def main(files, out):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(var='eng') # load engineering variables
# for nc in list_files:
# print nc
# the engine that xarray uses can be changed as specified here
# http://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html#xarray.open_dataset
with xr.open_mfdataset(list_files, engine='netcdf4') as ds_disk:
# change dimensions from 'obs' to 'time'
ds_disk = ds_disk.swap_dims({'obs': 'time'})
ds_variables = ds_disk.data_vars.keys() # List of dataset variables
stream = ds_disk.stream # List stream name associated with the data
title_pre = mk_str(ds_disk.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds_disk.attrs, 's') # , var, tt0, tt1, 's')
save_dir = os.path.join(out, ds_disk.subsite, ds_disk.node, ds_disk.stream, 'pcolor')
cf.create_dir(save_dir)
# t0, t1 = cf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = t0 + t1 - (t0 / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
# time_list = [[t0, t1]]
# for period in time_list:
# tt0 = period[0]
# tt1 = period[1]
# sub_ds = ds_disk.sel(time=slice(str(tt0), str(tt1)))
bins = ds_disk['bin_depths']
north = ds_disk['northward_seawater_velocity']
east = ds_disk['eastward_seawater_velocity']
# up = ds_disk['upward_seawater_velocity']
# error = ds_disk['error_velocity']
time = dict(data=ds_disk['time'].data, info=dict(label=ds_disk['time'].standard_name, units='GMT'))
bins = dict(data=bins.data.T, info=dict(label=bins.long_name, units=bins.units))
north = dict(data=north.data.T, info=dict(label=north.long_name, units=north.units))
east = dict(data=east.data.T, info=dict(label=east.long_name, units=east.units))
# up = dict(data=up.data.T, info=dict(label=up.long_name, units=up.units))
# error = dict(data=error.data.T, info=dict(label=error.long_name, units=error.units))
sname = save_pre + 'ADCP'
title = title_pre
fig, axs = pf.adcp(time, bins, north, east, title)
pf.resize(width=12, height=8.5) # Resize figure
pf.save_fig(save_dir, sname, res=250) # Save figure
plt.close('all')
def main(files, out, time_break, depth):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(var='eng') # load engineering variables
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
stream = ds.stream # List stream name associated with the data
title_pre = mk_str(ds.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds.attrs, 's') # , var, tt0, tt1, 's')
platform = ds.subsite
node = ds.node
sensor = ds.sensor
deployment = 'D0000{}'.format(str(np.unique(ds.deployment)[0]))
stream = ds.stream
save_dir = os.path.join(out, platform, deployment, node, sensor, stream, 'depth_profiles')
cf.create_dir(save_dir)
# try:
# eng = stream_vars[stream] # select specific streams engineering variables
# except KeyError:
# eng = ['']
misc = ['quality', 'string', 'timestamp', 'deployment', 'id', 'provenance', 'qc', 'time', 'mission', 'obs',
'volt', 'ref', 'sig', 'amp', 'rph', 'calphase', 'phase', 'therm']
# reg_ex = re.compile('|'.join(eng+misc)) # make regular expression
reg_ex = re.compile('|'.join(misc))
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
# t0, t1 = pf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = (pd.to_datetime(t0) + (pd.to_datetime(t1) - pd.to_datetime(t0)) / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
times = np.unique(ds[time_break])
for t in times:
time_ind = t == ds[time_break].data
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop('comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
y = dict(data=ds[depth].data[time_ind], info=dict(label='Pressure', units='dbar', var=var,
platform=platform, node=node, sensor=sensor))
try:
z_lab = sci.long_name
except AttributeError:
z_lab = sci.standard_name
z = dict(data=sci.data[time_ind], info=dict(label=z_lab, units=sci.units, var=var,
platform=platform, node=node, sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.depth_cross_section(z, y, x, title=title)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
# try:
# y_lab = sci.standard_name
# except AttributeError:
# y_lab = var
# y = dict(data=sci.data, info=dict(label=y_lab, units=sci.units))
del x, y
def main(files, out, east_var, north_var, up_var, err_var):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(var='eng') # load engineering variables
# for nc in list_files:
# print nc
# the engine that xarray uses can be changed as specified here
# http://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html#xarray.open_dataset
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds_disk:
#with xr.open_mfdataset(nc, engine='netcdf4') as ds_disk:
# change dimensions from 'obs' to 'time'
ds_disk = ds_disk.swap_dims({'obs': 'time'})
ds_variables = ds_disk.data_vars.keys() # List of dataset variables
stream = ds_disk.stream # List stream name associated with the data
deployment = 'D0000{}'.format(str(numpy.unique(ds_disk.deployment)[0]))
title_pre = mk_str(ds_disk.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds_disk.attrs, 's') # , var, tt0, tt1, 's')
save_dir = os.path.join(out, ds_disk.subsite, deployment, ds_disk.node, ds_disk.stream, 'pcolor')
cf.create_dir(save_dir)
# t0, t1 = cf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = t0 + t1 - (t0 / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
# time_list = [[t0, t1]]
# for period in time_list:
# tt0 = period[0]
# tt1 = period[1]
# sub_ds = ds_disk.sel(time=slice(str(tt0), str(tt1)))
north = ds_disk[north_var]
east = ds_disk[east_var]
up = ds_disk[up_var]
error = ds_disk[err_var]
try:
bins = ds_disk['bin_depths']
bins = dict(data=bins.data.T, info=dict(label=bins.long_name, units=bins.units))
except KeyError:
# use the matrix indices to plot
bins = numpy.zeros_like(east.data)
for i, item in enumerate(east):
for jj, xtem in enumerate(east[i]):
bins[i][jj] = jj
bins = numpy.reshape(bins,(bins.shape[-1],bins.shape[0]))
bins = dict(data=bins, label='bin_indices', units='')
# the correct way to do this is to calculate the bin_depths, for that you need:
# 9 First Cell Range(meters) (rounded bin_1_distance average, m)
# 73 deployment depth of the ADCP instrument (pull from asset-management, depth in m)
# 21 number of bins (num_cells, m)
# 4 cell length (cell_length, m)
# equation with the numbers above would be:
# depths = 73 - 9 - ([1:21]-1)*4;
time = dict(data=ds_disk['time'].data, info=dict(label=ds_disk['time'].standard_name, units='GMT'))
#bins = dict(data=bins.data.T, info=dict(label=bins.long_name, units=bins.units))
north = dict(data=north.data.T, info=dict(label=north.long_name, units=north.units))
east = dict(data=east.data.T, info=dict(label=east.long_name, units=east.units))
up = dict(data=up.data.T, info=dict(label=up.long_name, units=up.units))
error = dict(data=error.data.T, info=dict(label=error.long_name, units=error.units))
sname_ew = save_pre + 'E-W-ADCP'
title = title_pre
fig, axs = pf.adcp(time, bins, north, east, title)
pf.resize(width=12, height=8.5) # Resize figure
pf.save_fig(save_dir, sname_ew, res=250) # Save figure
sname_ur = save_pre + 'U-R-ADCP'
fig, axs = pf.adcp(time, bins, up, error, title)
pf.resize(width=12, height=8.5) # Resize figure
pf.save_fig(save_dir, sname_ur, res=250) # Save figure
plt.close('all')
def main(files, out):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(
var='eng') # load engineering variables
# for nc in list_files:
# print nc
# the engine that xarray uses can be changed as specified here
# http://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html#xarray.open_dataset
with xr.open_mfdataset(list_files, engine='netcdf4') as ds_disk:
# change dimensions from 'obs' to 'time'
ds_disk = ds_disk.swap_dims({'obs': 'time'})
ds_variables = ds_disk.data_vars.keys() # List of dataset variables
stream = ds_disk.stream # List stream name associated with the data
title_pre = mk_str(ds_disk.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds_disk.attrs, 's') # , var, tt0, tt1, 's')
save_dir = os.path.join(out, ds_disk.subsite, ds_disk.node,
ds_disk.stream, 'pcolor')
cf.create_dir(save_dir)
# t0, t1 = cf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = t0 + t1 - (t0 / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
# time_list = [[t0, t1]]
# for period in time_list:
# tt0 = period[0]
# tt1 = period[1]
# sub_ds = ds_disk.sel(time=slice(str(tt0), str(tt1)))
bins = ds_disk['bin_depths']
north = ds_disk['northward_seawater_velocity']
