def _run(args):
config = read_config(args.config)
files = config["options"]["files"]
var_keys = config["options"]["var_keys"]
output = config["options"]["output"]
binary_mult = config["options"]["binary_mult"]
binary_type = (config["options"]["binary_type"],)
paths = config["options"]["paths"]
out_prefix = config["options"]["out_prefix"]
verbose = config["options"]["verbose"]
mask = read_netcdf(paths["mask_path"], nc_vars=["mask"])["mask"]
yi, xi = np.nonzero(mask)
print("found {0} points in mask file.".format(len(yi)))
xlist = []
ylist = []
pointlist = []
append = False
for i, fname in enumerate(files):
d = read_netcdf(os.path.join(paths["in_path"], fname), verbose=verbose)
if i == 0:
# find point locations
xs = d["xc"]
ys = d["yc"]
posinds = np.nonzero(xs > 180)
xs[posinds] -= 360
print("adjusted xs lon minimum")
for y, x in pyzip(yi, xi):
active_flag = False
for key in var_keys:
if (d[key][:, y, x].all() is np.ma.masked) or (mask[y, x] == 0):
active_flag = True
if not active_flag:
point = (ys[y, x], xs[y, x])
xlist.append(x)
ylist.append(y)
pointlist.append(point)
else:
append = True
for y, x, point in pyzip(ylist, xlist, pointlist):
data = np.empty((d[var_keys[0]].shape[0], len(var_keys)))
for j, key in enumerate(var_keys):
data[:, j] = d[key][:, y, x]
if output["Binary"]:
write_binary(data * binary_mult, point, binary_type, out_prefix, paths["BinaryoutPath"], append)
if output["ASCII"]:
write_ascii(data, point, out_prefix, paths["ASCIIoutPath"], append)
return
def test_pyzip():
a = [1, 2, 3]
b = ['a', 'b', 'c']
z = pyzip(a, b)
assert hasattr(z, '__iter__')
lz = list(pyzip(a, b))
assert lz[0] == (1, 'a')
assert len(lz) == len(a) # == len(b)
def veg(data, xinds, yinds, veg_file, rootzones=3, global_lai=True):
"""Write VIC formatted veg parameter file"""
print('writing veg parameter file: {0}'.format(veg_file))
# counter for bad grid cells
count = 0
f = open(veg_file, 'w')
for y, x in pyzip(yinds, xinds):
gridcell = int(data['gridcell'][y, x])
n_veg = int(data['Nveg'][y, x])
cv = data['Cv'][:, y, x]
veg_class = np.nonzero(cv)[0]
if not len(veg_class) == n_veg:
count += 1
line1 = str(gridcell) + ' ' + str(n_veg) + '\n'
f.write(line1)
if n_veg > 0:
for j in veg_class:
line2 = [str(j + 1)]
line2.append(str(cv[j]))
for k in range(rootzones):
line2.append(str(data['root_depth'][j, k, y, x]))
line2.append(str(data['root_fract'][j, k, y, x]))
line2.append('\n')
f.write(' '.join(line2))
if global_lai:
line3 = []
for m in range(12):
line3.append(str(data['LAI'][j, m, y, x]))
line3.append('\n')
f.write(' '.join(line3))
f.close()
print('{0} grid cells have unequal veg_classes'.format(count))
print('finished writing veg parameter file: {0}'.format(veg_file))
return
def set_fileformat(self, fileformat):
"""sets and assigns fileformat specific attributes and methods"""
if fileformat == 'ascii':
delimeter = r'\t' # VIC ascii files are tab seperated
else:
delimeter = r',' # true csv
for p in self:
p.fileformat = fileformat
if fileformat in ['ascii', 'csv']:
p.open = p._open_ascii
p.delimeter = delimeter
p.read = p._read_ascii
elif fileformat == 'binary':
p.open = p._open_binary
p.read = p._read_binary
p.dt = np.dtype(list(pyzip(p.names, p.bin_dtypes)))
elif fileformat == 'netcdf':
p.open = p._open_netcdf
p.read = p._read_netcdf
else:
