data.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,14 +10,14 @@
     "import numpy as np\n",
     "\n",
     "from src.preprocessing.synthetic_data import SIR, I\n",
-    "from src.preprocessing.transform_data import transform_data\n",
+    "from src.preprocessing.transform_data import transform_data, transform_jh_germany_data, transform_paper_data\n",
     "\n",
     "from src.plotter import Plotter"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transform_paper_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -81,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -162,10 +171,313 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:48: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  infections[i] = date_df['Confirmed'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:49: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  deaths[i] = date_df['Deaths'].fillna(0).astype(int)\n",
+      "/home/rothenbeck/Covid_19_DINN/src/preprocessing/transform_data.py:50: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
+      "  recoveries[i] = date_df['Recovered'].fillna(0).astype(int)\n"
+     ]
+    }
+   ],
+   "source": [
+    "transform_jh_germany_data(plotter, model=\"SIR\")"
+   ]
   }
  ],
  "metadata": {

germany_dinn_sir.ipynb

@@ -420,7 +420,7 @@
     "    dataset = PandemicDataset(dataset_name, ['S', 'I', 'R'], 83100000, *covid_data)\n",
     "    problem = SIRProblem(dataset)\n",
     "    dinn = DINN(3, dataset, ['alpha', 'beta'], problem, plotter)\n",
-    "    dinn.configure_training(1e-3, 10000, scheduler_name='PolynomialLR', verbose=True)\n",
+    "    dinn.configure_training(1e-3, 10000, scheduler_name=Scheduler.POLYNOMIAL, verbose=True)\n",
     "    dinn.train(create_animation=True, animation_sample_rate=100, verbose=True)\n",
     "    dinn.plot_training_graphs()"
    ]