east = ds_disk['eastward_seawater_velocity']
# up = ds_disk['upward_seawater_velocity']
# error = ds_disk['error_velocity']
time = dict(data=ds_disk['time'].data,
info=dict(label=ds_disk['time'].standard_name,
units='GMT'))
bins = dict(data=bins.data.T,
info=dict(label=bins.long_name, units=bins.units))
north = dict(data=north.data.T,
info=dict(label=north.long_name, units=north.units))
east = dict(data=east.data.T,
info=dict(label=east.long_name, units=east.units))
# up = dict(data=up.data.T, info=dict(label=up.long_name, units=up.units))
# error = dict(data=error.data.T, info=dict(label=error.long_name, units=error.units))
sname = save_pre + 'ADCP'
title = title_pre
fig, axs = pf.adcp(time, bins, north, east, title)
pf.resize(width=12, height=8.5) # Resize figure
pf.save_fig(save_dir, sname, res=250) # Save figure
plt.close('all')
def main(files, out, time_break):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(
var='eng') # load engineering variables
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
stream = ds.stream # List stream name associated with the data
title_pre = mk_str(ds.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds.attrs, 's') # , var, tt0, tt1, 's')
platform = ds.subsite
node = ds.node
sensor = ds.sensor
save_dir = os.path.join(out, ds.subsite, ds.node, ds.stream,
'timeseries')
cf.create_dir(save_dir)
try:
eng = stream_vars[
stream] # select specific streams engineering variables
except KeyError:
eng = ['']
misc = [
'timestamp', 'provenance', 'qc', 'id', 'obs', 'deployment',
'volts', 'counts', 'quality_flag'
]
reg_ex = re.compile('|'.join(eng +
misc)) # make regular expression
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
# t0, t1 = pf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = (pd.to_datetime(t0) + (pd.to_datetime(t1) - pd.to_datetime(t0)) / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
times = np.unique(ds[time_break])
for t in times:
time_ind = t == ds[time_break].data
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(
x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(
x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop(
'comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
# define possible pressure variables
pressure_vars = [
'seawater_pressure', 'sci_water_pressure_dbar',
'ctdgv_m_glider_instrument_recovered-sci_water_pressure_dbar',
'ctdgv_m_glider_instrument-sci_water_pressure_dbar'
]
rePressure = re.compile('|'.join(pressure_vars))
# define y as pressure variable
pressure = [
s for s in sci.variables if rePressure.search(s)
]
pressure = ''.join(pressure)
y = sci.variables[pressure]
yN = pressure
y_units = sci.units
try:
y_lab = sci.long_name
except AttributeError:
y_lab = sci.standard_name
y = dict(data=sci.data[time_ind],
info=dict(label=y_lab,
units=sci.units,
var=var,
platform=platform,
node=node,
sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.auto_plot(x,
y,
title,
stdev=None,
line_style='r-o',
g_range=True)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
# plot z variable each time
fig, ax = pf.depth_cross_section(x,
y,
title,
stdev=1,
line_style='r-o',
g_range=True)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}_outliers_removed'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
del x, y
def main(files, out, time_break, depth, start, end, interactive):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(
var='eng') # load engineering variables
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
stream = ds.stream # List stream name associated with the data
title_pre = mk_str(ds.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds.attrs, 's') # , var, tt0, tt1, 's')
platform = ds.subsite
node = ds.node
sensor = ds.sensor
# save_dir = os.path.join(out,'xsection_depth_profiles')
save_dir = os.path.join(
out, ds.subsite, ds.subsite + '-' + ds.node + '-' + ds.sensor,
ds.stream, 'xsection_depth_profiles')
cf.create_dir(save_dir)
misc = [
'quality', 'string', 'timestamp', 'deployment', 'id',
'provenance', 'qc', 'time', 'mission', 'obs', 'volt', 'ref',
'sig', 'amp', 'rph', 'calphase', 'phase', 'therm', 'light'
]
reg_ex = re.compile('|'.join(misc))
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
