"
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]
},
{
"cell_type": "code",
Mod: -
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"execution_count": 6,
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"id": "437d9432-206e-4d23-a5cb-d9958b65f68a",
"metadata": {},
Mod: -
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"outputs": [],
"source": [
"from anisotropy.core.main import Anisotropy"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "756351e4-34b6-49d1-8cdb-29e3def4361d",
"metadata": {},
"outputs": [],
"source": [
"model = Anisotropy()\n",
"params = model.loadFromScratch(\"test_anisotropy.toml\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a6a7b464-045a-4d81-a7ad-eca75590179f",
"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
Mod: -
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"171"
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]
},
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"execution_count": 9,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
Mod: -
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"len(params)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cdbc661b-701f-475b-a72a-51b3bd2937ee",
"metadata": {},
"outputs": [],
"source": [
"dfparams = pandasify(params)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "73e04119-9f70-4e22-928b-caef022c365d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>structure_id</th>\n",
" <th>type</th>\n",
" <th>direction</th>\n",
" <th>theta</th>\n",
" <th>r0</th>\n",
" <th>L</th>\n",
" <th>radius</th>\n",
" <th>filletsEnabled</th>\n",
" <th>fillets</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.01</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.010101</td>\n",
" <td>True</td>\n",
" <td>0.191919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.02</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.020408</td>\n",
" <td>True</td>\n",
" <td>0.183900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.03</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.030928</td>\n",
" <td>True</td>\n",
" <td>0.175945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.04</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.041667</td>\n",
" <td>True</td>\n",
" <td>0.168056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 0.0, 0.0]</td>\n",
" <td>0.05</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.052632</td>\n",
" <td>True</td>\n",
" <td>0.160234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>80</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.24</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.315789</td>\n",
" <td>True</td>\n",
" <td>0.028070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>81</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.25</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.333333</td>\n",
" <td>True</td>\n",
" <td>0.022222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>82</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.26</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.351351</td>\n",
" <td>True</td>\n",
" <td>0.016517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>83</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.27</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.369863</td>\n",
" <td>True</td>\n",
" <td>0.010959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>84</td>\n",
" <td>simple</td>\n",
" <td>[1.0, 1.0, 1.0]</td>\n",
" <td>0.28</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.388889</td>\n",
" <td>True</td>\n",
" <td>0.005556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>84 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" structure_id type direction theta r0 L radius \\\n",
"0 1 simple [1.0, 0.0, 0.0] 0.01 1.0 2.0 1.010101 \n",
"1 2 simple [1.0, 0.0, 0.0] 0.02 1.0 2.0 1.020408 \n",
"2 3 simple [1.0, 0.0, 0.0] 0.03 1.0 2.0 1.030928 \n",
"3 4 simple [1.0, 0.0, 0.0] 0.04 1.0 2.0 1.041667 \n",
"4 5 simple [1.0, 0.0, 0.0] 0.05 1.0 2.0 1.052632 \n",
".. ... ... ... ... ... ... ... \n",
"79 80 simple [1.0, 1.0, 1.0] 0.24 1.0 2.0 1.315789 \n",
"80 81 simple [1.0, 1.0, 1.0] 0.25 1.0 2.0 1.333333 \n",
"81 82 simple [1.0, 1.0, 1.0] 0.26 1.0 2.0 1.351351 \n",
"82 83 simple [1.0, 1.0, 1.0] 0.27 1.0 2.0 1.369863 \n",
"83 84 simple [1.0, 1.0, 1.0] 0.28 1.0 2.0 1.388889 \n",
"\n",
" filletsEnabled fillets \n",
"0 True 0.191919 \n",
"1 True 0.183900 \n",
"2 True 0.175945 \n",
"3 True 0.168056 \n",
"4 True 0.160234 \n",
".. ... ... \n",
"79 True 0.028070 \n",
"80 True 0.022222 \n",
"81 True 0.016517 \n",
"82 True 0.010959 \n",
"83 True 0.005556 \n",
"\n",
"[84 rows x 9 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfparams.structure[dfparams.structure.type == \"simple\"]"
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]
},
{
"cell_type": "code",
"execution_count": null,
Mod: -
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"id": "c7abc63e-960c-4157-99b3-d947acf10c10",
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"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
Mod: database models
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"display_name": "Python 3 (ipykernel)",
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"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",
Mod: -
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"version": "3.10.0"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink