--- a/eldamwl_plot.py Mon Jan 29 11:16:12 2024 +0000
+++ b/eldamwl_plot.py Tue Jul 02 08:37:49 2024 +0000
@@ -1,6 +1,5 @@
-from typing import List, Any
-
-import xarray as xr
+# import nc as nc
+# import xarray as xr
from config import *
import plotly.graph_objects as go
from plotly.subplots import make_subplots
@@ -60,6 +59,19 @@
print(f"The group {e} does not exist.")
+def read_global_attribute(nc_file_path, attribute_name):
+ # Open the NetCDF file in read mode
+ with nc.Dataset(nc_file_path, 'r') as dataset:
+ # with netCDF4.Dataset(nc_file_path, 'r') as nc:
+ # Check if the attribute exists
+ if attribute_name in dataset.ncattrs():
+ # Read the attribute value
+ attribute_value = dataset.getncattr(attribute_name)
+ return attribute_value
+ else:
+ print(f"Attribute '{attribute_name}' not found in the NetCDF file.")
+
+
def read_variable_in_group2(nc_file_path, group_name, variable_name):
try:
with nc.Dataset(nc_file_path, 'r') as dataset:
@@ -89,7 +101,7 @@
dimension_values = {}
for dim_name in dimensions:
dim_values = group.variables[dim_name][:]
- dimension_values[dim_name] = dim_values
+ dimension_values[dim_name] = dim_values.tolist()
# Adjust the index array for slicing
index_array[variable.dimensions.index(dim_name)] = 0
@@ -154,17 +166,15 @@
for w in range(len(dimensions['wavelength'])):
tn = str(datetime.fromtimestamp(dimensions['time'][t]))
wn = str(dimensions['wavelength'][w])
- if timeinrows == True:
+ if timeinrows:
lgnd = wn + ' nm'
rown = t + 1
- colorint = 1
colorind = 0
- subtitle = str(datetime.fromtimestamp(dimensions['time'][t]))
+ # subtitle = str(datetime.fromtimestamp(dimensions['time'][t]))
else:
lgnd = wn + ' nm - ' + tn
rown = 1
colorind = t
- colorint = 1 - (0.4 * t)
# colour definition
if abs(dimensions['wavelength'][w] - 1064) < 1:
@@ -178,30 +188,164 @@
clr = 'rgb(0,0,0)'
data_values = data[variables[v]][w, t, :]
- error_values = positive_error_array[variables[v]][w, t, :]
- vertical_levels = y_array["data"][t, :]
+ if np.average(np.ma.masked_array(data_values, np.isnan(data_values))) is not np.ma.masked:
+ vertical_levels = y_array["data"][t, :]
+ error_values = positive_error_array[variables[v]][w, t, :]
+
+ # Add trace
+ fig.add_trace(go.Scatter(
+ x=data_values,
+ y=vertical_levels,
+ error_x=dict(array=error_values, width=0, thickness=0.1),
+ # ToDo Include distinction between positive and negative errors
+ mode='lines',
+ line=dict(width=2, color=clr),
+ name=lgnd),
+ row=rown, col=v + 1,
+ )
+
+ # ToDo Check formatting options (showlegend, mapbox)
+ fig.update_xaxes({"title": xaxis_label[v]}, row=rown, col=v + 1)
+ fig.update_xaxes({"mirror": True, "ticks": 'outside', "showline": True, "color": "black"}, row=rown,
+ col=v + 1)
+
+ fig.update_xaxes(ticks="outside")
+ fig.update_yaxes(ticks="outside", row=rown, col=v + 1)
+ # ToDo check tickformat (i.e. tickformat=":.2e")
+ if v == 0:
+ fig.update_yaxes({"title": yaxis_label}, row=rown, col=v + 1)
+
+ # ToDo add boxes
+ a = fig.layout['xaxis']['domain']
+ # print(a)
+ b = fig.layout['yaxis']['domain']
+ # print(b)
+ # fig.add_hline(y=b[0], line=dict(color="yellow", width=2), row=rown, col=v+1)
+ # fig.add_hline(y=b[1], line=dict(color="purple", width=2), row=rown, col=v+1)
+ # fig.add_vline(x=a[0], line=dict(color="orange", width=2), row=rown, col=v+1)
+ # fig.add_vline(x=a[1], line=dict(color="pink", width=2), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=a[0], y0=b[0], x1=a[1], y1=b[1],
+ # line=dict(color="grey", width=2, ), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=1, y0=1, x1=1, y1=0,
+ # line=dict(color="blue", width=2, ), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=0, y0=0, x1=1, y1=0,
