def get_timestep_ipw(tstep, input_list, ppt_list, myawsm):
"""
Pull out a time step from the forcing files (IPW) and
place that time step into a dict
Args:
tstep: datetime of timestep
input_list: numpy array (1D) of integer timesteps given
ppt_list: numpy array(1D) of integer timesteps for ppt_list
myawsm: AWSM instance for current run
Returns:
inpt: dictionary of forcing variable images
"""
inpt = {}
# map function from these values to the ones requried by snobal
map_val = {1: 'T_a', 5: 'S_n', 0: 'I_lw', 2: 'e_a', 3: 'u'}
map_val_prec = {0: 'm_pp', 1: 'percent_snow', 2: 'rho_snow', 3: 'T_pp'}
# get wy hour
wyhr = int(utils.water_day(tstep)[0] * 24)
# if we have inputs matching this water year hour
if np.any(input_list == wyhr):
i_in = ipw.IPW(os.path.join(myawsm.pathi, 'in.%04i' % (wyhr)))
# assign soil temp
inpt['T_g'] = myawsm.soil_temp * np.ones(
(myawsm.topo.ny, myawsm.topo.nx))
# myawsm._logger.info('T_g: {}'.format(myawsm.soil_temp))
# inpt['T_g'] = -2.5*np.ones((myawsm.topo.ny, myawsm.topo.nx))
for f, v in map_val.items():
# if no solar data, give it zero
if f == 5 and len(i_in.bands) < 6:
# myawsm._logger.info('No solar data for {}'.format(tstep))
inpt[v] = np.zeros((myawsm.topo.ny, myawsm.topo.nx))
else:
inpt[v] = i_in.bands[f].data
# assign ppt data if there
else:
raise ValueError('No input timesteps for {}'.format(tstep))
if np.any(ppt_list == wyhr):
i_ppt = ipw.IPW(os.path.join(myawsm.path_ppt, 'ppt.4b_%04i' % (wyhr)))
for f, v in map_val_prec.items():
inpt[v] = i_ppt.bands[f].data
else:
for f, v in map_val_prec.items():
inpt[v] = np.zeros((myawsm.topo.ny, myawsm.topo.nx))
# convert from C to K
inpt['T_a'] += FREEZE
inpt['T_pp'] += FREEZE
inpt['T_g'] += FREEZE
return inpt
def write_init(self):
"""
Write the iSnobal init file
"""
# get mask
mask = self.topo.mask
# make ipw init file
i_out = ipw.IPW()
i_out.new_band(self.init['elevation'])
i_out.new_band(self.init['z_0'])
i_out.new_band(self.init['z_s']*mask) # snow depth
i_out.new_band(self.init['rho']*mask) # snow density
i_out.new_band(self.init['T_s_0']*mask) # active layer temp
if self.start_wyhr > 0 or self.restart_crash:
i_out.new_band(self.init['T_s_l']*mask) # lower layer temp
i_out.new_band(self.init['T_s']*mask) # avgerage snow temp
i_out.new_band(self.init['h2o_sat']*mask) # percent saturatio
i_out.add_geo_hdr([self.topo.u, self.topo.v],
[self.topo.du, self.topo.dv],
self.topo.units, self.csys)
i_out.write(self.fp_init, 16)
def snow_nc(self):
''' Takes directory of ipw files from Hedrick18 and squishes them into a netCDF '''
# S is a dictionary list or something which is hardcoded band names and attributes for netCDF
# from AWSM/convertfiles/convertFiles
s = {}
s['name'] = [
'thickness', 'snow_density', 'specific_mass', 'liquid_water',
'temp_surf', 'temp_lower', 'temp_snowcover', 'thickness_lower',
'water_saturation'
]
s['units'] = [
'm', 'kg m-3', 'kg m-2', 'kg m-2', 'C', 'C', 'C', 'm', 'percent'
]
s['description'] = [
'Predicted thickness of the snowcover',
'Predicted average snow density',
'Predicted specific mass of the snowcover',
'Predicted mass of liquid water in the snowcover',
'Predicted temperature of the surface layer',
'Predicted temperature of the lower layer',
'Predicted temperature of the snowcover',
'Predicted thickness of the lower layer',
'Predicted percentage of liquid water'
]
# Tell file where to save and name
self.nc_file = os.path.join(self.file_path_out, self.file_name_out)
# Get X,Y and Time saved to nc file
snow = self.base_nc()
for i, v in enumerate(self.file_path_ipw):
snow.variables['time'][i] = self.file_num[
i] #file number is actually the hours after WY i.e. 23, 47, etc.
