measure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make plotly plots for measuring features in solar wind data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chris/python/miniconda3/envs/helio4/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import os\n",
    "import numpy as np\n",
    "from sunpy.time import parse_time\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime\n",
    "\n",
    "from heliocats import data as hd\n",
    "import copy\n",
    "\n",
    "data_path='/Users/chris/python/data/insitu_python/'\n",
    "\n",
    "#data_path='/Users/chris/python/data/may_2024_events/'\n",
    "\n",
    "\n",
    "#Plotly imports\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.express as px\n",
    "pio.renderers.default = 'browser'\n",
    "\n",
    "print(' ' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### print files in data_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".DS_Store\n",
      "ML_struct_arrays\n",
      "bepi_ib_2019_now_e2k.p\n",
      "bepi_ib_2019_now_rtn.p\n",
      "bepi_ib_e2k_test.p\n",
      "bepi_ib_rtn_test.p\n",
      "bepi_ob_2019_now_e2k.p\n",
      "bepi_ob_2019_now_rtn_file_before_new_conversion.p\n",
      "bepi_ob_e2k_test.p\n",
      "bepi_ob_rtn_test.p\n",
      "juno_2011_2016_rtn.p\n",
      "juno_2011_2016_rtn_old.p\n",
      "maven_2014_2018_removed.p\n",
      "maven_2014_2018_removed_smoothed.p\n",
      "maven_2014_2021_removed_no_plasma.p\n",
      "maven_2014_2021_removed_smoothed_no_plasma.p\n",
      "messenger_2007_2015_sceq_removed.p\n",
      "msl_2012_2019_rad.p\n",
      "new_data_headers.txt\n",
      "noaa_archive_gsm.p\n",
      "noaa_dst_last_2500_now.p\n",
      "noaa_dst_last_300files_now.p\n",
      "noaa_dst_last_35files_now.p\n",
      "noaa_rtsw\n",
      "noaa_rtsw_last_2500files_now.p\n",
      "noaa_rtsw_last_300files_now.p\n",
      "noaa_rtsw_last_35files_now.p\n",
      "noaa_rtsw_march_2023.p\n",
      "noaa_xray2_last_7days_now.p\n",
      "noaa_xray_last_7days_now.p\n",
      "omni_1963_now.p\n",
      "plots\n",
      "plots_positions\n",
      "plots_sun\n",
      "positions_psp_solo_sta_bepi_wind_juno_juice_ulysses_planets_HEEQ_10min_degrees.p\n",
      "positions_psp_solo_sta_bepi_wind_juno_juice_ulysses_planets_HEEQ_10min_rad.p\n",
      "positions_psp_solo_sta_bepi_wind_juno_juice_ulysses_planets_HEEQ_1hour_degrees.p\n",
      "positions_psp_solo_sta_bepi_wind_juno_juice_ulysses_planets_HEEQ_1hour_rad.p\n",
      "positions_psp_solo_sta_bepi_wind_planets_HEEQ_10min_degrees.p\n",
      "positions_psp_solo_sta_bepi_wind_planets_HEEQ_10min_rad.p\n",
      "positions_psp_solo_sta_bepi_wind_planets_HEEQ_1hour_degrees.p\n",
      "positions_psp_solo_sta_bepi_wind_planets_HEEQ_1hour_rad.p\n",
      "predicted-solar-cycle-2023_apr_19.json\n",
      "psp_2018_now_rtn.p\n",
      "psp_rtn_test.p\n",
      "rc_list.htm\n",
      "solo_2020_now_rtn.p\n",
      "solo_rtn_2024-03-20.p\n",
      "solo_rtn_2024-03-23.p\n",
      "solo_rtn_2024-03-25.p\n",
      "solo_rtn_test.p\n",
      "ssn.p\n",
      "ssn_13ms.p\n",
      "ssn_m.p\n",
      "ssn_prelim.p\n",
      "stereoa_2007_2020_sceq.p\n",
      "stereoa_2007_now_rtn.p\n",
      "stereoa_2020_now_sceq_beacon.p\n",
      "stereoa_beacon_gsm_last_300days_now.p\n",
      "stereoa_beacon_gsm_last_35days_now.p\n",
      "stereoa_beacon_gsm_last_400days_now.p\n",
      "stereoa_beacon_rtn_last_300days_now.p\n",
      "stereoa_beacon_rtn_last_35days_now.p\n",
      "stereoa_beacon_rtn_last_400days_now.p\n",
      "stereoa_rtn_test.p\n",
