Atmospheric Lidar Package: generic.py@563705a26f85 (annotated)

generic.py@563705a26f85 (annotated)

generic.py

Wed, 18 Jun 2014 17:56:46 +0300

author: Iannis <ulalume3@yahoo.com>
date: Wed, 18 Jun 2014 17:56:46 +0300
changeset 25: 563705a26f85
parent 24: b148076f7295
child 27: 74f7617f5356
permissions: -rw-r--r--

Athens files

 # General imports
 import datetime
 from operator import itemgetter
 # Science imports
 import numpy as np
 import matplotlib as mpl
 from matplotlib import pyplot as plt
 import netCDF4 as netcdf
 netcdf_format = 'NETCDF3_CLASSIC' # choose one of 'NETCDF3_CLASSIC', 'NETCDF3_64BIT', 'NETCDF4_CLASSIC' and 'NETCDF4'
 class BaseLidarMeasurement():
     """ This is the general measurement object.
     It is meant to become a general measurement object
     independent of the input files.
     Each subclass should implement the following:
     * the import_file method.
     * set the "extra_netcdf_parameters" variable to a dictionary that includes the appropriate parameters.
     You can override the get_PT method to define a custom procedure to get ground temperature and pressure.
     The one implemented by default is by using the MILOS meteorological station data.
     """
     def __init__(self, filelist= None):
         self.info = {}
         self.dimensions = {}
         self.variables =  {}
         self.channels = {}
         self.attributes = {}
         self.files = []
         self.dark_measurement = None
         if filelist:
             self.import_files(filelist)
     def import_files(self,filelist):
         for f in filelist:
             self.import_file(f)
         self.update()
     def import_file(self,filename):
         raise NotImplementedError('Importing files should be defined in the instrument-specific subclass.')
     def update(self):
         '''
         Update the the info, variables and dimensions of the lidar measurement based
         on the information found in the channels.
         Reading of the scan_angles parameter is not implemented.
         '''
         # Initialize
         start_time =[]
         stop_time = []
         points = []
         all_time_scales = []
         channel_name_list = []
         # Get the information from all the channels
         for channel_name, channel in self.channels.items():
             channel.update()
             start_time.append(channel.start_time)
             stop_time.append(channel.stop_time)
             points.append(channel.points)
             all_time_scales.append(channel.time)
             channel_name_list.append(channel_name)
         # Find the unique time scales used in the channels
         time_scales = set(all_time_scales)
         # Update the info dictionary
         self.info['start_time'] = min(start_time)
         self.info['stop_time'] = max(stop_time)
         self.info['duration'] = self.info['stop_time'] - self.info['start_time']
         # Update the dimensions dictionary
         self.dimensions['points'] = max(points)
         self.dimensions['channels'] = len(self.channels)
         # self.dimensions['scan angles'] = 1
         self.dimensions['nb_of_time_scales'] = len(time_scales)
         # Update the variables dictionary
         # Write time scales in seconds
         raw_Data_Start_Time = []
         raw_Data_Stop_Time = []
         for current_time_scale in list(time_scales):
             raw_start_time = np.array(current_time_scale) - min(start_time) # Time since start_time
             raw_start_in_seconds = np.array([t.seconds for t in raw_start_time]) # Convert in seconds
             raw_Data_Start_Time.append(raw_start_in_seconds) # And add to the list
             # Check if this time scale has measurements every 30 or 60 seconds.
             duration = self._get_duration(raw_start_in_seconds)
             raw_stop_in_seconds = raw_start_in_seconds + duration
             raw_Data_Stop_Time.append(raw_stop_in_seconds)
         self.variables['Raw_Data_Start_Time'] = raw_Data_Start_Time
         self.variables['Raw_Data_Stop_Time'] = raw_Data_Stop_Time
         # Make a dictionary to match time scales and channels
         channel_timescales = []
         for (channel_name, current_time_scale) in zip(channel_name_list, all_time_scales):
