def main(url_list, sDir, deployment_num, start_time, end_time, preferred_only,
n_std, surface_params, depth_params):
rd_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
if rd not in rd_list and 'ENG' not in rd:
rd_list.append(rd)
for r in rd_list:
print('\n{}'.format(r))
datasets = []
for u in url_list:
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join(
(splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = []
if preferred_only == 'yes':
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
for index, row in ps_df.iterrows():
for ii in range(n_streams):
try:
rms = '-'.join((r, row[ii]))
except TypeError:
continue
for dd in datasets:
spl = dd.split('/')[-2].split('-')
catalog_rms = '-'.join(
(spl[1], spl[2], spl[3], spl[4], spl[5], spl[6]))
fdeploy = dd.split('/')[-1].split('_')[0]
if rms == catalog_rms and fdeploy == row['deployment']:
fdatasets.append(dd)
else:
fdatasets = datasets
main_sensor = r.split('-')[-1]
fdatasets_sel = cf.filter_collocated_instruments(
main_sensor, fdatasets)
for fd in fdatasets_sel:
part_d = fd.split('/')[-1]
print('\n{}'.format(part_d))
ds = xr.open_dataset(fd, mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(
fd)
array = subsite[0:2]
sci_vars = cf.return_science_vars(stream)
if 'CE05MOAS' in r or 'CP05MOAS' in r: # for coastal gliders, get m_water_depth for bathymetry
eng = '-'.join((r.split('-')[0], r.split('-')[1],
'00-ENG000000', method, 'glider_eng'))
eng_url = [s for s in url_list if eng in s]
if len(eng_url) == 1:
eng_datasets = cf.get_nc_urls(eng_url)
# filter out collocated datasets
eng_dataset = [
j for j in eng_datasets
if (eng in j.split('/')[-1]
and deployment in j.split('/')[-1])
]
if len(eng_dataset) > 0:
ds_eng = xr.open_dataset(eng_dataset[0],
mask_and_scale=False)
t_eng = ds_eng['time'].values
m_water_depth = ds_eng['m_water_depth'].values
# m_altimeter_status = 0 means a good reading (not nan or -1)
eng_ind = ds_eng['m_altimeter_status'].values == 0
m_water_depth = m_water_depth[eng_ind]
t_eng = t_eng[eng_ind]
else:
print('No engineering file for deployment {}'.format(
deployment))
m_water_depth = None
t_eng = None
else:
m_water_depth = None
t_eng = None
else:
m_water_depth = None
t_eng = None
if deployment_num is not None:
if int(deployment.split('0')[-1]) is not deployment_num:
print(type(int(deployment.split('0')[-1])),
type(deployment_num))
continue
if start_time is not None and end_time is not None:
ds = ds.sel(time=slice(start_time, end_time))
if len(ds['time'].values) == 0:
print(
'No data to plot for specified time range: ({} to {})'.
format(start_time, end_time))
continue
stime = start_time.strftime('%Y-%m-%d')
etime = end_time.strftime('%Y-%m-%d')
ext = stime + 'to' + etime # .join((ds0_method, ds1_method
save_dir_profile = os.path.join(sDir, array, subsite, refdes,
'profile_plots', deployment,
ext)
save_dir_xsection = os.path.join(sDir, array, subsite, refdes,
'xsection_plots', deployment,
ext)
save_dir_4d = os.path.join(sDir, array, subsite, refdes,
'xsection_plots_4d', deployment,
ext)
else:
save_dir_profile = os.path.join(sDir, array, subsite, refdes,
'profile_plots', deployment)
save_dir_xsection = os.path.join(sDir, array, subsite, refdes,
'xsection_plots', deployment)
save_dir_4d = os.path.join(sDir, array, subsite, refdes,
'xsection_plots_4d', deployment)
tm = ds['time'].values
try:
ds_lat = ds['lat'].values
except KeyError:
ds_lat = None
print('No latitude variable in file')
try:
ds_lon = ds['lon'].values
except KeyError:
ds_lon = None
print('No longitude variable in file')
# get pressure variable
y, y_units, press = cf.add_pressure_to_dictionary_of_sci_vars(ds)
for sv in sci_vars:
print(sv)
if 'pressure' not in sv:
z = ds[sv].values
fv = ds[sv]._FillValue
sv_units = ds[sv].units
# Check if the array is all NaNs
if sum(np.isnan(z)) == len(z):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(z[z != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
# reject erroneous data
dtime, zpressure, ndata, lenfv, lennan, lenev, lengr, global_min, global_max, lat, lon = \
cf.reject_erroneous_data(r, sv, tm, y, z, fv, ds_lat, ds_lon)
# get rid of 0.0 data
if 'CTD' in r:
ind = zpressure > 0.0
else:
ind = ndata > 0.0
lenzero = np.sum(~ind)
dtime = dtime[ind]
zpressure = zpressure[ind]
ndata = ndata[ind]
if ds_lat is not None and ds_lon is not None:
lat = lat[ind]
lon = lon[ind]
else:
lat = None
lon = None
t0 = pd.to_datetime(
dtime.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(
dtime.max()).strftime('%Y-%m-%dT%H:%M:%S')
title = ' '.join((deployment, refdes,
method)) + '\n' + t0 + ' to ' + t1
# reject time range from data portal file export
t_portal, z_portal, y_portal, lat_portal, lon_portal = \
cf.reject_timestamps_dataportal(subsite, r, dtime, zpressure, ndata, lat, lon)
print(
'removed {} data points using visual inspection of data'
.format(len(ndata) - len(z_portal)))
# create data groups
columns = ['tsec', 'dbar', str(sv)]
# min_r = int(round(min(y_portal) - zcell_size))
# max_r = int(round(max(y_portal) + zcell_size))
# ranges = list(range(min_r, max_r, zcell_size))
#ranges = [0, 10, 20, 30, 40, 50, 60, 70, 80, 200]
range1 = list(
range(surface_params[0], surface_params[1],
surface_params[2]))
range2 = list(
range(depth_params[0],
depth_params[1] + depth_params[2],
depth_params[2]))
ranges = range1 + range2
groups, d_groups = gt.group_by_depth_range(
t_portal, y_portal, z_portal, columns, ranges)
if 'scatter' in sv:
n_std = None # to use percentile
else:
n_std = n_std
# get percentile analysis for printing on the profile plot
inpercentile = [surface_params[3]] * len(
range1) + [depth_params[3]] * len(range2)
n_std = [surface_params[3]] * len(
range1) + [depth_params[3]] * len(range2)
y_plt, n_med, n_min, n_max, n0_std, n1_std, l_arr, time_ex = reject_timestamps_in_groups(
groups, d_groups, n_std, inpercentile)
"""
Plot all data
"""
if len(tm) > 0:
cf.create_dir(save_dir_profile)
cf.create_dir(save_dir_xsection)
sname = '-'.join((r, method, sv))
sfileall = '_'.join(('all_data', sname))
'''
profile plot
'''
xlabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
clabel = 'Time'
fig, ax = pf.plot_profiles(z,
y,
tm,
ylabel,
xlabel,
clabel,
stdev=None)
ax.set_title(title, fontsize=9)
fig.tight_layout()
pf.save_fig(save_dir_profile, sfileall)
'''
xsection plot
'''
clabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
fig, ax, bar = pf.plot_xsection(subsite,
tm,
y,
z,
clabel,
ylabel,
t_eng,
m_water_depth,
inpercentile=None,
stdev=None)
ax.set_title(title, fontsize=9)
fig.tight_layout()
pf.save_fig(save_dir_xsection, sfileall)
"""
Plot cleaned-up data
"""
if len(dtime) > 0:
sfile = '_'.join(('rm_erroneous_data', sname))
'''
profile plot
'''
xlabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
clabel = 'Time'
fig, ax = pf.plot_profiles(z_portal,
y_portal,
t_portal,
ylabel,
xlabel,
clabel,
stdev=None)
ax.set_title(title, fontsize=9)
ax.plot(n_med, y_plt, '.k')
ax.fill_betweenx(y_plt,
n0_std,
n1_std,
color='m',
alpha=0.2)
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} zeros'.format(lenfv, lennan, lenev, lengr,
global_min, global_max,
lenzero) +
'\nexcluded {} suspect data points when inspected visually'
.format(len(ndata) - len(z_portal)) +
'\n(black) data median in {} dbar segments (break at {} dbar)'
.format([surface_params[2], depth_params[2]],
depth_params[0]) +
'\n(magenta) upper and lower {} percentile envelope in {} dbar segments'
.format(
[surface_params[3], depth_params[3]],
[surface_params[2], depth_params[2]]), )
ax.legend(leg_text,
loc='upper center',
bbox_to_anchor=(0.5, -0.17),
fontsize=6)
fig.tight_layout()
pf.save_fig(save_dir_profile, sfile)
'''
xsection plot
'''
clabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
# plot non-erroneous data
fig, ax, bar = pf.plot_xsection(subsite,
t_portal,
y_portal,
z_portal,
clabel,
ylabel,
t_eng,
m_water_depth,
inpercentile=None,
stdev=None)
ax.set_title(title, fontsize=9)
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} zeros'.format(lenfv, lennan, lenev, lengr,
global_min, global_max,
lenzero) +
'\nexcluded {} suspect data points when inspected visually'
.format(len(ndata) - len(z_portal)), )
ax.legend(leg_text,
loc='upper center',
bbox_to_anchor=(0.5, -0.17),
fontsize=6)
fig.tight_layout()
pf.save_fig(save_dir_xsection, sfile)
'''
4D plot for gliders only
'''
if 'MOAS' in r:
if ds_lat is not None and ds_lon is not None:
cf.create_dir(save_dir_4d)
clabel = sv + " (" + sv_units + ")"
zlabel = press[0] + " (" + y_units[0] + ")"
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
sct = ax.scatter(lon_portal,
lat_portal,
y_portal,
c=z_portal,
s=2)
cbar = plt.colorbar(sct,
label=clabel,
extend='both')
cbar.ax.tick_params(labelsize=8)
ax.invert_zaxis()
ax.view_init(25, 32)
ax.invert_xaxis()
ax.invert_yaxis()
ax.set_zlabel(zlabel, fontsize=9)
ax.set_ylabel('Latitude', fontsize=9)
ax.set_xlabel('Longitude', fontsize=9)
ax.set_title(title, fontsize=9)
pf.save_fig(save_dir_4d, sfile)
def main(url_list, sDir, deployment_num, start_time, end_time, preferred_only, n_std, inpercentile, zcell_size, zdbar):
rd_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
if rd not in rd_list and 'ENG' not in rd and 'ADCP' not in rd:
rd_list.append(rd)
for r in rd_list:
print('\n{}'.format(r))
datasets = []
for u in url_list:
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join((splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = []
if preferred_only == 'yes':
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
for index, row in ps_df.iterrows():
for ii in range(n_streams):
try:
rms = '-'.join((r, row[ii]))
