def _uv_plotting(df, lats, lons, data_type, analysis_use, bias_correction,
metadata, plot_type, outdir):
bias = 'adjusted' if bias_correction else 'unadjusted'
# Creates column name fo u and v data
u = f'u_{data_type}' if data_type in ['observation'] \
else f'u_{data_type}_{bias}'
v = f'v_{data_type}' if data_type in ['observation'] \
else f'v_{data_type}_{bias}'
if analysis_use:
data = {
'u': {
'assimilated': df['assimilated'][u].to_numpy(),
'rejected': df['rejected'][u].to_numpy(),
'monitored': df['monitored'][u].to_numpy()
},
'v': {
'assimilated': df['assimilated'][v].to_numpy(),
'rejected': df['rejected'][v].to_numpy(),
'monitored': df['monitored'][v].to_numpy()
}
}
else:
data = {
'u': df[u].to_numpy(),
'v': df[v].to_numpy(),
}
for key in data.keys():
metadata['Variable'] = key
if np.isin('histogram', plot_type):
plot_histogram(data[key], metadata, outdir)
if np.isin('spatial', plot_type):
plot_map(lats, lons, data[key], metadata, outdir)
def plotting(sat_config, diag_file, data_type, plot_type, outdir):
channel = sat_config['channel']
qcflag = sat_config['qc flag']
analysis_use = sat_config['analysis use'][0]
bias_correction = sat_config['bias correction'][0]
diag = Radiance(diag_file)
df = diag.get_data(channel=channel,
qcflag=qcflag,
analysis_use=analysis_use)
metadata = diag.metadata
metadata['Diag Type'] = data_type
bias = 'adjusted' if bias_correction else 'unadjusted'
column = f'{data_type}' if data_type in ['observation'] \
else f'{data_type}_{bias}'
if analysis_use:
lats = {
'assimilated': df['assimilated']['latitude'].to_numpy(),
'rejected': df['rejected']['latitude'].to_numpy(),
'monitored': df['monitored']['latitude'].to_numpy()
}
lons = {
'assimilated': df['assimilated']['longitude'].to_numpy(),
'rejected': df['rejected']['longitude'].to_numpy(),
'monitored': df['monitored']['longitude'].to_numpy()
}
data = {
'assimilated': df['assimilated'][column].to_numpy(),
'rejected': df['rejected'][column].to_numpy(),
'monitored': df['monitored'][column].to_numpy()
}
for key in data.keys():
data[key][data[key] > 1e5] = np.nan
else:
lats = df['latitude'].to_numpy()
lons = df['longitude'].to_numpy()
data = df[column].to_numpy()
data[data > 1e5] = np.nan
if np.isin('histogram', plot_type):
plot_histogram(data, metadata, outdir)
if np.isin('spatial', plot_type):
plot_map(lats, lons, data, metadata, outdir)
def plotting(sat_config, diag_file, data_type, plot_type, outdir):
analysis_use = ozone_config['analysis use'][0]
layer = ozone_config['layer'][0]
bias_correction = ozone_config['bias correction'][0]
diag = Ozone(diag_file)
df = diag.get_data(analysis_use=analysis_use)
metadata = diag.metadata
metadata['Diag Type'] = data_type
bias = 'adjusted' if bias_correction else 'unadjusted'
column = f'{data_type}' if data_type in ['observation'] \
else f'{data_type}_{bias}'
if layer == 0:
dict_key = 'column total'
else:
dict_key = list(df)[layer]
metadata['Layer'] = dict_key
if analysis_use:
lats = {
'assimilated': df[dict_key]['assimilated']['latitude'].to_numpy(),
'monitored': df[dict_key]['monitored']['latitude'].to_numpy()
}
lons = {
'assimilated': df[dict_key]['assimilated']['longitude'].to_numpy(),
'monitored': df[dict_key]['monitored']['longitude'].to_numpy()
}
data = {
'assimilated': df[dict_key]['assimilated'][column].to_numpy(),
'monitored': df[dict_key]['monitored'][column].to_numpy()
}
else:
lats = df[dict_key]['latitude'].to_numpy()
lons = df[dict_key]['longitude'].to_numpy()
data = df[dict_key][column].to_numpy()
if np.isin('histogram', plot_type):
plot_histogram(data, metadata, outdir)
if np.isin('spatial', plot_type):
plot_map(lats, lons, data, metadata, outdir)
def plotting_radiance(sat_config):
diagfile = sat_config['radiance input']['path'][0]
diag_type = sat_config['radiance input']['data type'][0].lower()
channel = sat_config['radiance input']['channel']
qcflag = sat_config['radiance input']['qc flag']
plot_type = sat_config['radiance input']['plot type']
outdir = sat_config['outdir']
diag = Radiance(diagfile)