      "stereob_2007_2014_rtn.p\n",
      "stereob_2007_2014_sceq.p\n",
      "ulysses_1990_2009_rtn.p\n",
      "vex_2007_2014_sceq_removed.p\n",
      "wind_1995_2021_heeq.p\n",
      "wind_1995_now_gse.p\n",
      "wind_1995_now_heeq.p\n",
      "wind_1995_now_rtn.p\n",
      "wind_gse_test.p\n",
      "wind_heeq_test.p\n",
      "wind_rtn_test.p\n"
     ]
    }
   ],
   "source": [
    "files = os.listdir(data_path)\n",
    "files.sort()\n",
    "fnames=[os.path.join(data_path, f) for f in files]\n",
    "for item in files:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Science level 2 solar wind magnetic field and plasma data (IMPACT) from STEREO A, obtained from https://spdf.gsfc.nasa.gov/pub/data/stereo/ahead/l2/impact/magplasma/1min/ Timerange: 2007-Jan-01 00:00 to 2023-Dec-31 23:49, with a time resolution of 1 min. The data are available in a numpy recarray, fields can be accessed by stereoa.time, stereoa.bx, stereoa.vt etc. Total number of data points: 8255308. Units are btxyz [nT, RTN], vtxy  [km s^-1, RTN], np[cm^-3], tp [K], heliospheric position x/y/z [km], r/lon/lat [AU, degree, HEEQ]. Made with heliocats/data_update_web_science.ipynb by E. Davies (twitter @spacedavies) and C. Möstl (@chrisoutofspace). File creation date: 2024-May-23 08:04 UTC'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#file='solo_2020_now_rtn.p'\n",
    "\n",
    "#file='psp_2018_now_rtn.p'\n",
    "#file='psp_rtn_test.p'\n",
    "\n",
    "#file='solo_rtn_2024-03-23.p'\n",
    "\n",
    "#file='solo_rtn_2024-03-25.p'\n",
    "\n",
    "\n",
    "#file='bepi_ob_rtn_test.p'\n",
    "#file='bepi_ob_2019_now_rtn.p'  #preferred ib over ob\n",
    "\n",
    "\n",
    "#file='bepi_ob_2019_now_rtn.p'  #preferred ib over ob\n",
    "#file='bepi_ob_2019_now_rtn.p'\n",
    "\n",
    "\n",
    "file='stereoa_2007_now_rtn.p'\n",
    "#file='stereoa_beacon_rtn_last_400days_now.p'\n",
    "#file='stereoa_beacon_gsm_last_35days_now.p'\n",
    "\n",
    "#file='noaa_archive_gsm.p'\n",
    "\n",
    "#file='stereoa_beacon_rtn_last_35days_now.p'\n",
    "#file='noaa_rtsw_last_35files_now.p'\n",
    "\n",
    "#file='wind_1995_now_rtn.p'\n",
    "\n",
    "\n",
    "#file='noaa_rtsw_last_300files_now.p'\n",
    "\n",
    "#file='solo_rtn_test.p'\n",
    "#file='psp_rtn_test.p'\n",
    "\n",
    "#file='ulysses_1990_2009_rtn.p'\n",
    "\n",
    "#file='juno_2011_2016_rtn.p'\n",
    "#file='juno_2011_2016_rtn_old.p'\n",
    "\n",
    "\n",
    "#file='omni_1963_now.p'\n",
    "\n",
    "[sc,header]=pickle.load(open(data_path+file, \"rb\" ) ) \n",
    "\n",
    "\n",
    "\n",
    "#start=datetime(2012,7,20)\n",
    "#end=datetime(2012,7,31)\n",
    "\n",
    "#start=datetime(1963,1,1)\n",
    "#end=datetime(2024,1,1)\n",
    "\n",
    "\n",
    "\n",
    "#start=datetime(1989,1,1)\n",
    "#end=datetime(2010,1,1)\n",
    "\n",
    "\n",
    "\n",
    "#cutout array for these dates\n",
    "#start=datetime(2005,2,23,6)\n",
    "start=datetime(2023,1,1,0)\n",
    "end=datetime(2024,8,31)\n",
    "\n",
    "\n",
    "#cutout array for these dates\n",
    "#start=datetime(2001,2,1)\n",
    "#end=datetime(2002,5,30)\n",
    "\n",
    "\n",
    "#scnum=mdates.date2num(sc.time)\n",
    "startind=np.argmin(abs(start-sc.time))\n",
    "endind=np.argmin(abs(end-sc.time))\n",
    "\n",
    "sc=sc[startind:endind]\n",
    "\n",
    "######## save cutout data if needed\n",
    "\n",
    "#pickle_file='data/bepi_cutout_2022_march_10_april_10.p'\n",
    "#pickle.dump([sc, header], open(pickle_file, \"wb\"))\n",
    "#[bepi,hbepi]=pickle.load(open(pickle_file, \"rb\" ) )\n",