except TypeError:
continue
for dd in datasets:
spl = dd.split('/')[-2].split('-')
catalog_rms = '-'.join((spl[1], spl[2], spl[3], spl[4], spl[5], spl[6]))
fdeploy = dd.split('/')[-1].split('_')[0]
if rms == catalog_rms and fdeploy == row['deployment']:
fdatasets.append(dd)
else:
fdatasets = datasets
main_sensor = r.split('-')[-1]
fdatasets_sel = cf.filter_collocated_instruments(main_sensor, fdatasets)
for fd in fdatasets_sel:
part_d = fd.split('/')[-1]
print('\n{}'.format(part_d))
ds = xr.open_dataset(fd, mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(fd)
array = subsite[0:2]
sci_vars = cf.return_science_vars(stream)
# if 'CE05MOAS' in r or 'CP05MOAS' in r: # for coastal gliders, get m_water_depth for bathymetry
# eng = '-'.join((r.split('-')[0], r.split('-')[1], '00-ENG000000', method, 'glider_eng'))
# eng_url = [s for s in url_list if eng in s]
# if len(eng_url) == 1:
# eng_datasets = cf.get_nc_urls(eng_url)
# # filter out collocated datasets
# eng_dataset = [j for j in eng_datasets if (eng in j.split('/')[-1] and deployment in j.split('/')[-1])]
# if len(eng_dataset) > 0:
# ds_eng = xr.open_dataset(eng_dataset[0], mask_and_scale=False)
# t_eng = ds_eng['time'].values
# m_water_depth = ds_eng['m_water_depth'].values
#
# # m_altitude = glider height above seafloor
# # m_depth = glider depth in the water column
# # m_altitude = ds_eng['m_altitude'].values
# # m_depth = ds_eng['m_depth'].values
# # calc_water_depth = m_altitude + m_depth
#
# # m_altimeter_status = 0 means a good reading (not nan or -1)
# try:
# eng_ind = ds_eng['m_altimeter_status'].values == 0
# except KeyError:
# eng_ind = (~np.isnan(m_water_depth)) & (m_water_depth >= 0)
#
# m_water_depth = m_water_depth[eng_ind]
# t_eng = t_eng[eng_ind]
#
# # get rid of any remaining nans or fill values
# eng_ind2 = (~np.isnan(m_water_depth)) & (m_water_depth >= 0)
# m_water_depth = m_water_depth[eng_ind2]
# t_eng = t_eng[eng_ind2]
# else:
# print('No engineering file for deployment {}'.format(deployment))
# m_water_depth = None
# t_eng = None
# else:
# m_water_depth = None
# t_eng = None
# else:
# m_water_depth = None
# t_eng = None
if deployment_num is not None:
if int(int(deployment[-4:])) is not deployment_num:
print(type(int(deployment[-4:])), type(deployment_num))
continue
if start_time is not None and end_time is not None:
ds = ds.sel(time=slice(start_time, end_time))
if len(ds['time'].values) == 0:
print('No data to plot for specified time range: ({} to {})'.format(start_time, end_time))
continue
stime = start_time.strftime('%Y-%m-%d')
etime = end_time.strftime('%Y-%m-%d')
ext = stime + 'to' + etime # .join((ds0_method, ds1_method
save_dir_profile = os.path.join(sDir, array, subsite, refdes, 'profile_plots', deployment, ext)
save_dir_xsection = os.path.join(sDir, array, subsite, refdes, 'xsection_plots', deployment, ext)
save_dir_4d = os.path.join(sDir, array, subsite, refdes, 'xsection_plots_4d', deployment, ext)
else:
save_dir_profile = os.path.join(sDir, array, subsite, refdes, 'profile_plots', deployment)
save_dir_xsection = os.path.join(sDir, array, subsite, refdes, 'xsection_plots', deployment)
save_dir_4d = os.path.join(sDir, array, subsite, refdes, 'xsection_plots_4d', deployment)
time1 = ds['time'].values
try:
ds_lat1 = ds['lat'].values
except KeyError:
ds_lat1 = None
print('No latitude variable in file')
try:
ds_lon1 = ds['lon'].values
except KeyError:
ds_lon1 = None
print('No longitude variable in file')
# get pressure variable
pvarname, y1, y_units, press, y_fillvalue = cf.add_pressure_to_dictionary_of_sci_vars(ds)
for sv in sci_vars:
print('')
print(sv)
if 'pressure' not in sv:
if sv == 'spkir_abj_cspp_downwelling_vector':
pxso.pf_xs_spkir(ds, sv, time1, y1, ds_lat1, ds_lon1, zcell_size, inpercentile, save_dir_profile,
save_dir_xsection, deployment, press, y_units, n_std, zdbar)
elif 'OPTAA' in r:
if sv not in ['wavelength_a', 'wavelength_c']:
pxso.pf_xs_optaa(ds, sv, time1, y1, ds_lat1, ds_lon1, zcell_size, inpercentile, save_dir_profile,
save_dir_xsection, deployment, press, y_units, n_std, zdbar)
else:
z1 = ds[sv].values
fv = ds[sv]._FillValue
sv_units = ds[sv].units
# Check if the array is all NaNs
if sum(np.isnan(z1)) == len(z1):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(z1[z1 != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
# remove unreasonable pressure data (e.g. for surface piercing profilers)
if zdbar:
po_ind = (0 < y1) & (y1 < zdbar)
tm = time1[po_ind]
y = y1[po_ind]
z = z1[po_ind]
ds_lat = ds_lat1[po_ind]
ds_lon = ds_lon1[po_ind]
else:
tm = time1
y = y1
z = z1
ds_lat = ds_lat1
ds_lon = ds_lon1
# reject erroneous data
dtime, zpressure, ndata, lenfv, lennan, lenev, lengr, global_min, global_max, lat, lon = \
cf.reject_erroneous_data(r, sv, tm, y, z, fv, ds_lat, ds_lon)
# get rid of 0.0 data
if sv == 'salinity':
ind = ndata > 30
elif sv == 'density':
ind = ndata > 1022.5
elif sv == 'conductivity':
ind = ndata > 3.45
else:
ind = ndata > 0
# if sv == 'sci_flbbcd_chlor_units':
# ind = ndata < 7.5
# elif sv == 'sci_flbbcd_cdom_units':
# ind = ndata < 25
# else:
# ind = ndata > 0.0
# if 'CTD' in r:
# ind = zpressure > 0.0
# else:
# ind = ndata > 0.0
lenzero = np.sum(~ind)
dtime = dtime[ind]
zpressure = zpressure[ind]
ndata = ndata[ind]
if ds_lat is not None and ds_lon is not None:
lat = lat[ind]
lon = lon[ind]
else:
lat = None
lon = None
if len(dtime) > 0:
# reject time range from data portal file export
t_portal, z_portal, y_portal, lat_portal, lon_portal = \
cf.reject_timestamps_dataportal(subsite, r, dtime, zpressure, ndata, lat, lon)
print('removed {} data points using visual inspection of data'.format(
len(ndata) - len(z_portal)))
# create data groups
if len(y_portal) > 0:
columns = ['tsec', 'dbar', str(sv)]
min_r = int(round(np.nanmin(y_portal) - zcell_size))
max_r = int(round(np.nanmax(y_portal) + zcell_size))
ranges = list(range(min_r, max_r, zcell_size))
groups, d_groups = gt.group_by_depth_range(t_portal, y_portal, z_portal, columns, ranges)
if 'scatter' in sv:
n_std = None # to use percentile
else:
n_std = n_std
# get percentile analysis for printing on the profile plot
y_avg, n_avg, n_min, n_max, n0_std, n1_std, l_arr, time_ex = cf.reject_timestamps_in_groups(
groups, d_groups, n_std, inpercentile)
"""
Plot all data
"""
if len(time1) > 0:
cf.create_dir(save_dir_profile)
cf.create_dir(save_dir_xsection)
sname = '-'.join((r, method, sv))
sfileall = '_'.join(('all_data', sname, pd.to_datetime(time1.min()).strftime('%Y%m%d')))
tm0 = pd.to_datetime(time1.min()).strftime('%Y-%m-%dT%H:%M:%S')
tm1 = pd.to_datetime(time1.max()).strftime('%Y-%m-%dT%H:%M:%S')
title = ' '.join((deployment, refdes, method)) + '\n' + tm0 + ' to ' + tm1
if 'SPKIR' in r:
title = title + '\nWavelength = 510 nm'
'''
profile plot
'''
xlabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
clabel = 'Time'
fig, ax = pf.plot_profiles(z1, y1, time1, ylabel, xlabel, clabel, stdev=None)
ax.set_title(title, fontsize=9)
fig.tight_layout()
pf.save_fig(save_dir_profile, sfileall)
'''
xsection plot
'''
clabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
fig, ax, bar = pf.plot_xsection(subsite, time1, y1, z1, clabel, ylabel, t_eng=None,
m_water_depth=None, inpercentile=None, stdev=None)
if fig:
ax.set_title(title, fontsize=9)
fig.tight_layout()
pf.save_fig(save_dir_xsection, sfileall)
"""
Plot cleaned-up data
"""
if len(dtime) > 0:
if len(y_portal) > 0:
sfile = '_'.join(('rm_erroneous_data', sname, pd.to_datetime(t_portal.min()).strftime('%Y%m%d')))
t0 = pd.to_datetime(t_portal.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(t_portal.max()).strftime('%Y-%m-%dT%H:%M:%S')
title = ' '.join((deployment, refdes, method)) + '\n' + t0 + ' to ' + t1
if 'SPKIR' in r:
title = title + '\nWavelength = 510 nm'
'''
profile plot
'''
xlabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
clabel = 'Time'
fig, ax = pf.plot_profiles(z_portal, y_portal, t_portal, ylabel, xlabel, clabel, stdev=None)
ax.set_title(title, fontsize=9)
ax.plot(n_avg, y_avg, '-k')
ax.fill_betweenx(y_avg, n0_std, n1_std, color='m', alpha=0.2)
if inpercentile:
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} unreasonable values'.format(lenfv, lennan, lenev, lengr, global_min, global_max, lenzero) +
'\nexcluded {} suspect data points when inspected visually'.format(
len(ndata) - len(z_portal)) +
'\n(black) data average in {} dbar segments'.format(zcell_size) +
'\n(magenta) {} percentile envelope in {} dbar segments'.format(
int(100 - inpercentile * 2), zcell_size),)
elif n_std:
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} unreasonable values'.format(lenfv, lennan, lenev, lengr, global_min, global_max,
lenzero) +
'\nexcluded {} suspect data points when inspected visually'.format(
len(ndata) - len(z_portal)) +
'\n(black) data average in {} dbar segments'.format(zcell_size) +
'\n(magenta) +/- {} SD envelope in {} dbar segments'.format(
int(n_std), zcell_size),)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
pf.save_fig(save_dir_profile, sfile)
'''
xsection plot
'''
clabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
# plot non-erroneous data
fig, ax, bar = pf.plot_xsection(subsite, t_portal, y_portal, z_portal, clabel, ylabel,
t_eng=None, m_water_depth=None, inpercentile=None,
stdev=None)
ax.set_title(title, fontsize=9)
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} unreasonable values'.format(lenfv, lennan, lenev, lengr, global_min, global_max, lenzero) +
'\nexcluded {} suspect data points when inspected visually'.format(
len(ndata) - len(z_portal)),
)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
