Scatter Plot .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c03eeab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from datetime import datetime \n",
    "import matplotlib as mpl\n",
    "import matplotlib.ticker as mticker\n",
    "import math\n",
    "from scipy.optimize import curve_fit\n",
    "from matplotlib import cm\n",
    "from scipy import stats\n",
    "\n",
    "\n",
    "#first importing the data using pandas \n",
    "sc_compact = 'sc_compact_1995_2019_d.csv'\n",
    "dataframe = pd.read_csv(sc_compact)\n",
    "\n",
    "omni_file = 'omni_min_2012.csv'\n",
    "dataframe_omni = pd.read_csv(omni_file)\n",
    "\n",
    "#dataframe had no datetime column so I created one\n",
    "dataframe['datetime'] = dataframe.year.astype(str).str.cat(dataframe.month.astype(str),sep='-').str.cat(dataframe.day.astype(str), sep='-').str.cat(dataframe.hour.astype(str),sep=',').str.cat(dataframe.minute.astype(str),sep=':')\n",
    "\n",
    "#making column with just time without date for graph\n",
    "dataframe_omni['time'] = dataframe_omni.hour.astype(str).str.cat(dataframe_omni.minute.astype(str),sep='-')\n",
    "\n",
    "#seperate dataframes for each event which took place ranging from 30 minutes before to 30 minutes after\n",
    "january22 = dataframe_omni[dataframe_omni.datetime.between('2012-01-22 05:39:00', '2012-01-22 06:41:00')]\n",
    "january24 = dataframe_omni[dataframe_omni.datetime.between('2012-01-24 14:31:00', '2012-01-24 15:33:00')]\n",
    "February26 = dataframe_omni[dataframe_omni.datetime.between('2012-02-26 21:08:00', '2012-02-26 22:10:00')]\n",
    "march7 = dataframe_omni[dataframe_omni.datetime.between('2012-03-07 03:47:00', '2012-03-07 04:49:00')]\n",
    "march8 = dataframe_omni[dataframe_omni.datetime.between('2012-03-08 10:31:00', '2012-03-08 11:33:00')]\n",
    "march12 = dataframe_omni[dataframe_omni.datetime.between('2012-03-12 08:43:00', '2012-03-12 09:45:00')]\n",
    "march15 = dataframe_omni[dataframe_omni.datetime.between('2012-03-15 12:35:00', '2012-03-15 13:37:00')]\n",
    "april23 = dataframe_omni[dataframe_omni.datetime.between('2012-04-23 02:50:00', '2012-04-23 03:52:00')]\n",
    "may21 = dataframe_omni[dataframe_omni.datetime.between('2012-05-21 19:05:00', '2012-05-21 20:07:00')]\n",
    "june161 = dataframe_omni[dataframe_omni.datetime.between('2012-06-16 09:24:00 ', '2012-06-16 10:26:00')]\n",
    "june162 = dataframe_omni[dataframe_omni.datetime.between('2012-06-16 19:47:00 ', '2012-06-16 20:49:00')]\n",
    "june163 = dataframe_omni[dataframe_omni.datetime.between('2012-06-16 20:44:00 ', '2012-06-16 21:46:00')]\n",
    "july14 = dataframe_omni[dataframe_omni.datetime.between('2012-07-14 17:40:00 ', '2012-07-14 18:42:00')]\n",
    "july20 =  dataframe_omni[dataframe_omni.datetime.between('2012-07-20 04:16:00', '2012-07-20 05:18:00')]\n",
    "july21= dataframe_omni[dataframe_omni.datetime.between('2012-07-21 15:34:00 ', '2012-07-21 16:36:00')]\n",
    "september3= dataframe_omni[dataframe_omni.datetime.between('2012-09-03 11:42:00 ', '2012-09-03 12:44:00')]\n",
    "september301 =  dataframe_omni[dataframe_omni.datetime.between('2012-09-30 11:00:00', '2012-09-30 12:02:00')]\n",
    "september302 = dataframe_omni[dataframe_omni.datetime.between('2012-09-30 22:34:00', '2012-09-30 23:36:00')]\n",
    "october31 =  dataframe_omni[dataframe_omni.datetime.between('2012-10-31 15:06:00', '2012-10-31 16:08:00')]\n",
    "november12 = dataframe_omni[dataframe_omni.datetime.between('2012-11-12 22:39:00', '2012-11-12 23:41:00')]\n",
    "november23 = dataframe_omni[dataframe_omni.datetime.between('2012-11-23 21:20:00', '2012-11-23 22:22:00')]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "081b989d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making time series of one event March 12th \n",
    "pressure = march12['flow_pressure'].tolist()\n",
    "time=march12[\"time\"].tolist()\n",
    "symh=march12[\"symh\"].tolist()\n",
    "\n",
    "sqrt_pressure = np.sqrt(pressure)\n",
    "\n",
    "pressure1 = pressure[0:30]\n",
    "pressure2 = pressure[31:60]\n",
    "\n",
    "pressure1 = [item for item in pressure1 if not(math.isnan(item)) == True]\n",
    "pressure2 =  [item for item in pressure2 if not(math.isnan(item)) == True]\n",
    "\n",
    "med_sqrt_pressure1 = np.median(np.sqrt(pressure1))\n",
    "med_sqrt_pressure2 = np.median(np.sqrt(pressure2))\n",
    "\n",
    "change_pressure = med_sqrt_pressure2 - med_sqrt_pressure1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aedef61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f9086b9dfa0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making an eleven panel figure with each parameter \n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3,sharex=True)\n",
    "\n",
    "#setting font sizes, heights and widths of each panel so the graph is clear\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=8)\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(8)\n",
    "\n",
    "#setting it to remove issue with whitespace between title and figure and at margins \n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=0.95)\n",
    "plt.rcParams['axes.xmargin'] = 0\n",
    "\n",
    "#to avoid overlap of the times on the x-axis, I am only displayign multiples of 10\n",
    "myLocator = mticker.MultipleLocator(10)\n",
    "ax1.xaxis.set_major_locator(myLocator)\n",
    "ax2.xaxis.set_major_locator(myLocator)\n",
    "ax3.xaxis.set_major_locator(myLocator)\n",
    "    \n",
    "#beggining to plot each with titles \n",
    "fig.suptitle('Data Recorded on March 12th 2012')\n",