if not time_break == None:
times = np.unique(ds[time_break])
for t in times:
time_ind = t == ds[time_break].data
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(
x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(
x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop(
'comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
y = dict(data=ds[depth].data[time_ind],
info=dict(label='Pressure',
units='dbar',
var=var,
platform=platform,
node=node,
sensor=sensor))
try:
z_lab = sci.long_name
except AttributeError:
z_lab = sci.standard_name
z = dict(data=sci.data[time_ind],
info=dict(label=z_lab,
units=str(sci.units),
var=var,
platform=platform,
node=node,
sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.depth_glider_cross_section(x,
y,
z,
title=title)
if interactive == True:
fig.canvas.mpl_connect(
'pick_event', lambda event: pf.onpick3(
event, x['data'], y['data'], z['data']))
plt.show()
else:
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name,
res=150) # Save figure
plt.close('all')
else:
ds = ds.sel(time=slice(start, end))
for var in sci_vars:
x = dict(data=ds['time'].data[:],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(
x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(
x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop(
'comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
y = dict(data=ds[depth].data[:],
info=dict(label='Pressure',
units='dbar',
var=var,
platform=platform,
node=node,
sensor=sensor))
try:
z_lab = sci.long_name
except AttributeError:
z_lab = sci.standard_name
z = dict(data=sci.data[:],
info=dict(label=z_lab,
units=sci.units,
var=var,
platform=platform,
node=node,
sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.depth_glider_cross_section(
x, y, z, title=title, interactive=interactive)
if interactive == True:
fig.canvas.mpl_connect(
'pick_event', lambda event: pf.onpick3(
event, x['data'], y['data'], z['data']))
plt.show()
else:
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name,
res=150) # Save figure
plt.close('all')
def main(files, out, time_break, depth, start, end, interactive):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(var='eng') # load engineering variables
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
stream = ds.stream # List stream name associated with the data
title_pre = mk_str(ds.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds.attrs, 's') # , var, tt0, tt1, 's')
platform = ds.subsite
node = ds.node
sensor = ds.sensor
# save_dir = os.path.join(out,'xsection_depth_profiles')
save_dir = os.path.join(out, ds.subsite, ds.subsite + '-' + ds.node + '-' + ds.sensor, ds.stream, 'xsection_depth_profiles')
cf.create_dir(save_dir)
misc = ['quality', 'string', 'timestamp', 'deployment', 'id', 'provenance', 'qc', 'time', 'mission', 'obs',
'volt', 'ref', 'sig', 'amp', 'rph', 'calphase', 'phase', 'therm', 'light']
reg_ex = re.compile('|'.join(misc))
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
if not time_break == None:
times = np.unique(ds[time_break])
for t in times:
time_ind = t == ds[time_break].data
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop('comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
y = dict(data=ds[depth].data[time_ind], info=dict(label='Pressure', units='dbar', var=var,
platform=platform, node=node, sensor=sensor))
try:
z_lab = sci.long_name
except AttributeError:
z_lab = sci.standard_name
z = dict(data=sci.data[time_ind], info=dict(label=z_lab, units=str(sci.units), var=var,
platform=platform, node=node, sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.depth_glider_cross_section(x, y, z, title=title)
if interactive == True:
fig.canvas.mpl_connect('pick_event', lambda event: pf.onpick3(event, x['data'], y['data'], z['data']))
plt.show()
else:
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
else:
ds = ds.sel(time=slice(start, end))
for var in sci_vars:
x = dict(data=ds['time'].data[:],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop('comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
y = dict(data=ds[depth].data[:], info=dict(label='Pressure', units='dbar', var=var,
platform=platform, node=node, sensor=sensor))
try:
z_lab = sci.long_name
