advanced/Generate_2D_slices_of_FPI_and_HPCA_data.ipynb

{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "name": "MMS 2D slices.ipynb",
   "provenance": [],
   "collapsed_sections": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "This notebook shows how to create 2D slices of 3D particle data from FPI and HPCA using PySPEDAS\n",
    "\n",
    "Note: these routines are still being beta tested; please report potential issues to egrimes@igpp.ucla.edu"
   ],
   "metadata": {
    "id": "poJAyrnHuSi_"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "This command sets up the PySPEDAS environment when running in Google Colab.  You can skip it or comment it out if you're running this notebook in an environment that already has PySPEDAS installed.\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p1dtOuzytrgv"
   },
   "outputs": [],
   "source": [
    "!pip install pyspedas"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from pyspedas.mms.particles.mms_part_slice2d import mms_part_slice2d"
   ],
   "metadata": {
    "id": "7kakDE8VuMR2"
   },
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Bi-directional field-aligned beam of 0-300 eV ions observed by FPI and HPCA"
   ],
   "metadata": {
    "id": "nFmIOsPO-AIx"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "time = '2017-09-10/09:32:20'"
   ],
   "metadata": {
    "id": "kp_bcSrYtwR5"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## FPI ions with geometric interpolation\n",
    "\n",
    "The data are rotated such that the x axis is parallel to B field and the bulk velocity defines the x-y plane, and plotted using geometric interpolation (each point on the plot is given the value of the bin it intersects)"
   ],
   "metadata": {
    "id": "EoGVv_lZ-ROs"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "mms_part_slice2d(time=time, instrument='fpi', species='i', rotation='bv', erange=[0, 300])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "GSe_TIihuRbk",
    "outputId": "50cdc385-b98b-47d4-9894-3a20a09a96a0"
   },
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDC username (blank for public access): \n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:24:14: Downloading mms1_fpi_fast_l2_dis-dist_20170910080000_v3.3.0.cdf to pydata/mms1/fpi/fast/l2/dis-dist/2017/09\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_dis_errorflags_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_count_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_angle_fast\n",
      "Time clip was applied to: mms1_dis_dist_fast\n",
      "Time clip was applied to: mms1_dis_disterr_fast\n",
      "Time clip was applied to: mms1_dis_energy_fast\n",
      "Time clip was applied to: mms1_dis_energy_delta_fast\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_plus_var\n",
      "Epoch_minus_var\n",
      "mms1_dis_errorflags_fast\n",
      "mms1_dis_compressionloss_fast\n",
      "mms1_dis_startdelphi_count_fast\n",
      "mms1_dis_startdelphi_angle_fast\n",
      "mms1_dis_dist_fast\n",
      "mms1_dis_disterr_fast\n",
      "mms1_dis_theta_fast\n",
      "mms1_dis_theta_delta_fast\n",
      "mms1_dis_energy_fast\n",
      "mms1_dis_energy_delta_fast\n",
      "mms1_dis_phi_fast\n",
      "mms1_dis_phi_delta_fast\n",
      "mms1_dis_errorflags_fast_dist\n",
      "mms1_dis_compressionloss_fast_dist\n",
      "mms1_dis_errorflags_fast_dist_flagbars_dist\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:24:22: Downloading mms1_fgm_srvy_l2_20170910_v5.104.0.cdf to pydata/mms1/fgm/srvy/l2/2017/09\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_fgm_b_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_dmpa_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_bcs_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_flag_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_hirange_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_bdeltahalf_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_stemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_etemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_mode_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_rdeltahalf_srvy_l2\n",
      "Loaded variables:\n",
      "Epoch\n",
      "mms1_fgm_b_gse_srvy_l2\n",
      "mms1_fgm_b_gsm_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2\n",
      "mms1_fgm_b_bcs_srvy_l2\n",
      "mms1_fgm_flag_srvy_l2\n",
      "Epoch_state\n",
      "mms1_fgm_hirange_srvy_l2\n",
      "mms1_fgm_bdeltahalf_srvy_l2\n",
      "mms1_fgm_stemp_srvy_l2\n",
      "mms1_fgm_etemp_srvy_l2\n",
      "mms1_fgm_mode_srvy_l2\n",
      "mms1_fgm_rdeltahalf_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2_bvec\n",
      "mms1_fgm_b_dmpa_srvy_l2_btot\n",
      "mms1_fgm_b_gse_srvy_l2_bvec\n",
      "mms1_fgm_b_gse_srvy_l2_btot\n",
      "mms1_fgm_b_gsm_srvy_l2_bvec\n",
      "mms1_fgm_b_gsm_srvy_l2_btot\n",
      "mms1_fgm_b_bcs_srvy_l2_bvec\n",
      "mms1_fgm_b_bcs_srvy_l2_btot\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:24:41: Downloading mms1_fpi_fast_l2_dis-moms_20170910080000_v3.3.0.cdf to pydata/mms1/fpi/fast/l2/dis-moms/2017/09\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_dis_errorflags_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_count_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_angle_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_px_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_mx_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_py_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_my_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_pz_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_mz_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_omni_fast\n",
      "Time clip was applied to: mms1_dis_spectr_bg_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_bg_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_err_fast\n",
      "Time clip was applied to: mms1_dis_densityextrapolation_low_fast\n",
      "Time clip was applied to: mms1_dis_densityextrapolation_high_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_spintone_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_gse_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_spintone_gse_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_err_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_gse_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_err_fast\n",
      "Time clip was applied to: mms1_dis_pres_bg_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_gse_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_err_fast\n",
      "Time clip was applied to: mms1_dis_heatq_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_heatq_gse_fast\n",
      "Time clip was applied to: mms1_dis_heatq_err_fast\n",
      "Time clip was applied to: mms1_dis_energy_fast\n",
      "Time clip was applied to: mms1_dis_energy_delta_fast\n",
      "Time clip was applied to: mms1_dis_temppara_fast\n",
      "Time clip was applied to: mms1_dis_tempperp_fast\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_plus_var\n",
      "Epoch_minus_var\n",
      "mms1_dis_errorflags_fast\n",
      "mms1_dis_compressionloss_fast\n",
      "mms1_dis_startdelphi_count_fast\n",
      "mms1_dis_startdelphi_angle_fast\n",
      "mms1_dis_energyspectr_px_fast\n",
      "mms1_dis_energyspectr_mx_fast\n",
      "mms1_dis_energyspectr_py_fast\n",
      "mms1_dis_energyspectr_my_fast\n",
      "mms1_dis_energyspectr_pz_fast\n",
      "mms1_dis_energyspectr_mz_fast\n",
      "mms1_dis_energyspectr_omni_fast\n",
      "mms1_dis_spectr_bg_fast\n",
      "mms1_dis_numberdensity_bg_fast\n",
      "mms1_dis_numberdensity_fast\n",
      "mms1_dis_numberdensity_err_fast\n",
      "mms1_dis_densityextrapolation_low_fast\n",
      "mms1_dis_densityextrapolation_high_fast\n",
      "mms1_dis_bulkv_dbcs_fast\n",
      "mms1_dis_bulkv_spintone_dbcs_fast\n",
      "mms1_dis_bulkv_gse_fast\n",
      "mms1_dis_bulkv_spintone_gse_fast\n",
      "mms1_dis_bulkv_err_fast\n",