data = diag.get_data(diag_type, channel=channel, qcflag=qcflag)
lats, lons = diag.get_lat_lon(channel=channel, qcflag=qcflag)
metadata = diag.metadata
if np.isin('histogram', plot_type):
plot_histogram(data, metadata, outdir)
if np.isin('spatial', plot_type):
plot_spatial(data, metadata, lats, lons, outdir)
return
def plotting(conv_config, diag_file, data_type, plot_type, outdir):
obsid = conv_config['observation id']
obsubtype = conv_config['observation subtype']
analysis_use = conv_config['analysis use'][0]
bias_correction = conv_config['bias correction'][0]
diag = Conventional(diag_file)
df = diag.get_data(obsid=obsid,
subtype=obsubtype,
analysis_use=analysis_use)
metadata = diag.metadata
metadata['Diag Type'] = data_type
bias = 'adjusted' if bias_correction else 'unadjusted'
column = f'{data_type}' if data_type in ['observation'] \
else f'{data_type}_{bias}'
if analysis_use:
lats = {
'assimilated': df['assimilated']['latitude'].to_numpy(),
'rejected': df['rejected']['latitude'].to_numpy(),
'monitored': df['monitored']['latitude'].to_numpy()
}
lons = {
'assimilated': df['assimilated']['longitude'].to_numpy(),
'rejected': df['rejected']['longitude'].to_numpy(),
'monitored': df['monitored']['longitude'].to_numpy()
}
if metadata['Obs Type'] == 'conv' and metadata['Variable'] == 'uv':
_uv_plotting(df, lats, lons, data_type, analysis_use, metadata,
plot_type, outdir)
else:
data = {
'assimilated': df['assimilated'][column].to_numpy(),
'rejected': df['rejected'][column].to_numpy(),
'monitored': df['monitored'][column].to_numpy()
}
if np.isin('histogram', plot_type):
plot_histogram(data, metadata, outdir)
if np.isin('spatial', plot_type):
plot_map(lats, lons, data, metadata, outdir)
else:
lats = df['latitude'].to_numpy()
lons = df['longitude'].to_numpy()
if metadata['Obs Type'] == 'conv' and metadata['Variable'] == 'uv':
_uv_plotting(df, lats, lons, data_type, analysis_use,
bias_correction, metadata, plot_type, outdir)
else:
data = df[column].to_numpy()
if np.isin('histogram', plot_type):
plot_histogram(data, metadata, outdir)
if np.isin('spatial', plot_type):
plot_map(lats, lons, data, metadata, outdir)
def plotting_conventional(conv_config):
diagfile = conv_config['conventional input']['path'][0]
diag_type = conv_config['conventional input']['data type'][0].lower()
obsid = conv_config['conventional input']['observation id']
analysis_use = conv_config['conventional input']['analysis use'][0]
plot_type = conv_config['conventional input']['plot type']
outdir = conv_config['outdir']
diag = Conventional(diagfile)
if analysis_use:
diag_components = diagfile.split('/')[-1].split('.')[0].split('_')
if diag_components[1] == 'conv' and diag_components[2] == 'uv':
u, v = diag.get_data(diag_type,
obsid=obsid,
analysis_use=analysis_use)
data = {
'assimilated': {
'u':
u['assimilated'],
'v':
v['assimilated'],
'windspeed':
np.sqrt(
np.square(u['assimilated']) +
np.square(v['assimilated']))
},
'monitored': {
'u':
u['monitored'],
'v':
v['monitored'],
'windspeed':
np.sqrt(
np.square(u['monitored']) + np.square(v['monitored']))
}
}
else:
data = diag.get_data(diag_type,
obsid=obsid,
analysis_use=analysis_use)
data = {
'assimilated': data['assimilated'],
'monitored': data['monitored']
}
lats, lons = diag.get_lat_lon(obsid=obsid, analysis_use=analysis_use)
metadata = diag.metadata
if np.isin('histogram', plot_type):
plot_histogram(data, metadata, outdir)
if np.isin('spatial', plot_type):
plot_spatial(data, metadata, lats, lons, outdir)
else:
diag_components = diagfile.split('/')[-1].split('.')[0].split('_')
if diag_components[1] == 'conv' and diag_components[2] == 'uv':
u, v = diag.get_data(diag_type,
obsid=obsid,
analysis_use=analysis_use)
data = {
'u': u,
'v': v,
'windspeed': np.sqrt(np.square(u) + np.square(v))
}
else:
data = diag.get_data(diag_type, obsid=obsid)
lats, lons = diag.get_lat_lon(obsid=obsid)
metadata = diag.metadata
if np.isin('histogram', plot_type):
plot_histogram(data, metadata, outdir)
if np.isin('spatial', plot_type):
plot_spatial(data, metadata, lats, lons, outdir)
return