{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standard imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-02-19T12:42:44.346360Z", "start_time": "2020-02-19T12:42:43.976396Z" } }, "outputs": [], "source": [ "# Data manipulation\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# Options for pandas\n", "pd.options.display.max_columns = 50\n", "pd.options.display.max_rows = 30\n", "\n", "from IPython import get_ipython\n", "ipython = get_ipython()\n", "\n", "# autoreload extension\n", "if 'autoreload' not in ipython.extension_manager.loaded:\n", " %load_ext autoreload\n", "\n", "%autoreload 2\n", "\n", "import matplotlib.pyplot as plt\n", "from matplotlib import gridspec\n", "%matplotlib inline\n", "\n", "import time\n", "np.random.seed(int(time.time()))" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-02-19T12:41:19.978956Z", "start_time": "2020-02-19T12:41:19.976845Z" } }, "source": [ "## Specific imports" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "ExecuteTime": { "end_time": "2020-02-19T13:30:47.386765Z", "start_time": "2020-02-19T13:30:47.352628Z" } }, "outputs": [], "source": [ "from generate_timeseries import Timeseries, make_params, is_stable\n", "from noise_parameters import NOISE\n", "from noise_properties_plotting import PlotTimeseriesComparison, PlotNoiseColorComparison, \\\n", " PiecewiseNormalize, noise_cmap_ww, noise_lim, example_noise_fit\n", "from noise_analysis import noise_color\n", "import os\n", "from scipy import stats\n", "\n", "#from scipy.optimize import curve_fit\n", "#from neutrality_analysis import KullbackLeibler_neutrality\n", "#from neutral_covariance_test import neutral_covariance_test\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Settings figures " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-02-19T12:42:46.948812Z", "start_time": "2020-02-19T12:42:46.632263Z" } }, "outputs": [], "source": [ "from elife_settings import set_elife_settings, ELIFE\n", "\n", "set_elife_settings()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experimental data" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2020-02-19T13:23:29.550553Z", "start_time": "2020-02-19T13:23:28.352572Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", " | slope_linear | \n", "std_slope_linear | \n", "pvalue | \n", "slope_spline | \n", "steady_state | \n", "
---|---|---|---|---|---|
species_1 | \n", "-0.707703 | \n", "0.019432 | \n", "9.946906e-07 | \n", "-1.226732 | \n", "1262.00000 | \n", "
species_2 | \n", "-0.756404 | \n", "0.019007 | \n", "1.455237e-07 | \n", "-0.785561 | \n", "641.00000 | \n", "
species_3 | \n", "-0.771402 | \n", "0.020433 | \n", "2.239110e-07 | \n", "-0.791151 | \n", "405.00000 | \n", "
species_5 | \n", "-0.763854 | \n", "0.016339 | \n", "1.295882e-08 | \n", "-1.550569 | \n", "367.00000 | \n", "
species_4 | \n", "-1.314492 | \n", "0.015137 | \n", "9.822188e-21 | \n", "-1.416094 | \n", "358.00000 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
species_99 | \n", "-1.112867 | \n", "0.019582 | \n", "2.382118e-13 | \n", "-1.245469 | \n", "5.00000 | \n", "
species_93 | \n", "-0.493099 | \n", "0.020793 | \n", "7.842600e-04 | \n", "-1.012660 | \n", "3.00000 | \n", "
species_60 | \n", "-0.267688 | \n", "0.022341 | \n", "7.507418e-02 | \n", "-2.207255 | \n", "3.00000 | \n", "
species_78 | \n", "-1.439354 | \n", "0.014124 | \n", "8.929784e-25 | \n", "-1.710482 | \n", "0.24259 | \n", "
species_97 | \n", "-0.449972 | \n", "0.014803 | \n", "2.921059e-04 | \n", "-1.930681 | \n", "0.00000 | \n", "
100 rows × 5 columns
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