ols and plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "750f746d-b13d-410e-a285-dbb227882d39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib version: 3.5.1\n",
      "numpy version 1.22.4\n",
      "pandas version: 1.4.3\n",
      "statsmodels version: 0.13.2\n",
      "dowhy version: 0.7.1\n",
      "Concact: Zehua Zeng (starlitnightly@163.com)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "print('matplotlib version:', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "print('numpy version', np.__version__)\n",
    "from numpy import median\n",
    "\n",
    "import pandas as pd\n",
    "print('pandas version:', pd.__version__)\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.formula.api import ols #加载ols模型\n",
    "from statsmodels.formula.api import poisson\n",
    "print('statsmodels version:', sm.__version__)\n",
    "\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets\n",
    "print('dowhy version:', dowhy.__version__)\n",
    "\n",
    "# Avoiding unnecessary log messges and warnings\n",
    "import logging\n",
    "logging.getLogger(\"dowhy\").setLevel(logging.WARNING)\n",
    "import warnings\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
    "\n",
    "print('Concact: Zehua Zeng (starlitnightly@163.com)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "810f4879-f12b-450c-9d53-aa26c54c244b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "# cmap\n",
    "emosa_colors = ['#FCEFE4','#FC7E0A']\n",
    "emosa_cmap = LinearSegmentedColormap.from_list('custom',emosa_colors)\n",
    "\n",
    "beta_colors = ['#F4E8FF','#7F00FF']\n",
    "beta_cmap = LinearSegmentedColormap.from_list('custom',beta_colors)\n",
    "\n",
    "smad_colors = ['#FFDFFF','#FF00FE']\n",
    "smad_cmap = LinearSegmentedColormap.from_list('custom',smad_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c3a8060-37b5-476b-a327-d3ebcfb7502b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanpy==1.9.1 anndata==0.8.0 umap==0.5.3 numpy==1.22.4 scipy==1.9.0 pandas==1.4.3 scikit-learn==1.1.2 statsmodels==0.13.2 python-igraph==0.9.11 pynndescent==0.5.7\n"
     ]
    }
   ],
   "source": [
    "import scanpy as sc\n",
    "sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)\n",
    "sc.logging.print_header()\n",
    "sc.settings.set_figure_params(dpi=80, facecolor='white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a61dbbdb-e82a-4fd1-ab5a-cabf028e6159",
   "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>Time</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>ndr1</th>\n",
       "      <th>ndr2</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9166.98</td>\n",
       "      <td>4351.54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7525.59</td>\n",
       "      <td>4074.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11753.21</td>\n",
       "      <td>6031.75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8580.33</td>\n",
       "      <td>6099.13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13392.53</td>\n",
       "      <td>10041.42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time  A  B  C      ndr1      ndr2  treatment\n",
       "0   3.7  1  1  1   9166.98   4351.54          1\n",
       "1   3.7  1  1  0   7525.59   4074.69          0\n",
       "2   4.0  1  1  1  11753.21   6031.75          1\n",
       "3   4.0  1  1  0   8580.33   6099.13          0\n",
       "4   4.3  1  1  1  13392.53  10041.42          1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish_data=pd.read_csv('data/fish_data.csv')\n",
    "fish_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0df6fee4-2e06-470a-b9c7-88215a0bd31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/latex": [
       "$\\displaystyle \\left\\{3.7, 4.0, 4.3, 4.7, 5.0, 6.0\\right\\}$"
      ],
      "text/plain": [
       "{3.7, 4.0, 4.3, 4.7, 5.0, 6.0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(fish_data['Time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d948764e-28e2-47b1-9e0b-c599332d7c84",
   "metadata": {},
   "source": [
    "# ndr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28e8fbc2-bb36-450e-a8b2-d5a36b08a7f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr1   R-squared:                       0.972\n",
      "Model:                            OLS   Adj. R-squared:                  0.930\n",
      "Method:                 Least Squares   F-statistic:                     23.00\n",
      "Date:                Wed, 05 Oct 2022   Prob (F-statistic):             0.0420\n",
      "Time:                        01:49:35   Log-Likelihood:                -42.592\n",
      "No. Observations:                   6   AIC:                             93.18\n",
      "Df Residuals:                       2   BIC:                             92.35\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1813.0488    654.861      2.769      0.109   -1004.591    4630.689\n",
      "A           2030.3462    507.253      4.003      0.057    -152.188    4212.881\n",
      "B           4090.7568    507.253      8.065      0.015    1908.222    6273.291\n",
      "C            824.2662    414.171      1.990      0.185    -957.766    2606.298\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.459\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.562\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr1 ~ A+B+C', data=fish_data.loc[fish_data['Time']==3.7], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8cfe19d0-4891-44c1-85b5-cbb9de514633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr1   R-squared:                       0.935\n",
      "Model:                            OLS   Adj. R-squared:                  0.838\n",
      "Method:                 Least Squares   F-statistic:                     9.630\n",
      "Date:                Wed, 05 Oct 2022   Prob (F-statistic):             0.0955\n",
      "Time:                        01:49:37   Log-Likelihood:                -46.245\n",
      "No. Observations:                   6   AIC:                             100.5\n",
      "Df Residuals:                       2   BIC:                             99.66\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   2060.5477   1203.832      1.712      0.229   -3119.123    7240.219\n",
      "A           2865.9032    932.484      3.073      0.092   -1146.253    6878.059\n",
      "B           4162.1653    932.484      4.464      0.047     150.009    8174.321\n",
      "C           2156.3075    761.370      2.832      0.105   -1119.604    5432.219\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.181\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.562\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr1 ~ A+B+C', data=fish_data.loc[fish_data['Time']==4], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d8aa084-1d7d-43c9-a52c-5fb862a14b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr1   R-squared:                       0.954\n",