             # The following lines are PEARL specific. The reason they are here is not clear.
             # if channel_name =='1064BLR':
             #     channel_name = '1064'
             for (ts,n) in zip(time_scales, range(len(time_scales))):
                 if current_time_scale == ts:
                     channel_timescales.append([channel_name,n])
         self.variables['id_timescale'] = dict(channel_timescales)
     def _get_duration(self, raw_start_in_seconds):
         ''' Return the duration for a given time scale. In some files (ex. Licel) this
         can be specified from the files themselves. In others this must be guessed.
         '''
         # The old method, kept here for reference
         #dt = np.mean(np.diff(raw_start_in_seconds))
         #for d in duration_list:
         #    if abs(dt - d) <15: #If the difference of measuremetns is 10s near the(30 or 60) seconds
         #        duration = d
         duration = np.diff(raw_start_in_seconds)[0]
         return duration
     def subset_by_channels(self, channel_subset):
         ''' Get a measurement object containing only the channels with names
         contained in the channel_sublet list '''
         m = self.__class__() # Create an object of the same type as this one.
         m.channels = dict([(channel, self.channels[channel]) for channel
                             in channel_subset])
         m.update()
         return m
     def subset_by_time(self, start_time, stop_time):
         if start_time > stop_time:
             raise ValueError('Stop time should be after start time')
         if (start_time < self.info['start_time']) or (stop_time > self.info['stop_time']):
             raise ValueError('The time interval specified is not part of the measurement')
         m = self.__class__() # Create an object of the same type as this one.
         for (channel_name, channel)  in self.channels.items():
             m.channels[channel_name] = channel.subset_by_time(start_time, stop_time)
         m.update()
         return m
     def r_plot(self):
         #Make a basic plot of the data.
         #Should include some dictionary with params to make plot stable.
         pass
     def r_pdf(self):
         # Create a pdf report using a basic plot and meta-data.
         pass
     def save(self):
         #Save the current state of the object to continue the analysis later.
         pass
     def get_PT(self):
         ''' Sets the pressure and temperature at station level .
         The results are stored in the info dictionary.
         '''
         self.info['Temperature'] = 10.0
         self.info['Pressure'] = 930.0
     def subtract_dark(self):
         if not self.dark_measurement:
             raise IOError('No dark measurements have been imported yet.')
         for (channel_name, dark_channel) in self.dark_measurement.channels.iteritems():
             dark_profile = dark_channel.average_profile()
             channel = self.channels[channel_name]
             for measurement_time, data in channel.data.iteritems():
                 channel.data[measurement_time] = data - dark_profile
             channel.update()
     def save_as_netcdf(self, filename = None):
         """Saves the measurement in the netcdf format as required by the SCC.
         Input: filename. If no filename is provided <measurement_id>.nc will
                be used.
