ssantisi
/
simulacion-permeabilidad
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
main
improvement-multiple_buffers
migrate_fortran
milestone_5
milestone_5_without_improvements-logs
milestone_5_without_improvements
improvement-remove_generate_array-logs
improvement-remove_generate_array
improvement-file_array-logs
improvement-file_array
improvement-double_with_dirty_mmap_fork
improvement-double_with_dirty_mmap
improvement-double_with_dirty_flag-logs
improvement-double_with_dirty_flag_bis
improvement-double_with_dirty_flag
improvement-double_buffer_array-logs
improvement-double_buffer_array
improvement-chunk_array-logs
improvement-chunk_array
milestone_2
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '4e338ab748'
${ noResults }
simulacion-permeabilidad/fftma_module/gen/analysis.ipynb

1157 lines
36 KiB
Plaintext
Raw Blame History

{
"cells": [
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def get_function_name(function_name):\n",
" return function_name[10:].rsplit(\".c\")[0]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"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>function_name</th>\n",
" <th>elapsed_time</th>\n",
" <th>executions</th>\n",
" <th>used_virtual_mem</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Py_getvalues</td>\n",
" <td>0.000007</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ran2</td>\n",
" <td>0.000065</td>\n",
" <td>41552</td>\n",
" <td>240.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>gasdev</td>\n",
" <td>0.000383</td>\n",
" <td>32768</td>\n",
" <td>240.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>generate</td>\n",
" <td>2.055015</td>\n",
" <td>1</td>\n",
" <td>219.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>maxfactor</td>\n",
" <td>0.000005</td>\n",
" <td>5</td>\n",
" <td>219.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>length</td>\n",
" <td>0.000071</td>\n",
" <td>3</td>\n",
" <td>219.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>cgrid</td>\n",
" <td>0.000265</td>\n",
" <td>1</td>\n",
" <td>219.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>cov_value</td>\n",
" <td>0.000156</td>\n",
" <td>24624</td>\n",
" <td>234.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>covariance</td>\n",
" <td>0.839737</td>\n",
" <td>1</td>\n",
" <td>228.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>fourt</td>\n",
" <td>0.003451</td>\n",
" <td>3</td>\n",
" <td>227.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>prebuild_gwn</td>\n",
" <td>0.000138</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>build_real</td>\n",
" <td>0.000424</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>clean_real</td>\n",
" <td>0.000158</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>fftma2</td>\n",
" <td>0.849271</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Py_kgeneration</td>\n",
" <td>2.904626</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" function_name elapsed_time executions used_virtual_mem\n",
"0 Py_getvalues 0.000007 1 0.0\n",
"1 ran2 0.000065 41552 240.5\n",
"2 gasdev 0.000383 32768 240.5\n",
"3 generate 2.055015 1 219.2\n",
"4 maxfactor 0.000005 5 219.2\n",
"5 length 0.000071 3 219.2\n",
"6 cgrid 0.000265 1 219.2\n",
"7 cov_value 0.000156 24624 234.7\n",
"8 covariance 0.839737 1 228.0\n",
"9 fourt 0.003451 3 227.9\n",
"10 prebuild_gwn 0.000138 1 227.4\n",
"11 build_real 0.000424 1 227.4\n",
"12 clean_real 0.000158 1 227.4\n",
"13 fftma2 0.849271 1 227.4\n",
"14 Py_kgeneration 2.904626 1 227.4"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = {}\n",
"with open(\"log_32.txt\") as log_file:\n",
" lines = log_file.readlines()\n",
" \n",
" for line in lines:\n",
" if \"MEM\" in line:\n",
" split_line = line.split()\n",
" idx_used_mem = split_line.index(\"USED\") + 4\n",
" function_name, used_virtual_mem = get_function_name(split_line[2]), float(split_line[idx_used_mem])\n",
" val = data.get(function_name, (0, 0, used_virtual_mem))\n",
" data[function_name] = (val[0], val[1], max(used_virtual_mem, val[2])) \n",
" if \"ELAPSED\" in line:\n",
" split_line = line.split()\n",
" idx_elapsed = split_line.index(\"ELAPSED\") + 2\n",
