def sum_swe_mod(lats, lons, swe, basin, model, scenario, month):
import numpy as np
import math
from snowpack_functions import lat_lon_adjust, get_dist, calc_area, unpack_netcdf_swe_month, mask_latlon
resol = 0.0625
swe_yearly_agg = np.ndarray(shape=(len(swe), 1), dtype=float)
## get historical SWE for model so that historical mean can be determined for mask
lats_hist, lons_hist, swe_hist, datess_hist = unpack_netcdf_swe_month(
basin, model, 'historical', month)
## arrays for latitude and longitude values included in the historical mean mask
lats_lons_inc_in_mask = list()
################# loop through array and get latitude, longitude and swe values for each #############
count_appending_latslons = 0
for i in np.arange(len(swe)): ### loop over year
count_appending_latslons += 1
## initialize value of SWE for each year to zero
swe_gridcell_total = 0
for j in np.arange(len(lats)): ### loop over latitude
for k in np.arange(len(lons)): ### loop over longitude
### don't calculate area for missing value elements
if (math.isnan(swe[i, j, k])) == False:
## REMOVE ADDITIONAL GRID CELLS ACCORDING TO LAT_LON_ADJUST FOR BOXES AND ADJUSTMENTS (LATER MASKS)
if_in_box = mask_latlon(lats[j], lons[k], basin)
adjust_mask = lat_lon_adjust(lats[j], lons[k], basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
## CALCULATE MEAN HISTORICAL SWE
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year, j, k]
mean_swe = historical_sum_swe / len(swe_hist)
########################### EXCLUDE GRID CELLS WITH MEAN HISTORICAL SWE < 10 MM
if (mean_swe >= 10):
### calculate area of grid cell
cellarea = calc_area(lats[j], lons[k], resol)
## calculate amount of swe in each grid cell: swe (in mm) * area of grid cell; adjust for units
swe_gridcell = cellarea * (swe[i, j, k] * 0.000001)
## sum up swe
swe_gridcell_total += swe_gridcell
## ONLY ADD LATS/LONS TO LIST FOR FIRST LOOP
if (count_appending_latslons < 2):
points = [lats[j], lons[k]]
lats_lons_inc_in_mask.append(points)
## summed up yearly April 1 aggregate snowpack
swe_yearly_agg[i] = swe_gridcell_total
##### save array to files for a multimodel average
## define path based on scenario
filearrayname = '/raid9/gergel/agg_snowpack/%s/%s_%s_%s.npz' % (
scenario, model, basin, month)
np.savez(filearrayname,
swe=swe_yearly_agg,
latslons=np.asarray(lats_lons_inc_in_mask))
return (swe_yearly_agg, lats_lons_inc_in_mask)
def sum_swe_mod(lats,lons,swe,basin,model,scenario,month):
import numpy as np
import math
from snowpack_functions import lat_lon_adjust,get_dist,calc_area,unpack_netcdf_swe_month,mask_latlon
resol=0.0625
swe_yearly_agg = np.ndarray(shape = (len(swe),1), dtype=float)
## get historical SWE for model so that historical mean can be determined for mask
lats_hist,lons_hist,swe_hist,datess_hist = unpack_netcdf_swe_month(basin,model,'historical',month)
## arrays for latitude and longitude values included in the historical mean mask
lats_lons_inc_in_mask = list()
################# loop through array and get latitude, longitude and swe values for each #############
count_appending_latslons = 0
for i in np.arange(len(swe)): ### loop over year
count_appending_latslons += 1
## initialize value of SWE for each year to zero
swe_gridcell_total = 0
for j in np.arange(len(lats)): ### loop over latitude
for k in np.arange(len(lons)): ### loop over longitude
### don't calculate area for missing value elements
if (math.isnan(swe[i,j,k])) == False:
## REMOVE ADDITIONAL GRID CELLS ACCORDING TO LAT_LON_ADJUST FOR BOXES AND ADJUSTMENTS (LATER MASKS)
if_in_box = mask_latlon(lats[j],lons[k],basin)
adjust_mask = lat_lon_adjust(lats[j],lons[k],basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
