def main(radial, database=False):
"""
:param radial:
:param database:
:return:
"""
# Get directory and filename of radial file
pathname, filename = os.path.split(radial)
pathname_qc = os.path.join(pathname.split('radials')[0], 'radials_qc', filename[5:9])
filename_qc = os.path.join(pathname_qc, filename)
# Create new dir from pathname if it doesn't already exist
cf.create_dir(pathname_qc)
# Parse the radial file with the generic codar CTF/LLUV parser located in functions/common
radial_file_data = cf.parse_lluv(radial)
# Create radial_data variable with alias for main datatable in radial_file_data
radial_data = radial_file_data['tables']['1']['data']
# QARTOD QC Tests
radial_data = qc.qc_location(radial_data)
radial_data = qc.qc_speed(radial_data, 250)
radial_file_data['header']['qc_qartod_radial_count'] = str(qc.qc_radial_count(radial_data, 150, 300))
# Modify any tableheader information that needs to be updated
radial_file_data['tables']['1']['TableColumns'] = radial_data.shape[1]
header_list = radial_data.columns.tolist()
header_list.remove('%%')
radial_file_data['tables']['1']['TableColumnTypes'] = ' '.join(header_list)
# Generate quality controlled radial file
create_ruv(radial_file_data, filename_qc)
def plot_ctdmo(data_dict, var, stdev=None):
colors10 = [
'red', 'firebrick', 'orange', 'mediumseagreen', 'blue', 'darkgreen',
'purple', 'indigo', 'slategray', 'black'
]
colors16 = [
'red', 'firebrick', 'orange', 'gold', 'mediumseagreen', 'darkcyan',
'blue', 'darkgreen', 'purple', 'lightgray', 'slategray', 'black',
'coral', 'gold', 'limegreen', 'midnightblue'
]
fig, ax1 = plt.subplots()
sensor_list = []
median_list = []
for i, (key, value) in enumerate(data_dict.items()):
if len(data_dict) < 11:
colors = colors10
else:
colors = colors16
t = value['time']
y = value['yD']
if stdev != None:
ind = cf.reject_outliers(value['yD'], stdev)
t = t[ind]
y = y[ind]
refdes = str(key)
sensor_list.append(refdes.split('-')[-1])
median_list.append(value['median'])
plt.scatter(t, y, c=colors[i], marker='.', s=.5)
if i == len(data_dict) - 1: # if the last dataset has been plotted
plt.grid()
plt.margins(y=.05, x=.05)
# refdes on secondary y-axis only for pressure and density
if var in ['ctdmo_seawater_pressure', 'density']:
ax2 = ax1.twinx()
ax2.set_ylim(ax1.get_ylim())
plt.yticks(median_list, sensor_list, fontsize=7.5)
plt.subplots_adjust(right=.85)
pf.format_date_axis(ax1, fig)
pf.y_axis_disable_offset(ax1)
subsite = refdes.split('-')[0]
title = subsite + ' ' + ('-'.join(
(value['dms'].split('-')[0], value['dms'].split('-')[1])))
ax1.set_ylabel((var + " (" + value['yunits'] + ")"), fontsize=9)
ax1.set_title(title, fontsize=10)
fname = '-'.join((subsite, value['dms'], var))
if stdev != None:
fname = '-'.join((fname, 'outliers_rejected'))
sdir = os.path.join(sDir, subsite, value['dms'].split('-')[0])
cf.create_dir(sdir)
pf.save_fig(sdir, fname)
def main(sDir, url_list, preferred_only):
rd_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)
for r in rd_list:
print('\n{}'.format(r))
subsite = r.split('-')[0]
array = subsite[0:2]
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))
if preferred_only == 'yes':
ps_df, n_streams = cf.get_preferred_stream_info(r)
fdatasets = []
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 = cf.filter_collocated_instruments(main_sensor, fdatasets)
num_data = len(fdatasets)
save_dir = os.path.join(sDir, array, subsite, r,
'preferred_method_plots')
cf.create_dir(save_dir)
print(len(fdatasets))
if len(fdatasets) > 3:
steps = list(range(3, len(fdatasets) + 3, 3))
for ii in steps:
plot_velocity_variables(r, fdatasets[ii - 3:ii], 3, save_dir)
else:
plot_velocity_variables(r, fdatasets, 3, save_dir)
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(sDir, f):
ff = pd.read_csv(os.path.join(sDir, f))
datasets = cf.get_nc_urls(ff['outputUrl'].tolist())
for d in datasets:
print(d)
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(
d)
save_dir = os.path.join(sDir, subsite, refdes, deployment)
cf.create_dir(save_dir)
sci_vars = cf.return_science_vars(stream)
colors = cm.jet(np.linspace(0, 1, len(sci_vars)))
with xr.open_dataset(d, mask_and_scale=False) as ds:
ds = ds.swap_dims({'obs': 'time'})
t = ds['time'].data
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')
title = ' '.join((deployment, refdes, method))
fig, ax = plt.subplots()
axes = [ax]
for i in range(len(sci_vars)):
if i > 0:
axes.append(ax.twinx()
) # twin the x-axis to make independent y-axes
fig.subplots_adjust(right=0.6)
right_additive = (0.98 - 0.6) / float(5)
for i in range(len(sci_vars)):
if i > 0:
axes[i].spines['right'].set_position(
('axes', 1. + right_additive * i))
y = ds[sci_vars[i]]
ind = cf.reject_outliers(y, 5)
yD = y.data[ind]
x = t[ind]
#yD = y.data
c = colors[i]
axes[i].plot(x, yD, '.', markersize=2, color=c)
axes[i].set_ylabel((y.name + " (" + y.units + ")"),
color=c,
fontsize=9)
axes[i].tick_params(axis='y', colors=c)
if i == len(
sci_vars) - 1: # if the last variable has been plotted
pf.format_date_axis(axes[i], fig)
axes[0].set_title((title + '\n' + t0 + ' - ' + t1), fontsize=9)
sfile = '_'.join((fname, 'timeseries'))
pf.save_fig(save_dir, sfile)
def main(url, out):
now = dt.datetime.now().strftime('%Y.%m.%dT%H.%M.00')
C = Crawl(url, select=[".*ncml"])
tds = 'https://opendap.oceanobservatories.org/thredds/dodsC/'
cf.create_dir(out)
fopen = open(out + '/' + now + '-nc-links.txt', 'w')
for dataset in C.datasets:
fopen.write(tds + dataset.id + '\n')
fopen.close()
def main(url, out):
now = dt.datetime.now().strftime('%Y.%m.%dT%H.%M.00')
C = Crawl(url, select=[".*ncml"])
tds = 'https://opendap.oceanobservatories.org/thredds/dodsC/'
cf.create_dir(out)
fopen = open(out+ '/' + now+'-nc-links.txt', 'w')
for dataset in C.datasets:
fopen.write(tds + dataset.id + '\n')
fopen.close()
def main(username, token, refdes, saveDir):
cf.create_dir(saveDir)
sensor_inv = 'https://ooinet.oceanobservatories.org/api/m2m/12576/sensor/inv/'
if not refdes:
f = 'uframe_annotations_all.csv'
elif ',' in refdes:
f = 'uframe_annotations_multiple_refdes.csv'
else:
f = 'uframe_annotations_{}.csv'.format(refdes)
fN = os.path.join(saveDir, f)
session = requests.session(
) # open the connection and leave it open for the session
with open(fN, 'a') as outfile:
writer = csv.writer(outfile)
writer.writerow([
'id', 'subsite', 'node', 'sensor', 'stream', 'method',
'parameters', 'beginDate', 'endDate', 'beginDT', 'endDT',
'exclusionFlag', 'qcFlag', 'source', 'annotation'
])
if not refdes: # if no refdes specified, provide all annotations
write_all_annotations(username, token, outfile, session)
else:
refdes_list = []
frefdes = data_request_tools.format_inputs(refdes)
for i in frefdes:
print(i)
if len(i) == 8:
url = sensor_inv + i
node_info = session.get(url, auth=(username, token))
nodes = node_info.json()
for n in nodes:
url2 = url + '/' + n
sensor_info = session.get(url2, auth=(username, token))
sensors = sensor_info.json()
for s in sensors:
refdes = '-'.join([i, n, s])
refdes_list.append(refdes)
elif len(i) == 14:
url = sensor_inv + i.split('-')[0] + '/' + i.split('-')[1]
sensor_info = session.get(url, auth=(username, token))
sensors = sensor_info.json()
for s in sensors:
refdes = '-'.join([i, s])
refdes_list.append(refdes)
elif len(i) == 27:
refdes_list.append(i)
refdes_unique = sorted(list(set(refdes_list)))
write_refdes_annotations(username, token, refdes_unique, outfile,
session)
def main(sDir, array, subsite, node, sensor, delivery_methods, begin, end, now):
cf.create_dir(sDir)
begin = data_request_tools.format_date(begin)
end = data_request_tools.format_date(end)
# basic checks that dates were specified properly
if end:
if not begin:
raise Exception('If an end date is specified, a begin date must also be specified.')
