diff --git a/blackdynamite/.bd/.gitkeep b/blackdynamite/.bd/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/blackdynamite/notebooks/post_treatment.ipynb b/blackdynamite/notebooks/post_treatment.ipynb new file mode 100644 index 0000000..b75a651 --- /dev/null +++ b/blackdynamite/notebooks/post_treatment.ipynb @@ -0,0 +1,232 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "15bf8e3e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:check_file_socket [base_zeo.py:53]: Found already running zeo server\n", + "WARNING:buildConnection [base_zeo.py:243]: Make a connection to zeo:///home/tarantino/repositories/renku-test/.bd/zeo.socket\n" + ] + } + ], + "source": [ + "import BlackDynamite as BD\n", + "import matplotlib.pyplot as plt\n", + "# basic connection\n", + "parser = BD.BDParser()\n", + "params = parser.parseBDParameters([])\n", + "mybase = BD.Base()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2f36108e", + "metadata": {}, + "outputs": [], + "source": [ + "# function to plot things (user's job)\n", + "def plot(run_list):\n", + " for r, j in run_list:\n", + " step, ekin = r.getScalarQuantity('ekin')\n", + " if ekin is None:\n", + " continue\n", + " print(j)\n", + " list_files = r.listFiles()\n", + " print(list_files)\n", + " fname = r.getFile(list_files[3])\n", + " print(fname + ':')\n", + " _file = open(fname)\n", + " print(_file.read())\n", + " plt.plot(step, ekin, 'o-',\n", + " label='$p_2 = {0}$'.format(j['param2']))\n", + "\n", + " vect = r.getVectorQuantity('random_vect', 1)\n", + " print(vect)\n", + " plt.legend(loc='best')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "64231a1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 2 runs: ✓ \n", + "JobZEO object:\n", + " id: 1\n", + " param1: 10\n", + " param2: 3.14\n", + " param3: toto\n", + "['doIt.py', 'launch.sh', 'test.e1', 'test.o1']\n", + "/home/tarantino/repositories/renku-test/BD-bd_study-runs/run-1/test.o1:\n", + "here is the job\n", + "1\n", + "10\n", + "3.14\n", + "toto\n", + "JobZEO object:\n", + " id: 1\n", + " param1: 10\n", + " param2: 3.14\n", + " param3: toto\n", + "\n", + "[0.962293822014714 0.9436352683204128 0.7994723354271817\n", + " 0.2214375683384744 0.767691322000609 0.2394817207276384\n", + " 0.26356320432040536 0.3649406864289889 0.5923733364484919\n", + " 0.8615272499687405]\n", + "JobZEO object:\n", + " id: 3\n", + " param1: 10\n", + " param2: 2.0\n", + " param3: toto\n", + "['doIt.py', 'launch.sh', 'test.e3', 'test.o3']\n", + "/home/tarantino/repositories/renku-test/BD-bd_study-runs/run-3/test.o3:\n", + "here is the job\n", + "3\n", + "10\n", + "2.0\n", + "toto\n", + "JobZEO object:\n", + " id: 3\n", + " param1: 10\n", + " param2: 2.0\n", + " param3: toto\n", + "\n", + "[0.53288814811771 0.7196847541570227 0.13287632085263779\n", + " 0.08738560316476174 0.11662260214240283 0.004668160209276628\n", + " 0.7452236886964579 0.2264382223157977 0.2183541656402137\n", + " 0.26491684322215436]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# selecting some runs\n", + "runSelector = BD.RunSelector(mybase)\n", + "run_list = runSelector.selectRuns(params)\n", + "plot(run_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7677d77f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 2 runs: ✓ \n", + "JobZEO object:\n", + " id: 1\n", + " param1: 10\n", + " param2: 3.14\n", + " param3: toto\n", + "['doIt.py', 'launch.sh', 'test.e1', 'test.o1']\n", + "/home/tarantino/repositories/renku-test/BD-bd_study-runs/run-1/test.o1:\n", + "here is the job\n", + "1\n", + "10\n", + "3.14\n", + "toto\n", + "JobZEO object:\n", + " id: 1\n", + " param1: 10\n", + " param2: 3.14\n", + " param3: toto\n", + "\n", + "[0.962293822014714 0.9436352683204128 0.7994723354271817\n", + " 0.2214375683384744 0.767691322000609 0.2394817207276384\n", + " 0.26356320432040536 0.3649406864289889 0.5923733364484919\n", + " 0.8615272499687405]\n", + "JobZEO object:\n", + " id: 3\n", + " param1: 10\n", + " param2: 2.0\n", + " param3: toto\n", + "['doIt.py', 'launch.sh', 'test.e3', 'test.o3']\n", + "/home/tarantino/repositories/renku-test/BD-bd_study-runs/run-3/test.o3:\n", + "here is the job\n", + "3\n", + "10\n", + "2.0\n", + "toto\n", + "JobZEO object:\n", + " id: 3\n", + " param1: 10\n", + " param2: 2.0\n", + " param3: toto\n", + "\n", + "[0.53288814811771 0.7196847541570227 0.13287632085263779\n", + " 0.08738560316476174 0.11662260214240283 0.004668160209276628\n", + " 0.7452236886964579 0.2264382223157977 0.2183541656402137\n", + " 0.26491684322215436]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# selecting some other runs\n", + "params['constraints'] = ['run_name = test', 'state = FINISHED', 'param2 > 1']\n", + "run_list = runSelector.selectRuns(params)\n", + "plot(run_list)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/blackdynamite/post_treatment.py b/blackdynamite/notebooks/post_treatment.py old mode 100755 new mode 100644 similarity index 96% rename from blackdynamite/post_treatment.py rename to blackdynamite/notebooks/post_treatment.py index 07fca53..f69caa8 --- a/blackdynamite/post_treatment.py +++ b/blackdynamite/notebooks/post_treatment.py @@ -1,57 +1,57 @@ #!/usr/bin/env python3 # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . ################################################################ import BlackDynamite as BD import matplotlib.pyplot as plt ################################################################ # basic connection parser = BD.BDParser() params = parser.parseBDParameters() mybase = BD.Base(**params) ################################################################ # function to plot things (user's job) def plot(run_list): for r, j in run_list: step, ekin = r.getScalarQuantity('ekin') if ekin is None: continue print(j) list_files = r.listFiles() print(list_files) fname = r.getFile(list_files[3]) print(fname + ':') _file = open(fname) print(_file.read()) plt.plot(step, ekin, 'o-', label='$p_2 = {0}$'.format(j['param2'])) vect = r.getVectorQuantity('random_vect', 1) print(vect) plt.legend(loc='best') plt.show() ################################################################ # selecting some runs runSelector = BD.RunSelector(mybase) run_list = runSelector.selectRuns(params) plot(run_list) # selecting some other runs -params['constraints'] = ['run_name ~ test', 'state = FINISHED', 'param2 > 1'] +params['constraints'] = ['run_name = test', 'state = FINISHED', 'param2 > 1'] run_list = runSelector.selectRuns(params) plot(run_list)