## CALCULATE MEAN HISTORICAL SWE
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year,j,k]
mean_swe = historical_sum_swe/len(swe_hist)
########################### EXCLUDE GRID CELLS WITH MEAN HISTORICAL SWE < 10 MM
if (mean_swe >= 10):
### calculate area of grid cell
cellarea = calc_area(lats[j],lons[k],resol)
## calculate amount of swe in each grid cell: swe (in mm) * area of grid cell; adjust for units
swe_gridcell = cellarea*(swe[i,j,k]*0.000001)
## sum up swe
swe_gridcell_total += swe_gridcell
## ONLY ADD LATS/LONS TO LIST FOR FIRST LOOP
if (count_appending_latslons < 2):
points = [lats[j],lons[k]]
lats_lons_inc_in_mask.append(points)
## summed up yearly April 1 aggregate snowpack
swe_yearly_agg[i] = swe_gridcell_total
##### save array to files for a multimodel average
## define path based on scenario
filearrayname = '/raid9/gergel/agg_snowpack/%s/%s_%s_%s.npz' %(scenario,model,basin,month)
np.savez(filearrayname,swe=swe_yearly_agg,latslons=np.asarray(lats_lons_inc_in_mask))
return (swe_yearly_agg,lats_lons_inc_in_mask)
direc = '/raid9/gergel/agg_snowpack/snotel_vic/vic_output/%s' %basin
site_ids = list()
for filename in os.listdir(direc): ## get list of snotel site ids
site_ids.append(filename)
if "11H59S" in site_ids: ## this is a missing snotel station in the Southern Rockies
site_ids.remove("11H59S")
arr_site_ids = np.asarray(site_ids)
vic_swe = list()
for site in arr_site_ids:
direcsite = '/raid9/gergel/agg_snowpack/snotel_vic/vic_output/%s/%s/fluxes__*' %(basin,site)
for pathfile in glob.glob(direcsite):
path,fname = os.path.split(pathfile)
elev,lat,lon = get_snotel_elevation(site)
snow_band,lat,lon = get_snow_band(fname,elev) ## get which snowband to use for snotel elevation
mask1 = lat_lon_adjust(float(lat),float(lon),basin)
mask2 = mask_latlon(float(lat),float(lon),basin)
if mask1 and mask2: ## apply further masking: include grid cell IF within mask
if snow_band == 0:
data = np.loadtxt(pathfile,dtype='float',usecols=(3,),delimiter='\t')
elif snow_band == 1:
data = np.loadtxt(pathfile,dtype='float',usecols=(4,),delimiter='\t')
elif snow_band == 2:
data = np.loadtxt(pathfile,dtype='float',usecols=(5,),delimiter='\t')
elif snow_band == 3:
data = np.loadtxt(pathfile,dtype='float',usecols=(6,),delimiter='\t')
else:
data = np.loadtxt(pathfile,dtype='float',usecols=(7,),delimiter='\t')
vic_swe.append(data[:]) ## add to vic swe list
## step 3: average over all vic simulations
avg_vic = np.mean(np.asarray(vic_swe),axis=0)
## get historical SWE data to determine which lats/lons to include in analysis
lats, lons, swe_hist, datess_swe_hist = unpack_netcdf_gen(
"SWE", basin, "historical")
## get RD, SD, TR historical classifications
for j in np.arange(len(lats)): ## loop over latitude
for k in np.arange(len(lons)): ## loop over longitude
for i in np.arange(len(swe)): ### loop over year
## array for temp average
temp_averrage = list()
### don't calculate area for missing value elements
if (math.isnan(swe[i, j, k])) == False:
## REMOVE ADDITIONAL GRID CELLS ACCORDING TO LAT_LON_ADJUST FOR BOXES AND ADJUSTMENTS (LATER MASKS)
if_in_box = mask_latlon(lats[j], lons[k], basin)
adjust_mask = lat_lon_adjust(lats[j], lons[k], basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
## CALCULATE MEAN HISTORICAL SWE
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year, j, k]
mean_swe = historical_sum_swe / len(swe_hist)
## EXCLUDE GRID CELLS WITH MEAN HISTORICAL SWE < 10 MM
if (mean_swe >= 10):
ind = i * 5
temp_maxx = (
temp_max[ind, j, k] + temp_max[ind + 1, j, k] +
temp_max[ind + 2, j, k] + temp_max[ind + 3, j, k] +
temp_max[ind + 4, j, k]) / 5
temp_minn = (
