def setUp(self):
self.gcomm = simplecomm.create_comm()
self.gsize = MPI_COMM_WORLD.Get_size()
self.grank = MPI_COMM_WORLD.Get_rank()
self.groups = ['a', 'b', 'c']
self.rank = int(self.grank // len(self.groups))
self.color = int(self.grank % len(self.groups))
self.group = self.groups[self.color]
self.monocomm, self.multicomm = self.gcomm.divide(self.group)
self.all_colors = [i % len(self.groups) for i in range(self.gsize)]
self.all_groups = [self.groups[i] for i in self.all_colors]
self.all_ranks = [int(i // len(self.groups)) for i in range(self.gsize)]
def setUp(self):
self.gcomm = simplecomm.create_comm()
self.gsize = MPI_COMM_WORLD.Get_size()
self.grank = MPI_COMM_WORLD.Get_rank()
self.groups = ['a', 'b', 'c']
self.rank = int(self.grank // len(self.groups))
self.color = int(self.grank % len(self.groups))
self.group = self.groups[self.color]
self.monocomm, self.multicomm = self.gcomm.divide(self.group)
self.all_colors = [i % len(self.groups) for i in range(self.gsize)]
self.all_groups = [self.groups[i] for i in self.all_colors]
self.all_ranks = [
int(i // len(self.groups)) for i in range(self.gsize)
]
def setUp(self):
# COMM_WORLD Communicator and its size and
# this MPI process's world rank
self.gcomm = simplecomm.create_comm()
self.gsize = MPI_COMM_WORLD.Get_size()
self.grank = MPI_COMM_WORLD.Get_rank()
# The group names to assume when dividing COMM_WORLD
self.groups = ['a', 'b', 'c']
# This MPI process's rank, color, and group after division
self.rank = self.grank / len(self.groups)
self.color = self.grank % len(self.groups)
self.group = self.groups[self.color]
# The divided communicators (monocolor and multicolor)
self.monocomm, self.multicomm = self.gcomm.divide(self.group)
# Every MPI process's color, group, and grank after division
self.all_colors = [i % len(self.groups) for i in xrange(self.gsize)]
self.all_groups = [self.groups[i] for i in self.all_colors]
self.all_ranks = [i / len(self.groups) for i in xrange(self.gsize)]
def setUp(self):
# COMM_WORLD Communicator and its size and
# this MPI process's world rank
self.gcomm = simplecomm.create_comm()
self.gsize = MPI_COMM_WORLD.Get_size()
self.grank = MPI_COMM_WORLD.Get_rank()
# The group names to assume when dividing COMM_WORLD
self.groups = ['a', 'b', 'c']
# This MPI process's rank, color, and group after division
self.rank = self.grank / len(self.groups)
self.color = self.grank % len(self.groups)
self.group = self.groups[self.color]
# The divided communicators (monocolor and multicolor)
self.monocomm, self.multicomm = self.gcomm.divide(self.group)
# Every MPI process's color, group, and grank after division
self.all_colors = [i % len(self.groups) for i in xrange(self.gsize)]
self.all_groups = [self.groups[i] for i in self.all_colors]
self.all_ranks = [i / len(self.groups) for i in xrange(self.gsize)]
def __init__(self,
specifier,
serial=False,
verbosity=1,
wmode='w',
once=False,
simplecomm=None):
"""
Constructor
Parameters:
specifier (Specifier): An instance of the Specifier class,
defining the input specification for this reshaper operation.
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
wmode (str): The mode to use for writing output. Can be 'w' for
normal write operation, 's' to skip the output generation for
existing time-series files, 'o' to overwrite existing
time-series files, 'a' to append to existing time-series files.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Type checking (or double-checking)
if not isinstance(specifier, Specifier):
err_msg = "Input must be given in the form of a Specifier object"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(wmode) is not str:
err_msg = "Write mode flag must be a str."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
if wmode not in ['w', 's', 'o', 'a']:
err_msg = "Write mode '{}' not recognized".format(wmode)
raise ValueError(err_msg)
# Whether to write a once file
self._use_once_file = once
# The output write mode to use
self._write_mode = wmode
# Internal timer data
self._timer = TimeKeeper()
# Dictionary storing read/write data amounts
self.assumed_block_size = float(4 * 1024 * 1024)
self._byte_counts = {}
self._timer.start('Initializing Simple Communicator')
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Reference to the simple communicator
self._simplecomm = simplecomm
self._timer.stop('Initializing Simple Communicator')
# Contruct the print header
header = ''.join([
'[',
str(self._simplecomm.get_rank()), '/',
str(self._simplecomm.get_size()), '] '
])
# Reference to the verbose printer tool
self._vprint = VPrinter(header=header, verbosity=verbosity)
# Debug output starting
if self._simplecomm.is_manager():
self._vprint('Initializing Reshaper...', verbosity=0)
# Validate the user input data
self._timer.start('Specifier Validation')
specifier.validate()
self._timer.stop('Specifier Validation')
if self._simplecomm.is_manager():
self._vprint(' Specifier validated', verbosity=1)
# Store the input file names
self._input_filenames = specifier.input_file_list
# Store the list of metadata names
self._metadata_names = specifier.time_variant_metadata
# Store the output file prefix and suffix
self._output_prefix = specifier.output_file_prefix
self._output_suffix = specifier.output_file_suffix
# Setup PyNIO options (including disabling the default PreFill option)
opt = nio_options()
opt.PreFill = False
# Determine the Format and CompressionLevel options
# from the NetCDF format string in the Specifier
if specifier.netcdf_format == 'netcdf':
opt.Format = 'Classic'
elif specifier.netcdf_format in ['netcdf4', 'netcdf4c']:
opt.Format = 'NetCDF4Classic'
opt.CompressionLevel = specifier.compression_level
self._nio_options = opt
if self._simplecomm.is_manager():
self._vprint(' PyNIO options set', verbosity=1)
# Helpful debugging message
if self._simplecomm.is_manager():
self._vprint('Reshaper initialized.', verbosity=0)
# Sync before continuing..
self._simplecomm.sync()
def __init__(self, specifier, serial=False, verbosity=1, wmode='w',
once=False, simplecomm=None):
"""
Constructor
Parameters:
specifier (Specifier): An instance of the Specifier class,
defining the input specification for this reshaper operation.
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
wmode (str): The mode to use for writing output. Can be 'w' for
normal write operation, 's' to skip the output generation for
existing time-series files, 'o' to overwrite existing
time-series files, 'a' to append to existing time-series files.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Type checking (or double-checking)
if not isinstance(specifier, Specifier):
err_msg = "Input must be given in the form of a Specifier object"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(wmode) is not str:
err_msg = "Write mode flag must be a str."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
if wmode not in ['w', 's', 'o', 'a']:
err_msg = "Write mode '{}' not recognized".format(wmode)
raise ValueError(err_msg)
# Whether to write a once file
self._use_once_file = once
# The output write mode to use
self._write_mode = wmode
# Internal timer data
self._timer = TimeKeeper()
# Dictionary storing read/write data amounts
self.assumed_block_size = float(4 * 1024 * 1024)
self._byte_counts = {}
self._timer.start('Initializing Simple Communicator')
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Reference to the simple communicator
self._simplecomm = simplecomm
self._timer.stop('Initializing Simple Communicator')
# Contruct the print header
header = ''.join(['[', str(self._simplecomm.get_rank()),
'/', str(self._simplecomm.get_size()), '] '])
# Reference to the verbose printer tool
self._vprint = VPrinter(header=header, verbosity=verbosity)
# Debug output starting
if self._simplecomm.is_manager():
self._vprint('Initializing Reshaper...', verbosity=0)
# Validate the user input data
self._timer.start('Specifier Validation')
specifier.validate()
self._timer.stop('Specifier Validation')
if self._simplecomm.is_manager():
self._vprint(' Specifier validated', verbosity=1)
# Store the input file names
self._input_filenames = specifier.input_file_list
# Store the list of metadata names
self._metadata_names = specifier.time_variant_metadata
# Store the output file prefix and suffix
self._output_prefix = specifier.output_file_prefix
self._output_suffix = specifier.output_file_suffix
# Setup PyNIO options (including disabling the default PreFill option)
opt = nio_options()
opt.PreFill = False
# Determine the Format and CompressionLevel options
# from the NetCDF format string in the Specifier
if specifier.netcdf_format == 'netcdf':
opt.Format = 'Classic'
elif specifier.netcdf_format in ['netcdf4', 'netcdf4c']:
opt.Format = 'NetCDF4Classic'
opt.CompressionLevel = specifier.compression_level
self._nio_options = opt
if self._simplecomm.is_manager():
self._vprint(' PyNIO options set', verbosity=1)
# Helpful debugging message
if self._simplecomm.is_manager():
self._vprint('Reshaper initialized.', verbosity=0)
# Sync before continuing..
self._simplecomm.sync()
def main(argv):
print 'Running pyEnsSumPop!'
# Get command line stuff and store in a dictionary
s = 'nyear= nmonth= npert= tag= res= mach= compset= sumfile= indir= tslice= verbose jsonfile= mpi_enable zscoreonly nrand= rand seq= jsondir='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSumPop_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict = {}
# Defaults
opts_dict['tag'] = 'cesm1_2_0'
opts_dict['compset'] = 'FC5'
opts_dict['mach'] = 'yellowstone'
opts_dict['tslice'] = 0
opts_dict['nyear'] = 3
opts_dict['nmonth'] = 12
opts_dict['npert'] = 40
opts_dict['nbin'] = 40
opts_dict['minrange'] = 0.0
opts_dict['maxrange'] = 4.0
opts_dict['res'] = 'ne30_ne30'
opts_dict['sumfile'] = 'ens.pop.summary.nc'
opts_dict['indir'] = './'
opts_dict['jsonfile'] = ''
opts_dict['verbose'] = True
opts_dict['mpi_enable'] = False
opts_dict['zscoreonly'] = False
opts_dict['popens'] = True
opts_dict['nrand'] = 40
opts_dict['rand'] = False
opts_dict['seq'] = 0
opts_dict['jsondir'] = '/glade/scratch/haiyingx/'
# This creates the dictionary of input arguments
print "before parseconfig"
opts_dict = pyEnsLib.getopt_parseconfig(opts, optkeys, 'ESP', opts_dict)
verbose = opts_dict['verbose']
nbin = opts_dict['nbin']
if verbose:
print opts_dict
# Now find file names in indir
input_dir = opts_dict['indir']
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(not opts_dict['mpi_enable'])
if opts_dict['jsonfile']:
# Read in the included var list
Var2d, Var3d = pyEnsLib.read_jsonlist(opts_dict['jsonfile'], 'ESP')
str_size = 0
for str in Var3d:
if str_size < len(str):
str_size = len(str)
for str in Var2d:
if str_size < len(str):
str_size = len(str)
in_files = []
if (os.path.exists(input_dir)):
# Pick up the 'nrand' random number of input files to generate summary files
if opts_dict['rand']:
in_files = pyEnsLib.Random_pickup_pop(input_dir, opts_dict,
opts_dict['nrand'])
else:
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files = sorted(in_files_temp)
# Make sure we have enough
num_files = len(in_files)
else:
print 'Input directory: ', input_dir, ' not found'
sys.exit(2)
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(not opts_dict['mpi_enable'])
#Partition the input file list
in_file_list = me.partition(in_files, func=EqualStride(), involved=True)
# Open the files in the input directory
o_files = []
for onefile in in_file_list:
if (os.path.isfile(input_dir + '/' + onefile)):
o_files.append(Nio.open_file(input_dir + '/' + onefile, "r"))
else:
print "COULD NOT LOCATE FILE " + input_dir + onefile + "! EXITING...."
sys.exit()
print in_file_list
# Store dimensions of the input fields
if (verbose == True):
print "Getting spatial dimensions"
nlev = -1
nlat = -1
nlon = -1
# Look at first file and get dims
input_dims = o_files[0].dimensions
ndims = len(input_dims)
# Make sure all files have the same dimensions
for key in input_dims:
if key == "z_t":
nlev = input_dims["z_t"]
elif key == "nlon":
nlon = input_dims["nlon"]
elif key == "nlat":
nlat = input_dims["nlat"]
for count, this_file in enumerate(o_files):
input_dims = this_file.dimensions
if ( nlev != int(input_dims["z_t"]) or ( nlat != int(input_dims["nlat"]))\
or ( nlon != int(input_dims["nlon"]))):
print "Dimension mismatch between ", in_file_list[
0], 'and', in_file_list[count], '!!!'
sys.exit()
# Create new summary ensemble file
this_sumfile = opts_dict["sumfile"]
if verbose:
print "Creating ", this_sumfile, " ..."
if (me.get_rank() == 0):
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
opt = Nio.options()
opt.PreFill = False
opt.Format = 'NetCDF4Classic'
nc_sumfile = Nio.open_file(this_sumfile, 'w', options=opt)
# Set dimensions
if (verbose == True):
print "Setting dimensions ....."
nc_sumfile.create_dimension('nlat', nlat)
nc_sumfile.create_dimension('nlon', nlon)
nc_sumfile.create_dimension('nlev', nlev)
nc_sumfile.create_dimension('time', None)
nc_sumfile.create_dimension('ens_size', opts_dict['npert'])
nc_sumfile.create_dimension('nbin', opts_dict['nbin'])
nc_sumfile.create_dimension('nvars', len(Var3d) + len(Var2d))
nc_sumfile.create_dimension('nvars3d', len(Var3d))
nc_sumfile.create_dimension('nvars2d', len(Var2d))
nc_sumfile.create_dimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
if (verbose == True):
print "Setting global attributes ....."
setattr(nc_sumfile, 'creation_date', now)
setattr(nc_sumfile, 'title', 'POP verification ensemble summary file')
setattr(nc_sumfile, 'tag', opts_dict["tag"])
setattr(nc_sumfile, 'compset', opts_dict["compset"])
setattr(nc_sumfile, 'resolution', opts_dict["res"])
setattr(nc_sumfile, 'machine', opts_dict["mach"])
# Create variables
if (verbose == True):
print "Creating variables ....."
v_lev = nc_sumfile.create_variable("lev", 'f', ('nlev', ))
v_vars = nc_sumfile.create_variable("vars", 'S1',
('nvars', 'str_size'))
v_var3d = nc_sumfile.create_variable("var3d", 'S1',
('nvars3d', 'str_size'))
v_var2d = nc_sumfile.create_variable("var2d", 'S1',
('nvars2d', 'str_size'))
v_time = nc_sumfile.create_variable("time", 'd', ('time', ))
v_ens_avg3d = nc_sumfile.create_variable(
"ens_avg3d", 'f', ('time', 'nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_stddev3d = nc_sumfile.create_variable(
"ens_stddev3d", 'f', ('time', 'nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_avg2d = nc_sumfile.create_variable(
"ens_avg2d", 'f', ('time', 'nvars2d', 'nlat', 'nlon'))
v_ens_stddev2d = nc_sumfile.create_variable(
"ens_stddev2d", 'f', ('time', 'nvars2d', 'nlat', 'nlon'))
v_RMSZ = nc_sumfile.create_variable(
"RMSZ", 'f', ('time', 'nvars', 'ens_size', 'nbin'))
if not opts_dict['zscoreonly']:
v_gm = nc_sumfile.create_variable("global_mean", 'f',
('time', 'nvars', 'ens_size'))
# Assign vars, var3d and var2d
if (verbose == True):
print "Assigning vars, var3d, and var2d ....."
eq_all_var_names = []
eq_d3_var_names = []
eq_d2_var_names = []
all_var_names = list(Var3d)
all_var_names += Var2d
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(Var3d)
for i in range(l_eq):
tt = list(Var3d[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(Var2d)
for i in range(l_eq):
tt = list(Var2d[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if (verbose == True):
print "Assigning time invariant metadata ....."
vars_dict = o_files[0].variables
lev_data = vars_dict["z_t"]
v_lev = lev_data
# Time-varient metadata
if verbose:
print "Assigning time variant metadata ....."
vars_dict = o_files[0].variables
time_value = vars_dict['time']
time_array = np.array([time_value])
time_array = pyEnsLib.gather_npArray_pop(time_array, me, (me.get_size(), ))
if me.get_rank() == 0:
v_time[:] = time_array[:]
# Calculate global mean, average, standard deviation
if verbose:
print "Calculating global means ....."
is_SE = False
tslice = 0
if not opts_dict['zscoreonly']:
gm3d, gm2d = pyEnsLib.generate_global_mean_for_summary(
o_files, Var3d, Var2d, is_SE, False, opts_dict)
if verbose:
print "Finish calculating global means ....."
# Calculate RMSZ scores
if (verbose == True):
print "Calculating RMSZ scores ....."
zscore3d, zscore2d, ens_avg3d, ens_stddev3d, ens_avg2d, ens_stddev2d, temp1, temp2 = pyEnsLib.calc_rmsz(
o_files, Var3d, Var2d, is_SE, opts_dict)
# Collect from all processors
if opts_dict['mpi_enable']:
# Gather the 3d variable results from all processors to the master processor
# Gather global means 3d results
if not opts_dict['zscoreonly']:
gmall = np.concatenate((gm3d, gm2d), axis=0)
#print "before gather, gmall.shape=",gmall.shape
gmall = pyEnsLib.gather_npArray_pop(
gmall, me,
(me.get_size(), len(Var3d) + len(Var2d), len(o_files)))
zmall = np.concatenate((zscore3d, zscore2d), axis=0)
zmall = pyEnsLib.gather_npArray_pop(
zmall, me,
(me.get_size(), len(Var3d) + len(Var2d), len(o_files), nbin))
#print 'zmall=',zmall
#print "after gather, gmall.shape=",gmall.shape
ens_avg3d = pyEnsLib.gather_npArray_pop(
ens_avg3d, me, (me.get_size(), len(Var3d), nlev, (nlat), nlon))
ens_avg2d = pyEnsLib.gather_npArray_pop(ens_avg2d, me,
(me.get_size(), len(Var2d),
(nlat), nlon))
ens_stddev3d = pyEnsLib.gather_npArray_pop(
ens_stddev3d, me, (me.get_size(), len(Var3d), nlev, (nlat), nlon))
ens_stddev2d = pyEnsLib.gather_npArray_pop(ens_stddev2d, me,
(me.get_size(), len(Var2d),
(nlat), nlon))
# Assign to file:
if me.get_rank() == 0:
#Zscoreall=np.concatenate((zscore3d,zscore2d),axis=0)
v_RMSZ[:, :, :, :] = zmall[:, :, :, :]
v_ens_avg3d[:, :, :, :, :] = ens_avg3d[:, :, :, :, :]
v_ens_stddev3d[:, :, :, :, :] = ens_stddev3d[:, :, :, :, :]
v_ens_avg2d[:, :, :, :] = ens_avg2d[:, :, :, :]
v_ens_stddev2d[:, :, :, :] = ens_stddev2d[:, :, :, :]
if not opts_dict['zscoreonly']:
v_gm[:, :, :] = gmall[:, :, :]
print "All done"
if rank == 0:
# Update system log with the dates that were just converted
debugMsg('before chunking.write_log', header=True, verbosity=1)
chunking.write_log('{0}/logs/ts_status.log'.format(caseroot), log)
debugMsg('after chunking.write_log', header=True, verbosity=1)
scomm.sync()
return 0
#===================================
if __name__ == "__main__":
# initialize simplecomm object
scomm = simplecomm.create_comm(serial=False)
# setup an overall timer
timer = timekeeper.TimeKeeper()
timer.start("Total Time")
# get commandline options
options = commandline_options()
debug = options.debug[0]
# initialize global vprinter object for printing debug messages
debugMsg = vprinter.VPrinter(header='', verbosity=0)
if options.debug:
header = 'cesm_tseries_generator: DEBUG... '
debugMsg = vprinter.VPrinter(header=header, verbosity=options.debug[0])
def main(argv):
# Get command line stuff and store in a dictionary
s = """verbose sumfile= indir= input_globs= tslice= nPC= sigMul=
minPCFail= minRunFail= numRunFile= printVars popens
jsonfile= mpi_enable nbin= minrange= maxrange= outfile=
casejson= npick= pepsi_gm pop_tol= web_enabled
pop_threshold= printStdMean fIndex= lev= eet= saveResults json_case= """
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.CECT_usage()
sys.exit(2)
# Set the default value for options
opts_dict = {}
opts_dict['input_globs'] = ''
opts_dict['indir'] = ''
opts_dict['tslice'] = 1
opts_dict['nPC'] = 50
opts_dict['sigMul'] = 2
opts_dict['verbose'] = False
opts_dict['minPCFail'] = 3
opts_dict['minRunFail'] = 2
opts_dict['numRunFile'] = 3
opts_dict['printVars'] = False
opts_dict['popens'] = False
opts_dict['jsonfile'] = ''
opts_dict['mpi_enable'] = False
opts_dict['nbin'] = 40
opts_dict['minrange'] = 0.0
opts_dict['maxrange'] = 4.0
opts_dict['outfile'] = 'testcase.result'
opts_dict['casejson'] = ''
opts_dict['npick'] = 10
opts_dict['pepsi_gm'] = False
opts_dict['test_failure'] = True
opts_dict['pop_tol'] = 3.0
opts_dict['pop_threshold'] = 0.90
opts_dict['printStdMean'] = False
opts_dict['lev'] = 0
opts_dict['eet'] = 0
opts_dict['json_case'] = ''
opts_dict['sumfile'] = ''
opts_dict['web_enabled'] = False
opts_dict['saveResults'] = False
# Call utility library getopt_parseconfig to parse the option keys
# and save to the dictionary
caller = 'CECT'
opts_dict = pyEnsLib.getopt_parseconfig(opts, optkeys, caller, opts_dict)
popens = opts_dict['popens']
#some mods for POP-ECT
if popens == True:
opts_dict['tslice'] = 0
opts_dict['numRunFile'] = 1
opts_dict['eet'] = 0
opts_dict['mpi_enable'] = False
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(not opts_dict['mpi_enable'])
# Print out timestamp, input ensemble file and new run directory
dt = datetime.now()
verbose = opts_dict['verbose']
if me.get_rank() == 0:
print(' ')
print('--------pyCECT--------')
print(' ')
print(dt.strftime("%A, %d. %B %Y %I:%M%p"))
print(' ')
if not opts_dict['web_enabled']:
print('Ensemble summary file = ' + opts_dict['sumfile'])
print(' ')
print('Testcase file directory = ' + opts_dict['indir'])
print(' ')
print(' ')
#make sure these are valid
if opts_dict['web_enabled'] == False and os.path.isfile(
opts_dict['sumfile']) == False:
print("ERROR: Summary file name is not valid.")
sys.exit()
if os.path.exists(opts_dict['indir']) == False:
print("ERROR: --indir path is not valid.")
sys.exit()
# Ensure sensible EET value
if opts_dict['eet'] and opts_dict['numRunFile'] > opts_dict['eet']:
pyEnsLib.CECT_usage()
sys.exit(2)
ifiles = []
in_files = []
# Random pick pop files from not_pick_files list
if opts_dict['casejson']:
with open(opts_dict['casejson']) as fin:
result = json.load(fin)
in_files_first = result['not_pick_files']
in_files = random.sample(in_files_first, opts_dict['npick'])
print('Testcase files:')
print('\n'.join(in_files))
elif opts_dict['json_case']:
json_file = opts_dict['json_case']
if (os.path.exists(json_file)):
fd = open(json_file)
metainfo = json.load(fd)
if 'CaseName' in metainfo:
casename = metainfo['CaseName']
if (os.path.exists(opts_dict['indir'])):
for name in casename:
wildname = '*.' + name + '.*'
full_glob_str = os.path.join(opts_dict['indir'],
wildname)
glob_file = glob.glob(full_glob_str)
in_files.extend(glob_file)
else:
print("ERROR: " + opts_dict['json_case'] + " does not exist.")
sys.exit()
print("in_files=", in_files)
else:
wildname = '*' + str(opts_dict['input_globs']) + '*'
# Open all input files
if (os.path.exists(opts_dict['indir'])):
full_glob_str = os.path.join(opts_dict['indir'], wildname)
glob_files = glob.glob(full_glob_str)
in_files.extend(glob_files)
num_file = len(in_files)
if num_file == 0:
print("ERROR: no matching files for wildcard=" + wildname +
" found in specified --indir")
sys.exit()
else:
print("Found " + str(num_file) +
" matching files in specified --indir")
if opts_dict['numRunFile'] > num_file:
print("ERROR: more files needed (" +
str(opts_dict['numRunFile']) +
") than available in the indir (" + str(num_file) + ").")
sys.exit()
in_files.sort()
#print in_files
if popens:
#Partition the input file list
in_files_list = me.partition(in_files,
func=EqualStride(),
involved=True)
else:
# Random pick cam files
in_files_list = pyEnsLib.Random_pickup(in_files, opts_dict)
for frun_file in in_files_list:
if frun_file.find(opts_dict['indir']) != -1:
frun_temp = frun_file
else:
frun_temp = opts_dict['indir'] + '/' + frun_file
if (os.path.isfile(frun_temp)):
ifiles.append(frun_temp)
else:
print("ERROR: COULD NOT LOCATE FILE " + frun_temp)
sys.exit()
if opts_dict['web_enabled']:
if len(opts_dict['sumfile']) == 0:
opts_dict[
'sumfile'] = '/glade/p/cesmdata/cseg/inputdata/validation/'
#need to open ifiles
opts_dict['sumfile'], machineid, compiler = pyEnsLib.search_sumfile(
opts_dict, ifiles)
if len(machineid) != 0 and len(compiler) != 0:
print(' ')
print('Validation file : machineid = ' + machineid +
', compiler = ' + compiler)
print('Found summary file : ' + opts_dict['sumfile'])
print(' ')
else:
print('Warning: machine and compiler are unknown')
if popens:
# Read in the included var list
if not os.path.exists(opts_dict['jsonfile']):
print(
"ERROR: POP-ECT requires the specification of a valid json file via --jsonfile."
)
sys.exit()
Var2d, Var3d = pyEnsLib.read_jsonlist(opts_dict['jsonfile'], 'ESP')
print(' ')
print('Z-score tolerance = ' + '{:3.2f}'.format(opts_dict['pop_tol']))
print('ZPR = ' + '{:.2%}'.format(opts_dict['pop_threshold']))
zmall, n_timeslice = pyEnsLib.pop_compare_raw_score(
opts_dict, ifiles, me.get_rank(), Var3d, Var2d)
np.set_printoptions(threshold=sys.maxsize)
if opts_dict['mpi_enable']:
zmall = pyEnsLib.gather_npArray_pop(
zmall, me, (me.get_size(), len(Var3d) + len(Var2d),
len(ifiles), opts_dict['nbin']))
if me.get_rank() == 0:
fout = open(opts_dict['outfile'], "w")
for i in range(me.get_size()):
for j in zmall[i]:
np.savetxt(fout, j, fmt='%-7.2e')
#cam
else:
# Read all variables from the ensemble summary file
ens_var_name, ens_avg, ens_stddev, ens_rmsz, ens_gm, num_3d, mu_gm, sigma_gm, loadings_gm, sigma_scores_gm, is_SE_sum, std_gm, std_gm_array, str_size = pyEnsLib.read_ensemble_summary(
opts_dict['sumfile'])
#Only doing gm
# Add ensemble rmsz and global mean to the dictionary "variables"
variables = {}
for k, v in ens_gm.items():
pyEnsLib.addvariables(variables, k, 'gmRange', v)
# Get 3d variable name list and 2d variable name list separately
var_name3d = []
var_name2d = []
for vcount, v in enumerate(ens_var_name):
if vcount < num_3d:
var_name3d.append(v)
else:
var_name2d.append(v)
# Get ncol and nlev value
npts3d, npts2d, is_SE = pyEnsLib.get_ncol_nlev(ifiles[0])
if (is_SE ^ is_SE_sum):
print(
'Warning: please note the ensemble summary file is different from the testing files: they use different grids'
)
# Compare the new run and the ensemble summary file
results = {}
countgm = np.zeros(len(ifiles), dtype=np.int32)
# Calculate the new run global mean
mean3d, mean2d, varlist = pyEnsLib.generate_global_mean_for_summary(
ifiles, var_name3d, var_name2d, is_SE, opts_dict['pepsi_gm'],
opts_dict)
means = np.concatenate((mean3d, mean2d), axis=0)
# Add the new run global mean to the dictionary "results"
for i in range(means.shape[1]):
for j in range(means.shape[0]):
pyEnsLib.addresults(results, 'means', means[j][i],
ens_var_name[j], 'f' + str(i))
# Evaluate the new run global mean if it is in the range of the ensemble summary global mean range
for fcount, fid in enumerate(ifiles):
countgm[fcount] = pyEnsLib.evaluatestatus('means', 'gmRange',
variables, 'gm', results,
'f' + str(fcount))
# Calculate the PCA scores of the new run
new_scores, var_list, comp_std_gm = pyEnsLib.standardized(
means, mu_gm, sigma_gm, loadings_gm, ens_var_name, opts_dict,
ens_avg, me)
run_index, decision = pyEnsLib.comparePCAscores(
ifiles, new_scores, sigma_scores_gm, opts_dict, me)
# If there is failure, plot out standardized mean and compared standardized mean in box plots
# if opts_dict['printStdMean'] and decision == 'FAILED':
if opts_dict['printStdMean']:
import seaborn as sns
import matplotlib
matplotlib.use('Agg') #don't display figures
import matplotlib.pyplot as plt
print(" ")
print(
'***************************************************************************** '
)
print(
'Test run variable standardized means (for reference only - not used to determine pass/fail)'
)
print(
'***************************************************************************** '
)
print(" ")
category = {
"all_outside99": [],
"two_outside99": [],
"one_outside99": [],
"all_oneside_outside1QR": []
}
b = list(pyEnsLib.chunk(ens_var_name, 10))
for f, alist in enumerate(b):
for fc, avar in enumerate(alist):
dist_995 = np.percentile(std_gm[avar], 99.5)
dist_75 = np.percentile(std_gm[avar], 75)
dist_25 = np.percentile(std_gm[avar], 25)
dist_05 = np.percentile(std_gm[avar], 0.5)
c = 0
d = 0
p = 0
q = 0
for i in range(comp_std_gm[f + fc].size):
if comp_std_gm[f + fc][i] > dist_995:
c = c + 1
elif comp_std_gm[f + fc][i] < dist_05:
d = d + 1
elif (comp_std_gm[f + fc][i] < dist_995
and comp_std_gm[f + fc][i] > dist_75):
p = p + 1
elif (comp_std_gm[f + fc][i] > dist_05
and comp_std_gm[f + fc][i] < dist_25):
q = q + 1
if c == 3 or d == 3:
category["all_outside99"].append((avar, f + fc))
elif c == 2 or d == 2:
category["two_outside99"].append((avar, f + fc))
elif c == 1 or d == 1:
category["one_outside99"].append((avar, f + fc))
if p == 3 or q == 3:
category["all_oneside_outside1QR"].append(
(avar, f + fc))
part_name = opts_dict['indir'].split('/')[-1]
if not part_name:
part_name = opts_dict['indir'].split('/')[-2]
for key in sorted(category):
list_array = []
list_array2 = []
list_var = []
value = category[key]
if key == "all_outside99":
print(
"*** ", len(value),
" variables have 3 test run global means outside of the 99th percentile."