pf.save_fig(save_dir_xsection, sfile)
'''
4D plot for gliders only
'''
if 'MOAS' in r:
if ds_lat is not None and ds_lon is not None:
cf.create_dir(save_dir_4d)
clabel = sv + " (" + sv_units + ")"
zlabel = press[0] + " (" + y_units[0] + ")"
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
sct = ax.scatter(lon_portal, lat_portal, y_portal, c=z_portal, s=2)
cbar = plt.colorbar(sct, label=clabel, extend='both')
cbar.ax.tick_params(labelsize=8)
ax.invert_zaxis()
ax.view_init(25, 32)
ax.invert_xaxis()
ax.invert_yaxis()
ax.set_zlabel(zlabel, fontsize=9)
ax.set_ylabel('Latitude', fontsize=9)
ax.set_xlabel('Longitude', fontsize=9)
ax.set_title(title, fontsize=9)
pf.save_fig(save_dir_4d, sfile)
def main(url_list, sDir, plot_type, deployment_num, start_time, end_time, preferred_only, glider, zdbar, n_std, inpercentile, zcell_size):
rd_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
if rd not in rd_list and 'ENG' not in rd:
rd_list.append(rd)
for r in rd_list:
print('\n{}'.format(r))
datasets = []
for u in url_list:
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join((splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = []
if preferred_only == 'yes':
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
for index, row in ps_df.iterrows():
for ii in range(n_streams):
try:
rms = '-'.join((r, row[ii]))
except TypeError:
continue
for dd in datasets:
spl = dd.split('/')[-2].split('-')
catalog_rms = '-'.join((spl[1], spl[2], spl[3], spl[4], spl[5], spl[6]))
fdeploy = dd.split('/')[-1].split('_')[0]
if rms == catalog_rms and fdeploy == row['deployment']:
fdatasets.append(dd)
else:
fdatasets = datasets
main_sensor = r.split('-')[-1]
fdatasets_sel = cf.filter_collocated_instruments(main_sensor, fdatasets)
for fd in fdatasets_sel:
part_d = fd.split('/')[-1]
print(part_d)
ds = xr.open_dataset(fd, mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(fd)
array = subsite[0:2]
sci_vars = cf.return_science_vars(stream)
if 'CE05MOAS' in r or 'CP05MOAS' in r: # for coastal gliders, get m_water_depth for bathymetry
eng = '-'.join((r.split('-')[0], r.split('-')[1], '00-ENG000000', method, 'glider_eng'))
eng_url = [s for s in url_list if eng in s]
if len(eng_url) == 1:
eng_datasets = cf.get_nc_urls(eng_url)
# filter out collocated datasets
eng_dataset = [j for j in eng_datasets if (eng in j.split('/')[-1] and deployment in j.split('/')[-1])]
if len(eng_dataset) > 0:
ds_eng = xr.open_dataset(eng_dataset[0], mask_and_scale=False)
t_eng = ds_eng['time'].values
m_water_depth = ds_eng['m_water_depth'].values
# m_altimeter_status = 0 means a good reading (not nan or -1)
eng_ind = ds_eng['m_altimeter_status'].values == 0
m_water_depth = m_water_depth[eng_ind]
t_eng = t_eng[eng_ind]
else:
print('No engineering file for deployment {}'.format(deployment))
if deployment_num is not None:
if int(deployment.split('0')[-1]) is not deployment_num:
print(type(int(deployment.split('0')[-1])), type(deployment_num))
continue
if start_time is not None and end_time is not None:
ds = ds.sel(time=slice(start_time, end_time))
if len(ds['time'].values) == 0:
print('No data to plot for specified time range: ({} to {})'.format(start_time, end_time))
continue
stime = start_time.strftime('%Y-%m-%d')
etime = end_time.strftime('%Y-%m-%d')
ext = stime + 'to' + etime # .join((ds0_method, ds1_method
save_dir = os.path.join(sDir, array, subsite, refdes, plot_type, deployment, ext)
else:
save_dir = os.path.join(sDir, array, subsite, refdes, plot_type, deployment)
cf.create_dir(save_dir)
tm = ds['time'].values
# get pressure variable
ds_vars = list(ds.data_vars.keys()) + [x for x in ds.coords.keys() if 'pressure' in x]
y, y_units, press = cf.add_pressure_to_dictionary_of_sci_vars(ds)
print(y_units, press)
# press = pf.pressure_var(ds, ds_vars)
# print(press)
# y = ds[press].values
# y_units = ds[press].units
for sv in sci_vars:
print(sv)
if 'sci_water_pressure' not in sv:
z = ds[sv].values
fv = ds[sv]._FillValue
z_units = ds[sv].units
# Check if the array is all NaNs
if sum(np.isnan(z)) == len(z):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(z[z != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
"""
clean up data
"""
# reject erroneous data
dtime, zpressure, ndata, lenfv, lennan, lenev, lengr, global_min, global_max = \
cf.reject_erroneous_data(r, sv, tm, y, z, fv)
# get rid of 0.0 data
if 'CTD' in r:
ind = zpressure > 0.0
else:
ind = ndata > 0.0
lenzero = np.sum(~ind)
dtime = dtime[ind]
zpressure = zpressure[ind]
ndata = ndata[ind]
# creating data groups
columns = ['tsec', 'dbar', str(sv)]
min_r = int(round(min(zpressure) - zcell_size))
max_r = int(round(max(zpressure) + zcell_size))
ranges = list(range(min_r, max_r, zcell_size))
groups, d_groups = gt.group_by_depth_range(dtime, zpressure, ndata, columns, ranges)
# rejecting timestamps from percentile analysis
y_avg, n_avg, n_min, n_max, n0_std, n1_std, l_arr, time_ex = cf.reject_timestamps_in_groups(
groups, d_groups, n_std, inpercentile)
t_nospct, z_nospct, y_nospct = cf.reject_suspect_data(dtime, zpressure, ndata, time_ex)
print('removed {} data points using {} percentile of data grouped in {} dbar segments'.format(
len(zpressure) - len(z_nospct), inpercentile, zcell_size))
# reject time range from data portal file export
t_portal, z_portal, y_portal = cf.reject_timestamps_dataportal(subsite, r,
t_nospct, y_nospct, z_nospct)
print('removed {} data points using visual inspection of data'.format(len(z_nospct) - len(z_portal)))
# reject data in a depth range
if zdbar:
y_ind = y_portal < zdbar
n_zdbar = np.sum(~y_ind)
t_array = t_portal[y_ind]
y_array = y_portal[y_ind]
z_array = z_portal[y_ind]
else:
n_zdbar = 0
t_array = t_portal
y_array = y_portal
z_array = z_portal
print('{} in water depth > {} dbar'.format(n_zdbar, zdbar))
"""
Plot data
"""
if len(dtime) > 0:
sname = '-'.join((r, method, sv))
clabel = sv + " (" + z_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
if glider == 'no':
t_eng = None
m_water_depth = None
# plot non-erroneous data
fig, ax, bar = pf.plot_xsection(subsite, dtime, zpressure, ndata, clabel, ylabel,
t_eng, m_water_depth, inpercentile, stdev=None)
t0 = pd.to_datetime(dtime.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(dtime.max()).strftime('%Y-%m-%dT%H:%M:%S')
title = ' '.join((deployment, refdes, method)) + '\n' + t0 + ' to ' + t1
ax.set_title(title, fontsize=9)
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} zeros'.format(lenfv, lennan, lenev, lengr, global_min, global_max, lenzero),
)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
sfile = '_'.join(('rm_erroneous_data', sname))
pf.save_fig(save_dir, sfile)
# plots removing all suspect data
if len(t_array) > 0:
if len(t_array) != len(dtime):
# plot bathymetry only within data time ranges
if glider == 'yes':
eng_ind = (t_eng >= np.min(t_array)) & (t_eng <= np.max(t_array))
t_eng = t_eng[eng_ind]
m_water_depth = m_water_depth[eng_ind]
fig, ax, bar = pf.plot_xsection(subsite, t_array, y_array, z_array, clabel, ylabel,
t_eng, m_water_depth, inpercentile, stdev=None)
t0 = pd.to_datetime(t_array.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(t_array.max()).strftime('%Y-%m-%dT%H:%M:%S')
title = ' '.join((deployment, refdes, method)) + '\n' + t0 + ' to ' + t1
ax.set_title(title, fontsize=9)
if zdbar:
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} zeros'.format(lenfv, lennan, lenev, lengr, global_min, global_max, lenzero)
+ '\nremoved {} in the upper and lower {}th percentile of data grouped in {} dbar segments'.format(
len(zpressure) - len(z_nospct), inpercentile, zcell_size)
+ '\nexcluded {} suspect data points when inspected visually'.format(
len(z_nospct) - len(z_portal))
+ '\nexcluded {} suspect data in water depth greater than {} dbar'.format(n_zdbar,
zdbar),
)
else:
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} zeros'.format(lenfv, lennan, lenev, lengr, global_min, global_max, lenzero)
+ '\nremoved {} in the upper and lower {}th percentile of data grouped in {} dbar segments'.format(
len(zpressure) - len(z_nospct), inpercentile, zcell_size)
+ '\nexcluded {} suspect data points when inspected visually'.format(
len(z_nospct) - len(z_portal)),
)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
sfile = '_'.join(('rm_suspect_data', sname))
pf.save_fig(save_dir, sfile)
def main(url_list, sDir, mDir, zcell_size, zdbar, start_time, end_time, inpercentile):
"""""
URL : path to instrument data by methods
sDir : path to the directory on your machine to save plots
mDir : path to the directory on your machine to save data ranges
zcell_size : depth cell size to group data
zdbar : define depth where suspect data are identified
start_time : select start date to slice timeseries
end_time : select end date to slice timeseries
"""""
rd_list = []
ms_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
ms = uu.split(rd + '-')[1].split('/')[0]
if rd not in rd_list:
rd_list.append(rd)
if ms not in ms_list:
ms_list.append(ms)
for r in rd_list:
print('\n{}'.format(r))
subsite = r.split('-')[0]
array = subsite[0:2]
main_sensor = r.split('-')[-1]
# read in the analysis file
dr_data = cf.refdes_datareview_json(r)
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
# get science variable long names from the Data Review Database
stream_sci_vars = cd.sci_var_long_names(r)
# check if the science variable long names are the same for each stream and initialize empty arrays
sci_vars_dict0 = cd.sci_var_long_names_check(stream_sci_vars)
# get the list of data files and filter out collocated instruments and other streams
datasets = []