+ # line=dict(color="red", width=2, ), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=0, y0=0, x1=0, y1=1,
+ # line=dict(color="green", width=2, ), row=rown, col=v+1)
+ # fig.add_hline(y=0, line=dict(color="yellow", width=2))
+ # print(fig.data[0]['x'])
+
+ # Set axis labels and title
+ fig.update_layout(
+ yaxis_title=yaxis_label,
+ title=title,
+ template="seaborn", # You can change the template as per your preference
+ # ['ggplot2', 'seaborn', 'simple_white', 'plotly', 'plotly_white', 'plotly_dark', 'presentation',
+ # 'xgridoff', 'ygridoff', 'gridon', 'none']
+ height=max(600, 450 * rown), width=min(max(400 * len(variables), 750), 1400),
+ )
+
+ return fig
+
+
+def plot_vertical_profiles_plotly_allres(variables, data, dimensions, y_array, positive_error_array,
+ negative_error_array, xaxis_label, yaxis_label, title):
+ """
+ ToDo Complete
+ Plot vertical profiles using plotly
+
+ Parameters:
+ -
+
+ Returns:
+ - fig: html
+ """
+
+ # Set number of columns, number of rows, and subtitles if needed
+ ncols = len(variables)
+ nrows = 1
+ subtitles = ''
- # Add trace
- fig.add_trace(go.Scatter(
- x=data_values,
- y=vertical_levels,
- error_x=dict(array=error_values, width=0, thickness=0.1),
- # ToDo Include distinction between positive and negative errors
- mode='lines',
- line=dict(width=2, color=clr),
- name=lgnd),
- row=rown, col=v + 1,
- )
+ # Create figure
+ fig = make_subplots(rows=nrows, cols=ncols,
+ subplot_titles=subtitles,
+ shared_yaxes=True)
+
+ # Define colours
+ clrs = [['rgb(0,35,255)', 'rgb(0,20,150)'], # UV
+ ['rgb(90,210,50)', 'rgb(40,90,25)'], # VIS
+ ['rgb(250,30,30)', 'rgb(130,30,10)']] # IR
+ clrs = np.array(clrs)
+
+ # Plot
+ for v in range(len(variables)):
+ for t in range(len(dimensions['time'])):
+ for w in range(len(dimensions['wavelength_res'])):
+ wl = float(dimensions['wavelength_res'][w].split(' ')[0])
+ wn = str(dimensions['wavelength_res'][w])
+ lgnd = wn.replace(' ', ' nm ')
+ rown = 1
+ if wn.find('LR') > 0:
+ colorind = 0
+ else:
+ colorind = 1
+
+ # colour definition
+ if abs(wl - 1064) < 1:
+ clr = clrs[2, colorind]
+ elif abs(wl - 532) < 1:
+ clr = clrs[1, colorind]
+ elif abs(wl - 355) < 1:
+ clr = clrs[0, colorind]
+ else:
+ clr = 'rgb(0,0,0)'
- # ToDo Check formatting options (showlegend, mapbox)
- fig.update_xaxes({"title": xaxis_label[v]}, row=rown, col=v + 1)
- fig.update_xaxes({"mirror": True, "ticks": 'outside', "showline": True, "color": "black"}, row=rown,
- col=v + 1)
+ data_values = data[variables[v]][w, t, :]
+ if np.average(np.ma.masked_array(data_values, np.isnan(data_values))) is not np.ma.masked:
+ vertical_levels = y_array["data"][t, :]
+ error_values = positive_error_array[variables[v]][w, t, :]
+ # Add trace
+ fig.add_trace(go.Scatter(
+ x=data_values,
+ y=vertical_levels,
+ error_x=dict(array=error_values, width=0, thickness=0.1),
+ # ToDo Include distinction between positive and negative errors
+ mode='lines',
+ line=dict(width=2, color=clr),
+ name=lgnd),
+ row=rown, col=v + 1,
+ )
+
+ # ToDo Check formatting options (showlegend, mapbox)
+ fig.update_xaxes({"title": xaxis_label[v]}, row=rown, col=v + 1)
+ fig.update_xaxes({"mirror": True, "ticks": 'outside', "showline": True, "color": "black"}, row=rown,
+ col=v + 1)
- fig.update_xaxes(ticks="outside")
- fig.update_yaxes(ticks="outside", row=rown, col=v + 1) # ToDo check tickformat (i.e. tickformat=":.2e")
- if v == 0:
- fig.update_yaxes({"title": yaxis_label}, row=rown, col=v + 1)
+ fig.update_xaxes(ticks="outside")
+ fig.update_yaxes(ticks="outside", row=rown, col=v + 1)
+ # ToDo check tickformat (i.e. tickformat=":.2e")
+ if v == 0:
+ fig.update_yaxes({"title": yaxis_label}, row=rown, col=v + 1)