    "plt.xlabel('Time')\n",
    "\n",
    "#plotting magnetic field in x,y,z directions \n",
    "ax1.plot(time,pressure)\n",
    "ax1.set_ylabel('Pressure (nPa)')\n",
    "ax1.axhline(y=0, xmin=0, xmax=3, c=\"black\",linestyle=':', zorder=0)\n",
    "\n",
    "#plotting flow pressure \n",
    "ax2.plot(time,sqrt_pressure)\n",
    "ax2.set_ylabel('$\\sqrt{Pressure}$ ($\\dfrac{nPa^1}{2}$)')\n",
    "ax2.set_ylim([0,10])\n",
    "#plotting\n",
    "ax3.plot(time,symh)\n",
    "ax3.set_ylabel('Sym-H (nT)')\n",
    "ax3.axhline(y=0, xmin=0, xmax=3, c=\"black\",linestyle=':', zorder=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3015459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2762716741354483, 0.4710446302966731, 0.6434530850314089, 0.6438189482849768, 0.7154546187578488, 0.8838495695685087, 0.9099867562225157, 0.9329224881799062, 0.9628208638938838, 1.0409847700023342, 1.0628310145000064, 1.0677166628233836, 1.1258747805195983, 1.2748265307306408, 1.47797206493537, 1.8439196093772843]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lilyrooney/opt/anaconda3/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/lilyrooney/opt/anaconda3/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "#getting amplitude \n",
    "sc_2012 = dataframe[dataframe.datetime.between('2012', '2013')]\n",
    "amplitude = sc_2012['average_amplitude'].tolist()\n",
    "\n",
    "\n",
    "pressure1 = january22['flow_pressure'].tolist()\n",
    "pressure2 = january24['flow_pressure'].tolist()\n",
    "pressure3 = February26['flow_pressure'].tolist()\n",
    "pressure4 = march7['flow_pressure'].tolist()\n",
    "pressure5 = march8['flow_pressure'].tolist()\n",
    "pressure6 = march12['flow_pressure'].tolist()\n",
    "pressure7 = march15['flow_pressure'].tolist()\n",
    "pressure8 = april23['flow_pressure'].tolist()\n",
    "pressure9 = may21['flow_pressure'].tolist()\n",
    "pressure10 = june161['flow_pressure'].tolist()\n",
    "pressure11=june162['flow_pressure'].tolist()\n",
    "pressure12 = june163['flow_pressure'].tolist()\n",
    "pressure13 = july14['flow_pressure'].tolist()\n",
    "pressure14 = july20['flow_pressure'].tolist()\n",
    "pressure15 = july21['flow_pressure'].tolist()\n",
    "pressure16 = september3['flow_pressure'].tolist()\n",
    "pressure17 = september301['flow_pressure'].tolist()\n",
    "pressure18 = september302['flow_pressure'].tolist()\n",
    "pressure19 = october31['flow_pressure'].tolist()\n",
    "pressure20 = november12['flow_pressure'].tolist()\n",
    "pressure21 = november23['flow_pressure'].tolist()\n",
    "\n",
    "a1 = pressure1[0:30] \n",
    "a2 = pressure2[0:30] \n",
    "a3 = pressure3[0:30] \n",
    "a4 = pressure4[0:30] \n",
    "a5= pressure5[0:30] \n",
    "a6= pressure6[0:30] \n",
    "a7= pressure7[0:30] \n",
    "a8= pressure8[0:30] \n",
    "a9= pressure9[0:30] \n",
    "a10= pressure10[0:30] \n",
    "a11= pressure11[0:30] \n",
    "a12= pressure12[0:30] \n",
    "a13= pressure13[0:30] \n",
    "a14= pressure14[0:30] \n",
    "a15= pressure15[0:30] \n",
    "a16= pressure16[0:30] \n",
    "a17= pressure17[0:30] \n",
    "a18= pressure18[0:30] \n",
    "a19= pressure19[0:30] \n",
    "a20= pressure20[0:30] \n",
    "a21= pressure21[0:30] \n",
    "\n",
    "\n",
    "b1 = pressure1[31:60]\n",
    "b2 = pressure2[31:60]\n",
    "b3 = pressure3[31:60]\n",
    "b4 = pressure4[31:60]\n",
    "b5= pressure5[31:60]\n",
    "b6= pressure6[31:60]\n",
    "b7= pressure7[31:60]\n",
    "b8= pressure8[31:60]\n",
    "b9= pressure9[31:60]\n",
    "b10= pressure10[31:60]\n",
    "b11= pressure11[31:60]\n",
    "b12= pressure12[31:60]\n",
    "b13= pressure13[31:60]\n",
    "b14= pressure14[31:60]\n",
    "b15=pressure15[31:60]\n",
    "b16= pressure16[31:60]\n",
    "b17= pressure17[31:60]\n",
    "b18= pressure18[31:60]\n",
    "b19= pressure19[31:60]\n",
    "b20= pressure20[31:60]\n",
    "b21= pressure21[31:60]\n",
    "\n",
    "a1 = [item for item in a1 if not(math.isnan(item)) == True]\n",
    "a2 = [item for item in a2 if not(math.isnan(item)) == True]\n",
    "a3 = [item for item in a3 if not(math.isnan(item)) == True]\n",
    "a4 = [item for item in a4 if not(math.isnan(item)) == True]\n",
    "a5 = [item for item in a5 if not(math.isnan(item)) == True]\n",
    "a6 = [item for item in a6 if not(math.isnan(item)) == True]\n",
    "a7 = [item for item in a7 if not(math.isnan(item)) == True]\n",
    "a8 = [item for item in a8 if not(math.isnan(item)) == True]\n",
    "a9 = [item for item in a9 if not(math.isnan(item)) == True]\n",
    "a10 = [item for item in a10 if not(math.isnan(item)) == True]\n",
    "a11 = [item for item in a11 if not(math.isnan(item)) == True]\n",
    "a12 = [item for item in a12 if not(math.isnan(item)) == True]\n",
    "a13 = [item for item in a13 if not(math.isnan(item)) == True]\n",
    "a14 = [item for item in a14 if not(math.isnan(item)) == True]\n",
    "a15 = [item for item in a15 if not(math.isnan(item)) == True]\n",
    "a16 = [item for item in a16 if not(math.isnan(item)) == True]\n",
    "a17 = [item for item in a17 if not(math.isnan(item)) == True]\n",
    "a18 = [item for item in a18 if not(math.isnan(item)) == True]\n",
    "a19 = [item for item in a19 if not(math.isnan(item)) == True]\n",
    "a20 = [item for item in a20 if not(math.isnan(item)) == True]\n",
    "a21 = [item for item in a21 if not(math.isnan(item)) == True]\n",
    "\n",
    "b1 = [item for item in b1 if not(math.isnan(item)) == True]\n",
    "b2 = [item for item in b2 if not(math.isnan(item)) == True]\n",