for u in url_list:
print(u)
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join((splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = cf.filter_collocated_instruments(main_sensor, datasets)
fdatasets = cf.filter_other_streams(r, ms_list, fdatasets)
# select the list of data files from the preferred dataset for each deployment
fdatasets_final = []
for ii in range(len(ps_df)):
for x in fdatasets:
if ps_df['deployment'][ii] in x and ps_df[0][ii] in x:
fdatasets_final.append(x)
# build dictionary of science data from the preferred dataset for each deployment
print('\nAppending data from files')
sci_vars_dict, y_unit, y_name, l0 = cd.append_evaluated_science_data(
sDir, ps_df, n_streams, r, fdatasets_final, sci_vars_dict0, zdbar, start_time, end_time)
# get end times of deployments
deployments = []
end_times = []
for index, row in ps_df.iterrows():
deploy = row['deployment']
deploy_info = cf.get_deployment_information(dr_data, int(deploy[-4:]))
deployments.append(int(deploy[-4:]))
end_times.append(pd.to_datetime(deploy_info['stop_date']))
# create data range output folders
save_dir_stat = os.path.join(mDir, array, subsite)
cf.create_dir(save_dir_stat)
# create plots output folder
save_fdir = os.path.join(sDir, array, subsite, r, 'data_range')
cf.create_dir(save_fdir)
stat_df = pd.DataFrame()
"""
create data ranges csv file and figures
"""
for m, n in sci_vars_dict.items():
for sv, vinfo in n['vars'].items():
print('\n' + vinfo['var_name'])
if len(vinfo['t']) < 1:
print('no variable data to plot')
continue
else:
sv_units = vinfo['units'][0]
fv = vinfo['fv'][0]
t = vinfo['t']
z = vinfo['values']
y = vinfo['pressure']
# Check if the array is all NaNs
if sum(np.isnan(z)) == len(z):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(z[z != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
if len(y) > 0:
if m == 'common_stream_placeholder':
sname = '-'.join((vinfo['var_name'], r))
else:
sname = '-'.join((vinfo['var_name'], r, m))
"""
create data ranges for non - pressure data only
"""
if 'pressure' in vinfo['var_name']:
pass
else:
columns = ['tsec', 'dbar', str(vinfo['var_name'])]
# create depth ranges
min_r = int(round(min(y) - zcell_size))
max_r = int(round(max(y) + zcell_size))
ranges = list(range(min_r, max_r, zcell_size))
# group data by depth
groups, d_groups = gt.group_by_depth_range(t, y, z, columns, ranges)
print('writing data ranges for {}'.format(vinfo['var_name']))
stat_data = groups.describe()[vinfo['var_name']]
stat_data.insert(loc=0, column='parameter', value=sv, allow_duplicates=False)
t_deploy = deployments[0]
for i in range(len(deployments))[1:len(deployments)]:
t_deploy = '{}, {}'.format(t_deploy, deployments[i])
stat_data.insert(loc=1, column='deployments', value=t_deploy, allow_duplicates=False)
stat_df = stat_df.append(stat_data, ignore_index=False)
"""
plot full time range free from errors and suspect data
"""
clabel = sv + " (" + sv_units + ")"
ylabel = (y_name[0][0] + " (" + y_unit[0][0] + ")")
t_eng = None
m_water_depth = None
# plot non-erroneous -suspect data
fig, ax, bar = pf.plot_xsection(subsite, t, y, z, clabel, ylabel, t_eng, m_water_depth,
inpercentile, stdev=None)
title0 = 'Data colored using the upper and lower {} percentile.'.format(inpercentile)
ax.set_title(r+'\n'+title0, fontsize=9)
leg_text = ('{} % erroneous values removed after Human In the Loop review'.format(
(len(t)/l0) * 100),)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
for ii in range(len(end_times)):
ax.axvline(x=end_times[ii], color='b', linestyle='--', linewidth=.8)
ax.text(end_times[ii], min(y)-5, 'End' + str(deployments[ii]),
fontsize=6, style='italic',
bbox=dict(boxstyle='round',
ec=(0., 0.5, 0.5),
fc=(1., 1., 1.),
))
# fig.tight_layout()
sfile = '_'.join(('data_range', sname))
pf.save_fig(save_fdir, sfile)
# write stat file
stat_df.to_csv('{}/{}_data_ranges.csv'.format(save_dir_stat, r), index=True, float_format='%11.6f')
def main(url_list, sDir, mDir, zcell_size, zdbar, start_time, end_time):
"""""
URL : path to instrument data by methods
sDir : path to the directory on your machine to save files
plot_type: folder name for a plot type
""" ""
rd_list = []
ms_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
ms = uu.split(rd + '-')[1].split('/')[0]
if rd not in rd_list:
rd_list.append(rd)
if ms not in ms_list:
ms_list.append(ms)
'''
separate different instruments
'''
for r in rd_list:
print('\n{}'.format(r))
subsite = r.split('-')[0]
array = subsite[0:2]
main_sensor = r.split('-')[-1]
# read in the analysis file
dr_data = cf.refdes_datareview_json(r)
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
# get science variable long names from the Data Review Database
stream_sci_vars = cd.sci_var_long_names(r)
#stream_vars = cd.var_long_names(r)
# check if the science variable long names are the same for each stream and initialize empty arrays
sci_vars_dict0 = cd.sci_var_long_names_check(stream_sci_vars)
# get the list of data files and filter out collocated instruments and other streams
datasets = []
for u in url_list:
print(u)
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join(
(splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = cf.filter_collocated_instruments(main_sensor, datasets)
fdatasets = cf.filter_other_streams(r, ms_list, fdatasets)
# select the list of data files from the preferred dataset for each deployment
fdatasets_final = []
for ii in range(len(ps_df)):
for x in fdatasets:
if ps_df['deployment'][ii] in x and ps_df[0][ii] in x:
fdatasets_final.append(x)
# build dictionary of science data from the preferred dataset for each deployment
print('\nAppending data from files')
et = []
sci_vars_dict, y_unit, y_name = cd.append_evaluated_science_data(
sDir, ps_df, n_streams, r, fdatasets_final, sci_vars_dict0, et,
start_time, end_time)
# get end times of deployments
deployments = []
end_times = []
for index, row in ps_df.iterrows():
deploy = row['deployment']
deploy_info = cf.get_deployment_information(
dr_data, int(deploy[-4:]))
deployments.append(int(deploy[-4:]))
end_times.append(pd.to_datetime(deploy_info['stop_date']))
"""
create a data-ranges table and figure for full data time range
"""
# create a folder to save data ranges
save_dir_stat = os.path.join(mDir, array, subsite)
cf.create_dir(save_dir_stat)
save_fdir = os.path.join(sDir, array, subsite, r, 'data_range')
cf.create_dir(save_fdir)
stat_df = pd.DataFrame()
for m, n in sci_vars_dict.items():
for sv, vinfo in n['vars'].items():
print(vinfo['var_name'])
if len(vinfo['t']) < 1:
print('no variable data to plot')
continue
else:
sv_units = vinfo['units'][0]
fv = vinfo['fv'][0]
t = vinfo['t']
z = vinfo['values']
y = vinfo['pressure']
# Check if the array is all NaNs
if sum(np.isnan(z)) == len(z):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(z[z != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
"""
clean up data
"""
# reject erroneous data
dtime, zpressure, ndata, lenfv, lennan, lenev, lengr, global_min, global_max = \
cf.reject_erroneous_data(r, sv, t, y, z, fv)
# reject timestamps from stat analysis
Dpath = '{}/{}/{}/{}/{}'.format(sDir, array, subsite, r,
'time_to_exclude')
onlyfiles = []
for item in os.listdir(Dpath):
if not item.startswith('.') and os.path.isfile(
os.path.join(Dpath, item)):
onlyfiles.append(join(Dpath, item))
dre = pd.DataFrame()
for nn in onlyfiles:
dr = pd.read_csv(nn)
dre = dre.append(dr, ignore_index=True)
drn = dre.loc[dre['Unnamed: 0'] == vinfo['var_name']]
list_time = []
for itime in drn.time_to_exclude:
ntime = itime.split(', ')
list_time.extend(ntime)
u_time_list = np.unique(list_time)
if len(u_time_list) != 0:
t_nospct, z_nospct, y_nospct = cf.reject_suspect_data(
dtime, zpressure, ndata, u_time_list)
print(
'{} using {} percentile of data grouped in {} dbar segments'
.format(
len(zpressure) - len(z_nospct), inpercentile,
zcell_size))
# reject time range from data portal file export
t_portal, z_portal, y_portal = cf.reject_timestamps_dataportal(
subsite, r, t_nospct, y_nospct, z_nospct)
print('{} using visual inspection of data'.format(
len(z_nospct) - len(z_portal), inpercentile,
zcell_size))
# reject data in a depth range
if zdbar is not None:
y_ind = y_portal < zdbar
t_array = t_portal[y_ind]
y_array = y_portal[y_ind]
z_array = z_portal[y_ind]
else:
y_ind = []
t_array = t_portal
y_array = y_portal
z_array = z_portal
print('{} in water depth > {} dbar'.format(
len(y_ind), zdbar))
if len(y_array) > 0:
if m == 'common_stream_placeholder':
sname = '-'.join((vinfo['var_name'], r))
else:
sname = '-'.join((vinfo['var_name'], r, m))
"""
create data ranges for non - pressure data only
"""
if 'pressure' in vinfo['var_name']:
pass
else:
columns = ['tsec', 'dbar', str(vinfo['var_name'])]
# create depth ranges
min_r = int(round(min(y_array) - zcell_size))
max_r = int(round(max(y_array) + zcell_size))
ranges = list(range(min_r, max_r, zcell_size))
# group data by depth
groups, d_groups = gt.group_by_depth_range(
t_array, y_array, z_array, columns, ranges)
print('writing data ranges for {}'.format(
vinfo['var_name']))
stat_data = groups.describe()[vinfo['var_name']]
stat_data.insert(loc=0,
column='parameter',
value=sv,
allow_duplicates=False)
t_deploy = deployments[0]
for i in range(
len(deployments))[1:len(deployments)]:
t_deploy = '{}, {}'.format(
t_deploy, deployments[i])
stat_data.insert(loc=1,
column='deployments',
value=t_deploy,
allow_duplicates=False)
stat_df = stat_df.append(stat_data, ignore_index=True)
"""
plot full time range free from errors and suspect data
"""
clabel = sv + " (" + sv_units + ")"