raise ValueError('Unknown file format: {0}'.format(fileformat))
return
def vic2nc(options, global_atts, domain_dict, fields):
""" Convert ascii VIC files to netCDF format"""
# determine run mode
if (options['memory_mode'] == 'standard') \
and (options['chunksize'] in ['all', 'All', 'ALL', 0]):
memory_mode = 'big_memory'
else:
memory_mode = options['memory_mode']
print("\n-------------------------------")
print("Configuration File Options")
print("-------------OPTIONS-------------")
for pair in options.items():
print("{0}: {1}".format(*pair))
print('Fields: {0}'.format(", ".join(fields.keys())))
if domain_dict:
print("-------------DOMAIN--------------")
for pair in domain_dict.items():
print("{0}: {1}".format(*pair))
print("--------GLOBAL_ATTRIBUTES--------")
for pair in global_atts.items():
print("{0}: {1}".format(*pair))
print("--------RUN MODE--------")
print('Memory Mode: {0}'.format(memory_mode))
if memory_mode == 'standard':
print('Chunksize={0}'.format(options['chunksize']))
print("---------------------------------\n")
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Make output directory
if not os.path.exists(options['out_directory']):
os.makedirs(options['out_directory'])
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Make pairs (i.e. find inds)
files = glob(options['input_files'])
points = get_file_coords(files)
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Get target grid information
if domain_dict:
domain = read_domain(domain_dict)
target_grid_file = path.split(domain_dict['filename'])[1]
global_atts['target_grid_file'] = target_grid_file
else:
# must be a regular grid, build from file names
domain = calc_grid(points.get_lats(), points.get_lons())
target_grid_file = None
domain_dict = {'y_x_dims': ['lat', 'lon']}
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Get grid index locations
points = get_grid_inds(domain, points)
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Get timestamps
if options['input_file_format'].lower() == 'ascii':
if ('bin_start_date' in options
and 'bin_end_date' in options
and 'bin_dt_sec' in options):
vic_datelist, vic_ordtime = make_dates(
options['bin_start_date'],
options['bin_end_date'],
options['bin_dt_sec'],
calendar=options['calendar'])
else:
vic_datelist = get_dates(files[0])
vic_ordtime = date2num(vic_datelist, TIMEUNITS,
calendar=options['calendar'])
elif options['input_file_format'].lower() in ['binary', 'netcdf']:
vic_datelist, vic_ordtime = make_dates(options['bin_start_date'],
options['bin_end_date'],
options['bin_dt_sec'],
calendar=options['calendar'])
else:
raise ValueError('Unknown input file format: {}. Valid options are \
ascii or binary'.format(options['input_file_format']))
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Determine time segmentation
if options['start_date']:
start_date = datetime.strptime(options['start_date'], TIMESTAMPFORM)
if start_date < vic_datelist[0]:
print("WARNING: Start date in configuration file is before "
"first date in file.")
start_date = vic_datelist[0]
print('WARNING: New start date is {0}'.format(start_date))
else:
start_date = vic_datelist[0]
if options['end_date']:
end_date = datetime.strptime(options['end_date'], TIMESTAMPFORM)
if end_date > vic_datelist[-1]:
print("WARNING: End date in configuration file is after "
"last date in file.")
