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tools.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "Hylke E. Beck"
__email__ = "[email protected]"
__date__ = "January 2022"
import os, sys, glob, time, pdb
import pandas as pd
import numpy as np
from netCDF4 import Dataset
from skimage.transform import resize
from skimage.transform import downscale_local_mean
from datetime import datetime, timedelta
from scipy import ndimage as nd
import rasterio
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
def load_country_code_map(filepath,mapsize):
src = rasterio.open(filepath)
country_code_map = src.read(1).astype(np.single)
country_code_map_refmatrix = src.get_transform()
src.close()
# Check if country border raster covers entire globe
assert (country_code_map_refmatrix[0]==-180) & (country_code_map_refmatrix[3]==90)
country_code_map = resize(country_code_map,mapsize,order=0,mode='edge',anti_aliasing=False)
country_code_map[country_code_map==158] = 156 # Add Taiwan to China
country_code_map[country_code_map==736] = 729 # South Sudan missing from map
country_code_map[country_code_map==0] = np.NaN
return country_code_map
def load_us_state_code_map(filepath,mapsize):
src = rasterio.open(filepath)
state_code_map = src.read(1).astype(np.single)
state_code_map = resize(state_code_map,mapsize,order=0,mode='edge',anti_aliasing=False)
state_code_map_refmatrix = src.get_transform()
src.close()
# Check if state border raster covers entire globe
assert (state_code_map_refmatrix[0]==-180) & (state_code_map_refmatrix[3]==90)
return state_code_map
def latlon2rowcol(lat,lon,res,lat_upper,lon_left):
row = np.round((lat_upper-lat)/res-0.5).astype(int)
col = np.round((lon-lon_left)/res-0.5).astype(int)
return row.squeeze(),col.squeeze()
def rowcol2latlon(row,col,res,lat_upper,lon_left):
lat = lat_upper-row*res-res/2
lon = lon_left+col*res+res/2
return lat.squeeze(),lon.squeeze()
def imresize_mean(oldarray,newshape):
'''Resample an array using simple averaging'''
oldshape = oldarray.shape
factor = oldshape[0]/newshape[0]
if factor==int(factor):
factor = int(factor)
newarray = downscale_local_mean(oldarray,(factor,factor))
else:
factor = 1
while newshape[0]*factor<oldshape[0]:
factor = factor+1
intarray = resize(oldarray,(int(newshape[0]*factor),int(newshape[1]*factor)),order=0,mode='constant',anti_aliasing=False)
newarray = downscale_local_mean(intarray,(factor,factor))
return newarray
def fill(data, invalid=None):
'''Nearest neighbor interpolation gap fill by Juh_'''
if invalid is None: invalid = np.isnan(data)
ind = nd.distance_transform_edt(invalid, return_distances=False, return_indices=True)
return data[tuple(ind)]
def load_config(filepath):
'''Load configuration file into dict'''
df = pd.read_csv(filepath,header=None,index_col=False)
config = {}
for ii in np.arange(len(df)):
string = df.iloc[ii,0].replace(" ","")
varname = string.rpartition('=')[0]
varcontents = string.rpartition('=')[2]
try:
varcontents = float(varcontents)
except:
pass
config[varname] = varcontents
return config
def initialize_netcdf(outfile,lat,lon,varname,units,least_significant_digit):
ncfile = Dataset(outfile, 'w', format='NETCDF4')
ncfile.history = 'Created on %s' % datetime.utcnow().strftime('%Y-%m-%d %H:%M')
ncfile.createDimension('lon', len(lon))
ncfile.createDimension('lat', len(lat))
ncfile.createDimension('time', None)
ncfile.createVariable('lon', 'f8', ('lon',))
ncfile.variables['lon'][:] = lon
ncfile.variables['lon'].units = 'degrees_east'
ncfile.variables['lon'].long_name = 'longitude'
ncfile.createVariable('lat', 'f8', ('lat',))
ncfile.variables['lat'][:] = lat
ncfile.variables['lat'].units = 'degrees_north'
ncfile.variables['lat'].long_name = 'latitude'
ncfile.createVariable('time', 'f8', 'time')
ncfile.variables['time'].units = 'days since 1979-01-02 00:00:00'
ncfile.variables['time'].long_name = 'time'
ncfile.variables['time'].calendar = 'proleptic_gregorian'
ncfile.createVariable(varname, np.single, ('time', 'lat', 'lon'),zlib=True,
chunksizes=(1,450,450,), fill_value=-9999,
least_significant_digit=least_significant_digit)
ncfile.variables[varname].units = units
return ncfile
def potential_evaporation(data,albedo,factor,doy,lat,elev):
"""
Calculate potential evaporation (mm/d) using an approach based on Penman-
Monteith. More details provided in the LISVAP documentation (Van der
Knijff, 2006).
Van der Knijff, J., 2006. LISVAP – Evaporation Pre-Processor for the
LISFLOOD Water Balance and Flood Simulation Model, User Manual. EUR
22639 EN, Office for Official Publications of the European
Communities, Luxembourg, 31 pp.