ylabel = (y_name[0][0] + " (" + y_unit[0][0] + ")")
title = ' '.join((r, m))
# plot non-erroneous -suspect data
fig, ax, bar = pf.plot_xsection(subsite,
t_array,
y_array,
z_array,
clabel,
ylabel,
inpercentile=None,
stdev=None)
ax.set_title(title, fontsize=9)
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}]'
.format(
len(z) - lenfv,
len(z) - lennan,
len(z) - lenev, lengr, global_min,
global_max) + '\n' +
('removed {} in the upper and lower {} percentile of data grouped in {} dbar segments'
.format(
len(zpressure) - len(z_nospct), inpercentile,
zcell_size)), )
ax.legend(leg_text,
loc='upper center',
bbox_to_anchor=(0.5, -0.17),
fontsize=6)
for ii in range(len(end_times)):
ax.axvline(x=end_times[ii],
color='b',
linestyle='--',
linewidth=.8)
ax.text(end_times[ii],
min(y_array) - 5,
'End' + str(deployments[ii]),
fontsize=6,
style='italic',
bbox=dict(
boxstyle='round',
ec=(0., 0.5, 0.5),
fc=(1., 1., 1.),
))
fig.tight_layout()
sfile = '_'.join(('data_range', sname))
pf.save_fig(save_fdir, sfile)
# write stat file
stat_df.to_csv('{}/{}_data_ranges.csv'.format(save_dir_stat, r),
index=True,
float_format='%11.6f')
def main(url_list, sDir, deployment_num, start_time, end_time, preferred_only,
zdbar, n_std, inpercentile, zcell_size):
rd_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
if rd not in rd_list and 'ENG' not in rd and 'ADCP' not in rd:
rd_list.append(rd)
for r in rd_list:
print('\n{}'.format(r))
datasets = []
for u in url_list:
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join(
(splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = []
if preferred_only == 'yes':
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
for index, row in ps_df.iterrows():
for ii in range(n_streams):
try:
rms = '-'.join((r, row[ii]))
except TypeError:
continue
for dd in datasets:
spl = dd.split('/')[-2].split('-')
catalog_rms = '-'.join(
(spl[1], spl[2], spl[3], spl[4], spl[5], spl[6]))
fdeploy = dd.split('/')[-1].split('_')[0]
if rms == catalog_rms and fdeploy == row['deployment']:
fdatasets.append(dd)
else:
fdatasets = datasets
main_sensor = r.split('-')[-1]
fdatasets_sel = cf.filter_collocated_instruments(
main_sensor, fdatasets)
for fd in fdatasets_sel:
part_d = fd.split('/')[-1]
print('\n{}'.format(part_d))
ds = xr.open_dataset(fd, mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(
fd)
array = subsite[0:2]
sci_vars = cf.return_science_vars(stream)
# if 'CE05MOAS' in r or 'CP05MOAS' in r: # for coastal gliders, get m_water_depth for bathymetry
# eng = '-'.join((r.split('-')[0], r.split('-')[1], '00-ENG000000', method, 'glider_eng'))
# eng_url = [s for s in url_list if eng in s]
# if len(eng_url) == 1:
# eng_datasets = cf.get_nc_urls(eng_url)
# # filter out collocated datasets
# eng_dataset = [j for j in eng_datasets if (eng in j.split('/')[-1] and deployment in j.split('/')[-1])]
# if len(eng_dataset) > 0:
# ds_eng = xr.open_dataset(eng_dataset[0], mask_and_scale=False)
# t_eng = ds_eng['time'].values
# m_water_depth = ds_eng['m_water_depth'].values
#
# # m_altimeter_status = 0 means a good reading (not nan or -1)
# try:
# eng_ind = ds_eng['m_altimeter_status'].values == 0
# except KeyError:
# eng_ind = (~np.isnan(m_water_depth)) & (m_water_depth >= 0)
#
# m_water_depth = m_water_depth[eng_ind]
# t_eng = t_eng[eng_ind]
#
# # get rid of any remaining nans or fill values
# eng_ind2 = (~np.isnan(m_water_depth)) & (m_water_depth >= 0)
# m_water_depth = m_water_depth[eng_ind2]
# t_eng = t_eng[eng_ind2]
# else:
# print('No engineering file for deployment {}'.format(deployment))
# m_water_depth = None
# t_eng = None
# else:
# m_water_depth = None
# t_eng = None
# else:
# m_water_depth = None
# t_eng = None
if deployment_num is not None:
if int(int(deployment[-4:])) is not deployment_num:
print(type(int(deployment[-4:])), type(deployment_num))
continue
if start_time is not None and end_time is not None:
ds = ds.sel(time=slice(start_time, end_time))
if len(ds['time'].values) == 0:
print(
'No data to plot for specified time range: ({} to {})'.
format(start_time, end_time))
continue
stime = start_time.strftime('%Y-%m-%d')
etime = end_time.strftime('%Y-%m-%d')
ext = stime + 'to' + etime # .join((ds0_method, ds1_method
save_dir_profile = os.path.join(sDir, array, subsite, refdes,
'profile_plots', deployment,
ext)
save_dir_xsection = os.path.join(sDir, array, subsite, refdes,
'xsection_plots', deployment,
ext)
save_dir_4d = os.path.join(sDir, array, subsite, refdes,
'xsection_plots_4d', deployment,
ext)
else:
save_dir_profile = os.path.join(sDir, array, subsite, refdes,
'profile_plots', deployment)
save_dir_xsection = os.path.join(sDir, array, subsite, refdes,
'xsection_plots', deployment)
save_dir_4d = os.path.join(sDir, array, subsite, refdes,
'xsection_plots_4d', deployment)
texclude_dir = os.path.join(sDir, array, subsite, refdes,
'time_to_exclude')
cf.create_dir(texclude_dir)
time1 = ds['time'].values
try:
ds_lat1 = ds['lat'].values
except KeyError:
ds_lat1 = None
print('No latitude variable in file')
try:
ds_lon1 = ds['lon'].values
except KeyError:
ds_lon1 = None
print('No longitude variable in file')
# get pressure variable
pvarname, y1, y_units, press, y_fillvalue = cf.add_pressure_to_dictionary_of_sci_vars(
ds)
# prepare file to list timestamps with suspect data for each data parameter
stat_data = pd.DataFrame(
columns=['deployments', 'time_to_exclude'])
file_exclude = '{}/{}_{}_{}_excluded_timestamps.csv'.format(
texclude_dir, deployment, refdes, method)
stat_data.to_csv(file_exclude, index=True)
# loop through sensor-data parameters
for sv in sci_vars:
print(sv)
if 'pressure' not in sv:
z1 = ds[sv].values
fv = ds[sv]._FillValue
sv_units = ds[sv].units
# Check if the array is all NaNs
if sum(np.isnan(z1)) == len(z1):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(z1[z1 != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
# remove unreasonable pressure data (e.g. for surface piercing profilers)
if zdbar:
po_ind = (0 < y1) & (y1 < zdbar)
n_zdbar = np.sum(~po_ind)
tm = time1[po_ind]
y = y1[po_ind]
z = z1[po_ind]
ds_lat = ds_lat1[po_ind]
ds_lon = ds_lon1[po_ind]
print('{} in water depth > {} dbar'.format(
n_zdbar, zdbar))
else:
tm = time1
y = y1
z = z1
ds_lat = ds_lat1
ds_lon = ds_lon1
# reject erroneous data
dtime, zpressure, ndata, lenfv, lennan, lenev, lengr, global_min, global_max, lat, lon = \
cf.reject_erroneous_data(r, sv, tm, y, z, fv, ds_lat, ds_lon)
# get rid of 0.0 data
if sv == 'salinity':
ind = ndata > 30
elif sv == 'density':
ind = ndata > 1022.5
elif sv == 'conductivity':
ind = ndata > 3.45
else:
ind = ndata > 0
# if sv == 'sci_flbbcd_chlor_units':
# ind = ndata < 7.5
# elif sv == 'sci_flbbcd_cdom_units':
# ind = ndata < 25
# else:
# ind = ndata > 0.0
# if 'CTD' in r:
# ind = zpressure > 0.0
# else:
# ind = ndata > 0.0
lenzero = np.sum(~ind)
dtime = dtime[ind]
zpressure = zpressure[ind]
ndata = ndata[ind]
if ds_lat is not None and ds_lon is not None:
lat = lat[ind]
lon = lon[ind]
else:
lat = None
lon = None
if len(dtime) > 0:
# reject time range from data portal file export
t_portal, z_portal, y_portal, lat_portal, lon_portal = \
cf.reject_timestamps_dataportal(subsite, r, dtime, zpressure, ndata, lat, lon)
print(
'removed {} data points using visual inspection of data'
.format(len(ndata) - len(z_portal)))
# create data groups
if len(y_portal) > 0:
columns = ['tsec', 'dbar', str(sv)]
min_r = int(round(min(y_portal) - zcell_size))
max_r = int(round(max(y_portal) + zcell_size))
ranges = list(range(min_r, max_r, zcell_size))
groups, d_groups = gt.group_by_depth_range(
t_portal, y_portal, z_portal, columns,
ranges)
if 'scatter' in sv:
n_std = None # to use percentile
else:
n_std = n_std
# identifying timestamps from percentile analysis
y_avg, n_avg, n_min, n_max, n0_std, n1_std, l_arr, time_ex = cf.reject_timestamps_in_groups(
groups, d_groups, n_std, inpercentile)
"""
writing timestamps to .csv file to use with data_range.py script
"""
if len(time_ex) != 0:
t_exclude = time_ex[0]
for i in range(
len(time_ex))[1:len(time_ex)]:
t_exclude = '{}, {}'.format(
t_exclude, time_ex[i])
stat_data = pd.DataFrame(
{
'deployments': deployment,
'time_to_exclude': t_exclude
},
index=[sv])
stat_data.to_csv(file_exclude,
index=True,
mode='a',
header=False)
# rejecting timestamps from percentile analysis
if len(time_ex) > 0:
t_nospct, z_nospct, y_nospct = cf.reject_suspect_data(
t_portal, y_portal, z_portal, time_ex)
else:
t_nospct = t_portal
z_nospct = z_portal
y_nospct = y_portal
"""
Plot data
"""
if len(t_nospct) > 0:
if len(t_nospct) != len(dtime):
cf.create_dir(save_dir_profile)
cf.create_dir(save_dir_xsection)
sname = '-'.join((r, method, sv))
sfile = '_'.join(
('rm_suspect_data', sname,
pd.to_datetime(
t_nospct.min()).strftime(
'%Y%m%d')))
t0 = pd.to_datetime(
t_nospct.min()).strftime(
'%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(
t_nospct.max()).strftime(
'%Y-%m-%dT%H:%M:%S')
title = ' '.join(
(deployment, refdes,
method)) + '\n' + t0 + ' to ' + t1
if zdbar:
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges '
'[{} - {}], {} unreasonable values'
.format(
lenfv, lennan, lenev,
lengr, global_min,
global_max, lenzero) +
'\nremoved {} in the upper and lower {} percentile of data grouped in {} '
'dbar segments'.format(
len(z_portal) -
len(z_nospct),
inpercentile, zcell_size) +
'\nexcluded {} suspect data points when inspected visually'
.format(
len(ndata) - len(z_portal))
+
'\nexcluded {} suspect data in water depth greater than {} dbar'
.format(n_zdbar, zdbar), )
elif n_std:
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} unreasonable values'.