end_date = vic_datelist[-1]
print('WARNING: New end date is {0}'.format(end_date))
else:
end_date = vic_datelist[-1]
# Ordinal Time
start_ord = date2num(start_date, TIMEUNITS, calendar=options['calendar'])
end_ord = date2num(end_date, TIMEUNITS, calendar=options['calendar'])
print("netCDF Start Date: {0}".format(start_date))
print("netCDF End Date: {0}".format(end_date))
segment_dates = []
if options['time_segment'] == 'day':
# calendar insensitive
num_segments = np.ceil(end_ord - start_ord)
if start_date.hour == 0:
segment_dates = num2date(np.arange(start_ord, end_ord + 1, 1),
TIMEUNITS, calendar=options['calendar'])
else:
# allow start at time other than 0
temp = [start_ord].append(np.arange(np.ceil(start_ord),
end_ord + 1, 1))
segment_dates = num2date(temp, TIMEUNITS,
calendar=options['calendar'])
elif options['time_segment'] == 'month':
num_segments = (end_date.year - start_date.year) * 12 \
+ end_date.month - start_date.month + 1
month = start_date.month
year = start_date.year
for i in pyrange(num_segments + 1):
segment_dates.append(datetime(year, month, 1))
month += 1
if month == 13:
month = 1
year += 1
elif options['time_segment'] == 'year':
num_segments = end_date.year - start_date.year + 1
year = start_date.year
for i in pyrange(num_segments + 1):
segment_dates.append(datetime(year, 1, 1))
year += 1
elif options['time_segment'] == 'decade':
num_segments = (end_date.year - start_date.year) / 10 + 1
year = start_date.year
for i in pyrange(num_segments + 1):
segment_dates.append(datetime(year, 1, 1))
year += 10
elif options['time_segment'] == 'all':
num_segments = 1
segment_dates = [start_date, end_date]
else:
raise ValueError('Unknown timesegment options \
{0}'.format(options['time_segment']))
print("Number of files: {0}".format(len(segment_dates) - 1))
assert len(segment_dates) == num_segments + 1
# Make sure the first and last dates are start/end_date
segment_dates[0] = start_date
segment_dates[-1] = end_date + timedelta(minutes=1)
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Setup Segments
segments = deque()
for num in pyrange(num_segments):
# Segment time bounds
t0 = segment_dates[num]
t1 = segment_dates[num + 1]
# Get segment inds
i0 = bisect_left(vic_datelist, t0)
i1 = bisect_left(vic_datelist, t1)
# Make segment filename (with path)
if options['time_segment'] == 'day':
filename = "{0}.{1}.nc".format(options['out_file_prefix'],
t0.strftime('%Y-%m-%d'))
elif options['time_segment'] == 'month':
filename = "{0}.{1}.nc".format(options['out_file_prefix'],
t0.strftime('%Y-%m'))
elif options['time_segment'] == 'year':
filename = "{0}.{1}.nc".format(options['out_file_prefix'],
t0.strftime('%Y'))
elif options['time_segment'] == 'all':
filename = "{0}.{1}-{2}.nc".format(options['out_file_prefix'],
t0.strftime('%Y%m%d'),
t1.strftime('%Y%m%d'))
filename = path.join(options['out_directory'], filename)
# Setup segment and initialize netcdf
segment = Segment(num, i0, i1, options['out_file_format'],
filename, memory_mode=memory_mode)
segment.nc_globals(**global_atts)
segment.nc_time(t0, t1, vic_ordtime, options['calendar'])
segment.nc_dimensions(snow_bands=options['snow_bands'],
veg_tiles=options['veg_tiles'],
soil_layers=options['soil_layers'])
segment.nc_domain(domain)
segment.nc_fields(fields,
domain_dict['y_x_dims'], options['precision'])
print(repr(segment))
segments.append(segment)
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
# Get column numbers and names (will help speed up reading)
names = []
usecols = []
dtypes = []
bin_dtypes = []
bin_mults = []
if options['precision'] == 'double':
prec = NC_DOUBLE
else:
prec = NC_FLOAT
for name, field in fields.items():
if not np.isscalar(field['column']):
# multiple levels
for i, col in enumerate(field['column']):
names.append(name + str(i))
usecols.append(col)
if 'type' in field:
if type(field['type']) == list:
dtypes.extend(field['type'])
else:
dtypes.extend([field['type']] * len(field['column']))
else:
dtypes.append([prec] * len(field['column']))
if options['input_file_format'].lower() == 'binary':
