Add plots of data from LiCSAlert figure to be used as zoomed images on

web.

LiCSAlert_batch_mode_examples.py

@@ -58,38 +58,4 @@
                           licsbas_dir = licsbas_dir, licsalert_dir = licsalert_dir,
                           licsalert_settings = licsalert_settings, icasar_settings = icasar_settings)
 
-
-
-#%% Example 3, Running LiCSAlert with a smaller signal (Campi Flegrei, processed with LiCSBAS)
-
-# LiCSBAS_out_folder_campi_flegrei = Path('./022D_04826_121209')
-# sys.path.append(str(ICASAR_path))                                                                       # Add ICASAR to the path so we can use one of its functions.  
-# import icasar
-# from icasar.icasar_funcs import LiCSBAS_to_ICASAR
-
-# LiCSAlert_settings = {"n_baseline_end" : 55,                                         # n_ifgs that are used in the baseline stage (i.e. by ICASAR)
-#                       "out_folder" : Path("LiCSAlert_campi_flegrei"),                # pathlib Path
-#                       "run_ICASAR" : False,                                           # If False, attempt to load results from previous run.  If True, run (which can be slow)
-#                       "figure_intermediate" : False,                                # if set to True, a figure is produced for all time steps in the monitoring data, which can be time consuming.  
-#                       "figure_type"         : 'png',                                 # either 'window' or 'png' (to save as pngs)
-#                       "downsample_run" : 0.5,                                        # data can be downsampled to speed things up
-#                       "downsample_plot" : 0.5,                                       # and a 2nd time for fast plotting.  Note this is applied to the restuls of the first downsampling, so is compound
-#                       "residual_type"        : 'cumulative'}                              # controls the type of residual used in the lower plot.  Either cumulative or window   
-
-
-# ICASAR_settings = {"n_comp" : 5,                                         # number of components to recover with ICA (ie the number of PCA sources to keep)
-#                    "bootstrapping_param" : (200, 0),                    # (number of runs with bootstrapping, number of runs without bootstrapping)                    "hdbscan_param" : (35, 10),                        # (min_cluster_size, min_samples)
-#                    "tsne_param" : (30, 12),                             # (perplexity, early_exaggeration)
-#                    "ica_param" : (1e-2, 150),                           # (tolerance, max iterations)
-#                    "hdbscan_param" : (100,10),                           # (min_cluster_size, min_samples) Discussed in more detail in Mcinnes et al. (2017). min_cluster_size sets the smallest collection of points that can be considered a cluster. min_samples sets how conservative the clustering is. With larger values, more points will be considered noise. 
-#                    "ifgs_format"        : 'cum',
-#                    "load_fastICA_results" : True,                      # If all the FastICA runs already exisit, setting this to True speeds up ICASAR as they don't need to be recomputed.  
-#                    "figures" : "png+window"}                            # if png, saved in a folder as .png.  If window, open as interactive matplotlib figures,
-
-
-# displacement_r2, tbaseline_info, _ = LiCSBAS_to_ICASAR(LiCSBAS_out_folder_campi_flegrei, figures=True)        # open various LiCSBAS products, spatial ones in displacement_r2, temporal ones in tbaseline_info
-# displacement_r2['ifg_dates'] = tbaseline_info['ifg_dates']                                                  # Unlike ICASAR, LiCSAlert always needs the ifg_dates too.  
-
-# #LiCSAlert_batch_mode(displacement_r2, ICASAR_settings = ICASAR_settings, **LiCSAlert_settings, ICASAR_path = ICASAR_path)
-