+
+ # ToDo add boxes
+ a = fig.layout['xaxis']['domain']
+ # print(a)
+ b = fig.layout['yaxis']['domain']
+ # print(b)
+ # fig.add_hline(y=b[0], line=dict(color="yellow", width=2), row=rown, col=v+1)
+ # fig.add_hline(y=b[1], line=dict(color="purple", width=2), row=rown, col=v+1)
+ # fig.add_vline(x=a[0], line=dict(color="orange", width=2), row=rown, col=v+1)
+ # fig.add_vline(x=a[1], line=dict(color="pink", width=2), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=a[0], y0=b[0], x1=a[1], y1=b[1],
+ # line=dict(color="grey", width=2, ), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=1, y0=1, x1=1, y1=0,
+ # line=dict(color="blue", width=2, ), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=0, y0=0, x1=1, y1=0,
+ # line=dict(color="red", width=2, ), row=rown, col=v+1)
+ # fig.add_shape(type="line", xref="paper", yref="paper", x0=0, y0=0, x1=0, y1=1,
+ # line=dict(color="green", width=2, ), row=rown, col=v+1)
+ # fig.add_hline(y=0, line=dict(color="yellow", width=2))
+ # print(fig.data[0]['x'])
# Set axis labels and title
fig.update_layout(
@@ -217,8 +361,10 @@
# Files for testing
-# netcdf_file = 'hpb_012_0000598_201810172100_201810172300_20181017oh00_ELDAmwl_v0.0.1.nc' # Backscatter and extinction, no error
-# netcdf_file = 'hpb_012_0000627_202308240200_202308240400_20230824hpb0200_ELDAmwl_v0.0.1.nc' # Extinction with error
+# Backscatter and extinction, no error:
+# netcdf_file = 'hpb_012_0000598_201810172100_201810172300_20181017oh00_ELDAmwl_v0.0.1.nc'
+# Extinction with error:
+# netcdf_file = 'hpb_012_0000627_202308240200_202308240400_20230824hpb0200_ELDAmwl_v0.0.1.nc'
try:
@@ -228,6 +374,11 @@
'Example: ./eldamwl_plot.py hpb_012_0000598_201810172100_201810172300_20181017oh00_ELDAmwl_v0.0.1.nc')
exit()
+# Print help
+if netcdf_file == '-h' or netcdf_file == '--h':
+ print('Syntax: ./eldamwl_plot.py hpb_012_0000598_201810172100_201810172300_20181017oh00_ELDAmwl_v0.0.1.nc')
+ exit()
+
netcdf_file_path = netcdf_path + netcdf_file
file_exists = exists(netcdf_file_path)
@@ -240,15 +391,20 @@
groups_names = ['Low resolution', 'High resolution']
# Variables that we want to plot
-variable = ['backscatter', 'extinction', 'lidarratio', 'volumedepolarization', 'particledepolarization'] # ToDo Test particledepolarization when available
+variable = ['backscatter', 'extinction', 'lidarratio', 'volumedepolarization',
+ 'particledepolarization'] # ToDo Test particledepolarization when available
# Errors associated to the variables
variable_error = ['error_backscatter', 'error_extinction', 'error_lidarratio',
'positive_systematic_error_volumedepolarization', 'error_particledepolarization']
variable_negative_error_volumedepol = 'negative_systematic_error_volumedepolarization'
-# Other variables:
+# ToDo Consider adding other variables:
# cloud_mask (time, level)
+# Read global attributes
+station_id = read_global_attribute(netcdf_file_path, 'station_ID')
+station_location = read_global_attribute(netcdf_file_path, 'location')
+# Read data
for g in range(len(groups)):
# Read the altitude variable
altitude = read_variable_in_group(netcdf_file_path, groups[g], 'altitude')
@@ -305,26 +461,48 @@
dimensions = dim
- # Ask user if he/she wants to plot different temporal profiles in different rows
+ # Save lowres and highres in variables to plot them together afterwards
+ if groups[g] == 'lowres_products':
+ altitude_lr = altitude
+ dimensions_lr = dim
+ data_lr = data
+ data_error_positive_lr = data_error_positive
+ data_error_negative_lr = data_error_negative
+ variables_with_data_lr = variables_with_data
+ variables_axis_lr = variables_axis
+ if groups[g] == 'highres_products':
