Overlap updates

data/stations.csv

@@ -1,3 +1,3 @@
 station_name, location, longitude, latitude, altitude, start_bin_height, end_bin_height, n_bins, pt_bin, dt, gdas1_folder, gdastxt_folder, lidar_src_calib_folder,lidar_src_folder,molecular_dataset,lidar_dataset_calib,db_file,aeronet_folder,aeronet_name,generation_folder,gen_signal_dataset,gen_lidar_dataset,gen_aerosol_dataset,gen_bg_dataset,gen_density_dataset, lidar_dataset, bg_dataset
 haifa, Haifa,35.02,32.8,230,78.75,22485.66016,3000,250, 50 * 1E-9, H:\data_haifa\DATA FROM TROPOS\GDAS\haifa, H:\data_haifa\DATA FROM TROPOS\GDAS\haifa_preproc, H:\data_haifa\DATA FROM TROPOS\data\level1a\PollyXT_TROPOS,H:\data_haifa\DATA FROM TROPOS\data\level0\PollyXT_TROPOS,D:\data_haifa\DATA FROM TROPOS\molecular_dataset,D:\data_haifa\DATA FROM TROPOS\lidar_dataset_calib,H:\data_haifa\DATA FROM TROPOS\data\level1a\PollyXT_TROPOS\pollyxt_tropos_calibration.db,D:\data_haifa\AERONET,Technion_Haifa_IL,D:\data_haifa\GENERATION\params_dataset,D:\data_haifa\GENERATION\signal_dataset,D:\data_haifa\GENERATION\lidar_dataset,D:\data_haifa\GENERATION\aerosol_dataset,D:\data_haifa\GENERATION\bg_dataset,D:\data_haifa\GENERATION\density_dataset,D:\data_haifa\DATA FROM TROPOS\lidar_dataset,D:\data_haifa\DATA FROM TROPOS\bg_dataset
-haifa_shubi, Haifa,35.02,32.8,230,78.75,22485.66016,3000, 250,50 * 1E-9, data_example/gdas, /media/shubi/My Passport/data_haifa/DATA FROM TROPOS/GDAS/haifa_preproc, /media/shubi/My Passport/data_haifa/DATA FROM TROPOS/data/level1a/PollyXT_TROPOS,/home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/data/level0,/media/shubi/My Passport/data_haifa/DATA FROM TROPOS/molecular_dataset,/home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/lidar_dataset_calib,/home/shubi/PycharmProjects/learning_lidar/data/data_example/pollyxt_tropos_calibration.db,D:\data_haifa\AERONET,Technion_Haifa_IL,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/bg_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/signal_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/lidar_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/aerosol_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/bg_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/density_dataset, /home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/lidar_dataset, /home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/bg_dataset
+haifa_shubi, Haifa,35.02,32.8,230,78.75,22485.66016,3000, 250,50 * 1E-9, data_example/gdas, /media/shubi/My Passport/data_haifa/DATA FROM TROPOS/GDAS/haifa_preproc, /media/shubi/My Passport/data_haifa/DATA FROM TROPOS/data/level1a/PollyXT_TROPOS,/home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/data/level0,/media/shubi/My Passport/data_haifa/DATA FROM TROPOS/molecular_dataset,/home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/lidar_dataset_calib,/home/shubi/PycharmProjects/learning_lidar/data/data_example/pollyxt_tropos_calibration.db,D:\data_haifa\AERONET,Technion_Haifa_IL,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/params_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/signal_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/lidar_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/aerosol_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/bg_dataset,/home/shubi/PycharmProjects/learning_lidar/data/GENERATION/density_dataset, /home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/lidar_dataset, /home/shubi/PycharmProjects/learning_lidar/data/DATA FROM TROPOS/bg_dataset

learning_lidar/generation/daily_signals_generation.py

@@ -1,70 +1,60 @@
+import datetime
 import os
 import logging
 from multiprocessing import Pool, cpu_count
 from itertools import repeat
 import pandas as pd
+import xarray as xr
 
 import learning_lidar.utils.global_settings as gs
 import learning_lidar.generation.generation_utils as gen_utils
-import learning_lidar.utils.utils as utils
 import learning_lidar.utils.vis_utils as vis_utils
 import learning_lidar.generation.daily_signals_generations_utils as gen_sig_utils
 from learning_lidar.utils.utils import create_and_configer_logger, get_base_arguments
 