    "\n",
    "plt.plot(sc.time,sc.bt)\n",
    "plt.plot(sc.time,sc.bx)\n",
    "plt.plot(sc.time,sc.by)\n",
    "plt.plot(sc.time,sc.bz)\n",
    "\n",
    "\n",
    "\n",
    "header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1773af3d0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sc.time,sc.r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Use plotly for measuring times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### with plasma data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nrows=5\n",
    "#init_notebook_mode(connected = True)\n",
    "#init_notebook_mode(connected = False)\n",
    "\n",
    "#fig=plt.figure(figsize=(10,6), dpi=150)\n",
    "fig = make_subplots(rows=nrows, cols=1, shared_xaxes=True,row_heights=[0.3,0.2, 0.2, 0.2, 0.1])\n",
    "\n",
    "\n",
    "#for column, color in zip(['b_x', 'b_y', 'b_z', 'b_tot'], ['red', 'green', 'blue', 'black']):\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bx, name='Bx',line_color='red'), row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"B [nT] RTN\", row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.by, name='By',line_color='green'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bz, name='Bz',line_color='blue'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bt, name='Bt',line_color='black'), row=1, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.vt, name='Vt',line_color='black'), row=2, col=1)\n",
    "fig.update_yaxes(title_text=\"V [km/s]\", row=2, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.np, name='Np',line_color='black'), row=3, col=1)\n",
    "fig.update_yaxes(title_text=\"Np [ccm-3]\", row=3, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.tp, name='Tp',line_color='black'), row=4, col=1)\n",
    "fig.update_yaxes(title_text=\"Tp [K]\", row=4, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.r, name='R',line_color='black'), row=5, col=1)\n",
    "fig.update_yaxes(title_text=\"R [AU]\", row=5, col=1)\n",
    "\n",
    "\n",
    "#fig.update_layout(title='sc data')\n",
    "\n",
    "#### save as vector graphics\n",
    "fig.show(config = {\n",
    "  'toImageButtonOptions': {\n",
    "    'format': 'svg', \n",
    "    'filename': 'custom_image',\n",
    "    'height': 800,\n",
    "    'width': 1000,\n",
    "    'scale':6 # Multiply title/legend/axis/canvas sizes by this factor\n",
    "  }\n",
    "})\n",
    "\n",
    "fig.write_html(f'measure.html')\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### no plasma data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def on_plot_click(trace, points, state):\n",
    "#    if points.point_inds:\n",
    "#        clicked_index = points.point_inds[0]\n",
    "#        clicked_x = df['x_values'][clicked_index]\n",
    "#        clicked_y = df['y_values'][clicked_index]\n",
    "#        print(f\"Clicked on point ({clicked_x}, {clicked_y})\")\n",
    "\n",
    "\n",
    "nrows=3\n",
    "#init_notebook_mode(connected = True)\n",
    "#init_notebook_mode(connected = False)\n",
    "\n",
    "#fig=plt.figure(figsize=(10,6), dpi=150)\n",
    "\n",
    "fig = make_subplots(rows=nrows, cols=1, shared_xaxes=True,row_heights=[0.6, 0.2,0.2])\n",
    "\n",
    "#for column, color in zip(['b_x', 'b_y', 'b_z', 'b_tot'], ['red', 'green', 'blue', 'black']):\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bx, name='Bx',line_color='red'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.by, name='By',line_color='green'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bz, name='Bz',line_color='blue'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bt, name='Bt',line_color='black'), row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"B [nT] RTN\", row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.r, name='R',line_color='black'), row=2, col=1)\n",