format(lenfv, lennan, lenev,
lengr, global_min,
global_max, lenzero) +
'\nremoved {} data points +/- {} SD of data grouped in {} dbar segments'
.format(
len(z_portal) -
len(z_nospct), n_std,
zcell_size) +
'\nexcluded {} suspect data points when inspected visually'
.format(
len(ndata) -
len(z_portal)), )
else:
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}], '
'{} unreasonable values'.
format(lenfv, lennan, lenev,
lengr, global_min,
global_max, lenzero) +
'\nremoved {} in the upper and lower {} percentile of data grouped in {} dbar segments'
.format(
len(z_portal) -
len(z_nospct),
inpercentile, zcell_size) +
'\nexcluded {} suspect data points when inspected visually'
.format(
len(ndata) -
len(z_portal)), )
'''
profile plot
'''
xlabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[
0] + ")"
clabel = 'Time'
# plot non-erroneous data
print('plotting profile')
fig, ax = pf.plot_profiles(z_nospct,
y_nospct,
t_nospct,
ylabel,
xlabel,
clabel,
stdev=None)
ax.set_title(title, fontsize=9)
ax.plot(n_avg, y_avg, '-k')
#ax.fill_betweenx(y_avg, n0_std, n1_std, color='m', alpha=0.2)
ax.legend(leg_text,
loc='upper center',
bbox_to_anchor=(0.5, -0.17),
fontsize=6)
fig.tight_layout()
pf.save_fig(save_dir_profile, sfile)
'''
xsection plot
'''
print('plotting xsection')
clabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[
0] + ")"
# plot bathymetry only within data time ranges
# if t_eng is not None:
# eng_ind = (t_eng >= np.nanmin(t_array)) & (t_eng <= np.nanmax(t_array))
# t_eng = t_eng[eng_ind]
# m_water_depth = m_water_depth[eng_ind]
# plot non-erroneous data
fig, ax, bar = pf.plot_xsection(
subsite,
t_nospct,
y_nospct,
z_nospct,
clabel,
ylabel,
t_eng=None,
m_water_depth=None,
inpercentile=inpercentile,
stdev=None)
ax.set_title(title, fontsize=9)
ax.legend(leg_text,
loc='upper center',
bbox_to_anchor=(0.5, -0.17),
fontsize=6)
fig.tight_layout()
pf.save_fig(save_dir_xsection, sfile)
def main(url_list, sDir, plot_type):
"""""
URL : path to instrument data by methods
sDir : path to the directory on your machine to save files
plot_type: folder name for a plot type
""" ""
rd_list = []
ms_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
ms = uu.split(rd + '-')[1].split('/')[0]
if rd not in rd_list:
rd_list.append(rd)
if ms not in ms_list:
ms_list.append(ms)
'''
separate different instruments
'''
for r in rd_list:
print('\n{}'.format(r))
subsite = r.split('-')[0]
array = subsite[0:2]
main_sensor = r.split('-')[-1]
ps_df, n_streams = cf.get_preferred_stream_info(r)
# read in the analysis file
dr_data = cf.refdes_datareview_json(r)
# get end times of deployments
deployments = []
end_times = []
for index, row in ps_df.iterrows():
deploy = row['deployment']
deploy_info = get_deployment_information(dr_data, int(deploy[-4:]))
deployments.append(int(deploy[-4:]))
end_times.append(pd.to_datetime(deploy_info['stop_date']))
# get the list of data files and filter out collocated instruments and other streams chat
datasets = []
for u in url_list:
print(u)
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join(
(splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = cf.filter_collocated_instruments(main_sensor, datasets)
fdatasets = cf.filter_other_streams(r, ms_list, fdatasets)
'''
separate the data files by methods
'''
for ms in ms_list: # np.unique(methodstream)
fdatasets_sel = [x for x in fdatasets if ms in x]
# create a folder to save figures
save_dir = os.path.join(sDir, array, subsite, r, plot_type,
ms.split('-')[0])
cf.create_dir(save_dir)
# create a dictionary for science variables from analysis file
stream_sci_vars_dict = dict()
for x in dr_data['instrument']['data_streams']:
dr_ms = '-'.join((x['method'], x['stream_name']))
if ms == dr_ms:
stream_sci_vars_dict[dr_ms] = dict(vars=dict())
sci_vars = dict()
for y in x['stream']['parameters']:
if y['data_product_type'] == 'Science Data':
sci_vars.update(
{y['name']: dict(db_units=y['unit'])})
if len(sci_vars) > 0:
stream_sci_vars_dict[dr_ms]['vars'] = sci_vars
# initialize an empty data array for science variables in dictionary
sci_vars_dict = cd.initialize_empty_arrays(stream_sci_vars_dict,
ms)
y_unit = []
y_name = []
for fd in fdatasets_sel:
ds = xr.open_dataset(fd, mask_and_scale=False)
print('\nAppending data file: {}'.format(fd.split('/')[-1]))
for var in list(sci_vars_dict[ms]['vars'].keys()):
sh = sci_vars_dict[ms]['vars'][var]
if ds[var].units == sh['db_units']:
if ds[var]._FillValue not in sh['fv']:
sh['fv'].append(ds[var]._FillValue)
if ds[var].units not in sh['units']:
sh['units'].append(ds[var].units)
# time
t = ds['time'].values
t0 = pd.to_datetime(
t.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(
t.max()).strftime('%Y-%m-%dT%H:%M:%S')
# sci variable
z = ds[var].values
sh['t'] = np.append(sh['t'], t)
sh['values'] = np.append(sh['values'], z)
# add pressure to dictionary of sci vars
if 'MOAS' in subsite:
if 'CTD' in main_sensor: # for glider CTDs, pressure is a coordinate
pressure = 'sci_water_pressure_dbar'
y = ds[pressure].values
if ds[pressure].units not in y_unit:
y_unit.append(ds[pressure].units)
if ds[pressure].long_name not in y_name:
y_name.append(ds[pressure].long_name)
else:
pressure = 'int_ctd_pressure'
y = ds[pressure].values
if ds[pressure].units not in y_unit:
y_unit.append(ds[pressure].units)
if ds[pressure].long_name not in y_name:
y_name.append(ds[pressure].long_name)
else:
pressure = pf.pressure_var(ds, ds.data_vars.keys())
y = ds[pressure].values
if ds[pressure].units not in y_unit:
y_unit.append(ds[pressure].units)
if ds[pressure].long_name not in y_name:
y_name.append(ds[pressure].long_name)
sh['pressure'] = np.append(sh['pressure'], y)
if len(y_unit) != 1:
print('pressure unit varies!')
else:
y_unit = y_unit[0]
if len(y_name) != 1:
print('pressure long name varies!')
else:
y_name = y_name[0]
for m, n in sci_vars_dict.items():
for sv, vinfo in n['vars'].items():
print('\nWorking on variable: {}'.format(sv))
if len(vinfo['t']) < 1:
print('no variable data to plot')
else:
sv_units = vinfo['units'][0]
fv = vinfo['fv'][0]
t0 = pd.to_datetime(min(
vinfo['t'])).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(max(
vinfo['t'])).strftime('%Y-%m-%dT%H:%M:%S')
t = vinfo['t']
x = vinfo['values']
y = vinfo['pressure']
# Check if the array is all NaNs
if sum(np.isnan(x)) == len(x):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(x[x != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
# reject fill values
fv_ind = x != fv
y_nofv = y[fv_ind]
t_nofv = t[fv_ind]
c_nofv = cm.rainbow(np.linspace(0, 1, len(t[fv_ind])))
x_nofv = x[fv_ind]
print(len(x) - len(fv_ind), ' fill values')
# reject NaNs
nan_ind = ~np.isnan(x)
t_nofv_nonan = t_nofv[nan_ind]
c_nofv_nonan = c_nofv[nan_ind]
y_nofv_nonan = y_nofv[nan_ind]
x_nofv_nonan = x_nofv[nan_ind]
print(len(x) - len(nan_ind), ' NaNs')
# reject extreme values
ev_ind = cf.reject_extreme_values(x_nofv_nonan)
t_nofv_nonan_noev = t_nofv_nonan[ev_ind]
c_nofv_nonan_noev = c_nofv_nonan[ev_ind]
y_nofv_nonan_noev = y_nofv_nonan[ev_ind]
x_nofv_nonan_noev = x_nofv_nonan[ev_ind]
print(len(z) - len(ev_ind), ' Extreme Values', '|1e7|')
# reject values outside global ranges:
global_min, global_max = cf.get_global_ranges(r, sv)
# platform not in qc-table (parad_k_par)
# global_min = 0
# global_max = 2500
print('global ranges for : {}-{} {} - {}'.format(
r, sv, global_min, global_max))
if isinstance(global_min, (int, float)) and isinstance(
global_max, (int, float)):
gr_ind = cf.reject_global_ranges(
x_nofv_nonan_noev, global_min, global_max)
t_nofv_nonan_noev_nogr = t_nofv_nonan_noev[gr_ind]
y_nofv_nonan_noev_nogr = y_nofv_nonan_noev[gr_ind]
x_nofv_nonan_noev_nogr = x_nofv_nonan_noev[gr_ind]
else:
t_nofv_nonan_noev_nogr = t_nofv_nonan_noev
y_nofv_nonan_noev_nogr = y_nofv_nonan_noev
x_nofv_nonan_noev_nogr = x_nofv_nonan_noev
if len(x_nofv_nonan_noev) > 0:
if m == 'common_stream_placeholder':
sname = '-'.join((r, sv))
else:
sname = '-'.join((r, m, sv))
if sv != 'pressure':
columns = ['tsec', 'dbar', str(sv)]
bin_size = 10
min_r = int(round(min(y_nofv_nonan_noev) - bin_size))
max_r = int(round(max(y_nofv_nonan_noev) + bin_size))
ranges = list(range(min_r, max_r, bin_size))
groups, d_groups = gt.group_by_depth_range(
t_nofv_nonan_noev_nogr, y_nofv_nonan_noev_nogr,
x_nofv_nonan_noev_nogr, columns, ranges)
y_avg, n_avg, n_min, n_max, n0_std, n1_std, l_arr = [], [], [], [], [], [], []
tm = 1
for ii in range(len(groups)):
nan_ind = d_groups[ii + tm].notnull()
xtime = d_groups[ii + tm][nan_ind]
colors = cm.rainbow(np.linspace(0, 1, len(xtime)))
ypres = d_groups[ii + tm + 1][nan_ind]
nval = d_groups[ii + tm + 2][nan_ind]
tm += 2
l_arr.append(len(
nval)) # count of data to filter out small groups
y_avg.append(ypres.mean())
n_avg.append(nval.mean())
n_min.append(nval.min())
n_max.append(nval.max())
n_std = 3
n0_std.append(nval.mean() + n_std * nval.std())
n1_std.append(nval.mean() - n_std * nval.std())
# Plot all data
ylabel = y_name + " (" + y_unit + ")"
xlabel = sv + " (" + sv_units + ")"