if 'bin_dtype' in field:
if type(field['bin_dtype']) == list:
bin_dtypes.extend(field['bin_dtype'])
else:
bin_dtypes.extend([field['bin_dtype']] *
len(field['column']))
else:
raise ValueError('bin_dtype not in field: {}'.format(name))
if 'bin_mult' in field:
if type(field['bin_mult']) == list:
bin_mults.extend(field['bin_mult'])
else:
bin_mults.extend([field['bin_mult']] *
len(field['column']))
else:
bin_mults.extend([1.0] * len(field['column']))
else:
# no levels
names.append(name)
usecols.append(field['column'])
if 'type' in field:
dtypes.append(field['type'])
else:
dtypes.append(prec)
if options['input_file_format'].lower() == 'binary':
if 'bin_dtype' in field:
bin_dtypes.append(field['bin_dtype'])
else:
raise ValueError('bin_dtype not in field: {}'.format(name))
if 'bin_mult' in field:
bin_mults.append(field['bin_mult'])
else:
bin_mults.append(1.0)
print('setting point attributes (fileformat, names, usecols, and dtypes)')
# pandas.read_table does not 'honor' the order of the columns in usecols
# it simply uses them in ascending order. So the names need to be sorted
# the same way. For example, if the columns in the VIC file are:
# 3: prcp; 4: evap; 5: runoff; 6; baseflow; 7: sm1; 8: sm2; 9: sm3; 10: swe
# and this is parsed from the configuration file as
# usecols = [3, 4, 5, 6, 10, 7, 8, 9]
# names=['prcp', 'evap', 'runoff', 'baseflow', 'swe', 'sm1', 'sm2', 'sm3']
# then without sorting, the netcdf file will have the wrong variables:
# nc_swe will contain sm1, nc_sm1 will contain sm2, nc_sm2: sm3 and
# nc_swe: sm3
# the following will ensure that the names are sorted in increasing column
# order. Note that sorted(usecols) is not strictly necessary, since
# apparently that is done in read_table, but it keeps the names and columns
# in the same order
names = [x for (y, x) in sorted(pyzip(usecols, names))]
usecols = sorted(usecols)
points.set_names(names)
points.set_usecols(usecols)
points.set_dtypes(dtypes)
# set binary attributes
if options['input_file_format'].lower() == 'binary':
points.set_bin_dtypes(bin_dtypes)
points.set_bin_mults(bin_mults)
points.set_fileformat(options['input_file_format'])
print('done')
# ---------------------------------------------------------------- #
# ---------------------------------------------------------------- #
if memory_mode == 'big_memory':
# ------------------------------------------------------------ #
# run in big memory mode
for i, segment in enumerate(segments):
segments[i].allocate()
while points:
point = points.popleft()
point.open()
point.read()
point.close()
for segment in segments:
segment.nc_add_data_to_array(point)
for segment in segments:
segment.nc_write_data_from_array()
segment.nc_close()
# ------------------------------------------------------------ #
elif memory_mode == 'standard':
# ------------------------------------------------------------ #
# Open VIC files and put data into netcdfs
chunk = Plist()
while points:
point = points.popleft()
point.open()
point.read()
point.close()
chunk.append(point)
if len(chunk) > int(options['chunksize']) or len(points) == 0:
for segment in segments:
segment.nc_add_data_standard(chunk)
chunk = Plist()
del point
# ------------------------------------------------------------ #
# ------------------------------------------------------------ #
# Close the netcdf files
for segment in segments:
segment.nc_close()
# ------------------------------------------------------------ #
elif memory_mode == 'original':
# ------------------------------------------------------------ #
# Run in original memory mode (a.k.a. vic2nc.c mode)
# Open all files
for point in points:
point.open()
while segments:
segment = segments.popleft()
segment.allocate()
count = segment.count
for point in points:
point.read(count)
segment.nc_add_data_to_array(point)
segment.nc_write_data_from_array()
segment.nc_close()
for point in points:
point.close()
# ------------------------------------------------------------ #
return
def nc_to_vic(config_file):
''' This function converts netCDF files to VIC ascii format files.
