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milestone_5_without_improvements-logs
chortas 3 years ago
parent 757a8befe2
commit 036d958eb8
2 changed files with 304 additions and 20 deletions
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1
.gitignore vendored
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@ -20,3 +20,4 @@ utilities/__pycache__/
fftma_module/gen/log_*
fftma_module/gen/out*.npy
.ipynb_checkpoints/analysis-checkpoint.ipynb
fftma_module/gen/.ipynb_checkpoints/

323
fftma_module/gen/analysis.ipynb
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@ -9,7 +9,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -34,7 +34,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@ -44,7 +44,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@ -89,7 +89,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@ -101,7 +101,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@ -1305,44 +1305,327 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 94,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
"Executing file number 1 out of 10\n",
"Executing file number 2 out of 10\n",
"Executing file number 3 out of 10\n",
"Executing file number 4 out of 10\n",
"Executing file number 5 out of 10\n",
"Executing file number 6 out of 10\n",
"Executing file number 7 out of 10\n",
"Executing file number 8 out of 10\n",
"Executing file number 9 out of 10\n",
"Executing file number 10 out of 10\n"
]
}
],
"source": [
"dfs = []\n",
"for i in range(10):\n",
" print(i)\n",
" print(\"Executing file number {} out of 10\".format(i+1))\n",
" df = create_df(\"log_256_{}.txt\".format(i+1))\n",
" dfs.append(df)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"functions = ['Py_kgeneration', 'generate', 'fftma2', 'covariance', 'gasdev', 'fourt', 'cov_value', 'ran2', 'build_real', 'prebuild_gwn', 'clean_real', 'cgrid', 'length', 'maxfactor']\n",
"def merge_dfs(dfs):\n",
" functions = ['Py_kgeneration', 'generate', 'fftma2', 'covariance', 'gasdev', 'fourt', 'cov_value', 'ran2', 'build_real', 'prebuild_gwn', 'clean_real', 'cgrid', 'length', 'maxfactor']\n",
" df_final = pd.concat(dfs, join='inner').sort_values(by=('time', 'sum'), ascending=False) \n",
"\n",
" memory_min, memory_max, memory_median = [], [], []\n",
" time_min, time_max, time_mean, time_sum, time_count = [], [], [], [], []\n",
"\n",
" for function in functions:\n",
" memory_min.append(df_final.loc[function, ('memory', 'min')].min())\n",
" time_min.append(df_final.loc[function, ('time', 'min')].min())\n",
" memory_max.append(df_final.loc[function, ('memory', 'max')].max())\n",
" time_max.append(df_final.loc[function, ('time', 'max')].max())\n",
" time_mean.append(df_final.loc[function, ('time', 'mean')].mean())\n",
" time_sum.append(df_final.loc[function, ('time', 'sum')].sum())\n",
" time_count.append(df_final.loc[function, ('time', 'count')].sum())\n",
" try:\n",
" memory_median.append(df_final.loc[function, ('memory', 'median')].median())\n",
" except:\n",
" memory_median.append(df_final.loc[function, ('memory', 'median')])\n",
" \n",
" df = pd.DataFrame({('memory', 'min'): memory_min, ('memory', 'max'): memory_max, ('memory', 'median'): memory_median, ('time', 'min'): time_min, ('time', 'max'): time_max, ('time', 'mean'): time_mean, ('time', 'sum'): time_sum, ('time', 'count'): time_count})\n",
"\n",
" df.index = functions\n",
" df.index.name = 'function'\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 96,
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">memory</th>\n",
" <th colspan=\"5\" halign=\"left\">time</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>max</th>\n",
" <th>median</th>\n",
" <th>min</th>\n",
" <th>count</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>min</th>\n",
" <th>sum</th>\n",
" </tr>\n",
" <tr>\n",
" <th>function</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Py_kgeneration</th>\n",
" <td>7421.6</td>\n",
" <td>7421.6</td>\n",
" <td>7421.6</td>\n",
" <td>1.0</td>\n",
" <td>1226.822575</td>\n",
" <td>1226.822575</td>\n",
" <td>1226.822575</td>\n",
" <td>1226.822575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>generate</th>\n",
" <td>6691.7</td>\n",
" <td>6691.7</td>\n",
" <td>6691.7</td>\n",
" <td>1.0</td>\n",
" <td>959.799368</td>\n",
" <td>959.799368</td>\n",
" <td>959.799368</td>\n",
" <td>959.799368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fftma2</th>\n",