for i2, v2 in enumerate(s['name']):
# snow.createVariable(v, 'f', self.dimensions[:3], chunksizes=(6, 10, 10))
if i == 0:
snow.createVariable(v2, 'f', self.dimensions[:3])
setattr(snow.variables[v2], 'units', s['units'][i2])
setattr(snow.variables[v2], 'description',
s['description'][i2])
snow.variables[v2][i, :, :] = ipw.IPW(v).bands[i2].data
else:
snow.variables[v2][i, :, :] = ipw.IPW(v).bands[i2].data
# snow.variables[v][0,:,:] = self.var_data[v]
# print(snow.variables[v][0,:,:].shape)
# print(self.var_data[v].shape)
print('Adding WY day: ', i)
self.finish_nc(snow)
def readImages(self):
"""
Read in the images from the config file
"""
if 'dem' not in self.topoConfig:
raise ValueError('DEM file not specified')
# read in the images
for v in self.images:
if v in self.topoConfig:
i = ipw.IPW(self.topoConfig[v])
setattr(self, v, i.bands[0].data.astype(np.float64))
if v == 'dem':
# get some general information about the model
# domain from the dem
self.ny = i.nlines
self.nx = i.nsamps
self.u = i.bands[0].bline
self.v = i.bands[0].bsamp
self.du = i.bands[0].dline
self.dv = i.bands[0].dsamp
self.units = i.bands[0].geounits
self.coord_sys_ID = i.bands[0].coord_sys_ID
else:
setattr(self, v, None)
# set roughness if not given
if 'roughness' in self.topoConfig:
self.roughness = ipw.IPW(
self.topoConfig['roughness']).bands[0].data.astype(np.float64)
else:
print('No surface roughness given in topo, setting to 5mm')
self.roughness = 0.005 * np.ones((self.ny, self.nx))
# create the x,y vectors
self.x = self.v + self.dv * np.arange(self.nx)
self.y = self.u + self.du * np.arange(self.ny)
[self.X, self.Y] = np.meshgrid(self.x, self.y)
def get_ipw_out(self):
"""
Set init fields for iSnobal out as init file
"""
i_in = ipw.IPW(self.init_file)
self.init['z_s'] = i_in.bands[0].data*self.topo.mask # snow depth
self.init['rho'] = i_in.bands[1].data*self.topo.mask # snow density
self.init['T_s_0'] = i_in.bands[4].data*self.topo.mask # active layer temp
self.init['T_s_l'] = i_in.bands[5].data*self.topo.mask # lower layer temp
self.init['T_s'] = i_in.bands[6].data*self.topo.mask # avgerage snow temp
self.init['h2o_sat'] = i_in.bands[8].data*self.topo.mask # percent saturation
def height_to_type(veg_height):
'''ZRU: this takes veg_height.ipw, reclassifies as 1,2,...N (N = number of distinct veg heights),
and outputs nparray. Note: classifications are ordered from shortest to tallest heights'''
mat = ipw.IPW(
veg_height).bands[0].data # grab veg_height array from ipw image
hts = [] #init list to collect unique heights
for i in np.arange(mat.shape[0]):
for j in np.arange(mat.shape[1]):
if mat[i, j] in hts:
pass
else:
hts.append(mat[i, j])
cls = mat.copy()
hts.sort()
for idx, vals in enumerate(hts):
cls[cls == vals] = idx + 1
return cls
def __init__(self, topoConfig, mask_isnobal, model_type, csys, dir_m):
"""
Args:
topoConfig: config section for topo from smrf config
mask_isnobal: Boolean for masking iSnobal
model_type: Type of snow model
csys: Coordinate system id
dir_m: Directory in which to write mask if needed
"""
self.topoConfig = topoConfig
#logger.debug('Reading in topo info for AWSM')
# read images
self.img_type = self.topoConfig['type']
if self.img_type == 'ipw':
self.readImages()
elif self.img_type == 'netcdf':
self.readNetCDF()
# assign path to mask, write mask if needed
# only needed if running iSnobal from ipw, not PySnobal
if mask_isnobal and model_type == 'isnobal':
if self.img_type == 'netcdf':
# assign path
self.fp_mask = os.path.join(dir_m, 'run_mask.ipw')