INPUTS
data: Dict with grids of tmean, tmin, tmax (all in degrees Celsius),
relhum (%), wind (m/s), pres(mbar), swd (W/m2), and lwd (W/m2)
albedo: Albedo (0 to 1)
factor: Empirical factor related to land cover (>0)
doy: Day of year (1 to 366)
lat: Latitude grid (degrees)
elev: Elevation grid (m asl)
OUTPUTS
pet: Potential evaporation (mm/d)
"""
# Difference between daily maximum and minimum temperature (degrees C)
DeltaT = data['tmax']-data['tmin']
DeltaT[DeltaT<0] = 0
# Empirical constant in windspeed formula (if DeltaT is less than 12
# degrees C, BU=0.54)
BU = 0.54+0.35*((DeltaT-12)/4)
BU[BU<0.54] = 0.54
# Goudriaan equation (1977) to calculate saturated vapour pressure (mbar)
ESat = 6.10588*np.exp((17.32491*data['tmean'])/(data['tmean']+238.102))
# Actual vapor pressure calculated from relative humidity (mbar)
EAct = data['relhum']*ESat/100
# Vapour pressure deficit (mbar)
VapPressDef = ESat-EAct
VapPressDef[VapPressDef<0] = 0
# Evaporative demand (mm/d)
EA = {}
for key in factor.keys():
EA[key] = 0.26*VapPressDef*(factor[key]+BU*data['wind'])
# Latent heat of vaporization (MJ/kg)
LatHeatVap = 2.501-0.002361*data['tmean']
# Allen et al. (1998) equation 8 (mbar/degrees C)
Psychro = 10*(1.013*10**-3*data['pres']/10)/(0.622*LatHeatVap)
# Slope of saturated vapour pressure curve (mbar/degrees C)
Delta = (238.102*17.32491*ESat)/((data['tmean']+238.102)**2)
#--------------------------------------------------------------------------
# Extra-terrestrial radiation
#--------------------------------------------------------------------------
# Solar declination (rad)
Declin_rad = np.arcsin(0.39795*np.cos(0.2163108+2*np.arctan(0.9671396*np.tan(0.00860*(doy-186)))))
# Convert latitude from degrees to radians
lat_rad = lat*np.pi/180
# Solar constant at top of the atmosphere (J/m2/s)
SolarConstant = 1370*(1+0.033*np.cos(2*np.pi*doy/365))
# Day length (h) equation from Forsythe et al. (1995; https://doi.org/10.1016/0304-3800(94)00034-F)
sinLD = np.sin(Declin_rad)*np.sin(lat_rad)
cosLD = np.cos(Declin_rad)*np.cos(lat_rad)
DayLength = 24-(24/np.pi)*np.arccos((np.sin(0.8333*np.pi/180)+sinLD)/cosLD)
DayLength = np.tile(fill(DayLength[:,:1]),(1,DayLength.shape[1])) # Nearest-neighbor gap filling
# Integral of solar height over the day (s)
IntSolarHeight = 3600*(DayLength*sinLD+(24/np.pi)*cosLD*np.sqrt(1-(sinLD/cosLD)**2))
IntSolarHeight[IntSolarHeight<0] = 0
IntSolarHeight = np.tile(fill(IntSolarHeight[:,:1]),(1,IntSolarHeight.shape[1])) # Nearest-neighbor gap filling
# Daily extra-terrestrial radiation (J/m2/d)
Ra = IntSolarHeight*SolarConstant
#--------------------------------------------------------------------------
# Net absorbed radiation
#--------------------------------------------------------------------------
# Clear-sky radiation (J/m2/d) from Allen et al. (1998; equation 37)
Rso = Ra*(0.75+(2*10**-5*elev))
# Adjustment factor for cloud cover
TransAtm_Allen = (data['swd']*86400+1)/(Rso+1)
AdjCC = 1.8*TransAtm_Allen-0.35
AdjCC[AdjCC<0.05] = 0.05
AdjCC[AdjCC>1] = 1
# Net emissivity
EmNet = 0.56-0.079*np.sqrt(EAct)
# Net longwave radiation (J/m2/d)
StefBoltzConstant = 4.903*10**-3 # J/K4/m2/d
RN = StefBoltzConstant*((data['tmean']+273.15)**4)*EmNet*AdjCC
# Net absorbed radiation of reference vegetation canopy (mm/d)
RNA = {}
for key in albedo.keys():
RNA[key] = ((1-albedo[key])*data['swd']*86400-RN)/(10**6*LatHeatVap)
RNA[key] = RNA[key].clip(0,None)
# Potential reference evapotranspiration rate (mm/d)
pet = {}
for key in albedo.keys():
pet[key] = ((Delta*RNA[key])+(Psychro*EA[key]))/(Delta+Psychro)
return pet
def makefig(folder,title,data,vmin,vmax):
if os.path.isdir(folder)==False:
os.makedirs(folder)
plt.figure()
ax = plt.gca()
im = ax.imshow(data,vmin=vmin,vmax=vmax)
ax.set_axis_off()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.title(title)
plt.savefig(os.path.join(folder,title+'.png'),dpi=300,bbox_inches='tight')
plt.close()