)
elif key == "two_outside99":
print(
"*** ", len(value),
" variables have 2 test run global means outside of the 99th percentile."
)
elif key == "one_outside99":
print(
"*** ", len(value),
" variables have 1 test run global mean outside of the 99th percentile."
)
elif key == "all_oneside_outside1QR":
print(
"*** ", len(value),
" variables have all test run global means outside of the first quartile (but not outside the 99th percentile)."
)
if len(value) > 0:
print(" => generating plot ...")
if len(value) > 20:
print(
" NOTE: truncating to only plot the first 20 variables."
)
value = value[0:20]
for each_var in value:
list_array.append(std_gm[each_var[0]])
list_array2.append(comp_std_gm[each_var[1]])
name = each_var[0]
if isinstance(name, str) == False:
name = name.decode("utf-8")
list_var.append(name)
if len(value) != 0:
ax = sns.boxplot(data=list_array,
whis=[0.5, 99.5],
fliersize=0.0)
sns.stripplot(data=list_array2, jitter=True, color="r")
plt.xticks(list(range(len(list_array))),
list_var,
fontsize=8,
rotation=-45)
if decision == 'FAILED':
plt.savefig(part_name + "_" + key + "_fail.png")
else:
plt.savefig(part_name + "_" + key + "_pass.png")
plt.close()
##
# Print file with info about new test runs....to a netcdf file
##
if opts_dict['saveResults']:
num_vars = comp_std_gm.shape[0]
tsize = comp_std_gm.shape[1]
esize = std_gm_array.shape[1]
this_savefile = 'savefile.nc'
if (verbose == True):
print("VERBOSE: Creating ", this_savefile, " ...")
if os.path.exists(this_savefile):
os.unlink(this_savefile)
nc_savefile = nc.Dataset(this_savefile,
"w",
format="NETCDF4_CLASSIC")
nc_savefile.createDimension('ens_size', esize)
nc_savefile.createDimension('test_size', tsize)
nc_savefile.createDimension('nvars', num_vars)
nc_savefile.createDimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
nc_savefile.creation_date = now
nc_savefile.title = 'PyCECT compare results file'
nc_savefile.summaryfile = opts_dict['sumfile']
#nc_savefile.testfiles = in_files
#variables
v_vars = nc_savefile.createVariable("vars", 'S1',
('nvars', 'str_size'))
v_std_gm = nc_savefile.createVariable("std_gm", 'f8',
('nvars', 'test_size'))
v_scores = nc_savefile.createVariable("scores", 'f8',
('nvars', 'test_size'))
v_ens_sigma_scores = nc_savefile.createVariable(
'ens_sigma_scores', 'f8', ('nvars', ))
v_ens_std_gm = nc_savefile.createVariable("ens_std_gm", 'f8',
('nvars', 'ens_size'))
#hard-coded size
str_out = nc.stringtochar(np.array(ens_var_name, 'S10'))
v_vars[:] = str_out
v_std_gm[:, :] = comp_std_gm[:, :]
v_scores[:, :] = new_scores[:, :]
v_ens_sigma_scores[:] = sigma_scores_gm[:]
v_ens_std_gm[:, :] = std_gm_array[:, :]
nc_savefile.close()
# Print variables (optional)
if opts_dict['printVars']:
print(" ")
print(
'***************************************************************************** '
)
print(
'Variable global mean information (for reference only - not used to determine pass/fail)'
)
print(
'***************************************************************************** '
)
for fcount, fid in enumerate(ifiles):
print(' ')
print('Run ' + str(fcount + 1) + ":")
print(' ')
print(
'***' + str(countgm[fcount]),
" of " + str(len(ens_var_name)) +
' variables are outside of ensemble global mean distribution***'
)
pyEnsLib.printsummary(results, 'gm', 'means', 'gmRange',
fcount, variables, 'global mean')
print(' ')
print(
'----------------------------------------------------------------------------'
)
if me.get_rank() == 0:
print(' ')
print("Testing complete.")
print(' ')
oldfile = os.path.join(olddir, filename)
if oldfile in oldfiles:
item_dict['old'] = oldfile
oldfiles.remove(oldfile)
items_to_check.append(item_dict)
else:
item_dict['old'] = None
unchecked_new_items.append(item_dict)
for oldfile in oldfiles:
item_dict = {'test': test_name}
item_dict['new'] = None
item_dict['old'] = oldfile
unchecked_old_items.append(item_dict)
# Get a basic MPI comm
comm = create_comm(serial=(opts.serial or opts.list_tests))
# Print tests that will be checked
if comm.is_manager():
print 'Checking test results.'
for test_name in tests_to_check:
print 'Test {0!s}:'.format(test_name)
num_chk = sum(1 for i in items_to_check if i['test'] == test_name)
num_new = num_chk + sum(1 for i in unchecked_new_items
if i['test'] == test_name)
num_old = num_chk + sum(1 for i in unchecked_old_items
if i['test'] == test_name)
print ' Checking {0!s} of {1!s}'.format(num_chk, num_new),
print 'new files generated against {0!s}'.format(num_old),
print 'old files found.'
def main(argv):
# Get command line stuff and store in a dictionary
s = 'tag= compset= esize= tslice= res= sumfile= indir= sumfiledir= mach= verbose jsonfile= mpi_enable maxnorm gmonly popens cumul regx= startMon= endMon= fIndex='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSum_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict={}
# Defaults
opts_dict['tag'] = 'cesm2_0_beta08'
opts_dict['compset'] = 'F2000'
opts_dict['mach'] = 'cheyenne'
opts_dict['esize'] = 350
opts_dict['tslice'] = 1
opts_dict['res'] = 'f19_f19'
opts_dict['sumfile'] = 'ens.summary.nc'
opts_dict['indir'] = './'
opts_dict['sumfiledir'] = './'
opts_dict['jsonfile'] = 'exclude_empty.json'
opts_dict['verbose'] = False
opts_dict['mpi_enable'] = False
opts_dict['maxnorm'] = False
opts_dict['gmonly'] = True
opts_dict['popens'] = False
opts_dict['cumul'] = False
opts_dict['regx'] = 'test'
opts_dict['startMon'] = 1
opts_dict['endMon'] = 1
opts_dict['fIndex'] = 151
# This creates the dictionary of input arguments
opts_dict = pyEnsLib.getopt_parseconfig(opts,optkeys,'ES',opts_dict)
verbose = opts_dict['verbose']
st = opts_dict['esize']
esize = int(st)
if not (opts_dict['tag'] and opts_dict['compset'] and opts_dict['mach'] or opts_dict['res']):
print 'Please specify --tag, --compset, --mach and --res options'
sys.exit()
# Now find file names in indir
input_dir = opts_dict['indir']
# The var list that will be excluded
ex_varlist=[]
inc_varlist=[]
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me=simplecomm.create_comm()
else:
me=simplecomm.create_comm(not opts_dict['mpi_enable'])
if me.get_rank() == 0:
print 'Running pyEnsSum!'
if me.get_rank() ==0 and (verbose == True):
print opts_dict
print 'Ensemble size for summary = ', esize
exclude=False
if me.get_rank() == 0:
if opts_dict['jsonfile']:
inc_varlist=[]
# Read in the excluded or included var list
ex_varlist,exclude=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ES')
if exclude == False:
inc_varlist=ex_varlist
ex_varlist=[]
# Read in the included var list
#inc_varlist=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ES')
# Broadcast the excluded var list to each processor
#if opts_dict['mpi_enable']:
# ex_varlist=me.partition(ex_varlist,func=Duplicate(),involved=True)
# Broadcast the excluded var list to each processor
if opts_dict['mpi_enable']:
exclude=me.partition(exclude,func=Duplicate(),involved=True)
if exclude:
ex_varlist=me.partition(ex_varlist,func=Duplicate(),involved=True)
else:
inc_varlist=me.partition(inc_varlist,func=Duplicate(),involved=True)
in_files=[]
if(os.path.exists(input_dir)):
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files=sorted(in_files_temp)
# Make sure we have enough
num_files = len(in_files)
if me.get_rank()==0 and (verbose == True):
print 'Number of files in input directory = ', num_files
if (num_files < esize):
if me.get_rank()==0 and (verbose == True):
print 'Number of files in input directory (',num_files,\
') is less than specified ensemble size of ', esize
sys.exit(2)
if (num_files > esize):
if me.get_rank()==0 and (verbose == True):
print 'NOTE: Number of files in ', input_dir, \
'is greater than specified ensemble size of ', esize ,\
'\nwill just use the first ', esize, 'files'
else:
if me.get_rank()==0:
print 'Input directory: ',input_dir,' not found'
sys.exit(2)
if opts_dict['cumul']:
if opts_dict['regx']:
in_files_list=get_cumul_filelist(opts_dict,opts_dict['indir'],opts_dict['regx'])
in_files=me.partition(in_files_list,func=EqualLength(),involved=True)
if me.get_rank()==0 and (verbose == True):
print 'in_files=',in_files
# Open the files in the input directory
o_files=[]
if me.get_rank() == 0 and opts_dict['verbose']:
print 'Input files are: '
print "\n".join(in_files)
#for i in in_files:
# print "in_files =",i
for onefile in in_files[0:esize]:
if (os.path.isfile(input_dir+'/' + onefile)):
o_files.append(Nio.open_file(input_dir+'/' + onefile,"r"))
else:
if me.get_rank()==0:
print "COULD NOT LOCATE FILE "+ input_dir + onefile + "! EXITING...."
sys.exit()
# Store dimensions of the input fields
if me.get_rank()==0 and (verbose == True):
print "Getting spatial dimensions"
nlev = -1
nilev = -1
ncol = -1
nlat = -1
nlon = -1
lonkey=''
latkey=''
# Look at first file and get dims
input_dims = o_files[0].dimensions
ndims = len(input_dims)
for key in input_dims:
if key == "lev":
nlev = input_dims["lev"]
elif key == "ilev":
nilev = input_dims["ilev"]
elif key == "ncol":
ncol = input_dims["ncol"]
elif (key == "nlon") or (key =="lon"):
nlon = input_dims[key]
lonkey=key
elif (key == "nlat") or (key == "lat"):
nlat = input_dims[key]
latkey=key
if (nlev == -1) :
if me.get_rank()==0:
print "COULD NOT LOCATE valid dimension lev => EXITING...."
sys.exit()
if (( ncol == -1) and ((nlat == -1) or (nlon == -1))):
if me.get_rank()==0:
print "Need either lat/lon or ncol => EXITING...."
sys.exit()
# Check if this is SE or FV data
if (ncol != -1):
is_SE = True
else:
is_SE = False
# Make sure all files have the same dimensions
if me.get_rank()==0 and (verbose == True):
print "Checking dimensions across files...."
print 'lev = ', nlev
if (is_SE == True):
print 'ncol = ', ncol
else:
print 'nlat = ', nlat
print 'nlon = ', nlon
for count, this_file in enumerate(o_files):
input_dims = this_file.dimensions
if (is_SE == True):
if ( nlev != int(input_dims["lev"]) or ( ncol != int(input_dims["ncol"]))):
if me.get_rank() == 0:
print "Dimension mismatch between ", in_files[0], 'and', in_files[0], '!!!'
sys.exit()
else:
if ( nlev != int(input_dims["lev"]) or ( nlat != int(input_dims[latkey]))\
or ( nlon != int(input_dims[lonkey]))):
if me.get_rank() == 0:
print "Dimension mismatch between ", in_files[0], 'and', in_files[0], '!!!'
sys.exit()
# Get 2d vars, 3d vars and all vars (For now include all variables)
vars_dict_all = o_files[0].variables
# Remove the excluded variables (specified in json file) from variable dictionary
#print len(vars_dict_all)
if exclude:
vars_dict=vars_dict_all
for i in ex_varlist:
if i in vars_dict:
del vars_dict[i]
#Given an included var list, remove all float var that are not on the list
else:
vars_dict=vars_dict_all.copy()
for k,v in vars_dict_all.iteritems():
if (k not in inc_varlist) and (vars_dict_all[k].typecode()=='f'):
#print vars_dict_all[k].typecode()
#print k
del vars_dict[k]
num_vars = len(vars_dict)
#print num_vars
#if me.get_rank() == 0:
# for k,v in vars_dict.iteritems():
# print 'vars_dict',k,vars_dict[k].typecode()
str_size = 0
d2_var_names = []
d3_var_names = []
num_2d = 0
num_3d = 0
# Which are 2d, which are 3d and max str_size
for k,v in vars_dict.iteritems():
var = k
vd = v.dimensions # all the variable's dimensions (names)
vr = v.rank # num dimension
vs = v.shape # dim values
is_2d = False
is_3d = False
if (is_SE == True): # (time, lev, ncol) or (time, ncol)
if ((vr == 2) and (vs[1] == ncol)):
is_2d = True
num_2d += 1
elif ((vr == 3) and (vs[2] == ncol and vs[1] == nlev )):
is_3d = True
num_3d += 1
else: # (time, lev, nlon, nlon) or (time, nlat, nlon)
if ((vr == 3) and (vs[1] == nlat and vs[2] == nlon)):
is_2d = True
num_2d += 1
elif ((vr == 4) and (vs[2] == nlat and vs[3] == nlon and (vs[1] == nlev or vs[1]==nilev ))):
is_3d = True
num_3d += 1
if (is_3d == True) :
str_size = max(str_size, len(k))
d3_var_names.append(k)
elif (is_2d == True):
str_size = max(str_size, len(k))
d2_var_names.append(k)
#else:
# print 'var=',k
if me.get_rank() == 0 and (verbose == True):
print 'Number of variables found: ', num_3d+num_2d
print '3D variables: '+str(num_3d)+', 2D variables: '+str(num_2d)
# Now sort these and combine (this sorts caps first, then lower case -
# which is what we want)
d2_var_names.sort()
d3_var_names.sort()
if esize<num_2d+num_3d:
if me.get_rank()==0:
print "************************************************************************************************************************************"
print " Error: the total number of 3D and 2D variables "+str(num_2d+num_3d)+" is larger than the number of ensemble files "+str(esize)
print " Cannot generate ensemble summary file, please remove more variables from your included variable list,"
print " or add more varaibles in your excluded variable list!!!"
print "************************************************************************************************************************************"
sys.exit()
# All vars is 3d vars first (sorted), the 2d vars
all_var_names = list(d3_var_names)
all_var_names += d2_var_names
n_all_var_names = len(all_var_names)
#if me.get_rank() == 0 and (verbose == True):
# print 'num vars = ', n_all_var_names, '(3d = ', num_3d, ' and 2d = ', num_2d, ")"
# Create new summary ensemble file
this_sumfile = opts_dict["sumfile"]
if me.get_rank() == 0 and (verbose == True):
print "Creating ", this_sumfile, " ..."
if(me.get_rank() ==0 | opts_dict["popens"]):
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
opt = Nio.options()
opt.PreFill = False
opt.Format = 'NetCDF4Classic'
nc_sumfile = Nio.open_file(this_sumfile, 'w', options=opt)
# Set dimensions
if me.get_rank() == 0 and (verbose == True):
print "Setting dimensions ....."
if (is_SE == True):
nc_sumfile.create_dimension('ncol', ncol)
else:
nc_sumfile.create_dimension('nlat', nlat)
nc_sumfile.create_dimension('nlon', nlon)
nc_sumfile.create_dimension('nlev', nlev)
nc_sumfile.create_dimension('ens_size', esize)
nc_sumfile.create_dimension('nvars', num_3d + num_2d)
nc_sumfile.create_dimension('nvars3d', num_3d)
nc_sumfile.create_dimension('nvars2d', num_2d)
nc_sumfile.create_dimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
if me.get_rank() == 0 and (verbose == True):
print "Setting global attributes ....."
setattr(nc_sumfile, 'creation_date',now)
setattr(nc_sumfile, 'title', 'CAM verification ensemble summary file')
setattr(nc_sumfile, 'tag', opts_dict["tag"])
setattr(nc_sumfile, 'compset', opts_dict["compset"])
setattr(nc_sumfile, 'resolution', opts_dict["res"])
setattr(nc_sumfile, 'machine', opts_dict["mach"])
# Create variables
if me.get_rank() == 0 and (verbose == True):
print "Creating variables ....."
v_lev = nc_sumfile.create_variable("lev", 'f', ('nlev',))
v_vars = nc_sumfile.create_variable("vars", 'S1', ('nvars', 'str_size'))
v_var3d = nc_sumfile.create_variable("var3d", 'S1', ('nvars3d', 'str_size'))
v_var2d = nc_sumfile.create_variable("var2d", 'S1', ('nvars2d', 'str_size'))
if not opts_dict['gmonly']:
if (is_SE == True):
v_ens_avg3d = nc_sumfile.create_variable("ens_avg3d", 'f', ('nvars3d', 'nlev', 'ncol'))
v_ens_stddev3d = nc_sumfile.create_variable("ens_stddev3d", 'f', ('nvars3d', 'nlev', 'ncol'))
v_ens_avg2d = nc_sumfile.create_variable("ens_avg2d", 'f', ('nvars2d', 'ncol'))
v_ens_stddev2d = nc_sumfile.create_variable("ens_stddev2d", 'f', ('nvars2d', 'ncol'))
else:
v_ens_avg3d = nc_sumfile.create_variable("ens_avg3d", 'f', ('nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_stddev3d = nc_sumfile.create_variable("ens_stddev3d", 'f', ('nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_avg2d = nc_sumfile.create_variable("ens_avg2d", 'f', ('nvars2d', 'nlat', 'nlon'))
v_ens_stddev2d = nc_sumfile.create_variable("ens_stddev2d", 'f', ('nvars2d', 'nlat', 'nlon'))
v_RMSZ = nc_sumfile.create_variable("RMSZ", 'f', ('nvars', 'ens_size'))
v_gm = nc_sumfile.create_variable("global_mean", 'f', ('nvars', 'ens_size'))
v_standardized_gm=nc_sumfile.create_variable("standardized_gm",'f',('nvars','ens_size'))
v_loadings_gm = nc_sumfile.create_variable('loadings_gm','f',('nvars','nvars'))
v_mu_gm = nc_sumfile.create_variable('mu_gm','f',('nvars',))
v_sigma_gm = nc_sumfile.create_variable('sigma_gm','f',('nvars',))
v_sigma_scores_gm = nc_sumfile.create_variable('sigma_scores_gm','f',('nvars',))
# Assign vars, var3d and var2d
if me.get_rank() == 0 and (verbose == True):
print "Assigning vars, var3d, and var2d ....."
eq_all_var_names =[]
eq_d3_var_names = []
eq_d2_var_names = []
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(d3_var_names)
for i in range(l_eq):
tt = list(d3_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(d2_var_names)
for i in range(l_eq):
tt = list(d2_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if me.get_rank() == 0 and (verbose == True):
print "Assigning time invariant metadata ....."
lev_data = vars_dict["lev"]
v_lev = lev_data
# Form ensembles, each missing one member; compute RMSZs and global means
#for each variable, we also do max norm also (currently done in pyStats)
tslice = opts_dict['tslice']
if not opts_dict['cumul']:
# Partition the var list
var3_list_loc=me.partition(d3_var_names,func=EqualStride(),involved=True)
var2_list_loc=me.partition(d2_var_names,func=EqualStride(),involved=True)
else:
var3_list_loc=d3_var_names
var2_list_loc=d2_var_names
# Calculate global means #
if me.get_rank() == 0 and (verbose == True):
print "Calculating global means ....."
if not opts_dict['cumul']:
gm3d,gm2d,var_list = pyEnsLib.generate_global_mean_for_summary(o_files,var3_list_loc,var2_list_loc , is_SE, False,opts_dict)
if me.get_rank() == 0 and (verbose == True):
print "Finish calculating global means ....."
# Calculate RMSZ scores
if (not opts_dict['gmonly']) | (opts_dict['cumul']):
if me.get_rank() == 0 and (verbose == True):
print "Calculating RMSZ scores ....."
zscore3d,zscore2d,ens_avg3d,ens_stddev3d,ens_avg2d,ens_stddev2d,temp1,temp2=pyEnsLib.calc_rmsz(o_files,var3_list_loc,var2_list_loc,is_SE,opts_dict)
# Calculate max norm ensemble
if opts_dict['maxnorm']:
if me.get_rank() == 0 and (verbose == True):
print "Calculating max norm of ensembles ....."
pyEnsLib.calculate_maxnormens(opts_dict,var3_list_loc)
pyEnsLib.calculate_maxnormens(opts_dict,var2_list_loc)
if opts_dict['mpi_enable'] & ( not opts_dict['popens']):
if not opts_dict['cumul']:
# Gather the 3d variable results from all processors to the master processor
slice_index=get_stride_list(len(d3_var_names),me)
# Gather global means 3d results
gm3d=gather_npArray(gm3d,me,slice_index,(len(d3_var_names),len(o_files)))
if not opts_dict['gmonly']:
# Gather zscore3d results
zscore3d=gather_npArray(zscore3d,me,slice_index,(len(d3_var_names),len(o_files)))
# Gather ens_avg3d and ens_stddev3d results
shape_tuple3d=get_shape(ens_avg3d.shape,len(d3_var_names),me.get_rank())
ens_avg3d=gather_npArray(ens_avg3d,me,slice_index,shape_tuple3d)
ens_stddev3d=gather_npArray(ens_stddev3d,me,slice_index,shape_tuple3d)
# Gather 2d variable results from all processors to the master processor
slice_index=get_stride_list(len(d2_var_names),me)
# Gather global means 2d results
gm2d=gather_npArray(gm2d,me,slice_index,(len(d2_var_names),len(o_files)))
var_list=gather_list(var_list,me)
if not opts_dict['gmonly']:
# Gather zscore2d results
zscore2d=gather_npArray(zscore2d,me,slice_index,(len(d2_var_names),len(o_files)))
# Gather ens_avg3d and ens_stddev2d results
shape_tuple2d=get_shape(ens_avg2d.shape,len(d2_var_names),me.get_rank())
ens_avg2d=gather_npArray(ens_avg2d,me,slice_index,shape_tuple2d)
ens_stddev2d=gather_npArray(ens_stddev2d,me,slice_index,shape_tuple2d)
else:
gmall=np.concatenate((temp1,temp2),axis=0)
gmall=pyEnsLib.gather_npArray_pop(gmall,me,(me.get_size(),len(d3_var_names)+len(d2_var_names)))
# Assign to file:
if me.get_rank() == 0 | opts_dict['popens'] :
if not opts_dict['cumul']:
gmall=np.concatenate((gm3d,gm2d),axis=0)
if not opts_dict['gmonly']:
Zscoreall=np.concatenate((zscore3d,zscore2d),axis=0)
v_RMSZ[:,:]=Zscoreall[:,:]
if not opts_dict['gmonly']:
if (is_SE == True):
v_ens_avg3d[:,:,:]=ens_avg3d[:,:,:]
v_ens_stddev3d[:,:,:]=ens_stddev3d[:,:,:]
v_ens_avg2d[:,:]=ens_avg2d[:,:]
v_ens_stddev2d[:,:]=ens_stddev2d[:,:]
else:
v_ens_avg3d[:,:,:,:]=ens_avg3d[:,:,:,:]
v_ens_stddev3d[:,:,:,:]=ens_stddev3d[:,:,:,:]
v_ens_avg2d[:,:,:]=ens_avg2d[:,:,:]
v_ens_stddev2d[:,:,:]=ens_stddev2d[:,:,:]
else:
gmall_temp=np.transpose(gmall[:,:])
gmall=gmall_temp
mu_gm,sigma_gm,standardized_global_mean,loadings_gm,scores_gm=pyEnsLib.pre_PCA(gmall,all_var_names,var_list,me)
v_gm[:,:]=gmall[:,:]
v_standardized_gm[:,:]=standardized_global_mean[:,:]
v_mu_gm[:]=mu_gm[:]
v_sigma_gm[:]=sigma_gm[:].astype(np.float32)
v_loadings_gm[:,:]=loadings_gm[:,:]
v_sigma_scores_gm[:]=scores_gm[:]
if me.get_rank() == 0:
print "All Done"
def main(argv):
# Get command line stuff and store in a dictionary
s = """verbose sumfile= indir= input_globs= tslice= nPC= sigMul=
minPCFail= minRunFail= numRunFile= printVarTest popens
jsonfile= mpi_enable nbin= minrange= maxrange= outfile=
casejson= npick= pepsi_gm pop_tol= web_enabled
pop_threshold= prn_std_mean fIndex= lev= eet= json_case= """
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.CECT_usage()
sys.exit(2)
# Set the default value for options
opts_dict = {}
opts_dict['input_globs'] = ''
opts_dict['indir'] = ''
opts_dict['tslice'] = 1
opts_dict['nPC'] = 50
opts_dict['sigMul'] = 2
opts_dict['verbose'] = False
opts_dict['minPCFail'] = 3
opts_dict['minRunFail'] = 2
opts_dict['numRunFile'] = 3
opts_dict['printVarTest'] = False
opts_dict['popens'] = False
opts_dict['jsonfile'] = ''
opts_dict['mpi_enable'] = False
opts_dict['nbin'] = 40
opts_dict['minrange'] = 0.0
opts_dict['maxrange'] = 4.0
opts_dict['outfile'] = 'testcase.result'
opts_dict['casejson'] = ''
opts_dict['npick'] = 10
opts_dict['pepsi_gm'] = False
opts_dict['test_failure'] = True
opts_dict['pop_tol'] = 3.0
opts_dict['pop_threshold'] = 0.90
opts_dict['prn_std_mean'] = False
opts_dict['lev'] = 0
opts_dict['eet'] = 0
opts_dict['json_case'] = ''
opts_dict['sumfile'] = ''
opts_dict['web_enabled'] = False
# Call utility library getopt_parseconfig to parse the option keys
# and save to the dictionary
caller = 'CECT'
gmonly = False
opts_dict = pyEnsLib.getopt_parseconfig(opts, optkeys, caller, opts_dict)
popens = opts_dict['popens']
#some mods for POP-ECT
if popens == True:
opts_dict['tslice'] = 0
opts_dict['numRunFile'] = 1
opts_dict['eet'] = 0
opts_dict['mpi_enable'] = False
#print opts_dict
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(not opts_dict['mpi_enable'])
# Print out timestamp, input ensemble file and new run directory
dt = datetime.now()
verbose = opts_dict['verbose']
if me.get_rank() == 0:
print '--------pyCECT--------'
print ' '
print dt.strftime("%A, %d. %B %Y %I:%M%p")
print ' '
if not opts_dict['web_enabled']:
print 'Ensemble summary file = ' + opts_dict['sumfile']
print ' '
print 'Testcase file directory = ' + opts_dict['indir']
print ' '
print ' '
# Ensure sensible EET value
if opts_dict['eet'] and opts_dict['numRunFile'] > opts_dict['eet']:
pyEnsLib.CECT_usage()
sys.exit(2)
ifiles = []
in_files = []
# Random pick pop files from not_pick_files list
if opts_dict['casejson']:
with open(opts_dict['casejson']) as fin:
result = json.load(fin)
in_files_first = result['not_pick_files']
in_files = random.sample(in_files_first, opts_dict['npick'])
print 'Testcase files:'
print '\n'.join(in_files)
elif opts_dict['json_case']:
json_file = opts_dict['json_case']
if (os.path.exists(json_file)):
fd = open(json_file)
metainfo = json.load(fd)
if 'CaseName' in metainfo:
casename = metainfo['CaseName']
if (os.path.exists(opts_dict['indir'])):
for name in casename:
wildname = '*.' + name + '.*'
full_glob_str = os.path.join(opts_dict['indir'],
wildname)
glob_file = glob.glob(full_glob_str)
in_files.extend(glob_file)
else:
print "ERROR: " + opts_dict['json_case'] + " does not exist."
sys.exit()
print "in_files=", in_files
else:
wildname = '*' + str(opts_dict['input_globs']) + '*'
# Open all input files
if (os.path.exists(opts_dict['indir'])):
full_glob_str = os.path.join(opts_dict['indir'], wildname)
glob_files = glob.glob(full_glob_str)
in_files.extend(glob_files)
num_file = len(in_files)
if num_file == 0:
print "ERROR: no matching files for wildcard=" + wildname + " found in specified --indir"
sys.exit()
else:
print "Found " + str(
num_file) + " matching files in specified --indir"
if opts_dict['numRunFile'] > num_file:
print "ERROR: more files needed (" + str(
opts_dict['numRunFile']
) + ") than available in the indir (" + str(num_file) + ")."