    "b3 = [item for item in b3 if not(math.isnan(item)) == True]\n",
    "b4 = [item for item in b4 if not(math.isnan(item)) == True]\n",
    "b5 = [item for item in b5 if not(math.isnan(item)) == True]\n",
    "b6 = [item for item in b6 if not(math.isnan(item)) == True]\n",
    "b7 = [item for item in b7 if not(math.isnan(item)) == True]\n",
    "b8 = [item for item in b8 if not(math.isnan(item)) == True]\n",
    "b9 = [item for item in b9 if not(math.isnan(item)) == True]\n",
    "b10= [item for item in b10 if not(math.isnan(item)) == True]\n",
    "b11= [item for item in b11 if not(math.isnan(item)) == True]\n",
    "b12= [item for item in b12 if not(math.isnan(item)) == True]\n",
    "b13 = [item for item in b13 if not(math.isnan(item)) == True]\n",
    "b14 = [item for item in b14 if not(math.isnan(item)) == True]\n",
    "b15 = [item for item in b15 if not(math.isnan(item)) == True]\n",
    "b16 = [item for item in b16 if not(math.isnan(item)) == True]\n",
    "b17 = [item for item in b17 if not(math.isnan(item)) == True]\n",
    "b18 = [item for item in b18 if not(math.isnan(item)) == True]\n",
    "b19 = [item for item in b19 if not(math.isnan(item)) == True]\n",
    "b20 = [item for item in b20 if not(math.isnan(item)) == True]\n",
    "b21 = [item for item in b21 if not(math.isnan(item)) == True]\n",
    "\n",
    "med_sqrt1 = np.median(np.sqrt(a1))\n",
    "med_sqrt2 = np.median(np.sqrt(a2))\n",
    "med_sqrt3 = np.median(np.sqrt(a3))\n",
    "med_sqrt4 = np.median(np.sqrt(a4))\n",
    "med_sqrt5 = np.median(np.sqrt(a5))\n",
    "med_sqrt6 = np.median(np.sqrt(a6))\n",
    "med_sqrt7 = np.median(np.sqrt(a7))\n",
    "med_sqrt8 = np.median(np.sqrt(a8))\n",
    "med_sqrt9 = np.median(np.sqrt(a9))\n",
    "med_sqrt10 = np.median(np.sqrt(a10))\n",
    "med_sqrt11 = np.median(np.sqrt(a11))\n",
    "med_sqrt12 = np.median(np.sqrt(a12))\n",
    "med_sqrt13 = np.median(np.sqrt(a13))\n",
    "med_sqrt14 = np.median(np.sqrt(a14))\n",
    "med_sqrt15 = np.median(np.sqrt(a15))\n",
    "med_sqrt16 = np.median(np.sqrt(a16))\n",
    "med_sqrt17 = np.median(np.sqrt(a17))\n",
    "med_sqrt18 = np.median(np.sqrt(a18))\n",
    "med_sqrt19 = np.median(np.sqrt(a19))\n",
    "med_sqrt20 = np.median(np.sqrt(a20))\n",
    "med_sqrt21 = np.median(np.sqrt(a21))\n",
    "\n",
    "med_sqrt1b = np.median(np.sqrt(b1))\n",
    "med_sqrt2b= np.median(np.sqrt(b2))\n",
    "med_sqrt3b= np.median(np.sqrt(b3))\n",
    "med_sqrt4b = np.median(np.sqrt(b4))\n",
    "med_sqrt5b = np.median(np.sqrt(b5))\n",
    "med_sqrt6b = np.median(np.sqrt(b6))\n",
    "med_sqrt7b = np.median(np.sqrt(b7))\n",
    "med_sqrt8b = np.median(np.sqrt(b8))\n",
    "med_sqrt9b = np.median(np.sqrt(b9))\n",
    "med_sqrt10b = np.median(np.sqrt(b10))\n",
    "med_sqrt11b = np.median(np.sqrt(b11))\n",
    "med_sqrt12b = np.median(np.sqrt(b12))\n",
    "med_sqrt13b = np.median(np.sqrt(b13))\n",
    "med_sqrt14b = np.median(np.sqrt(b14))\n",
    "med_sqrt15b = np.median(np.sqrt(b15))\n",
    "med_sqrt16b = np.median(np.sqrt(b16))\n",
    "med_sqrt17b = np.median(np.sqrt(b17))\n",
    "med_sqrt18b = np.median(np.sqrt(b18))\n",
    "med_sqrt19b = np.median(np.sqrt(b19))\n",
    "med_sqrt20b = np.median(np.sqrt(b20))\n",
    "med_sqrt21b = np.median(np.sqrt(b21))\n",
    "\n",
    "\n",
    "\n",
    "#removed the data from change 4 and 10 because they were NANs\n",
    "change1 = med_sqrt1b - med_sqrt1\n",
    "change2 = med_sqrt2b - med_sqrt2\n",
    "change3 = med_sqrt3b - med_sqrt3\n",
    "#change4 = med_sqrt4b \n",
    "#change5 = med_sqrt5b - med_sqrt5\n",
    "change6 = med_sqrt6b - med_sqrt6\n",
    "change7 = med_sqrt7b - med_sqrt7\n",
    "change8 = med_sqrt8b - med_sqrt8\n",
    "change9 = med_sqrt9b - med_sqrt9\n",
    "#change10 = med_sqrt10b \n",
    "#change11 = med_sqrt11b - med_sqrt11\n",
    "change12 = med_sqrt12b - med_sqrt12\n",
    "change13 = med_sqrt13b - med_sqrt13\n",
    "change14 = med_sqrt14b - med_sqrt14\n",
    "change15 = med_sqrt15b - med_sqrt15\n",
    "#change16 = med_sqrt16b - med_sqrt16\n",
    "change17 = med_sqrt17b - med_sqrt17\n",
    "change18 = med_sqrt18b - med_sqrt18\n",
    "change19 = med_sqrt19b - med_sqrt19\n",
    "change20 = med_sqrt20b - med_sqrt20\n",
    "change21 = med_sqrt21b - med_sqrt21\n",
    "\n",
    "changes = [change1, change2, change3, change6, change7, change8, change9, change12, change13, change14, change15, change17 ,change18 ,change19, change20,change21]\n",
    "changes.sort()\n",
    "print(changes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f4b1e31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08\n",
      "0.13\n",
      "0.16\n",
      "0.68\n",
      "0.37\n",
      "0.14\n",
      "0.05\n",
      "0.06\n",
      "0.22\n",
      "0.66\n",
      "0.26\n",
      "0.03\n",
      "0.18\n",
      "0.24\n",
      "0.16\n",
      "0.55\n",
      "0.16\n",
      "0.08\n",
      "0.0\n",
      "0.05\n",
      "0.17\n",
      "0.17\n",
      "0.83\n",
      "[6.2, 4.8, 4.6, 5.0, 3.4, 6.6, 5.2, 2.6, 3.6, 7.2, 5.4, 4.8, 4.4, 5.6, 4.6, 5.8]\n",
      "[29.880000000000003, 44.68000000000001, 16.9, 13.560000000000002, 48.82, 51.86, 25.6, 24.04, 22.08, 19.8, 22.6, 27.28, 41.06, 22.6, 13.34, 31.28, 17.1, 16.16, 23.2, 18.94, 20.74]\n",
      "[13.34, 16.9, 22.08, 20.74, 27.28, 17.1, 23.2, 29.880000000000003, 22.6, 25.6, 51.86, 24.04, 16.16, 41.06, 18.94, 44.68000000000001]\n"
     ]
    }
   ],
   "source": [
    "#getting the average durations\n",
    "duration = sc_2012['average_duration'].tolist()\n",
    "\n",
    "#finding percentage of NaNs in Flow pressure for each event\n",