# write mask ipw file
i_out = ipw.IPW()
i_out.new_band(self.mask)
i_out.add_geo_hdr([self.u, self.v], [self.du, self.dv],
self.units, csys)
i_out.write(self.fp_mask, 16)
elif self.img_type == 'ipw':
self.fp_mask = self.topoConfig['mask']
else:
self.fp_mask = None
# make masks one if not masking model
if not mask_isnobal:
self.mask = np.ones_like(self.dem)
def get_topo_stats(fp, filetype='netcdf'):
"""
Get stats about topo from the topo file
Returns:
ts - dictionary of topo header data
"""
fp = os.path.abspath(fp)
ts = {}
if filetype == 'netcdf':
ds = Dataset(fp, 'r')
ts['units'] = ds.variables['y'].units
y = ds.variables['y'][:]
x = ds.variables['x'][:]
ts['nx'] = len(x)
ts['ny'] = len(y)
ts['du'] = y[1] - y[0]
ts['dv'] = x[1] - x[0]
ts['v'] = x[0]
ts['u'] = y[0]
ts['x'] = x
ts['y'] = y
ds.close()
if filetype == 'ipw':
i = ipw.IPW(fp)
ts['nx'] = i.nsamps
ts['ny'] = i.nlines
ts['units'] = i.bands[0].units
ts['du'] = i.bands[0].dline
ts['dv'] = i.bands[0].dsamp
ts['v'] = float(i.bands[0].bsamp)
ts['u'] = float(i.bands[0].bline)
ts['csys'] = i.bands[0].coord_sys_ID
ts['x'] = ts['v'] + ts['dv'] * np.arange(ts['nx'])
ts['y'] = ts['u'] + ts['du'] * np.arange(ts['ny'])
return ts
def get_ipw(self):
"""
Set init fields for iSnobal out as init file
"""
i_in = ipw.IPW(self.init_file)
self.init['z_0'] = i_in.bands[1].data*self.topo.mask # snow depth
self.logger.warning('Using roughness from iSnobal ipw init file for initializing of model!')
self.init['z_s'] = i_in.bands[2].data*self.topo.mask # snow depth
self.init['rho'] = i_in.bands[3].data*self.topo.mask # snow density
self.init['T_s_0'] = i_in.bands[4].data*self.topo.mask # active layer temp
# get bands depending on if there is a lower layer or not
if len(i_in.bands) == 8:
self.init['T_s_l'] = i_in.bands[5].data*self.topo.mask # lower layer temp
self.init['T_s'] = i_in.bands[6].data*self.topo.mask # avgerage snow temp
self.init['h2o_sat'] = i_in.bands[7].data*self.topo.mask # percent saturation
elif len(i_in.bands) == 7:
self.init['T_s'] = i_in.bands[5].data*self.topo.mask # avgerage snow temp
self.init['h2o_sat'] = i_in.bands[6].data*self.topo.mask # percent saturation
du = -100
dv = 100
units = 'm'
csys = 'UTM'
nx = 1500
ny = 1500
nbits = 16
# create the x,y vectors
x = v + dv * np.arange(nx)
y = u + du * np.arange(ny)
#------------------------------------------------------------------------------
# band 0 - DEM
d = ipw.IPW('dem.ipw')
dem = d.bands[0].data
#------------------------------------------------------------------------------
# band 1 - roughness length
rough = 0.005 * np.ones(dem.shape)
#------------------------------------------------------------------------------
# band 2 - total snowcover depth
depth = np.zeros(dem.shape)
#------------------------------------------------------------------------------
# band 3 - average snowcover density
density = np.zeros(dem.shape)
#------------------------------------------------------------------------------
def do_update_isnobal(self, myawsm, update_info, update_snow, x, y):
"""
Function to read in an output file and update it with the lidar depth field
Argument:
myawsm: instantiated awsm class
update_info: update info pandas at correct index
update_snow: file pointer to snow update image
x: x vector
y: y vector
Returns:
init_file: file pointer to init image
"""
# get some info
update_number = update_info['number']
date = update_info['date_time']
wyhr = update_info['wyhr']
last_snow_image = ipw.IPW(update_snow)
z_s = last_snow_image.bands[0].data # Get modeled depth image.
