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  {
   "cell_type": "markdown",
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   "source": [
    "# Ion Distribution From MMS\n",
    "\n",
    "This notebook shows how to create 2D slices of 3D particle data from FPI using PySPEDAS. `pyspedas` is the Python implementation of SPEDAS orginally coded in IDL. It is not as feature-complete as the IDL version."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining MMS FPI Data\n",
    "\n",
    "Here is a recorded bi-directional field-aligned beam of 0-300 eV ions observed by FPI. Due to a bug in the `time` argument of `mms_part_slice2d`, we currently need to specify the start and end time of the interval as a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytplot import time_double, time_string\n",
    "\n",
    "time = \"2017-09-10/09:32:20\"\n",
    "window = 4.\n",
    "trange = [time_double(time)-window/2, time_double(time)+window/2]\n",
    "\n",
    "time_string(trange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is downloaded automatically to the `pydata` folder under the current directory if not found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspedas.mms.particles.mms_part_slice2d import mms_part_slice2d\n",
    "\n",
    "mms_part_slice2d(\n",
    "    time=time,\n",
    "    instrument=\"fpi\",\n",
    "    species=\"i\",\n",
    "    rotation=\"bv\",\n",
    "    erange=[0, 300],\n",
    "    cmap=\"turbo\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic operations\n",
    "\n",
    "To return the slice data structure instead of plotting, set the `return_slice` keyword to `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_slice = mms_part_slice2d(\n",
    "    return_slice=True,\n",
    "    time=time,\n",
    "    instrument=\"fpi\",\n",
    "    species=\"i\",\n",
    "    rotation=\"bv\",\n",
    "    erange=[0, 300],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slice is stored as a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "the_slice.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create 1D cuts through the 2D slice by specifying the velocity range:\n",
    "\n",
    "(hyzhou: why is the unit still the same as in 2D/3D?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspedas.particles.spd_slice2d.slice1d_plot import plot\n",
    "\n",
    "plot(the_slice, 'x', [-100, 100])  # summed from Vv=[-100, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mms_part_slice2d(interpolation=\"2d\", time=time, instrument=\"fpi\", species=\"i\", rotation=\"bv\", erange=[0, 300], cmap=\"turbo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slicemms2d = mms_part_slice2d(return_slice=True, interpolation=\"2d\", time=time, instrument=\"fpi\", species=\"i\", rotation=\"bv\", erange=[0, 300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import numpy as np\n",
    "\n",
    "plt.pcolormesh(slicemms2d[\"xgrid\"], slicemms2d[\"ygrid\"], slicemms2d[\"data\"].T,\n",
    "               norm=colors.LogNorm(1e-24, np.nanmax(slicemms2d[\"data\"])),\n",
    "               cmap=\"turbo\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  }
 ],
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