      "Model:                            OLS   Adj. R-squared:                  0.884\n",
      "Method:                 Least Squares   F-statistic:                     13.67\n",
      "Date:                Wed, 05 Oct 2022   Prob (F-statistic):             0.0689\n",
      "Time:                        01:49:38   Log-Likelihood:                -47.225\n",
      "No. Observations:                   6   AIC:                             102.5\n",
      "Df Residuals:                       2   BIC:                             101.6\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   2845.5176   1417.464      2.007      0.182   -3253.336    8944.371\n",
      "A           2937.1637   1097.963      2.675      0.116   -1786.988    7661.316\n",
      "B           1610.3042   1097.963      1.467      0.280   -3113.848    6334.456\n",
      "C           5215.4289    896.483      5.818      0.028    1358.175    9072.683\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.255\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.563\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr1 ~ A+B+C', data=fish_data.loc[fish_data['Time']==4.3], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7630083-cada-475a-a6a7-8accc172c0a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr1   R-squared:                       0.936\n",
      "Model:                            OLS   Adj. R-squared:                  0.841\n",
      "Method:                 Least Squares   F-statistic:                     9.808\n",
      "Date:                Wed, 05 Oct 2022   Prob (F-statistic):             0.0939\n",
      "Time:                        01:49:39   Log-Likelihood:                -50.174\n",
      "No. Observations:                   6   AIC:                             108.3\n",
      "Df Residuals:                       2   BIC:                             107.5\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   3129.8893   2317.215      1.351      0.309   -6840.281    1.31e+04\n",
      "A           2739.6324   1794.907      1.526      0.266   -4983.229    1.05e+04\n",
      "B           1123.3250   1794.907      0.626      0.595   -6599.536    8846.186\n",
      "C           7624.8566   1465.535      5.203      0.035    1319.167    1.39e+04\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.475\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.563\n",
      "Skew:                           0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr1 ~ A+B+C', data=fish_data.loc[fish_data['Time']==4.7], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "86c72eda-a1ca-4ea2-a8c4-d2459518f200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr1   R-squared:                       0.927\n",
      "Model:                            OLS   Adj. R-squared:                  0.817\n",
      "Method:                 Least Squares   F-statistic:                     8.454\n",
      "Date:                Wed, 05 Oct 2022   Prob (F-statistic):              0.108\n",
      "Time:                        01:49:40   Log-Likelihood:                -50.491\n",
      "No. Observations:                   6   AIC:                             109.0\n",
      "Df Residuals:                       2   BIC:                             108.1\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   7513.3900   2442.904      3.076      0.091   -2997.576     1.8e+04\n",
      "A          -4107.7205   1892.265     -2.171      0.162   -1.22e+04    4034.039\n",
      "B          -1104.9292   1892.265     -0.584      0.618   -9246.689    7036.830\n",
      "C           6963.8995   1545.028      4.507      0.046     316.181    1.36e+04\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.190\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.563\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr1 ~ A+B+C', data=fish_data.loc[fish_data['Time']==5.0], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "585bd3cd-2a30-4809-82bd-8f944875d197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr1   R-squared:                       0.906\n",
      "Model:                            OLS   Adj. R-squared:                  0.766\n",
      "Method:                 Least Squares   F-statistic:                     6.445\n",
      "Date:                Wed, 05 Oct 2022   Prob (F-statistic):              0.137\n",
      "Time:                        01:49:43   Log-Likelihood:                -49.505\n",
      "No. Observations:                   6   AIC:                             107.0\n",
      "Df Residuals:                       2   BIC:                             106.2\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   6720.3810   2072.570      3.243      0.083   -2197.168    1.56e+04\n",
      "A          -5420.4766   1605.406     -3.376      0.078   -1.23e+04    1487.027\n",
      "B           -116.8049   1605.406     -0.073      0.949   -7024.309    6790.699\n",
      "C           2766.8812   1310.808      2.111      0.169   -2873.072    8406.835\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.194\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.563\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr1 ~ A+B+C', data=fish_data.loc[fish_data['Time']==6.0], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ceb251c-9ee3-45c0-9b91-7824a4650744",
   "metadata": {},
   "source": [
    "# ndr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e0b6021-7c69-402d-b19c-fcac896a8cf6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89adae19-35e9-4c4d-84c8-82edc4449885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr2   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.997\n",
      "Method:                 Least Squares   F-statistic:                     483.6\n",
      "Date:                Thu, 28 Jul 2022   Prob (F-statistic):            0.00206\n",
      "Time:                        20:09:32   Log-Likelihood:                -30.558\n",
      "No. Observations:                   6   AIC:                             69.12\n",
      "Df Residuals:                       2   BIC:                             68.28\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1392.0812     88.128     15.796      0.004    1012.899    1771.264\n",
      "A           2394.6944     68.263     35.080      0.001    2100.981    2688.408\n",
      "B            339.6881     68.263      4.976      0.038      45.975     633.402\n",
      "C            173.3025     55.737      3.109      0.090     -66.513     413.118\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.493\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.563\n",