      "mms1_dis_prestensor_dbcs_fast\n",
      "mms1_dis_prestensor_gse_fast\n",
      "mms1_dis_prestensor_err_fast\n",
      "mms1_dis_pres_bg_fast\n",
      "mms1_dis_temptensor_dbcs_fast\n",
      "mms1_dis_temptensor_gse_fast\n",
      "mms1_dis_temptensor_err_fast\n",
      "mms1_dis_heatq_dbcs_fast\n",
      "mms1_dis_heatq_gse_fast\n",
      "mms1_dis_heatq_err_fast\n",
      "mms1_dis_energy_fast\n",
      "mms1_dis_energy_delta_fast\n",
      "mms1_dis_temppara_fast\n",
      "mms1_dis_tempperp_fast\n",
      "mms1_dis_errorflags_fast_moms\n",
      "mms1_dis_compressionloss_fast_moms\n",
      "mms1_dis_errorflags_fast_moms_flagbars_full\n",
      "mms1_dis_errorflags_fast_moms_flagbars_main\n",
      "mms1_dis_errorflags_fast_moms_flagbars_others\n",
      "mms1_dis_errorflags_fast_moms_flagbars_mini\n",
      "Averaging mms1_fgm_b_gse_srvy_l2_bvec\n",
      "Averaging mms1_dis_bulkv_gse_fast\n",
      "Aligning slice plane to: bv\n",
      "Finished slice at 2017-09-10 09:32:19.446664\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Return the slice data structure (instead of plotting) by setting the `return_slice` keyword to `True`"
   ],
   "metadata": {
    "id": "kgwe7HWyqEPL"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "the_slice = mms_part_slice2d(return_slice=True, time=time, instrument='fpi', species='i', rotation='bv', erange=[0, 300])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oGwQ2sRBqJr0",
    "outputId": "51473df8-906f-4aea-a7fb-db9f34edbfa2"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:29:15: Loading pydata/mms1/fpi/fast/l2/dis-dist/2017/09/mms1_fpi_fast_l2_dis-dist_20170910080000_v3.3.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_dis_errorflags_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_count_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_angle_fast\n",
      "Time clip was applied to: mms1_dis_dist_fast\n",
      "Time clip was applied to: mms1_dis_disterr_fast\n",
      "Time clip was applied to: mms1_dis_energy_fast\n",
      "Time clip was applied to: mms1_dis_energy_delta_fast\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_plus_var\n",
      "Epoch_minus_var\n",
      "mms1_dis_errorflags_fast\n",
      "mms1_dis_compressionloss_fast\n",
      "mms1_dis_startdelphi_count_fast\n",
      "mms1_dis_startdelphi_angle_fast\n",
      "mms1_dis_dist_fast\n",
      "mms1_dis_disterr_fast\n",
      "mms1_dis_theta_fast\n",
      "mms1_dis_theta_delta_fast\n",
      "mms1_dis_energy_fast\n",
      "mms1_dis_energy_delta_fast\n",
      "mms1_dis_phi_fast\n",
      "mms1_dis_phi_delta_fast\n",
      "mms1_dis_errorflags_fast_dist\n",
      "mms1_dis_compressionloss_fast_dist\n",
      "mms1_dis_errorflags_fast_dist_flagbars_dist\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:29:18: Loading pydata/mms1/fgm/srvy/l2/2017/09/mms1_fgm_srvy_l2_20170910_v5.104.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_fgm_b_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_dmpa_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_bcs_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_flag_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_hirange_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_bdeltahalf_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_stemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_etemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_mode_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_rdeltahalf_srvy_l2\n",
      "Loaded variables:\n",
      "Epoch\n",
      "mms1_fgm_b_gse_srvy_l2\n",
      "mms1_fgm_b_gsm_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2\n",
      "mms1_fgm_b_bcs_srvy_l2\n",
      "mms1_fgm_flag_srvy_l2\n",
      "Epoch_state\n",
      "mms1_fgm_hirange_srvy_l2\n",
      "mms1_fgm_bdeltahalf_srvy_l2\n",
      "mms1_fgm_stemp_srvy_l2\n",
      "mms1_fgm_etemp_srvy_l2\n",
      "mms1_fgm_mode_srvy_l2\n",
      "mms1_fgm_rdeltahalf_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2_bvec\n",
      "mms1_fgm_b_dmpa_srvy_l2_btot\n",
      "mms1_fgm_b_gse_srvy_l2_bvec\n",
      "mms1_fgm_b_gse_srvy_l2_btot\n",
      "mms1_fgm_b_gsm_srvy_l2_bvec\n",
      "mms1_fgm_b_gsm_srvy_l2_btot\n",
      "mms1_fgm_b_bcs_srvy_l2_bvec\n",
      "mms1_fgm_b_bcs_srvy_l2_btot\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:29:33: Loading pydata/mms1/fpi/fast/l2/dis-moms/2017/09/mms1_fpi_fast_l2_dis-moms_20170910080000_v3.3.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_dis_errorflags_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_count_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_angle_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_px_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_mx_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_py_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_my_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_pz_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_mz_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_omni_fast\n",
      "Time clip was applied to: mms1_dis_spectr_bg_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_bg_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_err_fast\n",
      "Time clip was applied to: mms1_dis_densityextrapolation_low_fast\n",
      "Time clip was applied to: mms1_dis_densityextrapolation_high_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_spintone_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_gse_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_spintone_gse_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_err_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_gse_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_err_fast\n",
      "Time clip was applied to: mms1_dis_pres_bg_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_gse_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_err_fast\n",
      "Time clip was applied to: mms1_dis_heatq_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_heatq_gse_fast\n",
      "Time clip was applied to: mms1_dis_heatq_err_fast\n",
      "Time clip was applied to: mms1_dis_energy_fast\n",
      "Time clip was applied to: mms1_dis_energy_delta_fast\n",
      "Time clip was applied to: mms1_dis_temppara_fast\n",
      "Time clip was applied to: mms1_dis_tempperp_fast\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_plus_var\n",
      "Epoch_minus_var\n",
      "mms1_dis_errorflags_fast\n",
      "mms1_dis_compressionloss_fast\n",
      "mms1_dis_startdelphi_count_fast\n",
      "mms1_dis_startdelphi_angle_fast\n",
      "mms1_dis_energyspectr_px_fast\n",
      "mms1_dis_energyspectr_mx_fast\n",
      "mms1_dis_energyspectr_py_fast\n",
      "mms1_dis_energyspectr_my_fast\n",
      "mms1_dis_energyspectr_pz_fast\n",
      "mms1_dis_energyspectr_mz_fast\n",
      "mms1_dis_energyspectr_omni_fast\n",
      "mms1_dis_spectr_bg_fast\n",
      "mms1_dis_numberdensity_bg_fast\n",
      "mms1_dis_numberdensity_fast\n",
      "mms1_dis_numberdensity_err_fast\n",
      "mms1_dis_densityextrapolation_low_fast\n",
      "mms1_dis_densityextrapolation_high_fast\n",
      "mms1_dis_bulkv_dbcs_fast\n",
      "mms1_dis_bulkv_spintone_dbcs_fast\n",
      "mms1_dis_bulkv_gse_fast\n",
      "mms1_dis_bulkv_spintone_gse_fast\n",
      "mms1_dis_bulkv_err_fast\n",
      "mms1_dis_prestensor_dbcs_fast\n",
      "mms1_dis_prestensor_gse_fast\n",
      "mms1_dis_prestensor_err_fast\n",
      "mms1_dis_pres_bg_fast\n",
      "mms1_dis_temptensor_dbcs_fast\n",
      "mms1_dis_temptensor_gse_fast\n",
      "mms1_dis_temptensor_err_fast\n",
      "mms1_dis_heatq_dbcs_fast\n",
      "mms1_dis_heatq_gse_fast\n",
      "mms1_dis_heatq_err_fast\n",
      "mms1_dis_energy_fast\n",
      "mms1_dis_energy_delta_fast\n",
      "mms1_dis_temppara_fast\n",
      "mms1_dis_tempperp_fast\n",
      "mms1_dis_errorflags_fast_moms\n",
      "mms1_dis_compressionloss_fast_moms\n",
      "mms1_dis_errorflags_fast_moms_flagbars_full\n",