petnat = petnat[237:-74,:,:]
petshort = petshort[237:-74,:,:]
pettall = pettall[237:-74,:,:]
evap = evap[237:-74,:,:]
transp = transp[237:-74,:,:]
else:
petnat = petnat[45:-2,:,:]
petshort = petshort[45:-2,:,:]
pettall = pettall[45:-2,:,:]
evap = evap[45:-2,:,:]
transp = transp[45:-2,:,:]
for j in np.arange(len(lats)): ## loop over latitude
for k in np.arange(len(lons)): ## loop over longitude
### don't calculate area for missing value elements
if (math.isnan(swe[0,j,k])) == False:
if_in_box = mask_latlon(lats[j],lons[k],basin)
adjust_mask = lat_lon_adjust(lats[j],lons[k],basin)
mean_swe = historical_sum_swe(j,k)
## new historical swe function based on livneh instead of vic simulations
if if_in_box and adjust_mask and mean_swe:
petsum = list()
aetsum = list()
if (scenario == "historical"):
for i in np.arange(30): ## now loop over year
ind = i*12
petsum.append(np.sum(np.asarray(petnat[ind:ind+12,j,k])) + np.sum(np.asarray(petshort[ind:ind+12,j,k])) + np.sum(np.asarray(pettall[ind:ind+12,j,k])))
aetsum.append(np.sum(np.asarray(evap[ind:ind+12,j,k])) + np.sum(np.asarray(transp[ind:ind+12,j,k])))
pet_agg.append(np.asarray(petsum).reshape(len(np.asarray(petsum)),1))
aet_agg.append(np.asarray(aetsum).reshape(1,len(np.asarray(aetsum)),1))
lats_inc.append(lats[j])
lons_inc.append(lons[k])
def swe_percs(lats,lons,swe,datess, elev_corr_info, basin,scenario):
import numpy as np
import math
from snowpack_functions import lat_lon_adjust,get_dist,calc_area,unpack_netcdf_swe_ensavg,mask_latlon,get_elev_for_lat_lon
## initialize lists for percentiles of elevations
swe_10 = list()
swe_25 = list()
swe_50 = list()
swe_75 = list()
swe_90 = list()
## get historical SWE for model so that historical mean can be determined for mask
lats_hist,lons_hist,swe_hist,datess_hist = unpack_netcdf_swe_ensavg(basin,'historical')
for i in np.arange(len(swe)): ### loop over year
## start a new list of SWE and Elevation points every year
swe_inc = list()
elev_inc = list()
## initialize SWE year total to zero
swe_tot = 0
for j in np.arange(len(lats)): ### loop over latitude
for k in np.arange(len(lons)): ### loop over longitude
### don't calculate area for missing value elements
if (math.isnan(swe[i,j,k])) == False:
## REMOVE ADDITIONAL GRID CELLS ACCORDING TO LAT_LON_ADJUST FOR BOXES AND ADJUSTMENTS (LATER MASKS)
if_in_box = mask_latlon(lats[j],lons[k],basin)
adjust_mask = lat_lon_adjust(lats[j],lons[k],basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
## CALCULATE MEAN HISTORICAL SWE
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year,j,k]
mean_swe = historical_sum_swe/len(swe_hist)
########################### EXCLUDE GRID CELLS WITH MEAN HISTORICAL SWE < 10 MM
if (mean_swe >= 10):
# get swe and elevation values and add to list for each grid cell for the given year
swe_inc.append(swe[i,j,k])
elev_inc.append(get_elev_for_lat_lon(elev_corr_info,lats[j],lons[k]))
## get total value of swe for given year
swe_tot += swe[i,j,k]
## total value of swe for given year is swe_tot
## sort elev_inc by elevation and swe_inc in same way (ascending order)
elev_sorted = sorted(elev_inc)
swe_sorted = [swe_inc for (elev_inc,swe_inc) in sorted(zip(elev_inc,swe_inc))]
print("the length of swe_sorted is %f" %len(swe_sorted))
## index lists for percentiles
p10 = list()
p25 = list()
p50 = list()
p75 = list()
p90 = list()
swe_cumsum = np.cumsum(swe_sorted)
for num in np.arange(len(swe_cumsum)):
if (swe_cumsum[num] >= 0.9*swe_tot):
p90.append(num)
print(num)
elif (swe_cumsum[num] >= 0.75*swe_tot):
p75.append(num)
print("25 percent")
elif (swe_cumsum[num] >= 0.5*swe_tot):
p50.append(num)
elif (swe_cumsum[num] >= 0.25*swe_tot):
p25.append(num)