(This function is adapted from tonic)
Parameters
----------
config_file: <str>
Path of config file for nc_to_vic
Returns
----------
Requires
----------
write_binary
'''
import numpy as np
import struct
import os
from tonic.io import read_netcdf, read_config
from tonic.pycompat import pyzip
config = read_config(config_file)
files = config['options']['files'] # should contain "{}", which will be replaced by YYYY
var_keys = config['options']['var_keys']
output_format = config['options']['output_format'] # Binary or ASCII
out_prefix = config['options']['out_prefix']
verbose = config['options']['verbose']
coord_keys = config['options']['coord_keys'] # varname of lon and lat in netCDF files
lon_name = coord_keys[0]
lat_name = coord_keys[1]
start_year = config['options']['start_year']
end_year = config['options']['end_year']
latlon_precision = config['options']['latlon_precision']
paths = config['paths']
mask_varname = paths['mask_varname']
mask = read_netcdf(paths['mask_path'], variables=['mask'])[0][mask_varname]
yi, xi = np.nonzero(mask)
print('found {0} points in mask file.'.format(len(yi)))
xlist = []
ylist = []
pointlist = []
append = False
for i, year in enumerate(range(start_year, end_year+1)):
print('Year {}'.format(year))
fname = files.format(year)
d = read_netcdf(os.path.join(paths['in_path'], fname),
verbose=verbose)[0]
if i == 0:
# find point locations
xs = d[lon_name]
ys = d[lat_name]
posinds = np.nonzero(xs > 180)
xs[posinds] -= 360
print('adjusted xs lon minimum')
for y, x in pyzip(yi, xi):
active_flag = False
for key in var_keys:
if (d[key][:, y, x].all() is np.ma.masked) \
or (mask[y, x] == 0):
active_flag = True
if not active_flag:
point = (ys[y], xs[x])
xlist.append(x)
ylist.append(y)
pointlist.append(point)
else:
append = True
for y, x, point in pyzip(ylist, xlist, pointlist):
data = np.empty((d[var_keys[0]].shape[0], len(var_keys)))
for j, key in enumerate(var_keys):
data[:, j] = d[key][:, y, x]
if output_format == 'Binary':
write_binary(data * binary_mult, point, binary_type,
out_prefix, paths['BinaryoutPath'], append)
if output_format == 'ASCII':
write_ascii(data, point, out_prefix, paths['ASCIIoutPath'],
latlon_precision, append)
return
def _run(args):
config = read_config(args.config)
files = config['options']['files']
var_keys = config['options']['var_keys']
output = config['options']['output']
binary_mult = config['options']['binary_mult']
binary_type = config['options']['binary_type'],
paths = config['options']['paths']
out_prefix = config['options']['out_prefix']
verbose = config['options']['verbose']
mask = read_netcdf(paths['mask_path'], nc_vars=['mask'])['mask']
yi, xi = np.nonzero(mask)
print('found {0} points in mask file.'.format(len(yi)))
xlist = []
ylist = []
pointlist = []
append = False
for i, fname in enumerate(files):
d = read_netcdf(os.path.join(paths['in_path'], fname), verbose=verbose)
if i == 0:
# find point locations
xs = d['xc']
ys = d['yc']
posinds = np.nonzero(xs > 180)
xs[posinds] -= 360
print('adjusted xs lon minimum')
for y, x in pyzip(yi, xi):
active_flag = False
for key in var_keys:
if (d[key][:, y, x].all() is np.ma.masked) \
or (mask[y, x] == 0):
active_flag = True
if not active_flag:
point = (ys[y, x], xs[y, x])
xlist.append(x)
ylist.append(y)
pointlist.append(point)
else:
append = True
for y, x, point in pyzip(ylist, xlist, pointlist):
data = np.empty((d[var_keys[0]].shape[0], len(var_keys)))
for j, key in enumerate(var_keys):
data[:, j] = d[key][:, y, x]
if output['Binary']:
write_binary(data * binary_mult, point, binary_type,
out_prefix, paths['BinaryoutPath'], append)
if output['ASCII']:
write_ascii(data, point, out_prefix, paths['ASCIIoutPath'],
append)
return
def grid_params(soil_dict, target_grid, snow_dict, veg_dict, veglib_dict,
version='4.1.2'):
"""
Reads the coordinate information from the soil_dict and target_grid and
maps all input dictionaries to the target grid. Returns a grid_dict with
the mapped input dictionary data.