    "for dataframe in (january22, january24, February26, march7, march8, march12, march15, april23, may21, june161, june162, june163, july14, july20, july21, september3, september301, september302, october31, november12, november23): \n",
    "    nan = round(dataframe.isnull().sum()/ len(dataframe), 2)\n",
    "    print(nan.flow_pressure)\n",
    "nans = nan.flow_pressure.tolist()\n",
    "print(nans)\n",
    "num = 1-nans \n",
    "print(num)\n",
    "\n",
    "#removing values where the NaN is <25%\n",
    "duration.remove(6.0)\n",
    "duration.remove(4.2)\n",
    "duration.remove(5.4)\n",
    "duration.remove(2.8)\n",
    "duration.pop(12)\n",
    "print(duration)\n",
    "\n",
    "#removing values where the NaN is <25%\n",
    "print(amplitude)\n",
    "amplitude.remove(13.560000000000002)\n",
    "amplitude.remove(48.82)\n",
    "amplitude.remove(19.8)\n",
    "amplitude.remove(31.28)\n",
    "amplitude.remove(22.6)\n",
    "myorder= [10,2,6,15,7,11,13,0,9,4,3,5,12,8,14,1]\n",
    "amplitude = [amplitude[i] for i in myorder]\n",
    "print(amplitude)\n",
    "duration = [duration[i] for i in myorder]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "671c6aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.29, 6.78, 1.3850000000000002, 4.449999999999999, 4.86, 4.375, 1.8699999999999999, 5.785, 4.25, 2.66, 0.7850000000000001, 3.3549999999999995, 4.46, 3.1799999999999997, 7.2700000000000005, 1.7099999999999997]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a1 = [item for item in a1 if not(math.isnan(item)) == True]\n",
    "a2 = [item for item in a2 if not(math.isnan(item)) == True]\n",
    "a3 = [item for item in a3 if not(math.isnan(item)) == True]\n",
    "a4 = [item for item in a4 if not(math.isnan(item)) == True]\n",
    "a5 = [item for item in a5 if not(math.isnan(item)) == True]\n",
    "a6 = [item for item in a6 if not(math.isnan(item)) == True]\n",
    "a7 = [item for item in a7 if not(math.isnan(item)) == True]\n",
    "a8 = [item for item in a8 if not(math.isnan(item)) == True]\n",
    "a9 = [item for item in a9 if not(math.isnan(item)) == True]\n",
    "a10 = [item for item in a10 if not(math.isnan(item)) == True]\n",
    "a11 = [item for item in a11 if not(math.isnan(item)) == True]\n",
    "a12 = [item for item in a12 if not(math.isnan(item)) == True]\n",
    "a13 = [item for item in a13 if not(math.isnan(item)) == True]\n",
    "a14 = [item for item in a14 if not(math.isnan(item)) == True]\n",
    "a15 = [item for item in a15 if not(math.isnan(item)) == True]\n",
    "a16 = [item for item in a16 if not(math.isnan(item)) == True]\n",
    "a17 = [item for item in a17 if not(math.isnan(item)) == True]\n",
    "a18 = [item for item in a18 if not(math.isnan(item)) == True]\n",
    "a19 = [item for item in a19 if not(math.isnan(item)) == True]\n",
    "a20 = [item for item in a20 if not(math.isnan(item)) == True]\n",
    "a21 = [item for item in a21 if not(math.isnan(item)) == True]\n",
    "\n",
    "b1 = [item for item in b1 if not(math.isnan(item)) == True]\n",
    "b2 = [item for item in b2 if not(math.isnan(item)) == True]\n",
    "b3 = [item for item in b3 if not(math.isnan(item)) == True]\n",
    "b4 = [item for item in b4 if not(math.isnan(item)) == True]\n",
    "b5 = [item for item in b5 if not(math.isnan(item)) == True]\n",
    "b6 = [item for item in b6 if not(math.isnan(item)) == True]\n",
    "b7 = [item for item in b7 if not(math.isnan(item)) == True]\n",
    "b8 = [item for item in b8 if not(math.isnan(item)) == True]\n",
    "b9 = [item for item in b9 if not(math.isnan(item)) == True]\n",
    "b10= [item for item in b10 if not(math.isnan(item)) == True]\n",
    "b11= [item for item in b11 if not(math.isnan(item)) == True]\n",
    "b12= [item for item in b12 if not(math.isnan(item)) == True]\n",
    "b13 = [item for item in b13 if not(math.isnan(item)) == True]\n",
    "b14 = [item for item in b14 if not(math.isnan(item)) == True]\n",
    "b15 = [item for item in b15 if not(math.isnan(item)) == True]\n",
    "b16 = [item for item in b16 if not(math.isnan(item)) == True]\n",
    "b17 = [item for item in b17 if not(math.isnan(item)) == True]\n",
    "b18 = [item for item in b18 if not(math.isnan(item)) == True]\n",
    "b19 = [item for item in b19 if not(math.isnan(item)) == True]\n",
    "b20 = [item for item in b20 if not(math.isnan(item)) == True]\n",
    "b21 = [item for item in b21 if not(math.isnan(item)) == True]\n",
    "\n",
    "\n",
    "med1 = np.median(a1)\n",
    "med2 = np.median(a2)\n",
    "med3 = np.median(a3)\n",
    "med4 = np.median(a4)\n",
    "med5 = np.median(a5)\n",
    "med6 = np.median(a6)\n",
    "med7 = np.median(a7)\n",
    "med8 = np.median(a8)\n",
    "med9 = np.median(a9)\n",
    "med10 = np.median(a10)\n",
    "med11 = np.median(a11)\n",
    "med12= np.median(a12)\n",
    "med13 = np.median(a13)\n",
    "med14 = np.median(a14)\n",
    "med15 = np.median(a15)\n",
    "med16 = np.median(a16)\n",
    "med17 = np.median(a17)\n",
    "med18 = np.median(a18)\n",
    "med19 = np.median(a19)\n",
    "med20 = np.median(a20)\n",
    "med21 = np.median(a21)\n",
    "\n",
    "\n",
    "med1b = np.median(b1)\n",
    "med2b = np.median(b2)\n",
    "med3b = np.median(b3)\n",
    "med4b = np.median(b4)\n",
    "med5b = np.median(b5)\n",
    "med6b = np.median(b6)\n",
    "med7b = np.median(b7)\n",
    "med8b = np.median(b8)\n",
    "med9b = np.median(b9)\n",
    "med10b = np.median(b10)\n",
    "med11b= np.median(b11)\n",
    "med12b= np.median(b12)\n",
    "med13b = np.median(b13)\n",