 
 # TODO:  add 2 flags - Debug and save figure.
 
-def generate_daily_lidar_measurement(station, day_date, save_ds=True, update_overlap_only=False):
+def generate_daily_lidar_measurement(station: gs.Station, day_date: datetime.date, save_ds: bool = True)\
+        -> (xr.Dataset, xr.Dataset):
+    ds_total = gen_sig_utils.calc_total_optical_density(station=station, day_date=day_date)
+    signal_ds = gen_sig_utils.calc_lidar_signal(station, day_date, ds_total)
+    measure_ds = gen_sig_utils.calc_daily_measurement(station=station, day_date=day_date, signal_ds=signal_ds,
+                                                      update_overlap_only=False)
 
-    overlap_params = pd.read_csv("../../data/overlap_params.csv", index_col=0)
-    try:
-        # TODO Generalize. Currently adapted to 4 months only - (4,5,9,10)
-        overlap_params_index = {4: 0, 5: 1, 9: 2, 10: 3}[day_date.month]
-        overlap_param = overlap_params.loc[overlap_params_index, :].values
-    except KeyError:
-        raise KeyError(f"This month is not currently supported in overlap function.")
+    if save_ds:
+        # TODO: check that the range_corr_p is added to measure_ds, and that the LCNET is uploading the new paths
+        #  (especially if range_corr_p )  . and if so, save only 2 single files of measure_ds, and signal_ds to save
+        #  time and space
+        # NOTE: saving to separated datasets (for the use of the learning phase),
+        # is done in dataseting.prepare_generated_samples()
+        gen_utils.save_generated_dataset(station, measure_ds, data_source='lidar', save_mode='single')
+        gen_utils.save_generated_dataset(station, signal_ds, data_source='signal', save_mode='single')
 
-    if update_overlap_only:
-        month_folder = utils.get_month_folder_name(station.gen_lidar_dataset, day_date) # drop
-        nc_path = os.path.join(month_folder, f"{day_date.strftime('%Y_%m_%d')}_{station.location}_generated_lidar.nc") # drop
-        # TODO: create get_daily_measure_ds similar to get_daily_gen_param_ds (and move it inside calc_daily_measurement use only a flag UPDATE_measure)
+    return measure_ds, signal_ds
 
-        measure_ds = gen_sig_utils.calc_daily_measurement(station, day_date, overlap_params=overlap_param,
-                                                          signal_ds=None, measure_ds_path=nc_path)
 
-        if save_ds:
-            # NOTE: saving to separated datasets (for the use of the learning phase),
-            # is done in dataseting.prepare_generated_samples()
-            gen_utils.save_generated_dataset(station, measure_ds, data_source='lidar', save_mode='single')
+def update_daily_lidar_measurement(station: gs.Station, day_date: datetime.date, save_ds: bool = True) -> xr.Dataset:
+    """
+    Updates a measure ds with overlap.
 
