# pyscissor
[](https://opensource.org/licenses/MIT)
A Python3 module for extracting data from netcdf file under a shapefile region.
<img src="data/sample.png" height="500" align="center">
### Installation
pyscissor can be installed using the following commands
```bash
$ git clone https://github.com/nzahasan/pyscissor.git
$ cd pyscissor
$ python3 setup.py install
```
or using pip
```bash
$ pip install pyscissor
```
### Using pyscissor
```python
import fiona
import numpy as np
from netCDF4 import Dataset
from shapely.geometry import shape
from pyscissor import scissor
# read shapefile
sf = fiona.open('data/shape.geojson')
shapely_shp =shape(sf.get(0)['geometry'])
# read netcdf
nf = Dataset('data/sample_2.nc','r')
lats = nf.variables['lat'][:]
lons = nf.variables['lon'][:]
# create scissor object
pys = scissor(shapely_shp,lats,lons)
weight_grid = pys.get_masked_weight() #=> returns a masked array containing weights
# get weighted average
avg = np.average(var,weights=weight_grid)
# if only intersection mask with shape is needed use `weight_grid.mask`
```
A detailed use case can be found in the following jupyter notebooks
- <a href="notebooks/example_01.ipynb">example_01.ipynb</a>
- <a href="notebooks/example_02.ipynb">example_02.ipynb</a>
- <a href="notebooks/example_03.ipynb">example_03.ipynb</a>
- <a href="notebooks/example_04.ipynb">example_04.ipynb</a>
### Using nc2ts_by_shp.py
this package contains a `nc2ts_by_shp.py` script. A command line tool that can be used to quickly extract
reduced(min/max/average/weighted average) time-series form netcdf file with shapefile
```bash
# with 3d array [data/sample_2.nc] generel case
$ nc2ts_by_shp.py -nc=sample_2.nc -nci='Y=lat;X=lon;T=time;V=tmin;' -s=shape_esri.zip \
-sp='ADM2_EN;ADM3_EN' -r=avg -o=test2.csv
# with 4d array [data/sample_1.nc]
$ nc2ts_by_shp.py -nc=sample_1.nc -nci='Y=lat;X=lon;T=time;V=temperature;slicer=[:,0,:,:]' -sf=shape_esri.zip \
-sfp='ADM2_EN;ADM3_EN' -r=wavg -o=test1.csv
```
Options:
-nc = netcdf file
-nci = netcdf variable and dimension information
available options:
X = x dimension variable name,
Y = y dimension variable name,
T = time dimension variable name,
V = variable name,
slicer = slicing index for obtaining 3d array [optional]
note: `slicer` is required if variable has more than three dimension
-sf = shape file ( can be zipped shapefile, shapefile or geojson )
-sfp = shapefile properties
only required when shapefile contains multiple records
-r = reducer, default is average
Available options: min,max,avg,wavg
-o = output file name
### Causes of Erroneous output
- when shapefile and netcdf file have different projection
- shapefile dosen't fully reside within netcdf bounds
Raw data
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