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Update docs
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@wpreimes
wpreimes committed Aug 4, 2023
1 parent adb08e3 commit c661a22
Showing 1 changed file with 42 additions and 34 deletions.
76 changes: 42 additions & 34 deletions docs/examples/ts2img.ipynb
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},
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"cell_type": "code",
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"execution_count": 25,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/wpreimes/shares/home/code/repurpose/src/repurpose/process.py:6: UserWarning: Numpy is already imported. Environment variables set in repurpose.utils wont have any effect!\n",
" warnings.warn(\"Numpy is already imported. Environment variables set in \"\n"
]
},
{
"data": {
"text/plain": "<matplotlib.legend.Legend at 0x7f0ed5420250>"
"text/plain": "<matplotlib.legend.Legend at 0x7f0ed531c910>"
},
"execution_count": 9,
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
Expand All @@ -63,7 +55,8 @@
"import numpy as np\n",
"from repurpose.process import rootdir\n",
"\n",
"source_grid = load_grid(os.path.join(rootdir(), \"docs\", \"examples\", \"assets\", \"warp_subgrid_AUT.nc\"))\n",
"source_grid = load_grid(os.path.join(rootdir(), \"docs\", \"examples\",\n",
" \"assets\", \"warp_subgrid_AUT.nc\"))\n",
"y = np.arange(46.3, 49.2, 0.1)\n",
"x = np.arange(9.2, 17.4, 0.1)\n",
"z = np.full((y.size-1, x.size-1), np.nan)\n",
Expand All @@ -76,7 +69,8 @@
"plt.ylabel('Latitude [°N]')\n",
"\n",
"legend = [Line2D([0], [0], color='k', label='Target (image) Grid, Regular 0.1°'),\n",
" Line2D([0], [0], marker='o', color='w', markerfacecolor='c', label='Source (timeseries) Grid, Irregular 12.5 km')]\n",
" Line2D([0], [0], marker='o', color='w', markerfacecolor='c',\n",
" label='Source (timeseries) Grid, Irregular 12.5 km')]\n",
"plt.legend(handles=legend, loc='upper left')"
],
"metadata": {
Expand All @@ -101,7 +95,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 26,
"id": "406f83ff-7725-4710-8048-1a2311b23963",
"metadata": {
"tags": []
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"\n",
"class Reader:\n",
" def __init__(self):\n",
" self.df = pd.read_csv(os.path.join(rootdir(), \"docs\", \"examples\", \"assets\", \"SM_AUT.csv\"), index_col=[0, 1], parse_dates=True)\n",
" self.df = pd.read_csv(\n",
" os.path.join(rootdir(), \"docs\", \"examples\", \"assets\", \"SM_AUT.csv\"),\n",
" index_col=[0, 1], parse_dates=True)\n",
" self.grid: CellGrid = source_grid\n",
"\n",
" def read(self, lon, lat):\n",
Expand All @@ -139,13 +135,13 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 27,
"outputs": [
{
"data": {
"text/plain": "<matplotlib.legend.Legend at 0x7f0ed559f580>"
"text/plain": "<matplotlib.legend.Legend at 0x7f0ed4ede950>"
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"execution_count": 11,
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
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},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 28,
"outputs": [],
"source": [
"from repurpose.ts2img import Ts2Img\n",
"from pygeogrids.grids import gridfromdims\n",
"\n",
"target_grid = gridfromdims(x, y)\n",
"timestamps = pd.date_range('2010-06-01T00:00:00', '2010-06-10T12:00:00', freq='12H')\n",
"timestamps = pd.date_range('2010-06-01T00:00:00',\n",
" '2010-06-10T12:00:00', freq='12H')\n",
"\n",
"converter = Ts2Img(ts_reader=source_reader, img_grid=target_grid, timestamps=timestamps, variables=['sm'], max_dist=10000, time_collocation=True)\n"
"converter = Ts2Img(ts_reader=source_reader,\n",
" img_grid=target_grid,\n",
" timestamps=timestamps,\n",
" variables=['sm'],\n",
" max_dist=10000,\n",
" time_collocation=True)\n"
],
"metadata": {
"collapsed": false,
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},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 29,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Images are stored in /tmp/tmp72x9hsa2\n"
"Images are stored in /tmp/tmp6_mbnlrl\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processed: 100%|██████████| 3/3 [00:10<00:00, 3.52s/it]\n",
"Processed: 100%|██████████| 14/14 [00:00<00:00, 109.31it/s]\n"
"Processed: 100%|██████████| 3/3 [00:07<00:00, 2.50s/it]\n",
"Processed: 100%|██████████| 14/14 [00:00<00:00, 106.15it/s]\n"
]
}
],
Expand All @@ -267,7 +269,11 @@
"var_attrs = {'sm': {'unit': 'percent saturation', 'long_name': 'soil moisture'}}\n",
"encoding = {'sm': {'scale_factor': 0.01, 'dtype': 'int32', '_FillValue': -9999}}\n",
"\n",
"converter.calc(path_out=path_out, format_out='slice', fn_template='ascat_0.1deg_{datetime}.nc',glob_attrs=glob_attrs, var_attrs=var_attrs,encoding=encoding, img_buffer=100, n_proc=1, drop_empty=True)"
"converter.calc(path_out=path_out, format_out='slice',\n",
" fn_template='ascat_0.1deg_{datetime}.nc',\n",
" glob_attrs=glob_attrs, var_attrs=var_attrs,\n",
" encoding=encoding, img_buffer=100, n_proc=1,\n",
" drop_empty=True)"
],
"metadata": {
"collapsed": false,
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},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 30,
"outputs": [
{
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"text/plain": "<matplotlib.collections.QuadMesh at 0x7f0ed4e22e30>"
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"execution_count": 14,
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
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"source": [
"import xarray as xr\n",
"\n",
"ds = xr.open_dataset(os.path.join(path_out, \"2010\", \"ascat_0.1deg_20100601120000.nc\"))\n",
"ds = xr.open_dataset(os.path.join(\n",
" path_out, \"2010\", \"ascat_0.1deg_20100601120000.nc\"))\n",
"\n",
"ds['sm'].plot(figsize=(10,5), cmap='RdBu')\n",
"\n",
"plt.figure()\n",
Expand All @@ -355,7 +363,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 31,
"outputs": [
{
"name": "stdout",
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},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 32,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Value and time stamp in image (after applying the timedetal_seconds to the image wide time stamp\n",
"Value and time stamp in image (after adding `timedelta_seconds` to the image wide time stamp\n",
"\n",
"sm: 5.64\n",
"timestamp+offset: 2010-06-01 19:23:48.750000\n"
Expand All @@ -403,7 +411,7 @@
"img_timestamp = pd.to_datetime(img['time'].values[0]).to_pydatetime()\n",
"timedelta = timedelta(seconds=float(img['timedelta_seconds'].values[0]))\n",
"\n",
"print(\"Value and time stamp in image (after applying the timedetal_seconds to the image wide time stamp\\n\")\n",
"print(\"Value and time stamp in image (after adding `timedelta_seconds` to the image wide time stamp\\n\")\n",
"print(\"sm:\", sm)\n",
"print(\"timestamp+offset:\", img_timestamp + timedelta)\n"
],
Expand Down

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