paper_sir_dinn.ipynb

@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "from src.dataset import PandemicDataset, Norms\n",
+    "from src.problem import SIRAlphaProblem\n",
+    "from src.dinn import DINN, Scheduler, Activation, Optimizer\n",
+    "from src.plotter import Plotter\n",
+    "# torch.manual_seed(18361969907809597111)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covid_data = np.genfromtxt(f'./datasets/SIR_Paper_Germany_14.csv', delimiter=',')\n",
+    "dataset = PandemicDataset(\"SIR_Paper_Germany_14\", \n",
+    "                          ['S', 'I', 'R'], \n",
+    "                          70000000, \n",
+    "                          *covid_data, \n",
+    "                          norm_name=Norms.MIN_MAX) \n",
+    "\n",
+    "ALPHA = 0.07"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Learning Rate:\t0.001\n",
+      "Optimizer:\tADAM\n",
+      "Scheduler:\tPOLYNOMIAL\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "plotter = Plotter()\n",
+    "\n",
+    "problem = SIRAlphaProblem(dataset, ALPHA)\n",
+    "dinn = DINN(3, \n",
+    "            dataset, \n",
+    "            ['beta'], \n",
+    "            problem, \n",
+    "            plotter,\n",
+    "            hidden_size=64, \n",
+    "            hidden_layers=12, \n",
+    "            activation_layer=torch.nn.Tanh(),\n",
+    "            activation_output=Activation.POWER)\n",
+    "dinn.configure_training(1e-3, \n",
+    "                        15000, \n",
+    "                        scheduler_class=Scheduler.POLYNOMIAL, \n",
+    "                        optimizer_class=Optimizer.ADAM,\n",
+    "                        lambda_obs=1e1, \n",
+    "                        verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch seed: 4462685050789905541\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch 0 | LR 0.0009999333333333333\n",
+      "physics loss:\t\t4.160715663590364e-05\n",
+      "observation loss:\t9.556413235291215\n",
+      "loss:\t\t\t9.55645484244785\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.6011705994606018\n",
+      "#################################\n",
+      "\n",
+      "Epoch 1000 | LR 0.0009332666666666524\n",
+      "physics loss:\t\t0.0009664472242609449\n",
+      "observation loss:\t0.0017112362767341596\n",
+      "loss:\t\t\t0.0026776835009951045\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.2550368905067444\n",
+      "#################################\n",
+      "\n",
+      "Epoch 2000 | LR 0.0008665999999999833\n",
+      "physics loss:\t\t0.001247615250807723\n",
+      "observation loss:\t0.0040488860826972005\n",
+      "loss:\t\t\t0.005296501333504924\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.253487229347229\n",
+      "#################################\n",
+      "\n",
+      "Epoch 3000 | LR 0.000799933333333315\n",
+      "physics loss:\t\t0.00044841892062854347\n",
+      "observation loss:\t0.012716515333301339\n",
+      "loss:\t\t\t0.013164934253929882\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.25267982482910156\n",
+      "#################################\n",
+      "\n",
+      "Epoch 4000 | LR 0.000733266666666649\n",
+      "physics loss:\t\t0.001045761999967263\n",
+      "observation loss:\t0.00025607909237615716\n",
+      "loss:\t\t\t0.00130184109234342\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.24764902889728546\n",
+      "#################################\n",
+      "\n",
+      "Epoch 5000 | LR 0.0006665999999999824\n",
+      "physics loss:\t\t0.0014444880255704616\n",
+      "observation loss:\t0.00014856713535789037\n",
+      "loss:\t\t\t0.001593055160928352\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.2412654012441635\n",
+      "#################################\n",
+      "\n",
+      "Epoch 6000 | LR 0.0005999333333333129\n",
+      "physics loss:\t\t0.001605141155981103\n",
+      "observation loss:\t0.00013662428319793931\n",
+      "loss:\t\t\t0.0017417654391790425\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.23936264216899872\n",
+      "#################################\n",
+      "\n",
+      "Epoch 7000 | LR 0.0005332666666666435\n",
+      "physics loss:\t\t0.00190968670757966\n",
+      "observation loss:\t0.00012329566674995849\n",
+      "loss:\t\t\t0.0020329823743296185\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.23441843688488007\n",
+      "#################################\n",
+      "\n",
+      "Epoch 8000 | LR 0.0004665999999999755\n",
+      "physics loss:\t\t0.0019683097313949755\n",
+      "observation loss:\t0.00011957883463252119\n",
+      "loss:\t\t\t0.0020878885660274966\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.2345646470785141\n",
+      "#################################\n",
+      "\n",
+      "Epoch 9000 | LR 0.0003999333333333152\n",
+      "physics loss:\t\t0.0023533661719838616\n",
+      "observation loss:\t0.00011510529573689898\n",
+      "loss:\t\t\t0.0024684714677207604\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.22906526923179626\n",
+      "#################################\n",
+      "\n",
+      "Epoch 10000 | LR 0.00033326666666665735\n",
+      "physics loss:\t\t0.0025831233331882735\n",
+      "observation loss:\t0.00011076522614630127\n",
+      "loss:\t\t\t0.002693888559334575\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.22521229088306427\n",
+      "#################################\n",
+      "\n",
+      "Epoch 11000 | LR 0.00026659999999999987\n",
+      "physics loss:\t\t0.002667692420961139\n",
+      "observation loss:\t0.00010629412421269853\n",
+      "loss:\t\t\t0.0027739865451738375\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.22304710745811462\n",
+      "#################################\n",
+      "\n",
+      "Epoch 12000 | LR 0.0001999333333333354\n",
+      "physics loss:\t\t0.002831200195029232\n",
+      "observation loss:\t0.00015541454626586456\n",
+      "loss:\t\t\t0.0029866147412950968\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.2231626659631729\n",
+      "#################################\n",
+      "\n",
+      "Epoch 13000 | LR 0.0001332666666666689\n",
+      "physics loss:\t\t0.002496475121496725\n",
+      "observation loss:\t9.74420291549779e-05\n",
+      "loss:\t\t\t0.002593917150651703\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.22342386841773987\n",
+      "#################################\n",
+      "\n",
+      "Epoch 14000 | LR 6.660000000000055e-05\n",
+      "physics loss:\t\t0.0024228574550409178\n",
+      "observation loss:\t9.359204257759588e-05\n",
+      "loss:\t\t\t0.0025164494976185137\n",
+      "---------------------------------\n",
+      "beta:\t\t\t0.2237681895494461\n",
+      "#################################\n"
+     ]
+    }
+   ],
+   "source": [
+    "dinn.train(create_animation=True, \n",
+    "           plot_I_prediction=True, \n",
+    "           verbose=True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dinn.plot_training_graphs()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PINN",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

src/dinn.py

@@ -8,38 +8,43 @@ from .dataset import PandemicDataset
 from .problem import PandemicProblem
 from .plotter import Plotter
 
