In [2]:

import xarray as xr
import pandas as pd
import os
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import regionmask

In [3]:

era_data_path="C:/Users/dboateng/Desktop/Datasets/ERA5/monthly_1950_2021/"
path_shapefile="C:/Users/dboateng/Desktop/Datasets/Station/Ghana/Ghana_ShapeFile/gh_wgs16dregions.shp"
afr_shape = "C:/Users/dboateng/Desktop/Datasets/Station/Ghana/Africa_shapefile/afr_g2014_2013_0.shp"

from1979to2012 = pd.date_range(start="1979-01-01", end="2012-12-31", freq="MS")

In [4]:

tp_monthly= xr.open_dataset(os.path.join(era_data_path, "tp_monthly.nc"))
tp_monthly = tp_monthly["tp"].sel(time=from1979to2012).mean(dim="time")

In [5]:

ghana_shape = gpd.read_file(path_shapefile)

In [15]:

np.array(ghana_shape.geometry[0])

Out[15]:

array(<POLYGON ((-3.088 6.975, -3.086 6.974, -3.084 6.974, -3.084 6.974, -3.084 6....>,
      dtype=object)

In [16]:

poly = regionmask.Regions(np.array(ghana_shape.geometry[0]))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Input In [16], in <cell line: 1>()
----> 1 poly = regionmask.Regions(np.array(ghana_shape.geometry[0]))

File ~\Anaconda3\envs\regionmask_env\lib\site-packages\regionmask\core\regions.py:95, in Regions.__init__(self, outlines, numbers, names, abbrevs, name, source, overlap)
     83 def __init__(
     84     self,
     85     outlines,
   (...)
     91     overlap=False,
     92 ):
     94     if numbers is None:
---> 95         numbers = range(len(outlines))
     97     if not _is_numeric(numbers):
     98         raise ValueError("'numbers' must be numeric")

TypeError: len() of unsized object

In [8]:

poly

Out[8]:

<regionmask.Regions>
Name:     unnamed
overlap:  False

Regions:
 0  r0  Region0
 1  r1  Region1
 2  r2  Region2
 3  r3  Region3
 4  r4  Region4
..  ..      ...
11 r11 Region11
12 r12 Region12
13 r13 Region13
14 r14 Region14
15 r15 Region15

[16 regions]

In [9]:

mask = poly.mask(tp_monthly, lat_name="latitude", lon_name="longitude", wrap_lon=True)

In [10]:

mask

Out[10]:

<xarray.DataArray 'mask' (latitude: 721, longitude: 1440)>
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]])
Coordinates:
    expver     float64 1.0
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8

In [11]:

pr_mask = tp_monthly.where(mask==0)

In [12]:

pr_mask

Out[12]:

<xarray.DataArray 'tp' (latitude: 721, longitude: 1440)>
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)
Coordinates:
    expver     float64 1.0
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0