+ altitude_hr = altitude
+ dimensions_hr = dim
+ data_hr = data
+ data_error_positive_hr = data_error_positive
+ data_error_negative_hr = data_error_negative
+ variables_with_data_hr = variables_with_data
+ variables_axis_hr = variables_axis
+
+ # If interactive is set to True, ask user if he/she wants to plot different temporal profiles in different rows
+ # If interactive is set to False, proceed with default values
if g == 0:
# Initialize
- timeinplots = False
+ timeinplots = True
timeinrows = False
- # Ask user how he/she wants the plot
- if len(dimensions['time']) > 1:
+ # If interactive (config.py) = True, ask user how he/she wants the plot
+ if interactive and len(dimensions['time']) > 1:
answer = input(
- f"There are {len(dimensions['time'])} temporal profiles, do you want to plot them on different plots [y/n]? ")
- if answer[0:1].lower() == 'y':
- timeinplots = True
- if timeinplots == False:
+ f"There are {len(dimensions['time'])} temporal profiles, "
+ f"do you want to plot them on different plots [y/n]? ")
+ if answer[0:1].lower() == 'n':
+ timeinplots = False
+ if not timeinplots:
answer = input(
- f"There are {len(dimensions['time'])} temporal profiles, do you want to plot them on different rows [y/n]? ")
+ f"There are {len(dimensions['time'])} temporal profiles, "
+ f"do you want to plot them on different rows [y/n]? ")
if answer[0:1].lower() == 'y':
timeinrows = True
if variables_with_data is not None:
timeiter = dimensions['time']
- if timeinplots == True:
+ if timeinplots:
+ # Default plotting
dimensions_t = dimensions
for t in range(len(dimensions['time'])):
dimensions_t['time'] = [timeiter[t]]
@@ -343,15 +521,14 @@
negative_error_array=data_error_negative,
xaxis_label=variables_axis,
yaxis_label=altitude['metadata']['long_name'] + ' [' + altitude['metadata']['units'] + ']',
- title=netcdf_file[0:3].upper() + " - " + str(
- datetime.fromtimestamp(dimensions_t['time'][0]).strftime(
- '%d/%m/%Y %H:%M:%S')) + " - ELDA MWL " +
- groups_names[g],
+ title=station_location + " (" + station_id.upper() + ") - " + str(
+ datetime.fromtimestamp(dimensions_t['time'][0]).strftime('%d/%m/%Y %H:%M:%S')) +
+ " - ELDA MWL " + groups_names[g],
timeinrows=True
)
# Show the plot (in Jupyter Notebook or Jupyter Lab)
- fig.show()
+ # fig.show()
# Save the plot
filename = "ELDAmwl_" + netcdf_file[0:3].upper() + "_" + datetime.fromtimestamp(
@@ -369,22 +546,22 @@
data_error_negative,
xaxis_label=variables_axis,
yaxis_label=altitude['metadata']['long_name'] + ' [' + altitude['metadata']['units'] + ']',
- title=netcdf_file[0:3].upper() + " - " + str(
+ title=station_location + " (" + station_id.upper() + ") - " + str(
datetime.fromtimestamp(dimensions['time'][0]).strftime('%d/%m/%Y')) + " - ELDA MWL " + groups_names[
g],
timeinrows=timeinrows
)
# Show the plot (in Jupyter Notebook or Jupyter Lab)
- fig.show()
+ # fig.show()
# Save the plot
- if timeinrows == True:
- filename = "ELDAmwl_" + netcdf_file[0:3].upper() + "_" + datetime.fromtimestamp(
+ if timeinrows:
+ filename = "ELDAmwl_" + station_id.upper() + "_" + datetime.fromtimestamp(
dimensions['time'][0]).strftime('%Y%m%d%H%M%S') + '-' + datetime.fromtimestamp(
dimensions['time'][-1]).strftime('%Y%m%d%H%M%S') + "_" + groups[g] + "_timeinrows.html"
else:
- filename = "ELDAmwl_" + netcdf_file[0:3].upper() + "_" + datetime.fromtimestamp(
+ filename = "ELDAmwl_" + station_id.upper() + "_" + datetime.fromtimestamp(
dimensions['time'][0]).strftime('%Y%m%d%H%M%S') + '-' + datetime.fromtimestamp(
dimensions['time'][-1]).strftime('%Y%m%d%H%M%S') + "_" + groups[g] + ".html"
@@ -392,3 +569,158 @@
else:
print(f'There is no data to be plotted in the {groups_names[g]} group.')