+
 class Optimizer(Enum):
-    ADAM=0
+    ADAM = 0
+
 
 class Scheduler(Enum):
-    CYCLIC=0
-    CONSTANT=1
-    LINEAR=2
-    POLYNOMIAL=3
+    CYCLIC = 0
+    CONSTANT = 1
+    LINEAR = 2
+    POLYNOMIAL = 3
+
 
 class Activation(Enum):
-    LINEAR=0
-    POWER=1
+    LINEAR = 0
+    POWER = 1
+
 
 def linear(x):
     return x
-        
+
+
 def power(x):
     return torch.float_power(x, 2)
 
 
 class DINN:
     class NN(torch.nn.Module):
-        def __init__(self, 
+        def __init__(self,
                      output_size: int,
                      input_size: int,
                      hidden_size: int,
-                     hidden_layers: int, 
+                     hidden_layers: int,
                      activation_layer,
                      t_init,
                      t_final,
                      output_activation_function=Activation.LINEAR,
-                     use_glorot_initialization = False,
+                     use_glorot_initialization=False,
                      use_t_scaled=True) -> None:
             """Neural Network
 
@@ -60,8 +65,10 @@ class DINN:
                 print('Set output activation to default: linear')
                 self.out_activation = self.linear
 
-            self.input = torch.nn.Sequential(torch.nn.Linear(input_size, hidden_size), activation_layer)
-            self.hidden = torch.nn.Sequential(*[torch.nn.Sequential(torch.nn.Linear(hidden_size, hidden_size), activation_layer) for _ in range(hidden_layers)])
+            self.input = torch.nn.Sequential(torch.nn.Linear(
+                input_size, hidden_size), activation_layer)
+            self.hidden = torch.nn.Sequential(*[torch.nn.Sequential(torch.nn.Linear(
+                hidden_size, hidden_size), activation_layer) for _ in range(hidden_layers)])
             self.output = torch.nn.Linear(hidden_size, output_size)
 
             if use_glorot_initialization:
@@ -69,7 +76,7 @@ class DINN:
                 for i in range(hidden_layers):
                     torch.nn.init.xavier_uniform_(self.hidden[i][0].weight)
                 torch.nn.init.xavier_uniform_(self.output.weight)
-            
+
             self.__t_init = t_init
             self.__t_final = t_final
             self.__use_t_scaled = use_t_scaled
@@ -77,14 +84,15 @@ class DINN:
         def forward(self, t):
             # normalize input
             if self.__use_t_scaled:
-                t_forward = (t - self.__t_init) / (self.__t_final - self.__t_init)
+                t_forward = (t - self.__t_init) / \
+                    (self.__t_final - self.__t_init)
             else:
                 t_forward = t
             x = self.input(t_forward)
             x = self.hidden(x)
             x = self.output(x)
             return self.out_activation(x)
-    
+
     def __init__(self,
                  output_size: int,
                  data: PandemicDataset,
@@ -93,12 +101,12 @@ class DINN:
                  plotter: Plotter,
                  state_variables=[],
                  parameter_regulator=torch.tanh,
-                 input_size=1, 
-                 hidden_size=20, 
-                 hidden_layers=7, 
+                 input_size=1,
+                 hidden_size=20,
+                 hidden_layers=7,
                  activation_layer=torch.nn.ReLU(),
                  activation_output=Activation.LINEAR,
-                 use_glorot_initialization = False) -> None:
+                 use_glorot_initialization=False) -> None:
         """Desease Informed Neural Network. Uses the PandemicProblem, DINN.NN and PandemicDataset to solve Inverse Problems and find the 
         parameters of a specific mathematical model.
 