      "Skew:                           0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr2 ~ A+B+C', data=fish_data.loc[fish_data['Time']==3.7], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "acfce56e-6e75-4055-a67d-6879ec055122",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr2   R-squared:                       0.976\n",
      "Model:                            OLS   Adj. R-squared:                  0.940\n",
      "Method:                 Least Squares   F-statistic:                     26.90\n",
      "Date:                Thu, 28 Jul 2022   Prob (F-statistic):             0.0361\n",
      "Time:                        20:09:38   Log-Likelihood:                -43.022\n",
      "No. Observations:                   6   AIC:                             94.04\n",
      "Df Residuals:                       2   BIC:                             93.21\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    524.3357    703.505      0.745      0.534   -2502.603    3551.274\n",
      "A           4629.3940    544.933      8.495      0.014    2284.737    6974.051\n",
      "B            936.5675    544.933      1.719      0.228   -1408.089    3281.224\n",
      "C            -49.7143    444.936     -0.112      0.921   -1964.118    1864.690\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.244\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.562\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr2 ~ A+B+C', data=fish_data.loc[fish_data['Time']==4], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a6bc46d9-8e8d-47ec-b7dd-0942e4aa9667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr2   R-squared:                       0.992\n",
      "Model:                            OLS   Adj. R-squared:                  0.981\n",
      "Method:                 Least Squares   F-statistic:                     87.98\n",
      "Date:                Thu, 28 Jul 2022   Prob (F-statistic):             0.0113\n",
      "Time:                        20:09:43   Log-Likelihood:                -42.388\n",
      "No. Observations:                   6   AIC:                             92.78\n",
      "Df Residuals:                       2   BIC:                             91.94\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   2997.2040    632.962      4.735      0.042     273.787    5720.621\n",
      "A           6154.2127    490.291     12.552      0.006    4044.663    8263.763\n",
      "B           -891.7324    490.291     -1.819      0.211   -3001.282    1217.817\n",
      "C           1745.1113    400.321      4.359      0.049      22.671    3467.551\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.278\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.562\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr2 ~ A+B+C', data=fish_data.loc[fish_data['Time']==4.3], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2119357-cc73-4c6e-8797-9d1513991962",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr2   R-squared:                       0.925\n",
      "Model:                            OLS   Adj. R-squared:                  0.812\n",
      "Method:                 Least Squares   F-statistic:                     8.202\n",
      "Date:                Thu, 28 Jul 2022   Prob (F-statistic):              0.111\n",
      "Time:                        20:09:46   Log-Likelihood:                -52.630\n",
      "No. Observations:                   6   AIC:                             113.3\n",
      "Df Residuals:                       2   BIC:                             112.4\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1058.5727   3489.257      0.303      0.790    -1.4e+04    1.61e+04\n",
      "A           1.175e+04   2702.767      4.348      0.049     123.526    2.34e+04\n",
      "B            839.0999   2702.767      0.310      0.786   -1.08e+04    1.25e+04\n",
      "C           2279.4485   2206.800      1.033      0.410   -7215.645    1.18e+04\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.474\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.562\n",
      "Skew:                           0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr2 ~ A+B+C', data=fish_data.loc[fish_data['Time']==4.7], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a87a280d-193b-4126-9388-3cec01ee72d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr2   R-squared:                       0.756\n",
      "Model:                            OLS   Adj. R-squared:                  0.390\n",
      "Method:                 Least Squares   F-statistic:                     2.066\n",
      "Date:                Thu, 28 Jul 2022   Prob (F-statistic):              0.343\n",
      "Time:                        20:09:50   Log-Likelihood:                -54.020\n",
      "No. Observations:                   6   AIC:                             116.0\n",
      "Df Residuals:                       2   BIC:                             115.2\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1551.9160   4399.075      0.353      0.758   -1.74e+04    2.05e+04\n",
      "A           6534.1997   3407.509      1.918      0.195   -8127.129    2.12e+04\n",
      "B           2631.4822   3407.509      0.772      0.521    -1.2e+04    1.73e+04\n",
      "C           4376.4542   2782.220      1.573      0.256   -7594.471    1.63e+04\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.178\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.563\n",
      "Skew:                          -0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr2 ~ A+B+C', data=fish_data.loc[fish_data['Time']==5.0], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "685651d0-ce86-415a-93fc-2e95931df063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   ndr2   R-squared:                       0.808\n",
      "Model:                            OLS   Adj. R-squared:                  0.521\n",
      "Method:                 Least Squares   F-statistic:                     2.811\n",
      "Date:                Sat, 30 Jul 2022   Prob (F-statistic):              0.273\n",
      "Time:                        10:27:45   Log-Likelihood:                -53.230\n",
      "No. Observations:                   6   AIC:                             114.5\n",
      "Df Residuals:                       2   BIC:                             113.6\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    808.1401   3855.972      0.210      0.853   -1.58e+04    1.74e+04\n",
      "A           1542.3565   2986.823      0.516      0.657   -1.13e+04    1.44e+04\n",
      "B           2932.9801   2986.823      0.982      0.430   -9918.283    1.58e+04\n",
      "C           6663.8366   2438.731      2.733      0.112   -3829.175    1.72e+04\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.265\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.562\n",
      "Skew:                           0.000   Prob(JB):                        0.755\n",