+
+# Plotting high and low resolution together
+if len(variables_with_data_lr) > 0 and len(variables_with_data_hr) > 0:
+ print(f'We have low and high resolution data, let''s combine it!')
+
+ # Variables with data
+ variables_with_data = list(variables_with_data_lr)
+ variables_with_data.extend(x for x in variables_with_data_hr if x not in variables_with_data)
+ print(f'Variables with data: {variables_with_data}')
+
+ # Variables axis
+ variables_axis = list(variables_axis_lr)
+ variables_axis.extend(x for x in variables_axis_hr if x not in variables_axis)
+
+ # Joining dimensions for both resolutions
+ # Wavelength
+ wav = list(dimensions_lr['wavelength'])
+ wav.extend(x for x in dimensions_hr['wavelength'] if x not in wav)
+ dimensions['wavelength'] = wav
+ # Add LR/HR to the wavelength
+ wlr = list(dimensions_lr['wavelength'])
+ for r in range(len(wlr)):
+ wlr[r] = str(wlr[r]) + ' (LR)'
+ whr = list(dimensions_hr['wavelength'])
+ for r in range(len(whr)):
+ whr[r] = str(whr[r]) + ' (HR)'
+ dimensions['wavelength_res'] = wlr + whr
+ # Time
+ tim = list(dimensions_lr['time'])
+ tim.extend(x for x in dimensions_hr['time'] if x not in tim)
+ dimensions['time'] = tim
+ # Level
+ lev = list(dimensions_lr['level'])
+ lev.extend(x for x in dimensions_hr['level'] if x not in lev)
+ dimensions['level'] = lev
+
+ # Populate the arrays with data and errors
+ for v in variables_with_data:
+ # Create array to store the joint data
+ # data_var = np.ma.masked_all((len(dimensions['wavelength_res']), len(tim), len(lev)))
+ data_var = np.ma.zeros((len(dimensions['wavelength_res']), len(tim), len(lev)))
+ data_error_positive_var = np.ma.zeros((len(dimensions['wavelength_res']), len(tim), len(lev)))
+ data_error_negative_var = np.ma.zeros((len(dimensions['wavelength_res']), len(tim), len(lev)))
+ # ToDo check if needed
+ data_var = np.ma.masked_values(data_var, 0)
+ data_error_positive_var = np.ma.masked_values(data_error_positive_var, 0)
+ data_error_negative_var = np.ma.masked_values(data_error_negative_var, 0)
+ for w in range(len(dimensions['wavelength_res'])):
+ for t in range(len(dimensions['time'])):
+ # Incorporate data into the main array
+ print(f"{v} - Wavelength: {dimensions['wavelength_res'][w]}. Time: {dimensions['time'][t]}.")
+
+ # High resolution
+ if dimensions['wavelength_res'][w].find('HR') >= 0:
+ # Is there HR data?
+ if v in data_hr:
+ try:
+ # Find position of w and t
+ wl = float(dimensions['wavelength_res'][w].split(' ')[0])
+ wp = dimensions_hr['wavelength'].index(wl)
+ tp = dimensions_hr['time'].index(dimensions['time'][t])
+ # Check if there is data
+ a = data_hr[v][wp, tp, :]
+ d = np.average(np.ma.masked_array(a, np.isnan(a)))
+ if np.ma.min(d) is not np.ma.masked:
+ print(f'\tWe have HR data for {v}')
+ # Save data into common structure
+ data_var[w, t, :] = a
+ if np.average(np.ma.masked_array(data_error_positive_hr[v][wp, tp, :], np.isnan(
+ data_error_positive_hr[v][wp, tp, :]))) is not np.ma.masked:
+ data_error_positive_var[w, t, :] = data_error_positive_hr[v][wp, tp, :]
+ if np.average(np.ma.masked_array(data_error_negative_hr[v][wp, tp, :], np.isnan(
+ data_error_negative_hr[v][wp, tp, :]))) is not np.ma.masked:
+ data_error_negative_var[w, t, :] = data_error_negative_hr[v][wp, tp, :]
+ except ValueError:
+ pass
+
+ # Low resolution
+ if dimensions['wavelength_res'][w].find('LR') >= 0:
+ # Is there LR data?
