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generate_masks.py
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generate_masks.py
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# script to take polygon(s) and turn into a mask on a particular lat-lon grid
# Modified from Peter Uhe's original script
# Sihan Li
# 27/07/2017
import math
import numpy as np
from matplotlib.path import Path
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.basemap import Basemap,cm
import os
import unicodedata
# Use osgeo (gdal library) to load shapefile
# Installed by '$conda install gdal'
from osgeo import ogr
########################################################################################
### Convert global coords to rotated (regional) coords
def glob2rot(lon, lat, pole_lon, pole_lat):
# Make sure rotlon is between 0 and 360
while (lon >= 360.0):
lon -= 360.0
while (lon < 0.0):
lon += 360.0
# Make sure pole_lon is between 0 and 360
while (pole_lon >= 360.0):
pole_lon -= 360.0
while (pole_lon < 0.0):
pole_lon += 360.0
# Convert inputs to radians
lon_r = math.radians(lon)
lat_r = math.radians(lat)
pole_lon_r = math.radians(pole_lon)
pole_lat_r = math.radians(pole_lat)
# Amount rotated about 180E meridian
if (pole_lon_r == 0.0):
sock = 0.0;
else:
sock = pole_lon_r - math.pi
# Need to get the screw in range -pi to pi
screw = lon_r - sock;
while (screw < -1.0 * math.pi):
screw += 2.0 * math.pi
while (screw > math.pi):
screw -= 2.0 * math.pi
bpart = math.cos(screw) * math.cos(lat_r)
x = (-1.0 * math.cos(pole_lat_r) * bpart) + (math.sin(pole_lat_r) * math.sin(lat_r))
if (x >= 1.0):
x = 1.0
if (x <= -1.0):
x = -1.0
lat2 = math.asin(x)
t1 = math.cos(pole_lat_r) * math.sin(lat_r)
t2 = math.sin(pole_lat_r) * bpart
x = (t1 + t2) / math.cos(lat2)
if (x >= 1.0):
x = 1.0
if (x <= -1.0):
x = -1.0
lon2 = -1.0 * math.acos(x)
if (screw >= 0.0 and screw <= math.pi):
lon2 *= -1.0
# Convert back to degrees
lon = math.degrees(lon2)
lat = math.degrees(lat2)
return lon, lat
########################################################################################
### Convert rotated (regional) to global grid
def rot2glob(lon, lat, pole_lon, pole_lat):
# Make sure rotlon is between 0 and 360
while (lon >= 360.0):
lon -= 360.0
while (lon < 0.0):
lon += 360.0
# Make sure pole_lon is between 0 and 360
while (pole_lon >= 360.0):
pole_lon -= 360.0
while (pole_lon < 0.0):
pole_lon += 360.0
# Convert inputs to radians
lon = math.radians(lon)
lat = math.radians(lat)
pole_lon = math.radians(pole_lon)
pole_lat = math.radians(pole_lat)
# Amount rotated about 180E meridian
if (pole_lon == 0.0):
sock = 0.0
else:
sock = pole_lon - math.pi
cpart = math.cos(lon) * math.cos(lat)
x = math.cos(pole_lat) * cpart + math.sin(pole_lat) * math.sin(lat)
if (x >= 1.0):
x = 1.0
if (x <= -1.0):
x = -1.0
lat_out = math.asin(x)
t1 = -1.0 * math.cos(pole_lat) * math.sin(lat)
t2 = math.sin(pole_lat) * cpart
x = (t1 + t2) / math.cos(lat_out)
if (x >= 1.0):
x = 1.0
if (x <= -1.0):
x = -1.0
lon_out = -1.0 * math.acos(x)
# Make sure rotlon is between -PI and PI
while (lon < -1*math.pi):
lon += 2.0*math.pi
while (lon > math.pi):
lon -= 2.0*math.pi
if (lon >= 0.0 and lon <= math.pi):
lon_out *= -1.0
lon_out += sock;
# Convert back to degrees
lon_out = math.degrees(lon_out)
lat_out = math.degrees(lat_out)
return lon_out, lat_out
########################################################################################
####################################################################################