if end:
if begin >= end:
raise Exception('End date entered ({:s}) is not after begin date ({:s})'.format(end, begin))
dmethods = data_request_tools.define_methods(delivery_methods)
gui_df_sci = gui_streams_science()
gui_df = data_request_tools.filter_dataframe(gui_df_sci, array, subsite, node, sensor, dmethods)
url_list = data_request_urls(gui_df, begin, end)
urls = pd.DataFrame(url_list)
urls.to_csv(os.path.join(sDir, 'data_request_urls_{}.csv'.format(now)), index=False, header=False)
return url_list
def main(sDir, thredds_urls):
server_url = 'https://opendap.oceanobservatories.org'
cf.create_dir(sDir)
if type(thredds_urls) == list:
thredds_list = thredds_urls
else:
thredds_file = pd.read_csv(os.path.join(sDir, thredds_urls))
thredds_list = thredds_file['outputUrl'].tolist()
for t in thredds_list:
print(t)
# Check that the data request has been fulfilled
cf.check_request_status(t)
# Create local folders and download files
print('Downloading files')
folder = t.split('/')[-2]
subsite = folder.split('-')[1]
refdes = '-'.join((subsite, folder.split('-')[2], folder.split('-')[3],
folder.split('-')[4]))
output_dir = os.path.join(sDir, subsite, refdes, folder)
cf.create_dir(output_dir)
catalog_url = t.replace('.html', '.xml')
files = []
datasets = get_elements(catalog_url, 'dataset', 'urlPath')
for d in datasets:
if d.endswith(('_provenance.json', '_annotations.json', '.nc')):
files.append(d)
count = 0
for f in files:
count += 1
file_url = '/'.join((server_url, 'thredds/fileServer', f))
file_name = '/'.join((output_dir, file_url.split('/')[-1]))
urlretrieve(file_url, file_name)
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(url_list, sDir, stime, etime):
if len(url_list) != 2:
print('Please provide 2 reference designators for plotting')
else:
uu0 = url_list[0]
uu1 = url_list[1]
rd0 = uu0.split('/')[-2][20:47]
rd1 = uu1.split('/')[-2][20:47]
array = rd0[0:2]
inst = rd0.split('-')[-1]
datasets0 = []
datasets1 = []
for i in range(len(url_list)):
udatasets = cf.get_nc_urls([url_list[i]])
if i == 0:
datasets0.append(udatasets)
else:
datasets1.append(udatasets)
datasets0 = list(itertools.chain(*datasets0))
datasets1 = list(itertools.chain(*datasets1))
main_sensor0 = rd0.split('-')[-1]
main_sensor1 = rd1.split('-')[-1]
fdatasets0_sel = cf.filter_collocated_instruments(
main_sensor0, datasets0)
fdatasets1_sel = cf.filter_collocated_instruments(
main_sensor1, datasets1)
deployments = [
dd.split('/')[-1].split('_')[0] for dd in fdatasets0_sel
]
for d in deployments:
fd0 = [x for x in fdatasets0_sel if d in x]
fd1 = [x for x in fdatasets1_sel if d in x]
ds0 = xr.open_dataset(fd0[0], mask_and_scale=False)
ds0 = ds0.swap_dims({'obs': 'time'})
ds1 = xr.open_dataset(fd1[0], mask_and_scale=False)
ds1 = ds1.swap_dims({'obs': 'time'})
if stime is not None and etime is not None:
ds0 = ds0.sel(time=slice(stime, etime))
ds1 = ds1.sel(time=slice(stime, etime))
if len(ds0['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(
fd0[0])
sci_vars = cf.return_science_vars(stream)
save_dir_profile = os.path.join(sDir, array, subsite, inst,
'profile_plots', deployment)
cf.create_dir(save_dir_profile)
# get pressure variable
pvarname, y1, y_units, press, y_fillvalue = cf.add_pressure_to_dictionary_of_sci_vars(
ds0)
for sv in sci_vars:
print('')
print(sv)
if 'pressure' not in sv:
fig, ax = plt.subplots()
plt.margins(y=.08, x=.02)
plt.grid()
title = ' '.join((deployment, subsite, inst, method))
sname = '-'.join((subsite, inst, method, sv))
for i in range(len(url_list)):
if i == 0:
ds = ds0
else:
ds = ds1
t = ds['time'].values
zpressure = ds[pvarname].values
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:
# get rid of 0.0 data
if sv == 'salinity':
ind = z1 > 1
elif sv == 'density':
ind = z1 > 1000
elif sv == 'conductivity':
ind = z1 > 0.1
elif sv == 'dissolved_oxygen':
ind = z1 > 160
elif sv == 'estimated_oxygen_concentration':
ind = z1 > 200
else:
ind = z1 > 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 = t[ind]
zpressure = zpressure[ind]
zdata = z1[ind]
if len(dtime) > 0:
ax.scatter(zdata,
zpressure,
s=2,
edgecolor='None')
xlabel = sv + " (" + sv_units + ")"
ylabel = press[0] + " (" + y_units[0] + ")"
ax.invert_yaxis()
# plt.xlim([-0.5, 0.5])
ax.set_xlabel(xlabel, fontsize=9)
ax.set_ylabel(ylabel, fontsize=9)
ax.set_title(title + '\nWFP02 (blue) & WFP03 (orange)',
fontsize=9)
fig.tight_layout()
pf.save_fig(save_dir_profile, sname)
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(nc, save_dir, display=False):
cf.create_dir(save_dir)
with xr.open_dataset(nc, mask_and_scale=False) as ds:
subsite = ds.subsite
node = ds.node
sensor = ds.sensor
stream = ds.stream
deployment = 'D0000{}'.format(str(np.unique(ds.deployment)[0]))
t0 = ds.time_coverage_start
t1 = ds.time_coverage_end
sub_dir = os.path.join(save_dir, subsite, '{}-{}-{}'.format(subsite, node, sensor), stream, deployment)
cf.create_dir(sub_dir)
misc = ['quality', 'string', 'timestamp', 'deployment', 'id', 'provenance', 'qc', 'time', 'mission', 'obs',
'volt', 'ref', 'sig', 'amp', 'rph', 'calphase', 'phase', 'therm']
reg_ex = re.compile(r'\b(?:%s)\b' % '|'.join(misc))
# keep variables that are not in the regular expression
vars = [s for s in ds.data_vars if not reg_ex.search(s)]
x = ds['time'].data
for v in vars: # List of dataset variables
# print v
# Filter out variables that are strings, datetimes, or qc related
if ds[v].dtype.kind == 'S' or ds[v].dtype == np.dtype('datetime64[ns]') or 'time' in v or 'qc_results' in v or 'qc_executed' in v:
continue
y = ds[v]
try:
y_units = y.units
except AttributeError:
y_units = None
y_data = y.data
if y_data.ndim > 1:
continue
source = ColumnDataSource(
data=dict(
x=x,
y=y_data,
)
)
gr = cf.get_global_ranges(subsite, node, sensor, v)
output_file('{}/{}-{}-{}.html'.format(sub_dir, v, ds.time_coverage_start.replace(':', ''), ds.time_coverage_end.replace(':', '')))
p = figure(width=1200,
height=800,
title='{}-{}-{}: {} - {} - {}, Stream: {}'.format(subsite, node, sensor, deployment, t0, t1, stream),
x_axis_label='Time (GMT)', y_axis_label='{} ({})'.format(v, y_units),
x_axis_type='datetime',
tools=[tools])
p.line('x', 'y', legend=v, line_width=3, source=source)
p.circle('x', 'y', fill_color='white', size=4, source=source)
if gr:
low_box = BoxAnnotation(top=gr[0], fill_alpha=0.05, fill_color='red')
mid_box = BoxAnnotation(top=gr[1], bottom=gr[0], fill_alpha=0.1, fill_color='green')
high_box = BoxAnnotation(bottom=gr[1], fill_alpha=0.05, fill_color='red')
p.add_layout(low_box)
p.add_layout(mid_box)
p.add_layout(high_box)
if display:
show(p)
else:
save(p)
reset_output()
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(sDir, url_list, start_time, end_time, preferred_only):
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 'PRESF' 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])
for ud in udatasets: # filter out collocated data files
if 'PRESF' in ud.split('/')[-1]:
datasets.append(ud)
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
fdatasets = np.unique(fdatasets).tolist()
for fd in fdatasets:
ds = xr.open_dataset(fd, mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
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)
sci_vars = cf.return_science_vars(stream)
print('\nPlotting {} {}'.format(r, deployment))
array = subsite[0:2]
filename = '_'.join(fname.split('_')[:-1])
save_dir = os.path.join(sDir, array, subsite, refdes,
'timeseries_plots', deployment)
cf.create_dir(save_dir)
tm = ds['time'].values
t0 = pd.to_datetime(tm.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(tm.max()).strftime('%Y-%m-%dT%H:%M:%S')
title = ' '.join((deployment, refdes, method))
for var in sci_vars:
print(var)
if var != 'id':
#if var == 'presf_wave_burst_pressure':
y = ds[var]
fv = y._FillValue
if len(y.dims) == 1:
# Check if the array is all NaNs
if sum(np.isnan(y.values)) == len(y.values):
print('Array of all NaNs - skipping plot.')
# Check if the array is all fill values
elif len(y[y != fv]) == 0:
print('Array of all fill values - skipping plot.')
else:
# reject fill values
ind = y.values != fv
t = tm[ind]
y = y[ind]
# Plot all data
fig, ax = pf.plot_timeseries(t,
y,
y.name,
stdev=None)
ax.set_title((title + '\n' + t0 + ' - ' + t1),
fontsize=9)
sfile = '-'.join((filename, y.name, t0[:10]))
pf.save_fig(save_dir, sfile)
# Plot data with outliers removed
fig, ax = pf.plot_timeseries(t, y, y.name, stdev=5)
ax.set_title((title + '\n' + t0 + ' - ' + t1),
fontsize=9)
sfile = '-'.join(
(filename, y.name, t0[:10])) + '_rmoutliers'
pf.save_fig(save_dir, sfile)
else:
v = y.values.T
n_nan = np.sum(np.isnan(v))
# convert fill values to nans
try:
v[v == fv] = np.nan
except ValueError:
v = v.astype(float)
v[v == fv] = np.nan
n_fv = np.sum(np.isnan(v)) - n_nan
# plot before global ranges are removed
fig, ax = pf.plot_presf_2d(tm, v, y.name, y.units)
ax.set_title((title + '\n' + t0 + ' - ' + t1),
fontsize=9)
sfile = '-'.join((filename, var, t0[:10]))
pf.save_fig(save_dir, sfile)
# reject data outside of global ranges
[g_min, g_max] = cf.get_global_ranges(r, var)
if g_min is not None and g_max is not None:
v[v < g_min] = np.nan
v[v > g_max] = np.nan
n_grange = np.sum(np.isnan(v)) - n_fv - n_nan
if n_grange > 0:
# don't plot if the array is all nans
if len(np.unique(
np.isnan(v))) == 1 and np.unique(
np.isnan(v))[0] == True:
continue
else:
# plot after global ranges are removed
fig, ax = pf.plot_presf_2d(
tm, v, y.name, y.units)
title2 = 'removed: {} global ranges [{}, {}]'.format(
n_grange, g_min, g_max)
ax.set_title((title + '\n' + t0 + ' - ' +
t1 + '\n' + title2),
fontsize=9)
sfile = '-'.join(
(filename, var, t0[:10], 'rmgr'))
pf.save_fig(save_dir, sfile)
def main(sDir, url_list, start_time, end_time, preferred_only):
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:
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):
rms = '-'.join((r, row[ii]))
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:
with xr.open_dataset(fd, mask_and_scale=False) as ds:
ds = ds.swap_dims({'obs': 'time'})
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)
print('\nPlotting {} {}'.format(r, deployment))
array = subsite[0:2]
save_dir = os.path.join(sDir, array, subsite, refdes,
'timeseries_panel_plots')
filename = '_'.join(fname.split('_')[:-1])
sci_vars = cf.return_science_vars(stream)
if len(sci_vars) > 1:
cf.create_dir(save_dir)
colors = cm.jet(np.linspace(0, 1, len(sci_vars)))
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')
title = ' '.join((deployment, refdes, method))
# Plot data with outliers removed
fig, ax = pf.plot_timeseries_panel(ds, t, sci_vars, colors,
5)
plt.xticks(fontsize=7)
ax[0].set_title((title + '\n' + t0 + ' - ' + t1),
fontsize=7)
sfile = '-'.join((filename, 'timeseries_panel', t0[:10]))
pf.save_fig(save_dir, sfile)
else:
print(
'Only one science variable in file, no panel plots necessary'
)
def main(url_list, sDir, plot_type, start_time, end_time, deployment_num):
for i, u in enumerate(url_list):
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]
datasets = cf.get_nc_urls([u])
datasets_sel = cf.filter_collocated_instruments(main_sensor, datasets)
save_dir = os.path.join(sDir, array, subsite, r, plot_type)
cf.create_dir(save_dir)
sname = '-'.join((r, ms, 'track'))
print('Appending....')
