From 98e16a70b034ddc352fa689250a85e05b0b5b586 Mon Sep 17 00:00:00 2001 From: weichangfeng Date: Tue, 1 Dec 2020 18:28:06 +0800 Subject: [PATCH] Upload New File --- datafile_info.ipynb | 440 ++++++++++++++++++++++++++++++++------------ 1 file changed, 320 insertions(+), 120 deletions(-) diff --git a/datafile_info.ipynb b/datafile_info.ipynb index ed88eeb..bd289e5 100644 --- a/datafile_info.ipynb +++ b/datafile_info.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,13 +11,28 @@ "import matplotlib.pyplot as plt" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#f1 = h5py.File('data_2020120101','r')\n", + "#f2 = h5py.File('data_2020120102','r')\n", + "#f3 = h5py.File('data_2020120103','r')\n", + "#f4 = h5py.File('data_2020120104','r')" + ] + }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "f1 = h5py.File('data_0.h5py','r')" + "f1 = h5py.File('data_0.h5py','r')\n", + "f2 = h5py.File('data_1.h5py','r')\n", + "f3 = h5py.File('data_2.h5py','r')\n", + "f4 = h5py.File('data_3.h5py','r')" ] }, { @@ -27,6 +42,13 @@ "First we can list keys in 'data_0.h5py':" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No parameter's info is found here. Then I look into one of them: 'mass_1_max'. " + ] + }, { "cell_type": "code", "execution_count": 4, @@ -35,110 +57,7 @@ { "data": { "text/plain": [ - "['KL_cycles',\n", - " 'M_max',\n", - " 'M_min',\n", - " 'Make_sky_plot',\n", - " 'a_1_max',\n", - " 'a_1_min',\n", - " 'a_2_max',\n", - " 'a_2_min',\n", - " 'batch_norm',\n", - " 'batch_size',\n", - " 'bilby_results_label',\n", - " 'by_channel',\n", - " 'conv_strides_q',\n", - " 'conv_strides_r1',\n", - " 'conv_strides_r2',\n", - " 'dec_max',\n", - " 'dec_min',\n", - " 'doPE',\n", - " 'drate',\n", - " 'duration',\n", - " 'filter_size_q',\n", - " 'filter_size_r1',\n", - " 'filter_size_r2',\n", - " 'geocent_time_max',\n", - " 'geocent_time_min',\n", - " 'gpu_num',\n", - " 'hyperparam_n_call',\n", - " 'hyperparam_optim',\n", - " 'hyperparam_optim_stop',\n", - " 'initial_training_rate',\n", - " 'load_by_chunks',\n", - " 'load_chunk_size',\n", - " 'load_iteration',\n", - " 'load_plot_data',\n", - " 'luminosity_distance_max',\n", - " 'luminosity_distance_min',\n", - " 'make_corner_plots',\n", - " 'make_kl_plot',\n", - " 'make_loss_plot',\n", - " 'make_pp_plot',\n", - " 'mass_1_max',\n", - " 'mass_1_min',\n", - " 'mass_2_max',\n", - " 'mass_2_min',\n", - " 'maxpool_q',\n", - " 'maxpool_r1',\n", - " 'maxpool_r2',\n", - " 'n_filters_q',\n", - " 'n_filters_r1',\n", - " 'n_filters_r2',\n", - " 'n_modes',\n", - " 'n_samples',\n", - " 'n_weights_q',\n", - " 'n_weights_r1',\n", - " 'n_weights_r2',\n", - " 'ndata',\n", - " 'num_iterations',\n", - " 'pe_dir',\n", - " 'phase_max',\n", - " 'phase_min',\n", - " 'phi_12_max',\n", - " 'phi_12_min',\n", - " 'phi_jl_max',\n", - " 'phi_jl_min',\n", - " 'plot_dir',\n", - " 'plot_interval',\n", - " 'pool_strides_q',\n", - " 'pool_strides_r1',\n", - " 'pool_strides_r2',\n", - " 'print_values',\n", - " 'psi_max',\n", - " 'psi_min',\n", - " 'r',\n", - " 'ra_max',\n", - " 'ra_min',\n", - " 'ramp',\n", - " 'ramp_end',\n", - " 'ramp_start',\n", - " 'rand_pars',\n", - " 'ref_geocent_time',\n", - " 'report_interval',\n", - " 'resume_training',\n", - " 'run_label',\n", - " 'save_interval',\n", - " 'snrs',\n", - " 'test_set_dir',\n", - " 'testing_data_seed',\n", - " 'theta_jn_max',\n", - " 'theta_jn_min',\n", - " 'tilt_1_max',\n", - " 'tilt_1_min',\n", - " 'tilt_2_max',\n", - " 'tilt_2_min',\n", - " 'tot_dataset_size',\n", - " 'train_set_dir',\n", - " 'training_data_seed',\n", - " 'tset_split',\n", - " 'weight_init',\n", - " 'weighted_pars_factor',\n", - " 'x_data',\n", - " 'y_data_noisefree',\n", - " 'y_data_noisy',\n", - " 