elif (swe_cumsum[num] >= 0.1*swe_tot):
p10.append(num)
else:
print("this swe sum is small")
swe_10.append(elev_sorted[np.min(p10)])
swe_25.append(elev_sorted[np.min(p25)])
swe_50.append(elev_sorted[np.min(p50)])
swe_75.append(elev_sorted[np.min(p75)])
swe_90.append(elev_sorted[np.min(p90)])
return (datess, swe_10,swe_25,swe_50,swe_75,swe_90)
for filename in os.listdir(direc): ## get list of snotel site ids
site_ids.append(filename)
if "11H59S" in site_ids: ## this is a missing snotel station in the Southern Rockies
site_ids.remove("11H59S")
arr_site_ids = np.asarray(site_ids)
vic_swe = list()
for site in arr_site_ids:
direcsite = '/raid9/gergel/agg_snowpack/snotel_vic/vic_output/%s/%s/fluxes__*' % (
basin, site)
for pathfile in glob.glob(direcsite):
path, fname = os.path.split(pathfile)
elev, lat, lon = get_snotel_elevation(site)
snow_band, lat, lon = get_snow_band(
fname, elev) ## get which snowband to use for snotel elevation
mask1 = lat_lon_adjust(float(lat), float(lon), basin)
mask2 = mask_latlon(float(lat), float(lon), basin)
if mask1 and mask2: ## apply further masking: include grid cell IF within mask
if snow_band == 0:
data = np.loadtxt(pathfile,
dtype='float',
usecols=(3, ),
delimiter='\t')
elif snow_band == 1:
data = np.loadtxt(pathfile,
dtype='float',
usecols=(4, ),
delimiter='\t')
elif snow_band == 2:
data = np.loadtxt(pathfile,
dtype='float',
usecols=(5, ),
def swe_elevation_mean(lats,lons,swe,datess,elev_corr_info,basin,scenario):
import numpy as np
import math
from snowpack_functions import unpack_netcdf_file_var,get_elev_for_lat_lon,mask_latlon,lat_lon_adjust
## get historical SWE for model so that historical mean can be determined for mask
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' %basin
file_hist = 'SWE_ensavg_%s_%s.nc' %("historical",basin)
file = 'SWE_ensavg_%s_%s.nc' %(scenario,basin)
lats_hist,lons_hist,swe_hist,datess_hist = unpack_netcdf_file_var(direc,file_hist,"swe")
## list for latitude and longitude values included in the historical mean mask
lats_lons_inc_in_mask = list()
## initialize lists for storing grid cell elevations
if (scenario == "historical"):
swe_inc = list()
elev_inc = list()
else:
swe_1 = list()
elev_1 = list()
swe_2 = list()
elev_2 = list()
swe_3 = list()
elev_3 = list()
count = 0
################# loop through array and get latitude, longitude and swe values for each #############
for j in np.arange(len(lats)): ### loop over latitude
for k in np.arange(len(lons)): ### loop over longitude
### don't calculate area for missing value elements
### isnan will convert masked values to nan's, so this statement will evaluate to false if
### the value exists (and thus is not masked)
historical_mean_swe = 0
## calculate historical mean SWE for grid cell
if (math.isnan(swe[0,j,k]) == False):
if_in_box = mask_latlon(lats[j],lons[k],basin)
adjust_mask = lat_lon_adjust(lats[j],lons[k],basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year,j,k]
historical_mean_swe = historical_sum_swe/len(swe_hist)
if (historical_mean_swe >= 10):
count += 1
## get elevation of grid cell
elevation_individual_gridcell = get_elev_for_lat_lon(elev_corr_info,lats[j],lons[k])
## add swe and elevation values to lists depending on scenario
if (scenario == "historical"):
sum_swe = 0
for year in np.arange(len(swe)): ## calculate mean swe
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(swe)
mean_swe = mean_swe*0.001 # convert mm to meters
swe_inc.append(mean_swe)
elev_inc.append(elevation_individual_gridcell)
else:
sum_swe = 0
for year in np.arange(4,34):
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(np.arange(4,33))
mean_swe = mean_swe*0.001 # convert mm to meters