         """
         params = self.extra_netcdf_parameters
         needed_parameters = ['Measurement_ID', 'Temperature', 'Pressure']
         for parameter in needed_parameters:
             stored_value = self.info.get(parameter, None)
             if stored_value is None:
                 raise ValueError('A value needs to be specified for %s' % parameter)
         if not filename:
             filename = "%s.nc" % self.info['Measurement_ID']
         dimensions = {'points': 1,
                 'channels': 1,
                 'time': None,
                 'nb_of_time_scales': 1,
                 'scan_angles': 1,}  # Mandatory dimensions. Time bck not implemented
         global_att = {'Measurement_ID': None,
                     'RawData_Start_Date': None,
                     'RawData_Start_Time_UT': None,
                     'RawData_Stop_Time_UT': None,
                     'RawBck_Start_Date': None,
                     'RawBck_Start_Time_UT': None,
                     'RawBck_Stop_Time_UT': None,
                     'Sounding_File_Name': None,
                     'LR_File_Name': None,
                     'Overlap_File_Name': None,
                     'Location': None,
                     'System': None,
                     'Latitude_degrees_north': None,
                     'Longitude_degrees_east': None,
                     'Altitude_meter_asl': None}
         channel_variables = \
                     {'channel_ID': (('channels', ), 'i'),
                     'Background_Low': (('channels', ), 'd'),
                     'Background_High': (('channels', ), 'd'),
                     'LR_Input': (('channels', ), 'i'),
                     'DAQ_Range': (('channels', ), 'd'),
                     'Depolarization_Factor': (('channels', ), 'd'), }
         channels = self.channels.keys()
         input_values = dict(self.dimensions, **self.variables)
         # Add some mandatory global attributes
         input_values['Measurement_ID'] = self.info['Measurement_ID']
         input_values['RawData_Start_Date'] = self.info['start_time'].strftime('%Y%m%d')
         input_values['RawData_Start_Time_UT'] = self.info['start_time'].strftime('%H%M%S')
         input_values['RawData_Stop_Time_UT'] = self.info['stop_time'].strftime('%H%M%S')
         # Add some optional global attributes
         input_values['System'] = params.general_parameters['System']
         input_values['Latitude_degrees_north'] = params.general_parameters['Latitude_degrees_north']
         input_values['Longitude_degrees_east'] = params.general_parameters['Longitude_degrees_east']
         input_values['Altitude_meter_asl'] = params.general_parameters['Altitude_meter_asl']
         # Open a netCDF4 file
         f = netcdf.Dataset(filename,'w', format = netcdf_format) # the format is specified in the begining of the file
         # Create the dimensions in the file
         for (d,v) in dimensions.iteritems():
             v = input_values.pop(d, v)
             f.createDimension(d,v)
         # Create global attributes
         for (attrib,value) in global_att.iteritems():
             val = input_values.pop(attrib,value)
             if val:
                 setattr(f, attrib, val)
         """ Variables """
         # Write the values of fixes channel parameters
         for (var,t) in channel_variables.iteritems():
             temp_v = f.createVariable(var,t[1],t[0])
             for (channel, n) in zip(channels, range(len(channels))):
                 temp_v[n] = params.channel_parameters[channel][var]
         # Write the id_timescale values
         temp_id_timescale = f.createVariable('id_timescale','i',('channels',))
         for (channel, n) in zip(channels, range(len(channels))):
             temp_id_timescale[n] = self.variables['id_timescale'][channel]
         # Laser pointing angle
         temp_v = f.createVariable('Laser_Pointing_Angle','d',('scan_angles',))
         temp_v[:] = params.general_parameters['Laser_Pointing_Angle']
         # Molecular calculation
         temp_v = f.createVariable('Molecular_Calc','i')
         temp_v[:] = params.general_parameters['Molecular_Calc']
         # Laser pointing angles of profiles
         temp_v = f.createVariable('Laser_Pointing_Angle_of_Profiles','i',('time','nb_of_time_scales'))
         for (time_scale,n) in zip(self.variables['Raw_Data_Start_Time'],
                                   range(len(self.variables['Raw_Data_Start_Time']))):
             temp_v[:len(time_scale), n] = 0  # The lidar has only one laser pointing angle
         # Raw data start/stop time
         temp_raw_start = f.createVariable('Raw_Data_Start_Time','i',('time','nb_of_time_scales'))
         temp_raw_stop = f.createVariable('Raw_Data_Stop_Time','i',('time','nb_of_time_scales'))
         for (start_time, stop_time,n) in zip(self.variables['Raw_Data_Start_Time'],
                                              self.variables['Raw_Data_Stop_Time'],
                                              range(len(self.variables['Raw_Data_Start_Time']))):
             temp_raw_start[:len(start_time),n] = start_time
             temp_raw_stop[:len(stop_time),n] = stop_time
         #Laser shots
         temp_v =  f.createVariable('Laser_Shots','i',('time','channels'))
         for (channel,n) in zip(channels, range(len(channels))):
             time_length = len(self.variables['Raw_Data_Start_Time'][self.variables['id_timescale'][channel]])