    "fig.update_yaxes(title_text=\"R [AU]\", row=2, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.lon, name='Longitude',line_color='black'), row=3, col=1)\n",
    "fig.update_yaxes(title_text=\"longitude [deg] HEEQ\", row=3, col=1)\n",
    "\n",
    "# Attach the custom click event callback\n",
    "#fig.data[0].on_click(on_plot_click)\n",
    "\n",
    "fig.show()\n",
    "#fig.update_layout(title='Wind')\n",
    "\n",
    "#fig.write_html(f'measure_mag.html')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "### Positions\n",
    "[psp, solo, sta, stb, bepi, l1, earth, mercury, venus, mars, jupiter, saturn, uranus, neptune]=pickle.load( open( 'results/positions/positions_psp_solo_sta_bepi_wind_planets_HEEQ_10min_degrees.p', \"rb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = make_subplots(rows=1, cols=1, shared_xaxes=True)\n",
    "fig.add_trace(go.Scatter(x=psp.time, y=psp.r, name='R',line_color='green'), row=1, col=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### check SolO ICMEs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "[ic,header,parameters] = pickle.load(open('icmecat/HELIO4CAST_ICMECAT_v22_pandas.p', \"rb\" ))\n",
    "\n",
    "soloi=np.where(ic.sc_insitu=='SolarOrbiter')[0]\n",
    "ics = np.array(ic.loc[soloi,'icme_start_time'])\n",
    "\n",
    "soloi2=np.where(ic.sc_insitu=='SolarOrbiter')[0]\n",
    "ics2 = np.array(ic.loc[soloi2,'mo_start_time'])\n",
    "\n",
    "soloi3=np.where(ic.sc_insitu=='SolarOrbiter')[0]\n",
    "ics3 = np.array(ic.loc[soloi3,'mo_end_time'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nrows=3\n",
    "#init_notebook_mode(connected = True)\n",
    "#init_notebook_mode(connected = False)\n",
    "\n",
    "#fig=plt.figure(figsize=(10,6), dpi=150)\n",
    "\n",
    "fig = make_subplots(rows=nrows, cols=1, shared_xaxes=True,row_heights=[0.6, 0.2,0.2])\n",
    "\n",
    "#for column, color in zip(['b_x', 'b_y', 'b_z', 'b_tot'], ['red', 'green', 'blue', 'black']):\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bx, name='Bx',line_color='red'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.by, name='By',line_color='green'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bz, name='Bz',line_color='blue'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bt, name='Bt',line_color='black'), row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"B [nT] RTN\", row=1, col=1)\n",
    "\n",
    "for i in np.arange(0,len(icsa)):\n",
    "    fig.add_vline(x=ics[i], line_width=3, line_dash='dash', line_color='black',row=1, col=1)\n",
    "    fig.add_vline(x=ics2[i], line_width=3, line_dash='dash', line_color='black',row=1, col=1)\n",
    "    fig.add_vline(x=ics3[i], line_width=3, line_dash='dash', line_color='black',row=1, col=1)\n",
    "\n",
    "    \n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.r, name='R',line_color='black'), row=2, col=1)\n",
    "fig.update_yaxes(title_text=\"R [AU]\", row=2, col=1)\n",
    "\n",
    "\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.lon, name='Longitude',line_color='black'), row=3, col=1)\n",
    "fig.update_yaxes(title_text=\"longitude [deg] HEEQ\", row=3, col=1)\n",
    "\n",
    "\n",
    "\n",
    "# Attach the custom click event callback\n",