sys.exit()
#in_files_temp=os.listdir(opts_dict['indir'])
in_files.sort()
#print in_files
if popens:
#Partition the input file list
in_files_list = me.partition(in_files,
func=EqualStride(),
involved=True)
else:
# Random pick non pop files
in_files_list = pyEnsLib.Random_pickup(in_files, opts_dict)
#in_files_list=in_files
for frun_file in in_files_list:
if frun_file.find(opts_dict['indir']) != -1:
frun_temp = frun_file
else:
frun_temp = opts_dict['indir'] + '/' + frun_file
if (os.path.isfile(frun_temp)):
ifiles.append(Nio.open_file(frun_temp, "r"))
else:
print "ERROR: COULD NOT LOCATE FILE " + frun_temp
sys.exit()
if opts_dict['web_enabled']:
if len(opts_dict['sumfile']) == 0:
opts_dict[
'sumfile'] = '/glade/p/cesmdata/cseg/inputdata/validation/'
opts_dict['sumfile'], machineid, compiler = pyEnsLib.search_sumfile(
opts_dict, ifiles)
if len(machineid) != 0 and len(compiler) != 0:
print ' '
print 'Validation file : machineid = ' + machineid + ', compiler = ' + compiler
print 'Found summary file : ' + opts_dict['sumfile']
print ' '
else:
print 'Warning: machine and compiler are unknown'
if popens:
# Read in the included var list
if not os.path.exists(opts_dict['jsonfile']):
print "ERROR: POP-ECT requires the specification of a valid json file via --jsonfile."
sys.exit()
Var2d, Var3d = pyEnsLib.read_jsonlist(opts_dict['jsonfile'], 'ESP')
print ' '
print 'Z-score tolerance = ' + '{:3.2f}'.format(opts_dict['pop_tol'])
print 'ZPR = ' + '{:.2%}'.format(opts_dict['pop_threshold'])
zmall, n_timeslice = pyEnsLib.pop_compare_raw_score(
opts_dict, ifiles, me.get_rank(), Var3d, Var2d)
#zmall = np.concatenate((Zscore3d,Zscore2d),axis=0)
np.set_printoptions(threshold=np.nan)
if opts_dict['mpi_enable']:
zmall = pyEnsLib.gather_npArray_pop(
zmall, me, (me.get_size(), len(Var3d) + len(Var2d),
len(ifiles), opts_dict['nbin']))
if me.get_rank() == 0:
fout = open(opts_dict['outfile'], "w")
for i in range(me.get_size()):
for j in zmall[i]:
np.savetxt(fout, j, fmt='%-7.2e')
#cam
else:
# Read all variables from the ensemble summary file
ens_var_name, ens_avg, ens_stddev, ens_rmsz, ens_gm, num_3d, mu_gm, sigma_gm, loadings_gm, sigma_scores_gm, is_SE_sum, std_gm = pyEnsLib.read_ensemble_summary(
opts_dict['sumfile'])
if len(ens_rmsz) == 0:
gmonly = True
# Add ensemble rmsz and global mean to the dictionary "variables"
variables = {}
if not gmonly:
for k, v in ens_rmsz.iteritems():
pyEnsLib.addvariables(variables, k, 'zscoreRange', v)
for k, v in ens_gm.iteritems():
pyEnsLib.addvariables(variables, k, 'gmRange', v)
# Get 3d variable name list and 2d variable name list separately
var_name3d = []
var_name2d = []
for vcount, v in enumerate(ens_var_name):
if vcount < num_3d:
var_name3d.append(v)
else:
var_name2d.append(v)
# Get ncol and nlev value
npts3d, npts2d, is_SE = pyEnsLib.get_ncol_nlev(ifiles[0])
if (is_SE ^ is_SE_sum):
print 'Warning: please note the ensemble summary file is different from the testing files, they use different grids'
# Compare the new run and the ensemble summary file to get rmsz score
results = {}
countzscore = np.zeros(len(ifiles), dtype=np.int32)
countgm = np.zeros(len(ifiles), dtype=np.int32)
if not gmonly:
for fcount, fid in enumerate(ifiles):
otimeSeries = fid.variables
for var_name in ens_var_name:
orig = otimeSeries[var_name]
Zscore, has_zscore = pyEnsLib.calculate_raw_score(
var_name, orig[opts_dict['tslice']], npts3d, npts2d,
ens_avg, ens_stddev, is_SE, opts_dict, 0, 0, 0)
if has_zscore:
# Add the new run rmsz zscore to the dictionary "results"
pyEnsLib.addresults(results, 'zscore', Zscore,
var_name, 'f' + str(fcount))
# Evaluate the new run rmsz score if is in the range of the ensemble summary rmsz zscore range
for fcount, fid in enumerate(ifiles):
countzscore[fcount] = pyEnsLib.evaluatestatus(
'zscore', 'zscoreRange', variables, 'ens', results,
'f' + str(fcount))
# Calculate the new run global mean
mean3d, mean2d, varlist = pyEnsLib.generate_global_mean_for_summary(
ifiles, var_name3d, var_name2d, is_SE, opts_dict['pepsi_gm'],
opts_dict)
means = np.concatenate((mean3d, mean2d), axis=0)
# Add the new run global mean to the dictionary "results"
for i in range(means.shape[1]):
for j in range(means.shape[0]):
pyEnsLib.addresults(results, 'means', means[j][i],
ens_var_name[j], 'f' + str(i))
# Evaluate the new run global mean if it is in the range of the ensemble summary global mean range
for fcount, fid in enumerate(ifiles):
countgm[fcount] = pyEnsLib.evaluatestatus('means', 'gmRange',
variables, 'gm', results,
'f' + str(fcount))
# Calculate the PCA scores of the new run
new_scores, var_list, comp_std_gm = pyEnsLib.standardized(
means, mu_gm, sigma_gm, loadings_gm, ens_var_name, opts_dict,
ens_avg, me)
run_index, decision = pyEnsLib.comparePCAscores(
ifiles, new_scores, sigma_scores_gm, opts_dict, me)
# If there is failure, plot out standardized mean and compared standardized mean in box plots
if opts_dict['prn_std_mean'] and decision == 'FAILED':
import seaborn as sns
category = {
"all_outside99": [],
"two_outside99": [],
"one_outside99": [],
"all_oneside_outside1QR": []
}
b = list(pyEnsLib.chunk(ens_var_name, 10))
for f, alist in enumerate(b):
for fc, avar in enumerate(alist):
dist_995 = np.percentile(std_gm[avar], 99.5)
dist_75 = np.percentile(std_gm[avar], 75)
dist_25 = np.percentile(std_gm[avar], 25)
dist_05 = np.percentile(std_gm[avar], 0.5)
c = 0
d = 0
p = 0
q = 0
for i in range(comp_std_gm[f + fc].size):
if comp_std_gm[f + fc][i] > dist_995:
c = c + 1
elif comp_std_gm[f + fc][i] < dist_05:
d = d + 1
elif (comp_std_gm[f + fc][i] < dist_995
and comp_std_gm[f + fc][i] > dist_75):
p = p + 1
elif (comp_std_gm[f + fc][i] > dist_05
and comp_std_gm[f + fc][i] < dist_25):
q = q + 1
if c == 3 or d == 3:
category["all_outside99"].append((avar, f + fc))
elif c == 2 or d == 2:
category["two_outside99"].append((avar, f + fc))
elif c == 1 or d == 1:
category["one_outside99"].append((avar, f + fc))
if p == 3 or q == 3:
category["all_oneside_outside1QR"].append(
(avar, f + fc))
part_name = opts_dict['indir'].split('/')[-1]
if not part_name:
part_name = opts_dict['indir'].split('/')[-2]
for key in sorted(category):
list_array = []
list_array2 = []
list_var = []
value = category[key]
print "value len=", key, len(value)
for each_var in value:
list_array.append(std_gm[each_var[0]])
list_array2.append(comp_std_gm[each_var[1]])
list_var.append(each_var[0])
if len(value) != 0:
ax = sns.boxplot(data=list_array,
whis=[0.5, 99.5],
fliersize=0.0)
sns.stripplot(data=list_array2, jitter=True, color="r")
sns.plt.xticks(range(len(list_array)),
list_var,
fontsize=8,
rotation=-45)
if decision == 'FAILED':
sns.plt.savefig(part_name + "_" + key + "_fail.png")
else:
sns.plt.savefig(part_name + "_" + key + "_pass.png")
sns.plt.clf()
'''
if len(run_index)>0:
json_file=opts_dict['json_case']
if (os.path.exists(json_file)):
fd=open(json_file)
metainfo=json.load(fd)
caseindex=metainfo['CaseIndex']
enspath=str(metainfo['EnsPath'][0])
#print caseindex
if (os.path.exists(enspath)):
i=0
comp_file=[]
search = '\.[0-9]{3}\.'
for name in in_files_list:
s=re.search(search,name)
in_files_index=s.group(0)
if in_files_index[1:4] in caseindex:
ens_index=str(caseindex[in_files_index[1:4]])
wildname='*.'+ens_index+'.*'
full_glob_str=os.path.join(enspath,wildname)
glob_file=glob.glob(full_glob_str)
comp_file.extend(glob_file)
print "comp_file=",comp_file
pyEnsLib.plot_variable(in_files_list,comp_file,opts_dict,var_list,run_index,me)
'''
# Print out
if opts_dict['printVarTest']:
print '*********************************************** '
print 'Variable-based testing (for reference only - not used to determine pass/fail)'
print '*********************************************** '
for fcount, fid in enumerate(ifiles):
print ' '
print 'Run ' + str(fcount + 1) + ":"
print ' '
if not gmonly:
print '***' + str(countzscore[fcount]), " of " + str(
len(ens_var_name)
) + ' variables are outside of ensemble RMSZ distribution***'
pyEnsLib.printsummary(results, 'ens', 'zscore',
'zscoreRange', (fcount), variables,
'RMSZ')
print ' '
print '***' + str(countgm[fcount]), " of " + str(
len(ens_var_name)
) + ' variables are outside of ensemble global mean distribution***'
pyEnsLib.printsummary(results, 'gm', 'means', 'gmRange',
fcount, variables, 'global mean')
print ' '
print '----------------------------------------------------------------------------'
if me.get_rank() == 0:
print ' '
print "Testing complete."
print ' '
def __init__(self, specifier, serial=False, verbosity=1, wmode='w', once=False, simplecomm=None):
"""
Constructor
Parameters:
specifier (Specifier): An instance of the Specifier class,
defining the input specification for this reshaper operation.
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
wmode (str): The mode to use for writing output. Can be 'w' for
normal write operation, 's' to skip the output generation for
existing time-series files, 'o' to overwrite existing
time-series files, 'a' to append to existing time-series files.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Type checking (or double-checking)
if not isinstance(specifier, Specifier):
err_msg = "Input must be given in the form of a Specifier object"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(wmode) is not str:
err_msg = "Write mode flag must be a str."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
if wmode not in ['w', 's', 'o', 'a']:
err_msg = "Write mode '{0}' not recognized".format(wmode)
raise ValueError(err_msg)
# Whether to write a once file
self._use_once_file = once
# The output write mode to use
self._write_mode = wmode
# Internal timer data
self._timer = TimeKeeper()
self._timer.start('Initializing Simple Communicator')
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Reference to the simple communicator
self._simplecomm = simplecomm
self._timer.stop('Initializing Simple Communicator')
# Dictionary storing read/write data amounts
self.assumed_block_size = float(4 * 1024 * 1024)
self._byte_counts = {}
# Contruct the print header
header = ''.join(['[', str(self._simplecomm.get_rank()),
'/', str(self._simplecomm.get_size()), '] '])
# Reference to the verbose printer tool
self._vprint = VPrinter(header=header, verbosity=verbosity)
# Debug output starting
if self._simplecomm.is_manager():
self._vprint('Initializing Reshaper...', verbosity=0)
self._vprint(' MPI Communicator Size: {}'.format(
self._simplecomm.get_size()), verbosity=1)
# Validate the user input data
self._timer.start('Specifier Validation')
specifier.validate()
self._timer.stop('Specifier Validation')
if self._simplecomm.is_manager():
self._vprint(' Specifier validated', verbosity=1)
# The I/O backend to use
if iobackend.is_available(specifier.io_backend):
self._backend = specifier.io_backend
else:
self._backend = iobackend.get_backend()
self._vprint((' I/O Backend {0} not available. Using {1} '
'instead').format(specifier.io_backend, self._backend), verbosity=1)
# Store the input file names
self._input_filenames = specifier.input_file_list
# Store the time-series variable names
self._time_series_names = specifier.time_series
if self._time_series_names is not None:
vnames = ', '.join(self._time_series_names)
if self._simplecomm.is_manager():
self._vprint('WARNING: Extracting only variables: {0}'.format(
vnames), verbosity=-1)
# Store the list of metadata names
self._metadata_names = specifier.time_variant_metadata
# Store whether to treat 1D time-variant variables as metadata
self._1d_metadata = specifier.assume_1d_time_variant_metadata
# Store the metadata filename
self._metadata_filename = specifier.metadata_filename
# Store time invariant variables that should be excluded from the timeseries files
self._exclude_list = specifier.exclude_list
# Store the output file prefix and suffix
self._output_prefix = specifier.output_file_prefix
self._output_suffix = specifier.output_file_suffix
# Setup NetCDF file options
self._netcdf_format = specifier.netcdf_format
self._netcdf_compression = specifier.compression_level
self._netcdf_least_significant_digit = specifier.least_significant_digit
if self._simplecomm.is_manager():
self._vprint(
' NetCDF I/O Backend: {0}'.format(self._backend), verbosity=1)
self._vprint(' NetCDF Output Format: {0}'.format(
self._netcdf_format), verbosity=1)
self._vprint(' NetCDF Compression: {0}'.format(
self._netcdf_compression), verbosity=1)
trunc_str = ('{} decimal places'.format(self._netcdf_least_significant_digit)
if self._netcdf_least_significant_digit else 'Disabled')
self._vprint(' NetCDF Truncation: {0}'.format(
trunc_str), verbosity=1)
# Helpful debugging message
if self._simplecomm.is_manager():
self._vprint('...Reshaper initialized.', verbosity=0)
# Sync before continuing..
self._simplecomm.sync()
def main(argv):
# Get command line stuff and store in a dictionary
s="""verbose sumfile= indir= input_globs= tslice= nPC= sigMul=
minPCFail= minRunFail= numRunFile= printVarTest popens
jsonfile= mpi_enable nbin= minrange= maxrange= outfile=
casejson= npick= pepsi_gm test_failure pop_tol= web_enabled
pop_threshold= prn_std_mean fIndex= lev= eet= json_case= """
optkeys = s.split()
try:
opts, args = getopt.getopt(argv,"h",optkeys)
except getopt.GetoptError:
pyEnsLib.CECT_usage()
sys.exit(2)
# Set the default value for options
opts_dict = {}
opts_dict['input_globs'] = ''
opts_dict['indir'] = ''
opts_dict['tslice'] = 1
opts_dict['nPC'] = 50
opts_dict['sigMul'] = 2
opts_dict['verbose'] = False
opts_dict['minPCFail'] = 3
opts_dict['minRunFail'] = 2
opts_dict['numRunFile'] = 3
opts_dict['printVarTest'] = False
opts_dict['popens'] = False
opts_dict['jsonfile'] = ''
opts_dict['mpi_enable'] = False
opts_dict['nbin'] = 40
opts_dict['minrange'] = 0.0
opts_dict['maxrange'] = 4.0
opts_dict['outfile'] = 'testcase.result'
opts_dict['casejson'] = ''
opts_dict['npick'] = 10
opts_dict['pepsi_gm'] = False
opts_dict['test_failure'] = True
opts_dict['pop_tol'] = 3.0
opts_dict['pop_threshold'] = 0.90
opts_dict['prn_std_mean'] = False
opts_dict['lev'] = 0
opts_dict['eet'] = 0
opts_dict['json_case'] = ''
opts_dict['sumfile'] = ''
opts_dict['web_enabled'] = False
# Call utility library getopt_parseconfig to parse the option keys
# and save to the dictionary
caller = 'CECT'
gmonly = False
opts_dict = pyEnsLib.getopt_parseconfig(opts,optkeys,caller,opts_dict)
popens = opts_dict['popens']
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me=simplecomm.create_comm()
else:
me=simplecomm.create_comm(not opts_dict['mpi_enable'])
# Print out timestamp, input ensemble file and new run directory
dt=datetime.now()
verbose = opts_dict['verbose']
if me.get_rank()==0:
print '--------pyCECT--------'
print ' '
print dt.strftime("%A, %d. %B %Y %I:%M%p")
print ' '
if not opts_dict['web_enabled']:
print 'Ensemble summary file = '+opts_dict['sumfile']
print ' '
print 'Testcase file directory = '+opts_dict['indir']
print ' '
print ' '
# Ensure sensible EET value
if opts_dict['eet'] and opts_dict['numRunFile'] > opts_dict['eet']:
pyEnsLib.CECT_usage()
sys.exit(2)
ifiles=[]
in_files=[]
# Random pick pop files from not_pick_files list
if opts_dict['casejson']:
with open(opts_dict['casejson']) as fin:
result=json.load(fin)
in_files_first=result['not_pick_files']
in_files=random.sample(in_files_first,opts_dict['npick'])
print 'Testcase files:'
print '\n'.join(in_files)
elif opts_dict['json_case']:
json_file=opts_dict['json_case']
if (os.path.exists(json_file)):
fd=open(json_file)
metainfo=json.load(fd)
if 'CaseName' in metainfo:
casename=metainfo['CaseName']
if (os.path.exists(opts_dict['indir'])):
for name in casename:
wildname='*.'+name+'.*'
full_glob_str=os.path.join(opts_dict['indir'],wildname)
glob_file=glob.glob(full_glob_str)
in_files.extend(glob_file)
else:
print "Error: "+opts_dict['json_case']+" does not exist"
sys.exit()
print "in_files=",in_files
else:
wildname='*'+opts_dict['input_globs']+'*'
# Open all input files
if (os.path.exists(opts_dict['indir'])):
full_glob_str=os.path.join(opts_dict['indir'],wildname)
glob_files=glob.glob(full_glob_str)
in_files.extend(glob_files)
num_file=len(in_files)
if opts_dict['numRunFile'] > num_file:
print "You requested more numRunFile than it is available at the indir, please change"
sys.exit()
#in_files_temp=os.listdir(opts_dict['indir'])
in_files.sort()
if popens:
#Partition the input file list
in_files_list=me.partition(in_files,func=EqualStride(),involved=True)
else:
# Random pick non pop files
in_files_list=pyEnsLib.Random_pickup(in_files,opts_dict)
#in_files_list=in_files
for frun_file in in_files_list:
if frun_file.find(opts_dict['indir']) != -1:
frun_temp=frun_file
else:
frun_temp=opts_dict['indir']+'/'+frun_file
if (os.path.isfile(frun_temp)):
ifiles.append(Nio.open_file(frun_temp,"r"))
else:
print "COULD NOT LOCATE FILE " +frun_temp+" EXISTING"
sys.exit()
if opts_dict['web_enabled']:
if len(opts_dict['sumfile'])==0:
opts_dict['sumfile']='/glade/p/cesmdata/cseg/inputdata/validation/'
opts_dict['sumfile'],machineid,compiler=pyEnsLib.search_sumfile(opts_dict,ifiles)
if len(machineid)!=0 and len(compiler)!=0:
print ' '
print 'Validation file : machineid = '+machineid+', compiler = '+compiler
print 'Found summery file : '+opts_dict['sumfile']
print ' '
else:
print 'Warning: machineid and compiler are unknown'
if popens:
# Read in the included var list
Var2d,Var3d=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ESP')
print ' '
print 'Z-score tolerance = '+'{:3.2f}'.format(opts_dict['pop_tol'])
print 'ZPR = '+'{:.2%}'.format(opts_dict['pop_threshold'])
zmall,n_timeslice=pyEnsLib.compare_raw_score(opts_dict,ifiles,me.get_rank(),Var3d,Var2d)
#zmall = np.concatenate((Zscore3d,Zscore2d),axis=0)
np.set_printoptions(threshold=np.nan)
if opts_dict['mpi_enable']:
zmall = pyEnsLib.gather_npArray_pop(zmall,me,(me.get_size(),len(Var3d)+len(Var2d),len(ifiles),opts_dict['nbin']))
if me.get_rank()==0:
fout = open(opts_dict['outfile'],"w")
for i in range(me.get_size()):
for j in zmall[i]:
np.savetxt(fout,j,fmt='%-7.2e')
else:
# Read all variables from the ensemble summary file
ens_var_name,ens_avg,ens_stddev,ens_rmsz,ens_gm,num_3d,mu_gm,sigma_gm,loadings_gm,sigma_scores_gm,is_SE_sum,std_gm=pyEnsLib.read_ensemble_summary(opts_dict['sumfile'])
if len(ens_rmsz) == 0:
gmonly = True
# Add ensemble rmsz and global mean to the dictionary "variables"
variables={}
if not gmonly:
for k,v in ens_rmsz.iteritems():
pyEnsLib.addvariables(variables,k,'zscoreRange',v)
for k,v in ens_gm.iteritems():
pyEnsLib.addvariables(variables,k,'gmRange',v)
# Get 3d variable name list and 2d variable name list seperately
var_name3d=[]
var_name2d=[]
for vcount,v in enumerate(ens_var_name):
if vcount < num_3d:
var_name3d.append(v)
else:
var_name2d.append(v)
# Get ncol and nlev value
npts3d,npts2d,is_SE=pyEnsLib.get_ncol_nlev(ifiles[0])
if (is_SE ^ is_SE_sum):
print 'Warning: please note the ensemble summary file is different from the testing files, they use different grids'
# Compare the new run and the ensemble summary file to get rmsz score
results={}
countzscore=np.zeros(len(ifiles),dtype=np.int32)
countgm=np.zeros(len(ifiles),dtype=np.int32)
if not gmonly:
for fcount,fid in enumerate(ifiles):
otimeSeries = fid.variables
for var_name in ens_var_name:
orig=otimeSeries[var_name]
Zscore,has_zscore=pyEnsLib.calculate_raw_score(var_name,orig[opts_dict['tslice']],npts3d,npts2d,ens_avg,ens_stddev,is_SE,opts_dict,0,0,0)
if has_zscore:
# Add the new run rmsz zscore to the dictionary "results"
pyEnsLib.addresults(results,'zscore',Zscore,var_name,'f'+str(fcount))
# Evaluate the new run rmsz score if is in the range of the ensemble summary rmsz zscore range
for fcount,fid in enumerate(ifiles):
countzscore[fcount]=pyEnsLib.evaluatestatus('zscore','zscoreRange',variables,'ens',results,'f'+str(fcount))
# Calculate the new run global mean
mean3d,mean2d,varlist=pyEnsLib.generate_global_mean_for_summary(ifiles,var_name3d,var_name2d,is_SE,opts_dict['pepsi_gm'],opts_dict)
means=np.concatenate((mean3d,mean2d),axis=0)
# Add the new run global mean to the dictionary "results"
for i in range(means.shape[1]):
for j in range(means.shape[0]):
pyEnsLib.addresults(results,'means',means[j][i],ens_var_name[j],'f'+str(i))
# Evaluate the new run global mean if it is in the range of the ensemble summary global mean range
for fcount,fid in enumerate(ifiles):
countgm[fcount]=pyEnsLib.evaluatestatus('means','gmRange',variables,'gm',results,'f'+str(fcount))
# Calculate the PCA scores of the new run
new_scores,var_list,comp_std_gm=pyEnsLib.standardized(means,mu_gm,sigma_gm,loadings_gm,ens_var_name,opts_dict,ens_avg,me)
run_index,decision=pyEnsLib.comparePCAscores(ifiles,new_scores,sigma_scores_gm,opts_dict,me)
# If there is failure, plot out standardized mean and compared standardized mean in box plots
if opts_dict['prn_std_mean'] and decision == 'FAILED':
import seaborn as sns
category={"all_outside99":[],"two_outside99":[],"one_outside99":[],"all_oneside_outside1QR":[]}
b=list(pyEnsLib.chunk(ens_var_name,10))
for f,alist in enumerate(b):
for fc,avar in enumerate(alist):
dist_995=np.percentile(std_gm[avar],99.5)
dist_75=np.percentile(std_gm[avar],75)
dist_25=np.percentile(std_gm[avar],25)
dist_05=np.percentile(std_gm[avar],0.5)
c=0
d=0
p=0
q=0
for i in range(comp_std_gm[f+fc].size):
if comp_std_gm[f+fc][i]>dist_995:
c=c+1
elif comp_std_gm[f+fc][i]<dist_05:
d=d+1
elif (comp_std_gm[f+fc][i]<dist_995 and comp_std_gm[f+fc][i]>dist_75):
p=p+1
elif (comp_std_gm[f+fc][i]>dist_05 and comp_std_gm[f+fc][i]<dist_25):
q=q+1
if c == 3 or d == 3:
category["all_outside99"].append((avar,f+fc))
elif c == 2 or d == 2:
category["two_outside99"].append((avar,f+fc))
elif c == 1 or d == 1:
category["one_outside99"].append((avar,f+fc))
if p == 3 or q == 3:
category["all_oneside_outside1QR"].append((avar,f+fc))
part_name=opts_dict['indir'].split('/')[-1]
if not part_name:
part_name=opts_dict['indir'].split('/')[-2]
for key in sorted(category):
list_array=[]
list_array2=[]
list_var=[]
value=category[key]
print "value len=",key,len(value)
for each_var in value:
list_array.append(std_gm[each_var[0]])
list_array2.append(comp_std_gm[each_var[1]])
list_var.append(each_var[0])
if len(value) !=0 :
ax=sns.boxplot(data=list_array,whis=[0.5,99.5],fliersize=0.0)
sns.stripplot(data=list_array2,jitter=True,color="r")
sns.plt.xticks(range(len(list_array)),list_var,fontsize=8,rotation=-45)
if decision == 'FAILED':
sns.plt.savefig(part_name+"_"+key+"_fail.png")
else:
sns.plt.savefig(part_name+"_"+key+"_pass.png")
sns.plt.clf()
'''
if len(run_index)>0:
json_file=opts_dict['json_case']
if (os.path.exists(json_file)):
fd=open(json_file)
metainfo=json.load(fd)
caseindex=metainfo['CaseIndex']
enspath=str(metainfo['EnsPath'][0])
#print caseindex
if (os.path.exists(enspath)):
i=0
comp_file=[]
search = '\.[0-9]{3}\.'
for name in in_files_list:
s=re.search(search,name)
in_files_index=s.group(0)
if in_files_index[1:4] in caseindex:
ens_index=str(caseindex[in_files_index[1:4]])
wildname='*.'+ens_index+'.*'
full_glob_str=os.path.join(enspath,wildname)
glob_file=glob.glob(full_glob_str)
comp_file.extend(glob_file)
print "comp_file=",comp_file
pyEnsLib.plot_variable(in_files_list,comp_file,opts_dict,var_list,run_index,me)
'''
# Print out
if opts_dict['printVarTest']:
print '*********************************************** '
print 'Variable-based testing (for reference only - not used to determine pass/fail)'
print '*********************************************** '
for fcount,fid in enumerate(ifiles):
print ' '
print 'Run '+str(fcount+1)+":"
print ' '
if not gmonly:
print '***'+str(countzscore[fcount])," of "+str(len(ens_var_name))+' variables are outside of ensemble RMSZ distribution***'
pyEnsLib.printsummary(results,'ens','zscore','zscoreRange',(fcount),variables,'RMSZ')
print ' '
print '***'+str(countgm[fcount])," of "+str(len(ens_var_name))+' variables are outside of ensemble global mean distribution***'
pyEnsLib.printsummary(results,'gm','means','gmRange',fcount,variables,'global mean')
print ' '
print '----------------------------------------------------------------------------'
if me.get_rank() == 0:
print ' '
print "Testing complete."
print ' '
def main(argv):
print('Running pyEnsSum!')