# z_s(mask==0) = NaN;
## Continue as before:
density = last_snow_image.bands[1].data.copy() # Get density image.
## ## ## ## ## ## ## ## ## %
# SPECIAL CASE... insert adjusted densities here:
# density = arcgridread_v2(['/Volumes/data/blizzard/Tuolumne/lidar/snowon/2017' ...
# '/adjusted_rho/TB2017' date_mmdd '_operational_rho_ARSgrid_50m.asc']);
## ## ## ## ## ## ## ## ## %
m_s = last_snow_image.bands[2].data.copy() # Get SWE image.
T_s_0 = last_snow_image.bands[4].data.copy(
) # Get active snow layer temperature image
T_s_l = last_snow_image.bands[5].data.copy(
) # Get lower snow layer temperature image
T_s = last_snow_image.bands[6].data.copy(
) # Get average snowpack temperature image
h2o_sat = last_snow_image.bands[8].data.copy(
) # Get liquid water saturation image
updated_fields = self.hedrick_updating_procedure(
m_s, T_s_0, T_s_l, T_s, h2o_sat, density, z_s, x, y, update_info)
# write init file
out_file = 'init_update_{}_wyhr{:04d}.ipw'.format(update_number, wyhr)
init_file = os.path.join(self.pathinit, out_file)
i_out = ipw.IPW()
i_out.new_band(updated_fields['dem'])
i_out.new_band(updated_fields['z0'])
i_out.new_band(updated_fields['D'])
i_out.new_band(updated_fields['rho'])
i_out.new_band(updated_fields['T_s_0'])
i_out.new_band(updated_fields['T_s_l'])
i_out.new_band(updated_fields['T_s'])
i_out.new_band(updated_fields['h2o_sat'])
#i_out.add_geo_hdr([u, v], [du, dv], units, csys)
i_out.add_geo_hdr([self.topo.u, self.topo.v],
[self.topo.du, self.topo.dv], self.topo.units,
self.csys)
i_out.write(init_file, self.nbits)
## Import newly-created init file and look at images to make sure they line up:
self._logger.info('Wrote ipw image for update {}'.format(wyhr))
return init_file
# ZRU 5/10/2019
# file pointers (mostly)
fp_veg_height = '/home/zachuhlmann/projects/zenodo_WRR_data/static_grids/tuolx_vegheight_50m.ipw'
fp_veg_type = '/home/zachuhlmann/projects/zenodo_WRR_data/static_grids/tuolx_vegnlcd_50m.ipw'
fp_veg_tau = '/home/zachuhlmann/projects/zenodo_WRR_data/static_grids/tuolx_vegtau_50m.ipw'
fp_veg_k = '/home/zachuhlmann/projects/zenodo_WRR_data/static_grids/tuolx_vegk_50m.ipw'
fp_dem = '/home/zachuhlmann/projects/zenodo_WRR_data/static_grids/tuolx_dem_50m.ipw'
fp_mask = '/home/zachuhlmann/projects/zenodo_WRR_data/static_grids/tuolx_hetchy_mask_50m.ipw'
ts = get_topo_stats(fp_veg_height,
filetype='ipw') # collects all the coordinate data
# Add all the layers
var_data = {}
var_data['veg_type'] = ipw.IPW(fp_veg_type).bands[0].data
var_data['veg_height'] = ipw.IPW(fp_veg_height).bands[0].data
var_data['veg_tau'] = ipw.IPW(fp_veg_tau).bands[0].data
var_data['veg_k'] = ipw.IPW(fp_veg_k).bands[0].data
var_data['dem'] = ipw.IPW(fp_dem).bands[0].data
var_data['mask'] = ipw.IPW(fp_mask).bands[0].data
var_data['x'] = ts['x']
var_data['y'] = ts['y']
tmp = var_data['veg_type'].round()
print('41 ', ((tmp == 41).sum()) / (tmp.shape[0] * tmp.shape[1]))
x = np.where(tmp == 41)
print(x[1][2])
print(x[0][2])
# # Not quite automated, but it will do
# var_list = ['veg_type', 'veg_height', 'veg_tau', 'veg_k', 'dem', 'mask']
def run():
fmt_file = fmt = '%Y%m%d'
#basin = 'SJ'
basin = 'TB'
wy = 2017
fpdir = '/home/micahsandusky/Code/awsfTesting/newupdatetest'
sj_updates = {}
sj_updates[2018] = ['2018-04-23', '2018-05-28']
sj_updates[2017] = []
# date_lst = ['2018-04-23', '2018-05-28']
tuol_updates = {}
tuol_updates[2013] = ['2013-04-03', '2013-04-29', '2013-05-03', '2013-05-25',
'2013-06-01', '2013-06-08']