    "#fig.data[0].on_click(on_plot_click)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for OMNI solar wind, Dst, and SSN in 2 plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "file='omni_1963_now.p'\n",
    "\n",
    "[sc,header]=pickle.load(open(data_path+file, \"rb\" ) ) \n",
    "\n",
    "#start=datetime(2012,7,20)\n",
    "#end=datetime(2012,7,31)\n",
    "\n",
    "start=datetime(1963,1,1)\n",
    "end=datetime(2024,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nrows=5\n",
    "#init_notebook_mode(connected = True)\n",
    "#init_notebook_mode(connected = False)\n",
    "\n",
    "#fig=plt.figure(figsize=(10,6), dpi=150)\n",
    "\n",
    "fig = make_subplots(rows=nrows, cols=1, shared_xaxes=True,row_heights=[0.3,0.2, 0.2, 0.2, 0.1])\n",
    "\n",
    "\n",
    "#for column, color in zip(['b_x', 'b_y', 'b_z', 'b_tot'], ['red', 'green', 'blue', 'black']):\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bx, name='Bx',line_color='red'), row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"B [nT] RTN\", row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.by, name='By',line_color='green'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bz, name='Bz',line_color='blue'), row=1, col=1)\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.bt, name='Bt',line_color='black'), row=1, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.vt, name='Vt',line_color='black'), row=2, col=1)\n",
    "fig.update_yaxes(title_text=\"V [km/s]\", row=2, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.np, name='Np',line_color='black'), row=3, col=1)\n",
    "fig.update_yaxes(title_text=\"Np [ccm-3]\", row=3, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.dst, name='Dst',line_color='black'), row=4, col=1)\n",
    "fig.update_yaxes(title_text=\"Dst [nT]\", row=4, col=1)\n",
    "\n",
    "\n",
    "#fig.update_layout(title='sc data')\n",
    "\n",
    "fig.write_html(f'omni.html')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Dst and Sunspot number\n",
    "\n",
    "nrows=2\n",
    "#init_notebook_mode(connected = True)\n",
    "#init_notebook_mode(connected = False)\n",
    "\n",
    "#fig=plt.figure(figsize=(10,6), dpi=150)\n",
    "\n",
    "fig = make_subplots(rows=nrows, cols=1, shared_xaxes=True,row_heights=[0.5,0.5])\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.spot, name='SSN',line_color='red'), row=1, col=1)\n",
    "fig.update_yaxes(title_text=\"SSN\", row=1, col=1)\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sc.time, y=sc.dst, name='Dst',line_color='black'), row=2, col=1)\n",
    "fig.update_yaxes(title_text=\"Dst [nT]\", row=2, col=1)\n",
    "\n",
    "\n",
    "#fig.update_layout(title='sc data')\n",
    "\n",
    "fig.write_html(f'dst_ssn.html')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('paper')\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "fig=plt.figure(1, figsize=(10,5), dpi=100) #full hd\n",
    "\n",
    "\n",
    "scale=1/0.40**(-1.5)\n",
    "\n",
    "scale2=1/0.40**(-2.0)\n",
    "scale3=1/0.40**(-1.0)\n",
    "\n",
    "\n",
    "bts=sc.bt*scale\n",
    "bxs=sc.bx*scale\n",
    "bys=sc.by*scale\n",
    "bzs=sc.bz*scale\n",
    "\n",
    "\n",
    "bzs2=sc.bz*scale2\n",
    "bzs3=sc.bz*scale3\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(sc.time,bts,'-k')\n",
    "#plt.plot(sc.time,bxs,'-r')\n",
    "#plt.plot(sc.time,bys,'-g')\n",
    "plt.plot(sc.time,bzs,'-b')\n",
    "\n",
    "#plt.fill_between(sc.time, bzs, color='skyblue', alpha=0.3)\n",
    "\n",
    "#plt.plot(sc.time,bzs2,'--b')\n",
    "#plt.plot(sc.time,bzs3,'--b')\n",
    "\n",
    "plt.ylabel('B [nT] RTN ') \n",