# Get command line stuff and store in a dictionary
s = 'tag= compset= esize= tslice= res= sumfile= indir= sumfiledir= mach= verbose jsonfile= mpi_enable maxnorm gmonly popens cumul regx= startMon= endMon= fIndex='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSum_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict={}
# Defaults
opts_dict['tag'] = ''
opts_dict['compset'] = ''
opts_dict['mach'] = ''
opts_dict['esize'] = 151
opts_dict['tslice'] = 0
opts_dict['res'] = ''
opts_dict['sumfile'] = 'ens.summary.nc'
opts_dict['indir'] = './'
opts_dict['sumfiledir'] = './'
opts_dict['jsonfile'] = ''
opts_dict['verbose'] = True
opts_dict['mpi_enable'] = False
opts_dict['maxnorm'] = False
opts_dict['gmonly'] = False
opts_dict['popens'] = False
opts_dict['cumul'] = False
opts_dict['regx'] = 'test'
opts_dict['startMon'] = 1
opts_dict['endMon'] = 1
opts_dict['fIndex'] = 151
# This creates the dictionary of input arguments
opts_dict = pyEnsLib.getopt_parseconfig(opts,optkeys,'ES',opts_dict)
verbose = opts_dict['verbose']
st = opts_dict['esize']
esize = int(st)
if (verbose == True):
print(opts_dict)
print('Ensemble size for summary = ', esize)
if not (opts_dict['tag'] and opts_dict['compset'] and opts_dict['mach'] or opts_dict['res']):
print('Please specify --tag, --compset, --mach and --res options')
sys.exit()
# Now find file names in indir
input_dir = opts_dict['indir']
# The var list that will be excluded
ex_varlist=[]
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me=simplecomm.create_comm()
else:
me=simplecomm.create_comm(not opts_dict['mpi_enable'])
if me.get_rank() == 0:
if opts_dict['jsonfile']:
# Read in the excluded var list
ex_varlist=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ES')
# Broadcast the excluded var list to each processor
if opts_dict['mpi_enable']:
ex_varlist=me.partition(ex_varlist,func=Duplicate(),involved=True)
in_files=[]
if(os.path.exists(input_dir)):
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files=sorted(in_files_temp)
#print in_files
# Make sure we have enough
num_files = len(in_files)
if (verbose == True):
print('Number of files in input directory = ', num_files)
if (num_files < esize):
print('Number of files in input directory (',num_files,
') is less than specified ensemble size of ', esize)
sys.exit(2)
if (num_files > esize):
print('NOTE: Number of files in ', input_dir,
'is greater than specified ensemble size of ', esize,
'\nwill just use the first ', esize, 'files')
else:
print('Input directory: ',input_dir,' not found')
sys.exit(2)
if opts_dict['cumul']:
if opts_dict['regx']:
in_files_list=get_cumul_filelist(opts_dict,opts_dict['indir'],opts_dict['regx'])
in_files=me.partition(in_files_list,func=EqualLength(),involved=True)
if me.get_rank()==0:
print('in_files=',in_files)
# Open the files in the input directory
o_files=[]
for onefile in in_files[0:esize]:
if (os.path.isfile(input_dir+'/' + onefile)):
o_files.append(Nio.open_file(input_dir+'/' + onefile,"r"))
else:
print("COULD NOT LOCATE FILE "+ input_dir + onefile + "! EXITING....")
sys.exit()
# Store dimensions of the input fields
if (verbose == True):
print("Getting spatial dimensions")
nlev = -1
ncol = -1
nlat = -1
nlon = -1
lonkey=''
latkey=''
# Look at first file and get dims
input_dims = o_files[0].dimensions
ndims = len(input_dims)
for key in input_dims:
if key == "lev":
nlev = input_dims["lev"]
elif key == "ncol":
ncol = input_dims["ncol"]
elif (key == "nlon") or (key =="lon"):
nlon = input_dims[key]
lonkey=key
elif (key == "nlat") or (key == "lat"):
nlat = input_dims[key]
latkey=key
if (nlev == -1) :
print("COULD NOT LOCATE valid dimension lev => EXITING....")
sys.exit()
if (( ncol == -1) and ((nlat == -1) or (nlon == -1))):
print("Need either lat/lon or ncol => EXITING....")
sys.exit()
# Check if this is SE or FV data
if (ncol != -1):
is_SE = True
else:
is_SE = False
# Make sure all files have the same dimensions
if (verbose == True):
print("Checking dimensions across files....")
print('lev = ', nlev)
if (is_SE == True):
print('ncol = ', ncol)
else:
print('nlat = ', nlat)
print('nlon = ', nlon)
for count, this_file in enumerate(o_files):
input_dims = this_file.dimensions
if (is_SE == True):
if ( nlev != int(input_dims["lev"]) or ( ncol != int(input_dims["ncol"]))):
print("Dimension mismatch between ", in_files[0], 'and', in_files[0], '!!!')
sys.exit()
else:
if ( nlev != int(input_dims["lev"]) or ( nlat != int(input_dims[latkey]))\
or ( nlon != int(input_dims[lonkey]))):
print("Dimension mismatch between ", in_files[0], 'and', in_files[0], '!!!')
sys.exit()
# Get 2d vars, 3d vars and all vars (For now include all variables)
vars_dict = o_files[0].variables
# Remove the excluded variables (specified in json file) from variable dictionary
if ex_varlist:
for i in ex_varlist:
if i in vars_dict:
del vars_dict[i]
num_vars = len(vars_dict)
if (verbose == True):
print('Number of variables (including metadata) found = ', num_vars)
str_size = 0
d2_var_names = []
d3_var_names = []
num_2d = 0
num_3d = 0
# Which are 2d, which are 3d and max str_size
for k,v in vars_dict.iteritems():
var = k
vd = v.dimensions # all the variable's dimensions (names)
vr = v.rank # num dimension
vs = v.shape # dim values
is_2d = False
is_3d = False
if (is_SE == True): # (time, lev, ncol) or (time, ncol)
if ((vr == 2) and (vs[1] == ncol)):
is_2d = True
num_2d += 1
elif ((vr == 3) and (vs[2] == ncol and vs[1] == nlev )):
is_3d = True
num_3d += 1
else: # (time, lev, nlon, nlon) or (time, nlat, nlon)
if ((vr == 3) and (vs[1] == nlat and vs[2] == nlon)):
is_2d = True
num_2d += 1
elif ((vr == 4) and (vs[2] == nlat and vs[3] == nlon and vs[1] == nlev )):
is_3d = True
num_3d += 1
if (is_3d == True) :
str_size = max(str_size, len(k))
d3_var_names.append(k)
elif (is_2d == True):
str_size = max(str_size, len(k))
d2_var_names.append(k)
# Now sort these and combine (this sorts caps first, then lower case -
# which is what we want)
d2_var_names.sort()
d3_var_names.sort()
# All vars is 3d vars first (sorted), the 2d vars
all_var_names = list(d3_var_names)
all_var_names += d2_var_names
n_all_var_names = len(all_var_names)
if (verbose == True):
print('num vars = ', n_all_var_names, '(3d = ', num_3d, ' and 2d = ', num_2d, ")")
# Create new summary ensemble file
this_sumfile = opts_dict["sumfile"]
if (verbose == True):
print("Creating ", this_sumfile, " ...")
if(me.get_rank() ==0 | opts_dict["popens"]):
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
opt = Nio.options()
opt.PreFill = False
opt.Format = 'NetCDF4Classic'
nc_sumfile = Nio.open_file(this_sumfile, 'w', options=opt)
# Set dimensions
if (verbose == True):
print("Setting dimensions .....")
if (is_SE == True):
nc_sumfile.create_dimension('ncol', ncol)
else:
nc_sumfile.create_dimension('nlat', nlat)
nc_sumfile.create_dimension('nlon', nlon)
nc_sumfile.create_dimension('nlev', nlev)
nc_sumfile.create_dimension('ens_size', esize)
nc_sumfile.create_dimension('nvars', num_3d + num_2d)
nc_sumfile.create_dimension('nvars3d', num_3d)
nc_sumfile.create_dimension('nvars2d', num_2d)
nc_sumfile.create_dimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
if (verbose == True):
print("Setting global attributes .....")
setattr(nc_sumfile, 'creation_date',now)
setattr(nc_sumfile, 'title', 'CAM verification ensemble summary file')
setattr(nc_sumfile, 'tag', opts_dict["tag"])
setattr(nc_sumfile, 'compset', opts_dict["compset"])
setattr(nc_sumfile, 'resolution', opts_dict["res"])
setattr(nc_sumfile, 'machine', opts_dict["mach"])
# Create variables
if (verbose == True):
print("Creating variables .....")
v_lev = nc_sumfile.create_variable("lev", 'f', ('nlev',))
v_vars = nc_sumfile.create_variable("vars", 'S1', ('nvars', 'str_size'))
v_var3d = nc_sumfile.create_variable("var3d", 'S1', ('nvars3d', 'str_size'))
v_var2d = nc_sumfile.create_variable("var2d", 'S1', ('nvars2d', 'str_size'))
if not opts_dict['gmonly']:
if (is_SE == True):
v_ens_avg3d = nc_sumfile.create_variable("ens_avg3d", 'f', ('nvars3d', 'nlev', 'ncol'))
v_ens_stddev3d = nc_sumfile.create_variable("ens_stddev3d", 'f', ('nvars3d', 'nlev', 'ncol'))
v_ens_avg2d = nc_sumfile.create_variable("ens_avg2d", 'f', ('nvars2d', 'ncol'))
v_ens_stddev2d = nc_sumfile.create_variable("ens_stddev2d", 'f', ('nvars2d', 'ncol'))
else:
v_ens_avg3d = nc_sumfile.create_variable("ens_avg3d", 'f', ('nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_stddev3d = nc_sumfile.create_variable("ens_stddev3d", 'f', ('nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_avg2d = nc_sumfile.create_variable("ens_avg2d", 'f', ('nvars2d', 'nlat', 'nlon'))
v_ens_stddev2d = nc_sumfile.create_variable("ens_stddev2d", 'f', ('nvars2d', 'nlat', 'nlon'))
v_RMSZ = nc_sumfile.create_variable("RMSZ", 'f', ('nvars', 'ens_size'))
v_gm = nc_sumfile.create_variable("global_mean", 'f', ('nvars', 'ens_size'))
v_loadings_gm = nc_sumfile.create_variable('loadings_gm','f',('nvars','nvars'))
v_mu_gm = nc_sumfile.create_variable('mu_gm','f',('nvars',))
v_sigma_gm = nc_sumfile.create_variable('sigma_gm','f',('nvars',))
v_sigma_scores_gm = nc_sumfile.create_variable('sigma_scores_gm','f',('nvars',))
# Assign vars, var3d and var2d
if (verbose == True):
print("Assigning vars, var3d, and var2d .....")
eq_all_var_names =[]
eq_d3_var_names = []
eq_d2_var_names = []
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(d3_var_names)
for i in range(l_eq):
tt = list(d3_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(d2_var_names)
for i in range(l_eq):
tt = list(d2_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if (verbose == True):
print("Assigning time invariant metadata .....")
lev_data = vars_dict["lev"]
v_lev = lev_data
# Form ensembles, each missing one member; compute RMSZs and global means
#for each variable, we also do max norm also (currently done in pyStats)
tslice = opts_dict['tslice']
if not opts_dict['cumul']:
# Partition the var list
var3_list_loc=me.partition(d3_var_names,func=EqualStride(),involved=True)
var2_list_loc=me.partition(d2_var_names,func=EqualStride(),involved=True)
else:
var3_list_loc=d3_var_names
var2_list_loc=d2_var_names
# Calculate global means #
if (verbose == True):
print("Calculating global means .....")
if not opts_dict['cumul']:
gm3d,gm2d = pyEnsLib.generate_global_mean_for_summary(o_files,var3_list_loc,var2_list_loc , is_SE, False,opts_dict)
if (verbose == True):
print("Finish calculating global means .....")
# Calculate RMSZ scores
if (verbose == True):
print("Calculating RMSZ scores .....")
if (not opts_dict['gmonly']) | (opts_dict['cumul']):
zscore3d,zscore2d,ens_avg3d,ens_stddev3d,ens_avg2d,ens_stddev2d,temp1,temp2=pyEnsLib.calc_rmsz(o_files,var3_list_loc,var2_list_loc,is_SE,opts_dict)
# Calculate max norm ensemble
if opts_dict['maxnorm']:
if (verbose == True):
print("Calculating max norm of ensembles .....")
pyEnsLib.calculate_maxnormens(opts_dict,var3_list_loc)
pyEnsLib.calculate_maxnormens(opts_dict,var2_list_loc)
if opts_dict['mpi_enable'] & ( not opts_dict['popens']):
if not opts_dict['cumul']:
# Gather the 3d variable results from all processors to the master processor
slice_index=get_stride_list(len(d3_var_names),me)
# Gather global means 3d results
gm3d=gather_npArray(gm3d,me,slice_index,(len(d3_var_names),len(o_files)))
if not opts_dict['gmonly']:
# Gather zscore3d results
zscore3d=gather_npArray(zscore3d,me,slice_index,(len(d3_var_names),len(o_files)))
# Gather ens_avg3d and ens_stddev3d results
shape_tuple3d=get_shape(ens_avg3d.shape,len(d3_var_names),me.get_rank())
ens_avg3d=gather_npArray(ens_avg3d,me,slice_index,shape_tuple3d)
ens_stddev3d=gather_npArray(ens_stddev3d,me,slice_index,shape_tuple3d)
# Gather 2d variable results from all processors to the master processor
slice_index=get_stride_list(len(d2_var_names),me)
# Gather global means 2d results
gm2d=gather_npArray(gm2d,me,slice_index,(len(d2_var_names),len(o_files)))
if not opts_dict['gmonly']:
# Gather zscore2d results
zscore2d=gather_npArray(zscore2d,me,slice_index,(len(d2_var_names),len(o_files)))
# Gather ens_avg3d and ens_stddev2d results
shape_tuple2d=get_shape(ens_avg2d.shape,len(d2_var_names),me.get_rank())
ens_avg2d=gather_npArray(ens_avg2d,me,slice_index,shape_tuple2d)
ens_stddev2d=gather_npArray(ens_stddev2d,me,slice_index,shape_tuple2d)
else:
gmall=np.concatenate((temp1,temp2),axis=0)
gmall=pyEnsLib.gather_npArray_pop(gmall,me,(me.get_size(),len(d3_var_names)+len(d2_var_names)))
# Assign to file:
if me.get_rank() == 0 | opts_dict['popens'] :
if not opts_dict['cumul']:
gmall=np.concatenate((gm3d,gm2d),axis=0)
if not opts_dict['gmonly']:
Zscoreall=np.concatenate((zscore3d,zscore2d),axis=0)
v_RMSZ[:,:]=Zscoreall[:,:]
if not opts_dict['gmonly']:
if (is_SE == True):
v_ens_avg3d[:,:,:]=ens_avg3d[:,:,:]
v_ens_stddev3d[:,:,:]=ens_stddev3d[:,:,:]
v_ens_avg2d[:,:]=ens_avg2d[:,:]
v_ens_stddev2d[:,:]=ens_stddev2d[:,:]
else:
v_ens_avg3d[:,:,:,:]=ens_avg3d[:,:,:,:]
v_ens_stddev3d[:,:,:,:]=ens_stddev3d[:,:,:,:]
v_ens_avg2d[:,:,:]=ens_avg2d[:,:,:]
v_ens_stddev2d[:,:,:]=ens_stddev2d[:,:,:]
else:
gmall_temp=np.transpose(gmall[:,:])
gmall=gmall_temp
mu_gm,sigma_gm,standardized_global_mean,loadings_gm,scores_gm=pyEnsLib.pre_PCA(gmall)
v_gm[:,:]=gmall[:,:]
v_mu_gm[:]=mu_gm[:]
v_sigma_gm[:]=sigma_gm[:].astype(np.float32)
v_loadings_gm[:,:]=loadings_gm[:,:]
v_sigma_scores_gm[:]=scores_gm[:]
print("All Done")
def get_cumul_filelist(opts_dict,indir,regx):
if not opts_dict['indir']:
print('input dir is not specified')
sys.exit(2)
#regx='(pgi(.)*-(01|02))'
regx_list=["mon","gnu","pgi"]
all_files=[]
for prefix in regx_list:
for i in range(opts_dict['fIndex'],opts_dict['fIndex']+opts_dict['esize']/3):
for j in range(opts_dict['startMon'],opts_dict['endMon']+1):
mon_str=str(j).zfill(2)
regx='(^'+prefix+'(.)*'+str(i)+'(.)*-('+mon_str+'))'
print('regx=',regx)
res=[f for f in os.listdir(indir) if re.search(regx,f)]
in_files=sorted(res)
all_files.extend(in_files)
print("all_files=",all_files)
#in_files=res
return all_files
#
# Get the shape of all variable list in tuple for all processor
#
def get_shape(shape_tuple,shape1,rank):
lst=list(shape_tuple)
lst[0]=shape1
shape_tuple=tuple(lst)
return shape_tuple
#
# Get the mpi partition list for each processor
#
def get_stride_list(len_of_list,me):
slice_index=[]
for i in range(me.get_size()):
index_arr=np.arange(len_of_list)
slice_index.append(index_arr[i::me.get_size()])
return slice_index
#
# Gather arrays from each processor by the var_list to the master processor and make it an array
#
def gather_npArray(npArray,me,slice_index,array_shape):
the_array=np.zeros(array_shape,dtype=np.float32)
if me.get_rank()==0:
k=0
for j in slice_index[me.get_rank()]:
the_array[j,:]=npArray[k,:]
k=k+1
for i in range(1,me.get_size()):
if me.get_rank() == 0:
rank,npArray=me.collect()
k=0
for j in slice_index[rank]:
the_array[j,:]=npArray[k,:]
k=k+1
if me.get_rank() != 0:
message={"from_rank":me.get_rank(),"shape":npArray.shape}
me.collect(npArray)
me.sync()
return the_array
if __name__ == "__main__":
main(sys.argv[1:])
def setUp(self):
self.gcomm = simplecomm.create_comm()
self.size = MPI_COMM_WORLD.Get_size()
self.rank = MPI_COMM_WORLD.Get_rank()
def main(argv):
# Get command line stuff and store in a dictionary
s='verbose sumfile= indir= input_globs= tslice= nPC= sigMul= minPCFail= minRunFail= numRunFile= printVarTest popens jsonfile= mpi_enable nbin= minrange= maxrange= outfile= casejson= npick= pepsi_gm test_failure pop_tol= pop_threshold='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv,"h",optkeys)
except getopt.GetoptError:
pyEnsLib.CECT_usage()
sys.exit(2)
# Set the default value for options
opts_dict = {}
opts_dict['input_globs'] = ''
opts_dict['indir'] = ''
opts_dict['tslice'] = 1
opts_dict['nPC'] = 50
opts_dict['sigMul'] = 2
opts_dict['verbose'] = False
opts_dict['minPCFail'] = 3
opts_dict['minRunFail'] = 2
opts_dict['numRunFile'] = 3
opts_dict['printVarTest'] = False
opts_dict['popens'] = False
opts_dict['jsonfile'] = ''
opts_dict['mpi_enable'] = False
opts_dict['nbin'] = 40
opts_dict['minrange'] = 0.0
opts_dict['maxrange'] = 4.0
opts_dict['outfile'] = 'testcase.result'
opts_dict['casejson'] = ''
opts_dict['npick'] = 10
opts_dict['pepsi_gm'] = False
opts_dict['test_failure'] = True
opts_dict['pop_tol'] = 3.0
opts_dict['pop_threshold'] = 0.90
# Call utility library getopt_parseconfig to parse the option keys
# and save to the dictionary
caller = 'CECT'
gmonly = False
opts_dict = pyEnsLib.getopt_parseconfig(opts,optkeys,caller,opts_dict)
popens = opts_dict['popens']
# Print out timestamp, input ensemble file and new run directory
dt=datetime.now()
verbose = opts_dict['verbose']
print('--------pyCECT--------')
print(' ')
print(dt.strftime("%A, %d. %B %Y %I:%M%p"))
print(' ')
print('Ensemble summary file = '+opts_dict['sumfile'])
print(' ')
print('Testcase file directory = '+opts_dict['indir'] )
print(' ')
print(' ')
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me=simplecomm.create_comm()
else:
me=simplecomm.create_comm(not opts_dict['mpi_enable'])
ifiles=[]
in_files=[]
# Random pick pop files from not_pick_files list
if opts_dict['casejson']:
with open(opts_dict['casejson']) as fin:
result=json.load(fin)
in_files_first=result['not_pick_files']
in_files=random.sample(in_files_first,opts_dict['npick'])
print('Testcase files:')
print('\n'.join(in_files))
else:
wildname='*'+opts_dict['input_globs']+'*'
# Open all input files
if (os.path.exists(opts_dict['indir'])):
full_glob_str=os.path.join(opts_dict['indir'],wildname)
glob_files=glob.glob(full_glob_str)
in_files.extend(glob_files)
#in_files_temp=os.listdir(opts_dict['indir'])
in_files.sort()
if popens:
#Partition the input file list
in_files_list=me.partition(in_files,func=EqualStride(),involved=True)
else:
# Random pick non pop files
in_files_list=pyEnsLib.Random_pickup(in_files,opts_dict)
for frun_file in in_files_list:
if frun_file.find(opts_dict['indir']) != -1:
frun_temp=frun_file
else:
frun_temp=opts_dict['indir']+'/'+frun_file
if (os.path.isfile(frun_temp)):
ifiles.append(Nio.open_file(frun_temp,"r"))
else:
print("COULD NOT LOCATE FILE " +frun_temp+" EXISTING")
sys.exit()
if popens:
# Read in the included var list
Var2d,Var3d=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ESP')
print(' ')
print('Z-score tolerance = '+'{:3.2f}'.format(opts_dict['pop_tol']))
print('ZPR = '+'{:.2%}'.format(opts_dict['pop_threshold']))
zmall,n_timeslice=pyEnsLib.compare_raw_score(opts_dict,ifiles,me.get_rank(),Var3d,Var2d)
#zmall = np.concatenate((Zscore3d,Zscore2d),axis=0)
np.set_printoptions(threshold=np.nan)
if opts_dict['mpi_enable']:
zmall = pyEnsLib.gather_npArray_pop(zmall,me,(me.get_size(),len(Var3d)+len(Var2d),len(ifiles),opts_dict['nbin']))
if me.get_rank()==0:
fout = open(opts_dict['outfile'],"w")
for i in range(me.get_size()):
for j in zmall[i]:
np.savetxt(fout,j,fmt='%-7.2e')
else:
# Read all variables from the ensemble summary file
ens_var_name,ens_avg,ens_stddev,ens_rmsz,ens_gm,num_3d,mu_gm,sigma_gm,loadings_gm,sigma_scores_gm,is_SE_sum=pyEnsLib.read_ensemble_summary(opts_dict['sumfile'])
if len(ens_rmsz) == 0:
gmonly = True
# Add ensemble rmsz and global mean to the dictionary "variables"
variables={}
if not gmonly:
for k,v in ens_rmsz.iteritems():
pyEnsLib.addvariables(variables,k,'zscoreRange',v)
for k,v in ens_gm.iteritems():
pyEnsLib.addvariables(variables,k,'gmRange',v)
# Get 3d variable name list and 2d variable name list seperately
var_name3d=[]
var_name2d=[]
for vcount,v in enumerate(ens_var_name):
if vcount < num_3d:
var_name3d.append(v)
else:
var_name2d.append(v)
# Get ncol and nlev value
npts3d,npts2d,is_SE=pyEnsLib.get_ncol_nlev(ifiles[0])
if (is_SE ^ is_SE_sum):
print('Warning: please note the ensemble summary file is different from the testing files, they use different grids')
# Compare the new run and the ensemble summary file to get rmsz score
results={}
countzscore=np.zeros(len(ifiles),dtype=np.int32)
countgm=np.zeros(len(ifiles),dtype=np.int32)
if not gmonly:
for fcount,fid in enumerate(ifiles):
otimeSeries = fid.variables
for var_name in ens_var_name:
orig=otimeSeries[var_name]
Zscore,has_zscore=pyEnsLib.calculate_raw_score(var_name,orig[opts_dict['tslice']],npts3d,npts2d,ens_avg,ens_stddev,is_SE,opts_dict,0,0,0)
if has_zscore:
# Add the new run rmsz zscore to the dictionary "results"
pyEnsLib.addresults(results,'zscore',Zscore,var_name,'f'+str(fcount))
# Evaluate the new run rmsz score if is in the range of the ensemble summary rmsz zscore range
for fcount,fid in enumerate(ifiles):
countzscore[fcount]=pyEnsLib.evaluatestatus('zscore','zscoreRange',variables,'ens',results,'f'+str(fcount))
# Calculate the new run global mean
mean3d,mean2d=pyEnsLib.generate_global_mean_for_summary(ifiles,var_name3d,var_name2d,is_SE,opts_dict['pepsi_gm'],opts_dict)
means=np.concatenate((mean3d,mean2d),axis=0)
# Add the new run global mean to the dictionary "results"
for i in range(means.shape[1]):
for j in range(means.shape[0]):
pyEnsLib.addresults(results,'means',means[j][i],ens_var_name[j],'f'+str(i))
# Evaluate the new run global mean if it is in the range of the ensemble summary global mean range
for fcount,fid in enumerate(ifiles):
countgm[fcount]=pyEnsLib.evaluatestatus('means','gmRange',variables,'gm',results,'f'+str(fcount))
# Calculate the PCA scores of the new run
new_scores=pyEnsLib.standardized(means,mu_gm,sigma_gm,loadings_gm)
pyEnsLib.comparePCAscores(ifiles,new_scores,sigma_scores_gm,opts_dict)
# Print out
if opts_dict['printVarTest']:
print('*********************************************** ')
print('Variable-based testing (for reference only - not used to determine pass/fail)')
print('*********************************************** ')
for fcount,fid in enumerate(ifiles):
print(' ')
print('Run '+str(fcount+1)+":")
print(' ')
if not gmonly:
print('***'+str(countzscore[fcount])," of "+str(len(ens_var_name))+' variables are outside of ensemble RMSZ distribution***')
pyEnsLib.printsummary(results,'ens','zscore','zscoreRange',(fcount),variables,'RMSZ')
print(' ')
print('***'+str(countgm[fcount])," of "+str(len(ens_var_name))+' variables are outside of ensemble global mean distribution***')
pyEnsLib.printsummary(results,'gm','means','gmRange',fcount,variables,'global mean')
print(' ')
print('----------------------------------------------------------------------------')
if __name__ == "__main__":
main(sys.argv[1:])
print(' ')
print("Testing complete.")
debugMsg('calling initialize_main', header=True, verbosity=1)
envDict = initialize_main(envDict, caseroot, debugMsg, options.standalone)
debugMsg('calling setup_config', header=True, verbosity=1)
setup_config(envDict)
main_comm.sync()
#===================================
if __name__ == "__main__":
# initialize simplecomm object
main_comm = simplecomm.create_comm(serial=True)
# setup an overall timer
timer = timekeeper.TimeKeeper()
# get commandline options
options = commandline_options()
# initialize global vprinter object for printing debug messages
print("debug level = {0}".format(options.debug[0]))
if options.debug:
header = "[" + str(main_comm.get_rank()) + "/" + str(main_comm.get_size()) + "]: DEBUG... "
debugMsg = vprinter.VPrinter(header=header, verbosity=options.debug[0])
try:
timer.start("Total Time")
def __init__(self,
specifiers,
serial=False,
verbosity=1,
skip_existing=False,
overwrite=False,
once=False,
simplecomm=None):
"""
Constructor
Parameters:
specifiers (dict): A dict of named Specifier instances, each
defining an input specification for this reshaper operation.