"""
print('gridding params now...')
yi, xi = latlon2yx(soil_dict['lats'], soil_dict['lons'],
target_grid[YVAR], target_grid[XVAR])
in_dicts = {'soil_dict': soil_dict}
out_dicts = OrderedDict()
if snow_dict:
in_dicts['snow_dict'] = snow_dict
else:
out_dicts['snow_dict'] = False
if veg_dict:
in_dicts['veg_dict'] = veg_dict
else:
out_dicts['veg_dict'] = False
# get "unmasked" mask
mask = target_grid['mask']
ysize, xsize = target_grid['mask'].shape
ymask, xmask = np.nonzero(mask != 1)
print('{0} masked values'.format(len(ymask)))
for name, mydict in in_dicts.items():
out_dict = OrderedDict()
for var in mydict:
if mydict[var].dtype in [np.int, np.int64, np.int32]:
fill_val = FILLVALUE_I
dtype = np.int
else:
fill_val = FILLVALUE_F
dtype = np.float
if mydict[var].ndim == 1:
out_dict[var] = np.ma.zeros((ysize, xsize),
dtype=dtype)
out_dict[var][yi, xi] = mydict[var]
out_dict[var][ymask, xmask] = fill_val
elif mydict[var].ndim == 2:
steps = mydict[var].shape[1]
out_dict[var] = np.ma.zeros((steps, ysize, xsize),
dtype=dtype)
for i in pyrange(steps):
out_dict[var][i, yi, xi] = mydict[var][:, i]
out_dict[var][:, ymask, xmask] = fill_val
elif mydict[var].ndim == 3:
j = mydict[var].shape[1]
k = mydict[var].shape[2]
out_dict[var] = np.ma.zeros((j, k, ysize, xsize),
dtype=dtype)
for jj in pyrange(j):
for kk in pyrange(k):
out_dict[var][jj, kk, yi, xi] = mydict[var][:, jj, kk]
for y, x in pyzip(ymask, xmask):
out_dict[var][:, :, y, x] = fill_val
out_dict[var] = np.ma.masked_values(out_dict[var], fill_val)
out_dicts[name] = out_dict
if veglib_dict and version == '5.0.dev':
# adjust vars for the following conditions
# bare soil tile
# Add bare soil tile
var = 'Cv'
bare = 1 - out_dicts['veg_dict'][var].sum(axis=0)
bare[bare < 0.0] = 0.0
nveg_clases = out_dicts['veg_dict'][var].shape[0] + 1
shape = (nveg_clases, ) + out_dicts['veg_dict'][var].shape[1:]
new = np.zeros(shape)
new[:-1, :, :] = out_dicts['veg_dict'][var]
new[-1, :, :] = bare
new /= new.sum(axis=0)
new[:, ymask, xmask] = FILLVALUE_F
out_dicts['veg_dict'][var] = new
# add dummy values for other veg vars
# double root_depth(veg_class, root_zone, nj, ni) ;
# double root_fract(veg_class, root_zone, nj, ni) ;
# double LAI(veg_class, month, nj, ni) ;
for var in ['root_depth', 'root_fract', 'LAI']:
shape = (nveg_clases, ) + out_dicts['veg_dict'][var].shape[1:]
new = np.zeros(shape) + FILLVALUE_F
new[:-1, :, :] = out_dicts['veg_dict'][var]
new[-1, :, :] = 0
out_dicts['veg_dict'][var] = np.ma.masked_values(new, FILLVALUE_F)