" function_name, elapsed = get_function_name(split_line[2]), float(split_line[idx_elapsed])\n",
" val = data.get(function_name, (elapsed, 0, 0))\n",
" data[function_name] = (max(elapsed, val[0]), val[1] + 1, val[2])\n",
"\n",
"values = data.values()\n",
"new_data = {\"function_name\": data.keys(), \"elapsed_time\": map(lambda x: x[0], values), \"executions\": map(lambda x: x[1], values), \"used_virtual_mem\": map(lambda x: x[2], values)}\n",
" \n",
"df = pd.DataFrame(new_data) \n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"df[\"total_time\"] = df[\"elapsed_time\"] * df[\"executions\"]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>function_name</th>\n",
" <th>elapsed_time</th>\n",
" <th>executions</th>\n",
" <th>used_virtual_mem</th>\n",
" <th>total_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>gasdev</td>\n",
" <td>0.000383</td>\n",
" <td>32768</td>\n",
" <td>240.5</td>\n",
" <td>12.550144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>cov_value</td>\n",
" <td>0.000156</td>\n",
" <td>24624</td>\n",
" <td>234.7</td>\n",
" <td>3.841344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Py_kgeneration</td>\n",
" <td>2.904626</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" <td>2.904626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ran2</td>\n",
" <td>0.000065</td>\n",
" <td>41552</td>\n",
" <td>240.5</td>\n",
" <td>2.700880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>generate</td>\n",
" <td>2.055015</td>\n",
" <td>1</td>\n",
" <td>219.2</td>\n",
" <td>2.055015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>fftma2</td>\n",
" <td>0.849271</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" <td>0.849271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>covariance</td>\n",
" <td>0.839737</td>\n",
" <td>1</td>\n",
" <td>228.0</td>\n",
" <td>0.839737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>fourt</td>\n",
" <td>0.003451</td>\n",
" <td>3</td>\n",
" <td>227.9</td>\n",
" <td>0.010353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>build_real</td>\n",
" <td>0.000424</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" <td>0.000424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>cgrid</td>\n",
" <td>0.000265</td>\n",
" <td>1</td>\n",
" <td>219.2</td>\n",
" <td>0.000265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>length</td>\n",
" <td>0.000071</td>\n",
" <td>3</td>\n",
" <td>219.2</td>\n",
" <td>0.000213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>clean_real</td>\n",
" <td>0.000158</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" <td>0.000158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>prebuild_gwn</td>\n",
" <td>0.000138</td>\n",
" <td>1</td>\n",
" <td>227.4</td>\n",
" <td>0.000138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>maxfactor</td>\n",
" <td>0.000005</td>\n",
" <td>5</td>\n",
" <td>219.2</td>\n",
" <td>0.000025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Py_getvalues</td>\n",
" <td>0.000007</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.000007</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" function_name elapsed_time executions used_virtual_mem total_time\n",
"2 gasdev 0.000383 32768 240.5 12.550144\n",
"7 cov_value 0.000156 24624 234.7 3.841344\n",
"14 Py_kgeneration 2.904626 1 227.4 2.904626\n",
"1 ran2 0.000065 41552 240.5 2.700880\n",
"3 generate 2.055015 1 219.2 2.055015\n",
"13 fftma2 0.849271 1 227.4 0.849271\n",
"8 covariance 0.839737 1 228.0 0.839737\n",
"9 fourt 0.003451 3 227.9 0.010353\n",
"11 build_real 0.000424 1 227.4 0.000424\n",
"6 cgrid 0.000265 1 219.2 0.000265\n",
"5 length 0.000071 3 219.2 0.000213\n",
"12 clean_real 0.000158 1 227.4 0.000158\n",
"10 prebuild_gwn 0.000138 1 227.4 0.000138\n",
"4 maxfactor 0.000005 5 219.2 0.000025\n",
"0 Py_getvalues 0.000007 1 0.0 0.000007"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sort_values(by=[\"total_time\", \"used_virtual_mem\"], ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>function_name</th>\n",
" <th>elapsed_time</th>\n",
" <th>executions</th>\n",
" <th>used_virtual_mem</th>\n",