    "med14b = np.median(b14)\n",
    "med15b = np.median(b15)\n",
    "med16b = np.median(b16)\n",
    "med17b = np.median(b17)\n",
    "med18b = np.median(b18)\n",
    "med19b = np.median(b19)\n",
    "med20b = np.median(b20)\n",
    "med21b = np.median(b21)\n",
    "\n",
    "#removed the data from change 4 and 10 because they were NANs\n",
    "changes1 = med1b - med1\n",
    "changes2 = med2b - med2\n",
    "changes3 = med3b - med3\n",
    "changes4 = med4b - med4\n",
    "changes5 = med5b - med5\n",
    "changes6 = med6b - med6\n",
    "changes7 = med7b - med7\n",
    "changes8 = med8b - med8\n",
    "changes9 = med9b - med9\n",
    "changes10 = med10b - med10\n",
    "changes11 = med11b - med11\n",
    "changes12 = med12b - med12\n",
    "changes13 = med13b - med13\n",
    "changes14 = med14b - med14\n",
    "changes15 = med15b - med15\n",
    "changes16 = med16b - med16\n",
    "changes17 = med17b - med17\n",
    "changes18 = med18b - med18\n",
    "changes19 = med19b - med19\n",
    "changes20 = med20b - med20\n",
    "changes21 = med21b - med21\n",
    "\n",
    "changes1 = [changes1, changes2, changes3, changes6, changes7, changes8, changes9, changes12, changes13, changes14, changes15, changes17 ,changes18 ,changes19, changes20,changes21]\n",
    "print(changes1)\n",
    "plt.scatter(changes1, amplitude)\n",
    "plt.title('Scatter Plot')\n",
    "plt.ylabel('Amplitude (nT)')\n",
    "plt.xlabel('$ \\Delta Pressure$ (${nPa}$)')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "3f370e47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The polynomial has the equation 0 =          2\n",
      "-0.6862 x + 17.38 x + 10.03\n",
      "R² = 1671.57\n",
      "chi² reduced =1.12\n",
      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting polynomial fit \n",
    "try:\n",
    "    import uncertainties.unumpy as unp\n",
    "    import uncertainties as unc\n",
    "except:\n",
    "    try:\n",
    "        from pip import main as pipmain\n",
    "    except:\n",
    "        from pip._internal import main as pipmain\n",
    "    pipmain(['install','uncertainties'])\n",
    "    import uncertainties.unumpy as unp\n",
    "    import uncertainties as unc\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "\n",
    "x = changes \n",
    "y = amplitude\n",
    "\n",
    "x = np.array(x, dtype=float) \n",
    "y = np.array(y, dtype=float)\n",
    "\n",
    "\n",
    "#making a model \n",
    "p = np.polyfit(x, y, 1)\n",
    "n = y.size\n",
    "m = p.size\n",
    "dof = n - m\n",
    "\n",
    "y_model = np.polyval(p, [x, y])\n",
    "model = np.poly1d(np.polyfit(x, y, 2))\n",
    "myline = np.linspace(0, 2, 100)\n",
    "print(f'The polynomial has the equation 0 = {model}')\n",
    "mymodel = model(myline)\n",
    "\n",
    "\n",
    "#statistical analysis\n",
    "y_bar = np.mean(y)\n",
    "# Equation to find R²\n",
    "R2 = np.sum((y_model - y_bar)**2) / np.sum((y - y_bar)**2)\n",
    "print(f'R² = {R2:.2f}')\n",
    "resid = y - y_model\n",
    "\n",
    "#chi^2 and reduced chi^2\n",
    "chi2 = np.sum((resid / y_model)**2)\n",
    "chi2_red = chi2 / dof\n",
    "print(f'chi² reduced ={chi2_red:.2f}')\n",
    "\n",
    "# Standard deviation of the error\n",
    "std = unp.std_devs(resid)\n",
    "t_critical = stats.t.ppf(1 - (0.05 / 2), dof)\n",
    "\n",
    "interval = t_critical*std\n",
    "print(std)\n",
    "\n",
    "xlim = plt.xlim(0,2)\n",
    "ylim = plt.ylim(0,55)\n",
    "plt.scatter(x, y, c='deeppink', marker='o', edgecolors='k', s=18, label=f'Data')\n",
    "plt.plot(myline, mymodel ,c='k', label=r'Polynomial Fit''\\n'f' Reduced Chi² = {chi2_red:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax.set_ylabel('Amplitude (nT)')\n",
    "ax.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($\\dfrac{nPa^1}{2}$)')\n",
    "ax.set_title('Polynomial Fit',fontweight=\"bold\")\n",
    "ax.legend(fontsize =7.5, loc='upper right' )\n",
    "ax.text(0,50,'$-0.6862x^2 + 17.38x + 10.03$')\n",
    "ax.grid(True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "443a5c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14.84415182  0.56129707]\n",
      "chi-squared statistic = 1254.90\n",
      "reduced chi-squared = 89.64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting exponential fit\n",
    "fig, ax = plt.subplots()\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=12)\n",
    "x=changes\n",
    "y=amplitude\n",
    "\n",
    "x = np.array(x, dtype=float) \n",
    "y = np.array(y, dtype=float)\n",
    "\n",
    "\n",
    "def func(x, a, b):\n",
    "    return (a * np.exp(b*x))\n",
    "\n",
    "popt, pcov = curve_fit(func, x, y, p0=[15,0.5])\n",
    "a, b = popt\n",
    "print(popt)\n",
    "\n",
    "def chi_squared_reduced(data, model, dof=None):\n",
    "    sq_residual = (data - model)**2\n",
    "    chi_sq = np.sum(sq_residual)\n",
    "    if dof is None:\n",
    "        return chi_sq\n",
    "    else:\n",
    "        nu = len(data) - dof\n",
    "        return chi_sq / nu\n",
    "\n",
    "# Define our data, model, uncertainty, and degrees of freedom\n",
    "I_data = y.copy()  # observed data\n",
    "I_modeled = func(x, *popt) # model fitted result\n",
    "\n",
    "# Calculate the Chi-Squared value (no dof)\n",
    "chisq = chi_squared_reduced(I_data, I_modeled)\n",
    "print(f\"chi-squared statistic = {chisq:1.2f}\")\n",
    "\n",
    "# Calculate the Reduced Chi-Squared value (with dof)\n",
    "dof = 2  # We have two free parameters\n",
    "rechisq = chi_squared_reduced(I_data, I_modeled, dof)\n",
