ts_inversion_numba_rewrite_BACKUP_492353.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Bodo Bookhagen and Ariane Mueting

"""

#Limit number of processes for lstsq inversion - you can parallelize multiple inversion steps through for loops
import os
os.environ["OMP_NUM_THREADS"] = "1" # export OMP_NUM_THREADS=1
import numpy as np
import os, glob, tqdm, gzip
import datetime as dt
import matplotlib.pyplot as plt
from osgeo import gdal
from numba import njit, prange
import pandas as pd
import datetime

def create_design_matrixII(df):
    dates = pd.unique(df[["date_ref", "date_sec"]].values.ravel())
    dates = np.sort(dates)

    timesteps = pd.DataFrame({"step": np.arange(0, len(dates)-1), "date_ref": dates[:-1], "date_sec":dates[1:]})
    
    design_matrix = np.zeros((len(df), len(dates)-1))

    for idx in range(len(df)):
        pair = df.iloc[idx]
        design_matrix[idx,:] = np.where((timesteps.date_ref >= pair.date_ref) & (timesteps.date_sec <= pair.date_sec), 1, 0)
        
    return design_matrix, dates

def create_design_matrix(num_ifgram, dates0, dates1):
    # create design matrix for num_ifgram and dates in dates0 (start), dates1 (end)
    # A is design matrix (also called G)
    unique_dates = np.union1d(np.unique(dates0), np.unique(dates1))
    num_date = len(unique_dates)

    tbase = [i.days + i.seconds / (24 * 60 * 60) for i in (unique_dates - unique_dates[0])]
    tbase = np.array(tbase, dtype=np.float32) / 365.25

    date12_list = []
    for i in range(len(dates0)):
        date12_list.append('%s_%s'%(dt.datetime.strftime(dates0[i], "%Y%m%d"), dt.datetime.strftime(dates1[i], "%Y%m%d")))

    A = np.zeros((num_ifgram, num_date), np.float32)

    date_list = list(unique_dates)
    date_list = [dt.datetime.strftime(d, "%Y%m%d") for d in date_list]

    for i in range(num_ifgram):
        ind1, ind2 = (date_list.index(d) for d in date12_list[i].split('_'))
        A[i, ind1] = -1
        A[i, ind2] = 1

    # Remove reference date as it can not be resolved
    ref_date = dt.datetime.strftime(min(dates0),"%Y%m%d")

    ind_r = date_list.index(ref_date)
    A = np.hstack((A[:, 0:ind_r], A[:, (ind_r+1):]))
    return A, ref_date, tbase



#@njit(parallel=True)
def linalg_weighted_numba(A, y, weights, tbase_diff, nre, rcond=1e-5):
    #numba-based inversion with weights
    num_date = A.shape[1] + 1
    ts = np.empty((num_date, nre), dtype=np.float32)
    ts.fill(np.nan)
    residuals = np.empty((A.shape[0], nre), dtype=np.float32)
    residuals.fill(np.nan)
    ranks = np.empty(nre, dtype=np.float32)
    ranks.fill(np.nan)

    #will do pixel-by-pixel inversion, because some pixels may not have data
    W = np.diag(weights).astype(np.float64)
    for i in prange(nre):
        y2 = y[:,i].astype(np.float64)
        if np.any(np.isnan(y2)) or np.any(np.isinf(y2)):
            continue
        Aw = np.dot(W, A.astype(np.float64))
        Bw = np.dot(y2, W)
        X, residual, ranks[i], _ = np.linalg.lstsq(Aw, Bw, rcond=rcond)
        if residual.size > 0:
            residuals[:,i] = residual
        else:
            residuals[:,i] = A.astype(np.float64).dot(X)
        ts_diff = X * tbase_diff[:,0]
        ts[0,:] = np.zeros(nre, dtype=np.float32)
        ts[1:, i] = np.cumsum(ts_diff)
    return ts, residuals, ranks