      "mms1_dis_errorflags_fast_moms_flagbars_main\n",
      "mms1_dis_errorflags_fast_moms_flagbars_others\n",
      "mms1_dis_errorflags_fast_moms_flagbars_mini\n",
      "Averaging mms1_fgm_b_gse_srvy_l2_bvec\n",
      "Averaging mms1_dis_bulkv_gse_fast\n",
      "Aligning slice plane to: bv\n",
      "Finished slice at 2017-09-10 09:32:19.446664\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "The slice is stored as a dictionary:"
   ],
   "metadata": {
    "id": "bTaAayTkqcLN"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "the_slice.keys()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pmvtBaekqiIU",
    "outputId": "1081b0ae-bba9-4cfa-d6af-57764bffcff6"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict_keys(['project_name', 'spacecraft', 'data_name', 'units_name', 'species', 'xyunits', 'rotation', 'energy', 'trange', 'zrange', 'rrange', 'rlog', 'n_samples', 'data', 'xgrid', 'ygrid'])"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "You can also create 1D cuts through the 2D slice:"
   ],
   "metadata": {
    "id": "aBWrAag8qmYX"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from pyspedas.particles.spd_slice2d.slice1d_plot import plot\n",
    "\n",
    "plot(the_slice, 'x', [-100, 100])  # summed from Vv=[-100, 100]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "ZgQfhlK9qpvF",
    "outputId": "ae66a6ed-d59a-4579-b20d-6bb210dd45a8"
   },
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## FPI ions with 2D interpolation\n",
    "\n",
    "The data are rotated such that the x axis is parallel to B field and the bulk velocity defines the x-y plane, and plotted using 2D interpolation (data points within the specified theta or z-axis range are projected onto the slice plane and linearly interpolated onto a regular 2D grid). The default theta range is [-20, +20]"
   ],
   "metadata": {
    "id": "mQ1McrYV-tdR"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "mms_part_slice2d(interpolation='2d', time=time, instrument='fpi', species='i', rotation='bv', erange=[0, 300])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "l8yijdFw-sXw",
    "outputId": "de8e159b-18fb-41ed-8a35-f90a895e1ce6"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:24:53: Loading pydata/mms1/fpi/fast/l2/dis-dist/2017/09/mms1_fpi_fast_l2_dis-dist_20170910080000_v3.3.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_dis_errorflags_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_count_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_angle_fast\n",
      "Time clip was applied to: mms1_dis_dist_fast\n",
      "Time clip was applied to: mms1_dis_disterr_fast\n",
      "Time clip was applied to: mms1_dis_energy_fast\n",
      "Time clip was applied to: mms1_dis_energy_delta_fast\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_plus_var\n",
      "Epoch_minus_var\n",
      "mms1_dis_errorflags_fast\n",
      "mms1_dis_compressionloss_fast\n",
      "mms1_dis_startdelphi_count_fast\n",
      "mms1_dis_startdelphi_angle_fast\n",
      "mms1_dis_dist_fast\n",
      "mms1_dis_disterr_fast\n",
      "mms1_dis_theta_fast\n",
      "mms1_dis_theta_delta_fast\n",
      "mms1_dis_energy_fast\n",
      "mms1_dis_energy_delta_fast\n",
      "mms1_dis_phi_fast\n",
      "mms1_dis_phi_delta_fast\n",
      "mms1_dis_errorflags_fast_dist\n",
      "mms1_dis_compressionloss_fast_dist\n",
      "mms1_dis_errorflags_fast_dist_flagbars_dist\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:24:56: Loading pydata/mms1/fgm/srvy/l2/2017/09/mms1_fgm_srvy_l2_20170910_v5.104.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_fgm_b_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_dmpa_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_bcs_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_flag_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_hirange_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_bdeltahalf_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_stemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_etemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_mode_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_rdeltahalf_srvy_l2\n",
      "Loaded variables:\n",
      "Epoch\n",
      "mms1_fgm_b_gse_srvy_l2\n",
      "mms1_fgm_b_gsm_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2\n",
      "mms1_fgm_b_bcs_srvy_l2\n",
      "mms1_fgm_flag_srvy_l2\n",
      "Epoch_state\n",
      "mms1_fgm_hirange_srvy_l2\n",
      "mms1_fgm_bdeltahalf_srvy_l2\n",
      "mms1_fgm_stemp_srvy_l2\n",
      "mms1_fgm_etemp_srvy_l2\n",
      "mms1_fgm_mode_srvy_l2\n",
      "mms1_fgm_rdeltahalf_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2_bvec\n",
      "mms1_fgm_b_dmpa_srvy_l2_btot\n",
      "mms1_fgm_b_gse_srvy_l2_bvec\n",
      "mms1_fgm_b_gse_srvy_l2_btot\n",
      "mms1_fgm_b_gsm_srvy_l2_bvec\n",
      "mms1_fgm_b_gsm_srvy_l2_btot\n",
      "mms1_fgm_b_bcs_srvy_l2_bvec\n",
      "mms1_fgm_b_bcs_srvy_l2_btot\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:25:13: Loading pydata/mms1/fpi/fast/l2/dis-moms/2017/09/mms1_fpi_fast_l2_dis-moms_20170910080000_v3.3.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_dis_errorflags_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_count_fast\n",
      "Time clip was applied to: mms1_dis_startdelphi_angle_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_px_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_mx_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_py_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_my_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_pz_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_mz_fast\n",
      "Time clip was applied to: mms1_dis_energyspectr_omni_fast\n",
      "Time clip was applied to: mms1_dis_spectr_bg_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_bg_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_fast\n",
      "Time clip was applied to: mms1_dis_numberdensity_err_fast\n",
      "Time clip was applied to: mms1_dis_densityextrapolation_low_fast\n",
      "Time clip was applied to: mms1_dis_densityextrapolation_high_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_spintone_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_gse_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_spintone_gse_fast\n",
      "Time clip was applied to: mms1_dis_bulkv_err_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_gse_fast\n",
      "Time clip was applied to: mms1_dis_prestensor_err_fast\n",
      "Time clip was applied to: mms1_dis_pres_bg_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_gse_fast\n",
      "Time clip was applied to: mms1_dis_temptensor_err_fast\n",
      "Time clip was applied to: mms1_dis_heatq_dbcs_fast\n",
      "Time clip was applied to: mms1_dis_heatq_gse_fast\n",
      "Time clip was applied to: mms1_dis_heatq_err_fast\n",
      "Time clip was applied to: mms1_dis_energy_fast\n",
      "Time clip was applied to: mms1_dis_energy_delta_fast\n",
      "Time clip was applied to: mms1_dis_temppara_fast\n",
      "Time clip was applied to: mms1_dis_tempperp_fast\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "That name is currently not in pytplot\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_plus_var\n",
      "Epoch_minus_var\n",
      "mms1_dis_errorflags_fast\n",
      "mms1_dis_compressionloss_fast\n",
      "mms1_dis_startdelphi_count_fast\n",
      "mms1_dis_startdelphi_angle_fast\n",
      "mms1_dis_energyspectr_px_fast\n",
      "mms1_dis_energyspectr_mx_fast\n",
      "mms1_dis_energyspectr_py_fast\n",
      "mms1_dis_energyspectr_my_fast\n",
      "mms1_dis_energyspectr_pz_fast\n",
      "mms1_dis_energyspectr_mz_fast\n",
      "mms1_dis_energyspectr_omni_fast\n",
      "mms1_dis_spectr_bg_fast\n",
      "mms1_dis_numberdensity_bg_fast\n",
      "mms1_dis_numberdensity_fast\n",
      "mms1_dis_numberdensity_err_fast\n",
      "mms1_dis_densityextrapolation_low_fast\n",
      "mms1_dis_densityextrapolation_high_fast\n",