-        return measure_ds
+    :param station: gs.station() object of the lidar station
+    :param day_date: datetime.date object of the required date
+    :param save_ds: bool, whether to save the genated measure ds or not
+    :return: xr.Dataset with the generated measure ds
+    """
 
+    measure_ds = gen_sig_utils.calc_daily_measurement(station, day_date, signal_ds=None, update_overlap_only=True)
 
+    if save_ds:
+        # NOTE: saving to separated datasets (for the use of the learning phase),
+        # is done in dataseting.prepare_generated_samples()
+        gen_utils.save_generated_dataset(station, measure_ds, data_source='lidar', save_mode='single')
 
-
-
-
-
-    else:
-        ds_total = gen_sig_utils.calc_total_optical_density(station=station, day_date=day_date)
-        signal_ds = gen_sig_utils.calc_lidar_signal(station, day_date, ds_total)
-        measure_ds = gen_sig_utils.calc_daily_measurement(station=station, day_date=day_date, signal_ds=signal_ds,
-                                                          overlap_params=overlap_param)
-
-        if save_ds:
-            # TODO: check that the range_corr_p is added to measure_ds, and that the LCNET is uploading the new paths
-            #  (especially if range_corr_p )  . and if so, save only 2 single files of measure_ds, and signal_ds to save
-            #  time and space
-            # NOTE: saving to separated datasets (for the use of the learning phase),
-            # is done in dataseting.prepare_generated_samples()
-            gen_utils.save_generated_dataset(station, measure_ds, data_source='lidar', save_mode='single')
-            gen_utils.save_generated_dataset(station, signal_ds, data_source='signal', save_mode='single')
-
-        return measure_ds, signal_ds
+    return measure_ds
 
 
 def daily_signals_generation_main(params):
-
     vis_utils.set_visualization_settings()
     gen_sig_utils.PLOT_RESULTS = params.plot_results
     # TODO: Toggle PLOT_RESULTS to True - doesn't seem to work. Omer - works for me. Adi, please check again..
@@ -83,9 +73,9 @@ def daily_signals_generation_main(params):
     num_days = len(days_list)
     num_processes = 1 if gen_sig_utils.PLOT_RESULTS else min((cpu_count() - 1, num_days))
 
+    func = generate_daily_lidar_measurement if not params.update_overlap_only else update_daily_lidar_measurement
     with Pool(num_processes) as p:
-        p.starmap(generate_daily_lidar_measurement, zip(repeat(station), days_list,
-                                                        repeat(params.save_ds), repeat(params.update_overlap_only)))
+        p.starmap(func, zip(repeat(station), days_list, repeat(params.save_ds)))
 
     logger.info(f"\nDone generating lidar signals & measurements "
                 f"for period: [{start_date.strftime('%Y-%m-%d')},{end_date.strftime('%Y-%m-%d')}]")

learning_lidar/generation/daily_signals_generations_utils.py

@@ -1,3 +1,4 @@
+import datetime
 import logging
 import os
 from datetime import timedelta
@@ -366,49 +367,34 @@ def calc_range_corr_measurement(station, day_date, pn_ds, r2_ds):
     return pr2n_ds
 
 
-def calc_daily_measurement(station, day_date, overlap_params, signal_ds, measure_ds_path=None):
+def calc_daily_measurement(station: gs.Station, day_date: datetime.date, signal_ds: xr.Dataset,
+                           update_overlap_only: bool=False) -> xr.Dataset:
     """
     Generate Lidar measurement, by combining background signal and the lidar signal,
     and then creating Poisson signal, which is the measurement of the mean lidar signal.
 
-    If measure_ds_path is given, an existing ds is loaded for the measurement, instead of computed from scratch
+    If update_overlap_only is given, an existing ds is loaded for the lidar and signal, instead of computed from scratch
 