sh = pd.DataFrame()
deployments = []
end_times = []
for ii, d in enumerate(datasets_sel):
print('\nDataset {} of {}: {}'.format(ii + 1, len(datasets_sel),
d.split('/')[-1]))
ds = xr.open_dataset(d, mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
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(
d)
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
# get end times of deployments
ps_df, n_streams = cf.get_preferred_stream_info(r)
dr_data = cf.refdes_datareview_json(r)
for index, row in ps_df.iterrows():
deploy = row['deployment']
deploy_info = cf.get_deployment_information(
dr_data, int(deploy[-4:]))
if int(deploy[-4:]) not in deployments:
deployments.append(int(deploy[-4:]))
if pd.to_datetime(deploy_info['stop_date']) not in end_times:
end_times.append(pd.to_datetime(deploy_info['stop_date']))
data = {'lat': ds['lat'].values, 'lon': ds['lon'].values}
new_r = pd.DataFrame(data,
columns=['lat', 'lon'],
index=ds['time'].values)
sh = sh.append(new_r)
xD = sh.lon.values
yD = sh.lat.values
tD = sh.index.values
clabel = 'Time'
ylabel = 'Latitude'
xlabel = 'Longitude'
fig, ax = pf.plot_profiles(xD,
yD,
tD,
ylabel,
xlabel,
clabel,
end_times,
deployments,
stdev=None)
ax.invert_yaxis()
ax.set_title('Glider Track - ' + r + '\n' + 'x: platform location',
fontsize=9)
ax.set_xlim(-71.75, -69.75)
ax.set_ylim(38.75, 40.75)
#cbar.ax.set_yticklabels(end_times)
# add Pioneer glider sampling area
ax.add_patch(
Rectangle((-71.5, 39.0),
1.58,
1.67,
linewidth=3,
edgecolor='b',
facecolor='none'))
ax.text(-71,
40.6,
'Pioneer Glider Sampling Area',
color='blue',
fontsize=8)
# add Pioneer AUV sampling area
# ax.add_patch(Rectangle((-71.17, 39.67), 0.92, 1.0, linewidth=3, edgecolor='m', facecolor='none'))
array_loc = cf.return_array_subsites_standard_loc(array)
ax.scatter(array_loc.lon,
array_loc.lat,
s=40,
marker='x',
color='k',
alpha=0.3)
#ax.legend(legn, array_loc.index, scatterpoints=1, loc='lower left', ncol=4, fontsize=8)
pf.save_fig(save_dir, sname)
def main(sDir, url_list, start_time, end_time, preferred_only):
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:
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):
rms = '-'.join((r, row[ii]))
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
for fd in fdatasets:
with xr.open_dataset(fd, mask_and_scale=False) as ds:
ds = ds.swap_dims({'obs': 'time'})
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)
print('\nPlotting {} {}'.format(r, deployment))
array = subsite[0:2]
save_dir = os.path.join(sDir, array, subsite, refdes, 'ts_plots')
cf.create_dir(save_dir)
tme = ds['time'].values
t0 = pd.to_datetime(tme.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(tme.max()).strftime('%Y-%m-%dT%H:%M:%S')
title = ' '.join((deployment, refdes, method))
filename = '-'.join(('_'.join(fname.split('_')[:-1]), 'ts', t0[:10]))
ds_vars = list(ds.data_vars.keys())
raw_vars = cf.return_raw_vars(ds_vars)
xvar = return_var(ds, raw_vars, 'salinity', 'Practical Salinity')
sal = ds[xvar].values
sal_fv = ds[xvar]._FillValue
yvar = return_var(ds, raw_vars, 'temp', 'Seawater Temperature')
temp = ds[yvar].values
temp_fv = ds[yvar]._FillValue
press = pf.pressure_var(ds, list(ds.coords.keys()))
if press is None:
press = pf.pressure_var(ds, list(ds.data_vars.keys()))
p = ds[press].values
# get rid of nans, 0.0s, fill values
sind1 = (~np.isnan(sal)) & (sal != 0.0) & (sal != sal_fv)
sal = sal[sind1]
temp = temp[sind1]
tme = tme[sind1]
p = p[sind1]
tind1 = (~np.isnan(temp)) & (temp != 0.0) & (temp != temp_fv)
sal = sal[tind1]
temp = temp[tind1]
tme = tme[tind1]
p = p[tind1]
# reject values outside global ranges:
global_min, global_max = cf.get_global_ranges(r, xvar)
if any(e is None for e in [global_min, global_max]):
sal = sal
temp = temp
tme = tme
p = p
else:
sgr_ind = cf.reject_global_ranges(sal, global_min, global_max)
sal = sal[sgr_ind]
temp = temp[sgr_ind]
tme = tme[sgr_ind]
p = p[sgr_ind]
global_min, global_max = cf.get_global_ranges(r, yvar)
if any(e is None for e in [global_min, global_max]):
sal = sal
temp = temp
tme = tme
p = p
else:
tgr_ind = cf.reject_global_ranges(temp, global_min, global_max)
sal = sal[tgr_ind]
temp = temp[tgr_ind]
tme = tme[tgr_ind]
p = p[tgr_ind]
# get rid of outliers
soind = cf.reject_outliers(sal, 5)
sal = sal[soind]
temp = temp[soind]
tme = tme[soind]
p = p[soind]
toind = cf.reject_outliers(temp, 5)
sal = sal[toind]
temp = temp[toind]
tme = tme[toind]
p = p[toind]
if len(sal) > 0: # if there are any data to plot
colors = cm.rainbow(np.linspace(0, 1, len(tme)))
# Figure out boundaries (mins and maxes)
#smin = sal.min() - (0.01 * sal.min())
#smax = sal.max() + (0.01 * sal.max())
if sal.max() - sal.min() < 0.2:
smin = sal.min() - (0.0005 * sal.min())
smax = sal.max() + (0.0005 * sal.max())
else:
smin = sal.min() - (0.001 * sal.min())
smax = sal.max() + (0.001 * sal.max())
if temp.max() - temp.min() <= 1:
tmin = temp.min() - (0.01 * temp.min())
tmax = temp.max() + (0.01 * temp.max())
elif 1 < temp.max() - temp.min() < 1.5:
tmin = temp.min() - (0.05 * temp.min())
tmax = temp.max() + (0.05 * temp.max())
else:
tmin = temp.min() - (0.1 * temp.min())
tmax = temp.max() + (0.1 * temp.max())
# Calculate how many gridcells are needed in the x and y directions and
# Create temp and sal vectors of appropriate dimensions
xdim = int(round((smax-smin)/0.1 + 1, 0))
if xdim == 1:
xdim = 2
si = np.linspace(0, xdim - 1, xdim) * 0.1 + smin
if 1.1 <= temp.max() - temp.min() < 1.7: # if the diff between min and max temp is small
ydim = int(round((tmax-tmin)/0.75 + 1, 0))
ti = np.linspace(0, ydim - 1, ydim) * 0.75 + tmin
elif temp.max() - temp.min() < 1.1:
ydim = int(round((tmax - tmin) / 0.1 + 1, 0))
ti = np.linspace(0, ydim - 1, ydim) * 0.1 + tmin
else:
ydim = int(round((tmax - tmin) + 1, 0))
ti = np.linspace(0, ydim - 1, ydim) + tmin
# Create empty grid of zeros
mdens = np.zeros((ydim, xdim))
# Loop to fill in grid with densities
for j in range(0, ydim):
for i in range(0, xdim):
mdens[j, i] = gsw.density.rho(si[i], ti[j], np.median(p)) # calculate density using median pressure value
fig, ax = pf.plot_ts(si, ti, mdens, sal, temp, colors)
ax.set_title((title + '\n' + t0 + ' - ' + t1 + '\ncolors = time (cooler: earlier)'), fontsize=9)
leg_text = ('Removed {} values (SD=5)'.format(len(ds[xvar].values) - len(sal)),)
ax.legend(leg_text, loc='best', fontsize=6)
pf.save_fig(save_dir, filename)
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(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, 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(sDir, url_list, deployment_num):
reviewlist = pd.read_csv(
'https://raw.githubusercontent.com/ooi-data-lab/data-review-prep/master/review_list/data_review_list.csv')
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:
rd_list.append(rd)
json_file_list = []
for r in rd_list:
dependencies = []
print('\n{}'.format(r))
data = OrderedDict(deployments=OrderedDict())
save_dir = os.path.join(sDir, r.split('-')[0], r)
cf.create_dir(save_dir)
# Deployment location test
deploy_loc_test = cf.deploy_location_check(r)
data['location_comparison'] = deploy_loc_test
for u in url_list:
splitter = u.split('/')[-2].split('-')
rd_check = '-'.join((splitter[1], splitter[2], splitter[3], splitter[4]))
catalog_rms = '-'.join((r, splitter[-2], splitter[-1]))
# complete the analysis by reference designator
if rd_check == r:
udatasets = cf.get_nc_urls([u])
# check for the OOI 1.0 datasets for review
rl_filtered = reviewlist.loc[
(reviewlist['Reference Designator'] == r) & (reviewlist['status'] == 'for review')]
review_deployments = rl_filtered['deploymentNumber'].tolist()
review_deployments_int = ['deployment%04d' % int(x) for x in review_deployments]
for rev_dep in review_deployments_int:
if deployment_num is not None:
if int(rev_dep[-4:]) is not deployment_num:
print('\nskipping {}'.format(rev_dep))
continue
rdatasets = [s for s in udatasets if rev_dep in s]
rdatasets.sort()
if len(rdatasets) > 0:
datasets = []
for dss in rdatasets: # filter out collocated data files
if catalog_rms == dss.split('/')[-1].split('_20')[0][15:]:
datasets.append(dss)
else:
drd = dss.split('/')[-1].split('_20')[0][15:42]
if drd not in dependencies and drd != r:
dependencies.append(drd)
notes = []
time_ascending = ''
sci_vars_dict = {}
#datasets = datasets[0:2] #### for testing
for i in range(len(datasets)):
ds = xr.open_dataset(datasets[i], mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
print('\nAppending data from {}: file {} of {}'.format(rev_dep, i+1, len(datasets)))
# when opening multiple datasets, don't check that the timestamps are in ascending order
time_ascending = 'not_tested'
if i == 0:
fname, subsite, refdes, method, data_stream, deployment = cf.nc_attributes(datasets[0])
fname = fname.split('_20')[0]
# Get info from the data review database
dr_data = cf.refdes_datareview_json(refdes)
stream_vars = cf.return_stream_vars(data_stream)
sci_vars = cf.return_science_vars(data_stream)
node = refdes.split('-')[1]
if 'cspp' in data_stream or 'WFP' in node:
sci_vars.append('int_ctd_pressure')
# Add pressure to the list of science variables
press = pf.pressure_var(ds, list(ds.coords.keys()))
if press is None:
press = pf.pressure_var(ds, list(ds.data_vars.keys()))
if press is not None:
sci_vars.append(press)
sci_vars.append('time')
sci_vars = list(np.unique(sci_vars))
if 'ADCP' in r:
sci_vars = [x for x in sci_vars if 'beam' not in x]
for sci_var in sci_vars:
if sci_var == 'time':
sci_vars_dict.update(
{sci_var: dict(values=np.array([], dtype=np.datetime64), units=[], fv=[])})
else:
sci_vars_dict.update({sci_var: dict(values=np.array([]), units=[], fv=[])})