             # Array slicing stoped working as usual ex. temp_v[:10] = 100 does not work. ??? np.ones was added.
             temp_v[:time_length, n] = np.ones(time_length) * params.channel_parameters[channel]['Laser_Shots']
         #Raw lidar data
         temp_v = f.createVariable('Raw_Lidar_Data','d',('time', 'channels','points'))
         for (channel,n) in zip(channels, range(len(channels))):
             c = self.channels[channel]
             temp_v[:len(c.time),n, :c.points] = c.matrix
         self.add_dark_measurements_to_netcdf(f, channels)
         #Pressure at lidar station
         temp_v = f.createVariable('Pressure_at_Lidar_Station','d')
         temp_v[:] = self.info['Pressure']
         #Temperature at lidar station
         temp_v = f.createVariable('Temperature_at_Lidar_Station','d')
         temp_v[:] = self.info['Temperature']
         self.save_netcdf_extra(f)
         f.close()
     def add_dark_measurements_to_netcdf(self, f, channels):
         # Get dark measurements. If it is not given in self.dark_measurement
         # try to get it using the get_dark_measurements method. If none is found
         # return without adding something.
         if self.dark_measurement is None:
             self.dark_measurement = self.get_dark_measurements()
         if self.dark_measurement is None:
             return
         dark_measurement = self.dark_measurement
         # Calculate the length of the time_bck dimensions
         number_of_profiles = [len(c.time) for c in dark_measurement.channels.values()]
         max_number_of_profiles = np.max(number_of_profiles)
         # Create the dimension
         f.createDimension('time_bck', max_number_of_profiles)
         # Save the dark measurement data
         temp_v = f.createVariable('Background_Profile','d',('time_bck', 'channels', 'points'))
         for (channel,n) in zip(channels, range(len(channels))):
             c = dark_measurement.channels[channel]
             temp_v[:len(c.time),n, :c.points] = c.matrix
         # Dark profile start/stop time
         temp_raw_start = f.createVariable('Raw_Bck_Start_Time','i',('time_bck','nb_of_time_scales'))
         temp_raw_stop = f.createVariable('Raw_Bck_Stop_Time','i',('time_bck','nb_of_time_scales'))
         for (start_time, stop_time,n) in zip(dark_measurement.variables['Raw_Data_Start_Time'],
                                              dark_measurement.variables['Raw_Data_Stop_Time'],
                                              range(len(dark_measurement.variables['Raw_Data_Start_Time']))):
             temp_raw_start[:len(start_time),n] = start_time
             temp_raw_stop[:len(stop_time),n] = stop_time
         # Dark measurement start/stop time
         f.RawBck_Start_Date = dark_measurement.info['start_time'].strftime('%Y%m%d')
         f.RawBck_Start_Time_UT = dark_measurement.info['start_time'].strftime('%H%M%S')
         f.RawBck_Stop_Time_UT = dark_measurement.info['stop_time'].strftime('%H%M%S')
     def save_netcdf_extra(self, f):
         pass
     def _gettime(self, date_str, time_str):
         t = datetime.datetime.strptime(date_str+time_str,'%d/%m/%Y%H.%M.%S')
         return t
     def plot(self):
         for channel in self.channels:
             self.channels[channel].plot(show_plot = False)
         plt.show()
     def get_dark_measurements(self):
         return None
     @property
     def mean_time(self):
         start_time = self.info['start_time']
         stop_time = self.info['stop_time']
         dt = stop_time - start_time
         t_mean = start_time + dt / 2
         return t_mean
 class Lidar_channel:
     def __init__(self,channel_parameters):
         c = 299792458 #Speed of light
         self.wavelength = channel_parameters['name']
         self.name = str(self.wavelength)
         self.binwidth = float(channel_parameters['binwidth']) # in microseconds
         self.data = {}
         self.resolution = self.binwidth * c / 2
         self.z = np.arange(len(channel_parameters['data'])) * self.resolution + self.resolution/2.0 # Change: add half bin in the z
         self.points = len(channel_parameters['data'])