except AttributeError:
z_lab = sci.standard_name
z = dict(data=sci.data[:], info=dict(label=z_lab, units=sci.units, var=var,
platform=platform, node=node, sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.depth_glider_cross_section(x, y, z, title=title, interactive=interactive)
if interactive == True:
fig.canvas.mpl_connect('pick_event', lambda event: pf.onpick3(event, x['data'], y['data'], z['data']))
plt.show()
else:
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
def main(nc, directory, out, time_break, breakdown):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
list_files = directory + "/*.nc"
# list_files = ['https://opendap.oceanobservatories.org/thredds/dodsC/ooi/friedrich-knuth-gmail/20170322T191659-RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample/deployment0003_RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample_20170312T000000.426102-20170322T190000.059973.nc',
# 'https://opendap.oceanobservatories.org/thredds/dodsC/ooi/friedrich-knuth-gmail/20170322T191659-RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample/deployment0003_RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample_20161222T000000.132709-20170311T235959.426096.nc']
# print list_files
stream_vars = pf.load_variable_dict(var='eng') # load engineering variables
with xr.open_dataset(nc, mask_and_scale=False) as ds_ncfile:
stream = ds_ncfile.stream # List stream name associated with the data
title_pre = mk_str(ds_ncfile.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds_ncfile.attrs, 's') # , var, tt0, tt1, 's')
platform = ds_ncfile.subsite
node = ds_ncfile.node
sensor = ds_ncfile.sensor
# save_dir = os.path.join(out, platform, node, stream, 'xsection_depth_profiles')
save_dir = os.path.join(out,'timeseries',breakdown)
cf.create_dir(save_dir)
with xr.open_mfdataset(list_files) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
# try:
# eng = stream_vars[stream] # select specific streams engineering variables
# except KeyError:
# eng = ['']
misc = ['quality', 'string', 'timestamp', 'deployment', 'id', 'provenance', 'qc', 'time', 'mission', 'obs',
'volt', 'ref', 'sig', 'amp', 'rph', 'calphase', 'phase', 'therm']
# reg_ex = re.compile('|'.join(eng+misc)) # make regular expression
reg_ex = re.compile('|'.join(misc))
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
# t0, t1 = pf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = (pd.to_datetime(t0) + (pd.to_datetime(t1) - pd.to_datetime(t0)) / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
times = np.unique(ds[time_break])
for t in times:
time_ind = t == ds[time_break].data
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop('comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
try:
y_lab = sci.long_name
except AttributeError:
y_lab = sci.standard_name
y = dict(data=sci.data[time_ind], info=dict(label=y_lab, units=str(sci.units), var=var,
platform=platform, node=node, sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.auto_plot(x, y, title, stdev=None, line_style='.', g_range=True)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
# try:
# y_lab = sci.standard_name
# except AttributeError:
# y_lab = var
# y = dict(data=sci.data, info=dict(label=y_lab, units=sci.units))
# plot timeseries with outliers removed
# fig, ax = pf.auto_plot(x, y, title, stdev=1, line_style='.', g_range=True)
# pf.resize(width=12, height=8.5) # Resize figure
# save_name = '{}-{}-{}_{}_{}-{}_outliers_removed'.format(platform, node, sensor, var, t0, t1)
# pf.save_fig(save_dir, save_name, res=150) # Save figure
# plt.close('all')
del x, y
def main(nc, directory, out, time_break, breakdown):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
list_files = directory + "/*.nc"
# list_files = ['https://opendap.oceanobservatories.org/thredds/dodsC/ooi/friedrich-knuth-gmail/20170322T191659-RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample/deployment0003_RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample_20170312T000000.426102-20170322T190000.059973.nc',
# 'https://opendap.oceanobservatories.org/thredds/dodsC/ooi/friedrich-knuth-gmail/20170322T191659-RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample/deployment0003_RS03AXPS-PC03A-4A-CTDPFA303-streamed-ctdpf_optode_sample_20161222T000000.132709-20170311T235959.426096.nc']
# print list_files
stream_vars = pf.load_variable_dict(