    "print(f\"reduced chi-squared = {rechisq:1.2f}\")\n",
    "\n",
    "xlim = plt.xlim()\n",
    "ylim = plt.ylim()\n",
    "t = np.linspace(0, 2, 19)\n",
    "ax.set_ylabel('Amplitude (nT)')\n",
    "ax.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($\\dfrac{nPa^1}{2}$)')\n",
    "ax.set_title('Exponential Fit',fontweight=\"bold\")\n",
    "plt.scatter(x, y, c='deeppink', marker='o', edgecolors='k', s=18, label=f'Data')\n",
    "plt.plot(x, func(x,*popt), c='k',label = r'Exponential Fit''\\n'f' Reduced Chi² = {rechisq:.2f}')\n",
    "plt.fill_between(x, func(x,*popt) + (1.96*std_err)/np.sqrt(len(y)), func(x,*popt) - (1.96*std_err)/np.sqrt(len(y)), facecolor='#C8A2C8', zorder=0,label=r'95% Confidence Interval')\n",
    "\n",
    "ax.legend()\n",
    "ax.text(0.3,50, '$14.844 e^{0.5613x}$', fontsize =12)\n",
    "plt.grid(True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "af02e6a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.680829376766084\n",
      "15.949572726705675\n",
      "The fitted straight line has equation y = 15.9x + 10.7\n",
      "R² = 0.31\n",
      "Reduced Chi^2 = 0.11\n",
      "9.41830733891356\n",
      "2.1447866879169273\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting straight line fit \n",
    "fig, ax = plt.subplots()\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "x=changes\n",
    "y=amplitude\n",
    "\n",
    "x = np.array(x, dtype=float) \n",
    "y = np.array(y, dtype=float)\n",
    "\n",
    "# fitting polynomial (1 means straight line)\n",
    "p = np.polyfit(x, y, 1)\n",
    "m = p[0]  \n",
    "c = p[1]\n",
    "\n",
    "print(c)\n",
    "print(m)\n",
    "print(f'The fitted straight line has equation y = {m:.1f}x {c:=+6.1f}')\n",
    "\n",
    "n = y.size\n",
    "m = p.size\n",
    "dof = n - m\n",
    "# Significance level\n",
    "alpha = 0.05\n",
    "# We're using a two-sided test\n",
    "tails = 2\n",
    "# The percent-point function (aka the quantile function) of the t-distribution\n",
    "# gives you the critical t-value that must be met in order to get significance\n",
    "t_critical = stats.t.ppf(1 - (alpha / tails), dof)\n",
    "y_model = np.polyval(p, x)\n",
    "#making model of 1-Dimensional polynomial \n",
    "model = np.poly1d(p)\n",
    "y_model = model(x)\n",
    "\n",
    "# Mean\n",
    "y_bar = np.mean(y)\n",
    "# Equation to find R²\n",
    "R2 = np.sum((y_model - y_bar)**2) / np.sum((y - y_bar)**2)\n",
    "print(f'R² = {R2:.2f}')\n",
    "\n",
    "# Calculate the residual errors in data \n",
    "resid = y - y_model\n",
    "# Chi^2 and reduced chi^2\n",
    "chi2 = sum((resid / y_model)**2)\n",
    "chi2_red = chi2 / dof\n",
    "print(f'Reduced Chi^2 = {chi2_red:.2f}')\n",
    "\n",
    "# Std of the error\n",
    "std_err = np.sqrt(sum(resid**2) / dof)\n",
    "print(std_err)\n",
    "print(t_critical)\n",
    "#plotting scatter, straight line and confidence interval \n",
    "plt.scatter(x, y, c='deeppink', marker='o', edgecolors='k', s=18, label=f'Data')\n",
    "xlim = plt.xlim()\n",
    "ylim = plt.ylim()\n",
    "plt.plot(np.array(xlim), p[1] + p[0] * np.array(xlim),c='k', label=r'Straight Line Fit''\\n'f' Reduced Chi² = {chi2_red:.2f}')\n",
    "\n",
    "#confidence interval \n",
    "x_fitted = np.linspace(xlim[0], xlim[1], 100)\n",
    "y_fitted = np.polyval(p, x_fitted)\n",
    "ci = t_critical * std_err * np.sqrt(1 / n + (x_fitted - np.mean(x))**2 / np.sum((x - np.mean(x))**2))\n",
    "plt.fill_between(x_fitted, y_fitted + (1.96*std_err)/np.sqrt(len(y)), y_fitted - (1.96*std_err)/np.sqrt(len(y)), facecolor='#C8A2C8', zorder=0,label=r'95% Confidence Interval')\n",
    "#print(ci)\n",
    "#setting graph \n",
    "ax.set_ylabel('Amplitude (nT)')\n",
    "ax.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($\\dfrac{nPa^1}{2}$)')\n",
    "ax.set_title('Straight Line Fit',fontweight=\"bold\")\n",
    "ax.legend(fontsize=8, loc='upper right')\n",
    "ax.text(0.3,50, 'y = 15.9x + 10.7')\n",
    "plt.ylim(0, ylim[1])\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "5cb07242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#trying for color bar \n",
    "fig, ax = plt.subplots()\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "duration = np.array(duration, dtype= float)\n",
    "plt.scatter(changes, amplitude, c=duration, cmap='spring')\n",
    "cbar=plt.colorbar()\n",
    "cbar.set_label('Duration Time (Minutes)', rotation=90)\n",
    "\n",
    "ax.set_ylabel('Amplitude (nT)')\n",
    "ax.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($\\dfrac{nPa^1}{2}$)')\n",
    "ax.set_title(r'Amplitude vs $\\Delta\\sqrt{Pressure}$''\\n'r'With Duration Time')\n",
    "ax.set_ylim(0,60)\n",
    "ax.set_xlim(0,2.2)\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "5c2733d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.13, 0.17, 0.33, 1.0, 0.63, 0.17, 0.1, 0.07, 0.33, 1.0, 0.43, 0.03, 0.2, 0.33, 0.13, 0.9, 0.17, 0.1, 0.0, 0.03, 0.13]\n",
      "[0.0, 0.07, 0.0, 0.34, 0.14, 0.14, 0.0, 0.07, 0.14, 0.34, 0.1, 0.03, 0.14, 0.17, 0.21, 0.21, 0.17, 0.07, 0.0, 0.0, 0.17]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#percentage of NaNs per event before vs after \n",
    "a1 = pressure1[0:30] \n",
    "a2 = pressure2[0:30] \n",
    "a3 = pressure3[0:30] \n",
    "a4 = pressure4[0:30] \n",
    "a5= pressure5[0:30] \n",
    "a6= pressure6[0:30] \n",
    "a7= pressure7[0:30] \n",
    "a8= pressure8[0:30] \n",
    "a9= pressure9[0:30] \n",
    "a10= pressure10[0:30] \n",
    "a11= pressure11[0:30] \n",
    "a12= pressure12[0:30] \n",
    "a13= pressure13[0:30] \n",