      "Kurtosis:                       1.500   Cond. No.                         6.34\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fernandozeng/miniforge3/envs/django/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "y, X = dmatrices('ndr2 ~ A+B+C', data=fish_data.loc[fish_data['Time']==6.0], return_type=\"dataframe\")\n",
    "model = sm.OLS(y, X)\n",
    "res = model.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458f6636-a433-4e7e-a2f4-dfdb43ec7b68",
   "metadata": {},
   "source": [
    "# ndr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c634a52-f6b0-474b-b20e-0a586e32b315",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_pd=pd.DataFrame(columns=['params','coef','pvalue','time'])\n",
    "for i in [3.7,4.0,4.3,4.7,5.0,6.0]:\n",
    "    res_test=pd.DataFrame()\n",
    "    y, X = dmatrices('ndr1 ~ A+B+C', data=fish_data.loc[fish_data['Time']==i], return_type=\"dataframe\")\n",
    "    model = sm.OLS(y, X)\n",
    "    res = model.fit()\n",
    "    res_test['params']=['Intercept','A','B','C']\n",
    "    res_test['coef']=res.params.values\n",
    "    res_test['pvalue']=res.pvalues.values\n",
    "    res_test['time']=[t for t in [i]*4]\n",
    "    res_pd=pd.concat([res_pd,res_test],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd8170cc-645c-4712-9d1f-5956f2b6c5af",
   "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>params</th>\n",
       "      <th>coef</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>1813.048844</td>\n",
       "      <td>0.109455</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2030.346245</td>\n",
       "      <td>0.057122</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>4090.756824</td>\n",
       "      <td>0.015030</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>824.266175</td>\n",
       "      <td>0.184849</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>2060.547703</td>\n",
       "      <td>0.229090</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      params         coef    pvalue  time\n",
       "0  Intercept  1813.048844  0.109455   3.7\n",
       "1          A  2030.346245  0.057122   3.7\n",
       "2          B  4090.756824  0.015030   3.7\n",
       "3          C   824.266175  0.184849   3.7\n",
       "4  Intercept  2060.547703  0.229090   4.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e019721-4485-4aa4-ad8e-ddca19fcbf18",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_analysis(A,B,C,inter):\n",
    "    A_adj=A/1000\n",
    "    B_adj=B/1000\n",
    "    C_adj=C/1000\n",
    "    all_adj=[]\n",
    "    \n",
    "    all_min=np.min(np.array([A_adj,B_adj,C_adj]))\n",
    "    for i in range(len(A_adj)):\n",
    "        all_test=np.array([A_adj[i],B_adj[i],C_adj[i]])\n",
    "        all_max=np.max(all_test)\n",
    "        all_test=(all_test-all_min)\n",
    "        all_adj.append(all_test)\n",
    "    return all_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e6aaed0-2fc9-40c2-9c41-4fd96429ac8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_analysis1(A,B,C):\n",
    "    A_adj=A/1000\n",
    "    B_adj=B/1000\n",
    "    C_adj=C/1000\n",
    "    all_adj=[]\n",
    "    \n",
    "    all_min=np.min(np.array([A_adj,B_adj,C_adj]))\n",
    "    for i in range(len(A_adj)):\n",
    "        all_test=np.array([A_adj[i],B_adj[i],C_adj[i]])\n",
    "        all_max=np.max(all_test)\n",
    "        all_test=(all_test-all_min)\n",
    "        all_sum=np.sum(all_test)\n",
    "        a1=[i/all_sum for i in all_test]\n",
    "        all_adj.append(a1)\n",
    "    return all_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5dc58381-88e2-4f3c-a09b-5c0dd98082ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_analysis2(data,pvalue=0.2):\n",
    "    result_pd=pd.DataFrame(columns=['params','prop','pvalue','time'])\n",
    "    data['coef']=data['coef']-data['coef'].min()\n",
    "    for i in set(data['time']):\n",
    "        new_pd=pd.DataFrame()\n",
    "        test_pd=data.loc[(data['time']==i)&(data['params']!='Intercept')]\n",
    "        test_pd.loc[test_pd['pvalue']>pvalue,'coef']=0\n",
    "        su=test_pd.loc[:,'coef'].sum()\n",
    "\n",
    "        new_pd['params']=test_pd.loc[:,'params'].values\n",
    "        new_pd['prop']=(test_pd.loc[:,'coef'].values/su)\n",
    "        new_pd['pvalue']=test_pd.loc[:,'pvalue'].values\n",
    "        new_pd['time']=[t for t in [i]*3]\n",
    "        \n",
    "        result_pd=pd.concat([result_pd,new_pd])\n",
    "    return result_pd\n",
    "#value_analysis2(res_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b8390acb-7ca3-4871-a87e-289959c1f8cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.45082289, 8.28637987, 8.35764039, 8.16010905, 1.31275614,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj=value_analysis(res_pd.loc[(res_pd['params']=='A')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='B')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='Intercept')]['coef'].values,\n",
    "              )\n",
    "np.array(all_adj)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4f0c3d41-d1d4-4baa-8999-9ae80f1cfe1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32106207, 0.32564816, 0.32114723, 0.294066  , 0.07287956,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj1=value_analysis1(res_pd.loc[(res_pd['params']=='A')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='B')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "               \n",
    "              )\n",
    "np.array(all_adj1)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "790ddec7-4bfd-4ab5-a116-4c7e40b27cf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4m/2xw3_2s503s9r616083n7w440000gn/T/ipykernel_48563/1778812987.py:66: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 340
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as mpatches\n",
    "#pp=plt.figure(figsize=(4,4))\n",
    "#用ax控制图片\n",
    "#ax=pp.add_subplot(1,1,1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='A')]\n",
    "for xp, yp, m,c in zip(range(6),np.array(all_adj)[:,0], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=emosa_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(range(6),np.array(all_adj)[:,0],color='#FCB471',label='Eomesa')\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='B')]\n",
    "for xp, yp, m,c in zip(range(6),np.array(all_adj)[:,1], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    if m=='*':\n",
    "        s=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=beta_cmap,vmin=0.05,vmax=1.5,)\n",
    "    elif m=='o':\n",
    "        s1=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=beta_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(range(6),np.array(all_adj)[:,1],color='#B063FF',label='b-catenin')\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='C')]\n",