clabel = 'Time'
fig, ax = pf.plot_profiles(x_nofv_nonan_noev_nogr,
y_nofv_nonan_noev_nogr,
t_nofv_nonan_noev_nogr,
ylabel,
xlabel,
clabel,
end_times,
deployments,
stdev=None)
title_text = ' '.join((r, ms.split('-')[-1])) + '\n' \
+ t0 + ' - ' + t1 + '\n' + str(bin_size) +\
' m average and ' + str(n_std) + ' std shown'
ax.set_title(title_text, fontsize=9)
ax.plot(n_avg, y_avg, '-k')
ax.fill_betweenx(y_avg,
n0_std,
n1_std,
color='m',
alpha=0.2)
pf.save_fig(save_dir, sname)
# Plot data with outliers removed
fig, ax = pf.plot_profiles(x_nofv_nonan_noev_nogr,
y_nofv_nonan_noev_nogr,
t_nofv_nonan_noev_nogr,
ylabel,
xlabel,
clabel,
end_times,
deployments,
stdev=5)
ax.set_title(' '.join((r, ms.split('-')[-1])) + '\n' \
+ t0 + ' - ' + t1, fontsize=9)
sfile = '_'.join((sname, 'rmoutliers'))
pf.save_fig(save_dir, sfile)
def main(url_list, sDir, plot_type, deployment_num, start_time, end_time, method_num, zdbar, n_std, inpercentile, zcell_size):
for i, u in enumerate(url_list):
print('\nUrl {} of {}: {}'.format(i + 1, len(url_list), u))
elements = u.split('/')[-2].split('-')
r = '-'.join((elements[1], elements[2], elements[3], elements[4]))
ms = u.split(r + '-')[1].split('/')[0]
subsite = r.split('-')[0]
array = subsite[0:2]
main_sensor = r.split('-')[-1]
# read URL to get data
datasets = cf.get_nc_urls([u])
datasets_sel = cf.filter_collocated_instruments(main_sensor, datasets)
# get sci data review list
dr_data = cf.refdes_datareview_json(r)
ps_df, n_streams = cf.get_preferred_stream_info(r)
# get end times of deployments
deployments = []
end_times = []
for index, row in ps_df.iterrows():
deploy = row['deployment']
deploy_info = cf.get_deployment_information(dr_data, int(deploy[-4:]))
deployments.append(int(deploy[-4:]))
end_times.append(pd.to_datetime(deploy_info['stop_date']))
# create a dictionary for science variables from analysis file
stream_sci_vars_dict = dict()
for x in dr_data['instrument']['data_streams']:
dr_ms = '-'.join((x['method'], x['stream_name']))
if ms == dr_ms:
stream_sci_vars_dict[dr_ms] = dict(vars=dict())
sci_vars = dict()
for y in x['stream']['parameters']:
if y['data_product_type'] == 'Science Data':
sci_vars.update({y['name']: dict(db_units=y['unit'])})
if len(sci_vars) > 0:
stream_sci_vars_dict[dr_ms]['vars'] = sci_vars
for ii, d in enumerate(datasets_sel):
part_d = d.split('/')[-1]
print('\nDataset {} of {}: {}'.format(ii + 1, len(datasets_sel), part_d))
with xr.open_dataset(d, mask_and_scale=False) as ds:
ds = ds.swap_dims({'obs': 'time'})
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(d)
if method_num is not None:
if method != method_num:
print(method_num, method)
continue
if deployment_num is not None:
if int(deployment.split('0')[-1]) is not deployment_num:
print(type(int(deployment.split('0')[-1])), type(deployment_num))
continue
if start_time is not None and end_time is not None:
ds = ds.sel(time=slice(start_time, end_time))
if len(ds['time'].values) == 0:
print('No data to plot for specified time range: ({} to {})'.format(start_time, end_time))
continue
stime = start_time.strftime('%Y-%m-%d')
etime = end_time.strftime('%Y-%m-%d')
ext = stime + 'to' + etime # .join((ds0_method, ds1_method
save_dir = os.path.join(sDir, array, subsite, refdes, plot_type, ms.split('-')[0], deployment, ext)
else:
save_dir = os.path.join(sDir, array, subsite, refdes, plot_type, ms.split('-')[0], deployment)
cf.create_dir(save_dir)
texclude_dir = os.path.join(sDir, array, subsite, refdes, 'time_to_exclude')
cf.create_dir(texclude_dir)
# initialize an empty data array for science variables in dictionary
sci_vars_dict = cd.initialize_empty_arrays(stream_sci_vars_dict, ms)
for var in list(sci_vars_dict[ms]['vars'].keys()):
sh = sci_vars_dict[ms]['vars'][var]
if ds[var].units == sh['db_units']:
if ds[var]._FillValue not in sh['fv']:
sh['fv'].append(ds[var]._FillValue)
if ds[var].units not in sh['units']:
sh['units'].append(ds[var].units)
sh['t'] = np.append(sh['t'], ds['time'].values) # t = ds['time'].values
sh['values'] = np.append(sh['values'], ds[var].values) # z = ds[var].values
y, y_unit, y_name = cf.add_pressure_to_dictionary_of_sci_vars(ds)
sh['pressure'] = np.append(sh['pressure'], y)
stat_data = pd.DataFrame(columns=['deployments', 'time_to_exclude'])
file_exclude = '{}/{}_{}_{}_excluded_timestamps.csv'.format(texclude_dir,
deployment, refdes, method)
stat_data.to_csv(file_exclude, index=True)
for m, n in sci_vars_dict.items():
for sv, vinfo in n['vars'].items():
print(sv)
if len(vinfo['t']) < 1:
print('no variable data to plot')
else:
sv_units = vinfo['units'][0]
fv = vinfo['fv'][0]
t0 = pd.to_datetime(min(vinfo['t'])).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(max(vinfo['t'])).strftime('%Y-%m-%dT%H:%M:%S')
colors = cm.rainbow(np.linspace(0, 1, len(vinfo['t'])))
t = vinfo['t']
z = vinfo['values']
y = vinfo['pressure']
# Check if the array is all NaNs
if sum(np.isnan(z)) == len(z):
print('Array of all NaNs - skipping plot.')
continue
# Check if the array is all fill values
elif len(z[z != fv]) == 0:
print('Array of all fill values - skipping plot.')
continue
else:
# reject erroneous data
dtime, zpressure, ndata, lenfv, lennan, lenev, lengr, global_min, global_max = \
cf.reject_erroneous_data(r, sv, t, y, z, fv)
# create data groups
columns = ['tsec', 'dbar', str(sv)]
min_r = int(round(min(zpressure) - zcell_size))
max_r = int(round(max(zpressure) + zcell_size))
ranges = list(range(min_r, max_r, zcell_size))
groups, d_groups = gt.group_by_depth_range(dtime, zpressure, ndata, columns, ranges)
# ... excluding timestamps
if 'scatter' in sv:
n_std = None #to use percentile
else:
n_std = n_std
# rejecting timestamps from percentile analysis
y_avg, n_avg, n_min, n_max, n0_std, n1_std, l_arr, time_ex, \
t_nospct, z_nospct, y_nospct = cf.reject_timestamps_in_groups(groups, d_groups, n_std,
dtime, zpressure, ndata,
inpercentile)
print('{} using {} percentile of data grouped in {} dbar segments'.format(
len(zpressure) - len(z_nospct), inpercentile, zcell_size))
"""
writing timestamps to .csv file to use with data_range.py script
"""
if len(time_ex) != 0:
t_exclude = time_ex[0]
for i in range(len(time_ex))[1:len(time_ex)]:
t_exclude = '{}, {}'.format(t_exclude, time_ex[i])
stat_data = pd.DataFrame({'deployments': deployment,
'time_to_exclude': t_exclude}, index=[sv])
stat_data.to_csv(file_exclude, index=True, mode='a', header=False)
# reject time range from data portal file export
t_portal, z_portal, y_portal = cf.reject_timestamps_dataportal(subsite, r,
t_nospct, z_nospct, y_nospct)
print('{} using visual inspection of data'.format(len(z_nospct) - len(z_portal),
inpercentile, zcell_size))
# reject data in a depth range
if zdbar is not None:
y_ind = y_portal < zdbar
t_array = t_portal[y_ind]
y_array = y_portal[y_ind]
z_array = z_portal[y_ind]
else:
y_ind = []
t_array = t_portal
y_array = y_portal
z_array = z_portal
print('{} in water depth > {} dbar'.format(len(y_ind), zdbar))
"""
Plot data
"""
if len(t_array) > 0:
if m == 'common_stream_placeholder':
sname = '-'.join((sv, r))
else:
sname = '-'.join((sv, r, m))
xlabel = sv + " (" + sv_units + ")"
ylabel = y_name[0] + " (" + y_unit[0] + ")"
clabel = 'Time'
title = ' '.join((deployment, r, m))
# plot non-erroneous data
fig, ax = pf.plot_profiles(ndata, zpressure, dtime,
ylabel, xlabel, clabel, end_times, deployments, stdev=None)
ax.set_title(title, fontsize=9)
ax.plot(n_avg, y_avg, '-k')
ax.fill_betweenx(y_avg, n0_std, n1_std, color='m', alpha=0.2)
leg_text = (
'removed {} fill values, {} NaNs, {} Extreme Values (1e7), {} Global ranges [{} - {}]'.format(
len(z) - lenfv, len(z) - lennan, len(z) - lenev, lengr, global_min, global_max) + '\n' +
('(black) data average in {} dbar segments'.format(zcell_size)) + '\n' +
('(magenta) upper and lower {} percentile envelope in {} dbar segments'.format(
inpercentile, zcell_size)),)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
sfile = '_'.join(('rm_erroneous_data', sname))
pf.save_fig(save_dir, sfile)
# plot excluding time ranges for suspect data
if len(z_nospct) != len(zpressure):
fig, ax = pf.plot_profiles(z_nospct, y_nospct, t_nospct,
ylabel, xlabel, clabel, end_times, deployments, stdev=None)
ax.set_title(title, fontsize=9)
leg_text = (
'removed {} in the upper and lower {} percentile of data grouped in {} dbar segments'.format(
len(zpressure) - len(z_nospct), inpercentile, zcell_size),)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
sfile = '_'.join(('rm_suspect_data', sname))
pf.save_fig(save_dir, sfile)
# plot excluding time ranges from data portal export
if len(z_nospct) - len(z_portal):
fig, ax = pf.plot_profiles(z_portal, y_portal, t_portal,
ylabel, xlabel, clabel, end_times, deployments, stdev=None)
ax.set_title(title, fontsize=9)
leg_text = ('excluded {} suspect data when inspected visually'.format(
len(z_nospct) - len(z_portal)),)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
sfile = '_'.join(('rm_v_suspect_data', sname))