#@njit(parallel=True)
def SBAS_noweights_numba(A, y, tbase_diff, nre, rcond=1e-5):
    #numba-based inversion with no weights
    num_date = A.shape[1] + 1
    ts = np.empty((num_date, nre), dtype=np.float32)
    ts.fill(np.nan)
    residuals = np.empty((A.shape[0], nre), dtype=np.float32)
    residuals.fill(np.nan)
    ranks = np.empty(nre, dtype=np.float32)
    ranks.fill(np.nan)

    #will do pixel-by-pixel inversion, because some pixels may not have data
    for i in prange(nre):
        y2 = y[:,i].astype(np.float64)
        if np.any(np.isnan(y2)) or np.any(np.isinf(y2)):
            continue
        X, residual, ranks[i], _ = np.linalg.lstsq(A.astype(np.float64), y2, rcond=rcond)
        if residual.size > 0:
            residuals[:,i] = residual
        else:
            residuals[:,i] = A.astype(np.float64).dot(X)
        ts_diff = X * tbase_diff[:,0]
        ts[0,:] = np.zeros(nre, dtype=np.float32)
        ts[1:, i] = np.cumsum(ts_diff)
    return ts, residuals, ranks

def SBAS_noweights_numbaII(A, y, nre, rcond=1e-5):
    #numba-based inversion with no weights
    num_date = A.shape[1] + 1
    ts = np.empty((num_date, nre), dtype=np.float32)
    ts.fill(np.nan)
    ts[0,:] = np.zeros(nre, dtype=np.float32) #first date will have zero displacement
    residuals = np.empty((A.shape[0], nre), dtype=np.float32)
    residuals.fill(np.nan)
    ranks = np.empty(nre, dtype=np.float32)
    ranks.fill(np.nan)

    #will do pixel-by-pixel inversion, because some pixels may not have data
    for i in prange(nre):
        y2 = y[:,i].astype(np.float64)
        if np.any(np.isnan(y2)) or np.any(np.isinf(y2)):
            continue
        X, residual, ranks[i], _ = np.linalg.lstsq(A.astype(np.float64), y2, rcond=rcond)
        if residual.size > 0:
            residuals[:,i] = residual
        else:
            residuals[:,i] = A.astype(np.float64).dot(X)
        ts[1:, i] = np.cumsum(X)
    return ts, residuals, ranks

#@njit(parallel=True)
def NSBAS_noweights_numba(A, y, tbase_diff, tbase, nre, gamma=1e-4, rcond=1e-5):
    #numba-based inversion with no weights
    num_date = A.shape[1] + 1
    num_im = A.shape[0]
    ts = np.empty((num_date, nre), dtype=np.float32)
    ts.fill(np.nan)
    residuals = np.empty((A.shape[0], nre), dtype=np.float32)
    residuals.fill(np.nan)
    ranks = np.empty(nre, dtype=np.float32)
    ranks.fill(np.nan)
    vconst = np.empty(nre, dtype=np.float32)
    vconst.fill(np.nan)
    vel = np.empty(nre, dtype=np.float32)
    vel.fill(np.nan)

    ### Set matrix of NSBAS part (bottom)
    Gbl = np.tril(np.ones((num_date, num_date-1), dtype=np.float32), k=-1) #lower tri matrix without diag
    Gbr = -np.ones((num_date, 2), dtype=np.float32)
    Gbr[:, 0] = -tbase
    # Gbr[:, 0] = tbase_diff
    Gb = np.concatenate((Gbl, Gbr), axis=1)*gamma
    Gt = np.concatenate((A, np.zeros((num_im, 2), dtype=np.float32)), axis=1)
    Gt = np.concatenate((A, np.ones((num_im, 2), dtype=np.float32)), axis=1)
    Gall = np.float32(np.concatenate((Gt, Gb)))