    "a14= pressure14[0:30] \n",
    "a15= pressure15[0:30] \n",
    "a16= pressure16[0:30] \n",
    "a17= pressure17[0:30] \n",
    "a18= pressure18[0:30] \n",
    "a19= pressure19[0:30] \n",
    "a20= pressure20[0:30] \n",
    "a21= pressure21[0:30] \n",
    "\n",
    "nan = []\n",
    "for i in (a1, a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13,a14,a15,a16,a17,a18,a19,a20,a21): \n",
    "    x = round(np.isnan(i).sum()/ len(i), 2)\n",
    "    nan.append(x)\n",
    "print(nan)\n",
    "\n",
    "b1 = pressure1[31:60]\n",
    "b2 = pressure2[31:60]\n",
    "b3 = pressure3[31:60]\n",
    "b4 = pressure4[31:60]\n",
    "b5= pressure5[31:60]\n",
    "b6= pressure6[31:60]\n",
    "b7= pressure7[31:60]\n",
    "b8= pressure8[31:60]\n",
    "b9= pressure9[31:60]\n",
    "b10= pressure10[31:60]\n",
    "b11= pressure11[31:60]\n",
    "b12= pressure12[31:60]\n",
    "b13= pressure13[31:60]\n",
    "b14= pressure14[31:60]\n",
    "b15=pressure15[31:60]\n",
    "b16= pressure16[31:60]\n",
    "b17= pressure17[31:60]\n",
    "b18= pressure18[31:60]\n",
    "b19= pressure19[31:60]\n",
    "b20= pressure20[31:60]\n",
    "b21= pressure21[31:60]\n",
    "\n",
    "nan1= []\n",
    "for i in (b1, b2,b3,b4,b5,b6,b7,b8,b9,b10,b11,b12,b13,b14,b15,b16,b17,b18,b19,b20,b21): \n",
    "    x = round(np.isnan(i).sum()/ len(i), 2)\n",
    "    nan1.append(x)\n",
    "print(nan1)\n",
    "\n",
    "duration1 = sc_2012['average_duration'].tolist()\n",
    "#fig, ax = plt.subplots()\n",
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "plt.rc('font', size=15)\n",
    "plt.rc('axes', titlesize=15)\n",
    "plt.scatter(nan, nan1, color='blue', label = 'Events')\n",
    "plt.axvline(x = 0.4, color='r', linestyle='-', label = 'Threshhold Line')\n",
    "\n",
    "plt.legend(fontsize =12, loc='best' )\n",
    "plt.ylabel('% NaN 0-30 minutes after event')\n",
    "plt.xlabel('% NaN 30-0 minutes before event')\n",
    "plt.title('NaN Percentages',fontweight=\"bold\")\n",
    "plt.ylim(-0.01,0.35)\n",
    "plt.xlim(0,1.1)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "62357cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.6861987795936302 17.379339082133328 10.034357305855615\n",
      "R^2: 0.35499402466588503\n",
      "Uncertainty\n",
      "a: -1+/-12\n",
      "b: 17+/-26\n",
      "c: 10+/-13\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using 95% interval from medium article \n",
    "#plotting polynomial fit \n",
    "try:\n",
    "    import uncertainties.unumpy as unp\n",
    "    import uncertainties as unc\n",
    "except:\n",
    "    try:\n",
    "        from pip import main as pipmain\n",
    "    except:\n",
    "        from pip._internal import main as pipmain\n",
    "    pipmain(['install','uncertainties'])\n",
    "    import uncertainties.unumpy as unp\n",
    "    import uncertainties as unc\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "\n",
    "x = changes \n",
    "y = amplitude\n",
    "\n",
    "x = np.array(x, dtype=float) \n",
    "y = np.array(y, dtype=float)\n",
    "n = len(y)\n",
    "\n",
    "def f(x,a,b,c):\n",
    "    return a*x**2 + b*x + c\n",
    "\n",
    "popt, pcov = curve_fit(f,x,y)\n",
    "a = popt[0]\n",
    "b = popt[1]\n",
    "c = popt[2]\n",
    "\n",
    "print(a,b,c)\n",
    "r2 = 1.0-(sum((y-f(x,a,b,c))**2)/((n-1.0)*np.var(y,ddof=2)))\n",
    "print('R^2: ' + str(r2))\n",
    "\n",
    "# calculate parameter confidence interval\n",
    "a,b,c = unc.correlated_values(popt, pcov)\n",
    "print('Uncertainty')\n",
    "print('a: ' + str(a))\n",
    "print('b: ' + str(b))\n",
    "print('c: ' + str(c))\n",
    "\n",
    "# plot data\n",
    "plt.scatter(x, y,c='deeppink', s=18, edgecolor='k', label='Data')\n",
    "\n",
    "# calculate regression confidence interval\n",
    "px = np.linspace(0, 2, 500)\n",
    "py = a*px**2 +b*px +c\n",
    "nom = unp.nominal_values(py)\n",
    "std = unp.std_devs(py)\n",
    "\n",
    "# plot the regression\n",
    "plt.plot(px, nom, c='black', label='Polynomial Fit')\n",
    "\n",
    "\n",
    "plt.fill_between(px, nom + (1.96*std)/np.sqrt(len(y)), nom - (1.96*std)/np.sqrt(len(y)), facecolor='#C8A2C8', zorder=0,label=r'95% Confidence Interval')\n",
    "\n",
    "ax.set_ylim(0,55)\n",
    "ax.set_xlim(0.2,1.6)\n",
    "ax.set_ylabel('Amplitude (nT)')\n",
    "ax.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($\\dfrac{nPa^1}{2}$)')\n",
    "ax.set_title('Polynomial Scatter Plot',fontweight=\"bold\")\n",
    "ax.legend(fontsize =7.5, loc='upper right' )\n",
    "ax.text(0.2,50,'$-0.6862x^2 + 17.38x + 10.03$')\n",
    "ax.grid(True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "6f521c78",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.6861987795936302 17.379339082133328 10.034357305855615\n",
      "R^2: 0.35499402466588503\n",
      "Uncertainty\n",
      "a: -1+/-12\n",
      "b: 17+/-26\n",
      "c: 10+/-13\n",
      "[14.84413318  0.56129818]\n",
      "chi-squared statistic = 1254.90\n",
      "reduced chi-squared = 89.64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.0, 1.0, 'C)')"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making one figure of the three fits \n",
    "fig = plt.figure()\n",
    "#setting the gridspec \n",
    "gs = fig.add_gridspec(3, 1, hspace=0.25, wspace=0.25)\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "fig.set_figheight(15)\n",
    "fig.set_figwidth(9)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=0.92)\n",
    "\n",
    "(ax1),(ax2),(ax3) = gs.subplots(sharex=False, sharey=False)\n",