deploy_info = get_deployment_information(dr_data, int(deployment[-4:]))
# Grab deployment Variables
deploy_start = str(deploy_info['start_date'])
deploy_stop = str(deploy_info['stop_date'])
deploy_lon = deploy_info['longitude']
deploy_lat = deploy_info['latitude']
deploy_depth = deploy_info['deployment_depth']
# Calculate days deployed
if deploy_stop != 'None':
r_deploy_start = pd.to_datetime(deploy_start).replace(hour=0, minute=0, second=0)
if deploy_stop.split('T')[1] == '00:00:00':
r_deploy_stop = pd.to_datetime(deploy_stop)
else:
r_deploy_stop = (pd.to_datetime(deploy_stop) + timedelta(days=1)).replace(hour=0, minute=0, second=0)
n_days_deployed = (r_deploy_stop - r_deploy_start).days
else:
n_days_deployed = None
# Add reference designator to dictionary
try:
data['refdes']
except KeyError:
data['refdes'] = refdes
# append data for the deployment into a dictionary
for s_v in sci_vars_dict.keys():
vv = ds[s_v]
try:
if vv.units not in sci_vars_dict[s_v]['units']:
sci_vars_dict[s_v]['units'].append(vv.units)
except AttributeError:
print('')
try:
if vv._FillValue not in sci_vars_dict[s_v]['fv']:
sci_vars_dict[s_v]['fv'].append(vv._FillValue)
except AttributeError:
print('')
if len(vv.dims) == 1:
if s_v in ['wavelength_a', 'wavelength_c']:
# if the array is not same as the array that was already appended for these
# two OPTAA variables, append. if it's already there, don't append
if np.sum(vv.values == sci_vars_dict[s_v]['values']) != len(vv.values):
sci_vars_dict[s_v]['values'] = np.append(sci_vars_dict[s_v]['values'],
vv.values)
else:
sci_vars_dict[s_v]['values'] = np.append(sci_vars_dict[s_v]['values'], vv.values)
elif len(vv.dims) == 2: # appending 2D datasets
vD = vv.values.T
if len(sci_vars_dict[s_v]['values']) == 0:
sci_vars_dict[s_v]['values'] = vD
else:
sci_vars_dict[s_v]['values'] = np.concatenate((sci_vars_dict[s_v]['values'], vD), axis=1)
deployments = data['deployments'].keys()
data_start = pd.to_datetime(min(sci_vars_dict['time']['values'])).strftime('%Y-%m-%dT%H:%M:%S')
data_stop = pd.to_datetime(max(sci_vars_dict['time']['values'])).strftime('%Y-%m-%dT%H:%M:%S')
# Add deployment and info to dictionary and initialize delivery method sub-dictionary
if deployment not in deployments:
data['deployments'][deployment] = OrderedDict(deploy_start=deploy_start,
deploy_stop=deploy_stop,
n_days_deployed=n_days_deployed,
lon=deploy_lon,
lat=deploy_lat,
deploy_depth=deploy_depth,
method=OrderedDict())
# Add delivery methods to dictionary and initialize stream sub-dictionary
methods = data['deployments'][deployment]['method'].keys()
if method not in methods:
data['deployments'][deployment]['method'][method] = OrderedDict(
stream=OrderedDict())
# Add streams to dictionary and initialize file sub-dictionary
streams = data['deployments'][deployment]['method'][method]['stream'].keys()
if data_stream not in streams:
data['deployments'][deployment]['method'][method]['stream'][
data_stream] = OrderedDict(file=OrderedDict())
# Get a list of data gaps >1 day
time_df = pd.DataFrame(sci_vars_dict['time']['values'], columns=['time'])
time_df = time_df.sort_values(by=['time'])
gap_list = cf.timestamp_gap_test(time_df)
# Calculate the sampling rate to the nearest second
time_df['diff'] = time_df['time'].diff().astype('timedelta64[s]')
rates_df = time_df.groupby(['diff']).agg(['count'])
n_diff_calc = len(time_df) - 1
rates = dict(n_unique_rates=len(rates_df), common_sampling_rates=dict())
for i, row in rates_df.iterrows():
percent = (float(row['time']['count']) / float(n_diff_calc))
if percent > 0.1:
rates['common_sampling_rates'].update({int(i): '{:.2%}'.format(percent)})
sampling_rt_sec = None
for k, v in rates['common_sampling_rates'].items():
if float(v.strip('%')) > 50.00:
sampling_rt_sec = k
if not sampling_rt_sec:
sampling_rt_sec = 'no consistent sampling rate: {}'.format(rates['common_sampling_rates'])
# Don't do : Check that the timestamps in the file are unique
time_test = ''
# Count the number of days for which there is at least 1 timestamp
n_days = len(np.unique(sci_vars_dict['time']['values'].astype('datetime64[D]')))
# Compare variables in file to variables in Data Review Database
ds_variables = list(ds.data_vars.keys()) + list(ds.coords.keys())
ds_variables = eliminate_common_variables(ds_variables)
ds_variables = [x for x in ds_variables if 'qc' not in x]
[_, unmatch1] = compare_lists(stream_vars, ds_variables)
[_, unmatch2] = compare_lists(ds_variables, stream_vars)
# calculate mean pressure from data, excluding outliers +/- 3 SD
try:
pressure = sci_vars_dict[press]
if len(pressure) > 1:
# reject NaNs
p_nonan = pressure['values'][~np.isnan(pressure['values'])]
# reject fill values
p_nonan_nofv = p_nonan[p_nonan != pressure['fv'][0]]
# reject data outside of global ranges
[pg_min, pg_max] = cf.get_global_ranges(r, press)
if pg_min is not None and pg_max is not None:
pgr_ind = cf.reject_global_ranges(p_nonan_nofv, pg_min, pg_max)
p_nonan_nofv_gr = p_nonan_nofv[pgr_ind]
else:
p_nonan_nofv_gr = p_nonan_nofv
if (len(p_nonan_nofv_gr) > 0):
[press_outliers, pressure_mean, _, pressure_max, _, _] = cf.variable_statistics(p_nonan_nofv_gr, 3)
pressure_mean = round(pressure_mean, 2)
pressure_max = round(pressure_max, 2)
else:
press_outliers = None
pressure_mean = None
pressure_max = None
if len(pressure) > 0 and len(p_nonan) == 0:
notes.append('Pressure variable all NaNs')
elif len(pressure) > 0 and len(p_nonan) > 0 and len(p_nonan_nofv) == 0:
notes.append('Pressure variable all fill values')
elif len(pressure) > 0 and len(p_nonan) > 0 and len(p_nonan_nofv) > 0 and len(p_nonan_nofv_gr) == 0:
notes.append('Pressure variable outside of global ranges')
else: # if there is only 1 data point
press_outliers = 0
pressure_mean = round(ds[press].values.tolist()[0], 2)
pressure_max = round(ds[press].values.tolist()[0], 2)
try:
pressure_units = pressure['units'][0]
except AttributeError:
pressure_units = 'no units attribute for pressure'
if pressure_mean:
if 'SF' in node:
pressure_compare = int(round(pressure_max))
else:
pressure_compare = int(round(pressure_mean))
if pressure_units == '0.001 dbar':
pressure_max = round((pressure_max / 1000), 2)
pressure_mean = round((pressure_mean / 1000), 2)
pressure_compare = round((pressure_compare / 1000), 2)
notes.append('Pressure converted from 0.001 dbar to dbar for pressure comparison')
elif pressure_units == 'daPa':
pressure_max = round((pressure_max / 1000), 2)
pressure_mean = round((pressure_mean / 1000), 2)
pressure_compare = round((pressure_compare / 1000), 2)
notes.append('Pressure converted from daPa to dbar for pressure comparison')
else:
pressure_compare = None
if (not deploy_depth) or (not pressure_mean):
pressure_diff = None
else:
pressure_diff = pressure_compare - deploy_depth
except KeyError:
press = 'no seawater pressure in file'
pressure_diff = None
pressure_mean = None
pressure_max = None
pressure_compare = None
press_outliers = None
pressure_units = None
# Add files and info to dictionary
filenames = data['deployments'][deployment]['method'][method]['stream'][data_stream][
'file'].keys()
if fname not in filenames:
data['deployments'][deployment]['method'][method]['stream'][data_stream]['file'][
fname] = OrderedDict(
file_downloaded=pd.to_datetime(splitter[0][0:15]).strftime('%Y-%m-%dT%H:%M:%S'),
file_coordinates=list(ds.coords.keys()),
sampling_rate_seconds=sampling_rt_sec,
sampling_rate_details=rates,
data_start=data_start,
data_stop=data_stop,
time_gaps=gap_list,
unique_timestamps=time_test,
n_timestamps=len(sci_vars_dict['time']['values']),
n_days=n_days,
notes=notes,
ascending_timestamps=time_ascending,
pressure_comparison=dict(pressure_mean=pressure_mean, units=pressure_units,
num_outliers=press_outliers, diff=pressure_diff,
pressure_max=pressure_max, variable=press,
pressure_compare=pressure_compare),
vars_in_file=ds_variables,
vars_not_in_file=[x for x in unmatch1 if 'time' not in x],
vars_not_in_db=unmatch2,
sci_var_stats=OrderedDict())
# calculate statistics for science variables, excluding outliers +/- 5 SD
for sv in sci_vars_dict.keys():
if sv != 't_max': # for ADCP
if sv != 'time':
print(sv)
var = sci_vars_dict[sv]
vD = var['values']
var_units = var['units']
#if 'timedelta' not in str(vD.dtype):
vnum_dims = len(np.shape(vD))
# for OPTAA wavelengths, print the array
if sv == 'wavelength_a' or sv == 'wavelength_c':
[g_min, g_max] = cf.get_global_ranges(r, sv)
n_all = len(var)
mean = list(vD)
num_outliers = None
vmin = None
vmax = None
sd = None
n_stats = 'not calculated'
n_nan = None
n_fv = None
n_grange = 'no global ranges'
fv = var['fv'][0]
else:
if vnum_dims > 2:
print('variable has more than 2 dimensions')
num_outliers = None
mean = None
vmin = None
vmax = None
sd = None
n_stats = 'variable has more than 2 dimensions'
n_nan = None
n_fv = None
n_grange = None
fv = None
n_all = None
else:
if vnum_dims > 1:
n_all = [len(vD), len(vD.flatten())]
else:
n_all = len(vD)
n_nan = int(np.sum(np.isnan(vD)))
fv = var['fv'][0]
vD[vD == fv] = np.nan # turn fill values to nans
n_fv = int(np.sum(np.isnan(vD))) - n_nan
[g_min, g_max] = cf.get_global_ranges(r, sv)
if list(np.unique(np.isnan(vD))) != [True]:
# reject data outside of global ranges
if g_min is not None and g_max is not None:
# turn data outside of global ranges to nans
#var_gr = var_nofv.where((var_nofv >= g_min) & (var_nofv <= g_max))
vD[vD < g_min] = np.nan
vD[vD > g_max] = np.nan
n_grange = int(np.sum(np.isnan(vD)) - n_fv - n_nan)
else:
n_grange = 'no global ranges'
if list(np.unique(np.isnan(vD))) != [True]:
if sv == 'spkir_abj_cspp_downwelling_vector':