" <th>total_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ran2</td>\n",
" <td>0.000220</td>\n",
" <td>333450</td>\n",
" <td>548.2</td>\n",
" <td>73.359000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>gasdev</td>\n",
" <td>0.000707</td>\n",
" <td>262144</td>\n",
" <td>548.2</td>\n",
" <td>185.335808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>generate</td>\n",
" <td>20.687490</td>\n",
" <td>1</td>\n",
" <td>425.6</td>\n",
" <td>20.687490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>maxfactor</td>\n",
" <td>0.000005</td>\n",
" <td>4</td>\n",
" <td>425.6</td>\n",
" <td>0.000020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>length</td>\n",
" <td>0.000059</td>\n",
" <td>3</td>\n",
" <td>425.6</td>\n",
" <td>0.000177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>cgrid</td>\n",
" <td>0.000199</td>\n",
" <td>1</td>\n",
" <td>425.6</td>\n",
" <td>0.000199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>cov_value</td>\n",
" <td>0.000158</td>\n",
" <td>156816</td>\n",
" <td>425.6</td>\n",
" <td>24.776928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>covariance</td>\n",
" <td>7.813795</td>\n",
" <td>1</td>\n",
" <td>313.0</td>\n",
" <td>7.813795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>clean_real</td>\n",
" <td>0.000966</td>\n",
" <td>1</td>\n",
" <td>311.3</td>\n",
" <td>0.000966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Py_kgeneration</td>\n",
" <td>28.676072</td>\n",
" <td>1</td>\n",
" <td>311.3</td>\n",
" <td>28.676072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>fourt</td>\n",
" <td>0.069088</td>\n",
" <td>3</td>\n",
" <td>311.1</td>\n",
" <td>0.207264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>prebuild_gwn</td>\n",
" <td>0.001624</td>\n",
" <td>1</td>\n",
" <td>308.7</td>\n",
" <td>0.001624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>build_real</td>\n",
" <td>0.004294</td>\n",
" <td>1</td>\n",
" <td>308.4</td>\n",
" <td>0.004294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>fftma2</td>\n",
" <td>7.988212</td>\n",
" <td>1</td>\n",
" <td>308.4</td>\n",
" <td>7.988212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Py_getvalues</td>\n",
" <td>0.000009</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.000009</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" function_name elapsed_time executions used_virtual_mem total_time\n",
"1 ran2 0.000220 333450 548.2 73.359000\n",
"2 gasdev 0.000707 262144 548.2 185.335808\n",
"3 generate 20.687490 1 425.6 20.687490\n",
"4 maxfactor 0.000005 4 425.6 0.000020\n",
"5 length 0.000059 3 425.6 0.000177\n",
"6 cgrid 0.000199 1 425.6 0.000199\n",
"7 cov_value 0.000158 156816 425.6 24.776928\n",
"8 covariance 7.813795 1 313.0 7.813795\n",
"12 clean_real 0.000966 1 311.3 0.000966\n",
"14 Py_kgeneration 28.676072 1 311.3 28.676072\n",
"9 fourt 0.069088 3 311.1 0.207264\n",
"10 prebuild_gwn 0.001624 1 308.7 0.001624\n",
"11 build_real 0.004294 1 308.4 0.004294\n",
"13 fftma2 7.988212 1 308.4 7.988212\n",
"0 Py_getvalues 0.000009 1 0.0 0.000009"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sort_values(by=[\"used_virtual_mem\"], ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Import libraries\n",
"from matplotlib_venn import venn3\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"relations = {\n",
" \"generate\": [\"gasdev\"],\n",
" \"fftma2\": [\"covariance\", \"fourt\", \"prebuild_gwn\"]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(dpi=125)\n",
"plt.title('Py_kgeneration')\n",
"plt.pie([0.010957, 0.012503], labels=[\"generate\", \"fftma2\"], normalize=True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import libraries\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"\n",
"\n",
"# Creating dataset\n",
"size = 6\n",
"cars = ['AUDI', 'BMW', 'FORD',\n",
"\t\t'TESLA', 'JAGUAR', 'MERCEDES']\n",
"\n",
"data = np.array([[23, 16], [17, 23],\n",
"\t\t\t\t[35, 11], [29, 33],\n",
"\t\t\t\t[12, 27], [41, 42]])\n",
"\n",
"# normalizing data to 2 pi\n",
"norm = data / np.sum(data)*2 * np.pi\n",
"\n",
"# obtaining ordinates of bar edges\n",
"left = np.cumsum(np.append(0,\n",
"\t\t\t\t\t\tnorm.flatten()[:-1])).reshape(data.shape)\n",