Keyword Arguments:
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
skip_existing (bool): Flag specifying whether to skip the generation
of time-series for variables with time-series files that already
exist. Default is False.
overwrite (bool): Flag specifying whether to forcefully overwrite
output files if they already exist. Default is False.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Check types
if not isinstance(specifiers, dict):
err_msg = "Input must be given in a dictionary of Specifiers"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(skip_existing) is not bool:
err_msg = "Skip_existing flag must be True or False."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if simplecomm is not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
# Whether to write to a once file
self._use_once_file = once
# Whether to write to a once file
self._skip_existing = skip_existing
# Whether to write to overwrite output files
self._overwrite = overwrite
# Store the list of specifiers
self._specifiers = specifiers
# Store the serial specifier
self._serial = serial
# Check for a SimpleComm, and if none create it
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Pointer to its own messenger
self._simplecomm = simplecomm
# Store the verbosity
self._verbosity = verbosity
# Set the verbose printer
self._vprint = VPrinter(verbosity=verbosity)
# Storage for timing data
self._times = {}
# Orders for printing timing data
self._time_orders = {}
# Storage for all byte counters
self._byte_counts = {}
oldfile = os.path.join(olddir, filename)
if oldfile in oldfiles:
item_dict["old"] = oldfile
oldfiles.remove(oldfile)
items_to_check.append(item_dict)
else:
item_dict["old"] = None
unchecked_new_items.append(item_dict)
for oldfile in oldfiles:
item_dict = {"test": test_name}
item_dict["new"] = None
item_dict["old"] = oldfile
unchecked_old_items.append(item_dict)
# Get a basic MPI comm
comm = create_comm(serial=(args.serial or args.list_tests))
# Print tests that will be checked
if comm.is_manager():
if args.multispec:
print "Checking multitest results."
else:
print "Checking individual test results."
print
for test_name in tests_to_check:
print "Test {0!s}:".format(test_name)
num_chk = sum(1 for i in items_to_check if i["test"] == test_name)
num_new = num_chk + sum(1 for i in unchecked_new_items if i["test"] == test_name)
num_old = num_chk + sum(1 for i in unchecked_old_items if i["test"] == test_name)
print " Checking {0!s} of {1!s}".format(num_chk, num_new),
def main(argv=None):
args = cli(argv)
# Create the necessary SimpleComm
scomm = create_comm(serial=args.serial)
# Do setup only on manager node
if scomm.is_manager():
# Check that the specfile exists
if not exists(args.stdfile):
raise OSError(("Output specification file {!r} not "
"found").format(args.stdfile))
# Read the specfile into a dictionary
print("Reading standardization file: {}".format(args.stdfile))
dsdict = json_load(open(args.stdfile, "r"),
object_pairs_hook=OrderedDict)
# Parse the output Dataset
print(
"Creating output dataset descriptor from standardization file...")
outds = OutputDatasetDesc(dsdict=dsdict)
else:
outds = None
# Send the output descriptor to all nodes
outds = scomm.partition(outds, func=Duplicate(), involved=True)
# Sync
scomm.sync()
# Continue setup only on manager node
if scomm.is_manager():
# Gather the list of input files
infiles = []
for infile in args.infiles:
infiles.extend(glob(infile))
# If no input files, stop here
if len(infiles) == 0:
print("Standardization file validated.")
return
# Parse the input Dataset
print(
"Creating input dataset descriptor from {} input files...".format(
len(infiles)))
inpds = InputDatasetDesc(filenames=infiles)
else:
inpds = None
# Send the input descriptor to all nodes
inpds = scomm.partition(inpds, func=Duplicate(), involved=True)
# Sync and continue process on all nodes
scomm.sync()
# Check for warn/error
if args.error:
simplefilter("error", ValidationWarning)
# Try importing all of the necessary user-defined modules
if args.module is not None:
for i, modpath in enumerate(args.module):
load_source("user{}".format(i), modpath)
# Setup the PyConform data flow on all nodes
if scomm.is_manager():
print("Creating the data flow...")
dataflow = DataFlow(inpds, outds)
# Execute the data flow (write to files)
history = not args.no_history
dataflow.execute(
chunks=dict(args.chunks),
scomm=scomm,
history=history,
deflate=args.deflate,
debug=args.debug,
)
debugMsg('calling setup_config', header=True, verbosity=1)
setup_config(envDict)
debugMsg('expanding variables in batch script',
header=True,
verbosity=1)
expand_batch_vars(envDict, imb_name)
main_comm.sync()
#===================================
if __name__ == "__main__":
# initialize simplecomm object
main_comm = simplecomm.create_comm(serial=True)
# setup an overall timer
timer = timekeeper.TimeKeeper()
# get commandline options
options = commandline_options()
# initialize global vprinter object for printing debug messages
print("debug level = {0}".format(options.debug[0]))
if options.debug:
header = "[" + str(main_comm.get_rank()) + "/" + str(
main_comm.get_size()) + "]: DEBUG... "
debugMsg = vprinter.VPrinter(header=header, verbosity=options.debug[0])
try:
def main(argv):
# Get command line stuff and store in a dictionary
s = 'tag= compset= esize= tslice= res= sumfile= indir= sumfiledir= mach= verbose jsonfile= mpi_enable maxnorm gmonly popens cumul regx= startMon= endMon= fIndex='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSum_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict={}
# Defaults
opts_dict['tag'] = 'cesm2_0_beta10'
opts_dict['compset'] = 'F2000climo'
opts_dict['mach'] = 'cheyenne'
opts_dict['esize'] = 350
opts_dict['tslice'] = 1
opts_dict['res'] = 'f19_f19'
opts_dict['sumfile'] = 'ens.summary.nc'
opts_dict['indir'] = './'
opts_dict['sumfiledir'] = './'
opts_dict['jsonfile'] = 'exclude_empty.json'
opts_dict['verbose'] = False
opts_dict['mpi_enable'] = False
opts_dict['maxnorm'] = False
opts_dict['gmonly'] = True
opts_dict['popens'] = False
opts_dict['cumul'] = False
opts_dict['regx'] = 'test'
opts_dict['startMon'] = 1
opts_dict['endMon'] = 1
opts_dict['fIndex'] = 151
# This creates the dictionary of input arguments
opts_dict = pyEnsLib.getopt_parseconfig(opts,optkeys,'ES',opts_dict)
verbose = opts_dict['verbose']
st = opts_dict['esize']
esize = int(st)
if opts_dict['popens' == True]:
print "Error: Please use pyEnsSumPop.py for a POP ensemble (not --popens)."
sys.exit()
if not (opts_dict['tag'] and opts_dict['compset'] and opts_dict['mach'] or opts_dict['res']):
print 'Please specify --tag, --compset, --mach and --res options'
sys.exit()
# Now find file names in indir
input_dir = opts_dict['indir']
# The var list that will be excluded
ex_varlist=[]
inc_varlist=[]
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me=simplecomm.create_comm()
else:
me=simplecomm.create_comm(not opts_dict['mpi_enable'])
if me.get_rank() == 0:
print 'Running pyEnsSum!'
if me.get_rank() ==0 and (verbose == True):
print opts_dict
print 'Ensemble size for summary = ', esize
exclude=False
if me.get_rank() == 0:
if opts_dict['jsonfile']:
inc_varlist=[]
# Read in the excluded or included var list
ex_varlist,exclude=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ES')
if exclude == False:
inc_varlist=ex_varlist
ex_varlist=[]
# Read in the included var list
#inc_varlist=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ES')
# Broadcast the excluded var list to each processor
#if opts_dict['mpi_enable']:
# ex_varlist=me.partition(ex_varlist,func=Duplicate(),involved=True)
# Broadcast the excluded var list to each processor
if opts_dict['mpi_enable']:
exclude=me.partition(exclude,func=Duplicate(),involved=True)
if exclude:
ex_varlist=me.partition(ex_varlist,func=Duplicate(),involved=True)
else:
inc_varlist=me.partition(inc_varlist,func=Duplicate(),involved=True)
in_files=[]
if(os.path.exists(input_dir)):
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files=sorted(in_files_temp)
# Make sure we have enough
num_files = len(in_files)
if me.get_rank()==0 and (verbose == True):
print 'Number of files in input directory = ', num_files
if (num_files < esize):
if me.get_rank()==0 and (verbose == True):
print 'Number of files in input directory (',num_files,\
') is less than specified ensemble size of ', esize
sys.exit(2)
if (num_files > esize):
if me.get_rank()==0 and (verbose == True):
print 'NOTE: Number of files in ', input_dir, \
'is greater than specified ensemble size of ', esize ,\
'\nwill just use the first ', esize, 'files'
else:
if me.get_rank()==0:
print 'Input directory: ',input_dir,' not found'
sys.exit(2)
if opts_dict['cumul']:
if opts_dict['regx']:
in_files_list=get_cumul_filelist(opts_dict,opts_dict['indir'],opts_dict['regx'])
in_files=me.partition(in_files_list,func=EqualLength(),involved=True)
if me.get_rank()==0 and (verbose == True):
print 'in_files=',in_files
# Open the files in the input directory
o_files=[]
if me.get_rank() == 0 and opts_dict['verbose']:
print 'Input files are: '
print "\n".join(in_files)
#for i in in_files:
# print "in_files =",i
for onefile in in_files[0:esize]:
if (os.path.isfile(input_dir+'/' + onefile)):
o_files.append(Nio.open_file(input_dir+'/' + onefile,"r"))
else:
if me.get_rank()==0:
print "COULD NOT LOCATE FILE ", input_dir+'/'+onefile , "! EXITING...."
sys.exit()
# Store dimensions of the input fields
if me.get_rank()==0 and (verbose == True):
print "Getting spatial dimensions"
nlev = -1
nilev = -1
ncol = -1
nlat = -1
nlon = -1
lonkey=''
latkey=''
# Look at first file and get dims
input_dims = o_files[0].dimensions
ndims = len(input_dims)
for key in input_dims:
if key == "lev":
nlev = input_dims["lev"]
elif key == "ilev":
nilev = input_dims["ilev"]
elif key == "ncol":
ncol = input_dims["ncol"]
elif (key == "nlon") or (key =="lon"):
nlon = input_dims[key]
lonkey=key
elif (key == "nlat") or (key == "lat"):
nlat = input_dims[key]
latkey=key
if (nlev == -1) :
if me.get_rank()==0:
print "COULD NOT LOCATE valid dimension lev => EXITING...."
sys.exit()
if (( ncol == -1) and ((nlat == -1) or (nlon == -1))):
if me.get_rank()==0:
print "Need either lat/lon or ncol => EXITING...."
sys.exit()
# Check if this is SE or FV data
if (ncol != -1):
is_SE = True
else:
is_SE = False
# Make sure all files have the same dimensions
if me.get_rank()==0 and (verbose == True):
print "Checking dimensions across files...."
print 'lev = ', nlev
if (is_SE == True):
print 'ncol = ', ncol
else:
print 'nlat = ', nlat
print 'nlon = ', nlon
for count, this_file in enumerate(o_files):
input_dims = this_file.dimensions
if (is_SE == True):
if ( nlev != int(input_dims["lev"]) or ( ncol != int(input_dims["ncol"]))):
if me.get_rank() == 0:
print "Dimension mismatch between ", in_files[0], 'and', in_files[0], '!!!'
sys.exit()
else:
if ( nlev != int(input_dims["lev"]) or ( nlat != int(input_dims[latkey]))\
or ( nlon != int(input_dims[lonkey]))):
if me.get_rank() == 0:
print "Dimension mismatch between ", in_files[0], 'and', in_files[0], '!!!'
sys.exit()
# Get 2d vars, 3d vars and all vars (For now include all variables)
vars_dict_all = o_files[0].variables
# Remove the excluded variables (specified in json file) from variable dictionary
#print len(vars_dict_all)
if exclude:
vars_dict=vars_dict_all
for i in ex_varlist:
if i in vars_dict:
del vars_dict[i]
#Given an included var list, remove all float var that are not on the list
else:
vars_dict=vars_dict_all.copy()
for k,v in vars_dict_all.iteritems():
if (k not in inc_varlist) and (vars_dict_all[k].typecode()=='f'):
#print vars_dict_all[k].typecode()
#print k
del vars_dict[k]
num_vars = len(vars_dict)
#print num_vars
#if me.get_rank() == 0:
# for k,v in vars_dict.iteritems():
# print 'vars_dict',k,vars_dict[k].typecode()
str_size = 0
def __init__(self,in_directory,
out_directory,
prefix,
suffix,
file_pattern='null',
date_pattern='null',
m_id = ['-999'],
hist_type='slice',
avg_list=[],
weighted=False,
split=False,
split_files='null',
split_orig_size='null',
ncformat='netcdf4c',
varlist=[],
serial=False,
mean_diff_rms_obs_dir='null',
region_nc_var='null',
regions={},
region_wgt_var='null',
obs_file='null',
reg_obs_file_suffix='null',
obs_dir='null',
main_comm=None,
clobber=False,
ice_obs_file='null',
reg_file = 'null',
ncl_location='null',
year0=-99,
year1=-99,
collapse_dim='',
vertical_levels=60):
'''
Initializes the internal data with optional arguments
@param in_directory Where the input directory resides (needs full path).
@param out_directory Where the output will be produced (needs full path).
@param prefix String specifying the full file name before the date string.
@param suffix String specifying the suffix of the file names
@param file_pattern File pattern used put the prefix, date, and suffix together for input files.
@param date_pattern The pattern used to decipher the date string within the file name.
@param m_id Array of member identifiers. All averages will be done on each member individually and then across all members.
@param hist_type Type of file ('slice' or 'series'). Default is 'slice'.
@param avg_list List of averages that need to be computed. Elements should contain aveType:year0:year1.
year2 is only required for multi year averaging.
@param weighted Boolean variable to selected if weights will be applied to the averaging.
True = weights will be applied. Default is False.
@param split Boolean variable. True = the file is split spatially and the final average needs to be pieced together.
(ie. CICE times series files) Default is False.
@param split_files The strings indicating the naming difference between split files. Expects a string with elements separated by a comma.
Defualt is 'null'.
@param split_orig_size A string listing the lat and lon values of the origianl grid size. Needed in case some of the grid has been deleted.
(example: 'lon=288,lat=192'). Default is 'null'.
@param ncformat Format to output the averaged file(s) in. Default is 'netcdf4c'. Other options: 'netcdf','netcdf4','netcdf4c'
@param varlist Optional variables list, if not averaging all variables
@param serial Boolean to run in serial mode. True=serial (without MPI) False=run in parallel(with MPI) False requires mpi4py to be installed.
Default is False.
@param regions Dictionary that contains regions to average over. Fromat is 'string region name: int region value'. Default is an empty dictionary.
@param region_nc_var String that identifies the netcdf variable that contains the region mask used by a regional average.
@param region_wgt_var String that identifies the netcdf variable that contains the weights.
@param obs_file Observational file used for the creation of the mean_diff_rms file. This file must contain all of the variables within the
variable list (or if a variable list is not specified, must contain all hist file variables). Dimension must be nlon and nlat.
@param reg_obs_file_suffix The suffix of the regional, weighted averages of the 'obs_file'. Used for the creation of the mean_diff_rms file.
@param obs_dir Full path to the observational files used for the mean_diff_rms file.
@param main_comm A simplecomm to be used by the PyAverager. If not specified, one will be created by this specifier. Default None.
@param clobber Remove netcdf output file(s) if they exist. Default False - will exit if an output file of the same name exists.
@param ice_obs_file Full path to the observational file used to create the cice model pre_proc file
@param reg_file Full path to the regional file used to create the cice model pre_proc file
@param ncl_location Location of where the ncl scripts reside
@param year0 The first year - only used to create the cice pre_proc file.
@param year1 The last year - only used to create the cice pre_proc file.
@param collapse_dims Used to collapse/average over one dim.
@param vertical_levels Number of ocean vertical levels
'''
# Where the input is located
self.in_directory = in_directory
# Where the output should be produced
self.out_directory = out_directory
# Full file name up to the date string
self.prefix = prefix
# The suffix of the data files
self.suffix = suffix
# Type of file
self.hist_type = hist_type
# List of averages to compute
self.avg_list = avg_list
# Should weights be applied?
self.weighted = weighted
# Are files split spatially?
self.split = split
# Split file name indicators
self.split_files = split_files
# The original grid size of the split files
self.split_orig_size = split_orig_size
# The netcdf output format
self.ncformat = ncformat
# Varlist to average (if not all variables)
self.varlist = varlist
# Run in serial mode? If True, will be ran without MPI
self.serial = serial
# Directory where to find the regional obds files for the mean_diff_rms climo file
self.mean_diff_rms_obs_dir = mean_diff_rms_obs_dir
# Regions to average over
self.regions = regions
# Netcdf variable name that contains a region mask
self.region_nc_var = region_nc_var
# Netcdf variable name that contains the weights
self.region_wgt_var = region_wgt_var
# String that indicates the suffix of the regional obs files used for the mean_diff_rms file
self.reg_obs_file_suffix = reg_obs_file_suffix
# String that indicates the name of the observational file
self.obs_file = obs_file
# String indicating the path to the observational files used for the mean_diff_rms file
self.obs_dir = obs_dir
# File pattern used to piece together a full file name
if (file_pattern == 'null'):
if (hist_type == 'slice'):
self.file_pattern = ['$prefix','.','$date_pattern','.','$suffix']
if (hist_type == 'series'):
if split:
self.file_pattern = ['$prefix','.','$var','_','$hem','.','$date_pattern','.','$suffix']
else:
self.file_pattern = ['$prefix','.','$var','.','$date_pattern','.','$suffix']
else:
self.file_pattern = file_pattern
# The date pattern to decipher the date within the file name
self.date_pattern = date_pattern
self.m_id = m_id
# Get first and last years used in the averaging by parsing the avg_list
dates = []
for avg in avg_list:
avg_descr = avg.split(':')
for yr in avg_descr[1:]:
dates.append(int(yr))
if (year0 == -99 and year1 == -99):
self.year0 = int(min(dates))
self.year1 = int(max(dates))
else:
self.year0 = int(year0)
self.year1 = int(year1)
# Initialize a simple_comm object if one was not passed in by the user
if (main_comm is None):
from asaptools import simplecomm
self.main_comm = simplecomm.create_comm(serial=serial)
else:
self.main_comm = main_comm
# True/False, rm average file(s) is it has already been created
self.clobber = clobber
# File that contains the weight/area information
self.ice_obs_file = ice_obs_file
# File that exists or will be created that contains a region mask for ice
self.reg_file = reg_file
# Location of the ncl script that will be used to create reg_file if it doesn't exist
self.ncl_location = ncl_location
# Used to collapse/average over one dim.
self.collapse_dim = collapse_dim
# Used to specify the number of ocean vertical levels
self.vertical_levels = vertical_levels
def setUp(self):
self.scomm = simplecomm.create_comm(serial=True)
self.pcomm = simplecomm.create_comm(serial=False)
self.size = MPI_COMM_WORLD.Get_size()
self.rank = MPI_COMM_WORLD.Get_rank()
def main(argv):
print "Running pyEnsSum!"
# Get command line stuff and store in a dictionary
s = "tag= compset= esize= tslice= res= sumfile= indir= sumfiledir= mach= verbose jsonfile= mpi_enable maxnorm gmonly popens cumul regx= startMon= endMon= fIndex="
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSum_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict = {}
# Defaults
opts_dict["tag"] = ""
opts_dict["compset"] = ""
opts_dict["mach"] = ""
opts_dict["esize"] = 151
opts_dict["tslice"] = 0
opts_dict["res"] = ""
opts_dict["sumfile"] = "ens.summary.nc"
opts_dict["indir"] = "./"
opts_dict["sumfiledir"] = "./"
opts_dict["jsonfile"] = ""
opts_dict["verbose"] = True
opts_dict["mpi_enable"] = False
opts_dict["maxnorm"] = False
opts_dict["gmonly"] = False
opts_dict["popens"] = False
opts_dict["cumul"] = False
opts_dict["regx"] = "test"
opts_dict["startMon"] = 1
opts_dict["endMon"] = 1
opts_dict["fIndex"] = 151
# This creates the dictionary of input arguments
opts_dict = pyEnsLib.getopt_parseconfig(opts, optkeys, "ES", opts_dict)
verbose = opts_dict["verbose"]
st = opts_dict["esize"]
esize = int(st)
if verbose == True:
print opts_dict
print "Ensemble size for summary = ", esize
if not (opts_dict["tag"] and opts_dict["compset"] and opts_dict["mach"] or opts_dict["res"]):
print "Please specify --tag, --compset, --mach and --res options"
sys.exit()
# Now find file names in indir
input_dir = opts_dict["indir"]
# The var list that will be excluded
ex_varlist = []
# Create a mpi simplecomm object
if opts_dict["mpi_enable"]:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(not opts_dict["mpi_enable"])
if me.get_rank() == 0:
if opts_dict["jsonfile"]:
# Read in the excluded var list
ex_varlist = pyEnsLib.read_jsonlist(opts_dict["jsonfile"], "ES")
# Broadcast the excluded var list to each processor
if opts_dict["mpi_enable"]:
ex_varlist = me.partition(ex_varlist, func=Duplicate(), involved=True)
in_files = []
if os.path.exists(input_dir):
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files = sorted(in_files_temp)
# Make sure we have enough
num_files = len(in_files)
if verbose == True:
print "Number of files in input directory = ", num_files
if num_files < esize:
print "Number of files in input directory (", num_files, ") is less than specified ensemble size of ", esize
sys.exit(2)
if num_files > esize:
print "NOTE: Number of files in ", input_dir, "is greater than specified ensemble size of ", esize, "\nwill just use the first ", esize, "files"
else:
print "Input directory: ", input_dir, " not found"
sys.exit(2)
if opts_dict["cumul"]:
if opts_dict["regx"]:
in_files_list = get_cumul_filelist(opts_dict, opts_dict["indir"], opts_dict["regx"])
in_files = me.partition(in_files_list, func=EqualLength(), involved=True)
if me.get_rank() == 0:
print "in_files=", in_files
# Open the files in the input directory
o_files = []
for onefile in in_files[0:esize]:
if os.path.isfile(input_dir + "/" + onefile):
o_files.append(Nio.open_file(input_dir + "/" + onefile, "r"))
else:
print "COULD NOT LOCATE FILE " + input_dir + onefile + "! EXITING...."
sys.exit()
# Store dimensions of the input fields
if verbose == True:
print "Getting spatial dimensions"
nlev = -1
ncol = -1
nlat = -1
nlon = -1
lonkey = ""
latkey = ""
# Look at first file and get dims
input_dims = o_files[0].dimensions
ndims = len(input_dims)
for key in input_dims:
if key == "lev":
nlev = input_dims["lev"]
elif key == "ncol":
ncol = input_dims["ncol"]
elif (key == "nlon") or (key == "lon"):
nlon = input_dims[key]
lonkey = key
elif (key == "nlat") or (key == "lat"):
nlat = input_dims[key]
latkey = key
if nlev == -1:
print "COULD NOT LOCATE valid dimension lev => EXITING...."
sys.exit()
if (ncol == -1) and ((nlat == -1) or (nlon == -1)):
print "Need either lat/lon or ncol => EXITING...."
sys.exit()
# Check if this is SE or FV data
if ncol != -1:
is_SE = True
else:
is_SE = False
# Make sure all files have the same dimensions
if verbose == True:
print "Checking dimensions across files...."
print "lev = ", nlev
if is_SE == True:
print "ncol = ", ncol
else:
print "nlat = ", nlat
print "nlon = ", nlon
for count, this_file in enumerate(o_files):
input_dims = this_file.dimensions
if is_SE == True:
if nlev != int(input_dims["lev"]) or (ncol != int(input_dims["ncol"])):
print "Dimension mismatch between ", in_files[0], "and", in_files[0], "!!!"
sys.exit()
else:
if nlev != int(input_dims["lev"]) or (nlat != int(input_dims[latkey])) or (nlon != int(input_dims[lonkey])):
print "Dimension mismatch between ", in_files[0], "and", in_files[0], "!!!"
sys.exit()
# Get 2d vars, 3d vars and all vars (For now include all variables)
vars_dict = o_files[0].variables
# Remove the excluded variables (specified in json file) from variable dictionary
if ex_varlist:
for i in ex_varlist:
del vars_dict[i]
num_vars = len(vars_dict)
if verbose == True:
print "Number of variables (including metadata) found = ", num_vars
str_size = 0
d2_var_names = []
d3_var_names = []
num_2d = 0
num_3d = 0
# Which are 2d, which are 3d and max str_size
for k, v in vars_dict.iteritems():
var = k
vd = v.dimensions # all the variable's dimensions (names)
vr = v.rank # num dimension
vs = v.shape # dim values
is_2d = False
is_3d = False
if is_SE == True: # (time, lev, ncol) or (time, ncol)
if (vr == 2) and (vs[1] == ncol):
is_2d = True
num_2d += 1
elif (vr == 3) and (vs[2] == ncol and vs[1] == nlev):
is_3d = True
num_3d += 1
else: # (time, lev, nlon, nlon) or (time, nlat, nlon)
if (vr == 3) and (vs[1] == nlat and vs[2] == nlon):
is_2d = True
num_2d += 1
elif (vr == 4) and (vs[2] == nlat and vs[3] == nlon and vs[1] == nlev):
is_3d = True
num_3d += 1
if is_3d == True:
str_size = max(str_size, len(k))
d3_var_names.append(k)
elif is_2d == True:
str_size = max(str_size, len(k))
d2_var_names.append(k)
# Now sort these and combine (this sorts caps first, then lower case -
# which is what we want)
d2_var_names.sort()
d3_var_names.sort()
# All vars is 3d vars first (sorted), the 2d vars
all_var_names = list(d3_var_names)
all_var_names += d2_var_names
n_all_var_names = len(all_var_names)
if verbose == True:
print "num vars = ", n_all_var_names, "(3d = ", num_3d, " and 2d = ", num_2d, ")"
# Create new summary ensemble file
this_sumfile = opts_dict["sumfile"]
if verbose == True:
print "Creating ", this_sumfile, " ..."
if me.get_rank() == 0 | opts_dict["popens"]:
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
opt = Nio.options()
opt.PreFill = False
opt.Format = "NetCDF4Classic"
nc_sumfile = Nio.open_file(this_sumfile, "w", options=opt)
# Set dimensions
if verbose == True:
print "Setting dimensions ....."
if is_SE == True:
nc_sumfile.create_dimension("ncol", ncol)
else:
nc_sumfile.create_dimension("nlat", nlat)
nc_sumfile.create_dimension("nlon", nlon)
nc_sumfile.create_dimension("nlev", nlev)
nc_sumfile.create_dimension("ens_size", esize)
nc_sumfile.create_dimension("nvars", num_3d + num_2d)
nc_sumfile.create_dimension("nvars3d", num_3d)
nc_sumfile.create_dimension("nvars2d", num_2d)
nc_sumfile.create_dimension("str_size", str_size)
# Set global attributes
now = time.strftime("%c")
if verbose == True:
print "Setting global attributes ....."
setattr(nc_sumfile, "creation_date", now)
setattr(nc_sumfile, "title", "CAM verification ensemble summary file")
setattr(nc_sumfile, "tag", opts_dict["tag"])
setattr(nc_sumfile, "compset", opts_dict["compset"])
setattr(nc_sumfile, "resolution", opts_dict["res"])
setattr(nc_sumfile, "machine", opts_dict["mach"])
# Create variables
if verbose == True:
print "Creating variables ....."
v_lev = nc_sumfile.create_variable("lev", "f", ("nlev",))
v_vars = nc_sumfile.create_variable("vars", "S1", ("nvars", "str_size"))
v_var3d = nc_sumfile.create_variable("var3d", "S1", ("nvars3d", "str_size"))
v_var2d = nc_sumfile.create_variable("var2d", "S1", ("nvars2d", "str_size"))
if not opts_dict["gmonly"]:
if is_SE == True:
v_ens_avg3d = nc_sumfile.create_variable("ens_avg3d", "f", ("nvars3d", "nlev", "ncol"))
v_ens_stddev3d = nc_sumfile.create_variable("ens_stddev3d", "f", ("nvars3d", "nlev", "ncol"))
v_ens_avg2d = nc_sumfile.create_variable("ens_avg2d", "f", ("nvars2d", "ncol"))
v_ens_stddev2d = nc_sumfile.create_variable("ens_stddev2d", "f", ("nvars2d", "ncol"))
else:
v_ens_avg3d = nc_sumfile.create_variable("ens_avg3d", "f", ("nvars3d", "nlev", "nlat", "nlon"))
v_ens_stddev3d = nc_sumfile.create_variable("ens_stddev3d", "f", ("nvars3d", "nlev", "nlat", "nlon"))
v_ens_avg2d = nc_sumfile.create_variable("ens_avg2d", "f", ("nvars2d", "nlat", "nlon"))
v_ens_stddev2d = nc_sumfile.create_variable("ens_stddev2d", "f", ("nvars2d", "nlat", "nlon"))
v_RMSZ = nc_sumfile.create_variable("RMSZ", "f", ("nvars", "ens_size"))
v_gm = nc_sumfile.create_variable("global_mean", "f", ("nvars", "ens_size"))
v_loadings_gm = nc_sumfile.create_variable("loadings_gm", "f", ("nvars", "nvars"))
v_mu_gm = nc_sumfile.create_variable("mu_gm", "f", ("nvars",))
v_sigma_gm = nc_sumfile.create_variable("sigma_gm", "f", ("nvars",))
v_sigma_scores_gm = nc_sumfile.create_variable("sigma_scores_gm", "f", ("nvars",))
# Assign vars, var3d and var2d
if verbose == True:
print "Assigning vars, var3d, and var2d ....."
eq_all_var_names = []
eq_d3_var_names = []
eq_d2_var_names = []
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if l_tt < str_size:
extra = list(" ") * (str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(d3_var_names)
for i in range(l_eq):
tt = list(d3_var_names[i])
l_tt = len(tt)
if l_tt < str_size:
extra = list(" ") * (str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(d2_var_names)
for i in range(l_eq):
tt = list(d2_var_names[i])
l_tt = len(tt)
if l_tt < str_size:
extra = list(" ") * (str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if verbose == True:
print "Assigning time invariant metadata ....."
lev_data = vars_dict["lev"]
v_lev = lev_data
# Form ensembles, each missing one member; compute RMSZs and global means
# for each variable, we also do max norm also (currently done in pyStats)
tslice = opts_dict["tslice"]
if not opts_dict["cumul"]:
# Partition the var list
var3_list_loc = me.partition(d3_var_names, func=EqualStride(), involved=True)
var2_list_loc = me.partition(d2_var_names, func=EqualStride(), involved=True)
else:
var3_list_loc = d3_var_names
var2_list_loc = d2_var_names
# Calculate global means #
if verbose == True:
print "Calculating global means ....."
if not opts_dict["cumul"]:
gm3d, gm2d = pyEnsLib.generate_global_mean_for_summary(
o_files, var3_list_loc, var2_list_loc, is_SE, False, opts_dict
)
if verbose == True:
print "Finish calculating global means ....."
# Calculate RMSZ scores
if verbose == True:
print "Calculating RMSZ scores ....."
if (not opts_dict["gmonly"]) | (opts_dict["cumul"]):
zscore3d, zscore2d, ens_avg3d, ens_stddev3d, ens_avg2d, ens_stddev2d, temp1, temp2 = pyEnsLib.calc_rmsz(
o_files, var3_list_loc, var2_list_loc, is_SE, opts_dict
)
# Calculate max norm ensemble
if opts_dict["maxnorm"]:
if verbose == True:
print "Calculating max norm of ensembles ....."
pyEnsLib.calculate_maxnormens(opts_dict, var3_list_loc)
pyEnsLib.calculate_maxnormens(opts_dict, var2_list_loc)
if opts_dict["mpi_enable"] & (not opts_dict["popens"]):
if not opts_dict["cumul"]:
# Gather the 3d variable results from all processors to the master processor
slice_index = get_stride_list(len(d3_var_names), me)
# Gather global means 3d results
gm3d = gather_npArray(gm3d, me, slice_index, (len(d3_var_names), len(o_files)))
if not opts_dict["gmonly"]:
# Gather zscore3d results
zscore3d = gather_npArray(zscore3d, me, slice_index, (len(d3_var_names), len(o_files)))
# Gather ens_avg3d and ens_stddev3d results
shape_tuple3d = get_shape(ens_avg3d.shape, len(d3_var_names), me.get_rank())
ens_avg3d = gather_npArray(ens_avg3d, me, slice_index, shape_tuple3d)
ens_stddev3d = gather_npArray(ens_stddev3d, me, slice_index, shape_tuple3d)
# Gather 2d variable results from all processors to the master processor
slice_index = get_stride_list(len(d2_var_names), me)
# Gather global means 2d results
gm2d = gather_npArray(gm2d, me, slice_index, (len(d2_var_names), len(o_files)))
if not opts_dict["gmonly"]:
# Gather zscore2d results
zscore2d = gather_npArray(zscore2d, me, slice_index, (len(d2_var_names), len(o_files)))
# Gather ens_avg3d and ens_stddev2d results
shape_tuple2d = get_shape(ens_avg2d.shape, len(d2_var_names), me.get_rank())
ens_avg2d = gather_npArray(ens_avg2d, me, slice_index, shape_tuple2d)
ens_stddev2d = gather_npArray(ens_stddev2d, me, slice_index, shape_tuple2d)
else:
gmall = np.concatenate((temp1, temp2), axis=0)
gmall = pyEnsLib.gather_npArray_pop(gmall, me, (me.get_size(), len(d3_var_names) + len(d2_var_names)))
# Assign to file:
if me.get_rank() == 0 | opts_dict["popens"]:
if not opts_dict["cumul"]:
gmall = np.concatenate((gm3d, gm2d), axis=0)
if not opts_dict["gmonly"]:
Zscoreall = np.concatenate((zscore3d, zscore2d), axis=0)
v_RMSZ[:, :] = Zscoreall[:, :]
if not opts_dict["gmonly"]:
if is_SE == True:
v_ens_avg3d[:, :, :] = ens_avg3d[:, :, :]
v_ens_stddev3d[:, :, :] = ens_stddev3d[:, :, :]
v_ens_avg2d[:, :] = ens_avg2d[:, :]
v_ens_stddev2d[:, :] = ens_stddev2d[:, :]
else:
v_ens_avg3d[:, :, :, :] = ens_avg3d[:, :, :, :]
v_ens_stddev3d[:, :, :, :] = ens_stddev3d[:, :, :, :]
v_ens_avg2d[:, :, :] = ens_avg2d[:, :, :]
v_ens_stddev2d[:, :, :] = ens_stddev2d[:, :, :]
else:
gmall_temp = np.transpose(gmall[:, :])
gmall = gmall_temp
mu_gm, sigma_gm, standardized_global_mean, loadings_gm, scores_gm = pyEnsLib.pre_PCA(gmall)
v_gm[:, :] = gmall[:, :]
v_mu_gm[:] = mu_gm[:]
v_sigma_gm[:] = sigma_gm[:].astype(np.float32)
v_loadings_gm[:, :] = loadings_gm[:, :]
v_sigma_scores_gm[:] = scores_gm[:]
print "All Done"
def __init__(self, specifiers, serial=False, verbosity=1, wmode='w',
once=False, simplecomm=None):
"""
Constructor
Parameters:
specifiers (dict): A dict of named Specifier instances, each
defining an input specification for this reshaper operation.