    "plt.title('SolO RTN Bn (blue) and Bt (black) scaled with exponents -1.0 to -2.0')\n",
    "\n",
    "\n",
    "plt.fill_between(sc.time, bzs3, bzs2,color='lightblue', interpolate=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_coupling_newell(by, bz, v):\n",
    "    #Empirical Formula for dFlux/dt - the Newell coupling\n",
    "    #e.g. paragraph 25 in Newell et al. 2010 doi:10.1029/2009JA014805\n",
    "    #input: needs arrays for by, bz, v    \n",
    "    \n",
    "    bt = np.sqrt(by**2 + bz**2)\n",
    "    bztemp = bz\n",
    "    bztemp[bz == 0] = 0.001\n",
    "    tc = np.arctan2(by,bztemp)     #calculate clock angle (theta_c = t_c)\n",
    "    neg_tc = bt*np.cos(tc)*bz < 0  #similar to IDL code sol_coup.pro\n",
    "    tc[neg_tc] = tc[neg_tc] + np.pi\n",
    "    sintc = np.abs(np.sin(tc/2.))\n",
    "    nc = (v**1.33333)*(sintc**2.66667)*(bt**0.66667)\n",
    "    \n",
    "    return nc\n",
    "\n",
    "\n",
    "def nc_weights(nc):    \n",
    "\n",
    "    #this function is for 1 hour time resolution\n",
    "    nc_weight=np.zeros(len(nc))\n",
    "\n",
    "    prev_hour_weight = 0.65    # reduce weighting by factor with each hour back\n",
    "    \n",
    "    weights=np.zeros(4)\n",
    "\n",
    "    weights[3] = 1  #the current fraction of hour is weighted as linear\n",
    "    weights[2] = prev_hour_weight  \n",
    "    weights[1] = prev_hour_weight**2  \n",
    "    weights[0] = prev_hour_weight**3      \n",
    "    \n",
    "    for i in np.arange(4,len(nc)):\n",
    "        #add and normalize\n",
    "        nc_weight[i] = np.round(np.nansum(nc[i-3:i+1]*weights)/ np.nansum(weights),1)    \n",
    "    \n",
    "    return nc_weight\n",
    "\n",
    "print(' ')\n",
    "print('calculate Newell coupling without propagation first')\n",
    "print(' ')\n",
    "#n-> dst\n",
    "#w-> solar wind\n",
    "\n",
    "\n",
    "\n",
    "vassumed=np.zeros(np.size(bzs))+820\n",
    "\n",
    "#minute resolution\n",
    "solo_nc=calc_coupling_newell(bys,bzs,vassumed)/4421\n",
    "#interpolate to 1 hour first\n",
    "\n",
    "\n",
    "time_hourly=np.array([\n",
    "datetime(2024, 3, 23, 12, 0),\n",
    "datetime(2024, 3, 23, 13, 0),\n",
    "datetime(2024, 3, 23, 14, 0),\n",
    "datetime(2024, 3, 23, 15, 0),\n",
    "datetime(2024, 3, 23, 16, 0),\n",
    "datetime(2024, 3, 23, 17, 0),\n",
    "datetime(2024, 3, 23, 18, 0),\n",
    "]\n",
    ")\n",
    "solo_nci=np.interp(mdates.date2num(time_hourly),mdates.date2num(sc.time),solo_nc)\n",
    "\n",
    "\n",
    "\n",
    "#then do weighting\n",
    "solo_nc_fin=nc_weights(solo_nci)\n",
    "\n",
    "\n",
    "plt.title('Newell coupling from Solar Orbiter data')\n",
    "\n",
    "plt.ylabel('Nc')\n",
    "\n",
    "plt.plot(sc.time,solo_nc,alpha=0.5)\n",
    "plt.plot(time_hourly,solo_nci,'ok')\n",
    "plt.plot(time_hourly,solo_nc_fin,'-r')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#check positions in positions file and in data file\n",
    "\n",
    "#plt.plot(solo.time,solo.lat,'b-')\n",
    "#plt.plot(pos[1].time,np.rad2deg(pos[1].lat),'k-')   \n",
    "\n",
    "#from plotly.subplots import make_subplots\n",
    "#fig = make_subplots(rows=1, cols=1, shared_xaxes=True)\n",
    "#fig.add_trace(go.Scatter(x=solo.time, y=solo.lat, name='lat solo',line_color='red'))\n",
    "#fig.add_trace(go.Scatter(x=mdates.num2date(pos[1].time), y=np.rad2deg(pos[1].lat), name='lat pos',line_color='green'))\n",
    "\n",
    "#fig.write_html(f'measure.html')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#print('load Solar Orbiter SCEQ')\n",