@@ -115,17 +123,18 @@ class DINN:
             hidden_layers (int, optional): Number of the hidden layers for the NN. Defaults to 7.
             activation_layer (optional): Class of the activation function. Defaults to torch.nn.ReLU().
         """
-        assert len(state_variables) + data.number_groups == output_size, f'The number of groups plus the number of state variable must result in the output size\nGroups:\t{data.number_groups}\nState variables:\t{len(state_variables)}\noutput_size: {output_size}\n'
+        assert len(state_variables) + \
+            data.number_groups == output_size, f'The number of groups plus the number of state variable must result in the output size\nGroups:\t{data.number_groups}\nState variables:\t{len(state_variables)}\noutput_size: {output_size}\n'
         self.device = torch.device(data.device_name)
         self.device_name = data.device_name
         self.plotter = plotter
 
-        self.model = DINN.NN(output_size, 
-                             input_size, 
-                             hidden_size, 
-                             hidden_layers, 
+        self.model = DINN.NN(output_size,
+                             input_size,
+                             hidden_size,
+                             hidden_layers,
                              activation_layer,
-                             data.t_init, 
+                             data.t_init,
                              data.t_final,
                              activation_output,
                              use_glorot_initialization=use_glorot_initialization,
@@ -138,7 +147,8 @@ class DINN:
 
         self.parameters_tilda = {}
         for parameter in parameter_list:
-            self.parameters_tilda.update({parameter : torch.nn.Parameter(torch.rand(1, requires_grad=True, device=self.device_name))})
+            self.parameters_tilda.update({parameter: torch.nn.Parameter(
+                torch.rand(1, requires_grad=True, device=self.device_name))})
 
         # new model has to be configured and then trained
         self.__is_configured = False
@@ -164,7 +174,7 @@ class DINN:
             torch.Parameter: Regulated parameter object of the search parameter.
         """
         return self.parameter_regulator(self.parameters_tilda[parameter_name])
-    
+
     def get_parameters_tilda(self):
         """Function to get the original value (not forced into any range).
 
@@ -181,19 +191,18 @@ class DINN:
         """
         return [self.parameter_regulator(parameter) for parameter in self.get_parameters_tilda()]
 
-    
     def get_output(self, index):
         output = self.model(self.data.t_batch)
         return output[:, index]
-    
-    def configure_training(self, 
-                           lr:float, 
-                           epochs:int, 
-                           optimizer_class=Optimizer.ADAM, 
-                           scheduler_class=Scheduler.CYCLIC, 
-                           scheduler_factor = 1, 
-                           lambda_obs = 1,
-                           lambda_physics = 1,
+
+    def configure_training(self,
+                           lr: float,
+                           epochs: int,
+                           optimizer_class=Optimizer.ADAM,
+                           scheduler_class=Scheduler.CYCLIC,
+                           scheduler_factor=1,
+                           lambda_obs=1,
+                           lambda_physics=1,
                            verbose=False):
         """This method sets the optimizer, scheduler, learning rate and number of epochs for the following training process.
 
@@ -204,7 +213,8 @@ class DINN:
             scheduler_name (str, optional): Name of the scheduler class that is supposed to be used. Defaults to 'CyclicLR'.
             verbose (bool, optional): Controles if the configuration process, is to be verbosed. Defaults to False.
         """
-        parameter_list = list(self.model.parameters()) + list(self.parameters_tilda.values())
+        parameter_list = list(self.model.parameters()) + \
+            list(self.parameters_tilda.values())
         self.epochs = epochs
         self.lambda_obs = lambda_obs
         self.lambda_physics = lambda_physics
@@ -215,34 +225,41 @@ class DINN:
                 self.optimizer = torch.optim.Adam(parameter_list, lr=lr)
                 if verbose:
                     print('---------------------------------')
-                    print(f' Entered unknown optimizer name: {optimizer_class.name}\n Defaulted to ADAM.')
+                    print(
+                        f' Entered unknown optimizer name: {optimizer_class.name}\n Defaulted to ADAM.')
                     print('---------------------------------')
                 optimizer_class = Optimizer.ADAM
 