# Load a text file containing lists of vertices (space separated, one vertex per line)
# Creates matplotlib.path.Path objects representing the polygons.
# Lines starting with '#' are ignored
# A blank line indicates the end of a polygon, so multiple polygons can be defined with new lines in between
def load_polygons(fname):
polygons=[]
tmp=[]
# Import Polygons of mask in fname (separate polygons are separated by a blank line)
for line in open(fname,'r'):
if line[0]=='#':
# skip commented lines
continue
elif line.strip()!='':
# Add coordinates to list
tuple=line.strip().split()
tmp.append(tuple) # lon,lat
else: # blank line
# Finished reading data for this polygon
# create polygon path out of list of vertices
if tmp !=[]:
polygons.append(Path(np.array(tmp)))
tmp=[]
# If the file didn't end in a blank line, add the final polygon
if tmp !=[]:
polygons.append(Path(np.array(tmp)))
# print polygons
# polygons=glob2rot(polygons[:,1], polygons[:,2], 236.68,79.95)
return polygons
###################################################################################
def load_grid(fname,latname='gloabl_latitude0',lonname='global_longitude0'):
# load region grid and returns list of points (lon,lat)
with Dataset(fname,'r') as f:
# Load 1d arrays of lon and lat
lat=f.variables[latname][:]
lon=f.variables[lonname][:]
if len(lat.shape)==2:
# 2D lat and lon:
lonxx=lon
latyy=lat
else:
# Create 2D arrays of lon and lat
lonxx,latyy=np.meshgrid(lon,lat)
#print lonxx
#print latyy
return lonxx,latyy
##################################################################################
# Function to create mask given polygons object and points array
def create_mask(polygons,points,nlat,nlon):
# Convert polygons to mask (true if inside the polygon region)
# add the masks for multiple polygons together
for i,polygon in enumerate(polygons):
# Determine if points inside polygon
tmp_mask = polygon.contains_points(points)
# Reshape mask to dimensions of the grid
tmp_mask=np.reshape(tmp_mask,[nlat,nlon])
try:
mask=tmp_mask | mask
except:
mask=tmp_mask
return ~mask # Invert the mask so true is outside the region
#################################################################################
# Wrapper function to return the mask given the polygons file and grid file
def load_and_create_mask(f_polygons,f_grid,latname='global_latitude0',lonname='global_longitude0'):
# Load inputs and create mask
polygons=load_polygons(f_polygons)
# Load 2D lon and lat arrays for grid
lonxx,latyy=load_grid(f_grid,latname,lonname)
nlat,nlon=lonxx.shape
# Stack points into a N x 2 array (where N = nlat x nlon)
points = np.vstack((lonxx.flatten(),latyy.flatten())).T
# Call create_mask function for polygons and grid points
return create_mask(polygons,points,nlat,nlon)
#################################################################################
def add_to_text(fileh,polygon):
for coord in polygon.vertices:
fileh.write(str(coord[0])+' '+str(coord[1])+'\n')
fileh.write('\n')
#################################################################################
def get_rotated_pole(nc_in_file,in_var):
# get the rotated pole longitude / latitude (for calculating weights)
try:
grid_map_name = getattr(nc_in_var,"grid_mapping")
grid_map_var = nc_in_file.variables[grid_map_name]
plon = getattr(grid_map_var,"grid_north_pole_longitude")
plat = getattr(grid_map_var,"grid_north_pole_latitude")
except:
plon = 0.0
plat = 90.0
return plon, plat
##############################################################################
def create_netcdf(template,data,outname,template_var='field16'):
# create outfile object
outfile=Dataset(outname,'w')
# Create dimensions copied from template file
temp=template.variables[template_var]
for dim in temp.dimensions:
if dim[:3]=='lat' or dim[:3] =='lon':
leng=len(template.dimensions[dim])
outfile.createDimension(dim[:3],leng)
outfile.createVariable(dim[:3],'f',(dim[:3],))
outfile.variables[dim[:3]][:]=template.variables[dim][:]
print template.variables[dim].__dict__
for att in template.variables[dim].ncattrs():
outfile.variables[dim[:3]].__setattr__(att,template.variables[dim].__getattribute__(att))
# Create data variable (named region_mask)
outfile.createVariable('region_mask','f',['lat','lon'])
outfile.variables['region_mask'][:]=(data-1)*-1
outfile.close()
#############################################################################
# Create a mask, from textfile for a specific grid
# f_grid: (filename of netcdf file contatining grid information)
# latname, lonname: name of latitude and longitude variables in netcdf file
#
def create_mask_fromtext(f_grid,textfile,region_name='region',latname='latitude0',lonname='longitude0',plot=False,template_var='field16'):
# first create folders (if needed)
if plot and not os.path.exists('plots'):
os.mkdir('plots')
#if not os.path.exists('masks_netcdf'):
# os.mkdir('masks_netcdf')
#if not os.path.exists('masks_text'):
# os.mkdir('masks_text')
# Load Shape file
polygons=load_polygons(textfile)
# Load lat lon grid (for mask)
lonxx,latyy=load_grid(f_grid,latname=latname,lonname=lonname)
nlat,nlon=lonxx.shape
# Update lon to be from -180 to 180
# NOTE: (this is only if the shapefile uses lat coordinates from -180-180 )
# Comment out otherwise
#lonxx[lonxx>180]=lonxx[lonxx>180]-360
# Turn lat and lon into a list of coordinates
points = np.vstack((lonxx.flatten(),latyy.flatten())).T
print points
if plot:
# Set up Basemap projection (may need fine tuning)
m = Basemap(projection='robin',lon_0=180)
xx,yy=m(lonxx,latyy) # basemap coordinates
plot_mask = np.zeros([nlat,nlon])
# Create mask out of polygon, matching points from grid
mask = create_mask(polygons,points,nlat,nlon)
#print mask
# Create netcdf for combined mask
create_netcdf(Dataset(f_grid,'r'),mask,'region_mask/masks_netcdf/mask_'+region_name+'.nc',template_var='field16')
if plot:
plt.clf()
m.contourf(xx,yy,mask)
plt.colorbar()
m.drawcoastlines(linewidth=0.2)
m.drawcountries(linewidth=0.2)
plt.title('Mask: '+region_name)
plt.savefig('plots/mask_'+region_name+'.png')
#################################################################################
if __name__=='__main__':
# Set up input files
# Template grid for output mask
f_grid='region_mask/region_template/SAS50/sas50_region_template.nc'
# Text file with region boundaries
f_text = 'region_mask/source_file/Thailand.txt'
create_mask_fromtext(f_grid,f_text, region_name = 'Thailand',latname='global_latitude0',lonname='global_longitude0', plot=False,template_var='field16')