def extract_map_data(nc, data_var, t_index, z_index):
""" TODO: add a docstring! """
# Find the number of dimensions for the data variable
num_dims = len(data_var.dimensions)
if num_dims < 2 or num_dims > 4:
# This shows an example of raising an exception if bad input means that we can't proceed further
raise ValueError(
"Cannot extract data from variable with %d dimensions" % num_dims)
# ... Complete this function!
# Consider the abnormal situation that the longitude and latitude cannot
# be found in the nc variable
if netcdf_utils.find_latitude_var == False or netcdf_utils.find_longitude_var == False:
raise ValueError(
"the variable does not have latitude and longitude variables")
# First check the existance of time dimension
if not netcdf_utils.find_time_var(nc, data_var):
# Then check the existance of vertical dimension if cannot find the
# timen dimension
if not netcdf_utils.find_vertical_var(nc, data_var):
# If both of time and vertical dimension do not exist
# only return 2-D array
return data_var[:, :]
else:
# If only the time dimension does not exist then return the 3-D
# array with z_index
return data_var[z_index, :, :]
else:
# If time dimension exists, check the existance of vertical dimension
if not netcdf_utils.find_vertical_var(nc, data_var):
# if vertical dimension does not exist, return the 3-D array
# with time dimension
return data_var[t_index, :, :]
else:
# If both time and vertical dimension exist, ruturn the 4-D array
return data_var[t_index, z_index, :, :]
def extract_vertical_data(nc, data_var, direction, value, t_index):
"""
This function extracts data ready to be plotted on the vertical cross section.
:param nc: a NetCDF Dataset object
:param data_var: a NetCDF Variable object representing the variable to be extracted
:param direction: the direction of the vertical section as a string
:param value: the latitude or longitude that section represents
:return: the extracted vertical profile data and coordinate data for plotting
"""
# Find the number of dimensions for the data variable
num_dims = len(data_var.dimensions)
if num_dims < 3 or num_dims > 4:
raise ValueError("Cannot extract data from variable with %d dimensions" % num_dims)
lon_var = nu.find_longitude_var(nc, data_var)
lat_var = nu.find_latitude_var(nc, data_var)
t_var = nu.find_time_var(nc, data_var)
z_var = nu.find_vertical_var(nc, data_var)
if lon_var is None or lat_var is None:
# Vertical cross section need both latitude and longitude
raise ValueError("Cannot plot vertical cross section without both \
longitude and latitude")
else:
# Determine the direction of this section and find the index of the coordinate value
if (direction == 'NS'):
sec_index = nu.find_nearest_lon_index(nc, data_var, value)
# Check the existing of vertical dimension
if z_var is None:
raise ValueError("Cannont plot vertical cross section without vertical dimension")
elif t_var is None:
# Return 3-D array, x- and z- coordinate data if time dimension does not exist
return data_var[:,:,sec_index], lat_var, z_var
else:
# Return 4-D array, x- and z- coordinate data if time dimension exists
return data_var[t_index,:,:,sec_index], lat_var, z_var
elif (direction == 'EW'):
sec_index = nu.find_nearest_lat_index(nc, data_var, value)
if z_var is None:
raise ValueError("Cannont plot vertical cross section without vertical dimension")
elif t_var is None:
return data_var[:,sec_index,:], lon_var, z_var
else:
return data_var[t_index,:,sec_index,:], lon_var, z_var
else:
raise ValueError("Need to choose the direction from NS or EW")
def extract_map_data(nc, data_var, t_index, z_index):
"""
This function extracts data ready to be plotted on a map. It takes 4 arguments:
nc: a NetCDF Dataset object
data_var: a NetCDF Variable object representing the variable to be extracted
t_index: the desired index along the time axis (if present) - an integer
z_index: the desired index along the z-axis (if present) - an integer
The function returns a 2D numpy array of map data
"""
# Find the number of dimensions for the data variable
num_dims = len(data_var.dimensions)
if num_dims < 2 or num_dims > 4:
# This shows an example of raising an exception if bad input means that we can't proceed further
raise ValueError("Cannot extract data from variable with %d dimensions" % num_dims)
lon_var = nu.find_longitude_var(nc, data_var)
lat_var = nu.find_latitude_var(nc, data_var)
if lon_var is None or lat_var is None:
raise ValueError("Need both latitude and longitude dimensions to extract map data")
if num_dims == 2:
# Simple case - just return all the data
return data_var[:,:]
elif num_dims == 3:
# Determine whether to use time or z axis
z_var = nu.find_vertical_var(nc, data_var)
time_var = nu.find_time_var(nc, data_var)
# Neither axis is present - throw an error
if z_var is None and time_var is None:
raise ValueError("Data is 3D but there is no vertical or time axis")
if z_var is not None:
# We have a z-axis
return data_var[z_index,:,:]
else:
# We have a time axis
return data_var[t_index,:,:]
else:
# num_dims must equal 4
return data_var[t_index,z_index,:,:]
def plot_timeseries(filename, varname, lon, lat, z):
"""
This function plots the time series from NetCDF data.
