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kaipy/tests/notebookfortest.ipynb

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{
"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
}
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