swe_1.append(mean_swe)
elev_1.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(34,64):
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(np.arange(4,33))
mean_swe = mean_swe*0.001 # convert mm to meters
swe_2.append(mean_swe)
elev_2.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(64,94):
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(np.arange(4,33))
mean_swe = mean_swe*0.001 # convert mm to meters
swe_3.append(mean_swe)
elev_3.append(elevation_individual_gridcell)
print(count)
##### save arrays to files for a multimodel average (and for spatial plots with lats and lons)
## define path based on scenario
filearrayname = '/raid9/gergel/agg_snowpack/elevations/ensavg_%s_%s.npz' %(basin,scenario)
if (scenario == "historical"):
np.savez(filearrayname,swe=np.asarray(swe_inc),elevations=np.asarray(elev_inc))
return (swe_inc,elev_inc)
else:
np.savez(filearrayname,swe1=np.asarray(swe_1),swe2=np.asarray(swe_2),swe3=np.asarray(swe_3),elev1=np.asarray(elev_1),elev2=np.asarray(elev_2),elev3=np.asarray(elev_3))
return (swe_1,swe_2,swe_3,elev_1,elev_2,elev_3)
def swe_elevation_mean(lats, lons, swe, datess, elev_corr_info, basin,
scenario):
import numpy as np
import math
from snowpack_functions import unpack_netcdf_file_var, get_elev_for_lat_lon, mask_latlon, lat_lon_adjust
## get historical SWE for model so that historical mean can be determined for mask
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' % basin
file_hist = 'SWE_ensavg_%s_%s.nc' % ("historical", basin)
file = 'SWE_ensavg_%s_%s.nc' % (scenario, basin)
lats_hist, lons_hist, swe_hist, datess_hist = unpack_netcdf_file_var(
direc, file_hist, "swe")
## list for latitude and longitude values included in the historical mean mask
lats_lons_inc_in_mask = list()
## initialize lists for storing grid cell elevations
if (scenario == "historical"):
swe_inc = list()
elev_inc = list()
else:
swe_1 = list()
elev_1 = list()
swe_2 = list()
elev_2 = list()
swe_3 = list()
elev_3 = list()
count = 0
################# loop through array and get latitude, longitude and swe values for each #############
for j in np.arange(len(lats)): ### loop over latitude
for k in np.arange(len(lons)): ### loop over longitude
### don't calculate area for missing value elements
### isnan will convert masked values to nan's, so this statement will evaluate to false if
### the value exists (and thus is not masked)
historical_mean_swe = 0
## calculate historical mean SWE for grid cell
if (math.isnan(swe[0, j, k]) == False):
if_in_box = mask_latlon(lats[j], lons[k], basin)
adjust_mask = lat_lon_adjust(lats[j], lons[k], basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year, j, k]
historical_mean_swe = historical_sum_swe / len(swe_hist)
if (historical_mean_swe >= 10):
count += 1
## get elevation of grid cell
elevation_individual_gridcell = get_elev_for_lat_lon(
elev_corr_info, lats[j], lons[k])
## add swe and elevation values to lists depending on scenario
if (scenario == "historical"):
sum_swe = 0
for year in np.arange(
len(swe)): ## calculate mean swe
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(swe)
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_inc.append(mean_swe)
elev_inc.append(elevation_individual_gridcell)
else:
sum_swe = 0
for year in np.arange(4, 34):
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(np.arange(4, 33))
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_1.append(mean_swe)
elev_1.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(34, 64):
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(np.arange(4, 33))
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_2.append(mean_swe)