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
wmode (str): The mode to use for writing output. Can be 'w' for
normal write operation, 's' to skip the output generation for
existing time-series files, 'o' to overwrite existing
time-series files, 'a' to append to existing time-series files.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Check types
if not isinstance(specifiers, dict):
err_msg = "Input must be given in a dictionary of Specifiers"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(wmode) is not str:
err_msg = "Write mode flag must be a str."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
if wmode not in ['w', 's', 'o', 'a']:
err_msg = "Write mode '{}' not recognized".format(wmode)
raise ValueError(err_msg)
# Whether to write to a once file
self._use_once_file = once
# Output file write mode
self._write_mode = wmode
# Store the list of specifiers
self._specifiers = specifiers
# Store the serial specifier
self._serial = serial
# Check for a SimpleComm, and if none create it
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Pointer to its own messenger
self._simplecomm = simplecomm
# Store the verbosity
self._verbosity = verbosity
# Set the verbose printer
self._vprint = VPrinter(verbosity=verbosity)
# Storage for timing data
self._times = {}
# Orders for printing timing data
self._time_orders = {}
# Storage for all byte counters
self._byte_counts = {}
def __init__(self, specifier, serial=False, verbosity=1,
skip_existing=False, overwrite=False,
once=False, simplecomm=None):
"""
Constructor
Parameters:
specifier (Specifier): An instance of the Specifier class,
defining the input specification for this reshaper operation.
Keyword Arguments:
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
skip_existing (bool): Flag specifying whether to skip the generation
of time-series for variables with time-series files that already
exist. Default is False.
overwrite (bool): Flag specifying whether to forcefully overwrite
output files if they already exist. Default is False.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Type checking (or double-checking)
if not isinstance(specifier, Specifier):
err_msg = "Input must be given in the form of a Specifier object"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(skip_existing) is not bool:
err_msg = "Skip_existing flag must be True or False."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not (isinstance(simplecomm, SimpleComm) or \
isinstance(simplecomm, SimpleCommMPI)):
err_msg = ("Simple communicator object is not a SimpleComm or ",
"SimpleCommMPI")
raise TypeError(err_msg)
# Whether to write a once file
self._use_once_file = once
# Internal timer data
self._timer = TimeKeeper()
# Dictionary storing read/write data amounts
self.assumed_block_size = float(4 * 1024 * 1024)
self._byte_counts = {}
self._timer.start('Initializing Simple Communicator')
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Reference to the simple communicator
self._simplecomm = simplecomm
self._timer.stop('Initializing Simple Communicator')
# Contruct the print header
header = ''.join(['[', str(self._simplecomm.get_rank()),
'/', str(self._simplecomm.get_size()), '] '])
# Reference to the verbose printer tool
self._vprint = VPrinter(header=header, verbosity=verbosity)
# Debug output starting
if self._simplecomm.is_manager():
self._vprint('Initializing Reshaper', verbosity=1)
# Validate the user input data
self._timer.start('Specifier Validation')
specifier.validate()
self._timer.stop('Specifier Validation')
if self._simplecomm.is_manager():
self._vprint('Specifier validated', verbosity=1)
# Setup PyNIO options (including disabling the default PreFill option)
opt = Nio.options()
opt.PreFill = False
# Determine the Format and CompressionLevel options
# from the NetCDF format string in the Specifier
if specifier.netcdf_format == 'netcdf':
opt.Format = 'Classic'
elif specifier.netcdf_format == 'netcdf4':
opt.Format = 'NetCDF4Classic'
opt.CompressionLevel = 0
elif specifier.netcdf_format == 'netcdf4c':
opt.Format = 'NetCDF4Classic'
opt.CompressionLevel = specifier.netcdf_deflate
if self._simplecomm.is_manager():
self._vprint('PyNIO compression level: {0}'.format(\
specifier.netcdf_deflate), verbosity=2)
self._nio_options = opt
if self._simplecomm.is_manager():
self._vprint('PyNIO options set', verbosity=2)
# Open all of the input files
self._timer.start('Open Input Files')
self._input_files = []
for filename in specifier.input_file_list:
self._input_files.append(Nio.open_file(filename, "r"))
self._timer.stop('Open Input Files')
if self._simplecomm.is_manager():
self._vprint('Input files opened', verbosity=2)
# Validate the input files themselves
self._timer.start('Input File Validation')
self._validate_input_files(specifier)
self._timer.stop('Input File Validation')
if self._simplecomm.is_manager():
self._vprint('Input files validated', verbosity=2)
# Sort the input files by time
self._timer.start('Sort Input Files')
self._sort_input_files_by_time(specifier)
self._timer.stop('Sort Input Files')
if self._simplecomm.is_manager():
self._vprint('Input files sorted', verbosity=2)
# Retrieve and sort the variables in each time-slice file
# (To determine if it is time-invariant metadata, time-variant
# metadata, or if it is a time-series variable)
self._timer.start('Sort Variables')
self._sort_variables(specifier)
self._timer.stop('Sort Variables')
if self._simplecomm.is_manager():
self._vprint('Variables sorted', verbosity=2)
# Validate the output files
self._timer.start('Output File Validation')
self._validate_output_files(specifier, skip_existing, overwrite)
self._timer.stop('Output File Validation')
if self._simplecomm.is_manager():
self._vprint('Output files validated', verbosity=2)
# Helpful debugging message
if self._simplecomm.is_manager():
self._vprint('Reshaper initialized.', verbosity=1)
# Sync before continuing..
self._simplecomm.sync()
def __init__(self, specifiers, serial=False, verbosity=1,
skip_existing=False, overwrite=False,
once=False, simplecomm=None):
"""
Constructor
Parameters:
specifiers (dict): A dict of named Specifier instances, each
defining an input specification for this reshaper operation.
Keyword Arguments:
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
skip_existing (bool): Flag specifying whether to skip the generation
of time-series for variables with time-series files that already
exist. Default is False.
overwrite (bool): Flag specifying whether to forcefully overwrite
output files if they already exist. Default is False.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Check types
if not isinstance(specifiers, dict):
err_msg = "Input must be given in a dictionary of Specifiers"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(skip_existing) is not bool:
err_msg = "Skip_existing flag must be True or False."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if simplecomm is not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
# Whether to write to a once file
self._use_once_file = once
# Whether to write to a once file
self._skip_existing = skip_existing
# Whether to write to overwrite output files
self._overwrite = overwrite
# Store the list of specifiers
self._specifiers = specifiers
# Store the serial specifier
self._serial = serial
# Check for a SimpleComm, and if none create it
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Pointer to its own messenger
self._simplecomm = simplecomm
# Store the verbosity
self._verbosity = verbosity
# Set the verbose printer
self._vprint = VPrinter(verbosity=verbosity)
# Storage for timing data
self._times = {}
# Orders for printing timing data
self._time_orders = {}
# Storage for all byte counters
self._byte_counts = {}
oldfile = os.path.join(olddir, filename)
if oldfile in oldfiles:
item_dict['old'] = oldfile
oldfiles.remove(oldfile)
items_to_check.append(item_dict)
else:
item_dict['old'] = None
unchecked_new_items.append(item_dict)
for oldfile in oldfiles:
item_dict = {'test': test_name}
item_dict['new'] = None
item_dict['old'] = oldfile
unchecked_old_items.append(item_dict)
# Get a basic MPI comm
comm = create_comm(serial=(opts.serial or opts.list_tests))
# Print tests that will be checked
if comm.is_manager():
print 'Checking test results.'
for test_name in tests_to_check:
print 'Test {0!s}:'.format(test_name)
num_chk = sum(1 for i in items_to_check if i['test'] == test_name)
num_new = num_chk + sum(
1 for i in unchecked_new_items if i['test'] == test_name)
num_old = num_chk + sum(
1 for i in unchecked_old_items if i['test'] == test_name)
print ' Checking {0!s} of {1!s}'.format(num_chk, num_new),
print 'new files generated against {0!s}'.format(num_old),
print 'old files found.'
def __init__(self,
specifiers,
serial=False,
verbosity=1,
wmode='w',
once=False,
simplecomm=None):
"""
Constructor
Parameters:
specifiers (dict): A dict of named Specifier instances, each
defining an input specification for this reshaper operation.
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
wmode (str): The mode to use for writing output. Can be 'w' for
normal write operation, 's' to skip the output generation for
existing time-series files, 'o' to overwrite existing
time-series files, 'a' to append to existing time-series files.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Check types
if not isinstance(specifiers, dict):
err_msg = "Input must be given in a dictionary of Specifiers"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(wmode) is not str:
err_msg = "Write mode flag must be a str."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
if wmode not in ['w', 's', 'o', 'a']:
err_msg = "Write mode '{}' not recognized".format(wmode)
raise ValueError(err_msg)
# Whether to write to a once file
self._use_once_file = once
# Output file write mode
self._write_mode = wmode
# Store the list of specifiers
self._specifiers = specifiers
# Store the serial specifier
self._serial = serial
# Check for a SimpleComm, and if none create it
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Pointer to its own messenger
self._simplecomm = simplecomm
# Store the verbosity
self._verbosity = verbosity
# Set the verbose printer
self._vprint = VPrinter(verbosity=verbosity)
# Storage for timing data
self._times = {}
# Orders for printing timing data
self._time_orders = {}
# Storage for all byte counters
self._byte_counts = {}
def main(argv):
print 'Running pyEnsSumPop!'
# Get command line stuff and store in a dictionary
s = 'nyear= nmonth= npert= tag= res= mach= compset= sumfile= indir= tslice= verbose jsonfile= mpi_enable zscoreonly nrand= rand seq= jsondir='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSumPop_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict={}
# Defaults
opts_dict['tag'] = 'cesm1_2_0'
opts_dict['compset'] = 'FC5'
opts_dict['mach'] = 'yellowstone'
opts_dict['tslice'] = 0
opts_dict['nyear'] = 3
opts_dict['nmonth'] = 12
opts_dict['npert'] = 40
opts_dict['nbin'] = 40
opts_dict['minrange'] = 0.0
opts_dict['maxrange'] = 4.0
opts_dict['res'] = 'ne30_ne30'
opts_dict['sumfile'] = 'ens.pop.summary.nc'
opts_dict['indir'] = './'
opts_dict['jsonfile'] = ''
opts_dict['verbose'] = True
opts_dict['mpi_enable'] = False
opts_dict['zscoreonly'] = False
opts_dict['popens'] = True
opts_dict['nrand'] = 40
opts_dict['rand'] = False
opts_dict['seq'] = 0
opts_dict['jsondir'] = '/glade/scratch/haiyingx/'
# This creates the dictionary of input arguments
print "before parseconfig"
opts_dict = pyEnsLib.getopt_parseconfig(opts,optkeys,'ESP',opts_dict)
verbose = opts_dict['verbose']
nbin = opts_dict['nbin']
if verbose:
print opts_dict
# Now find file names in indir
input_dir = opts_dict['indir']
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me=simplecomm.create_comm()
else:
me=simplecomm.create_comm(not opts_dict['mpi_enable'])
if opts_dict['jsonfile']:
# Read in the included var list
Var2d,Var3d=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ESP')
str_size=0
for str in Var3d:
if str_size < len(str):
str_size=len(str)
for str in Var2d:
if str_size < len(str):
str_size=len(str)
in_files=[]
if(os.path.exists(input_dir)):
# Pick up the 'nrand' random number of input files to generate summary files
if opts_dict['rand']:
in_files=pyEnsLib.Random_pickup_pop(input_dir,opts_dict,opts_dict['nrand'])
else:
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files=sorted(in_files_temp)
# Make sure we have enough
num_files = len(in_files)
else:
print 'Input directory: ',input_dir,' not found'
sys.exit(2)
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me=simplecomm.create_comm()
else:
me=simplecomm.create_comm(not opts_dict['mpi_enable'])
#Partition the input file list
in_file_list=me.partition(in_files,func=EqualStride(),involved=True)
# Open the files in the input directory
o_files=[]
for onefile in in_file_list:
if (os.path.isfile(input_dir+'/' + onefile)):
o_files.append(Nio.open_file(input_dir+'/' + onefile,"r"))
else:
print "COULD NOT LOCATE FILE "+ input_dir + onefile + "! EXITING...."
sys.exit()
print in_file_list
# Store dimensions of the input fields
if (verbose == True):
print "Getting spatial dimensions"
nlev = -1
nlat = -1
nlon = -1
# Look at first file and get dims
input_dims = o_files[0].dimensions
ndims = len(input_dims)
# Make sure all files have the same dimensions
for key in input_dims:
if key == "z_t":
nlev = input_dims["z_t"]
elif key == "nlon":
nlon = input_dims["nlon"]
elif key == "nlat":
nlat = input_dims["nlat"]
for count, this_file in enumerate(o_files):
input_dims = this_file.dimensions
if ( nlev != int(input_dims["z_t"]) or ( nlat != int(input_dims["nlat"]))\
or ( nlon != int(input_dims["nlon"]))):
print "Dimension mismatch between ", in_file_list[0], 'and', in_file_list[count], '!!!'
sys.exit()
# Create new summary ensemble file
this_sumfile = opts_dict["sumfile"]
if verbose:
print "Creating ", this_sumfile, " ..."
if (me.get_rank() == 0 ):
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
opt =Nio.options()
opt.PreFill = False
opt.Format = 'NetCDF4Classic'
nc_sumfile = Nio.open_file(this_sumfile, 'w', options=opt)
# Set dimensions
if (verbose == True):
print "Setting dimensions ....."
nc_sumfile.create_dimension('nlat', nlat)
nc_sumfile.create_dimension('nlon', nlon)
nc_sumfile.create_dimension('nlev', nlev)
nc_sumfile.create_dimension('time',None)
nc_sumfile.create_dimension('ens_size', opts_dict['npert'])
nc_sumfile.create_dimension('nbin', opts_dict['nbin'])
nc_sumfile.create_dimension('nvars', len(Var3d) + len(Var2d))
nc_sumfile.create_dimension('nvars3d', len(Var3d))
nc_sumfile.create_dimension('nvars2d', len(Var2d))
nc_sumfile.create_dimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
if (verbose == True):
print "Setting global attributes ....."
setattr(nc_sumfile, 'creation_date',now)
setattr(nc_sumfile, 'title', 'POP verification ensemble summary file')
setattr(nc_sumfile, 'tag', opts_dict["tag"])
setattr(nc_sumfile, 'compset', opts_dict["compset"])
setattr(nc_sumfile, 'resolution', opts_dict["res"])
setattr(nc_sumfile, 'machine', opts_dict["mach"])
# Create variables
if (verbose == True):
print "Creating variables ....."
v_lev = nc_sumfile.create_variable("lev", 'f', ('nlev',))
v_vars = nc_sumfile.create_variable("vars", 'S1', ('nvars', 'str_size'))
v_var3d = nc_sumfile.create_variable("var3d", 'S1', ('nvars3d', 'str_size'))
v_var2d = nc_sumfile.create_variable("var2d", 'S1', ('nvars2d', 'str_size'))
v_time = nc_sumfile.create_variable("time",'d',('time',))
v_ens_avg3d = nc_sumfile.create_variable("ens_avg3d", 'f', ('time','nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_stddev3d = nc_sumfile.create_variable("ens_stddev3d", 'f', ('time','nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_avg2d = nc_sumfile.create_variable("ens_avg2d", 'f', ('time','nvars2d', 'nlat', 'nlon'))
v_ens_stddev2d = nc_sumfile.create_variable("ens_stddev2d", 'f', ('time','nvars2d', 'nlat', 'nlon'))
v_RMSZ = nc_sumfile.create_variable("RMSZ", 'f', ('time','nvars', 'ens_size','nbin'))
if not opts_dict['zscoreonly']:
v_gm = nc_sumfile.create_variable("global_mean", 'f', ('time','nvars', 'ens_size'))
# Assign vars, var3d and var2d
if (verbose == True):
print "Assigning vars, var3d, and var2d ....."
eq_all_var_names =[]
eq_d3_var_names = []
eq_d2_var_names = []
all_var_names = list(Var3d)
all_var_names += Var2d
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(Var3d)
for i in range(l_eq):
tt = list(Var3d[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(Var2d)
for i in range(l_eq):
tt = list(Var2d[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ')*(str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if (verbose == True):
print "Assigning time invariant metadata ....."
vars_dict = o_files[0].variables
lev_data = vars_dict["z_t"]
v_lev = lev_data
# Time-varient metadata
if verbose:
print "Assigning time variant metadata ....."
vars_dict = o_files[0].variables
time_value = vars_dict['time']
time_array = np.array([time_value])
time_array = pyEnsLib.gather_npArray_pop(time_array,me,(me.get_size(),))
if me.get_rank() == 0:
v_time[:]=time_array[:]
# Calculate global mean, average, standard deviation
if verbose:
print "Calculating global means ....."
is_SE = False
tslice=0
if not opts_dict['zscoreonly']:
gm3d,gm2d = pyEnsLib.generate_global_mean_for_summary(o_files,Var3d,Var2d, is_SE,False,opts_dict)
if verbose:
print "Finish calculating global means ....."
# Calculate RMSZ scores
if (verbose == True):
print "Calculating RMSZ scores ....."
zscore3d,zscore2d,ens_avg3d,ens_stddev3d,ens_avg2d,ens_stddev2d,temp1,temp2=pyEnsLib.calc_rmsz(o_files,Var3d,Var2d,is_SE,opts_dict)
# Collect from all processors
if opts_dict['mpi_enable'] :
# Gather the 3d variable results from all processors to the master processor
# Gather global means 3d results
if not opts_dict['zscoreonly']:
gmall=np.concatenate((gm3d,gm2d),axis=0)
#print "before gather, gmall.shape=",gmall.shape
gmall=pyEnsLib.gather_npArray_pop(gmall,me,(me.get_size(),len(Var3d)+len(Var2d),len(o_files)))
zmall=np.concatenate((zscore3d,zscore2d),axis=0)
zmall=pyEnsLib.gather_npArray_pop(zmall,me,(me.get_size(),len(Var3d)+len(Var2d),len(o_files),nbin))
#print 'zmall=',zmall
#print "after gather, gmall.shape=",gmall.shape
ens_avg3d=pyEnsLib.gather_npArray_pop(ens_avg3d,me,(me.get_size(),len(Var3d),nlev,(nlat),nlon))
ens_avg2d=pyEnsLib.gather_npArray_pop(ens_avg2d,me,(me.get_size(),len(Var2d),(nlat),nlon))
ens_stddev3d=pyEnsLib.gather_npArray_pop(ens_stddev3d,me,(me.get_size(),len(Var3d),nlev,(nlat),nlon))
ens_stddev2d=pyEnsLib.gather_npArray_pop(ens_stddev2d,me,(me.get_size(),len(Var2d),(nlat),nlon))
# Assign to file:
if me.get_rank() == 0 :
#Zscoreall=np.concatenate((zscore3d,zscore2d),axis=0)
v_RMSZ[:,:,:,:]=zmall[:,:,:,:]
v_ens_avg3d[:,:,:,:,:]=ens_avg3d[:,:,:,:,:]
v_ens_stddev3d[:,:,:,:,:]=ens_stddev3d[:,:,:,:,:]
v_ens_avg2d[:,:,:,:]=ens_avg2d[:,:,:,:]
v_ens_stddev2d[:,:,:,:]=ens_stddev2d[:,:,:,:]
if not opts_dict['zscoreonly']:
v_gm[:,:,:]=gmall[:,:,:]
print "All done"
def execute(self,
chunks={},
serial=False,
history=False,
scomm=None,
deflate=None):
"""
Execute the Data Flow
Parameters:
chunks (dict): A dictionary of output dimension names and chunk sizes for each
dimension given. Output dimensions not included in the dictionary will not be
chunked. (Use OrderedDict to preserve order of dimensions, where the first
dimension will be assumed to correspond to the fastest-varying index and the last
dimension will be assumed to correspond to the slowest-varying index.)
serial (bool): Whether to run in serial (True) or parallel (False)
history (bool): Whether to write a history attribute generated during execution
for each variable in the file
scomm (SimpleComm): An externally created SimpleComm object to use for managing
parallel operation
deflate (int): Override all output file deflate levels with given value
"""
# Check chunks type
if not isinstance(chunks, dict):
raise TypeError('Chunks must be specified with a dictionary')
# Make sure that the specified chunking dimensions are valid
for odname, odsize in chunks.iteritems():
if odname not in self._o2imap:
raise ValueError(
'Cannot chunk over unknown output dimension {!r}'.format(
odname))
if not isinstance(odsize, int):
raise TypeError(
('Chunk size invalid for output dimension {!r}: '
'{}').format(odname, odsize))
# Check that we are not chunking over any "sum-like" dimensions
sumlike_chunk_dims = sorted(d for d in chunks
if d in self._sumlike_dimensions)
if len(sumlike_chunk_dims) > 0:
raise ValueError((
'Cannot chunk over dimensions that are summed over (or "sum-like")'
': {}'.format(', '.join(sumlike_chunk_dims))))
# Create the simple communicator, if necessary
if scomm is None:
scomm = create_comm(serial=bool(serial))
elif isinstance(scomm, SimpleComm):
if scomm.is_manager():
print 'Inheriting SimpleComm object from parent. (Ignoring serial argument.)'
else:
raise TypeError('Communication object is not a SimpleComm!')
# Start general output
prefix = '[{}/{}]'.format(scomm.get_rank(), scomm.get_size())
if scomm.is_manager():
print 'Beginning execution of data flow...'
print 'Mapping Input Dimensions to Output Dimensions:'
for d in sorted(self._i2omap):
print ' {} --> {}'.format(d, self._i2omap[d])
if len(chunks) > 0:
print 'Chunking over Output Dimensions:'
for d in chunks:
print ' {}: {}'.format(d, chunks[d])
else:
print 'Not chunking output.'
# Partition the output files/variables over available parallel (MPI) ranks
fnames = scomm.partition(self._filesizes.items(),
func=WeightBalanced(),
involved=True)
if scomm.is_manager():
print 'Writing {} files across {} MPI processes.'.format(
len(self._filesizes), scomm.get_size())
scomm.sync()
# Standard output
print '{}: Writing {} files: {}'.format(prefix, len(fnames),
', '.join(fnames))
scomm.sync()
# Loop over output files and write using given chunking
for fname in fnames:
print '{}: Writing file: {}'.format(prefix, fname)
if history:
self._writenodes[fname].enable_history()
else:
self._writenodes[fname].disable_history()
self._writenodes[fname].execute(chunks=chunks, deflate=deflate)
print '{}: Finished writing file: {}'.format(prefix, fname)
scomm.sync()
if scomm.is_manager():
print 'All output variables written.'
print
def __init__(self,
in_directory,
out_directory,
prefix,
suffix,
file_pattern='null',
date_pattern='null',
m_id=['-999'],
hist_type='slice',
avg_list=[],
weighted=False,
split=False,
split_files='null',
split_orig_size='null',
ncformat='netcdf4c',
varlist=[],
serial=False,
mean_diff_rms_obs_dir='null',
region_nc_var='null',
regions={},
region_wgt_var='null',
obs_file='null',
reg_obs_file_suffix='null',
obs_dir='null',
main_comm=None,
clobber=False,
ice_obs_file='null',
reg_file='null',
ncl_location='null',
year0=-99,
year1=-99,
collapse_dim='',
vertical_levels=60):
'''
Initializes the internal data with optional arguments
@param in_directory Where the input directory resides (needs full path).
@param out_directory Where the output will be produced (needs full path).
@param prefix String specifying the full file name before the date string.
@param suffix String specifying the suffix of the file names
@param file_pattern File pattern used put the prefix, date, and suffix together for input files.
@param date_pattern The pattern used to decipher the date string within the file name.
@param m_id Array of member identifiers. All averages will be done on each member individually and then across all members.
@param hist_type Type of file ('slice' or 'series'). Default is 'slice'.
@param avg_list List of averages that need to be computed. Elements should contain aveType:year0:year1.
year2 is only required for multi year averaging.
@param weighted Boolean variable to selected if weights will be applied to the averaging.
True = weights will be applied. Default is False.
@param split Boolean variable. True = the file is split spatially and the final average needs to be pieced together.
(ie. CICE times series files) Default is False.
@param split_files The strings indicating the naming difference between split files. Expects a string with elements separated by a comma.
Defualt is 'null'.
@param split_orig_size A string listing the lat and lon values of the origianl grid size. Needed in case some of the grid has been deleted.
(example: 'lon=288,lat=192'). Default is 'null'.
@param ncformat Format to output the averaged file(s) in. Default is 'netcdf4c'. Other options: 'netcdf','netcdf4','netcdf4c'
@param varlist Optional variables list, if not averaging all variables
@param serial Boolean to run in serial mode. True=serial (without MPI) False=run in parallel(with MPI) False requires mpi4py to be installed.
Default is False.
@param regions Dictionary that contains regions to average over. Fromat is 'string region name: int region value'. Default is an empty dictionary.
@param region_nc_var String that identifies the netcdf variable that contains the region mask used by a regional average.
@param region_wgt_var String that identifies the netcdf variable that contains the weights.
@param obs_file Observational file used for the creation of the mean_diff_rms file. This file must contain all of the variables within the
variable list (or if a variable list is not specified, must contain all hist file variables). Dimension must be nlon and nlat.
@param reg_obs_file_suffix The suffix of the regional, weighted averages of the 'obs_file'. Used for the creation of the mean_diff_rms file.
@param obs_dir Full path to the observational files used for the mean_diff_rms file.
@param main_comm A simplecomm to be used by the PyAverager. If not specified, one will be created by this specifier. Default None.
@param clobber Remove netcdf output file(s) if they exist. Default False - will exit if an output file of the same name exists.
@param ice_obs_file Full path to the observational file used to create the cice model pre_proc file
@param reg_file Full path to the regional file used to create the cice model pre_proc file
@param ncl_location Location of where the ncl scripts reside
@param year0 The first year - only used to create the cice pre_proc file.
@param year1 The last year - only used to create the cice pre_proc file.
@param collapse_dims Used to collapse/average over one dim.