         match scheduler_class:
             case Scheduler.CYCLIC:
-                self.scheduler = torch.optim.lr_scheduler.CyclicLR(self.optimizer, base_lr=lr * 10, max_lr=lr * 1e3, step_size_up=1000, mode="exp_range", gamma=0.85, cycle_momentum=False)
+                self.scheduler = torch.optim.lr_scheduler.CyclicLR(
+                    self.optimizer, base_lr=lr * 10, max_lr=lr * 1e3, step_size_up=1000, mode="exp_range", gamma=0.85, cycle_momentum=False)
             case Scheduler.CONSTANT:
-                self.scheduler = torch.optim.lr_scheduler.ConstantLR(self.optimizer, factor=1, total_iters=4)
+                self.scheduler = torch.optim.lr_scheduler.ConstantLR(
+                    self.optimizer, factor=1, total_iters=4)
             case Scheduler.LINEAR:
-                self.scheduler = torch.optim.lr_scheduler.LinearLR(self.optimizer, start_factor=lr, total_iters=epochs/scheduler_factor)
+                self.scheduler = torch.optim.lr_scheduler.LinearLR(
+                    self.optimizer, start_factor=lr, total_iters=epochs / scheduler_factor)
             case Scheduler.POLYNOMIAL:
-                self.scheduler = torch.optim.lr_scheduler.PolynomialLR(self.optimizer, total_iters=epochs/scheduler_factor, power=1.0)
+                self.scheduler = torch.optim.lr_scheduler.PolynomialLR(
+                    self.optimizer, total_iters=epochs / scheduler_factor, power=1.0)
             case _:
-                self.scheduler = torch.optim.lr_scheduler.CyclicLR(self.optimizer, base_lr=lr * 10, max_lr=lr * 1e3, step_size_up=1000, mode="exp_range", gamma=0.85, cycle_momentum=False)
+                self.scheduler = torch.optim.lr_scheduler.CyclicLR(
+                    self.optimizer, base_lr=lr * 10, max_lr=lr * 1e3, step_size_up=1000, mode="exp_range", gamma=0.85, cycle_momentum=False)
                 if verbose:
                     print('---------------------------------')
-                    print(f' Entered unknown scheduler name: {scheduler_class.name}\n Defaulted to CYCLIC.')
+                    print(
+                        f' Entered unknown scheduler name: {scheduler_class.name}\n Defaulted to CYCLIC.')
                     print('---------------------------------')
                 scheduler_class = Scheduler.CYCLIC
 
         if verbose:
-            print(f'\nLearning Rate:\t{lr}\nOptimizer:\t{optimizer_class.name}\nScheduler:\t{scheduler_class.name}\n')
+            print(
+                f'\nLearning Rate:\t{lr}\nOptimizer:\t{optimizer_class.name}\nScheduler:\t{scheduler_class.name}\n')
 
         self.__is_configured = True
 
-    
-    def train(self, 
+    def train(self,
               create_animation=False,
               animation_sample_rate=500,
               verbose=False,
@@ -258,7 +275,7 @@ class DINN:
         assert self.__is_configured, 'The model has to be configured before training through the use of self.configure training.'
         if verbose:
             print(f'torch seed: {torch.seed()}')
-        
+
         # arrays to hold values for plotting
         self.losses = np.zeros(self.epochs)
         self.obs_losses = np.zeros(self.epochs)
@@ -268,7 +285,8 @@ class DINN:
         for epoch in range(self.epochs):
             # get the prediction and the fitting residuals
             prediction = self.model(self.data.t_batch)
-            residuals = self.problem.residual(prediction, *self.get_regulated_param_list())
+            residuals = self.problem.residual(
+                prediction, *self.get_regulated_param_list())
             self.optimizer.zero_grad()
 