    #will do pixel-by-pixel inversion, because some pixels may not have data
    for i in prange(nre):
        y2 = np.concatenate((y[:, i], np.zeros((num_date), dtype=np.float32))).transpose()
        if np.any(np.isnan(y2)) or np.any(np.isinf(y2)):
            continue
        X, residual, ranks[i], _ = np.linalg.lstsq(Gall.astype(np.float64), y2, rcond=rcond)
        if residual.size > 0:
            residuals[:,i] = residual
        else:
            residuals[:,i] = A.astype(np.float64).dot(X[1:-1])
        ts_diff = X[:num_date-1] * tbase_diff[:,0] #Incremental displacement (num_date-1, n_pt)
        # ts_diff = X[:num_date-1] * tbase_diff[1:] #Incremental displacement (num_date-1, n_pt)
        vel[i] = X[num_date-1] #Velocity (n_pt)
        vconst[i] = X[num_date] #Constant part of linear velocity (c of vt+c) (n_pt)

        ts[0,:] = np.zeros(nre, dtype=np.float32)
        ts[1:, i] = np.cumsum(ts_diff)
    return ts, residuals, ranks, vel, vconst

def NSBAS_noweights_numbaII(A, y, tbase_diff, tbase, nre, gamma=1e-4, rcond=1e-5):
    #numba-based inversion with no weights
    num_date = A.shape[1] + 1
    num_im = A.shape[0]
    ts = np.empty((num_date, nre), dtype=np.float32)
    ts.fill(np.nan)
    residuals = np.empty((A.shape[0], nre), dtype=np.float32)
    residuals.fill(np.nan)
    ranks = np.empty(nre, dtype=np.float32)
    ranks.fill(np.nan)
    vconst = np.empty(nre, dtype=np.float32)
    vconst.fill(np.nan)
    vel = np.empty(nre, dtype=np.float32)
    vel.fill(np.nan)

    ### Set matrix of NSBAS part (bottom)
    Gbl = np.tril(np.ones((num_date, num_date-1), dtype=np.float32), k=-1) #lower tri matrix without diag
    Gbr = -np.ones((num_date, 2), dtype=np.float32)
    Gbr[:, 0] = -tbase
    # Gbr[:, 0] = tbase_diff
    Gb = np.concatenate((Gbl, Gbr), axis=1)*gamma
    Gt = np.concatenate((A, np.zeros((num_im, 2), dtype=np.float32)), axis=1)
    Gt = np.concatenate((A, np.ones((num_im, 2), dtype=np.float32)), axis=1)
    Gall = np.float32(np.concatenate((Gt, Gb)))

    #will do pixel-by-pixel inversion, because some pixels may not have data
    for i in prange(nre):
        y2 = np.concatenate((y[:, i], np.zeros((num_date), dtype=np.float32))).transpose()
        if np.any(np.isnan(y2)) or np.any(np.isinf(y2)):
            continue
        X, residual, ranks[i], _ = np.linalg.lstsq(Gall.astype(np.float64), y2, rcond=rcond)
        if residual.size > 0:
            residuals[:,i] = residual
        else:
            residuals[:,i] = A.astype(np.float64).dot(X[1:-1])
        ts_diff = X[:num_date-1] * tbase_diff[:,0] #Incremental displacement (num_date-1, n_pt)
        # ts_diff = X[:num_date-1] * tbase_diff[1:] #Incremental displacement (num_date-1, n_pt)
        vel[i] = X[num_date-1] #Velocity (n_pt)
        vconst[i] = X[num_date] #Constant part of linear velocity (c of vt+c) (n_pt)

        ts[0,:] = np.zeros(nre, dtype=np.float32)
        ts[1:, i] = np.cumsum(ts_diff)
    return ts, residuals, ranks, vel, vconst