@param vertical_levels Number of ocean vertical levels
'''
# Where the input is located
self.in_directory = in_directory
# Where the output should be produced
self.out_directory = out_directory
# Full file name up to the date string
self.prefix = prefix
# The suffix of the data files
self.suffix = suffix
# Type of file
self.hist_type = hist_type
# List of averages to compute
self.avg_list = avg_list
# Should weights be applied?
self.weighted = weighted
# Are files split spatially?
self.split = split
# Split file name indicators
self.split_files = split_files
# The original grid size of the split files
self.split_orig_size = split_orig_size
# The netcdf output format
self.ncformat = ncformat
# Varlist to average (if not all variables)
self.varlist = varlist
# Run in serial mode? If True, will be ran without MPI
self.serial = serial
# Directory where to find the regional obds files for the mean_diff_rms climo file
self.mean_diff_rms_obs_dir = mean_diff_rms_obs_dir
# Regions to average over
self.regions = regions
# Netcdf variable name that contains a region mask
self.region_nc_var = region_nc_var
# Netcdf variable name that contains the weights
self.region_wgt_var = region_wgt_var
# String that indicates the suffix of the regional obs files used for the mean_diff_rms file
self.reg_obs_file_suffix = reg_obs_file_suffix
# String that indicates the name of the observational file
self.obs_file = obs_file
# String indicating the path to the observational files used for the mean_diff_rms file
self.obs_dir = obs_dir
# File pattern used to piece together a full file name
if (file_pattern == 'null'):
if (hist_type == 'slice'):
self.file_pattern = [
'$prefix', '.', '$date_pattern', '.', '$suffix'
]
if (hist_type == 'series'):
if split:
self.file_pattern = [
'$prefix', '.', '$var', '_', '$hem', '.',
'$date_pattern', '.', '$suffix'
]
else:
self.file_pattern = [
'$prefix', '.', '$var', '.', '$date_pattern', '.',
'$suffix'
]
else:
self.file_pattern = file_pattern
# The date pattern to decipher the date within the file name
self.date_pattern = date_pattern
self.m_id = m_id
# Get first and last years used in the averaging by parsing the avg_list
dates = []
for avg in avg_list:
avg_descr = avg.split(':')
for yr in avg_descr[1:]:
dates.append(int(yr))
if (year0 == -99 and year1 == -99):
self.year0 = int(min(dates))
self.year1 = int(max(dates))
else:
self.year0 = int(year0)
self.year1 = int(year1)
# Initialize a simple_comm object if one was not passed in by the user
if (main_comm is None):
from asaptools import simplecomm
self.main_comm = simplecomm.create_comm(serial=serial)
else:
self.main_comm = main_comm
# True/False, rm average file(s) is it has already been created
self.clobber = clobber
# File that contains the weight/area information
self.ice_obs_file = ice_obs_file
# File that exists or will be created that contains a region mask for ice
self.reg_file = reg_file
# Location of the ncl script that will be used to create reg_file if it doesn't exist
self.ncl_location = ncl_location
# Used to collapse/average over one dim.
self.collapse_dim = collapse_dim
# Used to specify the number of ocean vertical levels
self.vertical_levels = vertical_levels
workdir = '{0}/climo/{1}/{2}/{3}/'.format(envDict['PTMPDIR_'+t], envDict['caseid_'+t], subdir, m_dir)
timer_tag = '{0}_{1}'.format(t, climo_file)
timer.start(timer_tag)
debugMsg('Before call to lnd_regrid using workdir = {0}/{1}'.format(workdir, ext_dir), header=True, verbosity=1)
diagUtilsLib.lnd_regrid(climo_file, regrid_script, t, workdir, ext_dir, envDict)
timer.stop(timer_tag)
debugMsg("Total time to regrid file {0} = {1}".format(climo_file, timer.get_time(timer_tag)), header=True, verbosity=1)
#===================================
if __name__ == "__main__":
# initialize simplecomm object
main_comm = simplecomm.create_comm(serial=False)
# setup an overall timer
timer = timekeeper.TimeKeeper()
# get commandline options
options = commandline_options()
# initialize global vprinter object for printing debug messages
if options.debug:
header = "[" + str(main_comm.get_rank()) + "/" + str(main_comm.get_size()) + "]: DEBUG... "
debugMsg = vprinter.VPrinter(header=header, verbosity=options.debug[0])
try:
timer.start("Total Time")
status = main(options, main_comm, debugMsg, timer)
def setUp(self):
self.scomm = simplecomm.create_comm(serial=True)
self.pcomm = simplecomm.create_comm(serial=False)
self.size = MPI_COMM_WORLD.Get_size()
self.rank = MPI_COMM_WORLD.Get_rank()
def __init__(self,
specifier,
serial=False,
verbosity=1,
skip_existing=False,
overwrite=False,
once=False,
simplecomm=None):
"""
Constructor
Parameters:
specifier (Specifier): An instance of the Specifier class,
defining the input specification for this reshaper operation.
Keyword Arguments:
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
skip_existing (bool): Flag specifying whether to skip the generation
of time-series for variables with time-series files that already
exist. Default is False.
overwrite (bool): Flag specifying whether to forcefully overwrite
output files if they already exist. Default is False.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Type checking (or double-checking)
if not isinstance(specifier, Specifier):
err_msg = "Input must be given in the form of a Specifier object"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(skip_existing) is not bool:
err_msg = "Skip_existing flag must be True or False."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not (isinstance(simplecomm, SimpleComm) or \
isinstance(simplecomm, SimpleCommMPI)):
err_msg = (
"Simple communicator object is not a SimpleComm or ",
"SimpleCommMPI")
raise TypeError(err_msg)
# Whether to write a once file
self._use_once_file = once
# Internal timer data
self._timer = TimeKeeper()
# Dictionary storing read/write data amounts
self.assumed_block_size = float(4 * 1024 * 1024)
self._byte_counts = {}
self._timer.start('Initializing Simple Communicator')
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Reference to the simple communicator
self._simplecomm = simplecomm
self._timer.stop('Initializing Simple Communicator')
# Contruct the print header
header = ''.join([
'[',
str(self._simplecomm.get_rank()), '/',
str(self._simplecomm.get_size()), '] '
])
# Reference to the verbose printer tool
self._vprint = VPrinter(header=header, verbosity=verbosity)
# Debug output starting
if self._simplecomm.is_manager():
self._vprint('Initializing Reshaper', verbosity=1)
# Validate the user input data
self._timer.start('Specifier Validation')
specifier.validate()
self._timer.stop('Specifier Validation')
if self._simplecomm.is_manager():
self._vprint('Specifier validated', verbosity=1)
# Setup PyNIO options (including disabling the default PreFill option)
opt = Nio.options()
opt.PreFill = False
# Determine the Format and CompressionLevel options
# from the NetCDF format string in the Specifier
if specifier.netcdf_format == 'netcdf':
opt.Format = 'Classic'
elif specifier.netcdf_format == 'netcdf4':
opt.Format = 'NetCDF4Classic'
opt.CompressionLevel = 0
elif specifier.netcdf_format == 'netcdf4c':
opt.Format = 'NetCDF4Classic'
opt.CompressionLevel = specifier.netcdf_deflate
if self._simplecomm.is_manager():
self._vprint('PyNIO compression level: {0}'.format(\
specifier.netcdf_deflate), verbosity=2)
self._nio_options = opt
if self._simplecomm.is_manager():
self._vprint('PyNIO options set', verbosity=2)
# Open all of the input files
self._timer.start('Open Input Files')
self._input_files = []
for filename in specifier.input_file_list:
self._input_files.append(Nio.open_file(filename, "r"))
self._timer.stop('Open Input Files')
if self._simplecomm.is_manager():
self._vprint('Input files opened', verbosity=2)
# Validate the input files themselves
self._timer.start('Input File Validation')
self._validate_input_files(specifier)
self._timer.stop('Input File Validation')
if self._simplecomm.is_manager():
self._vprint('Input files validated', verbosity=2)
# Sort the input files by time
self._timer.start('Sort Input Files')
self._sort_input_files_by_time(specifier)
self._timer.stop('Sort Input Files')
if self._simplecomm.is_manager():
self._vprint('Input files sorted', verbosity=2)
# Retrieve and sort the variables in each time-slice file
# (To determine if it is time-invariant metadata, time-variant
# metadata, or if it is a time-series variable)
self._timer.start('Sort Variables')
self._sort_variables(specifier)
self._timer.stop('Sort Variables')
if self._simplecomm.is_manager():
self._vprint('Variables sorted', verbosity=2)
# Validate the output files
self._timer.start('Output File Validation')
self._validate_output_files(specifier, skip_existing, overwrite)
self._timer.stop('Output File Validation')
if self._simplecomm.is_manager():
self._vprint('Output files validated', verbosity=2)
# Helpful debugging message
if self._simplecomm.is_manager():
self._vprint('Reshaper initialized.', verbosity=1)
# Sync before continuing..
self._simplecomm.sync()
def main(argv):
# Get command line stuff and store in a dictionary
s = 'tag= compset= esize= tslice= res= sumfile= indir= sumfiledir= mach= verbose jsonfile= mpi_enable maxnorm gmonly popens cumul regx= startMon= endMon= fIndex='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSum_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict = {}
# Defaults
opts_dict['tag'] = 'cesm2_0_beta08'
opts_dict['compset'] = 'F2000'
opts_dict['mach'] = 'cheyenne'
opts_dict['esize'] = 350
opts_dict['tslice'] = 1
opts_dict['res'] = 'f19_f19'
opts_dict['sumfile'] = 'ens.summary.nc'
opts_dict['indir'] = './'
opts_dict['sumfiledir'] = './'
opts_dict['jsonfile'] = 'exclude_empty.json'
opts_dict['verbose'] = False
opts_dict['mpi_enable'] = False
opts_dict['maxnorm'] = False
opts_dict['gmonly'] = True
opts_dict['popens'] = False
opts_dict['cumul'] = False
opts_dict['regx'] = 'test'
opts_dict['startMon'] = 1
opts_dict['endMon'] = 1
opts_dict['fIndex'] = 151
# This creates the dictionary of input arguments
opts_dict = pyEnsLib.getopt_parseconfig(opts, optkeys, 'ES', opts_dict)
verbose = opts_dict['verbose']
st = opts_dict['esize']
esize = int(st)
if not (opts_dict['tag'] and opts_dict['compset'] and opts_dict['mach']
or opts_dict['res']):
print 'Please specify --tag, --compset, --mach and --res options'
sys.exit()
# Now find file names in indir
input_dir = opts_dict['indir']
# The var list that will be excluded
ex_varlist = []
inc_varlist = []
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(not opts_dict['mpi_enable'])
if me.get_rank() == 0:
print 'Running pyEnsSum!'
if me.get_rank() == 0 and (verbose == True):
print opts_dict
print 'Ensemble size for summary = ', esize
exclude = False
if me.get_rank() == 0:
if opts_dict['jsonfile']:
inc_varlist = []
# Read in the excluded or included var list
ex_varlist, exclude = pyEnsLib.read_jsonlist(
opts_dict['jsonfile'], 'ES')
if exclude == False:
inc_varlist = ex_varlist
ex_varlist = []
# Read in the included var list
#inc_varlist=pyEnsLib.read_jsonlist(opts_dict['jsonfile'],'ES')
# Broadcast the excluded var list to each processor
#if opts_dict['mpi_enable']:
# ex_varlist=me.partition(ex_varlist,func=Duplicate(),involved=True)
# Broadcast the excluded var list to each processor
if opts_dict['mpi_enable']:
exclude = me.partition(exclude, func=Duplicate(), involved=True)
if exclude:
ex_varlist = me.partition(ex_varlist,
func=Duplicate(),
involved=True)
else:
inc_varlist = me.partition(inc_varlist,
func=Duplicate(),
involved=True)
in_files = []
if (os.path.exists(input_dir)):
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files = sorted(in_files_temp)
# Make sure we have enough
num_files = len(in_files)
if me.get_rank() == 0 and (verbose == True):
print 'Number of files in input directory = ', num_files
if (num_files < esize):
if me.get_rank() == 0 and (verbose == True):
print 'Number of files in input directory (',num_files,\
') is less than specified ensemble size of ', esize
sys.exit(2)
if (num_files > esize):
if me.get_rank() == 0 and (verbose == True):
print 'NOTE: Number of files in ', input_dir, \
'is greater than specified ensemble size of ', esize ,\
'\nwill just use the first ', esize, 'files'
else:
if me.get_rank() == 0:
print 'Input directory: ', input_dir, ' not found'
sys.exit(2)
if opts_dict['cumul']:
if opts_dict['regx']:
in_files_list = get_cumul_filelist(opts_dict, opts_dict['indir'],
opts_dict['regx'])
in_files = me.partition(in_files_list,
func=EqualLength(),
involved=True)
if me.get_rank() == 0 and (verbose == True):
print 'in_files=', in_files
# Open the files in the input directory
o_files = []
if me.get_rank() == 0 and opts_dict['verbose']:
print 'Input files are: '
print "\n".join(in_files)
#for i in in_files:
# print "in_files =",i
for onefile in in_files[0:esize]:
if (os.path.isfile(input_dir + '/' + onefile)):
o_files.append(Nio.open_file(input_dir + '/' + onefile, "r"))
else:
if me.get_rank() == 0:
print "COULD NOT LOCATE FILE " + input_dir + onefile + "! EXITING...."
sys.exit()
# Store dimensions of the input fields
if me.get_rank() == 0 and (verbose == True):
print "Getting spatial dimensions"
nlev = -1
nilev = -1
ncol = -1
nlat = -1
nlon = -1
lonkey = ''
latkey = ''
# Look at first file and get dims
input_dims = o_files[0].dimensions
ndims = len(input_dims)
for key in input_dims:
if key == "lev":
nlev = input_dims["lev"]
elif key == "ilev":
nilev = input_dims["ilev"]
elif key == "ncol":
ncol = input_dims["ncol"]
elif (key == "nlon") or (key == "lon"):
nlon = input_dims[key]
lonkey = key
elif (key == "nlat") or (key == "lat"):
nlat = input_dims[key]
latkey = key
if (nlev == -1):
if me.get_rank() == 0:
print "COULD NOT LOCATE valid dimension lev => EXITING...."
sys.exit()
if ((ncol == -1) and ((nlat == -1) or (nlon == -1))):
if me.get_rank() == 0:
print "Need either lat/lon or ncol => EXITING...."
sys.exit()
# Check if this is SE or FV data
if (ncol != -1):
is_SE = True
else:
is_SE = False
# Make sure all files have the same dimensions
if me.get_rank() == 0 and (verbose == True):
print "Checking dimensions across files...."
print 'lev = ', nlev
if (is_SE == True):
print 'ncol = ', ncol
else:
print 'nlat = ', nlat
print 'nlon = ', nlon
for count, this_file in enumerate(o_files):
input_dims = this_file.dimensions
if (is_SE == True):
if (nlev != int(input_dims["lev"])
or (ncol != int(input_dims["ncol"]))):
if me.get_rank() == 0:
print "Dimension mismatch between ", in_files[
0], 'and', in_files[0], '!!!'
sys.exit()
else:
if ( nlev != int(input_dims["lev"]) or ( nlat != int(input_dims[latkey]))\
or ( nlon != int(input_dims[lonkey]))):
if me.get_rank() == 0:
print "Dimension mismatch between ", in_files[
0], 'and', in_files[0], '!!!'
sys.exit()
# Get 2d vars, 3d vars and all vars (For now include all variables)
vars_dict_all = o_files[0].variables
# Remove the excluded variables (specified in json file) from variable dictionary
#print len(vars_dict_all)
if exclude:
vars_dict = vars_dict_all
for i in ex_varlist:
if i in vars_dict:
del vars_dict[i]
#Given an included var list, remove all float var that are not on the list
else:
vars_dict = vars_dict_all.copy()
for k, v in vars_dict_all.iteritems():
if (k not in inc_varlist) and (vars_dict_all[k].typecode() == 'f'):
#print vars_dict_all[k].typecode()
#print k
del vars_dict[k]
num_vars = len(vars_dict)
#print num_vars
#if me.get_rank() == 0:
# for k,v in vars_dict.iteritems():
# print 'vars_dict',k,vars_dict[k].typecode()
str_size = 0
d2_var_names = []
d3_var_names = []
num_2d = 0
num_3d = 0
# Which are 2d, which are 3d and max str_size
for k, v in vars_dict.iteritems():
var = k
vd = v.dimensions # all the variable's dimensions (names)
vr = v.rank # num dimension
vs = v.shape # dim values
is_2d = False
is_3d = False
if (is_SE == True): # (time, lev, ncol) or (time, ncol)
if ((vr == 2) and (vs[1] == ncol)):
is_2d = True
num_2d += 1
elif ((vr == 3) and (vs[2] == ncol and vs[1] == nlev)):
is_3d = True
num_3d += 1
else: # (time, lev, nlon, nlon) or (time, nlat, nlon)
if ((vr == 3) and (vs[1] == nlat and vs[2] == nlon)):
is_2d = True
num_2d += 1
elif ((vr == 4) and (vs[2] == nlat and vs[3] == nlon and
(vs[1] == nlev or vs[1] == nilev))):
is_3d = True
num_3d += 1
if (is_3d == True):
str_size = max(str_size, len(k))
d3_var_names.append(k)
elif (is_2d == True):
str_size = max(str_size, len(k))
d2_var_names.append(k)
#else:
# print 'var=',k
if me.get_rank() == 0 and (verbose == True):
print 'Number of variables found: ', num_3d + num_2d
print '3D variables: ' + str(num_3d) + ', 2D variables: ' + str(num_2d)
# Now sort these and combine (this sorts caps first, then lower case -
# which is what we want)
d2_var_names.sort()
d3_var_names.sort()
if esize < num_2d + num_3d:
if me.get_rank() == 0:
print "************************************************************************************************************************************"
print " Error: the total number of 3D and 2D variables " + str(
num_2d + num_3d
) + " is larger than the number of ensemble files " + str(esize)
print " Cannot generate ensemble summary file, please remove more variables from your included variable list,"
print " or add more varaibles in your excluded variable list!!!"
print "************************************************************************************************************************************"
sys.exit()
# All vars is 3d vars first (sorted), the 2d vars
all_var_names = list(d3_var_names)
all_var_names += d2_var_names
n_all_var_names = len(all_var_names)
#if me.get_rank() == 0 and (verbose == True):
# print 'num vars = ', n_all_var_names, '(3d = ', num_3d, ' and 2d = ', num_2d, ")"
# Create new summary ensemble file
this_sumfile = opts_dict["sumfile"]
if me.get_rank() == 0 and (verbose == True):
print "Creating ", this_sumfile, " ..."
if (me.get_rank() == 0 | opts_dict["popens"]):
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
opt = Nio.options()
opt.PreFill = False
opt.Format = 'NetCDF4Classic'
nc_sumfile = Nio.open_file(this_sumfile, 'w', options=opt)
# Set dimensions
if me.get_rank() == 0 and (verbose == True):
print "Setting dimensions ....."
if (is_SE == True):
nc_sumfile.create_dimension('ncol', ncol)
else:
nc_sumfile.create_dimension('nlat', nlat)
nc_sumfile.create_dimension('nlon', nlon)
nc_sumfile.create_dimension('nlev', nlev)
nc_sumfile.create_dimension('ens_size', esize)
nc_sumfile.create_dimension('nvars', num_3d + num_2d)
nc_sumfile.create_dimension('nvars3d', num_3d)
nc_sumfile.create_dimension('nvars2d', num_2d)
nc_sumfile.create_dimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
if me.get_rank() == 0 and (verbose == True):
print "Setting global attributes ....."
setattr(nc_sumfile, 'creation_date', now)
setattr(nc_sumfile, 'title', 'CAM verification ensemble summary file')
setattr(nc_sumfile, 'tag', opts_dict["tag"])
setattr(nc_sumfile, 'compset', opts_dict["compset"])
setattr(nc_sumfile, 'resolution', opts_dict["res"])
setattr(nc_sumfile, 'machine', opts_dict["mach"])
# Create variables
if me.get_rank() == 0 and (verbose == True):
print "Creating variables ....."
v_lev = nc_sumfile.create_variable("lev", 'f', ('nlev', ))
v_vars = nc_sumfile.create_variable("vars", 'S1',
('nvars', 'str_size'))
v_var3d = nc_sumfile.create_variable("var3d", 'S1',
('nvars3d', 'str_size'))
v_var2d = nc_sumfile.create_variable("var2d", 'S1',
('nvars2d', 'str_size'))
if not opts_dict['gmonly']:
if (is_SE == True):
v_ens_avg3d = nc_sumfile.create_variable(
"ens_avg3d", 'f', ('nvars3d', 'nlev', 'ncol'))
v_ens_stddev3d = nc_sumfile.create_variable(
"ens_stddev3d", 'f', ('nvars3d', 'nlev', 'ncol'))
v_ens_avg2d = nc_sumfile.create_variable(
"ens_avg2d", 'f', ('nvars2d', 'ncol'))
v_ens_stddev2d = nc_sumfile.create_variable(
"ens_stddev2d", 'f', ('nvars2d', 'ncol'))
else:
v_ens_avg3d = nc_sumfile.create_variable(
"ens_avg3d", 'f', ('nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_stddev3d = nc_sumfile.create_variable(
"ens_stddev3d", 'f', ('nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_avg2d = nc_sumfile.create_variable(
"ens_avg2d", 'f', ('nvars2d', 'nlat', 'nlon'))
v_ens_stddev2d = nc_sumfile.create_variable(
"ens_stddev2d", 'f', ('nvars2d', 'nlat', 'nlon'))
v_RMSZ = nc_sumfile.create_variable("RMSZ", 'f',
('nvars', 'ens_size'))
v_gm = nc_sumfile.create_variable("global_mean", 'f',
('nvars', 'ens_size'))
v_standardized_gm = nc_sumfile.create_variable("standardized_gm", 'f',
('nvars', 'ens_size'))
v_loadings_gm = nc_sumfile.create_variable('loadings_gm', 'f',
('nvars', 'nvars'))
v_mu_gm = nc_sumfile.create_variable('mu_gm', 'f', ('nvars', ))
v_sigma_gm = nc_sumfile.create_variable('sigma_gm', 'f', ('nvars', ))
v_sigma_scores_gm = nc_sumfile.create_variable('sigma_scores_gm', 'f',
('nvars', ))
# Assign vars, var3d and var2d
if me.get_rank() == 0 and (verbose == True):
print "Assigning vars, var3d, and var2d ....."
eq_all_var_names = []
eq_d3_var_names = []
eq_d2_var_names = []
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(d3_var_names)
for i in range(l_eq):
tt = list(d3_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(d2_var_names)
for i in range(l_eq):
tt = list(d2_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if me.get_rank() == 0 and (verbose == True):
print "Assigning time invariant metadata ....."
lev_data = vars_dict["lev"]
v_lev = lev_data
# Form ensembles, each missing one member; compute RMSZs and global means
#for each variable, we also do max norm also (currently done in pyStats)
tslice = opts_dict['tslice']
if not opts_dict['cumul']:
# Partition the var list
var3_list_loc = me.partition(d3_var_names,
func=EqualStride(),
involved=True)
var2_list_loc = me.partition(d2_var_names,
func=EqualStride(),
involved=True)
else:
var3_list_loc = d3_var_names
var2_list_loc = d2_var_names
# Calculate global means #
if me.get_rank() == 0 and (verbose == True):
print "Calculating global means ....."
if not opts_dict['cumul']:
gm3d, gm2d, var_list = pyEnsLib.generate_global_mean_for_summary(
o_files, var3_list_loc, var2_list_loc, is_SE, False, opts_dict)
if me.get_rank() == 0 and (verbose == True):
print "Finish calculating global means ....."
# Calculate RMSZ scores
if (not opts_dict['gmonly']) | (opts_dict['cumul']):
if me.get_rank() == 0 and (verbose == True):
print "Calculating RMSZ scores ....."
zscore3d, zscore2d, ens_avg3d, ens_stddev3d, ens_avg2d, ens_stddev2d, temp1, temp2 = pyEnsLib.calc_rmsz(
o_files, var3_list_loc, var2_list_loc, is_SE, opts_dict)
# Calculate max norm ensemble
if opts_dict['maxnorm']:
if me.get_rank() == 0 and (verbose == True):
print "Calculating max norm of ensembles ....."
pyEnsLib.calculate_maxnormens(opts_dict, var3_list_loc)
pyEnsLib.calculate_maxnormens(opts_dict, var2_list_loc)
if opts_dict['mpi_enable'] & (not opts_dict['popens']):
if not opts_dict['cumul']:
# Gather the 3d variable results from all processors to the master processor
slice_index = get_stride_list(len(d3_var_names), me)
# Gather global means 3d results
gm3d = gather_npArray(gm3d, me, slice_index,
(len(d3_var_names), len(o_files)))
if not opts_dict['gmonly']:
# Gather zscore3d results
zscore3d = gather_npArray(zscore3d, me, slice_index,
(len(d3_var_names), len(o_files)))
# Gather ens_avg3d and ens_stddev3d results
shape_tuple3d = get_shape(ens_avg3d.shape, len(d3_var_names),
me.get_rank())
ens_avg3d = gather_npArray(ens_avg3d, me, slice_index,
shape_tuple3d)
ens_stddev3d = gather_npArray(ens_stddev3d, me, slice_index,
shape_tuple3d)
# Gather 2d variable results from all processors to the master processor
slice_index = get_stride_list(len(d2_var_names), me)
# Gather global means 2d results
gm2d = gather_npArray(gm2d, me, slice_index,
(len(d2_var_names), len(o_files)))
var_list = gather_list(var_list, me)
if not opts_dict['gmonly']:
# Gather zscore2d results
zscore2d = gather_npArray(zscore2d, me, slice_index,
(len(d2_var_names), len(o_files)))
# Gather ens_avg3d and ens_stddev2d results
shape_tuple2d = get_shape(ens_avg2d.shape, len(d2_var_names),
me.get_rank())
ens_avg2d = gather_npArray(ens_avg2d, me, slice_index,
shape_tuple2d)
ens_stddev2d = gather_npArray(ens_stddev2d, me, slice_index,
shape_tuple2d)
else:
gmall = np.concatenate((temp1, temp2), axis=0)
gmall = pyEnsLib.gather_npArray_pop(
gmall, me,
(me.get_size(), len(d3_var_names) + len(d2_var_names)))
# Assign to file:
if me.get_rank() == 0 | opts_dict['popens']:
if not opts_dict['cumul']:
gmall = np.concatenate((gm3d, gm2d), axis=0)
if not opts_dict['gmonly']:
Zscoreall = np.concatenate((zscore3d, zscore2d), axis=0)
v_RMSZ[:, :] = Zscoreall[:, :]
if not opts_dict['gmonly']:
if (is_SE == True):
v_ens_avg3d[:, :, :] = ens_avg3d[:, :, :]
v_ens_stddev3d[:, :, :] = ens_stddev3d[:, :, :]
v_ens_avg2d[:, :] = ens_avg2d[:, :]
v_ens_stddev2d[:, :] = ens_stddev2d[:, :]
else:
v_ens_avg3d[:, :, :, :] = ens_avg3d[:, :, :, :]
v_ens_stddev3d[:, :, :, :] = ens_stddev3d[:, :, :, :]
v_ens_avg2d[:, :, :] = ens_avg2d[:, :, :]
v_ens_stddev2d[:, :, :] = ens_stddev2d[:, :, :]
else:
gmall_temp = np.transpose(gmall[:, :])
gmall = gmall_temp
mu_gm, sigma_gm, standardized_global_mean, loadings_gm, scores_gm = pyEnsLib.pre_PCA(
gmall, all_var_names, var_list, me)
v_gm[:, :] = gmall[:, :]
v_standardized_gm[:, :] = standardized_global_mean[:, :]
v_mu_gm[:] = mu_gm[:]
v_sigma_gm[:] = sigma_gm[:].astype(np.float32)
v_loadings_gm[:, :] = loadings_gm[:, :]
v_sigma_scores_gm[:] = scores_gm[:]
if me.get_rank() == 0:
print "All Done"
def main(argv):
# Get command line stuff and store in a dictionary
s = 'tag= compset= esize= tslice= res= sumfile= indir= sumfiledir= mach= verbose jsonfile= mpi_enable maxnorm gmonly popens cumul regx= startMon= endMon= fIndex= mpi_disable'
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSum_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict = {}
# Defaults
opts_dict['tag'] = 'cesm2_0'
opts_dict['compset'] = 'F2000climo'
opts_dict['mach'] = 'cheyenne'
opts_dict['esize'] = 350
opts_dict['tslice'] = 1
opts_dict['res'] = 'f19_f19'
opts_dict['sumfile'] = 'ens.summary.nc'
opts_dict['indir'] = './'
opts_dict['sumfiledir'] = './'
opts_dict['jsonfile'] = 'exclude_empty.json'
opts_dict['verbose'] = False
opts_dict['mpi_enable'] = True
opts_dict['mpi_disable'] = False
opts_dict['maxnorm'] = False
opts_dict['gmonly'] = True
opts_dict['popens'] = False
opts_dict['cumul'] = False
opts_dict['regx'] = 'test'
opts_dict['startMon'] = 1
opts_dict['endMon'] = 1
opts_dict['fIndex'] = 151
# This creates the dictionary of input arguments
opts_dict = pyEnsLib.getopt_parseconfig(opts, optkeys, 'ES', opts_dict)
verbose = opts_dict['verbose']
st = opts_dict['esize']
esize = int(st)
if opts_dict['popens'] == True:
print(
"ERROR: Please use pyEnsSumPop.py for a POP ensemble (not --popens) => EXITING...."
)
sys.exit()
if not (opts_dict['tag'] and opts_dict['compset'] and opts_dict['mach']
or opts_dict['res']):
print(
'ERROR: Please specify --tag, --compset, --mach and --res options => EXITING....'