tuol_updates[2014] = ['2014-03-23', '2014-04-07', '2014-04-13', '2014-04-20',
'2014-04-28', '2014-05-02', '2014-05-11', '2014-05-17',
'2014-05-27', '2014-05-31', '2014-06-05']
tuol_updates[2015] = ['2015-02-18', '2015-03-06', '2015-03-25', '2015-04-03',
'2015-04-09', '2015-04-15', '2015-04-27', '2015-05-01',
'2015-05-28', '2015-06-08']
tuol_updates[2016] = ['2016-03-26', '2016-04-07', '2016-04-16',
'2016-04-26', '2016-05-09', '2016-05-27', '2016-06-07',
'2016-06-13', '2016-06-20', '2016-06-25', '2016-07-01',
'2016-07-08']
tuol_updates[2017] = ['2017-01-29', '2017-03-03', '2017-04-01',
'2017-05-02', '2017-06-04', '2017-07-09', '2017-07-17',
'2017-08-16']
#tuol_updates[2016] = ['2016-04-16', '2016-04-26']
if basin == 'TB':
date_lst = tuol_updates[wy]
# put into datetime
date_lst = [pd.to_datetime(dt+' 23:00') for dt in date_lst]
tzinfo = pytz.timezone('UTC')
tmp_date = date_lst[0]
tmp_date = tmp_date.replace(tzinfo=tzinfo)
# find start of water year
tmpwy = utils.water_day(tmp_date)[1]
wy = tmpwy
start_date = pd.to_datetime('{:d}-10-01'.format(tmpwy-1))
# get the paths
fp_lst = ['wy{}/{}{}_SUPERsnow_depth.asc'.format(wy, basin, dt.strftime(fmt_file))
for dt in date_lst]
fp_lst = [os.path.join(fpdir,fpu) for fpu in fp_lst]
if basin == 'TB':
dem_fp = '/data/blizzard/tuolumne/common_data/topo/tuolx_dem_50m.ipw'
gisPath = '/home/micahsandusky/Code/awsfTesting/initUpdate/'
maskPath = os.path.join(gisPath, 'tuolx_mask_50m.ipw')
if wy < 2017:
maskPath = os.path.join(gisPath, 'tuolx_hetchy_mask_50m.ipw')
elif basin == 'SJ':
dem_fp = '/data/blizzard/sanjoaquin/common_data/topo/SJ_dem_50m.ipw'
gisPath = '/data/blizzard/sanjoaquin/common_data/topo/'
maskPath = os.path.join(gisPath, 'SJ_Millerton_mask_50m.ipw')
else:
raise ValueError('Wrong basin name')
mask = ipw.IPW(maskPath).bands[0].data[:]
output_path = os.path.join(fpdir, 'wy{}'.format(wy))
fname = 'flight_depths_{}'.format(basin)
nanval = -9999.0
nanup = 1000.0
# #### Now actually do the stuff ####
# date to use for finding wy
fname = fname+'_{}'.format(tmpwy)
# get topo stats from dem
ts = get_topo_stats(dem_fp, filetype = 'ipw')
x = ts['x'] # + ts['dv']*np.arange(ts['nx'])
y = ts['y'] # + ts['du']*np.arange(ts['ny'])
# get depth array
depth_arr = read_flight(fp_lst, ts, nanval = nanval, nanup = nanup)
print(depth_arr)
# create netcdfs
ds = output_files(output_path, fname, start_date, x, y)
# write to file
for idt, dt in enumerate(date_lst):
data = depth_arr[idt,:]#*mask'
data[mask == 0.0] = np.nan
output_timestep(ds, data, dt, idt, start_date)
# close file
ds.close()
update_num))
previous_update_dir = 'run.{}'.format(q)
output_dir = 'output_update'
add_in = '_update'
else:
previous_update_dir = 'run.'.format(previous_update_dir)
output_dir = 'output_update'
#gisPath = '/data/blizzard/Tuolumne/common_data/topo/'
gisPath = '/home/micahsandusky/Code/awsfTesting/initUpdate/'
###chdir(os.path.join(basePath,dataDir))
###runPath = os.path.join(basePath,runDir,previous_update_dir,output_dir)
#last_snow_image = ipw.IPW(os.path.join(runPath, 'snow.{}'.format(wyhr)))
#last_snow_image = ipw.IPW('/home/micahsandusky/Code/awsfTesting/initUpdate/snow.5999')
#last_snow_image = ipw.IPW('/home/micahsandusky/Code/awsfTesting/newupdatetest/snow.5999')
last_snow_image = ipw.IPW(
'/home/micahsandusky/Code/awsfTesting/newupdatetest/snow.2879')
demPath = os.path.join(gisPath, 'tuolx_dem_50m.{}'.format(filetype[0:3]))
if int(wy) <= 2015: # Model domain was only above Hetchy before 2016.