         self.rc = []
         self.duration  = 60
     def calculate_rc(self):
         background = np.mean(self.matrix[:,4000:], axis = 1) #Calculate the background from 30000m and above
         self.rc = (self.matrix.transpose()- background).transpose() * (self.z **2)
     def update(self):
         self.start_time = min(self.data.keys())
         self.stop_time = max(self.data.keys()) + datetime.timedelta(seconds = self.duration)
         self.time = tuple(sorted(self.data.keys()))
         sorted_data = sorted(self.data.iteritems(), key=itemgetter(0))
         self.matrix =  np.array(map(itemgetter(1),sorted_data))
     def _nearest_dt(self,dtime):
         margin = datetime.timedelta(seconds = 300)
         if ((dtime + margin) < self.start_time)| ((dtime - margin)   > self.stop_time):
             print "Requested date not covered in this file"
             raise
         dt = abs(self.time - np.array(dtime))
         dtmin = min(dt)
         if dtmin > datetime.timedelta(seconds = 60):
             print "Nearest profile more than 60 seconds away. dt = %s." % dtmin
         ind_t = np.where(dt == dtmin)
         ind_a= ind_t[0]
         if len(ind_a) > 1:
             ind_a = ind_a[0]
         chosen_time = self.time[ind_a]
         return chosen_time, ind_a
     def subset_by_time(self, start_time, stop_time):
         time_array = np.array(self.time)
         condition = (time_array >= start_time) & (time_array <= stop_time)
         subset_time = time_array[condition]
         subset_data = dict([(c_time, self.data[c_time]) for c_time in subset_time])
         #Create a list with the values needed by channel's __init__()
         parameters_values = {'name': self.wavelength,
                              'binwidth': self.binwidth,
                              'data': subset_data[subset_time[0]],}
         c = Lidar_channel(parameters_values)
         c.data = subset_data
         c.update()
         return c
     def profile(self,dtime, signal_type = 'rc'):
         t, idx = self._nearest_dt(dtime)
         if signal_type == 'rc':
             data = self.rc
         else:
             data = self.matrix
         prof = data[idx,:][0]
         return prof, t
     def get_slice(self, starttime, endtime, signal_type = 'rc'):
         if signal_type == 'rc':
             data = self.rc
         else:
             data = self.matrix
         tim = np.array(self.time)
         starttime = self._nearest_dt(starttime)[0]
         endtime = self._nearest_dt(endtime)[0]
         condition = (tim >= starttime) & (tim <= endtime)
         sl  = data[condition, :]
         t = tim[condition]
         return sl,t
     def profile_for_duration(self, tim, duration = datetime.timedelta(seconds = 0), signal_type = 'rc'):
         """ Calculates the profile around a specific time (tim). """
         starttime = tim - duration/2
         endtime = tim + duration/2
         d,t = self.get_slice(starttime, endtime, signal_type = signal_type)
         prof = np.mean(d, axis = 0)
         tmin = min(t)
         tmax = max(t)
         tav = tmin + (tmax-tmin)/2
         return prof,(tav, tmin,tmax)
     def average_profile(self):
         """ Return the average profile (NOT range corrected) for all the duration of the measurement. """
         prof = np.mean(self.matrix, axis = 0)
         return prof
     def plot(self, signal_type = 'rc', filename = None, zoom = [0,12000,0,-1], show_plot = True, cmap = plt.cm.jet, z0 = None, title = None, vmin = 0, vmax = 1.3 * 10 ** 7):
         #if filename is not None:
         #    matplotlib.use('Agg')
         fig = plt.figure()
         ax1 = fig.add_subplot(111)
         self.draw_plot(ax1, cmap = cmap, signal_type = signal_type, zoom = zoom, z0 = z0, vmin = vmin, vmax = vmax)
         if title:
             ax1.set_title(title)
         else:
             ax1.set_title("%s signal - %s" % (signal_type.upper(), self.name))
         if filename is not None:
             pass
             #plt.savefig(filename)
         else:
             if show_plot:
                 plt.show()
         #plt.close() ???