"\n",
"# Creating color scale\n",
"cmap = plt.get_cmap(\"tab20c\")\n",
"outer_colors = cmap(np.arange(6)*4)\n",
"inner_colors = cmap(np.array([1, 2, 5, 6, 9,\n",
"\t\t\t\t\t\t\t10, 12, 13, 15,\n",
"\t\t\t\t\t\t\t17, 18, 20 ]))\n",
"\n",
"# Creating plot\n",
"fig, ax = plt.subplots(figsize =(10, 7),\n",
"\t\t\t\t\tsubplot_kw = dict(polar = True))\n",
"\n",
"ax.bar(x = left[:, 0],\n",
"\twidth = norm.sum(axis = 1),\n",
"\tbottom = 1-size,\n",
"\theight = size,\n",
"\tcolor = outer_colors,\n",
"\tedgecolor ='w',\n",
"\tlinewidth = 1,\n",
"\talign =\"edge\")\n",
"\n",
"ax.bar(x = left.flatten(),\n",
"\twidth = norm.flatten(),\n",
"\tbottom = 1-2 * size,\n",
"\theight = size,\n",
"\tcolor = inner_colors,\n",
"\tedgecolor ='w',\n",
"\tlinewidth = 1,\n",
"\talign =\"edge\")\n",
"\n",
"ax.set(title =\"Nested pie chart\")\n",
"ax.set_axis_off()\n",
"\n",
"# show plot\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"data = []\n",
"with open(\"log_64.txt\") as log_file:\n",
" lines = log_file.readlines()\n",
" \n",
" for line in lines:\n",
" row = {}\n",
" if \"MEM\" in line:\n",
" split_line = line.split()\n",
" idx_used_mem = split_line.index(\"USED\") + 4\n",
" function_name, used_virtual_mem = get_function_name(split_line[2]), float(split_line[idx_used_mem])\n",
" #val = data.get(function_name, (0, 0, used_virtual_mem))\n",
" #data[function_name] = (val[0], val[1], max(used_virtual_mem, val[2]))\n",
"\n",
" row[\"function\"] = function_name\n",
" row[\"memory\"] = used_virtual_mem \n",
"\n",
" if \"ELAPSED\" in line:\n",
" split_line = line.split()\n",
" idx_elapsed = split_line.index(\"ELAPSED\") + 2\n",
" function_name, elapsed = get_function_name(split_line[2]), float(split_line[idx_elapsed].rsplit(\",\")[0])\n",
" #val = data.get(function_name, (elapsed, 0, 0))\n",
" #data[function_name] = (max(elapsed, val[0]), val[1] + 1, val[2])\n",
" #data.append({\"function\": function_name, \"time\": elapsed, \"memory\": 0})\n",
" row[\"function\"] = function_name\n",
" row[\"time\"] = elapsed\n",
"\n",
" if row: data.append(row)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.DataFrame(data) "
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"df2 = df2.fillna(np.nan)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"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>function</th>\n",
" <th>time</th>\n",
" <th>memory</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Py_getvalues</td>\n",
" <td>0.000008</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ran2</td>\n",
" <td>0.000003</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ran2</td>\n",
" <td>0.000023</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>gasdev</td>\n",
" <td>0.000046</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>gasdev</td>\n",
" <td>0.000006</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" function time memory\n",
"0 Py_getvalues 0.000008 NaN\n",
"1 ran2 0.000003 0.0\n",
"2 ran2 0.000023 0.0\n",
"3 gasdev 0.000046 0.0\n",
"4 gasdev 0.000006 0.0"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"df2_grouped = df2.groupby(['function']).agg({'time': ['min', 'max', 'mean', 'sum', 'count'], 'memory': ['min', 'max', 'median']})"
]
},
{
"cell_type": "code",
"execution_count": 92,
"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=\"5\" halign=\"left\">time</th>\n",
" <th colspan=\"3\" halign=\"left\">memory</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>sum</th>\n",
" <th>count</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>median</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>11.316621</td>\n",
" <td>11.316621</td>\n",
" <td>11.316621</td>\n",
" <td>11.316621</td>\n",
" <td>1</td>\n",
" <td>-293.7</td>\n",
" <td>-293.7</td>\n",
" <td>-293.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>generate</th>\n",
" <td>7.424831</td>\n",
" <td>7.424831</td>\n",
" <td>7.424831</td>\n",
" <td>7.424831</td>\n",
" <td>1</td>\n",
" <td>-226.3</td>\n",
" <td>-226.3</td>\n",
" <td>-226.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gasdev</th>\n",
" <td>0.000003</td>\n",
" <td>0.000277</td>\n",
" <td>0.000021</td>\n",