      "mms1_dis_bulkv_dbcs_fast\n",
      "mms1_dis_bulkv_spintone_dbcs_fast\n",
      "mms1_dis_bulkv_gse_fast\n",
      "mms1_dis_bulkv_spintone_gse_fast\n",
      "mms1_dis_bulkv_err_fast\n",
      "mms1_dis_prestensor_dbcs_fast\n",
      "mms1_dis_prestensor_gse_fast\n",
      "mms1_dis_prestensor_err_fast\n",
      "mms1_dis_pres_bg_fast\n",
      "mms1_dis_temptensor_dbcs_fast\n",
      "mms1_dis_temptensor_gse_fast\n",
      "mms1_dis_temptensor_err_fast\n",
      "mms1_dis_heatq_dbcs_fast\n",
      "mms1_dis_heatq_gse_fast\n",
      "mms1_dis_heatq_err_fast\n",
      "mms1_dis_energy_fast\n",
      "mms1_dis_energy_delta_fast\n",
      "mms1_dis_temppara_fast\n",
      "mms1_dis_tempperp_fast\n",
      "mms1_dis_errorflags_fast_moms\n",
      "mms1_dis_compressionloss_fast_moms\n",
      "mms1_dis_errorflags_fast_moms_flagbars_full\n",
      "mms1_dis_errorflags_fast_moms_flagbars_main\n",
      "mms1_dis_errorflags_fast_moms_flagbars_others\n",
      "mms1_dis_errorflags_fast_moms_flagbars_mini\n",
      "Averaging mms1_fgm_b_gse_srvy_l2_bvec\n",
      "Averaging mms1_dis_bulkv_gse_fast\n",
      "Aligning slice plane to: bv\n",
      "Finished slice at 2017-09-10 09:32:19.446664\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## HPCA H+ with geometric interpolation\n",
    "\n",
    "Same as above, but with HPCA H+ data"
   ],
   "metadata": {
    "id": "erJiIaBi_LVA"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "mms_part_slice2d(time=time, instrument='hpca', species='hplus', rotation='bv', erange=[0, 300])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "YsAk42vLuZKk",
    "outputId": "7592268b-99b2-4449-9498-0e104dc5bcf7"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:25:15: Downloading mms1_hpca_srvy_l2_ion_20170910060000_v4.1.0.cdf to pydata/mms1/hpca/srvy/l2/ion/2017/09\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_hpca_azimuth_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_polar_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_energy_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_sweep_table_number\n",
      "Time clip was applied to: mms1_hpca_start_azimuth\n",
      "Time clip was applied to: mms1_hpca_science_mode\n",
      "Time clip was applied to: mms1_hpca_hplus_flux\n",
      "Time clip was applied to: mms1_hpca_hplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_heplus_flux\n",
      "Time clip was applied to: mms1_hpca_heplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_heplusplus_flux\n",
      "Time clip was applied to: mms1_hpca_heplusplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_oplus_flux\n",
      "Time clip was applied to: mms1_hpca_oplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_B_GSE_sweep_avg\n",
      "Time clip was applied to: mms1_hpca_B_GSM_sweep_avg\n",
      "Time clip was applied to: mms1_hpca_azimuth_angles_degrees\n",
      "Time clip was applied to: mms1_hpca_azimuth_angles_per_ev_degrees\n",
      "Time clip was applied to: mms1_hpca_MCP_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_TOF_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_decimation_factor_index\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_MINUS\n",
      "Epoch_PLUS\n",
      "mms1_hpca_polar_anode_number\n",
      "mms1_hpca_azimuth_decimation_factor\n",
      "mms1_hpca_polar_decimation_factor\n",
      "mms1_hpca_energy_decimation_factor\n",
      "mms1_hpca_sweep_table_number\n",
      "mms1_hpca_start_azimuth\n",
      "mms1_hpca_science_mode\n",
      "mms1_hpca_ion_energy\n",
      "mms1_hpca_hplus_flux\n",
      "mms1_hpca_hplus_phase_space_density\n",
      "mms1_hpca_heplus_flux\n",
      "mms1_hpca_heplus_phase_space_density\n",
      "mms1_hpca_heplusplus_flux\n",
      "mms1_hpca_heplusplus_phase_space_density\n",
      "mms1_hpca_oplus_flux\n",
      "mms1_hpca_oplus_phase_space_density\n",
      "mms1_hpca_B_GSE_sweep_avg\n",
      "mms1_hpca_B_GSM_sweep_avg\n",
      "Epoch_Angles\n",
      "Epoch_MINUS_Angles\n",
      "Epoch_PLUS_Angles\n",
      "mms1_hpca_azimuth_angles_degrees\n",
      "mms1_hpca_azimuth_angles_per_ev_degrees\n",
      "mms1_hpca_centroid_elevation_angle\n",
      "mms1_hpca_azimuth_index\n",
      "mms1_hpca_energy_step_number\n",
      "mms1_hpca_MCP_VMON_MIN_converted\n",
      "mms1_hpca_TOF_VMON_MIN_converted\n",
      "mms1_hpca_decimation_factor_index\n",
      "mms1_hpca_acquisition_time\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:25:38: Loading pydata/mms1/fgm/srvy/l2/2017/09/mms1_fgm_srvy_l2_20170910_v5.104.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_fgm_b_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_dmpa_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_bcs_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_flag_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_hirange_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_bdeltahalf_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_stemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_etemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_mode_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_rdeltahalf_srvy_l2\n",
      "Loaded variables:\n",
      "Epoch\n",
      "mms1_fgm_b_gse_srvy_l2\n",
      "mms1_fgm_b_gsm_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2\n",
      "mms1_fgm_b_bcs_srvy_l2\n",
      "mms1_fgm_flag_srvy_l2\n",
      "Epoch_state\n",
      "mms1_fgm_hirange_srvy_l2\n",
      "mms1_fgm_bdeltahalf_srvy_l2\n",
      "mms1_fgm_stemp_srvy_l2\n",
      "mms1_fgm_etemp_srvy_l2\n",
      "mms1_fgm_mode_srvy_l2\n",
      "mms1_fgm_rdeltahalf_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2_bvec\n",
      "mms1_fgm_b_dmpa_srvy_l2_btot\n",
      "mms1_fgm_b_gse_srvy_l2_bvec\n",
      "mms1_fgm_b_gse_srvy_l2_btot\n",
      "mms1_fgm_b_gsm_srvy_l2_bvec\n",
      "mms1_fgm_b_gsm_srvy_l2_btot\n",
      "mms1_fgm_b_bcs_srvy_l2_bvec\n",
      "mms1_fgm_b_bcs_srvy_l2_btot\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:25:53: Downloading mms1_hpca_srvy_l2_moments_20170910060000_v4.1.0.cdf to pydata/mms1/hpca/srvy/l2/moments/2017/09\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_hpca_spin_number\n",
      "Time clip was applied to: mms1_hpca_sweep_table_number\n",
      "Time clip was applied to: mms1_hpca_science_mode\n",
      "Time clip was applied to: mms1_hpca_hplus_number_density\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_hplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_hplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_heplus_number_density\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_heplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_heplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_heplusplus_number_density\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_heplusplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_heplusplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_oplus_number_density\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_oplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_oplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_B_GSE_spin_avg\n",
      "Time clip was applied to: mms1_hpca_B_GSM_spin_avg\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_hplus_tperp\n",
      "Time clip was applied to: mms1_hpca_hplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_hplus_vperp\n",
      "Time clip was applied to: mms1_hpca_hplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_hplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_hplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_tperp\n",
      "Time clip was applied to: mms1_hpca_heplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_heplus_vperp\n",
      "Time clip was applied to: mms1_hpca_heplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_heplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_tperp\n",