    "#filesolo='solo_2020_april_2022_sep_mag_sceq.p'\n",
    "#solo=pickle.load(open(data_path+filesolo, \"rb\" ) ) \n",
    "#sc=solo\n",
    "#sc_label='Solar Orbiter'\n",
    "#start1='2022-Jan-1 00:00'\n",
    "#end1='2022-Jun-30 00:00'\n",
    "\n",
    "\n",
    "#print('load Solar Orbiter SCEQ')\n",
    "#filesolo='solo_2020_april_2022_june_rtn.p'\n",
    "#solo=pickle.load(open(data_path+filesolo, \"rb\" ) ) \n",
    "#sc=solo\n",
    "#sc_label='Solar Orbiter'\n",
    "#start1='2022-Jan-1 00:00'\n",
    "#end1='2022-Jun-30 00:00'\n",
    "\n",
    "#print('PSP SCEQ')\n",
    "#filepsp='psp_2022_add_mag_sceq.p'\n",
    "#psp=pickle.load(open(data_path+filepsp, \"rb\" ) ) \n",
    "#filepsp='psp_2022_add_mag_rtn.p'\n",
    "#psp=pickle.load(open(data_path+filepsp, \"rb\" ) ) \n",
    "\n",
    "#sc=psp\n",
    "#sc_label='PSP'\n",
    "#start1='2022-Jan-1 00:00'\n",
    "#end1='2022-Mar-31 00:00'\n",
    "\n",
    "\n",
    "#print('wind HEEQ')\n",
    "#filesolo='wind_2018_now_heeq.p'\n",
    "#[wind,windh]=pickle.load(open(data_path+filesolo, \"rb\" ) ) \n",
    "#sc=wind\n",
    "#sc_label='Wind'\n",
    "#start1='2022-Sep-1 00:00'\n",
    "#end1='2022-Nov-11 00:00'\n",
    "\n",
    "\n",
    "#print('load bepi')\n",
    "#file='bepi_2021_2022_ib_sceq.p'\n",
    "#file='bepi_2021_2022_ib_sceq.p'\n",
    "\n",
    "#bepi=pickle.load(open(data_path+file, \"rb\" ) ) \n",
    "#sc=bepi\n",
    "#sc_label='Bepi'\n",
    "#start1='2022-Apr-1 00:00'\n",
    "#end1='2022-Nov-31 00:00'\n",
    "\n",
    "\n",
    "#print('load sta')\n",
    "#filesolo='stereoa_2020_now_sceq_beacon.p'\n",
    "#[sta,stah]=pickle.load(open(data_path+filesolo, \"rb\" ) ) \n",
    "#sc=sta\n",
    "#sc_label='STEREO-A'\n",
    "#start1='2022-Feb-1 00:00'\n",
    "#end1='2022-Nov-11 00:00'\n",
    "\n",
    "\n",
    "\n",
    "####### select parts for plotting\n",
    "sc=sc[-100000:-1]\n",
    "\n",
    "#sc[0:1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "####### check STEREO-A distance to Earth\n",
    "\n",
    "\n",
    "#cutout array for these dates\n",
    "start=datetime(2023,1,1,0)\n",
    "end=datetime(2024,8,31)\n",
    "\n",
    "\n",
    "file='stereoa_2007_now_rtn.p'\n",
    "\n",
    "[sta,header]=pickle.load(open(data_path+file, \"rb\" ) ) \n",
    "\n",
    "\n",
    "startind=np.argmin(abs(start-sta.time))\n",
    "endind=np.argmin(abs(end-sta.time))\n",
    "\n",
    "sta=sta[startind:endind]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "file='wind_1995_now_rtn.p'\n",
    "\n",
    "[win,header]=pickle.load(open(data_path+file, \"rb\" ) ) \n",
    "\n",
    "\n",
    "startind=np.argmin(abs(start-win.time))\n",
    "endind=np.argmin(abs(end-win.time))\n",
    "\n",
    "win=win[startind:endind]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1776c1e70>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(win.time,win.r,'-k')\n",
    "plt.plot(sta.time,sta.r,'-r')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (375839,) (470944,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(win\u001b[38;5;241m.\u001b[39mtime,\u001b[43mwin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mr\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43msta\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mr\u001b[49m)\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (375839,) (470944,) "
     ]
    }
   ],
   "source": [
    "plt.plot(win.time,win.r-sta.r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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