    "for xp, yp, m,c in zip(range(6),np.array(all_adj)[:,2], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=smad_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(range(6),np.array(all_adj)[:,2],color='#FF6CFE',label='Smad2')\n",
    "plt.grid(False)\n",
    "\n",
    "legend1=ax.legend(bbox_to_anchor=(1.05, 1.1), ncol=1,fontsize=12)\n",
    "ax.add_artist(legend1)\n",
    "\n",
    "\n",
    "names=['p<0.05','p>0.05']\n",
    "legend2 = ax.legend([s,s1], names,bbox_to_anchor=(1.05, 0.7), ncol=1,fontsize=12,title='Type')\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr1',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Regression coefficient',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "\n",
    "plt.xticks(range(6),[3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "\n",
    "axins = inset_axes(ax,\n",
    "                   width=\"5%\",  # width = 5% of parent_bbox width\n",
    "                   height=\"30%\",  # height : 50%\n",
    "                   loc='lower left',\n",
    "                   bbox_to_anchor=(1.15, -0.1, 0.5, 1),\n",
    "                   bbox_transform=ax.transAxes,\n",
    "                   borderpad=0,\n",
    "                   )\n",
    "norm = mpl.colors.Normalize(vmin=0.05, vmax=1.5)\n",
    "cmap = mpl.cm.ScalarMappable(norm=norm, cmap='gray_r')\n",
    "cmap.set_array([])\n",
    "fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n",
    "plt.savefig(\"result/Fig_ndr1_4.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "657a11e4-217c-4046-a667-d9c32231a767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 352
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,0],\n",
    "        bottom=np.zeros(len(plot_t['time'])),\n",
    "        label='Eomesa',width=0.8,#edgecolor='black',\n",
    "        fc = '#FCB471')\n",
    "\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,1],\n",
    "        bottom=np.array(all_adj1)[:,0],\n",
    "        label='b-catenin',width=0.8,#edgecolor='black',\n",
    "        fc = '#B063FF')\n",
    "\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,2],\n",
    "        bottom=np.array(all_adj1)[:,0]+np.array(all_adj1)[:,1],\n",
    "        label='Smad2',width=0.8,#edgecolor='black',\n",
    "        fc = '#FF6CFE')\n",
    "\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 0.5), ncol=1,fontsize=12)\n",
    "\n",
    "plt.grid(False)\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr1',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Relative Contribution',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "plt.xticks(range(6),[3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.savefig(\"result/Fig_ndr1_2.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec644bb9-e4fe-41f6-86d1-f21da0bf7fba",
   "metadata": {},
   "source": [
    "# ndr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e543e351-d39e-4af6-a4fe-da3269879a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_pd=pd.DataFrame(columns=['params','coef','pvalue','time'])\n",
    "for i in [3.7,4.0,4.3,4.7,5.0,6.0]:\n",
    "    res_test=pd.DataFrame()\n",
    "    y, X = dmatrices('ndr2 ~ A+B+C', data=fish_data.loc[fish_data['Time']==i], return_type=\"dataframe\")\n",
    "    model = sm.OLS(y, X)\n",
    "    res = model.fit()\n",
    "    res_test['params']=['Intercept','A','B','C']\n",
    "    res_test['coef']=res.params.values\n",
    "    res_test['pvalue']=res.pvalues.values\n",
    "    res_test['time']=[t for t in [i]*4]\n",
    "    res_pd=pd.concat([res_pd,res_test],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1f404798-ecfd-428e-a7e1-1add72861356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.28642675,  5.52112637,  7.04594512, 12.64432555,  7.4259321 ,\n",
       "        2.43408888])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj=value_analysis(res_pd.loc[(res_pd['params']=='A')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='B')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='Intercept')]['coef'].values,\n",
    "              )\n",
    "np.array(all_adj)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bbcbdeca-8de0-4236-bfa8-1a27c3133faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58866131, 0.67401135, 0.72767725, 0.72062473, 0.45790094,\n",
       "       0.17619977])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj1=value_analysis1(res_pd.loc[(res_pd['params']=='A')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='B')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "               #res_pd.loc[(res_pd['params']=='Intercept')]['coef'].values,\n",
    "              )\n",
    "np.array(all_adj1)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "78ae59a9-cc90-4e5f-a161-58ecaffbb7a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4m/2xw3_2s503s9r616083n7w440000gn/T/ipykernel_48563/2607685284.py:66: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 334
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as mpatches\n",
    "#pp=plt.figure(figsize=(4,4))\n",
    "#用ax控制图片\n",
    "#ax=pp.add_subplot(1,1,1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='A')]\n",
    "for xp, yp, m,c in zip(range(6),np.array(all_adj)[:,0], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    if m=='*':\n",
    "        s=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=emosa_cmap,vmin=0.05,vmax=1.5,)\n",
    "    elif m=='o':\n",
    "        s1=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=emosa_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(range(6),np.array(all_adj)[:,0],color='#FCB471',label='Eomesa')\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='B')]\n",
    "for xp, yp, m,c in zip(range(6),np.array(all_adj)[:,1], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=beta_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(range(6),np.array(all_adj)[:,1],color='#B063FF',label='b-catenin')\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='C')]\n",
    "for xp, yp, m,c in zip(range(6),np.array(all_adj)[:,2], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=smad_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(range(6),np.array(all_adj)[:,2],color='#FF6CFE',label='Smad2')\n",
    "plt.grid(False)\n",
    "\n",
    "legend1=ax.legend(bbox_to_anchor=(1.5, 1.1), ncol=1,fontsize=12)\n",
    "ax.add_artist(legend1)\n",
    "\n",
    "\n",
    "names=['p<0.05','p>0.05']\n",
    "legend2 = ax.legend([s,s1], names,bbox_to_anchor=(1.05, 0.7), ncol=1,fontsize=12,title='Type')\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr2',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Regression coefficient',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "\n",
    "plt.xticks(range(6),[3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "\n",
    "axins = inset_axes(ax,\n",
    "                   width=\"5%\",  # width = 5% of parent_bbox width\n",