elev_2.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(64, 94):
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(np.arange(4, 33))
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_3.append(mean_swe)
elev_3.append(elevation_individual_gridcell)
print(count)
##### save arrays to files for a multimodel average (and for spatial plots with lats and lons)
## define path based on scenario
filearrayname = '/raid9/gergel/agg_snowpack/elevations/ensavg_%s_%s.npz' % (
basin, scenario)
if (scenario == "historical"):
np.savez(filearrayname,
swe=np.asarray(swe_inc),
elevations=np.asarray(elev_inc))
return (swe_inc, elev_inc)
else:
np.savez(filearrayname,
swe1=np.asarray(swe_1),
swe2=np.asarray(swe_2),
swe3=np.asarray(swe_3),
elev1=np.asarray(elev_1),
elev2=np.asarray(elev_2),
elev3=np.asarray(elev_3))
return (swe_1, swe_2, swe_3, elev_1, elev_2, elev_3)
snotel_swe = list()
#snotel_swe = np.ndarray(shape=(len(arr_site_ids),len(arr_dates)),dtype=float)
rowcount = 0
for site in arr_site_ids:
snotel_site_swe = list()
snotel_dates = list()
print(site)
filename = 'swe.%s.dat' % site
elev, lat, lon = get_snotel_elevation(
site) ## get elevation of snotel site
lat_sno, lon_sno = find_gridcell(
float(lat),
float(lon)) ## figure out which gridcell the snotel site is in
mask3 = lat_lon_adjust(float(lat_sno), float(lon_sno),
basin) ## apply first lat/lon mask
mask4 = mask_latlon(float(lat_sno), float(lon_sno),
basin) ## apply second lat/lon mask
if mask3 and mask4:
snotel_data = np.loadtxt(os.path.join(direc_snotel, filename),
dtype='str',
delimiter='\t')
for day in np.arange(len(snotel_data)):
eachday = snotel_data[day].split()
if np.float(eachday[0][:4]) >= 1987 and np.float(
eachday[0][:4]) <= 2005:
snotel_dates.append(
datetime.datetime.strptime(eachday[0], '%Y%m%d'))
snotel_site_swe.append(np.float(eachday[1]))
arr_snotel_site_swe = np.asarray(snotel_site_swe)
print(len(arr_snotel_site_swe))
arr_snotel_site_swe[arr_snotel_site_swe <
0] = np.nan ## change -99 values in swe to nan
## end date + 1 (will only produce specified end date - 1)
end_date = datetime.datetime(2006, 1, 1)
arr_dates = [base + datetime.timedelta(days=i) for i in range(0, (end_date-base).days)]
direc_snotel = '/raid9/gergel/vic_sim_obs/snotel_data/US_swe'
snotel_swe = list()
#snotel_swe = np.ndarray(shape=(len(arr_site_ids),len(arr_dates)),dtype=float)
rowcount = 0
for site in arr_site_ids:
snotel_site_swe = list()
snotel_dates = list()
print(site)
filename = 'swe.%s.dat' %site
elev,lat,lon = get_snotel_elevation(site) ## get elevation of snotel site
lat_sno,lon_sno = find_gridcell(float(lat),float(lon)) ## figure out which gridcell the snotel site is in
mask3 = lat_lon_adjust(float(lat_sno),float(lon_sno),basin) ## apply first lat/lon mask
mask4 = mask_latlon(float(lat_sno),float(lon_sno),basin) ## apply second lat/lon mask
if mask3 and mask4:
snotel_data = np.loadtxt(os.path.join(direc_snotel,filename),dtype='str',delimiter='\t')
for day in np.arange(len(snotel_data)):
eachday = snotel_data[day].split()
if np.float(eachday[0][:4]) >= 1987 and np.float(eachday[0][:4]) <= 2005:
snotel_dates.append(datetime.datetime.strptime(eachday[0],'%Y%m%d'))
snotel_site_swe.append(np.float(eachday[1]))
arr_snotel_site_swe = np.asarray(snotel_site_swe)
print(len(arr_snotel_site_swe))
arr_snotel_site_swe[arr_snotel_site_swe < 0]=np.nan ## change -99 values in swe to nan
# snotel_swe.append(arr_snotel_site_swe)
## deal with missing values using pandas merge
df_full = pd.DataFrame({'cola':arr_dates})
df_part = pd.DataFrame({'cola':snotel_dates,'swe':arr_snotel_site_swe.tolist()})
## now join dataframes so that missing values are populated with nans