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Add explanation for unamed-variables error and a masked-data example #85

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Add explanation for unamed-variables error and a masked-data example #85

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167 changes: 145 additions & 22 deletions doc/tutorial.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -38,8 +39,7 @@
"%matplotlib inline\n",
"\n",
"nt, nx = 100, 30\n",
"da = xr.DataArray(np.random.randn(nt, nx), dims=['time', 'x'],\n",
" name='foo') # all inputs need a name\n",
"da = xr.DataArray(np.random.randn(nt, nx), dims=['time', 'x'])\n",
"display(da)\n",
"da.plot()"
]
Expand All @@ -55,16 +55,60 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
"tags": []
},
"outputs": [],
"source": [
"from xhistogram.xarray import histogram\n",
"\n",
"bins = np.linspace(-4, 4, 20)\n",
"h = histogram(da, bins=[bins])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This error occurred because no name was given to `da` (e.g. `da.name == None`) and `histogram` needs that to determine the name of the output dimension.\n",
"We can solve this by either renaming `da` at the `histogram` input as\n",
"\n",
"```python\n",
"h = histogram(da.rename(\"foo\"), bins=[bins])\n",
"```\n",
"\n",
"or redefining `da` by giving the name at the input"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"da = xr.DataArray(np.random.randn(nt, nx), dims=['time', 'x'], name = \"foo\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can calculate the histogram and visualize it"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
},
"tags": []
},
"outputs": [],
"source": [
"h = histogram(da, bins=[bins])\n",
"display(h)\n",
"h.plot()"
Expand All @@ -90,7 +134,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -113,7 +158,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -136,7 +182,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -158,7 +205,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -182,7 +230,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand Down Expand Up @@ -219,7 +268,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -232,7 +282,7 @@
" xr.open_dataset(Temp_url).tmn.load(),\n",
" xr.open_dataset(Salt_url).smn.load(),\n",
" xr.open_dataset(Oxy_url).omn.load()])\n",
"ds"
"display(ds)"
]
},
{
Expand All @@ -249,7 +299,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -264,7 +315,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand Down Expand Up @@ -292,7 +344,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -301,7 +354,7 @@
"\n",
"# Create a dz variable\n",
"dz = np.diff(ds.lev)\n",
"dz =np.insert(dz, 0, dz[0])\n",
"dz = np.insert(dz, 0, dz[0])\n",
"dz = xr.DataArray(dz, coords= {'lev':ds.lev}, dims='lev')\n",
"\n",
"# weight by volume of grid cell (resolution = 5degree, 1degree=110km)\n",
Expand All @@ -319,6 +372,75 @@
"The ridges of this above plot are indicative of T/S classes with a lot of volume, and some of them are indicative of Mode Waters (example Eighteen Degree water with T$\\sim18^oC$, and S$\\sim36.5psu$. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's suppose that we have a mask for different regions in the planet. For the sake of simplicity, we will create a mask to separate the globe into three regions.\n",
"\n",
"- Tropical: `np.abs(latitude) <= 30`\n",
"- Temperate: `30 < np.abs(latitude) <= 60`\n",
"- Polar: `60 < np.abs(latitude)`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"zones = np.abs(ds.lat / 30).round().rename(\"zones\")\n",
"display(zones)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now there are two ways of doing that, we can create a new dimension on the histogram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"zonebins = np.arange(-0.5, 4, 1)\n",
"hTSw = histogram(ds.smn, ds.tmn, zones, bins=[sbins, tbins, zonebins], weights=dVol)\n",
"np.log10(hTSw.T).plot(levels=31, vmin=11.5, vmax=16, cmap='brg', col = \"zones_bin\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or use groupby function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"hTSw = ds.assign(dVol = dVol).groupby(zones).apply(lambda ds: histogram(ds.smn, ds.tmn, bins=[sbins, tbins], weights=ds.dVol))\n",
"np.log10(hTSw.T).plot(levels=31, vmin=11.5, vmax=16, cmap='brg', col = \"zones\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second option is more verbose, but more efficient and also works with text or mask values that does not vary monotonically."
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -339,7 +461,8 @@
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"tags": []
},
"outputs": [],
"source": [
Expand Down Expand Up @@ -381,9 +504,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "coringa0.3.0",
"language": "python",
"name": "python3"
"name": "coringa0.3.0"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -395,7 +518,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
"version": "3.9.15"
}
},
"nbformat": 4,
Expand Down