    "                   height=\"30%\",  # height : 50%\n",
    "                   loc='lower left',\n",
    "                   bbox_to_anchor=(1.15, -.1, 0.5, 1),\n",
    "                   bbox_transform=ax.transAxes,\n",
    "                   borderpad=0,\n",
    "                   )\n",
    "norm = mpl.colors.Normalize(vmin=0.05, vmax=1.5)\n",
    "cmap = mpl.cm.ScalarMappable(norm=norm, cmap='gray_r')\n",
    "cmap.set_array([])\n",
    "fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n",
    "plt.savefig(\"result/Fig_ndr2_4.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02c9b1b4-e0ef-4806-bab8-c83fa86cdeb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 352
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,0],\n",
    "        bottom=np.zeros(len(plot_t['time'])),\n",
    "        label='Eomesa',width=0.8,#edgecolor='black',\n",
    "        fc = '#FCB471')\n",
    "\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,1],\n",
    "        bottom=np.array(all_adj1)[:,0],\n",
    "        label='b-catenin',width=0.8,#edgecolor='black',\n",
    "        fc = '#B063FF')\n",
    "\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,2],\n",
    "        bottom=np.array(all_adj1)[:,0]+np.array(all_adj1)[:,1],\n",
    "        label='Smad2',width=0.8,#edgecolor='black',\n",
    "        fc = '#FF6CFE')\n",
    "\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 0.5), ncol=1,fontsize=12)\n",
    "\n",
    "plt.grid(False)\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr2',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Relative Contribution',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "plt.xticks(range(6),[3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.savefig(\"result/Fig_ndr2_2.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "075c10e2-bc86-46a5-a4d2-18987c1f1e64",
   "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>params</th>\n",
       "      <th>coef</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3843.395089</td>\n",
       "      <td>0.032306</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>3075.583701</td>\n",
       "      <td>0.064760</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>824.266175</td>\n",
       "      <td>0.476236</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4926.450932</td>\n",
       "      <td>0.045302</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B</td>\n",
       "      <td>2729.213690</td>\n",
       "      <td>0.182026</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>2156.307528</td>\n",
       "      <td>0.242948</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>5782.681307</td>\n",
       "      <td>0.034463</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>141.722353</td>\n",
       "      <td>0.937386</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>5215.428932</td>\n",
       "      <td>0.044717</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>5869.521672</td>\n",
       "      <td>0.044593</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>-246.491191</td>\n",
       "      <td>0.903343</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C</td>\n",
       "      <td>7624.856629</td>\n",
       "      <td>0.022705</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3405.669500</td>\n",
       "      <td>0.237009</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>B</td>\n",
       "      <td>948.931000</td>\n",
       "      <td>0.724497</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C</td>\n",
       "      <td>6963.899500</td>\n",
       "      <td>0.057063</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>1299.904309</td>\n",
       "      <td>0.670905</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B</td>\n",
       "      <td>2593.433393</td>\n",
       "      <td>0.442443</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C</td>\n",
       "      <td>2766.881239</td>\n",
       "      <td>0.391517</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       params         coef    pvalue  time\n",
       "0   Intercept  3843.395089  0.032306   3.7\n",
       "1           B  3075.583701  0.064760   3.7\n",
       "2           C   824.266175  0.476236   3.7\n",
       "3   Intercept  4926.450932  0.045302   4.0\n",
       "4           B  2729.213690  0.182026   4.0\n",
       "5           C  2156.307528  0.242948   4.0\n",
       "6   Intercept  5782.681307  0.034463   4.3\n",
       "7           B   141.722353  0.937386   4.3\n",
       "8           C  5215.428932  0.044717   4.3\n",
       "9   Intercept  5869.521672  0.044593   4.7\n",
       "10          B  -246.491191  0.903343   4.7\n",
       "11          C  7624.856629  0.022705   4.7\n",
       "12  Intercept  3405.669500  0.237009   5.0\n",
       "13          B   948.931000  0.724497   5.0\n",
       "14          C  6963.899500  0.057063   5.0\n",
       "15  Intercept  1299.904309  0.670905   6.0\n",
       "16          B  2593.433393  0.442443   6.0\n",
       "17          C  2766.881239  0.391517   6.0"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_pd=pd.DataFrame(columns=['params','coef','pvalue','time'])\n",
    "for i in [3.7,4.0,4.3,4.7,5.0,6.0]:\n",
    "    res_test=pd.DataFrame()\n",
    "    y, X = dmatrices('ndr1 ~ B+C', data=fish_data.loc[fish_data['Time']==i], return_type=\"dataframe\")\n",
    "    model = sm.OLS(y, X)\n",
    "    res = model.fit()\n",
    "    res_test['params']=['Intercept','B','C']\n",
    "    res_test['coef']=res.params.values\n",
    "    res_test['pvalue']=res.pvalues.values\n",
    "    res_test['time']=[t for t in [i]*3]\n",
    "    res_pd=pd.concat([res_pd,res_test],ignore_index=True)\n",
    "res_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "48f4f216-eff9-44b6-83d5-21e4c2c6aa61",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_analysis3(A,B,inter):\n",
    "    A_adj=A/1000\n",
    "    B_adj=B/1000\n",
    "    all_adj=[]\n",
    "    \n",
    "    all_min=np.min(np.array([A_adj,B_adj]))\n",
    "    for i in range(len(A_adj)):\n",
    "        all_test=np.array([A_adj[i],B_adj[i]])\n",
    "        all_max=np.max(all_test)\n",
    "        all_test=(all_test-all_min)\n",
    "        all_adj.append(all_test)\n",
    "    return all_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "a92a4eeb-5564-48b5-9dcd-d4749ec344c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_analysis4(A,B):\n",
    "    A_adj=A/1000\n",
    "    B_adj=B/1000\n",
    "\n",
    "    all_adj=[]\n",
    "    \n",
    "    all_min=np.min(np.array([A_adj,B_adj]))\n",
    "    for i in range(len(A_adj)):\n",
    "        all_test=np.array([A_adj[i],B_adj[i]])\n",
    "        all_max=np.max(all_test)\n",
    "        all_test=(all_test-all_min)\n",
    "        all_sum=np.sum(all_test)\n",
    "        a1=[i/all_sum for i in all_test]\n",