# don't remove outliers from dataset
[num_outliers, mean, vmin, vmax, sd, n_stats] = cf.variable_statistics_spkir(vD)
else:
if vnum_dims > 1:
var_gr = vD.flatten()
else:
var_gr = vD
# drop nans before calculating stats
var_gr = var_gr[~np.isnan(var_gr)]
[num_outliers, mean, vmin, vmax, sd, n_stats] = cf.variable_statistics(var_gr, 5)
else:
num_outliers = None
mean = None
vmin = None
vmax = None
sd = None
n_stats = 0
n_grange = None
else:
num_outliers = None
mean = None
vmin = None
vmax = None
sd = None
n_stats = 0
n_grange = None
if vnum_dims > 1:
sv = '{} (dims: {})'.format(sv, list(np.shape(var['values'])))
else:
sv = sv
#if 'timedelta' not in str(var.values.dtype):
data['deployments'][deployment]['method'][method]['stream'][data_stream]['file'][
fname]['sci_var_stats'][sv] = dict(n_outliers=num_outliers, mean=mean, min=vmin,
max=vmax, stdev=sd, n_stats=n_stats, units=var_units,
n_nans=n_nan, n_fillvalues=n_fv, fill_value=str(fv),
global_ranges=[g_min, g_max], n_grange=n_grange,
n_all=n_all)
sfile = os.path.join(save_dir, '{}-{}-file_analysis.json'.format(rev_dep, r))
with open(sfile, 'w') as outfile:
json.dump(data, outfile)
json_file_list.append(str(sfile))
depfile = os.path.join(save_dir, '{}-dependencies.txt'.format(r))
with open(depfile, 'w') as depf:
depf.write(str(dependencies))
return json_file_list
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(url_list, sDir, plot_type, start_time, end_time, deployment_num, bfiles):
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:
rd_list.append(rd)
for r in rd_list:
if 'ENG' not in r:
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:
if 'bottom_track_earth' not in splitter[-1]:
udatasets = cf.get_nc_urls([u])
datasets.append(udatasets)
datasets = list(itertools.chain(*datasets))
fdatasets = []
# 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)
main_sensor = r.split('-')[-1]
fdatasets_sel = cf.filter_collocated_instruments(main_sensor, fdatasets)
subsite = r.split('-')[0]
array = subsite[0:2]
save_dir = os.path.join(sDir, array, subsite, r, plot_type)
cf.create_dir(save_dir)
sname = '_'.join((r, plot_type))
sh = pd.DataFrame()
deployments = []
for ii, d in enumerate(fdatasets_sel):
print('\nDataset {} of {}: {}'.format(ii + 1, len(fdatasets_sel), d.split('/')[-1]))
deploy = d.split('/')[-1].split('_')[0]
if deployment_num:
if int(deploy[-4:]) is not deployment_num:
continue
ds = xr.open_dataset(d, mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
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
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')
if ds_lat is not None and ds_lon is not None:
data = {'lat': ds_lat, 'lon': ds_lon}
new_r = pd.DataFrame(data, columns=['lat', 'lon'], index=ds['time'].values)
sh = sh.append(new_r)
# append the deployments that are actually plotted
if int(deploy[-4:]) not in deployments:
deployments.append(int(deploy[-4:]))
# plot data by deployment
sfile = '-'.join((deploy, sname))
if array == 'CE':
ttl = 'Glider Track - ' + r + ' - ' + deploy + '\nx: Mooring Locations'
else:
ttl = 'Glider Track - ' + r + ' - ' + deploy + '\nx: Mooring Locations' + '\n blue box: Glider Sampling Area'
#fig, ax = pf.plot_profiles(ds_lon, ds_lat, ds['time'].values, ylabel, xlabel, clabel, stdev=None)
plot_map(save_dir, sfile, ttl, ds_lon, ds_lat, ds['time'].values, array, bfiles, plot_type)
sh = sh.resample('H').median() # resample hourly
xD = sh.lon.values
yD = sh.lat.values
tD = sh.index.values
title = 'Glider Track - ' + r + '\nDeployments: ' + str(deployments) + ' x: Mooring Locations' + '\n blue box: Glider Sampling Area'
save_dir_main = os.path.join(sDir, array, subsite, r)
plot_map(save_dir_main, sname, title, xD, yD, tD, array, bfiles, plot_type, add_box='yes')
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(sDir, plotting_sDir, url_list, sd_calc):
dr = pd.read_csv('https://datareview.marine.rutgers.edu/notes/export')
drn = dr.loc[dr.type == 'exclusion']
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:
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 = []
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
pms = []
for index, row in ps_df.iterrows():
for ii in range(n_streams):
try:
rms = '-'.join((r, row[ii]))
pms.append(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)
main_sensor = r.split('-')[-1]
fdatasets_sel = cf.filter_collocated_instruments(
main_sensor, fdatasets)
# find time ranges to exclude from analysis for data review database
subsite = r.split('-')[0]
subsite_node = '-'.join((subsite, r.split('-')[1]))
drne = drn.loc[drn.reference_designator.isin(
[subsite, subsite_node, r])]
et = []
for i, row in drne.iterrows():
sdate = cf.format_dates(row.start_date)
edate = cf.format_dates(row.end_date)
et.append([sdate, edate])
# 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
sci_vars_dict = cd.sci_var_long_names_check(stream_sci_vars)
# get the preferred stream information
ps_df, n_streams = cf.get_preferred_stream_info(r)
# build dictionary of science data from the preferred dataset for each deployment
print('\nAppending data from files')
sci_vars_dict, pressure_unit, pressure_name = cd.append_science_data(
ps_df, n_streams, r, fdatasets_sel, sci_vars_dict, et)
# analyze combined dataset
print('\nAnalyzing combined dataset and writing summary file')
array = subsite[0:2]
save_dir = os.path.join(sDir, array, subsite)
cf.create_dir(save_dir)
rows = []
if ('FLM' in r) and (
'CTDMO' in r
): # calculate Flanking Mooring CTDMO stats based on pressure
headers = [
'common_stream_name', 'preferred_methods_streams',
'deployments', 'long_name', 'units', 't0', 't1', 'fill_value',
'global_ranges', 'n_all', 'press_min_max',
'n_excluded_forpress', 'n_nans', 'n_fillvalues', 'n_grange',
'define_stdev', 'n_outliers', 'n_stats', 'mean', 'min', 'max',
'stdev', 'note'
]
else:
headers = [
'common_stream_name', 'preferred_methods_streams',
'deployments', 'long_name', 'units', 't0', 't1', 'fill_value',
'global_ranges', 'n_all', 'n_nans', 'n_fillvalues', 'n_grange',
'define_stdev', 'n_outliers', 'n_stats', 'mean', 'min', 'max',
'stdev'
]
for m, n in sci_vars_dict.items():
print('\nSTREAM: ', m)
if m == 'common_stream_placeholder':
m = 'science_data_stream'
if m == 'metbk_hourly': # don't calculate ranges for metbk_hourly
continue
if ('FLM' in r) and (
'CTDMO' in r
): # calculate Flanking Mooring CTDMO stats based on pressure
# index the pressure variable to filter and calculate stats on the rest of the variables
sv_press = 'Seawater Pressure'
vinfo_press = n['vars'][sv_press]
# first, index where data are nans, fill values, and outside of global ranges
fv_press = list(np.unique(vinfo_press['fv']))[0]
pdata = vinfo_press['values']
[pind, __, __, __, __,
__] = index_dataset(r, vinfo_press['var_name'], pdata,
fv_press)
pdata_filtered = pdata[pind]
[__, pmean, __, __, psd,
__] = cf.variable_statistics(pdata_filtered, None)
# index of pressure = average of all 'valid' pressure data +/- 1 SD
ipress_min = pmean - psd
ipress_max = pmean + psd
ind_press = (pdata >= ipress_min) & (pdata <= ipress_max)
# calculate stats for all variables
print('\nPARAMETERS:')
for sv, vinfo in n['vars'].items():
print(sv)
fv_lst = np.unique(vinfo['fv']).tolist()
if len(fv_lst) == 1:
fill_value = fv_lst[0]
else:
print('No unique fill value for {}'.format(sv))
lunits = np.unique(vinfo['units']).tolist()
n_all = len(vinfo['t'])
# filter data based on pressure index
t_filtered = vinfo['t'][ind_press]
data_filtered = vinfo['values'][ind_press]
deploy_filtered = vinfo['deployments'][ind_press]
n_excluded = n_all - len(t_filtered)
[dataind, g_min, g_max, n_nan, n_fv,
n_grange] = index_dataset(r, vinfo['var_name'],
data_filtered, fill_value)
t_final = t_filtered[dataind]
data_final = data_filtered[dataind]
deploy_final = deploy_filtered[dataind]
t0 = pd.to_datetime(
min(t_final)).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(
max(t_final)).strftime('%Y-%m-%dT%H:%M:%S')
deploy = list(np.unique(deploy_final))
deployments = [int(dd) for dd in deploy]
if len(data_final) > 1:
[num_outliers, mean, vmin, vmax, sd, n_stats
] = cf.variable_statistics(data_final, sd_calc)
else:
mean = None
vmin = None
vmax = None
sd = None
n_stats = None
note = 'restricted stats calculation to data points where pressure is within defined ranges' \
' (average of all pressure data +/- 1 SD)'
rows.append([
m,
list(np.unique(pms)), deployments, sv, lunits, t0, t1,
fv_lst, [g_min, g_max], n_all,
[round(ipress_min, 2),
round(ipress_max,
2)], n_excluded, n_nan, n_fv, n_grange, sd_calc,
num_outliers, n_stats, mean, vmin, vmax, sd, note
])
# plot CTDMO data used for stats
psave_dir = os.path.join(plotting_sDir, array, subsite, r,
'timeseries_plots_stats')
cf.create_dir(psave_dir)
dr_data = cf.refdes_datareview_json(r)
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']))
sname = '-'.join((r, sv))
fig, ax = pf.plot_timeseries_all(t_final,
data_final,
sv,
lunits[0],
stdev=None)
ax.set_title(
(r + '\nDeployments: ' + str(sorted(deployments)) +
'\n' + t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
pf.save_fig(psave_dir, sname)
if sd_calc:
sname = '-'.join((r, sv, 'rmoutliers'))
fig, ax = pf.plot_timeseries_all(t_final,
data_final,
sv,
lunits[0],
stdev=sd_calc)
ax.set_title(
(r + '\nDeployments: ' + str(sorted(deployments)) +
'\n' + t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
pf.save_fig(psave_dir, sname)
else:
if not sd_calc:
sdcalc = None
print('\nPARAMETERS: ')
for sv, vinfo in n['vars'].items():
print(sv)
fv_lst = np.unique(vinfo['fv']).tolist()
if len(fv_lst) == 1:
fill_value = fv_lst[0]
else:
print(fv_lst)