-    :param overlap_params: list of parameters for overlap sigmoid function
-    :param measure_ds_path: path to existing measure ds
+    :param update_overlap_only: bool, whether to load a precomputed lidar and signal dataset or not
     :param station: gs.station() object of the lidar station
     :param day_date: datetime.date object of the required date
     :param signal_ds: xr.Dataset(), containing the daily lidar signal (clean)
     :return: measure_ds: xr.Dataset(), containing the daily lidar measurement (with background and applied photon noise)
     """
-    """
-    if update:
-        load measure ds 
-        load signal ds (pass signal_ds=None)
+    # get the ingredients
+    if update_overlap_only:
+        measure_ds = gen_utils.get_daily_gen_ds(station, day_date, type_='lidar')
+        signal_ds = gen_utils.get_daily_gen_ds(station, day_date, type_='signal')
         p_mean = measure_ds.p_mean
-    p_bg = get_daily_bg(station, day_date)
+
+    p_bg = get_daily_bg(station, day_date)  # daily background: p_bg
+    # Expand p_bg to coordinates : 'Wavelength','Height', 'Time
     bg_ds = p_bg.broadcast_like(signal_ds.range_corr)
-    p_mean = calc_mean_measurement(station, day_date, signal_ds, bg_ds) if not update
-    overlap_ds = get_daily_overlap() (load daily overlap params (similar to load daily measurement) , sigmoid, attr)
-    """
-    # get the inigridients
-    if measure_ds_path:
-        measure_ds = xr_utils.load_dataset(measure_ds_path)
-        bg_ds = measure_ds.p_bg
-        p_mean = measure_ds.p_mean
-    else:
-        p_bg = get_daily_bg(station, day_date)  # daily background: p_bg
-        # Expand p_bg to coordinates : 'Wavelength','Height', 'Time
-        bg_ds = p_bg.broadcast_like(signal_ds.range_corr)
-        p_mean = calc_mean_measurement(station, day_date, signal_ds, bg_ds)  # me
-
-    Height_indx = p_mean.Height
-    # Apply Overlap function
-    # TODO use gen_utils.create_ratio(total_bins=Height_indx.size, mode='overlap')?
-    #  It has additional options.
-    overlap = sigmoid(Height_indx, *overlap_params)
-    overlap_ds = xr.Dataset(data_vars={'overlap': ('Height', overlap)},
-                            coords={'Height': Height_indx.values},
-                            attrs={'name': 'Overlap Function'})
+
+    p_mean = calc_mean_measurement(station, day_date, signal_ds, bg_ds) if not update_overlap_only else p_mean
+
+    overlap_ds = gen_utils.get_daily_overlap(station, day_date, height_indx=p_mean.Height)
+
     # here the calculation stars
     p_mean = xr.apply_ufunc(lambda x, r: (x * r),
                             p_mean, overlap_ds.overlap,

learning_lidar/generation/generation_utils.py

@@ -6,11 +6,13 @@
 from matplotlib import pyplot as plt
 from scipy.ndimage import gaussian_filter1d
 from tqdm import tqdm
+import xarray as xr
 
 import learning_lidar.preprocessing.preprocessing_utils as prep_utils
 import learning_lidar.utils.xr_utils as xr_utils
 from learning_lidar.generation.generate_density_utils import PLOT_RESULTS
 from learning_lidar.utils.global_settings import eps
+import learning_lidar.utils.global_settings as gs
 
 
 def get_gen_dataset_file_name(station, day_date, wavelength='*',
@@ -72,6 +74,8 @@ def save_generated_dataset(station, dataset, data_source='lidar', save_mode='bot
         base_folder = station.gen_density_dataset
     elif data_source == 'bg':
         base_folder = station.gen_bg_dataset
+    else:
+        raise Exception("Unsupported data_source.")
     month_folder = prep_utils.get_month_folder_name(base_folder, date_datetime)
 
     xr_utils.get_daily_ds_date(dataset)
@@ -118,7 +122,6 @@ def get_month_gen_params_path(station, day_date, type='density_params'):
         'density_params' - for density sampler generator,
         'bg'- for generated background signal,
         'LC' - for generated Lidar Constant signal
-        # TODO : add 'overlap'
     :param station: gs.station() object of the lidar station
     :param day_date: datetime.date object of the required date
     :return: str. Path to monthly dataset of generation parameters.
@@ -129,23 +132,45 @@ def get_month_gen_params_path(station, day_date, type='density_params'):
     _, monthdays = calendar.monthrange(year, month)
     month_end_day = datetime(year, month, monthdays, 0, 0)
 