" <td>5.484581</td>\n",
" <td>262144</td>\n",
" <td>-20.0</td>\n",
" <td>5.3</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fftma2</th>\n",
" <td>3.891431</td>\n",
" <td>3.891431</td>\n",
" <td>3.891431</td>\n",
" <td>3.891431</td>\n",
" <td>1</td>\n",
" <td>-69.5</td>\n",
" <td>-69.5</td>\n",
" <td>-69.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>covariance</th>\n",
" <td>3.765731</td>\n",
" <td>3.765731</td>\n",
" <td>3.765731</td>\n",
" <td>3.765731</td>\n",
" <td>1</td>\n",
" <td>-65.0</td>\n",
" <td>-65.0</td>\n",
" <td>-65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cov_value</th>\n",
" <td>0.000007</td>\n",
" <td>0.000108</td>\n",
" <td>0.000013</td>\n",
" <td>2.062640</td>\n",
" <td>156816</td>\n",
" <td>-1.9</td>\n",
" <td>1.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ran2</th>\n",
" <td>0.000002</td>\n",
" <td>0.000114</td>\n",
" <td>0.000005</td>\n",
" <td>1.534732</td>\n",
" <td>333450</td>\n",
" <td>-19.7</td>\n",
" <td>2.6</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fourt</th>\n",
" <td>0.032778</td>\n",
" <td>0.051545</td>\n",
" <td>0.039664</td>\n",
" <td>0.118991</td>\n",
" <td>3</td>\n",
" <td>-1.9</td>\n",
" <td>-0.0</td>\n",
" <td>-0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_real</th>\n",
" <td>0.004053</td>\n",
" <td>0.004053</td>\n",
" <td>0.004053</td>\n",
" <td>0.004053</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>prebuild_gwn</th>\n",
" <td>0.001132</td>\n",
" <td>0.001132</td>\n",
" <td>0.001132</td>\n",
" <td>0.001132</td>\n",
" <td>1</td>\n",
" <td>-2.5</td>\n",
" <td>-2.5</td>\n",
" <td>-2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>clean_real</th>\n",
" <td>0.000830</td>\n",
" <td>0.000830</td>\n",
" <td>0.000830</td>\n",
" <td>0.000830</td>\n",
" <td>1</td>\n",
" <td>-0.7</td>\n",
" <td>-0.7</td>\n",
" <td>-0.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cgrid</th>\n",
" <td>0.000148</td>\n",
" <td>0.000148</td>\n",
" <td>0.000148</td>\n",
" <td>0.000148</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>length</th>\n",
" <td>0.000015</td>\n",
" <td>0.000064</td>\n",
" <td>0.000039</td>\n",
" <td>0.000118</td>\n",
" <td>3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>maxfactor</th>\n",
" <td>0.000004</td>\n",
" <td>0.000025</td>\n",
" <td>0.000011</td>\n",
" <td>0.000043</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Py_getvalues</th>\n",
" <td>0.000008</td>\n",
" <td>0.000008</td>\n",
" <td>0.000008</td>\n",
" <td>0.000008</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time memory \\\n",
" min max mean sum count min \n",
"function \n",
"Py_kgeneration 11.316621 11.316621 11.316621 11.316621 1 -293.7 \n",
"generate 7.424831 7.424831 7.424831 7.424831 1 -226.3 \n",
"gasdev 0.000003 0.000277 0.000021 5.484581 262144 -20.0 \n",
"fftma2 3.891431 3.891431 3.891431 3.891431 1 -69.5 \n",
"covariance 3.765731 3.765731 3.765731 3.765731 1 -65.0 \n",
"cov_value 0.000007 0.000108 0.000013 2.062640 156816 -1.9 \n",
"ran2 0.000002 0.000114 0.000005 1.534732 333450 -19.7 \n",
"fourt 0.032778 0.051545 0.039664 0.118991 3 -1.9 \n",
"build_real 0.004053 0.004053 0.004053 0.004053 1 0.0 \n",
"prebuild_gwn 0.001132 0.001132 0.001132 0.001132 1 -2.5 \n",
"clean_real 0.000830 0.000830 0.000830 0.000830 1 -0.7 \n",
"cgrid 0.000148 0.000148 0.000148 0.000148 1 0.0 \n",
"length 0.000015 0.000064 0.000039 0.000118 3 0.0 \n",
"maxfactor 0.000004 0.000025 0.000011 0.000043 4 0.0 \n",
"Py_getvalues 0.000008 0.000008 0.000008 0.000008 1 NaN \n",
"\n",
" \n",
" max median \n",
"function \n",
"Py_kgeneration -293.7 -293.7 \n",
"generate -226.3 -226.3 \n",
"gasdev 5.3 0.0 \n",
"fftma2 -69.5 -69.5 \n",
"covariance -65.0 -65.0 \n",
"cov_value 1.5 0.0 \n",
"ran2 2.6 0.0 \n",
"fourt -0.0 -0.2 \n",
"build_real 0.0 0.0 \n",
"prebuild_gwn -2.5 -2.5 \n",
"clean_real -0.7 -0.7 \n",
"cgrid 0.0 0.0 \n",
"length 0.0 0.0 \n",
"maxfactor 0.0 0.0 \n",
"Py_getvalues NaN NaN "
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2_grouped.sort_values(by=('time', 'sum'), ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue View Git Blame Copy Permalink