print('No unique fill value for {}'.format(sv))
lunits = np.unique(vinfo['units']).tolist()
t = vinfo['t']
if len(t) > 1:
data = vinfo['values']
n_all = len(t)
if 'SPKIR' in r or 'presf_abc_wave_burst' in m:
if 'SPKIR' in r:
[dd_data, g_min, g_max, n_nan, n_fv,
n_grange] = index_dataset_2d(
r, 'spkir_abj_cspp_downwelling_vector',
data, fill_value)
else:
[dd_data, g_min, g_max, n_nan, n_fv,
n_grange] = index_dataset_2d(
r, 'presf_wave_burst_pressure', data,
fill_value)
t_final = t
t0 = pd.to_datetime(
min(t_final)).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(
max(t_final)).strftime('%Y-%m-%dT%H:%M:%S')
deploy_final = vinfo['deployments']
deploy = list(np.unique(deploy_final))
deployments = [int(dd) for dd in deploy]
num_outliers = []
mean = []
vmin = []
vmax = []
sd = []
n_stats = []
for i in range(len(dd_data)):
dd = data[i]
# drop nans before calculating stats
dd = dd[~np.isnan(dd)]
[
num_outliersi, meani, vmini, vmaxi, sdi,
n_statsi
] = cf.variable_statistics(dd, sd_calc)
num_outliers.append(num_outliersi)
mean.append(meani)
vmin.append(vmini)
vmax.append(vmaxi)
sd.append(sdi)
n_stats.append(n_statsi)
else:
[dataind, g_min, g_max, n_nan, n_fv,
n_grange] = index_dataset(r, vinfo['var_name'],
data, fill_value)
t_final = t[dataind]
if len(t_final) > 0:
t0 = pd.to_datetime(
min(t_final)).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(
max(t_final)).strftime('%Y-%m-%dT%H:%M:%S')
data_final = data[dataind]
# if sv == 'Dissolved Oxygen Concentration':
# xx = (data_final > 0) & (data_final < 400)
# data_final = data_final[xx]
# t_final = t_final[xx]
# if sv == 'Seawater Conductivity':
# xx = (data_final > 1) & (data_final < 400)
# data_final = data_final[xx]
# t_final = t_final[xx]
deploy_final = vinfo['deployments'][dataind]
deploy = list(np.unique(deploy_final))
deployments = [int(dd) for dd in deploy]
if len(data_final) > 1:
[
num_outliers, mean, vmin, vmax, sd,
n_stats
] = cf.variable_statistics(
data_final, sd_calc)
else:
sdcalc = None
num_outliers = None
mean = None
vmin = None
vmax = None
sd = None
n_stats = None
else:
sdcalc = None
num_outliers = None
mean = None
vmin = None
vmax = None
sd = None
n_stats = None
deployments = None
t0 = None
t1 = None
else:
sdcalc = None
num_outliers = None
mean = None
vmin = None
vmax = None
sd = None
n_stats = None
deployments = None
t0 = None
t1 = None
t_final = []
if sd_calc:
print_sd = sd_calc
else:
print_sd = sdcalc
rows.append([
m,
list(np.unique(pms)), deployments, sv, lunits, t0, t1,
fv_lst, [g_min, g_max], n_all, n_nan, n_fv, n_grange,
print_sd, num_outliers, n_stats, mean, vmin, vmax, sd
])
if len(t_final) > 0:
# plot data used for stats
psave_dir = os.path.join(
plotting_sDir, array, subsite, r,
'timeseries_reviewed_datarange')
cf.create_dir(psave_dir)
dr_data = cf.refdes_datareview_json(r)
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']))
sname = '-'.join((r, sv))
# plot hourly averages for streaming data
if 'streamed' in sci_vars_dict[list(
sci_vars_dict.keys())[0]]['ms'][0]:
sname = '-'.join((sname, 'hourlyavg'))
df = pd.DataFrame({
'dfx': t_final,
'dfy': data_final
})
dfr = df.resample('H', on='dfx').mean()
# Plot all data
fig, ax = pf.plot_timeseries_all(dfr.index,
dfr['dfy'],
sv,
lunits[0],
stdev=None)
ax.set_title((r + '\nDeployments: ' +
str(sorted(deployments)) + '\n' +
t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
pf.save_fig(psave_dir, sname)
if sd_calc:
sname = '-'.join(
(sname, 'hourlyavg_rmoutliers'))
fig, ax = pf.plot_timeseries_all(dfr.index,
dfr['dfy'],
sv,
lunits[0],
stdev=sd_calc)
ax.set_title((r + '\nDeployments: ' +
str(sorted(deployments)) + '\n' +
t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
pf.save_fig(psave_dir, sname)
elif 'SPKIR' in r:
fig, ax = pf.plot_spkir(t_final, dd_data, sv,
lunits[0])
ax.set_title((r + '\nDeployments: ' +
str(sorted(deployments)) + '\n' +
t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
pf.save_fig(psave_dir, sname)
# plot each wavelength
wavelengths = [
'412nm', '443nm', '490nm', '510nm', '555nm',
'620nm', '683nm'
]
for wvi in range(len(dd_data)):
fig, ax = pf.plot_spkir_wv(
t_final, dd_data[wvi], sv, lunits[0], wvi)
ax.set_title((r + '\nDeployments: ' +
str(sorted(deployments)) + '\n' +
t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
snamewvi = '-'.join((sname, wavelengths[wvi]))
pf.save_fig(psave_dir, snamewvi)
elif 'presf_abc_wave_burst' in m:
fig, ax = pf.plot_presf_2d(t_final, dd_data, sv,
lunits[0])
ax.set_title((r + '\nDeployments: ' +
str(sorted(deployments)) + '\n' +
t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
snamewave = '-'.join((sname, m))
pf.save_fig(psave_dir, snamewave)
else: # plot all data if not streamed
fig, ax = pf.plot_timeseries_all(t_final,
data_final,
sv,
lunits[0],
stdev=None)
ax.set_title((r + '\nDeployments: ' +
str(sorted(deployments)) + '\n' +
t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
pf.save_fig(psave_dir, sname)
if sd_calc:
sname = '-'.join((r, sv, 'rmoutliers'))
fig, ax = pf.plot_timeseries_all(t_final,
data_final,
sv,
lunits[0],
stdev=sd_calc)
ax.set_title((r + '\nDeployments: ' +
str(sorted(deployments)) + '\n' +
t0 + ' - ' + t1),
fontsize=8)
for etimes in end_times:
ax.axvline(x=etimes,
color='k',
linestyle='--',
linewidth=.6)
pf.save_fig(psave_dir, sname)
fsum = pd.DataFrame(rows, columns=headers)
fsum.to_csv('{}/{}_data_ranges.csv'.format(save_dir, r), index=False)
def main(folder, 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
"""
with xr.open_mfdataset(folder, 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
file = os.path.join(root,filename)
f = xr.open_dataset(file)
f = f.swap_dims({'obs':'time'})
fN = f.source
platform = f.subsite
node = f.node
sensor = f.sensor
title = platform + '-' + node + '-' + sensor
global fName
head, tail = os.path.split(filename)
fName = tail.split('.', 1)[0]
d = fName.split('_')[0]
save_dir = os.path.join(rootdir, 'timeseries', d)
cf.create_dir(save_dir)
varList = []
for vars in f.variables:
varList.append(str(vars))
yVars = [s for s in varList if not reg_ex.search(s)]
for v in yVars:
print v
t = f['time'].data
t_dict = dict(data = t, info = dict(label='Time', units='GMT'))
y = f[v]
@usage:
sDir: directory where outputs are saved
username: OOI API username
token: OOI API password
"""
import datetime as dt
import functions.common as cf
import scripts
sDir = '/Users/lgarzio/Documents/OOI'
username = '******'
token = 'token'
cf.create_dir(sDir)
now = dt.datetime.now().strftime('%Y%m%dT%H%M')
arrays = input(
'\nPlease select arrays (CE, CP, GA, GI, GP, GS, RS). Must be comma separated (if choosing multiple) or press enter to select all: '
) or ''
array = scripts.data_request_tools.format_inputs(arrays)
subsites = input(
'\nPlease fully-qualified subsites (e.g. GI01SUMO, GI05MOAS). Must be comma separated (if choosing multiple) or press enter to select all: '
) or ''
subsite = scripts.data_request_tools.format_inputs(subsites)
nodes = input(
'\nPlease select fully-qualified nodes. (e.g. GL469, GL477). Must be comma separated (if choosing multiple) or press enter to select all: '
) or ''
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 = []
deployments = []
for url in url_list:
splitter = url.split('/')[-2].split('-')
rd_check = '-'.join(
(splitter[1], splitter[2], splitter[3], splitter[4]))
catalog_rms = '-'.join((r, splitter[-2], splitter[-1]))
if rd_check == r:
udatasets = cf.get_nc_urls([url])
for u in udatasets: # filter out collocated data files
if catalog_rms == u.split('/')[-1].split('_20')[0][15:]:
datasets.append(u)
deployments.append(
int(u.split('/')[-1].split('_')[0][-4:]))
deployments = np.unique(deployments).tolist()
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 dep in deployments:
if deployment_num is not None:
if dep is not deployment_num:
print('\nskipping deployment {}'.format(dep))
continue
rdatasets = [
s for s in fdatasets_sel if 'deployment%04d' % dep in s
]
rdatasets.sort()
if len(rdatasets) > 0:
sci_vars_dict = {}
# rdatasets = rdatasets[0:2] #### for testing
for i in range(len(rdatasets)):
ds = xr.open_dataset(rdatasets[i], mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
print('\nAppending data from {}: file {} of {}'.format(
'deployment%04d' % dep, i + 1, len(rdatasets)))
array = r[0:2]
subsite = r.split('-')[0]
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, r, 'profile_plots',
'deployment%04d' % dep, ext)
save_dir_xsection = os.path.join(
sDir, array, subsite, r, 'xsection_plots',
'deployment%04d' % dep, ext)
else:
save_dir_profile = os.path.join(
sDir, array, subsite, r, 'profile_plots',
'deployment%04d' % dep)
save_dir_xsection = os.path.join(
sDir, array, subsite, r, 'xsection_plots',
'deployment%04d' % dep)
if len(sci_vars_dict) == 0:
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(
rdatasets[0])
sci_vars = cf.return_science_vars(stream)
if 'CTDPF' not in r:
sci_vars.append('int_ctd_pressure')
sci_vars.append('time')
sci_vars = list(np.unique(sci_vars))
# initialize the dictionary
for sci_var in sci_vars:
if sci_var == 'time':
sci_vars_dict.update({
sci_var:
dict(values=np.array([],
dtype=np.datetime64),
units=[],
fv=[])
})
else:
sci_vars_dict.update({
sci_var:
dict(values=np.array([]), units=[], fv=[])
})
# append data for the deployment into the dictionary
for s_v in sci_vars_dict.keys():
vv = ds[s_v]
try:
if vv.units not in sci_vars_dict[s_v]['units']:
sci_vars_dict[s_v]['units'].append(vv.units)
except AttributeError:
print('')
try:
if vv._FillValue not in sci_vars_dict[s_v]['fv']:
sci_vars_dict[s_v]['fv'].append(vv._FillValue)
vv_data = vv.values
try:
vv_data[
vv_data == vv.
