Small changes to example file (change name as now not just for Sierra

Negra).

LiCSAlert_batch_mode_examples.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar  8 10:55:48 2020
+
+
+Things to address:
+    
+    downsampling is compound?
+
+
+@author: matthew
+"""
+
+import sys
+import pickle
+from pathlib import Path
+import copy
+import numpy as np
+
+sys.path.append("./lib")
+from LiCSAlert_functions import LiCSAlert_batch_mode
+
+#%% Load Sentinel-1 data for Sierra Negra, note that this wasn't processed with LiCSBAS, and doesn't include the DEM.  
+
+displacement_r2 = {}                                                                    # initiate a dictionary to store some info in.  
+with open(f"sierra_negra_example_data.pkl", 'rb') as f:
+    phUnw_files = pickle.load(f)                                                        # these are the names of the interferograms used (e.g. YYYYMMDD_YYYYMMDD.unw)
+    displacement_r2["incremental"] = pickle.load(f)                                     # the incremental interferograms as row vectors.
+    displacement_r2["mask"] = pickle.load(f)                                            # a mask to convert a row vector back to a rank 2 array (col_to_ma in LiCSAlert_functions.py does this easily.  )
+    cumulative_baselines = pickle.load(f)                                               # the cumulative baselines. ie if there are acquisitions every 12 days, these would be 12,24,36 etc.  
+    acq_dates = pickle.load(f)                                                          # acquisition dates.  This should be one longer than the names of the interferograms
+    lons = pickle.load(f)                                                               # matrix of longitudes for bottom row (ie rank 1)
+    lats = pickle.load(f)                                                               # matrix of latitdues for left column (ie rank 1)
+f.close()
+
+displacement_r2['lons'] = np.repeat(lons[np.newaxis, :],lats.shape[0], 0 )              # convert from rank 1 to rank 2
+displacement_r2['lats'] = np.repeat(lats[::-1, np.newaxis],lons.shape[0], 1 )           # convert from rank 1 to rank 2
+del lons, lats
+
+
+displacement_r2['ifg_dates'] = [phUnw_file.split('.')[0] for phUnw_file in phUnw_files] # use the file names to get the dates that each interferogram spans.  
+displacement_r2_copy = copy.deepcopy(displacement_r2)                                   # make a copy for use with the 2nd example  
+
+
+
+#%% Example 1, creating one LiCSAlert figure for the complete time series
+ICASAR_path = Path("/home/matthew/university_work/15_my_software_releases/ICASAR-2.3.0/lib/")                               # location of ICASAR functions
+# ICASAR_path = Path("/home/matthew/university_work/01_blind_signal_separation_python/13_ICASAR/ICASAR_GitHub/lib")           # development version
+
+# LiCSAlert_settings = {"n_baseline_end" : 35,                                         # n_ifgs that are used in the baseline stage (i.e. by ICASAR)
+#                       "out_folder" : "LiCSAlert_01_Sierra_Negra_no_intermediate",    # no spaces, snake or camel case
+#                       "run_ICASAR" : True,                                           # If False, attempt to load results from previous run.  If True, run (which can be slow)
+#                       "intermediate_figures" : False,                                # if set to True, a figure is produced for all time steps in the monitoring data, which can be time consuming.  
+#                       "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
+#                       "ic_classifying_model" : Path("/home/matthew/university_work/02_neural_networks_python/01_phd_script_approach/04_vgg16/03_best_2head_synth_real_v2/01_vgg16_2head_block5_trained.h5")}        # path to the keras model that can be used to classify ICs and deformation or atmosphere
+
+# ICASAR_settings = {"n_comp" : 6,                                                     # 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)
+#                     "figures" : "png",                                               # if png, saved in a folder as .png.  If window, open as interactive matplotlib figures, if window+png then both.  
+#                     "create_all_ifgs_flag" : False,                                  # Creates all possible pairs of ifgs between all acquisitions.  Results can be more complex with this set to True, but also better at recovering small magnitude signals
+#                     "load_fastICA_results" : True}                                   # If True, ICASAR will try to load the results of FastICA from previous runs.  This is useful if you wish to fine tune the hdbscan or tsne settings quickly.     
+
+
+
+# LiCSAlert_batch_mode(displacement_r2, ICASAR_settings = ICASAR_settings, **LiCSAlert_settings, ICASAR_path = ICASAR_path)
+
+
+# #%% Example 2, creating the LiCSAlert figure at all time steps
+
+# LiCSAlert_settings["intermediate_figures"] = True                # change one key/variable in the dict so that all the intermediate ifgs are made by LiCSAlert (which can be slow, but is good for animations)
+# LiCSAlert_settings["out_folder"] = "LiCSAlert_02_Sierra_Negra_intermediate"
+# LiCSAlert_batch_mode(displacement_r2_copy, ICASAR_settings = ICASAR_settings, **LiCSAlert_settings,ICASAR_path = ICASAR_path)
+
+
+#%% Example 3, Running LiCSAlert with a smaller signal (The LiCSBAS example at Campi Flegrei)
+
+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.  
+from ICASAR_functions import LiCSBAS_to_ICASAR                                                          # which is for importing time series processed by LiCSBAS.  
+
+LiCSAlert_settings = {"n_baseline_end" : 55,                                         # n_ifgs that are used in the baseline stage (i.e. by ICASAR)
+                      "out_folder" : "LiCSAlert_03_Campi_Flegrei",    # no spaces, snake or camel case
+                      "run_ICASAR" : True,                                           # If False, attempt to load results from previous run.  If True, run (which can be slow)
+                      "intermediate_figures" : False,                                # if set to True, a figure is produced for all time steps in the monitoring data, which can be time consuming.  
+                      "downsample_run" : 1.,                                        # 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
+
+
+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. 
+                   "create_all_ifgs_flag" : True,                       # small signals are hard for ICA to extact from time series, so make it easier by creating all possible long temporal baseline ifgs from the incremental data.  
+                   "load_fastICA_results" : False,                      # 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)
+
+