    "fig.suptitle(r'Comparing Fits for the Amplitude of SYM-H''\\n'f' against Change in the Square of Pressure', fontweight=\"bold\")\n",
    "\n",
    "#data for the scatter plots\n",
    "x = changes \n",
    "y = amplitude\n",
    "\n",
    "x = np.array(x, dtype=float) \n",
    "y = np.array(y, dtype=float)\n",
    "\n",
    "#plotting straight line fit\n",
    "ax1.scatter(x, y, c='deeppink', marker='o', edgecolors='k', s=18, label=f'Data')\n",
    "xlim = ax1.set_xlim(0.2,1.6)\n",
    "ylim = ax1.set_ylim()\n",
    "ax1.plot(np.array(xlim), p[1] + p[0] * np.array(xlim),c='k', label=r'Straight Line Fit''\\n'f' Reduced Chi² = {chi2_red:.2f}')\n",
    "\n",
    "#confidence interval \n",
    "x_fitted = np.linspace(xlim[0], xlim[1], 100)\n",
    "y_fitted = np.polyval(p, x_fitted)\n",
    "ci = t_critical * std_err * np.sqrt(1 / n + (x_fitted - np.mean(x))**2 / np.sum((x - np.mean(x))**2))\n",
    "ax1.fill_between(x_fitted, y_fitted + (1.96*std_err)/np.sqrt(len(y)), y_fitted - (1.96*std_err)/np.sqrt(len(y)), facecolor='#C8A2C8', zorder=0,label=r'95% Confidence Interval')\n",
    "\n",
    "ax1.set_ylim(0,55)\n",
    "ax1.set_xlim(0.27,1.6)\n",
    "ax1.set_ylabel('Amplitude (nT)')\n",
    "ax1.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($nPa^{\\dfrac{1}{2}}$)')\n",
    "ax1.set_title('Straight Line Fit',fontweight=\"bold\")\n",
    "ax1.legend(fontsize=8, loc='upper right')\n",
    "ax1.text(0.27,50, 'y = 15.9x + 10.7',fontsize = 12)\n",
    "ax1.set_ylim(0, ylim[1])\n",
    "ax1.grid(True)\n",
    "\n",
    "#plotting exponential fit\n",
    "def f(x,a,b,c):\n",
    "    return a*x**2 + b*x + c\n",
    "\n",
    "popt, pcov = curve_fit(f,x,y)\n",
    "a = popt[0]\n",
    "b = popt[1]\n",
    "c = popt[2]\n",
    "\n",
    "print(a,b,c)\n",
    "r2 = 1.0-(sum((y-f(x,a,b,c))**2)/((n-1.0)*np.var(y,ddof=2)))\n",
    "print('R^2: ' + str(r2))\n",
    "\n",
    "# calculate parameter confidence interval\n",
    "a,b,c = unc.correlated_values(popt, pcov)\n",
    "print('Uncertainty')\n",
    "print('a: ' + str(a))\n",
    "print('b: ' + str(b))\n",
    "print('c: ' + str(c))\n",
    "\n",
    "\n",
    "ax2.scatter(x, y,c='deeppink', s=18, edgecolor='k', label='Data')\n",
    "\n",
    "# calculate regression confidence interval\n",
    "px = np.linspace(0, 2, 500)\n",
    "py = a*px**2 +b*px +c\n",
    "nom = unp.nominal_values(py)\n",
    "std = unp.std_devs(py)\n",
    "\n",
    "# plot the regression\n",
    "ax2.plot(px, nom, c='black', label=r'Polynomial Fit''\\n'f' Reduced Chi² = 1.12')\n",
    "\n",
    "\n",
    "ax2.fill_between(px, nom + (1.96*std)/np.sqrt(len(y)), nom - (1.96*std)/np.sqrt(len(y)), facecolor='#C8A2C8', zorder=0,label=r'95% Confidence Interval')\n",
    "\n",
    "ax2.set_ylim(0,55)\n",
    "ax2.set_xlim(0.27,1.6)\n",
    "ax2.set_ylabel('Amplitude (nT)')\n",
    "ax2.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($nPa^{\\dfrac{1}{2}}$)')\n",
    "ax2.set_title('Polynomial Line Fit',fontweight=\"bold\")\n",
    "ax2.legend(fontsize =7.5, loc='upper right' )\n",
    "ax2.text(0.27,50,'$-0.6862x^2 + 17.38x + 10.03$',fontsize = 12)\n",
    "ax2.grid(True)\n",
    "\n",
    "#plotting exponential fit \n",
    "def func(x, a, b):\n",
    "    return (a * np.exp(b*x))\n",
    "\n",
    "popt, pcov = curve_fit(func, x, y, p0=[12,0.2])\n",
    "a, b = popt\n",
    "print(popt)\n",
    "\n",
    "def chi_squared_reduced(data, model, dof=None):\n",
    "    sq_residual = (data - model)**2\n",
    "    chi_sq = np.sum(sq_residual)\n",
    "    if dof is None:\n",
    "        return chi_sq\n",
    "    else:\n",
    "        nu = len(data) - dof\n",
    "        return chi_sq / nu\n",
    "\n",
    "# Define our data, model, uncertainty, and degrees of freedom\n",
    "I_data = y.copy()  # observed data\n",
    "I_modeled = func(x, *popt) # model fitted result\n",
    "\n",
    "# Calculate the Chi-Squared value (no dof)\n",
    "chisq = chi_squared_reduced(I_data, I_modeled)\n",
    "print(f\"chi-squared statistic = {chisq:1.2f}\")\n",
    "\n",
    "# Calculate the Reduced Chi-Squared value (with dof)\n",
    "dof = 2  # We have two free parameters\n",
    "rechisq = chi_squared_reduced(I_data, I_modeled, dof)\n",
    "print(f\"reduced chi-squared = {rechisq:1.2f}\")\n",
    "\n",
    "t = np.linspace(0, 2, 19)\n",
    "ax3.set_ylabel('Amplitude (nT)')\n",
    "ax3.set_xlabel('$ \\Delta\\sqrt{Pressure}$ ($nPa^{\\dfrac{1}{2}}$)')\n",
    "ax3.set_title('Exponential Line Fit',fontweight=\"bold\")\n",
    "ax3.scatter(x, y, c='deeppink', marker='o', edgecolors='k', s=18, label=f'Data')\n",
    "ax3.plot(x, func(x,*popt), c='k',label = r'Exponential Line Fit''\\n'f' Reduced Chi² = {rechisq:.2f}')\n",
    "ax3.fill_between(x, func(x,*popt) + (1.96*std_err)/np.sqrt(len(y)), func(x,*popt) - (1.96*std_err)/np.sqrt(len(y)), facecolor='#C8A2C8', zorder=0,label=r'95% Confidence Interval')\n",
    "\n",
    "ax3.set_ylim(0,55)\n",
    "ax3.set_xlim(0.272,1.6)\n",
    "ax3.legend(fontsize =7.5, loc='upper right' )\n",
    "ax3.text(0.3,50, '$14.844 e^{0.5613x}$',fontsize = 12)\n",
    "ax3.grid(True)\n",
    "\n",
    "#setting labels a,b,c\n",
    "ax1.set_title('A)', fontweight=\"bold\", loc='left', fontsize='10')\n",
    "ax2.set_title('B)', fontweight=\"bold\", loc='left', fontsize='10')\n",
    "ax3.set_title('C)', fontweight=\"bold\", loc='left', fontsize='10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b27ee78c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f525e15b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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