maskPath = os.path.join(gisPath,
'tuolx_hetchy_mask_50m.{}'.format(filetype[0:3]))
else:
maskPath = os.path.join(gisPath, 'tuolx_mask_50m.{}'.format(filetype[0:3]))
z0Path = os.path.join(gisPath, 'tuolx_z0_50m.{}'.format(filetype[0:3]))
if filetype == 'ipw':
demstruct = ipw.IPW(demPath)
dem = demstruct.bands[0].data
mask = ipw.IPW(maskPath)
mask = mask.bands[0].data
z0 = ipw.IPW(z0Path)
import h5py from smrf import ipw import numpy as np import matplotlib.pyplot as plt import getCDF as gcdf ''' sanity check to make sure topo_wrr18.nc was created properly. really, why 42s are where there should be 41 in vegtype''' fp_topo = '/home/zachuhlmann/projects/Hedrick_WRR_2018/tuol_topo_wrr18.nc' fp_veg_type = '/home/zachuhlmann/projects/zenodo_WRR_data/static_grids/tuolx_vegnlcd_50m.ipw' gcdf_obj = gcdf.GetCDF() gcdf_obj.get_topo(fp_topo) ipw_array = ipw.IPW(fp_veg_type).bands[0].data ipw_array = ipw_array.astype(np.int8) nc_array = np.array(gcdf_obj.topo['veg_type']) # ipw_array[gcdf_obj.mask == False] = np.nan ipw_array = np.ma.masked_where(gcdf_obj.mask == False, nc_array) ipw_array = ipw_array[gcdf_obj.trim_to_NA_extent()] ipw_array = np.reshape(ipw_array, (gcdf_obj.nrows_trim, gcdf_obj.ncols_trim)) nc_array = np.ma.masked_where(gcdf_obj.mask == False, nc_array) nc_array = nc_array[gcdf_obj.trim_to_NA_extent()] nc_array = np.reshape(nc_array, (gcdf_obj.nrows_trim, gcdf_obj.ncols_trim)) fig, axs = plt.subplots(ncols =3, nrows =1) axs[0].imshow(ipw_array) axs[1].imshow(nc_array) mp = axs[2].imshow(ipw_array - nc_array) fig.colorbar(mp, ax=axs[2], fraction=0.04, pad=0.04, orientation = 'horizontal', extend = 'max')
from smrf import ipw
file_name = 'topo.nc'
f = {
'dem': 'dem.ipw',
'mask': 'mask.ipw',
'veg_height': 'veg_height.ipw',
'veg_k': 'veg_k.ipw',
'veg_tau': 'veg_tau.ipw',
'veg_type': 'veg_type.ipw'
}
# get the x,y
d = ipw.IPW(f['dem'])
x = d.bands[0].x
y = d.bands[0].y
# create the netCDF file
s = nc.Dataset(file_name, 'w', format='NETCDF4', clobber=True)
# add dimensions
dimensions = ['y', 'x']
s.createDimension(dimensions[0], y.shape[0])
s.createDimension(dimensions[1], x.shape[0])
# create the variables
s.createVariable('y', 'f', dimensions[0])
s.createVariable('x', 'f', dimensions[1])
def ipw2nc_mea(myawsm, runtype):
'''
Function to create netcdf files from iSnobal output. Reads the snow and em
ouptuts in the 'output' folder and stores them in snow.nc and em.nc one
directory up.