+    folder_name = prep_utils.get_month_folder_name(station.generation_folder, day_date)
+
     nc_name = f"generated_{type}_{station.location}_{month_start_day.strftime('%Y-%m-%d')}_" \
               f"{month_end_day.strftime('%Y-%m-%d')}.nc"
 
-    folder_name = prep_utils.get_month_folder_name(station.generation_folder, day_date)
-
     gen_source_path = os.path.join(folder_name, nc_name)
     return gen_source_path
 
 
+def get_daily_ds_path(station: gs.Station, day_date: datetime.date, type_: str) -> str:
+    """
+    Get the path to the daily generated measure (lidar) or signal ds
+
+    :param type_: str, 'lidar' for measure dataset. 'signal' for signal dataset
+    :param station: gs.station() object of the lidar station
+    :param day_date: datetime.date object of the required date
+    :return: str. Path to monthly dataset of generation parameters.
+    """
+    if type_ == 'lidar':
+        parent_folder = station.gen_lidar_dataset
+    elif type_ == 'signal':
+        parent_folder = station.gen_signal_dataset
+    else:
+        raise Exception("Unsupported type. Should by 'lidar' or 'signal'")
+
+    month_folder = prep_utils.get_month_folder_name(parent_folder, day_date)
+    file_name = get_gen_dataset_file_name(station, day_date, wavelength='*', data_source=type_)
+    gen_source_path = os.path.join(month_folder, file_name)
+    return gen_source_path
+
+
 def get_month_gen_params_ds(station, day_date, type='density_params'):
     """
     Returns the monthly parameters of density creation as a dataset.
     :param type: type of generated parameter:
         'density_params' - for density sampler generator,
         'bg'- for generated background signal,
         'LC' - for generated Lidar Constant signal
-        # TODO : add 'overlap'
+        'overlap' - for generated overlap parameters
     :param station: gs.station() object of the lidar station
     :param day_date: datetime.date object of the required date
     :return: day_params_ds: xarray.Dataset(). Monthly dataset of generation parameters.
@@ -173,6 +198,40 @@ def get_daily_gen_param_ds(station, day_date, type='density_params'):
     return day_params_ds
 
 
+def get_daily_gen_ds(station: gs.Station, day_date: datetime.date, type_: str) -> xr.Dataset:
+    """
+    Returns the daily parameters of measures (lidar) or signal creation as a dataset.
+
+    :param type_: str, should be one of 'signal' / 'lidar'
+    :param station: gs.station() object of the lidar station
+    :param day_date: datetime.date object of the required date
+    :return: day_params_ds: xarray.Dataset(). Daily dataset of generation parameters.
+    """
+    daily_ds_path = get_daily_ds_path(station, day_date, type_)
+    ds = xr_utils.load_dataset(daily_ds_path)
+    return ds
+
+
+def get_daily_overlap(station: gs.Station, day_date: datetime.date, height_indx: xr.DataArray) -> xr.Dataset:
+    """
+    Generates overlap values per height index, from overlap params
+
+    :param station: gs.station() object of the lidar station
+    :param day_date: datetime.date object of the required date
+    :param height_indx: xr.DataArray, stores the height index per Height
+
+    :return: xr.Dataset with the overlap values per height index
+    """
+
+    overlap_params = get_month_gen_params_ds(station, day_date, type='overlap')
+    overlap = sigmoid(height_indx, *overlap_params.to_array().values)
+    overlap_ds = xr.Dataset(data_vars={'overlap': ('Height', overlap)},
+                            coords={'Height': height_indx.values},
+                            attrs={'name': 'Overlap Function'})
+
+    return overlap_ds
+
+
 def dt2binscale(dt_time, res_sec=30):
     """
     TODO consider to move this function to Station ?