    "        all_adj.append(a1)\n",
    "    return all_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "bea52217-bd19-4795-9e62-1cc0bed22bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.32207489, 2.97570488, 0.38821354, 0.        , 1.19542219,\n",
       "       2.83992458])"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj=value_analysis3(res_pd.loc[(res_pd['params']=='B')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='Intercept')]['coef'].values,\n",
    "              )\n",
    "np.array(all_adj)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "158627d3-27d5-4ed4-9df6-b7b61efed907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75624897, 0.55325888, 0.06635977, 0.        , 0.14221375,\n",
       "       0.48518375])"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj1=value_analysis4(res_pd.loc[(res_pd['params']=='B')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "              )\n",
    "np.array(all_adj1)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "822d51ee-468c-4e56-b3ab-8fca82aaac93",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4m/2xw3_2s503s9r616083n7w440000gn/T/ipykernel_2169/2908348217.py:66: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 270,
       "width": 320
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as mpatches\n",
    "#pp=plt.figure(figsize=(4,4))\n",
    "#用ax控制图片\n",
    "#ax=pp.add_subplot(1,1,1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='B')]\n",
    "for xp, yp, m,c in zip(plot_t['time'],np.array(all_adj)[:,0], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    if m=='*':\n",
    "        s=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=beta_cmap,vmin=0.05,vmax=1.5,)\n",
    "    elif m=='o':\n",
    "        s1=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=beta_cmap,vmin=0.05,vmax=1.5,)\n",
    "        \n",
    "plt.plot(plot_t['time'],np.array(all_adj)[:,0],color='#B063FF',label='b-catenin')\n",
    "\n",
    "\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='C')]\n",
    "for xp, yp, m,c in zip(plot_t['time'],np.array(all_adj)[:,1], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    if m=='*':\n",
    "        s=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=smad_cmap,vmin=0.05,vmax=1.5,)\n",
    "    elif m=='o':\n",
    "        s1=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=smad_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(plot_t['time'],np.array(all_adj)[:,1],color='#FF6CFE',label='Smad2')\n",
    "plt.grid(False)\n",
    "\n",
    "legend1=ax.legend(bbox_to_anchor=(0.7, 0.9), ncol=1,fontsize=12)\n",
    "ax.add_artist(legend1)\n",
    "\n",
    "\n",
    "names=['p<0.05','p>0.05']\n",
    "legend2 = ax.legend([s,s1], names,bbox_to_anchor=(1.05, 0.9), ncol=1,fontsize=12,title='Type')\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr1',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Regression coefficient',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "\n",
    "plt.xticks([3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "\n",
    "axins = inset_axes(ax,\n",
    "                   width=\"5%\",  # width = 5% of parent_bbox width\n",
    "                   height=\"30%\",  # height : 50%\n",
    "                   loc='lower left',\n",
    "                   bbox_to_anchor=(1.15, 0., 0.5, 1),\n",
    "                   bbox_transform=ax.transAxes,\n",
    "                   borderpad=0,\n",
    "                   )\n",
    "norm = mpl.colors.Normalize(vmin=0.05, vmax=1.5)\n",
    "cmap = mpl.cm.ScalarMappable(norm=norm, cmap='gray_r')\n",
    "cmap.set_array([])\n",
    "fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n",
    "plt.savefig(\"Fig_ndr1_11.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "fd7cc7ba-b5a3-4ce5-bd7c-8e233d2fee0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 352
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,0],\n",
    "        bottom=np.zeros(len(plot_t['time'])),\n",
    "\n",
    "        label='b-catenin',width=0.8,#edgecolor='black',\n",
    "        fc = '#B063FF')\n",
    "\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,1],\n",
    "        bottom=np.array(all_adj1)[:,0],\n",
    "        label='Smad2',width=0.8,#edgecolor='black',\n",
    "        fc = '#FF6CFE')\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 0.5), ncol=1,fontsize=12)\n",
    "\n",
    "plt.grid(False)\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr1',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Relative Contribution',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "plt.xticks(range(6),[3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.savefig(\"Fig_ndr1_12.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "b2ae3f7f-d885-44ec-8c37-ba8a3f1f6c4d",
   "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>params</th>\n",
       "      <th>coef</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>1731.769362</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>2224.850304</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>173.302526</td>\n",
       "      <td>0.374381</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>1460.903151</td>\n",
       "      <td>0.083568</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>4161.110273</td>\n",
       "      <td>0.006338</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>-49.714303</td>\n",
       "      <td>0.936189</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>2105.471625</td>\n",
       "      <td>0.028868</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A</td>\n",
       "      <td>6600.078938</td>\n",
       "      <td>0.001346</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>1745.111250</td>\n",
       "      <td>0.046522</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>1897.672567</td>\n",
       "      <td>0.379309</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A</td>\n",
       "      <td>11333.043221</td>\n",
       "      <td>0.010238</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C</td>\n",
       "      <td>2279.448509</td>\n",
       "      <td>0.304530</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>4183.398183</td>\n",
       "      <td>0.204455</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A</td>\n",
       "      <td>5218.458640</td>\n",
       "      <td>0.153501</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C</td>\n",
       "      <td>4376.454190</td>\n",
       "      <td>0.189443</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3741.120184</td>\n",
       "      <td>0.220453</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A</td>\n",
       "      <td>75.866438</td>\n",
       "      <td>0.978314</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C</td>\n",
       "      <td>6663.836630</td>\n",