Args:
myawsm: AWSM instance
runtype: either 'smrf' for standard run or 'forecast' for gridded data run
'''
myawsm._logger.info("making the NetCDF files from ipw"
" files for {}".format(runtype))
if runtype != 'smrf' and runtype != 'forecast':
myawsm._logger.error('Wrong run type given to ipw2nc. '
'not smrf or forecast')
sys.exit()
myawsm._logger.info("convert all .ipw output files to netcdf files")
#######################################################################
# Convert all .ipw output files to netcdf files #####################
#######################################################################
time_zone = myawsm.tmz
# create the x,y vectors
x = myawsm.topo.x
y = myawsm.topo.y
# ========================================================================
# NetCDF EM image
# ========================================================================
m = {}
m['name'] = [
'net_rad', 'sensible_heat', 'latent_heat', 'snow_soil',
'precip_advected', 'sum_EB', 'evaporation', 'snowmelt', 'SWI',
'cold_content'
]
m['units'] = [
'W m-2', 'W m-2', 'W m-2', 'W m-2', 'W m-2', 'W m-2', 'kg m-2',
'kg m-2', 'kg or mm m-2', 'J m-2'
]
m['description'] = [
'Average net all-wave radiation', 'Average sensible heat transfer',
'Average latent heat exchange', 'Average snow/soil heat exchange',
'Average advected heat from precipitation',
'Average sum of EB terms for snowcover', 'Total evaporation',
'Total snowmelt', 'Total runoff', 'Snowcover cold content'
]
if runtype == 'smrf':
netcdfFile = os.path.join(myawsm.pathrr, 'em.nc')
elif runtype == 'forecast':
netcdfFile = os.path.join(myawsm.pathrr, 'em_forecast.nc')
dimensions = ('time', 'y', 'x')
em = nc.Dataset(netcdfFile, 'w')
# create the dimensions
em.createDimension('time', None)
em.createDimension('y', myawsm.topo.ny)
em.createDimension('x', myawsm.topo.nx)
# create some variables
em.createVariable('time', 'f', dimensions[0])
em.createVariable('y', 'f', dimensions[1])
em.createVariable('x', 'f', dimensions[2])
setattr(em.variables['time'], 'units', 'hours since %s' % myawsm.wy_start)
setattr(em.variables['time'], 'calendar', 'standard')
setattr(em.variables['time'], 'time_zone', time_zone)
em.variables['x'][:] = x
em.variables['y'][:] = y
# em image
for i, v in enumerate(m['name']):
em.createVariable(v, 'f', dimensions[:3], chunksizes=(24, 10, 10))
setattr(em.variables[v], 'units', m['units'][i])
setattr(em.variables[v], 'description', m['description'][i])
em.setncattr_string('source', 'AWSM {}'.format(myawsm.gitVersion))
# ========================================================================
# NetCDF SNOW image
# ========================================================================
s = {}
s['name'] = [
'thickness', 'snow_density', 'specific_mass', 'liquid_water',
'temp_surf', 'temp_lower', 'temp_snowcover', 'thickness_lower',
'water_saturation'
]
s['units'] = [
'm', 'kg m-3', 'kg m-2', 'kg m-2', 'C', 'C', 'C', 'm', 'percent'
]
s['description'] = [
'Predicted thickness of the snowcover',
'Predicted average snow density',
'Predicted specific mass of the snowcover',
'Predicted mass of liquid water in the snowcover',
'Predicted temperature of the surface layer',
'Predicted temperature of the lower layer',
'Predicted temperature of the snowcover',
'Predicted thickness of the lower layer',
'Predicted percentage of liquid water'
' saturation of the snowcover'
]
if runtype == 'smrf':
netcdfFile = os.path.join(myawsm.pathrr, 'snow.nc')
elif runtype == 'forecast':
netcdfFile = os.path.join(myawsm.pathrr, 'snow_forescast.nc')
dimensions = ('time', 'y', 'x')
snow = nc.Dataset(netcdfFile, 'w')
# create the dimensions
snow.createDimension('time', None)
snow.createDimension('y', myawsm.topo.ny)
snow.createDimension('x', myawsm.topo.nx)
# create some variables
snow.createVariable('time', 'f', dimensions[0])