+ if v in data_lr:
+ # Find position of w and t
+ try:
+ wl = float(dimensions['wavelength_res'][w].split(' ')[0])
+ wp = dimensions_hr['wavelength'].index(wl)
+ if wl in dimensions_lr['wavelength']:
+ wp = dimensions_lr['wavelength'].index(dimensions['wavelength'][w])
+ else:
+ wp = -1
+ # if dimensions['time'][t] in dimensions_lr['time']:
+ tp = dimensions_lr['time'].index(dimensions['time'][t])
+ # else:
+ # tp = -1
+ if wp > -1 and tp > -1:
+ # Check if there is data
+ a = data_lr[v][wp, tp, :]
+ d = np.average(np.ma.masked_array(a, np.isnan(a)))
+ if np.ma.min(d) is not np.ma.masked:
+ print(f'\tWe have LR data for {v}')
+ # Save data into common structure
+ data_var[w, t, :] = a
+ if np.average(np.ma.masked_array(data_error_positive_lr[v][wp, tp, :], np.isnan(
+ data_error_positive_lr[v][wp, tp, :]))) is not np.ma.masked:
+ data_error_positive_var[w, t, :] = data_error_positive_lr[v][wp, tp, :]
+ if np.average(np.ma.masked_array(data_error_negative_lr[v][wp, tp, :], np.isnan(
+ data_error_negative_lr[v][wp, tp, :]))) is not np.ma.masked:
+ data_error_negative_var[w, t, :] = data_error_negative_lr[v][wp, tp, :]
+ except ValueError:
+ # i.e. no 1064 wavelength in dimensions_lr
+ pass
+ # except IndexError:
+ # pass
+
+ # Store data in the global matrices
+ if variables_with_data.index(v) == 0:
+ data_all = {v: data_var}
+ data_error_positive = {v: data_error_positive_var}
+ data_error_negative = {v: data_error_negative_var}
+ else:
+ data_all[v] = data_var
+ data_error_positive[v] = data_error_positive_var
+ data_error_negative[v] = data_error_negative_var
+
+ # Plot
+ timeiter = dimensions['time']
+ # Default plotting
+ dimensions_t = dimensions
+ for t in range(len(dimensions['time'])):
+ print(f'plotting hr & lr for {timeiter[t]}')
+ dimensions_t['time'] = [timeiter[t]]
+ for vr in range(len(variables_with_data)):
+ if vr == 0:
+ data_t = {variables_with_data[vr]: data_all[variables_with_data[vr]][:, t:t + 1, :]}
+ else:
+ data_t[variables_with_data[vr]] = data_all[variables_with_data[vr]][:, t:t + 1, :]
+
+ fig = plot_vertical_profiles_plotly_allres(
+ variables=variables_with_data,
+ data=data_t,
+ dimensions=dimensions_t,
+ y_array=altitude,
+ positive_error_array=data_error_positive,
+ negative_error_array=data_error_negative,
+ xaxis_label=variables_axis,
+ yaxis_label=altitude['metadata']['long_name'] + ' [' + altitude['metadata']['units'] + ']',
+ title=station_location + " (" + station_id.upper() + ") - " + str(datetime.fromtimestamp(dimensions_t['time'][0]).strftime('%d/%m/%Y %H:%M:%S')) + " - ELDA MWL - High and low resolution")
+
+ # Show the plot (in Jupyter Notebook or Jupyter Lab)
+ fig.show()
+
+ # Save the plot
+ filename = "ELDAmwl_" + netcdf_file[0:3].upper() + "_" + datetime.fromtimestamp(
+ dimensions['time'][0]).strftime('%Y%m%d-%H%M%S') + "_all.html"
+ fig.write_html(html_path + filename)
+