" <td>872.0</td>\n",
" <td>872.0</td>\n",
" <td>872.0</td>\n",
" <td>1.0</td>\n",
" <td>267.021516</td>\n",
" <td>267.021516</td>\n",
" <td>267.021516</td>\n",
" <td>267.021516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>covariance</th>\n",
" <td>870.5</td>\n",
" <td>870.5</td>\n",
" <td>870.5</td>\n",
" <td>1.0</td>\n",
" <td>247.512194</td>\n",
" <td>247.512194</td>\n",
" <td>247.512194</td>\n",
" <td>247.512194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gasdev</th>\n",
" <td>8.7</td>\n",
" <td>0.0</td>\n",
" <td>-13.5</td>\n",
" <td>16777216.0</td>\n",
" <td>0.001358</td>\n",
" <td>0.000033</td>\n",
" <td>0.000000</td>\n",
" <td>564.182445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fourt</th>\n",
" <td>11.5</td>\n",
" <td>-1.4</td>\n",
" <td>-16.2</td>\n",
" <td>3.0</td>\n",
" <td>8.429829</td>\n",
" <td>6.378454</td>\n",
" <td>5.015006</td>\n",
" <td>19.135362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cov_value</th>\n",
" <td>0.7</td>\n",
" <td>0.0</td>\n",
" <td>-13.9</td>\n",
" <td>8855600.0</td>\n",
" <td>0.000437</td>\n",
" <td>0.000002</td>\n",
" <td>0.000001</td>\n",
" <td>21.579349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ran2</th>\n",
" <td>0.9</td>\n",
" <td>0.0</td>\n",
" <td>-0.8</td>\n",
" <td>21359556.0</td>\n",
" <td>0.000381</td>\n",
" <td>0.000002</td>\n",
" <td>0.000000</td>\n",
" <td>45.002553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_real</th>\n",
" <td>-0.2</td>\n",
" <td>-0.2</td>\n",
" <td>-0.2</td>\n",
" <td>1.0</td>\n",
" <td>0.151968</td>\n",
" <td>0.151968</td>\n",
" <td>0.151968</td>\n",
" <td>0.151968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prebuild_gwn</th>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" <td>6.5</td>\n",
" <td>1.0</td>\n",
" <td>0.108160</td>\n",
" <td>0.108160</td>\n",
" <td>0.108160</td>\n",
" <td>0.108160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>clean_real</th>\n",
" <td>127.2</td>\n",
" <td>127.2</td>\n",
" <td>127.2</td>\n",
" <td>1.0</td>\n",
" <td>0.095267</td>\n",
" <td>0.095267</td>\n",
" <td>0.095267</td>\n",
" <td>0.095267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cgrid</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.000160</td>\n",
" <td>0.000160</td>\n",
" <td>0.000160</td>\n",
" <td>0.000160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>length</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.000043</td>\n",
" <td>0.000034</td>\n",
" <td>0.000021</td>\n",
" <td>0.000102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>maxfactor</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.000002</td>\n",
" <td>0.000002</td>\n",
" <td>0.000001</td>\n",
" <td>0.000008</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" memory time \\\n",
" max median min count max mean \n",
"function \n",
"Py_kgeneration 7421.6 7421.6 7421.6 1.0 1226.822575 1226.822575 \n",
"generate 6691.7 6691.7 6691.7 1.0 959.799368 959.799368 \n",
"fftma2 872.0 872.0 872.0 1.0 267.021516 267.021516 \n",
"covariance 870.5 870.5 870.5 1.0 247.512194 247.512194 \n",
"gasdev 8.7 0.0 -13.5 16777216.0 0.001358 0.000033 \n",
"fourt 11.5 -1.4 -16.2 3.0 8.429829 6.378454 \n",
"cov_value 0.7 0.0 -13.9 8855600.0 0.000437 0.000002 \n",
"ran2 0.9 0.0 -0.8 21359556.0 0.000381 0.000002 \n",
"build_real -0.2 -0.2 -0.2 1.0 0.151968 0.151968 \n",
"prebuild_gwn 6.5 6.5 6.5 1.0 0.108160 0.108160 \n",
"clean_real 127.2 127.2 127.2 1.0 0.095267 0.095267 \n",
"cgrid 0.0 0.0 0.0 1.0 0.000160 0.000160 \n",
"length 0.0 0.0 0.0 3.0 0.000043 0.000034 \n",
"maxfactor 0.0 0.0 0.0 5.0 0.000002 0.000002 \n",
"\n",
"#df_final = pd.concat(dfs).sort_values(by=('time', 'sum'), ascending=False) "
" \n",
" min sum \n",
"function \n",
"Py_kgeneration 1226.822575 1226.822575 \n",
"generate 959.799368 959.799368 \n",
"fftma2 267.021516 267.021516 \n",
"covariance 247.512194 247.512194 \n",
"gasdev 0.000000 564.182445 \n",
"fourt 5.015006 19.135362 \n",
"cov_value 0.000001 21.579349 \n",
"ran2 0.000000 45.002553 \n",
"build_real 0.151968 0.151968 \n",
"prebuild_gwn 0.108160 0.108160 \n",
"clean_real 0.095267 0.095267 \n",
"cgrid 0.000160 0.000160 \n",
"length 0.000021 0.000102 \n",
"maxfactor 0.000001 0.000008 "
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merge_dfs(dfs)"
]
},
{

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