var='eng') # load engineering variables
with xr.open_dataset(nc, mask_and_scale=False) as ds_ncfile:
stream = ds_ncfile.stream # List stream name associated with the data
title_pre = mk_str(ds_ncfile.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds_ncfile.attrs, 's') # , var, tt0, tt1, 's')
platform = ds_ncfile.subsite
node = ds_ncfile.node
sensor = ds_ncfile.sensor
# save_dir = os.path.join(out, platform, node, stream, 'xsection_depth_profiles')
save_dir = os.path.join(out, 'timeseries', breakdown)
cf.create_dir(save_dir)
with xr.open_mfdataset(list_files) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
# try:
# eng = stream_vars[stream] # select specific streams engineering variables
# except KeyError:
# eng = ['']
misc = [
'quality', 'string', 'timestamp', 'deployment', 'id', 'provenance',
'qc', 'time', 'mission', 'obs', 'volt', 'ref', 'sig', 'amp', 'rph',
'calphase', 'phase', 'therm'
]
# reg_ex = re.compile('|'.join(eng+misc)) # make regular expression
reg_ex = re.compile('|'.join(misc))
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
# t0, t1 = pf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = (pd.to_datetime(t0) + (pd.to_datetime(t1) - pd.to_datetime(t0)) / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
times = np.unique(ds[time_break])
for t in times:
time_ind = t == ds[time_break].data
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(
x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(
x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop('comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
try:
y_lab = sci.long_name
except AttributeError:
y_lab = sci.standard_name
y = dict(data=sci.data[time_ind],
info=dict(label=y_lab,
units=str(sci.units),
var=var,
platform=platform,
node=node,
sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.auto_plot(x,
y,
title,
stdev=None,
line_style='.',
g_range=True)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(platform, node, sensor,
var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
# try:
# y_lab = sci.standard_name
# except AttributeError:
# y_lab = var
# y = dict(data=sci.data, info=dict(label=y_lab, units=sci.units))
# plot timeseries with outliers removed
# fig, ax = pf.auto_plot(x, y, title, stdev=1, line_style='.', g_range=True)
# pf.resize(width=12, height=8.5) # Resize figure
# save_name = '{}-{}-{}_{}_{}-{}_outliers_removed'.format(platform, node, sensor, var, t0, t1)
# pf.save_fig(save_dir, save_name, res=150) # Save figure
# plt.close('all')
del x, y
def main(files, out, east_var, north_var, up_var, err_var):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(
var='eng') # load engineering variables
# for nc in list_files:
# print nc
# the engine that xarray uses can be changed as specified here
# http://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html#xarray.open_dataset
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds_disk:
#with xr.open_mfdataset(nc, engine='netcdf4') as ds_disk:
# change dimensions from 'obs' to 'time'
ds_disk = ds_disk.swap_dims({'obs': 'time'})
ds_variables = ds_disk.data_vars.keys(
) # List of dataset variables
stream = ds_disk.stream # List stream name associated with the data
deployment = 'D0000{}'.format(
str(numpy.unique(ds_disk.deployment)[0]))
title_pre = mk_str(ds_disk.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds_disk.attrs, 's') # , var, tt0, tt1, 's')
save_dir = os.path.join(out, ds_disk.subsite, deployment,
ds_disk.node, ds_disk.stream, 'pcolor')
cf.create_dir(save_dir)
# t0, t1 = cf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = t0 + t1 - (t0 / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
# time_list = [[t0, t1]]
# for period in time_list:
# tt0 = period[0]
# tt1 = period[1]
# sub_ds = ds_disk.sel(time=slice(str(tt0), str(tt1)))
north = ds_disk[north_var]
east = ds_disk[east_var]
up = ds_disk[up_var]
error = ds_disk[err_var]
try:
bins = ds_disk['bin_depths']
bins = dict(data=bins.data.T,
info=dict(label=bins.long_name, units=bins.units))
except KeyError:
# use the matrix indices to plot
bins = numpy.zeros_like(east.data)
for i, item in enumerate(east):
for jj, xtem in enumerate(east[i]):
bins[i][jj] = jj
bins = numpy.reshape(bins, (bins.shape[-1], bins.shape[0]))
bins = dict(data=bins, label='bin_indices', units='')
# the correct way to do this is to calculate the bin_depths, for that you need:
# 9 First Cell Range(meters) (rounded bin_1_distance average, m)
# 73 deployment depth of the ADCP instrument (pull from asset-management, depth in m)
# 21 number of bins (num_cells, m)
# 4 cell length (cell_length, m)
# equation with the numbers above would be:
# depths = 73 - 9 - ([1:21]-1)*4;
time = dict(data=ds_disk['time'].data,
info=dict(label=ds_disk['time'].standard_name,
units='GMT'))
#bins = dict(data=bins.data.T, info=dict(label=bins.long_name, units=bins.units))
north = dict(data=north.data.T,
info=dict(label=north.long_name, units=north.units))
east = dict(data=east.data.T,
info=dict(label=east.long_name, units=east.units))
up = dict(data=up.data.T,
info=dict(label=up.long_name, units=up.units))
error = dict(data=error.data.T,
info=dict(label=error.long_name, units=error.units))
sname_ew = save_pre + 'E-W-ADCP'
title = title_pre
fig, axs = pf.adcp(time, bins, north, east, title)
pf.resize(width=12, height=8.5) # Resize figure
pf.save_fig(save_dir, sname_ew, res=250) # Save figure
sname_ur = save_pre + 'U-R-ADCP'
fig, axs = pf.adcp(time, bins, up, error, title)
pf.resize(width=12, height=8.5) # Resize figure
pf.save_fig(save_dir, sname_ur, res=250) # Save figure
plt.close('all')
def main(files, out, time_break):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(var='eng') # load engineering variables
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
stream = ds.stream # List stream name associated with the data
title_pre = mk_str(ds.attrs, 't') # , var, tt0, tt1, 't')
save_pre = mk_str(ds.attrs, 's') # , var, tt0, tt1, 's')
platform = ds.subsite
node = ds.node
sensor = ds.sensor
save_dir = os.path.join(out, ds.subsite, ds.node, ds.stream, 'timeseries')
cf.create_dir(save_dir)
try:
eng = stream_vars[stream] # select specific streams engineering variables
except KeyError:
eng = ['']
misc = ['timestamp', 'provenance', 'qc', 'id', 'obs', 'deployment',
'volts', 'counts', 'quality_flag']
reg_ex = re.compile('|'.join(eng+misc)) # make regular expression
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
# t0, t1 = pf.get_rounded_start_and_end_times(ds_disk['time'].data)
# tI = (pd.to_datetime(t0) + (pd.to_datetime(t1) - pd.to_datetime(t0)) / 2)
# time_list = [[t0, t1], [t0, tI], [tI, t1]]
times = np.unique(ds[time_break])
for t in times:
time_ind = t == ds[time_break].data
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
# sci = sub_ds[var]
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop('comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
# define possible pressure variables
pressure_vars = ['seawater_pressure', 'sci_water_pressure_dbar',
'ctdgv_m_glider_instrument_recovered-sci_water_pressure_dbar',
'ctdgv_m_glider_instrument-sci_water_pressure_dbar']
rePressure = re.compile('|'.join(pressure_vars))
# define y as pressure variable
pressure = [s for s in sci.variables if rePressure.search(s)]
pressure = ''.join(pressure)
y = sci.variables[pressure]
yN = pressure
y_units = sci.units
try:
y_lab = sci.long_name
except AttributeError:
y_lab = sci.standard_name
y = dict(data=sci.data[time_ind], info=dict(label=y_lab, units=sci.units, var=var,
platform=platform, node=node, sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.auto_plot(x, y, title, stdev=None, line_style='r-o', g_range=True)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
# plot z variable each time
fig, ax = pf.depth_cross_section(x, y, title, stdev=1, line_style='r-o', g_range=True)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}_outliers_removed'.format(platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
del x, y
def main(files,
out,
start,
end,
time_break='full',
stdev=None,
color=None,
interactive=False):
"""
files: url to an .nc/.ncml file or the path to a text file containing .nc/.ncml links. A # at the front will skip links in the text file.