The function extracts the relevant data using functions from netcdf_utils and extract
It plots the data using functions from plotting.
:param filename: location of a NetCDF file as a string (in file system)
:param varname: the identifier of the variable that is to be plotted
:param lon: the value of longitude in degrees
:param lat: the value of latitude in degrees
:param z: the value of vertical coordinate variable
:return: no return
"""
nc = netCDF4.Dataset(filename)
data_var = nc.variables[varname]
# Extract the required data and coordinate data
data, coor_t = extract.extract_timeseries(nc, data_var, lon, lat, z)
# Determine the title of the plot
z_var = netcdf_utils.find_vertical_var(nc, data_var)
if z_var:
title = 'Time series for {name} ({unit})\nat {lat} degrees latitude and ' \
'{lon} degrees longitude\n on {z} ({z_unit}) layer'\
.format(
name=netcdf_utils.get_attribute(data_var, 'standard_name', data_var._name),
unit=netcdf_utils.get_attribute(data_var, 'units', 'no units'),
lat=lat, lon=lon, z=z, \
z_unit=netcdf_utils.get_attribute(z_var, 'units', 'no units')\
)
plotting.display_timeseries_plot(data, data_var, coor_t, title)
else:
title = 'Time series for {name} ({unit})\nat {lat} degrees latitude and ' \
'{lon} degrees longitude'\
.format(
name=netcdf_utils.get_attribute(data_var, 'standard_name', data_var._name),
unit=netcdf_utils.get_attribute(data_var, 'units', 'no units'),
lat=lat, lon=lon
)
plotting.display_timeseries_plot(data, data_var, coor_t, title)
def extract_timeseries(nc, data_var, lon, lat, z):
"""
This function extracts data ready to be plotted the time series.
:param nc: a NetCDF Dataset object
:param data_var: a NetCDF Variable object representing the variable to be extracted
:param lon: the value of longitude in degrees
:param lat: the value of latitude in degrees
:param z: the value of vertical coordinate variable
:return: the extracted data and coordinate data for plotting
"""
# Find the number of dimensions for the data variable
num_dims = len(data_var.dimensions)
# To plot time series the data should have x-, y- and t- dimensions at least
if num_dims < 3 or num_dims > 4:
raise ValueError("Cannot extract data from variable with %d dimensions" % num_dims)
lon_var = nu.find_longitude_var(nc, data_var)
lat_var = nu.find_latitude_var(nc, data_var)
t_var = nu.find_time_var(nc, data_var)
z_var = nu.find_vertical_var(nc, data_var)
if t_var is None:
# Time series plot must contain the time dimension
raise ValueError("The data does not have time dimension")
# Consider other dimensions when time diemension exists
elif lon_var is None or lat_var is None:
# Time series plot needs both latitude and longitude to determine location
raise ValueError("Cannot plot time series plot without both longitude \
and latitude")
# Check the existence of vertical axis variable
elif z_var is not None:
lon_index = nu.find_nearest_lon_index(nc, data_var, lon)
lat_index = nu.find_nearest_lat_index(nc, data_var, lat)
z_index = nu.find_nearest_z_index_index(nc, data_var, z)
return data_var[:,z_index,lat_index,lon_index], t_var
else:
lon_index = nu.find_nearest_lon_index(nc, data_var, lon)
lat_index = nu.find_nearest_lat_index(nc, data_var, lat)
return data_var[:,lat_index,lon_index], t_var