{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "from kaipy.kaixdmf import AddGrid, AddData, AddDI, getRootVars, getVars, printVidAndLocs, AddVectors, getLoc, addHyperslab\n",
    "\n",
    "import xml.etree.ElementTree as et"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Cell'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getLoc([4, 4, 4], [3, 3, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Node'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getLoc([3, 3, 3], [3, 3, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Other'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getLoc([3, 3, 3], [5, 5, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grid = et.Element(\"Grid\")\n",
    "addHyperslab(Grid, \"density\", \"3 3 3\", \"3 3 3\", \"0 0 0\", \"1 1 1\", \"3 3 3\", \"3 3 3\", \"test.h5:/density\")\n",
    "vAtt = Grid.find(\"Attribute\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert vAtt.get(\"Name\") == \"density\"\n",
    "assert vAtt.get(\"AttributeType\") == \"Scalar\"\n",
    "assert vAtt.get(\"Center\") == \"Node\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "slabDI = vAtt.find(\"DataItem\")\n",
    "assert slabDI.get(\"ItemType\") == \"HyperSlab\"\n",
    "assert slabDI.get(\"Dimensions\") == \"3 3 3\"\n",
    "cutDI = slabDI.find(\"DataItem\")\n",
    "assert cutDI.get(\"Dimensions\") == \"3 3 3\"\n",
    "assert cutDI.get(\"Format\") == \"XML\"\n",
    "assert cutDI.text == \"\\n0 0 0\\n1 1 1\\n3 3 3\\n\"\n",
    "datDI = slabDI.find(\"DataItem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "datDI = slabDI.find(\"DataItem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3 3 3'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datDI.get(\"Dimensions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert datDI.get(\"Dimensions\") == \"3 3 3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(datDI.get(\"DataType\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from kaipy.kdefs import *\n",
    "import alive_progress.animations.bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "barDef = alive_progress.animations.bars.bar_factory(tip=\"><('>\", chars='∙',background='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'function' object has no attribute 'tip'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbarDef\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtip\u001b[49m()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'tip'"
     ]
    }
   ],
   "source": [
    "barDef.tip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/wiltbemj/conda-envs/kaipy-pytest/lib/python3.8/site-packages/spacepy/time.py:2448: UserWarning: Leapseconds may be out of date. Use spacepy.toolbox.update(leapsecs=True)\n",
      "  _read_leaps()\n"
     ]
    }
   ],
   "source": [
    "import pytest\n",
    "import numpy as np\n",
    "import datetime\n",
    "from kaipy.transform import SMtoGSM, GSMtoSM, GSEtoGSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.12636355386656506, 2.0, 3.159751928910593)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ut = datetime.datetime(2009, 1, 27, 0, 0, 0)\n",
    "x, y, z = 1, 2, 3\n",
    "SMtoGSM(x, y, z, ut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1, 2, 3])\n",
    "y = np.array([4, 5, 6])\n",
    "z = np.array([7, 8, 9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ut = np.array([datetime.datetime(2009, 1, 27, 0, 0, 0),\n",
    "                datetime.datetime(2009, 1, 27, 1, 0, 0),\n",
    "                datetime.datetime(2009, 1, 27, 2, 0, 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_gsm, y_gsm, z_gsm = SMtoGSM(x, y, z, ut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.5419005900706804"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_gsm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kaipy.cmaps.kaimaps import load_colormap_from_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kaipy.cmaps.kaimaps as km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/glade/work/wiltbemj/src/kaipy-private/kaipy/cmaps/kaimaps.py'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.__file__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytest\n",
    "import numpy as np\n",
    "import h5py\n",
    "from kaipy.chimp.kCyl import getGrid, getSlc, PIso, getEQGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=np.random.rand(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fIn = '/glade/campaign/univ/ujhb0019/adamm/March172013_chimp/noWPI_noBwSclComp/eRBpsdH5All.ps.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(fIn, 'r') as hf:\n",
    "    xx = hf[\"X\"][()].T\n",
    "    yy = hf[\"Y\"][()].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 25, 31)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import datetime\n",
    "from kaipy.kaijson import CustomEncoder, customhook, dump, load, dumps, loads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'time': datetime.datetime(2020, 1, 1, 12, 0, 0)}\n",
    "json_str = json.dumps(data, cls=CustomEncoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"time\": \"2020-01-01T12:00:00Z\"}'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "val=loads(json_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': '2020-01-01T12:00:00Z'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fIn = '/glade/u/home/wiltbemj/src/kaipy-private/kaipy/gamera/lfmG.X.txt'\n",
    "xxi = np.loadtxt(fIn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(213, 193)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_gsm = 540626.9291896636"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isclose(y_gsm, 0.540 * 1e6, atol=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-626.9291896636132"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.540 * 1e6 - y_gsm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