pf.save_fig(save_dir, sfile)
# Plot excluding a selected depth value
if len(z_array) != len(z_array):
fig, ax = pf.plot_profiles(z_array, y_array, t_array,
ylabel, xlabel, clabel, end_times, deployments, stdev=None)
ax.set_title(title, fontsize=9)
leg_text = ('excluded {} suspect data in water depth greater than {} dbar'.format(len(y_ind), zdbar),)
ax.legend(leg_text, loc='upper center', bbox_to_anchor=(0.5, -0.17), fontsize=6)
fig.tight_layout()
sfile = '_'.join(('rm_depth_range', sname))
pf.save_fig(save_dir, sfile)
def main(url_list, sDir, plot_type, deployment_num, start_time, end_time):
"""""
URL : path to instrument data by methods
sDir : path to the directory on your machine to save files
plot_type: folder name for a plot type
""" ""
rd_list = []
ms_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
ms = uu.split(rd + '-')[1].split('/')[0]
if rd not in rd_list:
rd_list.append(rd)
if ms not in ms_list:
ms_list.append(ms)
'''
separate different instruments
'''
for r in rd_list:
print('\n{}'.format(r))
subsite = r.split('-')[0]
array = subsite[0:2]
main_sensor = r.split('-')[-1]
ps_df, n_streams = cf.get_preferred_stream_info(r)
# read in the analysis file
dr_data = cf.refdes_datareview_json(r)
# get end times of deployments
deployments = []
end_times = []
for index, row in ps_df.iterrows():
deploy = row['deployment']
deploy_info = get_deployment_information(dr_data, int(deploy[-4:]))
deployments.append(int(deploy[-4:]))
end_times.append(pd.to_datetime(deploy_info['stop_date']))
# get the list of data files and filter out collocated instruments and other streams chat
datasets = []
for u in url_list:
print(u)
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join(
(splitter[1], splitter[2], splitter[3], splitter[4]))
if rd_check == r:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = cf.filter_collocated_instruments(main_sensor, datasets)
fdatasets = cf.filter_other_streams(r, ms_list, fdatasets)
'''
separate the data files by methods
'''
for ms in ms_list:
fdatasets_sel = [x for x in fdatasets if ms in x]
# create a dictionary for science variables from analysis file
stream_sci_vars_dict = dict()
for x in dr_data['instrument']['data_streams']:
dr_ms = '-'.join((x['method'], x['stream_name']))
if ms == dr_ms:
stream_sci_vars_dict[dr_ms] = dict(vars=dict())
sci_vars = dict()
for y in x['stream']['parameters']:
if y['data_product_type'] == 'Science Data':
sci_vars.update(
{y['name']: dict(db_units=y['unit'])})
if len(sci_vars) > 0:
stream_sci_vars_dict[dr_ms]['vars'] = sci_vars
# initialize an empty data array for science variables in dictionary
sci_vars_dict = cd.initialize_empty_arrays(stream_sci_vars_dict,
ms)
print('\nAppending data from files: {}'.format(ms))
y_unit = []
y_name = []
for fd in fdatasets_sel:
ds = xr.open_dataset(fd, mask_and_scale=False)
print(fd)
if start_time is not None and end_time is not None:
ds = ds.sel(time=slice(start_time, end_time))
if len(ds['time'].values) == 0:
print(
'No data to plot for specified time range: ({} to {})'
.format(start_time, end_time))
continue
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(
fd)
if deployment_num is not None:
if int(deployment.split('0')[-1]) is not deployment_num:
print(type(int(deployment.split('0')[-1])),
type(deployment_num))
continue
save_dir = os.path.join(sDir, array, subsite, refdes,
plot_type,
ms.split('-')[0], deployment)
cf.create_dir(save_dir)
for var in list(sci_vars_dict[ms]['vars'].keys()):
sh = sci_vars_dict[ms]['vars'][var]
if ds[var].units == sh['db_units']:
if ds[var]._FillValue not in sh['fv']:
sh['fv'].append(ds[var]._FillValue)
if ds[var].units not in sh['units']:
sh['units'].append(ds[var].units)
# time
t = ds['time'].values
t0 = pd.to_datetime(
t.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(
t.max()).strftime('%Y-%m-%dT%H:%M:%S')
# sci variable
z = ds[var].values
sh['t'] = np.append(sh['t'], t)
sh['values'] = np.append(sh['values'], z)
# add pressure to dictionary of sci vars
if 'MOAS' in subsite:
if 'CTD' in main_sensor: # for glider CTDs, pressure is a coordinate
pressure = 'sci_water_pressure_dbar'
y = ds[pressure].values
if ds[pressure].units not in y_unit:
y_unit.append(ds[pressure].units)
if ds[pressure].long_name not in y_name:
y_name.append(ds[pressure].long_name)
else:
pressure = 'int_ctd_pressure'
y = ds[pressure].values
if ds[pressure].units not in y_unit:
y_unit.append(ds[pressure].units)
if ds[pressure].long_name not in y_name:
y_name.append(ds[pressure].long_name)
else:
pressure = pf.pressure_var(ds, ds.data_vars.keys())
y = ds[pressure].values
if ds[pressure].units not in y_unit:
y_unit.append(ds[pressure].units)
if ds[pressure].long_name not in y_name:
y_name.append(ds[pressure].long_name)
sh['pressure'] = np.append(sh['pressure'], y)
if len(y_unit) != 1:
print('pressure unit varies UHHHHHHHHH')
else:
y_unit = y_unit[0]
if len(y_name) != 1:
print('pressure long name varies UHHHHHHHHH')
else:
y_name = y_name[0]
for m, n in sci_vars_dict.items():
for sv, vinfo in n['vars'].items():
print(sv)
if len(vinfo['t']) < 1:
print('no variable data to plot')
else:
sv_units = vinfo['units'][0]
fv = vinfo['fv'][0]
t0 = pd.to_datetime(min(
vinfo['t'])).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(max(
vinfo['t'])).strftime('%Y-%m-%dT%H:%M:%S')
t = vinfo['t']
z = vinfo['values']
y = vinfo['pressure']
title = ' '.join((r, ms.split('-')[1]))
# Check if the array is all NaNs
if sum(np.isnan(z)) == len(z):
print('Array of all NaNs - skipping plot.')
# Check if the array is all fill values
elif len(z[z != fv]) == 0:
print('Array of all fill values - skipping plot.')
else:
# reject fill values
fv_ind = z != fv
y_nofv = y[fv_ind]
t_nofv = t[fv_ind]
z_nofv = z[fv_ind]
print(len(z) - len(fv_ind), ' fill values')
# reject NaNs
nan_ind = ~np.isnan(z)
t_nofv_nonan = t_nofv[nan_ind]
y_nofv_nonan = y_nofv[nan_ind]
z_nofv_nonan = z_nofv[nan_ind]
print(len(z) - len(nan_ind), ' NaNs')
# reject extreme values
ev_ind = cf.reject_extreme_values(z_nofv_nonan)
t_nofv_nonan_noev = t_nofv_nonan[ev_ind]
colors = cm.rainbow(
np.linspace(0, 1, len(t_nofv_nonan_noev)))
y_nofv_nonan_noev = y_nofv_nonan[ev_ind]
z_nofv_nonan_noev = z_nofv_nonan[ev_ind]
print(
len(z) - len(ev_ind), ' Extreme Values',
'|1e7|')
if len(y_nofv_nonan_noev) > 0:
if m == 'common_stream_placeholder':
sname = '-'.join((r, sv))
else:
sname = '-'.join((r, m, sv))
# Plot all data
ylabel = y_name + " (" + y_unit + ")"
xlabel = sv + " (" + sv_units + ")"
clabel = 'Time'
clabel = sv + " (" + sv_units + ")"
fig, ax = pf.plot_profiles(z_nofv_nonan_noev,
y_nofv_nonan_noev,
colors,
xlabel,
ylabel,
stdev=None)
ax.set_title((
title + '\n' + str(deployment_num) + ': ' + t0 +
' - ' + t1 + '\n' +
'used bin = 2 dbar to calculate an average profile (black line) and 3-STD envelope (shaded area)'
),
fontsize=9)
# group by depth range
columns = ['time', 'pressure', str(sv)]
# ranges = [0, 50, 100, 200, 400, 600]
ranges = list(
range(int(round(min(y_nofv_nonan_noev))),
int(round(max(y_nofv_nonan_noev))), 1))
groups, d_groups = gt.group_by_depth_range(
t_nofv_nonan_noev, y_nofv_nonan_noev,
z_nofv_nonan_noev, columns, ranges)
# describe_file = '_'.join((sname, 'statistics.csv'))
# # groups.describe().to_csv(save_dir + '/' + describe_file)
ind = groups.describe()[sv]['mean'].notnull()
groups.describe()[sv][ind].to_csv(
'{}/{}_statistics.csv'.format(save_dir, sname),
index=True)
tm = 1
fig, ax = pyplot.subplots(nrows=2, ncols=1)
pyplot.margins(y=.08, x=.02)
pyplot.grid()
y_avg, n_avg, n_min, n_max, n0_std, n1_std, l_arr = [], [], [], [], [], [], []
for ii in range(len(groups)):
nan_ind = d_groups[ii + tm].notnull()
xtime = d_groups[ii + tm][nan_ind]
colors = cm.rainbow(np.linspace(0, 1, len(xtime)))
ypres = d_groups[ii + tm + 1][nan_ind]
nval = d_groups[ii + tm + 2][nan_ind]
tm += 2
# fig, ax = pf.plot_xsection(subsite, xtime, ypres, nval, clabel, ylabel, stdev=None)
# ax.set_title((title + '\n' + t0 + ' - ' + t1), fontsize=9)
# pf.plot_profiles(nval, ypres, colors, ylabel, clabel, stdev=None)
# ax.set_title((title + '\n' + t0 + ' - ' + t1), fontsize=9)
ind2 = cf.reject_outliers(nval, 5)
xD = nval[ind2]
yD = ypres[ind2]
nZ = colors[ind2]
outliers = str(len(nval) - len(xD))
leg_text = ('removed {} outliers (SD={})'.format(
outliers, stdev), )
ax.scatter(xD, yD, c=nZ, s=2, edgecolor='None')
ax.invert_yaxis()
ax.set_xlabel(clabel, fontsize=9)
ax.set_ylabel(ylabel, fontsize=9)
ax.legend(leg_text, loc='best', fontsize=6)
ax.set_title((title + '\n' + t0 + ' - ' + t1),
fontsize=9)
l_arr.append(
len(nval)
) # count of data to filter out small groups
y_avg.append(ypres.mean())
n_avg.append(nval.mean())
n_min.append(nval.min())
n_max.append(nval.max())
n0_std.append(nval.mean() + 3 * nval.std())
n1_std.append(nval.mean() - 3 * nval.std())
ax.plot(n_avg, y_avg, '-k')
# ax.plot(n_min, y_avg, '-b')
# ax.plot(n_max, y_avg, '-b')
ax.fill_betweenx(y_avg,
n0_std,
n1_std,
color='m',
alpha=0.2)
sfile = '_'.join((sname, 'statistics'))
pf.save_fig(save_dir, sfile)