#@njit(parallel=True)
def NSBAS_noweights_numba(A, y, tbase_diff, tbase, nre, gamma=1e-4, rcond=1e-5):
    #numba-based inversion with no weights
    num_date = A.shape[1] + 1
    num_im = A.shape[0]
    ts = np.empty((num_date, nre), dtype=np.float32)
    ts.fill(np.nan)
    residuals = np.empty((A.shape[0], nre), dtype=np.float32)
    residuals.fill(np.nan)
    ranks = np.empty(nre, dtype=np.float32)
    ranks.fill(np.nan)
    vconst = np.empty(nre, dtype=np.float32)
    vconst.fill(np.nan)
    vel = np.empty(nre, dtype=np.float32)
    vel.fill(np.nan)

    ### Set matrix of NSBAS part (bottom)
    Gbl = np.tril(np.ones((num_date, num_date-1), dtype=np.float32), k=-1) #lower tri matrix without diag
    Gbr = -np.ones((num_date, 2), dtype=np.float32)
    Gbr[:, 0] = -tbase
    # Gbr[:, 0] = tbase_diff
    Gb = np.concatenate((Gbl, Gbr), axis=1)*gamma
    Gt = np.concatenate((A, np.zeros((num_im, 2), dtype=np.float32)), axis=1)
    Gt = np.concatenate((A, np.ones((num_im, 2), dtype=np.float32)), axis=1)
    Gall = np.float32(np.concatenate((Gt, Gb)))

    #will do pixel-by-pixel inversion, because some pixels may not have data
    for i in prange(nre):
        y2 = np.concatenate((y[:, i], np.zeros((num_date), dtype=np.float32))).transpose()
        if np.any(np.isnan(y2)) or np.any(np.isinf(y2)):
            continue
        X, residual, ranks[i], _ = np.linalg.lstsq(Gall.astype(np.float64), y2, rcond=rcond)
        if residual.size > 0:
            residuals[:,i] = residual
        else:
            residuals[:,i] = A.astype(np.float64).dot(X[1:-1])
        ts_diff = X[:num_date-1] * tbase_diff[:,0] #Incremental displacement (num_date-1, n_pt)
        # ts_diff = X[:num_date-1] * tbase_diff[1:] #Incremental displacement (num_date-1, n_pt)
        vel[i] = X[num_date-1] #Velocity (n_pt)
        vconst[i] = X[num_date] #Constant part of linear velocity (c of vt+c) (n_pt)

        ts[0,:] = np.zeros(nre, dtype=np.float32)
        ts[1:, i] = np.cumsum(ts_diff)
    return ts, residuals, ranks, vel, vconst


def read_file(fn, b=1):
    ds = gdal.Open(fn)
    data = ds.GetRasterBand(b).ReadAsArray()
    ds = None
    return data

def min_max_scaler(x):
    if len(x)>1:
        return (x-np.nanmin(x))/(np.nanmax(x)-np.nanmin(x))
    elif len(x) == 1:
        return np.array([1])
    else:
        return np.array([])

<<<<<<< HEAD
def get_scene_id(fn):
    
    #extract the scene id from a PS scene filename
    #assumes the filename still begins with the scene ID (should be default when downloading data)
    
    _, fn = os.path.split(fn) 
    
    #determine processing level of scenes
    if "_1B_" in fn:
        level = 1
    elif "_3B_" in fn:
        level = 3
    else:
        print("Could not determine processing level of the data. Make sure that either _1B_ or _3B_ is included in the filename of your scene.")
        return
    
    if fn.split("_").index(f"{level}B") == 4: #PSB.SD case
        scene_id = "_".join(fn.split("_")[0:4])
    elif fn.split("_").index(f"{level}B") == 3: #PS2 case
        scene_id = "_".join(fn.split("_")[0:3])
    else: 
        print("Couldn't guess the instrument type. Have you modifies filenames?")
        return
    return scene_id

        
def get_date(scene_id):
    
    #strip the time from th PS scene id
    
    return datetime.datetime.strptime(scene_id[0:8], "%Y%m%d")