# Distribute the veglib variables
# 1st - the 1d vars
# double lib_overstory(veg_class) ; --> (veg_class, nj, ni)
for var in ['overstory', 'rarc', 'rmin', 'wind_h', 'RGL', 'rad_atten',
'rad_atten', 'wind_atten', 'trunk_ratio', 'snow_albedo']:
lib_var = 'lib_{0}'.format(var)
new = np.zeros((nveg_clases, ysize, xsize)) + FILLVALUE_F
new[:-1, yi, xi] = veglib_dict[lib_var][:, np.newaxis]
new[-1, yi, xi] = 0
new[:, ymask, xmask] = fill_val
out_dicts['veg_dict'][var] = np.ma.masked_values(new, FILLVALUE_F)
# 2nd - the 2d vars
for var in ['albedo', 'veg_rough', 'displacement']:
lib_var = 'lib_{0}'.format(var)
shape = (nveg_clases, veglib_dict[lib_var].shape[1], ysize, xsize)
new = np.zeros(shape) + FILLVALUE_F
new[:-1, :, yi, xi] = veglib_dict[lib_var][:, :, np.newaxis]
new[-1, :, yi, xi] = 0
for y, x in pyzip(ymask, xmask):
new[:, :, y, x] = fill_val
out_dicts['veg_dict'][var] = np.ma.masked_values(new, FILLVALUE_F)
# 3rd - remove the redundant vars
# double lib_LAI(veg_class, month) ;
# removed from file
return out_dicts
def _run(args):
config = read_config(args.config)
files = [config['Basics']['files']]
var_keys = [config['Basics']['var_keys']]
output = config['Paths']['ASCIIoutPath']
#binary_mult = config['Basics']['binary_mult']
#binary_type = config['Basics']['binary_type'],
paths = config['Paths']
out_prefix = config['Basics']['out_prefix']
verbose = config['Basics']['verbose']
mask = read_netcdf(paths['mask_path'], variables=['mask'])[0]['mask']
yi, xi = np.nonzero(mask)
print(mask)
print('found {0} points in mask fqile.'.format(len(xi)))
#x = read_netcdf(os.path.join(paths['in_path'], 'pr_1979.nc'))
#print(x)
xlist = []
ylist = []
pointlist = []
append = False
for i, fname in enumerate(files):
d = read_netcdf(os.path.join(paths['in_path'], fname),
verbose=verbose)[0]
print(i)
if i == 0:
# find point locations
xs = d['lon']
ys = d['lat']
posinds = np.nonzero(xs > 180)
xs[posinds] -= 360
print('adjusted xs lon minimum')
for y, x in pyzip(yi, xi):
active_flag = False
for key in var_keys:
if (d[key][:, y, x].all() is np.ma.masked) \
or (mask[y, x] == 0):
active_flag = True
if not active_flag:
point = (ys[y], xs[x])
print(point)
xlist.append(x)
ylist.append(y)
pointlist.append(point)
else:
append = True
for y, x, point in pyzip(ylist, xlist, pointlist):
data = np.empty((d[var_keys[0]].shape[0], len(var_keys)))
for j, key in enumerate(var_keys):
data[:, j] = d[key][:, y, x]
#if output['Binary']:
# write_binary(data * binary_mult, point, binary_type,
# out_prefix, paths['BinaryoutPath'], append)
#if output['ASCII']:
write_ascii(data, point, out_prefix, paths['ASCIIoutPath'],
append)
return