      "Time clip was applied to: mms1_hpca_heplusplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vperp\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_tperp\n",
      "Time clip was applied to: mms1_hpca_oplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_oplus_vperp\n",
      "Time clip was applied to: mms1_hpca_oplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_oplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_MCP_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_TOF_VMON_MIN_converted\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_MINUS\n",
      "Epoch_PLUS\n",
      "mms1_hpca_spin_number\n",
      "mms1_hpca_ion_energy\n",
      "mms1_hpca_sweep_table_number\n",
      "mms1_hpca_science_mode\n",
      "mms1_hpca_hplus_number_density\n",
      "mms1_hpca_hplus_ion_bulk_velocity\n",
      "mms1_hpca_hplus_scalar_temperature\n",
      "mms1_hpca_hplus_ion_pressure\n",
      "mms1_hpca_hplus_temperature_tensor\n",
      "mms1_hpca_heplus_number_density\n",
      "mms1_hpca_heplus_ion_bulk_velocity\n",
      "mms1_hpca_heplus_scalar_temperature\n",
      "mms1_hpca_heplus_ion_pressure\n",
      "mms1_hpca_heplus_temperature_tensor\n",
      "mms1_hpca_heplusplus_number_density\n",
      "mms1_hpca_heplusplus_ion_bulk_velocity\n",
      "mms1_hpca_heplusplus_scalar_temperature\n",
      "mms1_hpca_heplusplus_ion_pressure\n",
      "mms1_hpca_heplusplus_temperature_tensor\n",
      "mms1_hpca_oplus_number_density\n",
      "mms1_hpca_oplus_ion_bulk_velocity\n",
      "mms1_hpca_oplus_scalar_temperature\n",
      "mms1_hpca_oplus_ion_pressure\n",
      "mms1_hpca_oplus_temperature_tensor\n",
      "mms1_hpca_B_GSE_spin_avg\n",
      "mms1_hpca_B_GSM_spin_avg\n",
      "mms1_hpca_hplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_hplus_tperp\n",
      "mms1_hpca_hplus_tparallel\n",
      "mms1_hpca_hplus_vperp\n",
      "mms1_hpca_hplus_vparallel\n",
      "mms1_hpca_hplus_vperp_GSM\n",
      "mms1_hpca_hplus_vparallel_GSM\n",
      "mms1_hpca_heplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_heplus_tperp\n",
      "mms1_hpca_heplus_tparallel\n",
      "mms1_hpca_heplus_vperp\n",
      "mms1_hpca_heplus_vparallel\n",
      "mms1_hpca_heplus_vperp_GSM\n",
      "mms1_hpca_heplus_vparallel_GSM\n",
      "mms1_hpca_heplusplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_heplusplus_tperp\n",
      "mms1_hpca_heplusplus_tparallel\n",
      "mms1_hpca_heplusplus_vperp\n",
      "mms1_hpca_heplusplus_vparallel\n",
      "mms1_hpca_heplusplus_vperp_GSM\n",
      "mms1_hpca_heplusplus_vparallel_GSM\n",
      "mms1_hpca_oplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_oplus_tperp\n",
      "mms1_hpca_oplus_tparallel\n",
      "mms1_hpca_oplus_vperp\n",
      "mms1_hpca_oplus_vparallel\n",
      "mms1_hpca_oplus_vperp_GSM\n",
      "mms1_hpca_oplus_vparallel_GSM\n",
      "mms1_hpca_MCP_VMON_MIN_converted\n",
      "mms1_hpca_TOF_VMON_MIN_converted\n",
      "Averaging mms1_fgm_b_gse_srvy_l2_bvec\n",
      "Averaging mms1_hpca_hplus_ion_bulk_velocity\n",
      "Aligning slice plane to: bv\n",
      "Finished slice at 2017-09-10 09:32:17.059004\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## HPCA H+ with 2d interpolation\n",
    "\n",
    "and with 2D interpolation. Note that with 2D interpolation, smoothing is enabled by default and set to 7 points; you can override this with the smooth keyword"
   ],
   "metadata": {
    "id": "e_U_9yNU_c2S"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "mms_part_slice2d(interpolation='2d', time=time, instrument='hpca', species='hplus', rotation='bv', erange=[0, 300])"
   ],
   "metadata": {
    "id": "0BpIKxJGur-F",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "outputId": "1ac6feaf-b7f4-4ad3-a1a4-68159026aeab"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:25:58: Loading pydata/mms1/hpca/srvy/l2/ion/2017/09/mms1_hpca_srvy_l2_ion_20170910060000_v4.1.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_hpca_azimuth_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_polar_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_energy_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_sweep_table_number\n",
      "Time clip was applied to: mms1_hpca_start_azimuth\n",
      "Time clip was applied to: mms1_hpca_science_mode\n",
      "Time clip was applied to: mms1_hpca_hplus_flux\n",
      "Time clip was applied to: mms1_hpca_hplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_heplus_flux\n",
      "Time clip was applied to: mms1_hpca_heplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_heplusplus_flux\n",
      "Time clip was applied to: mms1_hpca_heplusplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_oplus_flux\n",
      "Time clip was applied to: mms1_hpca_oplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_B_GSE_sweep_avg\n",
      "Time clip was applied to: mms1_hpca_B_GSM_sweep_avg\n",
      "Time clip was applied to: mms1_hpca_azimuth_angles_degrees\n",
      "Time clip was applied to: mms1_hpca_azimuth_angles_per_ev_degrees\n",
      "Time clip was applied to: mms1_hpca_MCP_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_TOF_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_decimation_factor_index\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_MINUS\n",
      "Epoch_PLUS\n",
      "mms1_hpca_polar_anode_number\n",
      "mms1_hpca_azimuth_decimation_factor\n",
      "mms1_hpca_polar_decimation_factor\n",
      "mms1_hpca_energy_decimation_factor\n",
      "mms1_hpca_sweep_table_number\n",
      "mms1_hpca_start_azimuth\n",
      "mms1_hpca_science_mode\n",
      "mms1_hpca_ion_energy\n",
      "mms1_hpca_hplus_flux\n",
      "mms1_hpca_hplus_phase_space_density\n",
      "mms1_hpca_heplus_flux\n",
      "mms1_hpca_heplus_phase_space_density\n",
      "mms1_hpca_heplusplus_flux\n",
      "mms1_hpca_heplusplus_phase_space_density\n",
      "mms1_hpca_oplus_flux\n",
      "mms1_hpca_oplus_phase_space_density\n",
      "mms1_hpca_B_GSE_sweep_avg\n",
      "mms1_hpca_B_GSM_sweep_avg\n",
      "Epoch_Angles\n",
      "Epoch_MINUS_Angles\n",
      "Epoch_PLUS_Angles\n",
      "mms1_hpca_azimuth_angles_degrees\n",
      "mms1_hpca_azimuth_angles_per_ev_degrees\n",
      "mms1_hpca_centroid_elevation_angle\n",
      "mms1_hpca_azimuth_index\n",
      "mms1_hpca_energy_step_number\n",
      "mms1_hpca_MCP_VMON_MIN_converted\n",
      "mms1_hpca_TOF_VMON_MIN_converted\n",
      "mms1_hpca_decimation_factor_index\n",
      "mms1_hpca_acquisition_time\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:26:16: Loading pydata/mms1/fgm/srvy/l2/2017/09/mms1_fgm_srvy_l2_20170910_v5.104.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_fgm_b_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_dmpa_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_bcs_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_flag_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_hirange_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_bdeltahalf_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_stemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_etemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_mode_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_rdeltahalf_srvy_l2\n",
      "Loaded variables:\n",
      "Epoch\n",
      "mms1_fgm_b_gse_srvy_l2\n",
      "mms1_fgm_b_gsm_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2\n",
      "mms1_fgm_b_bcs_srvy_l2\n",
      "mms1_fgm_flag_srvy_l2\n",
      "Epoch_state\n",
      "mms1_fgm_hirange_srvy_l2\n",
      "mms1_fgm_bdeltahalf_srvy_l2\n",
      "mms1_fgm_stemp_srvy_l2\n",
      "mms1_fgm_etemp_srvy_l2\n",
      "mms1_fgm_mode_srvy_l2\n",
      "mms1_fgm_rdeltahalf_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2_bvec\n",
      "mms1_fgm_b_dmpa_srvy_l2_btot\n",
      "mms1_fgm_b_gse_srvy_l2_bvec\n",
      "mms1_fgm_b_gse_srvy_l2_btot\n",
      "mms1_fgm_b_gsm_srvy_l2_bvec\n",
      "mms1_fgm_b_gsm_srvy_l2_btot\n",
      "mms1_fgm_b_bcs_srvy_l2_bvec\n",