=======
>>>>>>> 57fb918115e3a20fa5f9d3b5a4e8ac716a232d5f
def get_sun_pos(files):

    import subprocess
    import json

    bns = [os.path.basename(f) for f in files]

    ids1 = [("_").join(x.split("_")[0:3]) if len(x.split("_")[3]) == 8 else ("_").join(x.split("_")[0:4]) for x in bns]
    ids2 = [("_").join(x.split("_")[3:]) if len(x.split("_")[3]) == 8 else ("_").join(x.split("_")[4:]) for x in bns]
    ids2 = [("_").join(x.split("_")[0:3]) if (x.split("_")[3] == "L3B") else ("_").join(x.split("_")[0:4]) for x in ids2]

    ids = np.union1d(np.unique(ids1), np.unique(ids2))

    search = f"planet data filter --string-in id {','.join(ids)} > filter.json"
    result = subprocess.run(search, shell = True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    if result.stderr != "":
        print(result.stderr)
    search = "planet data search PSScene --limit 0 --filter filter.json > search.geojson"

    result = subprocess.run(search, shell = True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    if result.stderr != "":
        print(result.stderr)

    gj = [json.loads(line) for line in open("search.geojson", "r")]


    sun = pd.DataFrame({"id": [f["id"] for f in gj], "date": [f["id"].split("_")[0] for f in gj], "sun_az": [f["properties"]["sun_azimuth"] for f in gj], "sun_elev": [f["properties"]["sun_elevation"] for f in gj]})
    sun.date = pd.to_datetime(sun.date)
    return sun


if __name__ == '__main__':
  ######################################################################################################################
  #Arianes approach
  ######################################################################################################################
  
 
    #path = "/home/ariane/Documents/Project3/PlanetScope_Data/aoi7/all_scenes"
    path = "/raid-manaslu/amueting/PhD/Project3/PlanetScope_Data/aoi7/all_scenes"
    df = pd.read_csv(path+"/matches_by_group_PS2.csv")
    df = pd.concat([df, pd.read_csv(path+"/matches_by_group_PSBSD.csv")]).reset_index(drop = True)
    prefix_ext = "_L3B_polyfit"
    df["id_ref"] = df.ref.apply(get_scene_id)
    df["id_sec"] = df.sec.apply(get_scene_id)
    df["date_ref"] = df.id_ref.apply(get_date)
    df["date_sec"] = df.id_sec.apply(get_date)
    df["disp"] = df.apply(lambda x: os.path.join(path, "disparity_maps", f"{x.id_ref}_{x.id_sec}{prefix_ext}-F.tif"), axis = 1)
    df["exists"] = df.disp.apply(lambda x: os.path.isfile(x))
    df = df.loc[df.exists == True].reset_index(drop = True)
    dx_stack = np.asarray([read_file(f,1) for f in df.disp])
    dy_stack = np.asarray([read_file(f,2) for f in df.disp])
    
    
    A, dates = create_design_matrixII(df)
    
    
    # mask_fn = "/home/ariane/Documents/Project3/PlanetScope_Data/aoi7/masks/aoi7_region1.npy.gz"
    mask_fn = "/raid-manaslu/amueting/PhD/Project3/PlanetScope_Data/aoi7/masks/aoi7_region1.npy.gz"

    f = gzip.GzipFile(mask_fn, "r")
    mask = np.load(f)
    f = None
        
    # Extract values only for masked areas
    print('Extract relevant values and remove full array from memory')
    idxxy = np.where(mask.ravel() == 1)[0]
    num_ifgram = dx_stack.shape[0]
    nre = int(len(idxxy))
    dx_stack_masked = np.empty((num_ifgram, nre), dtype=np.float32)
    dx_stack_masked.fill(np.nan)
    dy_stack_masked = np.empty((num_ifgram, nre), dtype=np.float32)
    dy_stack_masked.fill(np.nan)
    