)
sys.exit()
if opts_dict['mpi_disable'] == True:
opts_dict['mpi_enable'] = False
# Now find file names in indir
input_dir = opts_dict['indir']
# The var list that will be excluded
ex_varlist = []
inc_varlist = []
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(not opts_dict['mpi_enable'])
if me.get_rank() == 0:
print('STATUS: Running pyEnsSum.py')
if me.get_rank() == 0 and (verbose == True):
print(opts_dict)
print('STATUS: Ensemble size for summary = ', esize)
exclude = False
if me.get_rank() == 0:
if opts_dict['jsonfile']:
inc_varlist = []
# Read in the excluded or included var list
ex_varlist, exclude = pyEnsLib.read_jsonlist(
opts_dict['jsonfile'], 'ES')
if exclude == False:
inc_varlist = ex_varlist
ex_varlist = []
# Broadcast the excluded var list to each processor
if opts_dict['mpi_enable']:
exclude = me.partition(exclude, func=Duplicate(), involved=True)
if exclude:
ex_varlist = me.partition(ex_varlist,
func=Duplicate(),
involved=True)
else:
inc_varlist = me.partition(inc_varlist,
func=Duplicate(),
involved=True)
in_files = []
if (os.path.exists(input_dir)):
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files = sorted(in_files_temp)
# Make sure we have enough
num_files = len(in_files)
if me.get_rank() == 0 and (verbose == True):
print('VERBOSE: Number of files in input directory = ', num_files)
if (num_files < esize):
if me.get_rank() == 0 and (verbose == True):
print('VERBOSE: Number of files in input directory (',num_files,\
') is less than specified ensemble size of ', esize)
sys.exit(2)
if (num_files > esize):
if me.get_rank() == 0 and (verbose == True):
print('VERBOSE: Note that the number of files in ', input_dir, \
'is greater than specified ensemble size of ', esize ,\
'\nwill just use the first ', esize, 'files')
else:
if me.get_rank() == 0:
print('ERROR: Input directory: ', input_dir, ' not found')
sys.exit(2)
if opts_dict['cumul']:
if opts_dict['regx']:
in_files_list = get_cumul_filelist(opts_dict, opts_dict['indir'],
opts_dict['regx'])
in_files = me.partition(in_files_list,
func=EqualLength(),
involved=True)
if me.get_rank() == 0 and (verbose == True):
print('VERBOSE: in_files = ', in_files)
# Check full file names in input directory (don't open yet)
full_in_files = []
if me.get_rank() == 0 and opts_dict['verbose']:
print('VERBOSE: Input files are: ')
for onefile in in_files[0:esize]:
fname = input_dir + '/' + onefile
if me.get_rank() == 0 and opts_dict['verbose']:
print(fname)
if (os.path.isfile(fname)):
full_in_files.append(fname)
else:
if me.get_rank() == 0:
print("ERROR: Could not locate file ", fname,
" => EXITING....")
sys.exit()
#open just the first file
first_file = nc.Dataset(full_in_files[0], "r")
# Store dimensions of the input fields
if me.get_rank() == 0 and (verbose == True):
print("VERBOSE: Getting spatial dimensions")
nlev = -1
nilev = -1
ncol = -1
nlat = -1
nlon = -1
lonkey = ''
latkey = ''
# Look at first file and get dims
input_dims = first_file.dimensions
ndims = len(input_dims)
for key in input_dims:
if key == "lev":
nlev = len(input_dims["lev"])
elif key == "ilev":
nilev = len(input_dims["ilev"])
elif key == "ncol":
ncol = len(input_dims["ncol"])
elif (key == "nlon") or (key == "lon"):
nlon = len(input_dims[key])
lonkey = key
elif (key == "nlat") or (key == "lat"):
nlat = len(input_dims[key])
latkey = key
if (nlev == -1):
if me.get_rank() == 0:
print(
"ERROR: could not locate a valid dimension (lev) => EXITING...."
)
sys.exit()
if ((ncol == -1) and ((nlat == -1) or (nlon == -1))):
if me.get_rank() == 0:
print("ERROR: Need either lat/lon or ncol => EXITING....")
sys.exit()
# Check if this is SE or FV data
if (ncol != -1):
is_SE = True
else:
is_SE = False
# output dimensions
if me.get_rank() == 0 and (verbose == True):
print('lev = ', nlev)
if (is_SE == True):
print('ncol = ', ncol)
else:
print('nlat = ', nlat)
print('nlon = ', nlon)
# Get 2d vars, 3d vars and all vars (For now include all variables)
vars_dict_all = first_file.variables
# Remove the excluded variables (specified in json file) from variable dictionary
if exclude:
vars_dict = vars_dict_all
for i in ex_varlist:
if i in vars_dict:
del vars_dict[i]
#Given an included var list, remove all the variables that are not on the list
else:
vars_dict = vars_dict_all.copy()
for k, v in vars_dict_all.items():
if (k not in inc_varlist) and (vars_dict_all[k].typecode() == 'f'):
del vars_dict[k]
num_vars = len(vars_dict)
str_size = 0
d2_var_names = []
d3_var_names = []
num_2d = 0
num_3d = 0
# Which are 2d, which are 3d and max str_size
for k, v in vars_dict.items():
var = k
vd = v.dimensions # all the variable's dimensions (names)
vr = len(v.dimensions) # num dimension
vs = v.shape # dim values
is_2d = False
is_3d = False
if (is_SE == True): # (time, lev, ncol) or (time, ncol)
if ((vr == 2) and (vs[1] == ncol)):
is_2d = True
num_2d += 1
elif ((vr == 3) and (vs[2] == ncol and vs[1] == nlev)):
is_3d = True
num_3d += 1
else: # (time, lev, nlon, nlon) or (time, nlat, nlon)
if ((vr == 3) and (vs[1] == nlat and vs[2] == nlon)):
is_2d = True
num_2d += 1
elif ((vr == 4) and (vs[2] == nlat and vs[3] == nlon and
(vs[1] == nlev or vs[1] == nilev))):
is_3d = True
num_3d += 1
if (is_3d == True):
str_size = max(str_size, len(k))
d3_var_names.append(k)
elif (is_2d == True):
str_size = max(str_size, len(k))
d2_var_names.append(k)
if me.get_rank() == 0 and (verbose == True):
print('VERBOSE: Number of variables found: ', num_3d + num_2d)
print('VERBOSE: 3D variables: ' + str(num_3d) + ', 2D variables: ' +
str(num_2d))
# Now sort these and combine (this sorts caps first, then lower case -
# which is what we want)
d2_var_names.sort()
d3_var_names.sort()
if esize < num_2d + num_3d:
if me.get_rank() == 0:
print(
"************************************************************************************************************************************"
)
print(" ERROR: the total number of 3D and 2D variables " +
str(num_2d + num_3d) +
" is larger than the number of ensemble files " + str(esize))
print(
" Cannot generate ensemble summary file, please remove more variables from your included variable list,"
)
print(
" or add more variables in your excluded variable list => EXITING...."
)
print(
"************************************************************************************************************************************"
)
sys.exit()
# All vars is 3d vars first (sorted), the 2d vars
all_var_names = list(d3_var_names)
all_var_names += d2_var_names
n_all_var_names = len(all_var_names)
# Rank 0 - Create new summary ensemble file
this_sumfile = opts_dict["sumfile"]
#check if directory is valid
sum_dir = os.path.dirname(this_sumfile)
if len(sum_dir) == 0:
sum_dir = '.'
if (os.path.exists(sum_dir) == False):
if me.get_rank() == 0:
print('ERROR: Summary file directory: ', sum_dir, ' not found')
sys.exit(2)
if (me.get_rank() == 0):
if (verbose == True):
print("VERBOSE: Creating ", this_sumfile, " ...")
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
nc_sumfile = nc.Dataset(this_sumfile, "w", format="NETCDF4_CLASSIC")
# Set dimensions
if (verbose == True):
print("VERBOSE: Setting dimensions .....")
if (is_SE == True):
nc_sumfile.createDimension('ncol', ncol)
else:
nc_sumfile.createDimension('nlat', nlat)
nc_sumfile.createDimension('nlon', nlon)
nc_sumfile.createDimension('nlev', nlev)
nc_sumfile.createDimension('ens_size', esize)
nc_sumfile.createDimension('nvars', num_3d + num_2d)
nc_sumfile.createDimension('nvars3d', num_3d)
nc_sumfile.createDimension('nvars2d', num_2d)
nc_sumfile.createDimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
if (verbose == True):
print("VERBOSE: Setting global attributes .....")
nc_sumfile.creation_date = now
nc_sumfile.title = 'CAM verification ensemble summary file'
nc_sumfile.tag = opts_dict["tag"]
nc_sumfile.compset = opts_dict["compset"]
nc_sumfile.resolution = opts_dict["res"]
nc_sumfile.machine = opts_dict["mach"]
# Create variables
if (verbose == True):
print("VERBOSE: Creating variables .....")
v_lev = nc_sumfile.createVariable("lev", 'f8', ('nlev', ))
v_vars = nc_sumfile.createVariable("vars", 'S1', ('nvars', 'str_size'))
v_var3d = nc_sumfile.createVariable("var3d", 'S1',
('nvars3d', 'str_size'))
v_var2d = nc_sumfile.createVariable("var2d", 'S1',
('nvars2d', 'str_size'))
v_gm = nc_sumfile.createVariable("global_mean", 'f8',
('nvars', 'ens_size'))
v_standardized_gm = nc_sumfile.createVariable("standardized_gm", 'f8',
('nvars', 'ens_size'))
v_loadings_gm = nc_sumfile.createVariable('loadings_gm', 'f8',
('nvars', 'nvars'))
v_mu_gm = nc_sumfile.createVariable('mu_gm', 'f8', ('nvars', ))
v_sigma_gm = nc_sumfile.createVariable('sigma_gm', 'f8', ('nvars', ))
v_sigma_scores_gm = nc_sumfile.createVariable('sigma_scores_gm', 'f8',
('nvars', ))
# Assign vars, var3d and var2d
if (verbose == True):
print("VERBOSE: Assigning vars, var3d, and var2d .....")
eq_all_var_names = []
eq_d3_var_names = []
eq_d2_var_names = []
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(d3_var_names)
for i in range(l_eq):
tt = list(d3_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(d2_var_names)
for i in range(l_eq):
tt = list(d2_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if (verbose == True):
print("VERBOSE: Assigning time invariant metadata .....")
# lev_data = np.zeros(num_lev,dtype=np.float64)
lev_data = first_file.variables["lev"]
v_lev[:] = lev_data[:]
#end of rank=0 work
# All:
tslice = opts_dict['tslice']
if not opts_dict['cumul']:
# Partition the var list
var3_list_loc = me.partition(d3_var_names,
func=EqualStride(),
involved=True)
var2_list_loc = me.partition(d2_var_names,
func=EqualStride(),
involved=True)
else:
var3_list_loc = d3_var_names
var2_list_loc = d2_var_names
#close first_file
first_file.close()
# Calculate global means #
if me.get_rank() == 0 and (verbose == True):
print("VERBOSE: Calculating global means .....")
if not opts_dict['cumul']:
gm3d, gm2d, var_list = pyEnsLib.generate_global_mean_for_summary(
full_in_files, var3_list_loc, var2_list_loc, is_SE, False,
opts_dict)
if me.get_rank() == 0 and (verbose == True):
print("VERBOSE: Finished calculating global means .....")
#gather to rank = 0
if opts_dict['mpi_enable']:
if not opts_dict['cumul']:
# Gather the 3d variable results from all processors to the master processor
slice_index = get_stride_list(len(d3_var_names), me)
# Gather global means 3d results
gm3d = gather_npArray(gm3d, me, slice_index,
(len(d3_var_names), len(full_in_files)))
# Gather 2d variable results from all processors to the master processor
slice_index = get_stride_list(len(d2_var_names), me)
# Gather global means 2d results
gm2d = gather_npArray(gm2d, me, slice_index,
(len(d2_var_names), len(full_in_files)))
#gather variables ro exclude (in pre_pca)
var_list = gather_list(var_list, me)
else:
gmall = np.concatenate((temp1, temp2), axis=0)
gmall = pyEnsLib.gather_npArray_pop(
gmall, me,
(me.get_size(), len(d3_var_names) + len(d2_var_names)))
# rank =0 : complete calculations for summary file
if me.get_rank() == 0:
if not opts_dict['cumul']:
gmall = np.concatenate((gm3d, gm2d), axis=0)
else:
gmall_temp = np.transpose(gmall[:, :])
gmall = gmall_temp
#PCA prep and calculation
mu_gm, sigma_gm, standardized_global_mean, loadings_gm, scores_gm, b_exit = pyEnsLib.pre_PCA(
gmall, all_var_names, var_list, me)
#if PCA calc encounters an error, then remove the summary file and exit
if b_exit:
nc_sumfile.close()
os.unlink(this_sumfile)
print("STATUS: Summary could not be created.")
sys.exit(2)
v_gm[:, :] = gmall[:, :]
v_standardized_gm[:, :] = standardized_global_mean[:, :]
v_mu_gm[:] = mu_gm[:]
v_sigma_gm[:] = sigma_gm[:]
v_loadings_gm[:, :] = loadings_gm[:, :]
v_sigma_scores_gm[:] = scores_gm[:]
print("STATUS: Summary file is complete.")
nc_sumfile.close()
def main(argv):
# Get command line stuff and store in a dictionary
s = 'nyear= nmonth= npert= tag= res= mach= compset= sumfile= indir= tslice= verbose jsonfile= mpi_enable mpi_disable nrand= rand seq= jsondir= esize='
optkeys = s.split()
try:
opts, args = getopt.getopt(argv, "h", optkeys)
except getopt.GetoptError:
pyEnsLib.EnsSumPop_usage()
sys.exit(2)
# Put command line options in a dictionary - also set defaults
opts_dict = {}
# Defaults
opts_dict['tag'] = 'cesm2_1_0'
opts_dict['compset'] = 'G'
opts_dict['mach'] = 'cheyenne'
opts_dict['tslice'] = 0
opts_dict['nyear'] = 1
opts_dict['nmonth'] = 12
opts_dict['esize'] = 40
opts_dict['npert'] = 0 #for backwards compatible
opts_dict['nbin'] = 40
opts_dict['minrange'] = 0.0
opts_dict['maxrange'] = 4.0
opts_dict['res'] = 'T62_g17'
opts_dict['sumfile'] = 'pop.ens.summary.nc'
opts_dict['indir'] = './'
opts_dict['jsonfile'] = 'pop_ensemble.json'
opts_dict['verbose'] = True
opts_dict['mpi_enable'] = True
opts_dict['mpi_disable'] = False
#opts_dict['zscoreonly'] = True
opts_dict['popens'] = True
opts_dict['nrand'] = 40
opts_dict['rand'] = False
opts_dict['seq'] = 0
opts_dict['jsondir'] = './'
# This creates the dictionary of input arguments
#print "before parseconfig"
opts_dict = pyEnsLib.getopt_parseconfig(opts, optkeys, 'ESP', opts_dict)
verbose = opts_dict['verbose']
nbin = opts_dict['nbin']
if opts_dict['mpi_disable']:
opts_dict['mpi_enable'] = False
#still have npert for backwards compatibility - check if it was set
#and override esize
if opts_dict['npert'] > 0:
user_size = opts_dict['npert']
print(
'WARNING: User specified value for --npert will override --esize. Please consider using --esize instead of --npert in the future.'
)
opts_dict['esize'] = user_size
# Now find file names in indir
input_dir = opts_dict['indir']
# Create a mpi simplecomm object
if opts_dict['mpi_enable']:
me = simplecomm.create_comm()
else:
me = simplecomm.create_comm(False)
if opts_dict['jsonfile']:
# Read in the included var list
Var2d, Var3d = pyEnsLib.read_jsonlist(opts_dict['jsonfile'], 'ESP')
str_size = 0
for str in Var3d:
if str_size < len(str):
str_size = len(str)
for str in Var2d:
if str_size < len(str):
str_size = len(str)
if me.get_rank() == 0:
print('STATUS: Running pyEnsSumPop!')
if verbose:
print("VERBOSE: opts_dict = ")
print(opts_dict)
in_files = []
if (os.path.exists(input_dir)):
# Pick up the 'nrand' random number of input files to generate summary files
if opts_dict['rand']:
in_files = pyEnsLib.Random_pickup_pop(input_dir, opts_dict,
opts_dict['nrand'])
else:
# Get the list of files
in_files_temp = os.listdir(input_dir)
in_files = sorted(in_files_temp)
num_files = len(in_files)
else:
if me.get_rank() == 0:
print('ERROR: Input directory: ', input_dir,
' not found => EXITING....')
sys.exit(2)
#make sure we have enough files
files_needed = opts_dict['nmonth'] * opts_dict['esize'] * opts_dict['nyear']
if (num_files < files_needed):
if me.get_rank() == 0:
print(
'ERROR: Input directory does not contain enough files (must be esize*nyear*nmonth = ',
files_needed, ' ) and it has only ', num_files, ' files).')
sys.exit(2)
#Partition the input file list (ideally we have one processor per month)
in_file_list = me.partition(in_files, func=EqualStride(), involved=True)
# Check the files in the input directory
full_in_files = []
if me.get_rank() == 0 and opts_dict['verbose']:
print('VERBOSE: Input files are:')
for onefile in in_file_list:
fname = input_dir + '/' + onefile
if opts_dict['verbose']:
print("my_rank = ", me.get_rank(), " ", fname)
if (os.path.isfile(fname)):
full_in_files.append(fname)
else:
print("ERROR: Could not locate file: " + fname + " => EXITING....")
sys.exit()
#open just the first file (all procs)
first_file = nc.Dataset(full_in_files[0], "r")
# Store dimensions of the input fields
if (verbose == True) and me.get_rank() == 0:
print("VERBOSE: Getting spatial dimensions")
nlev = -1
nlat = -1
nlon = -1
# Look at first file and get dims
input_dims = first_file.dimensions
ndims = len(input_dims)
# Make sure all files have the same dimensions
if (verbose == True) and me.get_rank() == 0:
print("VERBOSE: Checking dimensions ...")
for key in input_dims:
if key == "z_t":
nlev = len(input_dims["z_t"])
elif key == "nlon":
nlon = len(input_dims["nlon"])
elif key == "nlat":
nlat = len(input_dims["nlat"])
# Rank 0: prepare new summary ensemble file
this_sumfile = opts_dict["sumfile"]
if (me.get_rank() == 0):
if os.path.exists(this_sumfile):
os.unlink(this_sumfile)
if verbose:
print("VERBOSE: Creating ", this_sumfile, " ...")
nc_sumfile = nc.Dataset(this_sumfile, "w", format="NETCDF4_CLASSIC")
# Set dimensions
if verbose:
print("VERBOSE: Setting dimensions .....")
nc_sumfile.createDimension('nlat', nlat)
nc_sumfile.createDimension('nlon', nlon)
nc_sumfile.createDimension('nlev', nlev)
nc_sumfile.createDimension('time', None)
nc_sumfile.createDimension('ens_size', opts_dict['esize'])
nc_sumfile.createDimension('nbin', opts_dict['nbin'])
nc_sumfile.createDimension('nvars', len(Var3d) + len(Var2d))
nc_sumfile.createDimension('nvars3d', len(Var3d))
nc_sumfile.createDimension('nvars2d', len(Var2d))
nc_sumfile.createDimension('str_size', str_size)
# Set global attributes
now = time.strftime("%c")
if verbose:
print("VERBOSE: Setting global attributes .....")
nc_sumfile.creation_date = now
nc_sumfile.title = 'POP verification ensemble summary file'
nc_sumfile.tag = opts_dict["tag"]
nc_sumfile.compset = opts_dict["compset"]
nc_sumfile.resolution = opts_dict["res"]
nc_sumfile.machine = opts_dict["mach"]
# Create variables
if verbose:
print("VERBOSE: Creating variables .....")
v_lev = nc_sumfile.createVariable("z_t", 'f', ('nlev', ))
v_vars = nc_sumfile.createVariable("vars", 'S1', ('nvars', 'str_size'))
v_var3d = nc_sumfile.createVariable("var3d", 'S1',
('nvars3d', 'str_size'))
v_var2d = nc_sumfile.createVariable("var2d", 'S1',
('nvars2d', 'str_size'))
v_time = nc_sumfile.createVariable("time", 'd', ('time', ))
v_ens_avg3d = nc_sumfile.createVariable(
"ens_avg3d", 'f', ('time', 'nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_stddev3d = nc_sumfile.createVariable(
"ens_stddev3d", 'f', ('time', 'nvars3d', 'nlev', 'nlat', 'nlon'))
v_ens_avg2d = nc_sumfile.createVariable(
"ens_avg2d", 'f', ('time', 'nvars2d', 'nlat', 'nlon'))
v_ens_stddev2d = nc_sumfile.createVariable(
"ens_stddev2d", 'f', ('time', 'nvars2d', 'nlat', 'nlon'))
v_RMSZ = nc_sumfile.createVariable(
"RMSZ", 'f', ('time', 'nvars', 'ens_size', 'nbin'))
# Assign vars, var3d and var2d
if verbose:
print("VERBOSE: Assigning vars, var3d, and var2d .....")
eq_all_var_names = []
eq_d3_var_names = []
eq_d2_var_names = []
all_var_names = list(Var3d)
all_var_names += Var2d
l_eq = len(all_var_names)
for i in range(l_eq):
tt = list(all_var_names[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_all_var_names.append(tt)
l_eq = len(Var3d)
for i in range(l_eq):
tt = list(Var3d[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d3_var_names.append(tt)
l_eq = len(Var2d)
for i in range(l_eq):
tt = list(Var2d[i])
l_tt = len(tt)
if (l_tt < str_size):
extra = list(' ') * (str_size - l_tt)
tt.extend(extra)
eq_d2_var_names.append(tt)
v_vars[:] = eq_all_var_names[:]
v_var3d[:] = eq_d3_var_names[:]
v_var2d[:] = eq_d2_var_names[:]
# Time-invarient metadata
if verbose:
print("VERBOSE: Assigning time invariant metadata .....")
vars_dict = first_file.variables
lev_data = vars_dict["z_t"]
v_lev[:] = lev_data[:]
#end of rank 0
#All:
# Time-varient metadata
if verbose:
if me.get_rank() == 0:
print("VERBOSE: Assigning time variant metadata .....")
vars_dict = first_file.variables
time_value = vars_dict['time']
time_array = np.array([time_value])
time_array = pyEnsLib.gather_npArray_pop(time_array, me, (me.get_size(), ))
if me.get_rank() == 0:
v_time[:] = time_array[:]
#Assign zero values to first time slice of RMSZ and avg and stddev for 2d & 3d
#in case of a calculation problem before finishing
e_size = opts_dict['esize']
b_size = opts_dict['nbin']
z_ens_avg3d = np.zeros((len(Var3d), nlev, nlat, nlon), dtype=np.float32)
z_ens_stddev3d = np.zeros((len(Var3d), nlev, nlat, nlon), dtype=np.float32)
z_ens_avg2d = np.zeros((len(Var2d), nlat, nlon), dtype=np.float32)
z_ens_stddev2d = np.zeros((len(Var2d), nlat, nlon), dtype=np.float32)
z_RMSZ = np.zeros(((len(Var3d) + len(Var2d)), e_size, b_size),
dtype=np.float32)
#rank 0 (put zero values in summary file)
if me.get_rank() == 0:
v_RMSZ[0, :, :, :] = z_RMSZ[:, :, :]
v_ens_avg3d[0, :, :, :, :] = z_ens_avg3d[:, :, :, :]
v_ens_stddev3d[0, :, :, :, :] = z_ens_stddev3d[:, :, :, :]
v_ens_avg2d[0, :, :, :] = z_ens_avg2d[:, :, :]
v_ens_stddev2d[0, :, :, :] = z_ens_stddev2d[:, :, :]
#close file[0]
first_file.close()
# Calculate RMSZ scores
if (verbose == True and me.get_rank() == 0):
print("VERBOSE: Calculating RMSZ scores .....")
zscore3d, zscore2d, ens_avg3d, ens_stddev3d, ens_avg2d, ens_stddev2d = pyEnsLib.calc_rmsz(
full_in_files, Var3d, Var2d, opts_dict)
if (verbose == True and me.get_rank() == 0):
print("VERBOSE: Finished with RMSZ scores .....")
# Collect from all processors
if opts_dict['mpi_enable']:
# Gather the 3d variable results from all processors to the master processor
zmall = np.concatenate((zscore3d, zscore2d), axis=0)
zmall = pyEnsLib.gather_npArray_pop(
zmall, me,
(me.get_size(), len(Var3d) + len(Var2d), len(full_in_files), nbin))
ens_avg3d = pyEnsLib.gather_npArray_pop(
ens_avg3d, me, (me.get_size(), len(Var3d), nlev, (nlat), nlon))
ens_avg2d = pyEnsLib.gather_npArray_pop(ens_avg2d, me,
(me.get_size(), len(Var2d),
(nlat), nlon))
ens_stddev3d = pyEnsLib.gather_npArray_pop(
ens_stddev3d, me, (me.get_size(), len(Var3d), nlev, (nlat), nlon))
ens_stddev2d = pyEnsLib.gather_npArray_pop(ens_stddev2d, me,
(me.get_size(), len(Var2d),
(nlat), nlon))
# Assign to summary file:
if me.get_rank() == 0:
v_RMSZ[:, :, :, :] = zmall[:, :, :, :]
v_ens_avg3d[:, :, :, :, :] = ens_avg3d[:, :, :, :, :]
v_ens_stddev3d[:, :, :, :, :] = ens_stddev3d[:, :, :, :, :]
v_ens_avg2d[:, :, :, :] = ens_avg2d[:, :, :, :]
v_ens_stddev2d[:, :, :, :] = ens_stddev2d[:, :, :, :]
print("STATUS: PyEnsSumPop has completed.")
nc_sumfile.close()
def __init__(self, specifier, serial=False, verbosity=1, wmode='w', once=False, simplecomm=None):
"""
Constructor
Parameters:
specifier (Specifier): An instance of the Specifier class,
defining the input specification for this reshaper operation.
serial (bool): True or False, indicating whether the operation
should be performed in serial (True) or parallel
(False). The default is to assume parallel operation
(but serial will be chosen if the mpi4py cannot be
found when trying to initialize decomposition.
verbosity(int): Level of printed output (stdout). A value of 0
means no output, and a higher value means more output. The
default value is 1.
wmode (str): The mode to use for writing output. Can be 'w' for
normal write operation, 's' to skip the output generation for
existing time-series files, 'o' to overwrite existing
time-series files, 'a' to append to existing time-series files.
once (bool): True or False, indicating whether the Reshaper should
write all metadata to a 'once' file (separately).
simplecomm (SimpleComm): A SimpleComm object to handle the parallel
communication, if necessary
"""
# Type checking (or double-checking)
if not isinstance(specifier, Specifier):
err_msg = "Input must be given in the form of a Specifier object"
raise TypeError(err_msg)
if type(serial) is not bool:
err_msg = "Serial indicator must be True or False."
raise TypeError(err_msg)
if type(verbosity) is not int:
err_msg = "Verbosity level must be an integer."
raise TypeError(err_msg)
if type(wmode) is not str:
err_msg = "Write mode flag must be a str."
raise TypeError(err_msg)
if type(once) is not bool:
err_msg = "Once-file indicator must be True or False."
raise TypeError(err_msg)
if simplecomm is not None:
if not isinstance(simplecomm, SimpleComm):
err_msg = "Simple communicator object is not a SimpleComm"
raise TypeError(err_msg)
if wmode not in ['w', 's', 'o', 'a']:
err_msg = "Write mode '{0}' not recognized".format(wmode)
raise ValueError(err_msg)
# Whether to write a once file
self._use_once_file = once
# The output write mode to use
self._write_mode = wmode
# Internal timer data
self._timer = TimeKeeper()
self._timer.start('Initializing Simple Communicator')
if simplecomm is None:
simplecomm = create_comm(serial=serial)
# Reference to the simple communicator
self._simplecomm = simplecomm
self._timer.stop('Initializing Simple Communicator')
# Dictionary storing read/write data amounts
self.assumed_block_size = float(4 * 1024 * 1024)
self._byte_counts = {}
# Contruct the print header
header = ''.join(['[', str(self._simplecomm.get_rank()),
'/', str(self._simplecomm.get_size()), '] '])
# Reference to the verbose printer tool
self._vprint = VPrinter(header=header, verbosity=verbosity)
# Debug output starting
if self._simplecomm.is_manager():
self._vprint('Initializing Reshaper...', verbosity=0)
self._vprint(' MPI Communicator Size: {}'.format(
self._simplecomm.get_size()), verbosity=1)
# Validate the user input data
self._timer.start('Specifier Validation')
specifier.validate()
self._timer.stop('Specifier Validation')
if self._simplecomm.is_manager():
self._vprint(' Specifier validated', verbosity=1)
# The I/O backend to use
if iobackend.is_available(specifier.io_backend):
self._backend = specifier.io_backend
else:
self._backend = iobackend.get_backend()
self._vprint((' I/O Backend {0} not available. Using {1} '
'instead').format(specifier.io_backend, self._backend), verbosity=1)
# Store the input file names
self._input_filenames = specifier.input_file_list
# Store the time-series variable names
self._time_series_names = specifier.time_series
if self._time_series_names is not None:
vnames = ', '.join(self._time_series_names)
if self._simplecomm.is_manager():
self._vprint('WARNING: Extracting only variables: {0}'.format(
vnames), verbosity=-1)
# Store the list of metadata names
self._metadata_names = specifier.time_variant_metadata
# Store whether to treat 1D time-variant variables as metadata
self._1d_metadata = specifier.assume_1d_time_variant_metadata
# Store the metadata filename
self._metadata_filename = specifier.metadata_filename
# Store time invariant variables that should be excluded from the timeseries files
self._exclude_list = specifier.exclude_list
# Store the output file prefix and suffix
self._output_prefix = specifier.output_file_prefix
self._output_suffix = specifier.output_file_suffix
# Setup NetCDF file options
self._netcdf_format = specifier.netcdf_format
self._netcdf_compression = specifier.compression_level
self._netcdf_least_significant_digit = specifier.least_significant_digit
if self._simplecomm.is_manager():
self._vprint(
' NetCDF I/O Backend: {0}'.format(self._backend), verbosity=1)
self._vprint(' NetCDF Output Format: {0}'.format(
self._netcdf_format), verbosity=1)
self._vprint(' NetCDF Compression: {0}'.format(
self._netcdf_compression), verbosity=1)
trunc_str = ('{} decimal places'.format(self._netcdf_least_significant_digit)
if self._netcdf_least_significant_digit else 'Disabled')
self._vprint(' NetCDF Truncation: {0}'.format(
trunc_str), verbosity=1)
# Helpful debugging message
if self._simplecomm.is_manager():
self._vprint('...Reshaper initialized.', verbosity=0)
# Sync before continuing..
self._simplecomm.sync()
#!/usr/bin/env python
from asaptools import simplecomm
scomm = simplecomm.create_comm()
rank = scomm.get_rank()
size = scomm.get_size()
if scomm.is_manager():
l = range(10)
for i in l:
scomm.ration(i)
print '{0}/{1}: Sent {2!r}'.format(rank, size, i)
for i in range(scomm.get_size() - 1):
scomm.ration(None)
print '{0}/{1}: Sent None'.format(rank, size)
else:
i = -1
while i is not None:
i = scomm.ration()
print '{0}/{1}: Recvd {2!r}'.format(rank, size, i)
print '{0}/{1}: Out of loop'.format(rank, size)
scomm.sync()
if scomm.is_manager():