Pressure Histograms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0e0ae22",
   "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",
    "\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",
    "dataframe_omni.time\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 9:24:00 ', '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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d47ecc4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "066821d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#indexing out the numbers in the sym-H lists for histograms \n",
    "\n",
    "#values 30-15 minutes before onset \n",
    "a1 = pressure1[0:15] \n",
    "a2 = pressure2[0:15] \n",
    "a3 = pressure3[0:15] \n",
    "a4 = pressure4[0:15] \n",
    "a5= pressure5[0:15] \n",
    "a6= pressure6[0:15] \n",
    "a7= pressure7[0:15] \n",
    "a8= pressure8[0:15] \n",
    "a9= pressure9[0:15] \n",
    "a10= pressure10[0:15] \n",
    "a11= pressure11[0:15] \n",
    "a12= pressure12[0:15] \n",
    "a13= pressure13[0:15] \n",
    "a14= pressure14[0:15] \n",
    "a15= pressure15[0:15] \n",
    "a16= pressure16[0:15] \n",
    "a17= pressure17[0:15] \n",
    "a18= pressure18[0:15] \n",
    "a19= pressure19[0:15] \n",
    "a20= pressure20[0:15] \n",
    "a21= pressure21[0:15] \n",
    "\n",
    "#values for 15-0 minutes before onset \n",
    "b1 = pressure1[16:31]\n",
    "b2 = pressure2[16:31]\n",
    "b3 = pressure3[16:31]\n",
    "b4 = pressure4[16:31]\n",
    "b5= pressure5[16:31]\n",
    "b6= pressure6[16:31]\n",
    "b7= pressure7[16:31]\n",
    "b8= pressure8[16:31]\n",
    "b9= pressure9[16:31]\n",
    "b10= pressure10[16:31]\n",
    "b11= pressure11[16:31]\n",
    "b12= pressure12[16:31]\n",
    "b13= pressure13[16:31]\n",
    "b14= pressure14[16:31]\n",
    "b15=pressure15[16:31]\n",
    "b16= pressure16[16:31]\n",
    "b17= pressure17[16:31]\n",
    "b18= pressure18[16:31]\n",
    "b19= pressure19[16:31]\n",
    "b20= pressure20[16:31]\n",
    "b21= pressure21[16:31]\n",
    "\n",
    "#values for 0-15 minutes after onset \n",
    "c1 = pressure1[31:46]\n",
    "c2 = pressure2[31:46]\n",
    "c3= pressure3[31:46]\n",
    "c4= pressure4[31:46]\n",
    "c5= pressure5[31:46]\n",
    "c6= pressure6[31:46]\n",
    "c7= pressure7[31:46]\n",
    "c8= pressure8[31:46]\n",
    "c9= pressure9[31:46]\n",
    "c10= pressure10[31:46]\n",
    "c11= pressure11[31:46]\n",
    "c12 = pressure12[31:46]\n",
    "c13 = pressure13[31:46]\n",
    "c14= pressure14[31:46]\n",
    "c15= pressure15[31:46]\n",
    "c16= pressure16[31:46]\n",
    "c17=pressure17[31:46]\n",
    "c18= pressure18[31:46]\n",
    "c19= pressure19[31:46]\n",
    "c20= pressure20[31:46]\n",
    "c21= pressure21[31:46]\n",
    "\n",
    "#values for 15-30 minutes after the onset \n",
    "d1 = pressure1[46:61]\n",
    "d2= pressure2[46:61]\n",
    "d3= pressure3[46:61]\n",
    "d4= pressure4[46:61]\n",
    "d5= pressure5[46:61]\n",
    "d6= pressure6[46:61]\n",
    "d7= pressure7[46:61]\n",
    "d8= pressure8[46:61]\n",
    "d9= pressure9[46:61]\n",
    "d10= pressure10[46:61]\n",
    "d11= pressure11[46:61]\n",
    "d12= pressure12[46:61]\n",
    "d13= pressure13[46:61]\n",
    "d14= pressure14[46:61]\n",
    "d15= pressure15[46:61]\n",