    
    for i in tqdm.tqdm(range(dx_stack.shape[0])):
        dx_stack_masked[i,:] = dx_stack[i, :, :].ravel()[idxxy]
        dy_stack_masked[i,:] = dy_stack[i, :, :].ravel()[idxxy]


    del dx_stack, dy_stack
    
    print("\ndx")
    dx_ts_noweights_numba, dx_residuals_noweights_numba, dx_ranks_noweights_numba = SBAS_noweights_numbaII(A, dx_stack_masked, nre, rcond=1e-5)
    print("\ndy")
    dy_ts_noweights_numba, dx_residuals_noweights_numba, dx_ranks_noweights_numba = SBAS_noweights_numbaII(A, dy_stack_masked, nre, rcond=1e-5)


    png_out_path = "./png"
    if not os.path.exists(png_out_path):
        os.mkdir(png_out_path)
        
    fig, ax = plt.subplots(1, 2, figsize=(12,5))
    ax[0].plot(dates, np.nanmean(dx_ts_noweights_numba, axis=1), '-', color='darkblue', label='No weights')
    ax[0].set_title('Mean dx offset (n=%d)'%nre, fontsize=14)
    ax[0].set_xlabel('Time [y]')
    ax[0].set_ylabel('Cumulative dx offset [pix]')
    ax[0].legend()
    ax[0].grid()
    ax[1].plot(dates, np.nanmean(dy_ts_noweights_numba, axis=1), '-', color='darkblue', label='No weights')
    ax[1].set_title('Mean dy offset (n=%d)'%nre, fontsize=14)
    ax[1].set_xlabel('Time [y]')
    ax[1].set_ylabel('Cumulative dy offset [pix]')
    ax[1].legend()
    ax[1].grid()
    fig.tight_layout()
    fig.savefig(os.path.join(png_out_path, 'dx_dy_my_inversion.png'), dpi=300)

<<<<<<< HEAD
    ######################################################################################################################
    #Bodos approach
    ######################################################################################################################
    
    # files = glob.glob("/home/ariane/Documents/Project3/PlanetScope_Data/aoi7/all_scenes/disparity_maps/*L3B_polyfit-F.tif")
    # mask_fn = "/home/ariane/Documents/Project3/PlanetScope_Data/aoi7/masks/aoi7_region1.npy.gz"
    # files = glob.glob("/raid/Planet_NWArg/PS2_aoi7/disparity_maps/*L3B_polyfit-F.tif")
    # mask_fn = "/raid/Planet_NWArg/PS2_aoi7/masks/aoi7_region1.npy.gz"
    files = glob.glob("/raid-manaslu/amueting/PhD/Project3/PlanetScope_Data/aoi7/all_scenes/disparity_maps/*L3B_polyfit-F.tif")
    mask_fn = "/raid-manaslu/amueting/PhD/Project3/PlanetScope_Data/aoi7/masks/aoi7_region1.npy.gz"
=======
    files = glob.glob("/home/ariane/Documents/Project3/PlanetScope_Data/aoi7/*/disparity_maps/*L3B_polyfit-F.tif")
    mask_fn = "/home/ariane/Documents/Project3/PlanetScope_Data/aoi7/masks/aoi7_region1.npy.gz"
    # files = glob.glob("/raid/Planet_NWArg/PS2_aoi7/disparity_maps/*L3B_polyfit-F.tif")
    # mask_fn = "/raid/Planet_NWArg/PS2_aoi7/masks/aoi7_region1.npy.gz"
>>>>>>> 57fb918115e3a20fa5f9d3b5a4e8ac716a232d5f
    bns = [os.path.basename(f) for f in files]
    dx_stack = np.asarray([read_file(f,1) for f in files])
    dy_stack = np.asarray([read_file(f,2) for f in files])
    dates0 = [dt.datetime.strptime(f[0:8], "%Y%m%d") for f in bns]
    dates1 = [dt.datetime.strptime(f.split("_")[3], "%Y%m%d") if len(f.split("_")[3]) == 8 else dt.datetime.strptime(f.split("_")[4], "%Y%m%d") for f in bns]


    f = gzip.GzipFile(mask_fn, "r")
    mask = np.load(f)
    f = None

    area_name = "aoi7"
    deltay_stack_scale = 2
<<<<<<< HEAD
=======


    png_out_path = "./png"
    if not os.path.exists(png_out_path):
        os.mkdir(png_out_path)
>>>>>>> 57fb918115e3a20fa5f9d3b5a4e8ac716a232d5f