'y_normscale',\n", - " 'z_dimension']" + "" ] }, "execution_count": 4, @@ -147,63 +66,344 @@ } ], "source": [ - "list(f1.keys())" + "f1.keys()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 5, "metadata": {}, + "outputs": [], "source": [ - "No parameter's info is found here. Then I look into one of them: 'mass_1_max'. " + "times = np.linspace(0,1,256)++1126259642.5" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ref_geocent_time' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtimes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mref_geocent_time\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mref_geocent_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'ref_geocent_time' is not defined" + ] + } + ], + "source": [ + "times = np.linspace(ref_geocent_time,1.0+ref_geocent_time, 256)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0, 0.5, 'waveform')" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "f1['mass_1_max']" + "\n", + "plt.plot(times,f2['y_data_noisefree'][2])\n", + "plt.title('Hunter test (whitened) data')\n", + "plt.xlabel('times')\n", + "plt.ylabel('waveform')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'waveform')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.plot(times,f2['y_data_noisefree'][0])\n", + "plt.title('Hunter test (whitened) data')\n", + "plt.xlabel('times')\n", + "plt.ylabel('waveform')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'waveform')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "As we can see, it contains nothing." + "\n", + "plt.plot(times,f3['y_data_noisefree'][0])\n", + "plt.title('Hunter test (whitened) data')\n", + "plt.xlabel('times')\n", + "plt.ylabel('waveform')" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0, 0.5, 'waveform')" ] }, - "execution_count": 6, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "f1['y_data_noisefree']" + "\n", + "plt.plot(times,f4['y_data_noisefree'][0])\n", + "plt.title('Hunter test (whitened) data')\n", + "plt.xlabel('times')\n", + "plt.ylabel('waveform')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_data_o=np.zeros((4,9))\n", + "x_data_n=np.zeros((4,7))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_data_o[0]= f1['x_data']\n", + "x_data_o[1]= f2['x_data']\n", + "x_data_o[2]= f3['x_data']\n", + "x_data_o[3]= f4['x_data']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_data_o" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(4):\n", + " a = x_data_o[i].tolist()\n", + " a.pop(6)\n", + " a.pop(4)\n", + " x_data_n[i]= a\n", + "x_data_n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(4):\n", + " a = x_data_o[i].tolist()\n", + " a.pop(6)\n", + " a.pop(4)\n", + " x_data_n[i]= a\n", + "x_data_n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_data_n[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "times = np.linspace(-parameters['geocent_time'],1.0-parameters['geocent_time'], 256)\n", + "plt.plot(times,whitened_noisy_signal[0])\n", + "plt.title('whitened_noisy_signal_L1')\n", + "plt.xlabel('times')\n", + "plt.ylabel('waveform')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_data[3]= x_data[3].append(h5py.File('data_0.h5py', 'r')['y_data_noisy'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in [0,1,2,3]:\n", + " try:\n", + " x_data_temp=h5py.File('data_0.h5py', 'r')['x_data'][:]\n", + " \n", + " x_data['x_data'].append(np.expand_dims(x_data_temp['x_data'], axis=0))\n", + " except OSError:\n", + " print('Could not load requested file')\n", + " continue\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "truth=x_data[x_]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -228,7 +428,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4, -- GitLab