      "mms1_fgm_b_bcs_srvy_l2_btot\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:26:31: Loading pydata/mms1/hpca/srvy/l2/moments/2017/09/mms1_hpca_srvy_l2_moments_20170910060000_v4.1.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_hpca_spin_number\n",
      "Time clip was applied to: mms1_hpca_sweep_table_number\n",
      "Time clip was applied to: mms1_hpca_science_mode\n",
      "Time clip was applied to: mms1_hpca_hplus_number_density\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_hplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_hplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_heplus_number_density\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_heplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_heplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_heplusplus_number_density\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_heplusplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_heplusplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_oplus_number_density\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_oplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_oplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_B_GSE_spin_avg\n",
      "Time clip was applied to: mms1_hpca_B_GSM_spin_avg\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_hplus_tperp\n",
      "Time clip was applied to: mms1_hpca_hplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_hplus_vperp\n",
      "Time clip was applied to: mms1_hpca_hplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_hplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_hplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_tperp\n",
      "Time clip was applied to: mms1_hpca_heplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_heplus_vperp\n",
      "Time clip was applied to: mms1_hpca_heplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_heplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_tperp\n",
      "Time clip was applied to: mms1_hpca_heplusplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vperp\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_tperp\n",
      "Time clip was applied to: mms1_hpca_oplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_oplus_vperp\n",
      "Time clip was applied to: mms1_hpca_oplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_oplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_MCP_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_TOF_VMON_MIN_converted\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_MINUS\n",
      "Epoch_PLUS\n",
      "mms1_hpca_spin_number\n",
      "mms1_hpca_ion_energy\n",
      "mms1_hpca_sweep_table_number\n",
      "mms1_hpca_science_mode\n",
      "mms1_hpca_hplus_number_density\n",
      "mms1_hpca_hplus_ion_bulk_velocity\n",
      "mms1_hpca_hplus_scalar_temperature\n",
      "mms1_hpca_hplus_ion_pressure\n",
      "mms1_hpca_hplus_temperature_tensor\n",
      "mms1_hpca_heplus_number_density\n",
      "mms1_hpca_heplus_ion_bulk_velocity\n",
      "mms1_hpca_heplus_scalar_temperature\n",
      "mms1_hpca_heplus_ion_pressure\n",
      "mms1_hpca_heplus_temperature_tensor\n",
      "mms1_hpca_heplusplus_number_density\n",
      "mms1_hpca_heplusplus_ion_bulk_velocity\n",
      "mms1_hpca_heplusplus_scalar_temperature\n",
      "mms1_hpca_heplusplus_ion_pressure\n",
      "mms1_hpca_heplusplus_temperature_tensor\n",
      "mms1_hpca_oplus_number_density\n",
      "mms1_hpca_oplus_ion_bulk_velocity\n",
      "mms1_hpca_oplus_scalar_temperature\n",
      "mms1_hpca_oplus_ion_pressure\n",
      "mms1_hpca_oplus_temperature_tensor\n",
      "mms1_hpca_B_GSE_spin_avg\n",
      "mms1_hpca_B_GSM_spin_avg\n",
      "mms1_hpca_hplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_hplus_tperp\n",
      "mms1_hpca_hplus_tparallel\n",
      "mms1_hpca_hplus_vperp\n",
      "mms1_hpca_hplus_vparallel\n",
      "mms1_hpca_hplus_vperp_GSM\n",
      "mms1_hpca_hplus_vparallel_GSM\n",
      "mms1_hpca_heplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_heplus_tperp\n",
      "mms1_hpca_heplus_tparallel\n",
      "mms1_hpca_heplus_vperp\n",
      "mms1_hpca_heplus_vparallel\n",
      "mms1_hpca_heplus_vperp_GSM\n",
      "mms1_hpca_heplus_vparallel_GSM\n",
      "mms1_hpca_heplusplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_heplusplus_tperp\n",
      "mms1_hpca_heplusplus_tparallel\n",
      "mms1_hpca_heplusplus_vperp\n",
      "mms1_hpca_heplusplus_vparallel\n",
      "mms1_hpca_heplusplus_vperp_GSM\n",
      "mms1_hpca_heplusplus_vparallel_GSM\n",
      "mms1_hpca_oplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_oplus_tperp\n",
      "mms1_hpca_oplus_tparallel\n",
      "mms1_hpca_oplus_vperp\n",
      "mms1_hpca_oplus_vparallel\n",
      "mms1_hpca_oplus_vperp_GSM\n",
      "mms1_hpca_oplus_vparallel_GSM\n",
      "mms1_hpca_MCP_VMON_MIN_converted\n",
      "mms1_hpca_TOF_VMON_MIN_converted\n",
      "Averaging mms1_fgm_b_gse_srvy_l2_bvec\n",
      "Averaging mms1_hpca_hplus_ion_bulk_velocity\n",
      "Aligning slice plane to: bv\n",
      "Finished slice at 2017-09-10 09:32:17.059004\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Change the rotation such that the x axis is parallel to B field; the B x V(bulk) vector defines the x-y plane:"
   ],
   "metadata": {
    "id": "eMvaM5Njr9C_"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "mms_part_slice2d(interpolation='2d', time=time, instrument='hpca', species='hplus', rotation='be', erange=[0, 300])"
   ],
   "metadata": {
    "id": "uNJJ6eNG_gVT",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "outputId": "5278472a-2f9b-4d82-aa25-ce2c0bd29bf0"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:35:40: Loading pydata/mms1/hpca/srvy/l2/ion/2017/09/mms1_hpca_srvy_l2_ion_20170910060000_v4.1.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_hpca_azimuth_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_polar_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_energy_decimation_factor\n",
      "Time clip was applied to: mms1_hpca_sweep_table_number\n",
      "Time clip was applied to: mms1_hpca_start_azimuth\n",
      "Time clip was applied to: mms1_hpca_science_mode\n",
      "Time clip was applied to: mms1_hpca_hplus_flux\n",
      "Time clip was applied to: mms1_hpca_hplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_heplus_flux\n",
      "Time clip was applied to: mms1_hpca_heplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_heplusplus_flux\n",
      "Time clip was applied to: mms1_hpca_heplusplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_oplus_flux\n",
      "Time clip was applied to: mms1_hpca_oplus_phase_space_density\n",
      "Time clip was applied to: mms1_hpca_B_GSE_sweep_avg\n",
      "Time clip was applied to: mms1_hpca_B_GSM_sweep_avg\n",
      "Time clip was applied to: mms1_hpca_azimuth_angles_degrees\n",
      "Time clip was applied to: mms1_hpca_azimuth_angles_per_ev_degrees\n",
      "Time clip was applied to: mms1_hpca_MCP_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_TOF_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_decimation_factor_index\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_MINUS\n",
      "Epoch_PLUS\n",
      "mms1_hpca_polar_anode_number\n",
      "mms1_hpca_azimuth_decimation_factor\n",
      "mms1_hpca_polar_decimation_factor\n",
      "mms1_hpca_energy_decimation_factor\n",
      "mms1_hpca_sweep_table_number\n",
      "mms1_hpca_start_azimuth\n",
      "mms1_hpca_science_mode\n",
      "mms1_hpca_ion_energy\n",
      "mms1_hpca_hplus_flux\n",
      "mms1_hpca_hplus_phase_space_density\n",
      "mms1_hpca_heplus_flux\n",
      "mms1_hpca_heplus_phase_space_density\n",
      "mms1_hpca_heplusplus_flux\n",
      "mms1_hpca_heplusplus_phase_space_density\n",
      "mms1_hpca_oplus_flux\n",
      "mms1_hpca_oplus_phase_space_density\n",
      "mms1_hpca_B_GSE_sweep_avg\n",
      "mms1_hpca_B_GSM_sweep_avg\n",
      "Epoch_Angles\n",
      "Epoch_MINUS_Angles\n",