snow.createVariable('y', 'f', dimensions[1])
snow.createVariable('x', 'f', dimensions[2])
setattr(snow.variables['time'], 'units',
'hours since %s' % myawsm.wy_start)
setattr(snow.variables['time'], 'calendar', 'standard')
setattr(snow.variables['time'], 'time_zone', time_zone)
snow.variables['x'][:] = x
snow.variables['y'][:] = y
# snow image
for i, v in enumerate(s['name']):
snow.createVariable(v, 'f', dimensions[:3], chunksizes=(6, 10, 10))
setattr(snow.variables[v], 'units', s['units'][i])
setattr(snow.variables[v], 'description', s['description'][i])
h = '[{}] Data added or updated'.format(datetime.now().strftime("%Y%m%d"))
snow.setncattr_string('last modified', h)
snow.setncattr_string('AWSM version', myawsm.gitVersion)
if myawsm.do_smrf:
snow.setncattr_string('SMRF version', myawsm.smrf_version)
# =======================================================================
# Get all files in the directory, open ipw file, and add to netCDF
# =======================================================================
# get all the files in the directory
d = sorted(glob.glob("%s/snow*" % myawsm.pathro), key=os.path.getmtime)
d.sort(key=lambda f: os.path.splitext(f))
# find a drop any netcdfs in directory
d = [ddp for ddp in d if '.nc' not in ddp]
# pbar = progressbar.ProgressBar(max_value=len(d)).start()
j = 0
for idf, f in enumerate(d):
# print out counter at certain percentages. pbar doesn't play nice
# with logging
if j == int(len(d) / 4):
myawsm._logger.info("25 percent finished with "
"making NetCDF files!")
if j == int(len(d) / 2):
myawsm._logger.info("50 percent finished with "
"making NetCDF files!")
if j == int(3 * len(d) / 4):
myawsm._logger.info("75 percent finished with "
"making NetCDF files!")
# get the hr
nm = os.path.basename(f)
head = os.path.dirname(f)
hr = int(nm.split('.')[1])
# hr = int(hr)
snow.variables['time'][j] = hr # +1
em.variables['time'][j] = hr # +1
# Read the IPW file
i = ipw.IPW(f)
# output to the snow netcdf file
for b, var in enumerate(s['name']):
snow.variables[var][j, :] = i.bands[b].data
# output to the em netcdf file
# emFile = "%s/%s.%04i" % (head, 'em', hr)
emFile = os.path.join(head, 'em.%04i' % (hr))
i_em = ipw.IPW(emFile)
for b, var in enumerate(m['name']):
em.variables[var][j, :] = i_em.bands[b].data
snow.setncattr_string('last modified', h)
snow.setncattr_string('AWSM version', myawsm.gitVersion)
if myawsm.do_smrf:
snow.setncattr_string('SMRF version', myawsm.smrf_version)
em.sync()
snow.sync()
j += 1
# pbar.update(j)
# pbar.finish()
snow.close()
em.close()
myawsm._logger.info("Finished making the NetCDF "
"files from iSnobal output!")
nc_topo.variables['x'][:] = x
nc_topo.variables['y'][:] = y
# snow image
for i, v in enumerate(s['name']):
nc_topo.createVariable(v, 'f', ['y', 'x'], chunksizes=(10, 10))
setattr(nc_topo.variables[v], 'units', s['units'][i])
setattr(nc_topo.variables[v], 'description', s['description'][i])
setattr(nc_topo.variables[v], 'long_name', s['long_name'][i])
#===============================================================================
# open ipw file, and add to netCDF
#===============================================================================
for f, var in zip(s['file'], s['name']):
# Read the IPW file
i = ipw.IPW(f)
# assign to netcdf
tmp = i.bands[0].data
plt.imshow(tmp)
plt.colorbar()
plt.show()
nc_topo.variables[var][:] = i.bands[0].data
nc_topo.sync()
#===============================================================================
# set attributes
#===============================================================================
# the y variable attributes
nc_topo.variables['y'].setncattr('units', 'meters')
nc_topo.variables['y'].setncattr('description', 'UTM, north south')
nc_topo.variables['y'].setncattr('long_name', 'y coordinate')