    # Extract values only for masked areas
    print('Extract relevant values and remove full array from memory')
    idxxy = np.where(mask.ravel() == 1)[0]
    num_ifgram = dx_stack.shape[0]
    nre = int(len(idxxy))
    dx_stack_masked = np.empty((num_ifgram, nre), dtype=np.float32)
    dx_stack_masked.fill(np.nan)
    dy_stack_masked = np.empty((num_ifgram, nre), dtype=np.float32)
    dy_stack_masked.fill(np.nan)

    # Could also do this via numba, but looks fast enough right now
    for i in tqdm.tqdm(range(dx_stack.shape[0])):
        dx_stack_masked[i,:] = dx_stack[i, :, :].ravel()[idxxy]
        dy_stack_masked[i,:] = dy_stack[i, :, :].ravel()[idxxy]


    del dx_stack, dy_stack

    dates0 = np.asarray(dates0)
    dates1 = np.asarray(dates1)

    ddates = dates1 - dates0
    ddates_day = np.array([i.days for i in ddates])

    # create design_matrix
    A, ref_date, tbase = create_design_matrix(num_ifgram, dates0, dates1)
    tbase_diff = np.diff(tbase).reshape(-1, 1)
    tbase_diff2 = np.insert(tbase_diff, 0, 0)

    print('Number of correlations: %d'%num_ifgram)
    print('Number of unique Planet scenes: %d'%len(tbase))
    nIslands = np.min(A.shape) - np.linalg.matrix_rank(A)
    print('Number of connected components in network: %d '%nIslands)
    if nIslands > 1:
        print('\tThe network appears to be disconnected and contains island components')


    # SBAS - no weights
    print('\nRun linear SBAS inversion on each pixel with no weights')
    print('\t dx')
    dx_ts_SBAS_noweights_numba, dx_residuals_SBAS_noweights_numba, dx_ranks_SBAS_noweights_numba = SBAS_noweights_numba(A, dx_stack_masked, tbase_diff, nre, rcond=1e-5)
    print('\t dy')
    dy_ts_SBAS_noweights_numba, dy_residuals_SBAS_noweights_numba, dy_ranks_SBAS_noweights_numba = SBAS_noweights_numba(A, dy_stack_masked, tbase_diff, nre, rcond=1e-5)

    # NSBAS - no weights
    print('\nRun linear NSBAS inversion on each pixel with no weights')
    print('\t dx')
    dx_ts_NSBAS_noweights_numba, dx_residuals_NSBAS_noweights_numba, dx_ranks_NSBAS_noweights_numba, dx_ranks_NSBAS_noweights_vel, dx_ranks_NSBAS_noweights_vconst = NSBAS_noweights_numba(A, dx_stack_masked, tbase_diff, tbase, nre, rcond=1e-5)
    print('\t dy')
    dy_ts_NSBAS_noweights_numba, dy_residuals_NSBAS_noweights_numba, dy_ranks_NSBAS_noweights_numba, dx_ranks_NSBAS_noweights_vel, dx_ranks_NSBAS_noweights_vconst = NSBAS_noweights_numba(A, dy_stack_masked, tbase_diff, tbase, nre, rcond=1e-5)