             # calculate loss from the differential system
@@ -280,9 +298,10 @@ class DINN:
             # calculate loss from the dataset
             loss_obs = 0
             for i, group in enumerate(self.data.group_names):
-                loss_obs += torch.mean(torch.square(self.data.get_norm(group) - prediction[:, i]))
+                loss_obs += torch.mean(torch.square(
+                    self.data.get_norm(group) - prediction[:, i]))
             loss_obs *= self.lambda_obs
-            
+
             if do_split_training:
                 if epoch < start_split:
                     loss = loss_obs
@@ -300,41 +319,45 @@ class DINN:
             self.obs_losses[epoch] = loss_obs.item()
             self.physics_losses[epoch] = loss_physics.item()
             for i, parameter in enumerate(self.parameters_tilda.items()):
-                self.parameters[i][epoch] = self.get_regulated_param(parameter[0]).item()
+                self.parameters[i][epoch] = self.get_regulated_param(
+                    parameter[0]).item()
 
             # do snapshot for prediction animation
             if epoch % animation_sample_rate == 0 and create_animation:
                 # prediction
                 prediction = self.model(self.data.t_batch)
-                t = torch.arange(0, self.data.t_raw[-1].item(), (self.data.t_raw[-1] / self.data.t_raw.shape[0]).item())
-                groups = self.data.get_denormalized_data([prediction[:, i] for i in range(self.data.number_groups)])
-
-                plot_labels = [name + '_pred' for name in self.data.group_names] + [name + '_true' for name in self.data.group_names]
-                background_list = [0 for _ in self.data.group_names] + [1 for _ in self.data.group_names]
-                self.plotter.plot(t, 
-                                  list(groups) + list(self.data.data), 
+                t = torch.arange(
+                    0, self.data.t_raw[-1].item(), (self.data.t_raw[-1] / self.data.t_raw.shape[0]).item())
+                groups = self.data.get_denormalized_data(
+                    [prediction[:, i] for i in range(self.data.number_groups)])
+
+                plot_labels = ['I_pred', 'I_true']
+                background_list = [0, 1]
+                self.plotter.plot(t,
+                                  [groups[1]] + [self.data.data[1]],
                                   plot_labels,
-                                  'frame',
+                                  'Frame',
                                   f'epoch {epoch}',
                                   figure_shape=(12, 6),
                                   is_frame=True,
                                   is_background=background_list,
                                   lw=3,
                                   legend_loc='upper right',
-                                  ylim=(0, self.data.N), 
                                   xlabel='time / days',
                                   ylabel='amount of people')
 
             # print training advancements
-            if epoch % 1000 == 0 and verbose:          
-                print(f'\nEpoch {epoch} | LR {self.scheduler.get_last_lr()[0]}')
+            if epoch % 1000 == 0 and verbose:
+                print(
+                    f'\nEpoch {epoch} | LR {self.scheduler.get_last_lr()[0]}')
                 print(f'physics loss:\t\t{loss_physics.item()}')
                 print(f'observation loss:\t{loss_obs.item()}')
                 print(f'loss:\t\t\t{loss.item()}')
                 print('---------------------------------')
                 if len(self.parameters_tilda.items()) != 0:
                     for parameter in self.parameters_tilda.items():
-                        print(f'{parameter[0]}:\t\t\t{self.parameter_regulator(parameter[1]).item()}')
+                        print(
+                            f'{parameter[0]}:\t\t\t{self.parameter_regulator(parameter[1]).item()}')
                     print('#################################')
 