_FillValue] = np.nan # turn fill values to nans
except ValueError:
print('')
except AttributeError:
print('')
if len(vv.dims) > 1:
print('Skipping plot: variable has >1 dimension')
else:
sci_vars_dict[s_v]['values'] = np.append(
sci_vars_dict[s_v]['values'], vv.values)
# plot after appending all data into one file
data_start = pd.to_datetime(
min(sci_vars_dict['time']['values'])).strftime(
'%Y-%m-%dT%H:%M:%S')
data_stop = pd.to_datetime(max(
sci_vars_dict['time']['values'])).strftime(
'%Y-%m-%dT%H:%M:%S')
time1 = sci_vars_dict['time']['values']
ds_lat1 = np.empty(np.shape(time1))
ds_lon1 = np.empty(np.shape(time1))
# define pressure variable
try:
pname = 'seawater_pressure'
press = sci_vars_dict[pname]
except KeyError:
pname = 'int_ctd_pressure'
press = sci_vars_dict[pname]
y1 = press['values']
try:
y_units = press['units'][0]
except IndexError:
y_units = ''
for sv in sci_vars_dict.keys():
print('')
print(sv)
if sv not in [
'seawater_pressure', 'int_ctd_pressure', 'time'
]:
z1 = sci_vars_dict[sv]['values']
fv = sci_vars_dict[sv]['fv'][0]
sv_units = sci_vars_dict[sv]['units'][0]
# 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 > 20
# elif sv == 'density':
# ind = ndata > 1010
# elif sv == 'conductivity':
# ind = ndata > 2
# 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')
sfileall = '_'.join(
(sname, pd.to_datetime(
t_portal.min()).strftime('%Y%m%d')))
tm0 = pd.to_datetime(t_portal.min()).strftime(
'%Y-%m-%dT%H:%M:%S')
tm1 = pd.to_datetime(t_portal.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 = pname + " (" + y_units + ")"
clabel = 'Time'
# fig, ax = pf.plot_profiles(z1, y1, time1, ylabel, xlabel, clabel, stdev=None)
fig, ax = pf.plot_profiles(z_portal,
y_portal,
t_portal,
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 = pname + " (" + y_units + ")"
# fig, ax, bar = pf.plot_xsection(subsite, time1, y1, z1, clabel, ylabel, t_eng=None,
# m_water_depth=None, inpercentile=None, stdev=None)
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)
if fig:
ax.set_title(title, fontsize=9)
fig.tight_layout()
pf.save_fig(save_dir_xsection, sfileall)
"""
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(folder, 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
"""
with xr.open_mfdataset(folder, 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(sDir, url_list, preferred_only):
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]
# filter datasets
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 = []
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 = cf.filter_collocated_instruments(main_sensor, fdatasets)
# ps_df, n_streams = cf.get_preferred_stream_info(r)
# get end times of deployments
dr_data = cf.refdes_datareview_json(r)
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']))
# # filter datasets
# 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))
# main_sensor = r.split('-')[-1]
# fdatasets = cf.filter_collocated_instruments(main_sensor, datasets)
# fdatasets = cf.filter_other_streams(r, ms_list, fdatasets)
methodstream = []
for f in fdatasets:
methodstream.append('-'.join((f.split('/')[-2].split('-')[-2],
f.split('/')[-2].split('-')[-1])))
ms_dict = save_dir_path(ms_list)
for ms in np.unique(methodstream):
fdatasets_sel = [x for x in fdatasets if ms in x]
check_ms = ms.split('-')[1]
if 'recovered' in check_ms:
check_ms = check_ms.split('_recovered')[0]
if ms_dict['ms_count'][ms_dict['ms_unique'] == ms.split('-')
[0]] == 1:
save_dir = os.path.join(sDir, array, subsite, r,
'timeseries_yearly_plot',
ms.split('-')[0])
else:
save_dir = os.path.join(sDir, array, subsite, r,
'timeseries_yearly_plot',
ms.split('-')[0], check_ms)
cf.create_dir(save_dir)
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
sci_vars_dict = cd.initialize_empty_arrays(stream_sci_vars_dict,
ms)
print('\nAppending data from files: {}'.format(ms))
for fd in fdatasets_sel:
ds = xr.open_dataset(fd, mask_and_scale=False)
print(fd)
for var in list(sci_vars_dict[ms]['vars'].keys()):
sh = sci_vars_dict[ms]['vars'][var]
try:
ds[var]
print(var)
deployment_num = np.unique(ds['deployment'].values)[0]
sh['deployments'] = np.append(sh['deployments'],
deployment_num)
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)
tD = ds['time'].values
varD = ds[var].values
sh['t'] = np.append(sh['t'], tD)
sh['values'] = np.append(sh['values'], varD)
except KeyError:
print('KeyError: ', var)
print('\nPlotting data')
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]
deployments_num = vinfo['deployments']
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')
x = vinfo['t']
y = vinfo['values']
# reject NaNs
nan_ind = ~np.isnan(y)
x_nonan = x[nan_ind]
y_nonan = y[nan_ind]
# reject fill values
fv_ind = y_nonan != vinfo['fv'][0]
x_nonan_nofv = x_nonan[fv_ind]
y_nonan_nofv = y_nonan[fv_ind]
# reject extreme values
Ev_ind = cf.reject_extreme_values(y_nonan_nofv)
y_nonan_nofv_nE = y_nonan_nofv[Ev_ind]
x_nonan_nofv_nE = x_nonan_nofv[Ev_ind]
# reject values outside global ranges:
global_min, global_max = cf.get_global_ranges(r, sv)
print('global ranges: ', global_min, global_max)
if global_min and global_max:
gr_ind = cf.reject_global_ranges(
y_nonan_nofv_nE, global_min, global_max)
y_nonan_nofv_nE_nogr = y_nonan_nofv_nE[gr_ind]
x_nonan_nofv_nE_nogr = x_nonan_nofv_nE[gr_ind]
else:
y_nonan_nofv_nE_nogr = y_nonan_nofv_nE
x_nonan_nofv_nE_nogr = x_nonan_nofv_nE
# check array length
if len(y_nonan_nofv_nE_nogr) > 0:
if m == 'common_stream_placeholder':
sname = '-'.join((r, sv))
print(var, 'empty array')
else:
sname = '-'.join((r, m, sv))
# group data by year
groups, g_data = gt.group_by_time_range(
x_nonan_nofv_nE_nogr, y_nonan_nofv_nE_nogr,
'A')
# create bins
# groups_min = min(groups.describe()['DO']['min'])
# lower_bound = int(round(groups_min))
# groups_max = max(groups.describe()['DO']['max'])
# if groups_max < 1:
# upper_bound = 1
# step_bound = 1
# else:
# upper_bound = int(round(groups_max + (groups_max / 50)))
# step_bound = int(round((groups_max - groups_min) / 10))
#
# if step_bound == 0:
# step_bound += 1
#
# if (upper_bound - lower_bound) == step_bound:
# lower_bound -= 1
# upper_bound += 1
# if (upper_bound - lower_bound) < step_bound:
# print('<')
# step_bound = int(round(step_bound / 10))
# print(lower_bound, upper_bound, step_bound)
# bin_range = list(range(lower_bound, upper_bound, step_bound))
# print(bin_range)
# preparing color palette
colors = color_names[:len(groups)]
# colors = [color['color'] for color in
# list(pyplot.rcParams['axes.prop_cycle'][:len(groups)])]
fig0, ax0 = pyplot.subplots(nrows=2, ncols=1)
# subplot for histogram and basic statistics table
ax0[1].axis('off')
ax0[1].axis('tight')
the_table = ax0[1].table(
cellText=groups.describe().round(2).values,
rowLabels=groups.describe().index.year,
rowColours=colors,
colLabels=groups.describe().columns.levels[1],
loc='center')
the_table.set_fontsize(5)
# subplot for data
fig, ax = pyplot.subplots(nrows=len(groups),
ncols=1,
sharey=True)
if len(groups) == 1:
ax = [ax]
t = 1
for ny in range(len(groups)):
# prepare data for plotting
y_data = g_data[ny + (t + 1)].dropna(axis=0)
x_time = g_data[ny + t].dropna(axis=0)
t += 1
if len(y_data) != 0 and len(x_time) != 0:
n_year = x_time[0].year
col_name = str(n_year)
serie_n = pd.DataFrame(columns=[col_name],
index=x_time)
serie_n[col_name] = list(y_data[:])
# plot histogram
# serie_n.plot.hist(ax=ax0[0], bins=bin_range,
# histtype='bar', color=colors[ny], stacked=True)
if len(serie_n) != 1:
serie_n.plot.kde(ax=ax0[0],
color=colors[ny])
ax0[0].legend(fontsize=8,
bbox_to_anchor=(0., 1.12,
1.,
.102),
loc=3,
ncol=len(groups),
mode="expand",
borderaxespad=0.)
# ax0[0].set_xticks(bin_range)
ax0[0].set_xlabel('Observation Ranges',
fontsize=8)
ax0[0].set_ylabel(
'Density', fontsize=8
) #'Number of Observations'
ax0[0].set_title(
ms.split('-')[0] + ' (' + sv +
', ' + sv_units + ')' +
' Kernel Density Estimates',
fontsize=8)
# plot data
serie_n.plot(ax=ax[ny],
linestyle='None',
marker='.',
markersize=0.5,
color=colors[ny])
ax[ny].legend().set_visible(False)
# plot Mean and Standard deviation
ma = serie_n.rolling('86400s').mean()
mstd = serie_n.rolling('86400s').std()
ax[ny].plot(ma.index,
ma[col_name].values,
'k',
linewidth=0.15)
ax[ny].fill_between(
mstd.index,
ma[col_name].values -
2 * mstd[col_name].values,
ma[col_name].values +
2 * mstd[col_name].values,
color='b',
alpha=0.2)
# prepare the time axis parameters
datemin = datetime.date(n_year, 1, 1)
datemax = datetime.date(n_year, 12, 31)
ax[ny].set_xlim(datemin, datemax)
xlocator = mdates.MonthLocator(
) # every month
myFmt = mdates.DateFormatter('%m')
ax[ny].xaxis.set_minor_locator(
xlocator)
ax[ny].xaxis.set_major_formatter(myFmt)
# prepare the time axis parameters
# ax[ny].set_yticks(bin_range)
ylocator = MaxNLocator(prune='both',
nbins=3)
ax[ny].yaxis.set_major_locator(
ylocator)
# format figure
ax[ny].tick_params(axis='both',
color='r',
labelsize=7,
labelcolor='m')
if ny < len(groups) - 1:
ax[ny].tick_params(
which='both',
pad=0.1,
length=1,
labelbottom=False)
ax[ny].set_xlabel(' ')
else:
ax[ny].tick_params(which='both',
color='r',
labelsize=7,
labelcolor='m',
pad=0.1,
length=1,
rotation=0)
ax[ny].set_xlabel('Months',
rotation=0,
fontsize=8,
color='b')
ax[ny].set_ylabel(n_year,
rotation=0,
fontsize=8,
color='b',
labelpad=20)
ax[ny].yaxis.set_label_position(
"right")
if ny == 0:
if global_min and global_max:
ax[ny].set_title(
sv + '( ' + sv_units +
') -- Global Range: [' +
str(int(global_min)) +
',' +
str(int(global_max)) +
'] \n'
'Plotted: Data, Mean and 2STD (Method: One day rolling window calculations) \n',
fontsize=8)
else:
ax[ny].set_title(
sv + '( ' + sv_units +
') -- Global Range: [] \n'
'Plotted: Data, Mean and 2STD (Method: One day rolling window calculations) \n',
fontsize=8)
# plot global ranges
# ax[ny].axhline(y=global_min, color='r', linestyle='--', linewidth=.6)
# ax[ny].axhline(y=global_max, color='r', linestyle='--', linewidth=.6)
# mark deployment end times on figure
ymin, ymax = ax[ny].get_ylim()
#dep = 1
for etimes in range(len(end_times)):
if end_times[
etimes].year == n_year:
ax[ny].axvline(
x=end_times[etimes],
color='b',
linestyle='--',
linewidth=.6)
ax[ny].text(
end_times[etimes],
ymin,
'End' +
str(deployments_num[etimes]
),
fontsize=6,
style='italic',
bbox=dict(boxstyle='round',
ec=(0., 0.5,
0.5),
fc=(1., 1., 1.)))