       "      <td>0.070806</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       params          coef    pvalue  time\n",
       "0   Intercept   1731.769362  0.001896   3.7\n",
       "1           A   2224.850304  0.001078   3.7\n",
       "2           C    173.302526  0.374381   3.7\n",
       "3   Intercept   1460.903151  0.083568   4.0\n",
       "4           A   4161.110273  0.006338   4.0\n",
       "5           C    -49.714303  0.936189   4.0\n",
       "6   Intercept   2105.471625  0.028868   4.3\n",
       "7           A   6600.078938  0.001346   4.3\n",
       "8           C   1745.111250  0.046522   4.3\n",
       "9   Intercept   1897.672567  0.379309   4.7\n",
       "10          A  11333.043221  0.010238   4.7\n",
       "11          C   2279.448509  0.304530   4.7\n",
       "12  Intercept   4183.398183  0.204455   5.0\n",
       "13          A   5218.458640  0.153501   5.0\n",
       "14          C   4376.454190  0.189443   5.0\n",
       "15  Intercept   3741.120184  0.220453   6.0\n",
       "16          A     75.866438  0.978314   6.0\n",
       "17          C   6663.836630  0.070806   6.0"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_pd=pd.DataFrame(columns=['params','coef','pvalue','time'])\n",
    "for i in [3.7,4.0,4.3,4.7,5.0,6.0]:\n",
    "    res_test=pd.DataFrame()\n",
    "    y, X = dmatrices('ndr2 ~ A+C', data=fish_data.loc[fish_data['Time']==i], return_type=\"dataframe\")\n",
    "    model = sm.OLS(y, X)\n",
    "    res = model.fit()\n",
    "    res_test['params']=['Intercept','A','C']\n",
    "    res_test['coef']=res.params.values\n",
    "    res_test['pvalue']=res.pvalues.values\n",
    "    res_test['time']=[t for t in [i]*3]\n",
    "    res_pd=pd.concat([res_pd,res_test],ignore_index=True)\n",
    "res_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "4036e39d-4c6e-4712-ae80-7f0f30f12acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.27456461,  4.21082458,  6.64979324, 11.38275752,  5.26817294,\n",
       "        0.12558074])"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj=value_analysis3(res_pd.loc[(res_pd['params']=='A')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='Intercept')]['coef'].values,\n",
    "              )\n",
    "np.array(all_adj)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "fdca5a30-7460-4846-b343-cdfa56786d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91070688, 1.        , 0.78745926, 0.83013591, 0.54342763,\n",
       "       0.01836209])"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_adj1=value_analysis4(res_pd.loc[(res_pd['params']=='A')]['coef'].values,\n",
    "               res_pd.loc[(res_pd['params']=='C')]['coef'].values,\n",
    "              )\n",
    "np.array(all_adj1)[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "2f92b5ca-c322-47f2-b54b-a8757d4ee206",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4m/2xw3_2s503s9r616083n7w440000gn/T/ipykernel_2169/428047745.py:61: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 328
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as mpatches\n",
    "#pp=plt.figure(figsize=(4,4))\n",
    "#用ax控制图片\n",
    "#ax=pp.add_subplot(1,1,1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='A')]\n",
    "for xp, yp, m,c in zip(plot_t['time'],np.array(all_adj)[:,0], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    if m=='*':\n",
    "        s=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=emosa_cmap,vmin=0.05,vmax=1.5,)\n",
    "    elif m=='o':\n",
    "        s1=plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=emosa_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(plot_t['time'],np.array(all_adj)[:,0],color='#FCB471',label='Eomesa')\n",
    "\n",
    "\n",
    "plot_t=res_pd.loc[(res_pd['params']=='C')]\n",
    "for xp, yp, m,c in zip(plot_t['time'],np.array(all_adj)[:,1], \n",
    "                       ['*' if i<0.05 else 'o' for i in plot_t['pvalue']],-np.log10(plot_t['pvalue'])):\n",
    "    plt.scatter(xp, yp, marker=m, s=50,c=c,cmap=smad_cmap,vmin=0.05,vmax=1.5,)\n",
    "plt.plot(plot_t['time'],np.array(all_adj)[:,1],color='#FF6CFE',label='Smad2')\n",
    "plt.grid(False)\n",
    "\n",
    "legend1=ax.legend(bbox_to_anchor=(1.05, 1), ncol=1,fontsize=12)\n",
    "ax.add_artist(legend1)\n",
    "\n",
    "\n",
    "names=['p<0.05','p>0.05']\n",
    "legend2 = ax.legend([s,s1], names,bbox_to_anchor=(1.05, 0.9), ncol=1,fontsize=12,title='Type')\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr2',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Regression coefficient',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "\n",
    "plt.xticks([3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "\n",
    "axins = inset_axes(ax,\n",
    "                   width=\"5%\",  # width = 5% of parent_bbox width\n",
    "                   height=\"30%\",  # height : 50%\n",
    "                   loc='lower left',\n",
    "                   bbox_to_anchor=(1.15, 0., 0.5, 1),\n",
    "                   bbox_transform=ax.transAxes,\n",
    "                   borderpad=0,\n",
    "                   )\n",
    "norm = mpl.colors.Normalize(vmin=0.05, vmax=1.5)\n",
    "cmap = mpl.cm.ScalarMappable(norm=norm, cmap='gray_r')\n",
    "cmap.set_array([])\n",
    "fig.colorbar(cmap,cax=axins,label='-log10(Pvalue)')\n",
    "plt.savefig(\"Fig_ndr2_11.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "99e20c08-f494-47a5-99c5-240d37e34eec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x240 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 259,
       "width": 338
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=[3,3])\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,0],\n",
    "        bottom=np.zeros(len(plot_t['time'])),\n",
    "        label='Eomesa',width=0.8,#edgecolor='black',\n",
    "        fc = '#FCB471')\n",
    "\n",
    "plt.bar(range(6), \n",
    "        np.array(all_adj1)[:,1],\n",
    "        bottom=np.array(all_adj1)[:,0],\n",
    "        label='Smad2',width=0.8,#edgecolor='black',\n",
    "        fc = '#FF6CFE')\n",
    "\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 0.5), ncol=1,fontsize=12)\n",
    "\n",
    "plt.grid(False)\n",
    "\n",
    "#绘制图像标题\n",
    "ax=plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "plt.title('ndr2',fontsize=15,fontweight='bold')\n",
    "\n",
    "#设置横纵标题\n",
    "plt.ylabel('Relative Contribution',fontsize=15,fontweight='bold')                   \n",
    "plt.xlabel('Time',fontsize=15,fontweight='bold')\n",
    "plt.xticks(range(6),[3.7,4.0,4.3,4.7,5.0,6.0],fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.savefig(\"Fig_ndr2_12.png\",dpi=300,bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edc3df05-0df5-4ee7-a446-b5f06980d022",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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