         # create prediction animation
@@ -354,33 +377,37 @@ class DINN:
         epochs = np.arange(0, self.epochs, 1)
 
         # plot loss
-        self.plotter.plot(epochs, [self.losses, self.obs_losses, self.physics_losses], ['loss', 'observation loss', 'physics loss'], self.data.name + '_loss', 'Loss', (6, 6), y_log_scale=True, plot_legend=True, xlabel='epochs')
-        
+        self.plotter.plot(epochs, [self.losses, self.obs_losses, self.physics_losses], ['loss', 'observation loss',
+                          'physics loss'], self.data.name + '_loss', 'Loss', (6, 6), y_log_scale=True, plot_legend=True, xlabel='epochs')
+
         # plot parameters
         for i, parameter in enumerate(self.parameters):
             if len(ground_truth) > i:
-                self.plotter.plot(epochs, 
-                                  [parameter, 
-                                   np.ones_like(epochs) * ground_truth[i]], 
-                                   ['prediction', 'ground truth'], 
-                                   self.data.name + '_' + list(self.parameters_tilda.items())[i][0], 
-                                   list(self.parameters_tilda.items())[i][0], 
-                                   (6,6), 
-                                   is_background=[0, 1], 
-                                   xlabel='epochs')
+                self.plotter.plot(epochs,
+                                  [parameter,
+                                   np.ones_like(epochs) * ground_truth[i]],
+                                  ['prediction', 'ground truth'],
+                                  self.data.name + '_' +
+                                  list(self.parameters_tilda.items())[i][0],
+                                  list(self.parameters_tilda.items())[i][0],
+                                  (6, 6),
+                                  is_background=[0, 1],
+                                  xlabel='epochs')
             else:
-                self.plotter.plot(epochs, 
-                                  [parameter], 
-                                  ['prediction'], 
-                                  self.data.name + '_' + list(self.parameters_tilda.items())[i][0], 
-                                  list(self.parameters_tilda.items())[i][0], (6,6), 
-                                  xlabel='epochs', 
+                self.plotter.plot(epochs,
+                                  [parameter],
+                                  ['prediction'],
+                                  self.data.name + '_' +
+                                  list(self.parameters_tilda.items())[i][0],
+                                  list(self.parameters_tilda.items())[
+                                      i][0], (6, 6),
+                                  xlabel='epochs',
                                   plot_legend=False)
-                
-    def save_training_process(self, title, save_predictions = True):
-        losses = {'loss' : self.losses,
-                  'obs_loss' : self.obs_losses,
-                  'physics_loss' : self.physics_losses}
+
+    def save_training_process(self, title, save_predictions=True):
+        losses = {'loss': self.losses,
+                  'obs_loss': self.obs_losses,
+                  'physics_loss': self.physics_losses}
         for loss in losses.keys():
             with open(f'./results/training_metrics/{title}_{loss}.csv', 'w', newline='') as csvfile:
                 writer = csv.writer(csvfile, delimiter=',')
@@ -393,9 +420,11 @@ class DINN:
         if save_predictions:
             prediction = self.model(self.data.t_batch)
             for i, group in enumerate(self.data.group_names):
-                t = torch.linspace(0, self.data.t_raw[-1].item(), self.data.t_raw.shape[0]).detach().cpu().numpy()
+                t = torch.linspace(
+                    0, self.data.t_raw[-1].item(), self.data.t_raw.shape[0]).detach().cpu().numpy()
                 true = self.data.get_group(group).detach().cpu().numpy()
-                pred = self.data.get_denormalized_data([prediction[:, i]])[0].detach().cpu().numpy()
+                pred = self.data.get_denormalized_data([prediction[:, i]])[
+                    0].detach().cpu().numpy()
                 print(t.shape, true.shape)
                 with open(f'./results/I_predictions/{title}_I_prediction.csv', 'w', newline='') as csvfile:
                     writer = csv.writer(csvfile, delimiter=',')
@@ -405,13 +434,15 @@ class DINN:
 
     def plot_state_variables(self):
         prediction = self.model(self.data.t_batch)
-        for i in range(self.data.number_groups, self.data.number_groups+self.number_state_variables):
-            t = torch.linspace(0, self.data.t_raw[-1].item(), self.data.t_raw.shape[0])
+        for i in range(self.data.number_groups, self.data.number_groups + self.number_state_variables):
+            t = torch.linspace(
+                0, self.data.t_raw[-1].item(), self.data.t_raw.shape[0])
             self.plotter.plot(t,
                               [prediction[:, i]],
-                              [self.__state_variables[i-self.data.number_groups]],
+                              [self.__state_variables[i - self.data.number_groups]],
                               f'{self.data.name}_{self.__state_variables[i-self.data.number_groups]}',
-                              self.__state_variables[i-self.data.number_groups],
+                              self.__state_variables[i -
+                                                     self.data.number_groups],
                               figure_shape=(12, 6),
                               plot_legend=True,
-                              xlabel='time / days')
+                              xlabel='time / days')