# dep += 1
# ax[ny].set_ylim(5, 12)
# save figure to a file
sfile = '_'.join(('all', sname))
save_file = os.path.join(save_dir, sfile)
fig.savefig(str(save_file), dpi=150)
sfile = '_'.join(('Statistics', sname))
save_file = os.path.join(save_dir, sfile)
fig0.savefig(str(save_file), dpi=150)
pyplot.close()
def main(sDir, url_list):
# get summary lists of reference designators and delivery methods
rd_list = []
rdm_list = []
for uu in url_list:
elements = uu.split('/')[-2].split('-')
rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))
rdm = '-'.join((rd, elements[5]))
if rd not in rd_list:
rd_list.append(rd)
if rdm not in rdm_list:
rdm_list.append(rdm)
json_file_list = []
for r in rd_list:
rdm_filtered = [k for k in rdm_list if r in k]
dinfo = {}
save_dir = os.path.join(sDir, r.split('-')[0], r)
cf.create_dir(save_dir)
sfile = os.path.join(save_dir, '{}-method_comparison.json'.format(r))
if len(rdm_filtered) == 1:
print('Only one delivery method provided - no comparison.')
dinfo['note'] = 'no comparison - only one delivery method provided'
with open(sfile, 'w') as outfile:
json.dump(dinfo, outfile)
json_file_list.append(str(sfile))
continue
elif len(rdm_filtered) > 1 & len(rdm_filtered) <= 3:
print('\nComparing data from different methods for: {}'.format(r))
for i in range(len(rdm_filtered)):
urls = [x for x in url_list if rdm_filtered[i] in x]
for u in urls:
splitter = u.split('/')[-2].split('-')
catalog_rms = '-'.join((r, splitter[-2], splitter[-1]))
udatasets = cf.get_nc_urls([u])
idatasets = []
for dss in udatasets: # filter out collocated data files
if catalog_rms == dss.split('/')[-1].split(
'_20')[0][15:]:
idatasets.append(dss)
deployments = [
str(k.split('/')[-1][0:14]) for k in idatasets
]
udeploy = np.unique(deployments).tolist()
for ud in udeploy:
rdatasets = [s for s in idatasets if ud in s]
file_ms_lst = []
for dataset in rdatasets:
splt = dataset.split('/')[-1].split(
'_20')[0].split('-')
file_ms_lst.append('-'.join((splt[-2], splt[-1])))
file_ms = np.unique(file_ms_lst).tolist()[0]
try:
dinfo[file_ms]
except KeyError:
dinfo[file_ms] = {}
dinfo[file_ms].update({ud: rdatasets})
else:
print(
'More than 3 methods provided. Please provide fewer datasets for analysis.'
)
continue
dinfo_df = pd.DataFrame(dinfo)
umethods = []
ustreams = []
for k in dinfo.keys():
umethods.append(k.split('-')[0])
ustreams.append(k.split('-')[1])
if len(np.unique(ustreams)) > len(
np.unique(umethods)
): # if there is more than 1 stream per delivery method
mdict = dict()
method_stream_df = cf.stream_word_check(dinfo)
for cs in (np.unique(
method_stream_df['stream_name_compare'])).tolist():
print('Common stream_name: {}'.format(cs))
method_stream_list = []
for row in method_stream_df.itertuples():
index, method, stream_name, stream_name_compare = row
if stream_name_compare == cs:
method_stream_list.append('-'.join(
(method, stream_name)))
dinfo_df_filtered = dinfo_df[method_stream_list]
summary_dict = compare_data(dinfo_df_filtered)
# merge dictionaries for all streams for one reference designator
mdict = merge_two_dicts(mdict, summary_dict)
with open(sfile, 'w') as outfile:
json.dump(mdict, outfile)
else:
summary_dict = compare_data(dinfo_df)
with open(sfile, 'w') as outfile:
json.dump(summary_dict, outfile)
json_file_list.append(str(sfile))
return json_file_list
file = os.path.join(root, filename)
f = xr.open_dataset(file)
f = f.swap_dims({'obs': 'time'})
fN = f.source
platform = f.subsite
node = f.node
sensor = f.sensor
title = platform + '-' + node + '-' + sensor
global fName
head, tail = os.path.split(filename)
fName = tail.split('.', 1)[0]
d = fName.split('_')[0]
save_dir = os.path.join(rootdir, 'timeseries', d)
cf.create_dir(save_dir)
varList = []
for vars in f.variables:
varList.append(str(vars))
yVars = [s for s in varList if not reg_ex.search(s)]
for v in yVars:
print v
t = f['time'].data
t_dict = dict(data=t, info=dict(label='Time', units='GMT'))
y = f[v]
def main(sDir, url_list, start_time, end_time, deployment_num):
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 'OPTAA' in rd:
rd_list.append(rd)
for r in rd_list:
print('\n{}'.format(r))
datasets = []
deployments = []
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])
for ud in udatasets: # filter out collocated data files
if 'OPTAA' in ud.split('/')[-1]:
datasets.append(ud)
if ud.split('/')[-1].split('_')[0] not in deployments:
deployments.append(ud.split('/')[-1].split('_')[0])
deployments.sort()
fdatasets = np.unique(datasets).tolist()
for deploy in deployments:
if deployment_num is not None:
if int(deploy[-4:]) is not deployment_num:
print('\nskipping {}'.format(deploy))
continue
rdatasets = [s for s in fdatasets if deploy in s]
if len(rdatasets) > 0:
sci_vars_dict = {'optical_absorption': dict(atts=dict(fv=[], units=[])),
'beam_attenuation': dict(atts=dict(fv=[], units=[]))}
for i in range(len(rdatasets)):
#for i in range(0, 2): ##### for testing
ds = xr.open_dataset(rdatasets[i], mask_and_scale=False)
ds = ds.swap_dims({'obs': 'time'})
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
if i == 0:
fname, subsite, refdes, method, stream, deployment = cf.nc_attributes(rdatasets[0])
array = subsite[0:2]
filename = '_'.join(fname.split('_')[:-1])
save_dir = os.path.join(sDir, array, subsite, refdes, 'timeseries_plots', deployment)
cf.create_dir(save_dir)
for k in sci_vars_dict.keys():
print('\nAppending data from {}: {}'.format(deploy, k))
vv = ds[k]
fv = vv._FillValue
vvunits = vv.units
if fv not in sci_vars_dict[k]['atts']['fv']:
sci_vars_dict[k]['atts']['fv'].append(fv)
if vvunits not in sci_vars_dict[k]['atts']['units']:
sci_vars_dict[k]['atts']['units'].append(vvunits)
if k == 'optical_absorption':
wavelengths = ds['wavelength_a'].values
elif k == 'beam_attenuation':
wavelengths = ds['wavelength_c'].values
for j in range(len(wavelengths)):
if (wavelengths[j] > 671.) and (wavelengths[j] < 679.):
wv = str(wavelengths[j])
try:
sci_vars_dict[k][wv]
except KeyError:
sci_vars_dict[k].update({wv: dict(values=np.array([]), time=np.array([], dtype=np.datetime64))})
v = vv.sel(wavelength=j).values
sci_vars_dict[k][wv]['values'] = np.append(sci_vars_dict[k][wv]['values'], v)
sci_vars_dict[k][wv]['time'] = np.append(sci_vars_dict[k][wv]['time'], ds['time'].values)
title = ' '.join((deployment, refdes, method))
colors = ['purple', 'green', 'orange']
t0_array = np.array([], dtype=np.datetime64)
t1_array = np.array([], dtype=np.datetime64)
for var in sci_vars_dict.keys():
print('Plotting {}'.format(var))
plotting = [] # keep track if anything is plotted
fig1, ax1 = plt.subplots()
fig2, ax2 = plt.subplots()
[g_min, g_max] = cf.get_global_ranges(r, var)
for idk, dk in enumerate(sci_vars_dict[var]):
if dk != 'atts':
v = sci_vars_dict[var][dk]['values']
n_all = len(sci_vars_dict[var][dk]['values'])
n_nan = np.sum(np.isnan(v))
# convert fill values to nans
v[v == sci_vars_dict[var]['atts']['fv'][0]] = np.nan
n_fv = np.sum(np.isnan(v)) - n_nan
if n_nan + n_fv < n_all:
# plot before global ranges are removed
plotting.append('yes')
tm = sci_vars_dict[var][dk]['time']
t0_array = np.append(t0_array, tm.min())
t1_array = np.append(t1_array, tm.max())
ax1.scatter(tm, v, c=colors[idk - 1], label='{} nm'.format(dk), marker='.', s=1)
# reject data outside of global ranges
if g_min is not None and g_max is not None:
v[v < g_min] = np.nan
v[v > g_max] = np.nan
n_grange = np.sum(np.isnan(v)) - n_fv - n_nan
else:
n_grange = 'no global ranges'
# plot after global ranges are removed
ax2.scatter(tm, v, c=colors[idk - 1], label='{} nm: rm {} GR'.format(dk, n_grange),
marker='.', s=1)
if len(plotting) > 0:
t0 = pd.to_datetime(t0_array.min()).strftime('%Y-%m-%dT%H:%M:%S')
t1 = pd.to_datetime(t1_array.max()).strftime('%Y-%m-%dT%H:%M:%S')
ax1.grid()
pf.format_date_axis(ax1, fig1)
ax1.legend(loc='best', fontsize=7)
ax1.set_ylabel((var + " (" + sci_vars_dict[var]['atts']['units'][0] + ")"), fontsize=9)
ax1.set_title((title + '\n' + t0 + ' - ' + t1), fontsize=9)
sfile = '-'.join((filename, var, t0[:10]))
save_file = os.path.join(save_dir, sfile)
fig1.savefig(str(save_file), dpi=150)
ax2.grid()
pf.format_date_axis(ax2, fig2)
ax2.legend(loc='best', fontsize=7)
ax2.set_ylabel((var + " (" + vv.units + ")"), fontsize=9)
title_gr = 'GR: global ranges'
ax2.set_title((title + '\n' + t0 + ' - ' + t1 + '\n' + title_gr), fontsize=9)
sfile2 = '-'.join((filename, var, t0[:10], 'rmgr'))
save_file2 = os.path.join(save_dir, sfile2)
fig2.savefig(str(save_file2), dpi=150)
plt.close('all')