      "Epoch_PLUS_Angles\n",
      "mms1_hpca_azimuth_angles_degrees\n",
      "mms1_hpca_azimuth_angles_per_ev_degrees\n",
      "mms1_hpca_centroid_elevation_angle\n",
      "mms1_hpca_azimuth_index\n",
      "mms1_hpca_energy_step_number\n",
      "mms1_hpca_MCP_VMON_MIN_converted\n",
      "mms1_hpca_TOF_VMON_MIN_converted\n",
      "mms1_hpca_decimation_factor_index\n",
      "mms1_hpca_acquisition_time\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:35:58: Loading pydata/mms1/fgm/srvy/l2/2017/09/mms1_fgm_srvy_l2_20170910_v5.104.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_fgm_b_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_dmpa_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_b_bcs_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_flag_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gse_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_r_gsm_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_hirange_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_bdeltahalf_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_stemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_etemp_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_mode_srvy_l2\n",
      "Time clip was applied to: mms1_fgm_rdeltahalf_srvy_l2\n",
      "Loaded variables:\n",
      "Epoch\n",
      "mms1_fgm_b_gse_srvy_l2\n",
      "mms1_fgm_b_gsm_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2\n",
      "mms1_fgm_b_bcs_srvy_l2\n",
      "mms1_fgm_flag_srvy_l2\n",
      "Epoch_state\n",
      "mms1_fgm_hirange_srvy_l2\n",
      "mms1_fgm_bdeltahalf_srvy_l2\n",
      "mms1_fgm_stemp_srvy_l2\n",
      "mms1_fgm_etemp_srvy_l2\n",
      "mms1_fgm_mode_srvy_l2\n",
      "mms1_fgm_rdeltahalf_srvy_l2\n",
      "mms1_fgm_b_dmpa_srvy_l2_bvec\n",
      "mms1_fgm_b_dmpa_srvy_l2_btot\n",
      "mms1_fgm_b_gse_srvy_l2_bvec\n",
      "mms1_fgm_b_gse_srvy_l2_btot\n",
      "mms1_fgm_b_gsm_srvy_l2_bvec\n",
      "mms1_fgm_b_gsm_srvy_l2_btot\n",
      "mms1_fgm_b_bcs_srvy_l2_bvec\n",
      "mms1_fgm_b_bcs_srvy_l2_btot\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "30-Jun-22 17:36:14: Loading pydata/mms1/hpca/srvy/l2/moments/2017/09/mms1_hpca_srvy_l2_moments_20170910060000_v4.1.0.cdf\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time clip was applied to: mms1_hpca_spin_number\n",
      "Time clip was applied to: mms1_hpca_sweep_table_number\n",
      "Time clip was applied to: mms1_hpca_science_mode\n",
      "Time clip was applied to: mms1_hpca_hplus_number_density\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_hplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_hplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_heplus_number_density\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_heplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_heplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_heplusplus_number_density\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_heplusplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_heplusplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_oplus_number_density\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_bulk_velocity\n",
      "Time clip was applied to: mms1_hpca_oplus_scalar_temperature\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_pressure\n",
      "Time clip was applied to: mms1_hpca_oplus_temperature_tensor\n",
      "Time clip was applied to: mms1_hpca_B_GSE_spin_avg\n",
      "Time clip was applied to: mms1_hpca_B_GSM_spin_avg\n",
      "Time clip was applied to: mms1_hpca_hplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_hplus_tperp\n",
      "Time clip was applied to: mms1_hpca_hplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_hplus_vperp\n",
      "Time clip was applied to: mms1_hpca_hplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_hplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_hplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_tperp\n",
      "Time clip was applied to: mms1_hpca_heplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_heplus_vperp\n",
      "Time clip was applied to: mms1_hpca_heplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_heplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_heplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_tperp\n",
      "Time clip was applied to: mms1_hpca_heplusplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vperp\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_heplusplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_ion_bulk_velocity_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_tperp\n",
      "Time clip was applied to: mms1_hpca_oplus_tparallel\n",
      "Time clip was applied to: mms1_hpca_oplus_vperp\n",
      "Time clip was applied to: mms1_hpca_oplus_vparallel\n",
      "Time clip was applied to: mms1_hpca_oplus_vperp_GSM\n",
      "Time clip was applied to: mms1_hpca_oplus_vparallel_GSM\n",
      "Time clip was applied to: mms1_hpca_MCP_VMON_MIN_converted\n",
      "Time clip was applied to: mms1_hpca_TOF_VMON_MIN_converted\n",
      "Loaded variables:\n",
      "Epoch\n",
      "Epoch_MINUS\n",
      "Epoch_PLUS\n",
      "mms1_hpca_spin_number\n",
      "mms1_hpca_ion_energy\n",
      "mms1_hpca_sweep_table_number\n",
      "mms1_hpca_science_mode\n",
      "mms1_hpca_hplus_number_density\n",
      "mms1_hpca_hplus_ion_bulk_velocity\n",
      "mms1_hpca_hplus_scalar_temperature\n",
      "mms1_hpca_hplus_ion_pressure\n",
      "mms1_hpca_hplus_temperature_tensor\n",
      "mms1_hpca_heplus_number_density\n",
      "mms1_hpca_heplus_ion_bulk_velocity\n",
      "mms1_hpca_heplus_scalar_temperature\n",
      "mms1_hpca_heplus_ion_pressure\n",
      "mms1_hpca_heplus_temperature_tensor\n",
      "mms1_hpca_heplusplus_number_density\n",
      "mms1_hpca_heplusplus_ion_bulk_velocity\n",
      "mms1_hpca_heplusplus_scalar_temperature\n",
      "mms1_hpca_heplusplus_ion_pressure\n",
      "mms1_hpca_heplusplus_temperature_tensor\n",
      "mms1_hpca_oplus_number_density\n",
      "mms1_hpca_oplus_ion_bulk_velocity\n",
      "mms1_hpca_oplus_scalar_temperature\n",
      "mms1_hpca_oplus_ion_pressure\n",
      "mms1_hpca_oplus_temperature_tensor\n",
      "mms1_hpca_B_GSE_spin_avg\n",
      "mms1_hpca_B_GSM_spin_avg\n",
      "mms1_hpca_hplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_hplus_tperp\n",
      "mms1_hpca_hplus_tparallel\n",
      "mms1_hpca_hplus_vperp\n",
      "mms1_hpca_hplus_vparallel\n",
      "mms1_hpca_hplus_vperp_GSM\n",
      "mms1_hpca_hplus_vparallel_GSM\n",
      "mms1_hpca_heplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_heplus_tperp\n",
      "mms1_hpca_heplus_tparallel\n",
      "mms1_hpca_heplus_vperp\n",
      "mms1_hpca_heplus_vparallel\n",
      "mms1_hpca_heplus_vperp_GSM\n",
      "mms1_hpca_heplus_vparallel_GSM\n",
      "mms1_hpca_heplusplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_heplusplus_tperp\n",
      "mms1_hpca_heplusplus_tparallel\n",
      "mms1_hpca_heplusplus_vperp\n",
      "mms1_hpca_heplusplus_vparallel\n",
      "mms1_hpca_heplusplus_vperp_GSM\n",
      "mms1_hpca_heplusplus_vparallel_GSM\n",
      "mms1_hpca_oplus_ion_bulk_velocity_GSM\n",
      "mms1_hpca_oplus_tperp\n",
      "mms1_hpca_oplus_tparallel\n",
      "mms1_hpca_oplus_vperp\n",
      "mms1_hpca_oplus_vparallel\n",
      "mms1_hpca_oplus_vperp_GSM\n",
      "mms1_hpca_oplus_vparallel_GSM\n",
      "mms1_hpca_MCP_VMON_MIN_converted\n",
      "mms1_hpca_TOF_VMON_MIN_converted\n",
      "Averaging mms1_fgm_b_gse_srvy_l2_bvec\n",
      "Averaging mms1_hpca_hplus_ion_bulk_velocity\n",
      "Aligning slice plane to: be\n",
      "Finished slice at 2017-09-10 09:32:17.059004\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "To see the other options, run help() on mms_part_slice2d:"
   ],
   "metadata": {
    "id": "Xk-bqUQhszYu"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "help(mms_part_slice2d)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jr-HuPijpuVW",