out: Directory to save plots
"""
fname, ext = os.path.splitext(files)
if ext in '.nc':
list_files = [files]
elif ext in '.ncml':
list_files = [files]
else:
list_files = read_file(files)
stream_vars = pf.load_variable_dict(
var='eng') # load engineering variables
for nc in list_files:
print nc
with xr.open_dataset(nc, mask_and_scale=False) as ds:
# change dimensions from 'obs' to 'time'
ds = ds.swap_dims({'obs': 'time'})
ds_variables = ds.data_vars.keys() # List of dataset variables
title_pre = mk_str(ds.attrs, 't') # , var, tt0, tt1, 't')
platform = ds.subsite
node = ds.node
sensor = ds.sensor
deployment = 'D0000{}'.format(str(np.unique(ds.deployment)[0]))
stream = ds.stream
save_dir = os.path.join(out, platform, deployment, node, sensor,
stream, 'timeseries')
cf.create_dir(save_dir)
misc = [
'quality', 'string', 'timestamp', 'deployment', 'id',
'provenance', 'qc', 'time', 'mission', 'obs', 'volt', 'ref',
'sig', 'amp', 'rph', 'calphase', 'phase', 'therm',
'description'
]
# reg_ex = re.compile('|'.join(eng+misc)) # make regular expression
reg_ex = re.compile('|'.join(misc))
# keep variables that are not in the regular expression
sci_vars = [s for s in ds_variables if not reg_ex.search(s)]
# if not time_break is 'full' and not None:
# times = np.unique(ds[time_break])
# else:
# times = [0]
if not time_break == None:
times = np.unique(ds[time_break])
for t in times:
if not time_break is 'full':
time_ind = t == ds[time_break].data
else:
time_ind = np.ones(
ds['time'].data.shape,
dtype=bool) # index all times to be set to True
for var in sci_vars:
x = dict(data=ds['time'].data[time_ind],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(
x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(
x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop(
'comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
try:
y_lab = sci.long_name
except AttributeError:
try:
y_lab = sci.standard_name
except AttributeError:
print 'attribute list is not complete. long_ and standard_ names are not available'
y_lab = var
try:
y_unit = str(sci.units)
except AttributeError:
print 'attribute list is not complete. missing units'
y_unit = 'missing'
y = dict(data=sci.data[time_ind],
info=dict(label=y_lab,
units=y_unit,
var=var,
platform=platform,
node=node,
sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.auto_plot(x,
y,
title,
stdev=None,
line_style='.',
g_range=True,
color=color)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name,
res=150) # Save figure
plt.close('all')
# plot timeseries with outliers removed
fig, ax = pf.auto_plot(x,
y,
title,
stdev=stdev,
line_style='.',
g_range=True,
color=color)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}_outliers_removed'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name,
res=150) # Save figure
plt.close('all')
# del x, y
else:
ds = ds.sel(time=slice(start, end))
for var in sci_vars:
x = dict(data=ds['time'].data[:],
info=dict(label='Time', units='GMT'))
t0 = pd.to_datetime(
x['data'].min()).strftime('%Y-%m-%dT%H%M%00')
t1 = pd.to_datetime(
x['data'].max()).strftime('%Y-%m-%dT%H%M%00')
try:
sci = ds[var]
print var
except UnicodeEncodeError: # some comments have latex characters
ds[var].attrs.pop(
'comment') # remove from the attributes
sci = ds[var] # or else the variable won't load
try:
y_lab = sci.long_name
except AttributeError:
try:
y_lab = sci.standard_name
except AttributeError:
print 'attribute list is not complete. long_ and standard_ names are not available'
y_lab = var
try:
y_unit = sci.units
except AttributeError:
print 'attribute list is not complete. units are missing'
y_unit = 'missing'
y = dict(data=sci.data[:],
info=dict(label=y_lab,
units=y_unit,
var=var,
platform=platform,
node=node,
sensor=sensor))
title = title_pre + var
# plot timeseries with outliers
fig, ax = pf.auto_plot(x,
y,
title,
stdev=None,
line_style='.',
g_range=True,
color=color)
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name, res=150) # Save figure
plt.close('all')
# plot timeseries with outliers removed
fig, ax = pf.auto_plot(x,
y,
title,
stdev=stdev,
line_style='.',
g_range=True,
color=color,
interactive=interactive)
if interactive == True:
fig.canvas.mpl_connect(
'pick_event', lambda event: pf.onpick2(
event, x['data'], y['data']))
plt.show()
else:
pf.resize(width=12, height=8.5) # Resize figure
save_name = '{}-{}-{}_{}_{}-{}_outliers_removed'.format(
platform, node, sensor, var, t0, t1)
pf.save_fig(save_dir, save_name,
res=150) # Save figure
plt.close('all')