     def draw_plot(self,ax1, cmap = plt.cm.jet, signal_type = 'rc', zoom = [0,12000,0,-1], z0 = None, cmap_label = 'a.u.',  cb_format = None, vmin = 0, vmax = 1.3 * 10 ** 7):
         if signal_type == 'rc':
             if len(self.rc) == 0:
                 self.calculate_rc()
             data = self.rc
         else:
             data = self.matrix
         hmax_idx = self.index_at_height(zoom[1])
         # If z0 is given, then the plot is a.s.l.
         if z0:
             ax1.set_ylabel('Altitude a.s.l. [km]')
         else:
             ax1.set_ylabel('Altitude a.g.l. [km]')
             z0 = 0
         ax1.set_xlabel('Time UTC [hh:mm]')
         #y axis in km, xaxis /2 to make 30s measurements in minutes. Only for 1064
         #dateFormatter = mpl.dates.DateFormatter('%H.%M')
         #hourlocator = mpl.dates.HourLocator()
         #dayFormatter = mpl.dates.DateFormatter('\n\n%d/%m')
         #daylocator = mpl.dates.DayLocator()
         hourFormatter = mpl.dates.DateFormatter('%H:%M')
         hourlocator = mpl.dates.AutoDateLocator(minticks = 3, maxticks = 8, interval_multiples=True)
         #ax1.axes.xaxis.set_major_formatter(dayFormatter)
         #ax1.axes.xaxis.set_major_locator(daylocator)
         ax1.axes.xaxis.set_major_formatter(hourFormatter)
         ax1.axes.xaxis.set_major_locator(hourlocator)
         ts1 = mpl.dates.date2num(self.start_time)
         ts2 = mpl.dates.date2num(self.stop_time)
         im1 = ax1.imshow(data.transpose()[zoom[0]:hmax_idx,zoom[2]:zoom[3]],
             aspect = 'auto',
             origin = 'lower',
             cmap = cmap,
             vmin = vmin,
             #vmin = data[:,10:400].max() * 0.1,
             vmax = vmax,
             #vmax = data[:,10:400].max() * 0.9,
             extent = [ts1,ts2,self.z[zoom[0]]/1000.0 + z0/1000., self.z[hmax_idx]/1000.0 + z0/1000.],
             )
         if cb_format:
             cb1 = plt.colorbar(im1, format = cb_format)
         else:
             cb1 = plt.colorbar(im1)
         cb1.ax.set_ylabel(cmap_label)
         # Make the ticks of the colorbar smaller, two points smaller than the default font size
         cb_font_size = mpl.rcParams['font.size'] - 2
         for ticklabels in cb1.ax.get_yticklabels():
             ticklabels.set_fontsize(cb_font_size)
         cb1.ax.yaxis.get_offset_text().set_fontsize(cb_font_size)
     def index_at_height(self, height):
         idx = np.array(np.abs(self.z - height).argmin())
         if idx.size > 1:
             idx =idx[0]
         return idx
 def netcdf_from_files(LidarClass, filename, files, channels, measurement_ID):
     #Read the lidar files and select channels
     temp_m = LidarClass(files)
     m = temp_m.subset_by_channels(channels)
     m.get_PT()
     m.info['Measurement_ID'] = measurement_ID
     m.save_as_netcdf(filename)

Mercurial > public > atmospheric_lidar / annotate

generic.py@563705a26f85 (annotated)

generic.py