    "outputId": "6deccaa1-176a-45e0-a6c4-59ea989a2954"
   },
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Help on function mms_part_slice2d in module pyspedas.mms.particles.mms_part_slice2d:\n",
      "\n",
      "mms_part_slice2d(trange=None, time=None, samples=None, window=None, center_time=False, erange=None, thetarange=None, zdirrange=None, energy=False, log=False, probe='1', instrument='fpi', data_rate=None, level='l2', mag_data_rate=None, species=None, rotation='xy', custom_rotation=None, subtract_bulk=False, xrange=None, yrange=None, zrange=None, resolution=None, interpolation='geometric', smooth=None, save_png=None, save_svg=None, save_pdf=None, save_eps=None, return_slice=False, display=True)\n",
      "    This routine creates 2D slices of 3D distribution function data from the FPI and HPCA instruments.\n",
      "    This is essentially a wrapper around slice2d and slice2d_plot, that loads the data,\n",
      "    any required support data, calculates the slice with slice2d and plots it with slice2d_plot.\n",
      "    \n",
      "    Parameters\n",
      "    -----------\n",
      "        probe: int or str\n",
      "            MMS spacecraft probe # (1, 2, 3 or 4)\n",
      "    \n",
      "        instrument: str\n",
      "            MMS plasma instrument (fpi or hpca)\n",
      "    \n",
      "        species: str\n",
      "            Particle species; depends on the instrument:\n",
      "            FPI: 'e' for electrons, 'i' for ions\n",
      "            HPCA: 'hplus' for H+, 'oplus' for O+, 'heplus' for He+, 'heplusplus', for He++\n",
      "    \n",
      "        data_rate: str\n",
      "            FPI/HPCA data rate [fast (fpi), srvy (hpca), or brst (both fpi and hpca)]\n",
      "    \n",
      "        mag_data_rate: str\n",
      "            FGM data rate for transformations\n",
      "    \n",
      "        level: str\n",
      "            Data level (default: l2); lower levels require an SDC username and password\n",
      "    \n",
      "        trange: list of str or list of float\n",
      "            Two-element time range over which data will be averaged (optional; can also use the 'time' keyword)\n",
      "    \n",
      "        time: str or float\n",
      "            Time at which the slice will be computed (optional; can also use 'trange' above)\n",
      "    \n",
      "        samples: int\n",
      "            Number of nearest samples to TIME to average.\n",
      "    \n",
      "        window: float\n",
      "            Length in seconds from TIME over which data will be averaged\n",
      "    \n",
      "        center_time: bool\n",
      "            Flag denoting that TIME should be midpoint for window instead of beginning\n",
      "    \n",
      "        interpolation: str\n",
      "            Interpolation method; valid options:\n",
      "    \n",
      "            'geometric': Each point on the plot is given the value of the bin it intersects.\n",
      "                         This allows bin boundaries to be drawn at high resolutions.\n",
      "            '2d': Data points within the specified theta or z-axis range are projected onto\n",
      "                  the slice plane and linearly interpolated onto a regular 2D grid.\n",
      "    \n",
      "        rotation: str\n",
      "            Aligns the data relative to the magnetic field and/or bulk velocity.\n",
      "            This is applied after the CUSTOM_ROTATION. (BV and BE are invariant\n",
      "            between coordinate systems); valid options:\n",
      "    \n",
      "            'BV':  The x axis is parallel to B field; the bulk velocity defines the x-y plane\n",
      "            'BE':  The x axis is parallel to B field; the B x V(bulk) vector defines the x-y plane\n",
      "            'xy':  (default) The x axis is along the data's x axis and y is along the data's y axis\n",
      "            'xz':  The x axis is along the data's x axis and y is along the data's z axis\n",
      "            'yz':  The x axis is along the data's y axis and y is along the data's z axis\n",
      "            'xvel':  The x axis is along the data's x axis; the x-y plane is defined by the bulk velocity\n",
      "            'perp':  The x axis is the bulk velocity projected onto the plane normal to the B field; y is B x V(bulk)\n",
      "            'perp_xy':  The data's x & y axes are projected onto the plane normal to the B field\n",
      "            'perp_xz':  The data's x & z axes are projected onto the plane normal to the B field\n",
      "            'perp_yz':  The data's y & z axes are projected onto the plane normal to the B field\n",
      "    \n",
      "        custom_rotation: str or np.ndarray\n",
      "            Applies a custom rotation matrix to the data.  Input may be a\n",
      "            3x3 rotation matrix or a tplot variable containing matrices.\n",
      "            If the time window covers multiple matrices they will be averaged.\n",
      "            This is applied before other transformations\n",
      "    \n",
      "        energy: bool\n",
      "            Flag to plot data against energy (in eV) instead of velocity.\n",
      "    \n",
      "        log: bool\n",
      "            Flag to apply logarithmic scaling to the radial measure (i.e. energy/velocity).\n",
      "            (on by default if ENERGY is True)\n",
      "    \n",
      "        erange: list of float\n",
      "            Two element array specifying the energy range to be used in eV\n",
      "    \n",
      "        thetarange: list of float\n",
      "            (2D interpolation only) Angle range, in degrees [-90,90], used to calculate slice.\n",
      "            Default = [-20,20]; will override ZDIRRANGE.\n",
      "    \n",
      "        zdirrange: list of float\n",
      "            (2D interpolation only) Z-Axis range, in km/s, used to calculate slice.\n",
      "            Ignored if called with THETARANGE.\n",
      "    \n",
      "        subtract_bulk: bool\n",
      "            Subtract the bulk velocity vector\n",
      "    \n",
      "        resolution: int\n",
      "            Integer specifying the resolution along each dimension of the\n",
      "            slice (defaults:  2D interpolation: 150, geometric: 500)\n",
      "    \n",
      "        smooth: int\n",
      "            An odd integer >=3 specifying the width of a smoothing window in #\n",
      "            of points.  Smoothing is applied to the final plot using a gaussian\n",
      "            convolution. Even entries will be incremented, 0 and 1 are ignored.\n",
      "    \n",
      "        xrange: list of float\n",
      "            Keyword to limit the range of the slice's x-axis in the plot\n",
      "    \n",
      "        yrange: list of float\n",
      "            Keyword to limit the range of the slice's y-axis in the plot\n",
      "    \n",
      "        zrange: list of float\n",
      "            Keyword to limit the range of the slice's color bar\n",
      "    \n",
      "        save_png: str\n",
      "            Save the plot as a PNG file\n",
      "    \n",
      "        save_eps: str\n",
      "            Save the plot as an EPS file\n",
      "    \n",
      "        save_pdf: str\n",
      "            Save the plot as a PDF file\n",
      "    \n",
      "        save_svg: str\n",
      "            Save the plot as an SVG file\n",
      "    \n",
      "        display: bool\n",
      "            Flag to allow disabling displaying of the figure\n",
      "    \n",
      "        return_slice: bool\n",
      "            Flag to return the slice instead of displaying it\n",
      "    \n",
      "    Returns\n",
      "    --------\n",
      "        None (but creates a figure), unless return_slice is set to True\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "K27IBN-xs39k"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}