    "d16= pressure16[46:61]\n",
    "d17= pressure17[46:61]\n",
    "d18= pressure18[46:61]\n",
    "d19=pressure19[46:61]\n",
    "d20= pressure20[46:61]\n",
    "d21= pressure21[46:61]\n",
    "\n",
    "a = a1 + a2 + a3 +a4 +a5 +a6 +a7 + a8+  a9 + a10 +a11 + a12 +a13 +a14 + a15 + a16 + a17 +a18 + a19 +a20 +a21\n",
    "b = b1 + b2 + b3 + b4 +b5 + b6 + b7 + b8 + b9 + b10 +b11 + b12 + b13 +b14+b15 + b16 + b17 + b18 +b19 +b20 +b21\n",
    "c = c1 + c2 +c3 +c4 +c5 +c6 +c7 +c8 +c9+ c10+ c11 +c12 +c13 +c14 +c15 +c16 +c17 +c18 +c19 +c20 +c21\n",
    "d = d1 + d2 +d3 +d4 +d5 +d6 +d7 +d8 +d9+ d10+ d11 +d12 +d13 +d14 +d15 +d16 +d17 +d18 +d19 +d20 +d21\n",
    "\n",
    "#since some data in the pressure is recorded as NaN I removed these \n",
    "before = [item for item in a if not(math.isnan(item)) == True]\n",
    "pre_onset =  [item for item in b if not(math.isnan(item)) == True]\n",
    "onset = [item for item in c if not(math.isnan(item)) == True]\n",
    "past_onset = [item for item in d if not(math.isnan(item))== True]\n",
    "\n",
    "list12= dataframe_omni['flow_pressure'].tolist()\n",
    "pressure = [item for item in list12 if not(math.isnan(item)) == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f38b421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.0, 1.0, 'D)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#beginnig to plot the histograms \n",
    "fig = plt.figure()\n",
    "#setting the gridspec as a 2x2 figure\n",
    "gs = fig.add_gridspec(2, 2, hspace=0.5, wspace=0.5)\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(12)\n",
    "(ax1, ax2),(ax3, ax4) = gs.subplots(sharex=False, sharey=False)\n",
    "fig.suptitle(r'Histograms Displaying Solar Wind Pressure Values Over 15 Minute Intervals' \"\\n\" r' (Neglecting Values Over 25nPa)', fontweight=\"bold\")\n",
    "\n",
    "   \n",
    "#making the panels\n",
    "ax1.hist(before, bins=np.linspace(0,25,50), alpha=0.3, color = 'red',edgecolor='black',density=True)\n",
    "ax1.hist(pressure, bins = np.linspace(0,25,50), alpha=0.3, color = 'm',edgecolor='black',density=True )\n",
    "ax1.set_xlabel('Flow Pressure (nPa)')\n",
    "ax1.set_ylabel('Occurrence (Normalised)')\n",
    "#including median line and legend \n",
    "ax1.axvline(np.mean(before), color='k', linestyle='-', linewidth=1)\n",
    "#setting y-lim\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "ax1.text(10,0.25, 'Median: {:.2f}'.format(np.median(before)),color='red')\n",
    "ax1.text(10,0.2, '\\u03C3: {:.2f}'.format(np.std(before)),color='red')\n",
    "ax1.legend(['Median $SC^+$ Event','$SC^+$ Event Data','Total 2012 Data'], fontsize =8)\n",
    "\n",
    "\n",
    "ax2.hist(pre_onset, bins=np.linspace(0,25,50), alpha=0.3, color = 'red',edgecolor='black',density=True)\n",
    "ax2.hist(pressure, bins = np.linspace(0,25,50), alpha=0.3, color = 'm',edgecolor='black',density=True )\n",
    "ax2.set_xlabel('Flow Pressure (nPa)')\n",
    "ax2.set_ylabel('Occurrence (Normalised)')\n",
    "ax2.axvline(np.median(pre_onset), color='k', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "ax2.text(10, 0.25, 'Median: {:.2f}'.format(np.median(pre_onset)),color='red')\n",
    "ax2.text(10,0.2, '\\u03C3: {:.2f}'.format(np.std(pre_onset)),color='red')\n",