    fig, ax = plt.subplots(1, 2, figsize=(12,5))
    ax[0].plot(np.cumsum(tbase_diff2), np.nanmean(dx_ts_NSBAS_noweights_numba, axis=1), '-', color='darkblue', label='NSBAS')
    ax[0].plot(np.cumsum(tbase_diff2), np.nanmean(dx_ts_SBAS_noweights_numba, axis=1), '-', color='firebrick', label='SBAS')
    ax[0].set_title('Mean dx offset (n=%d)'%nre, fontsize=14)
    ax[0].set_xlabel('Time [y]')
    ax[0].set_ylabel('Cumulative dx offset [pix]')
    ax[0].legend()
    ax[0].grid()
    ax[1].plot(np.cumsum(tbase_diff2), np.nanmean(dy_ts_NSBAS_noweights_numba, axis=1), '-', color='darkblue', label='NSBAS')
    ax[1].plot(np.cumsum(tbase_diff2), np.nanmean(dy_ts_SBAS_noweights_numba, axis=1), '-', color='firebrick', label='SBAS')
    ax[1].set_title('Mean dy offset (n=%d)'%nre, fontsize=14)
    ax[1].set_xlabel('Time [y]')
    ax[1].set_ylabel('Cumulative dy offset [pix]')
    ax[1].legend()
    ax[1].grid()
    fig.tight_layout()
    fig.savefig(os.path.join(png_out_path, '%s_dx_dy_SBAS_NSBAS_inversion.png'%area_name), dpi=300)

    #weighted
    sun = get_sun_pos(files)
    dates_df = pd.DataFrame({'date0': dates0, 'date1': dates1})

    weight_df = pd.merge(dates_df, sun, left_on='date0', right_on='date', how='inner')
    weight_df = pd.merge(weight_df, sun, left_on='date1', right_on='date', how='inner', suffixes = ("_ref", "_sec"))
    weight_df.drop(["date_ref", "date_sec"], inplace = True, axis = 1)

    weights = 1-min_max_scaler(np.array(abs(weight_df.sun_az_ref - weight_df.sun_az_sec)))

    print('Run linear inversion on each pixel with weights')
    print('\t dx')
    dx_ts_weights_numba, dx_residuals_weights_numba, dx_ranks_weights_numba = linalg_weighted_numba(A, dx_stack_masked, weights, tbase_diff, nre, rcond=1e-5)
    print('\t dy')
    dy_ts_weights_numba, dy_residuals_weights_numba, dy_ranks_weights_numba = linalg_weighted_numba(A, dy_stack_masked, weights, tbase_diff, nre, rcond=1e-5)


    # dx and dy time series plot
    fig, ax = plt.subplots(1, 2, figsize=(12,5))
    ax[0].plot(np.cumsum(tbase_diff2), np.nanmean(dx_ts_SBAS_noweights_numba, axis=1), '-', color='darkblue', label='No weights')
    ax[0].plot(np.cumsum(tbase_diff2), np.nanmean(dx_ts_weights_numba, axis=1), '-', color='firebrick', label='Weighted')
    ax[0].set_title('Mean dx offset (n=%d)'%nre, fontsize=14)
    ax[0].set_xlabel('Time [y]')
    ax[0].set_ylabel('Cumulative dx offset [pix]')
    ax[0].legend()
    ax[0].grid()
    ax[1].plot(np.cumsum(tbase_diff2), np.nanmean(dy_ts_SBAS_noweights_numba, axis=1), '-', color='darkblue', label='No weights')
    ax[1].plot(np.cumsum(tbase_diff2), np.nanmean(dy_ts_weights_numba, axis=1), '-', color='firebrick', label='Weighted')
    ax[1].set_title('Mean dy offset (n=%d)'%nre, fontsize=14)
    ax[1].set_xlabel('Time [y]')
    ax[1].set_ylabel('Cumulative dy offset [pix]')
    ax[1].legend()
    ax[1].grid()
    fig.tight_layout()
    fig.savefig(os.path.join(png_out_path, '%s_dx_dy_timeseries_scaled_with_different_weights.png'%area_name), dpi=300)