    "ax2.legend(['Median $SC^+$ Event','$SC^+$ Event Data','Total 2012 Data'], fontsize =8)\n",
    "\n",
    "ax3.hist(onset,bins=np.linspace(0,25,50),alpha=0.3, color = 'red',edgecolor='black',density=True)\n",
    "ax3.hist(pressure, bins = np.linspace(0,25,50), alpha=0.3, color = 'm',edgecolor='black',density=True )\n",
    "ax3.set_xlabel('Flow Pressure (nPa)')\n",
    "ax3.set_ylabel('Occurrence (Normalised)')\n",
    "ax3.axvline(np.median(onset), color='k', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "ax3.text(10,0.2, '\\u03C3: {:.2f}'.format(np.std(onset)),color='red')\n",
    "ax3.text(10,0.25, 'Median: {:.2f}'.format(np.median(onset)),color='red')\n",
    "ax3.legend(['Median $SC^+$ Event','$SC^+$ Event Data','Total 2012 Data'], fontsize =8)\n",
    "\n",
    "ax4.hist(past_onset, bins=np.linspace(0,25,50),alpha=0.3, color = 'red',edgecolor='black',density=True)\n",
    "ax4.hist(pressure, bins = np.linspace(0,25,50),alpha=0.3, color = 'm',edgecolor='black',density=True )\n",
    "ax4.set_xlabel('Flow Pressure (nPa)')\n",
    "ax4.set_ylabel('Occurrence (Normalised)')\n",
    "median4 = np.median(past_onset)\n",
    "ax4.axvline(np.median(past_onset), color='k', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "ax4.text(10,0.2, '\\u03C3: {:.2f}'.format(np.std(past_onset)),color='red')\n",
    "ax4.text(10,0.25,'Median: {:.2f}'.format(np.median(past_onset)),color='red')\n",
    "ax4.legend(['Median $SC^+$ Event','$SC^+$ Event Data','Total 2012 Data'], fontsize =8)\n",
    "\n",
    "#setting indiviual titles \n",
    "ax1.set_title('30-15 Minutes Before Onset')\n",
    "ax2.set_title('15-0 Minutes Before Onset')\n",
    "ax3.set_title('0-15 Minutes After Onset')\n",
    "ax4.set_title('15-30 Minutes After Onset')\n",
    "\n",
    "#including a,b,c,d labels \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')\n",
    "ax4.set_title('D)', fontweight=\"bold\", loc='left', fontsize='10')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae4ef626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making histogram of flow pressure for all of 2012 to compare to \n",
    "list12= dataframe_omni['flow_pressure'].tolist()\n",
    "pressure = [item for item in list12 if not(math.isnan(item)) == True]\n",
    "\n",
    "#plotting\n",
    "fig, axs = plt.subplots(1, 1,figsize =(5, 5),tight_layout = True)\n",
    "plt.rc('font', size=10)\n",
    "plt.rc('axes', titlesize=10)\n",
    "axs.hist(pressure, bins = np.linspace(0,20,20), color ='palevioletred' )\n",
    "axs.set_xlabel('Flow Pressure (nPa)')\n",
    "axs.set_ylabel('Occurrence (Counts)')\n",
    "axs.axvline(np.median(pressure), color='k', linestyle='-', linewidth=1)\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "axs.text(np.std(pressure)*1,max_ylim*0.8, 'Standard Deviation: {:.2f}'.format(np.std(pressure)))\n",
    "axs.text(np.median(pressure)*1.1, max_ylim*0.9, 'Median: {:.2f}'.format(np.median(pressure)))\n",
    "axs.legend(['Median','Bins'])\n",
    "axs.set_title('Flow Pressure Values In 2012')\n",
    "axs.grid('on')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07a174b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34a3d70f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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