[Git][debian-gis-team/mintpy][upstream] New upstream version 1.4.1
Antonio Valentino (@antonio.valentino)
gitlab at salsa.debian.org
Tue Aug 16 08:40:31 BST 2022
Antonio Valentino pushed to branch upstream at Debian GIS Project / mintpy
Commits:
2aede911 by Antonio Valentino at 2022-08-16T07:26:02+00:00
New upstream version 1.4.1
- - - - -
20 changed files:
- .github/workflows/build-docker.yml
- + .github/workflows/publish-to-test-pypi.yml
- docs/environment.yml
- mintpy/__init__.py
- mintpy/__main__.py
- mintpy/asc_desc2horz_vert.py
- mintpy/geocode.py
- mintpy/load_data.py
- mintpy/objects/insar_vs_gps.py
- mintpy/reference_point.py
- mintpy/smallbaselineApp.py
- mintpy/timeseries2velocity.py
- mintpy/timeseries_rms.py
- mintpy/unwrap_error_phase_closure.py
- mintpy/utils/plot.py
- mintpy/utils/readfile.py
- mintpy/version.py
- mintpy/view.py
- requirements.txt
- setup.py
Changes:
=====================================
.github/workflows/build-docker.yml
=====================================
@@ -12,6 +12,7 @@ on:
jobs:
dockerize:
+ name: Build Docker image and push to GitHub Container Registry
runs-on: ubuntu-latest
steps:
- uses: actions/checkout at v2
@@ -31,7 +32,7 @@ jobs:
- name: Set environment variables for docker build
run: |
# Lowercase repo for Github Container Registry
- echo "REPO=${GITHUB_REPOSITORY,,}" >>${GITHUB_ENV}
+ echo "REPO=${GITHUB_REPOSITORY,,}" >> ${GITHUB_ENV}
# Ensure tags are checked out
git fetch origin +refs/tags/*:refs/tags/*
# Version number from tag
=====================================
.github/workflows/publish-to-test-pypi.yml
=====================================
@@ -0,0 +1,58 @@
+# link: https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/
+name: publish distributions 📦 to PyPI and TestPyPI
+
+on:
+ push:
+ branches:
+ - main
+ tags:
+ - v*
+
+jobs:
+ build-n-publish:
+ if: github.repository_owner == 'insarlab'
+
+ name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout at v3
+ with:
+ fetch-depth: 0
+
+ - name: Set up Python 3.10
+ uses: actions/setup-python at v3
+ with:
+ python-version: "3.10"
+
+ - name: Install pypa/build
+ run: >-
+ python -m
+ pip install
+ build
+ --user
+
+ - name: Build a binary wheel and a source tarball
+ run: >-
+ python -m
+ build
+ --sdist
+ --wheel
+ --outdir dist/
+ .
+
+ - name: Publish developed version 📦 to Test PyPI
+ if: github.ref == 'refs/heads/main'
+ uses: pypa/gh-action-pypi-publish at release/v1
+ with:
+ password: ${{ secrets.TEST_PYPI_API_TOKEN }}
+ repository_url: https://test.pypi.org/legacy/
+ skip_existing: false
+ verbose: true
+
+ - name: Publish released version 📦 to PyPI
+ if: startsWith(github.ref, 'refs/tags/v')
+ uses: pypa/gh-action-pypi-publish at release/v1
+ with:
+ password: ${{ secrets.PYPI_API_TOKEN }}
+ verbose: true
+
=====================================
docs/environment.yml
=====================================
@@ -23,11 +23,9 @@ dependencies:
- pyaps3>=0.3
- pykml>=0.2
- pyproj
+ - pyresample
- pysolid
- scikit-image
- scipy
- # for ARIA, FRInGE, HyP3, GMTSAR
+ # for ISCE, ARIA, FRInGE, HyP3, GMTSAR
- gdal>=3
- # for pyresample
- - pyresample
- - openmp
=====================================
mintpy/__init__.py
=====================================
@@ -1,5 +1,5 @@
# get version info
from mintpy.version import (
- version_num as __version__,
+ version as __version__,
logo as __logo__,
)
=====================================
mintpy/__main__.py
=====================================
@@ -513,7 +513,7 @@ def get_view_parser(subparsers=None):
def get_parser():
"""Instantiate the command line argument parser."""
parser = argparse.ArgumentParser(prog=PROG, description=__doc__)
- parser.add_argument("--version", action="version", version=f"%(prog)s {__version__}")
+ parser.add_argument("-v","--version", action="version", version=f"{__version__}")
# Sub-command management
sp = parser.add_subparsers(title="sub-commands", dest='func', required=True, metavar='')
=====================================
mintpy/asc_desc2horz_vert.py
=====================================
@@ -116,7 +116,7 @@ def cmd_line_parse(iargs=None):
if any(ref_diff > inps.max_ref_yx_diff for ref_diff in [ref_y_diff, ref_x_diff]):
msg = 'REF_LAT/LON difference between input files > {} pixels!\n'.format(inps.max_ref_yx_diff)
for fname, ref_lat, ref_lon in zip(inps.file, [ref_lat1, ref_lat2], [ref_lon1, ref_lon2]):
- msg += 'file1: {}\n'.format(fname)
+ msg += 'file: {}\n'.format(fname)
msg += '\tREF_LAT/LON: [{:.8f}, {:.8f}]\n'.format(ref_lat, ref_lon)
raise ValueError(msg)
=====================================
mintpy/geocode.py
=====================================
@@ -219,8 +219,11 @@ def read_template2inps(template_file, inps):
def check_num_processor(nprocs):
"""Check number of processors
Note by Yunjun, 2019-05-02:
- 1. conda install pyresample will install pykdtree and openmp, but it seems not working
+ 1. conda install pyresample will install pykdtree and openmp, but it seems not working:
geocode.py is getting slower with more processors
+ Test on a TS HDF5 file in size of (241, 2267, 2390)
+ Memory: up to 10GB
+ Run time: 2.5 mins for nproc=1, 3 mins for nproc=4
2. macports seems to have minor speedup when more processors
Thus, default number of processors is set to 1; although the capability of using multiple
processors is written here.
=====================================
mintpy/load_data.py
=====================================
@@ -766,7 +766,7 @@ def prepare_metadata(iDict):
geom_names = ['dem', 'lookupY', 'lookupX', 'incAngle', 'azAngle', 'shadowMask', 'waterMask']
geom_keys = ['mintpy.load.{}File'.format(i) for i in geom_names]
geom_files = [os.path.basename(iDict[key]) for key in geom_keys
- if (iDict.get(key, 'auto') != 'auto')]
+ if iDict.get(key, 'auto') not in ['auto', 'None', 'no', None, False]]
# compose list of input arguments
iargs = ['-m', meta_file, '-g', geom_dir]
=====================================
mintpy/objects/insar_vs_gps.py
=====================================
@@ -13,144 +13,14 @@ from scipy import stats
from scipy.interpolate import griddata
from datetime import datetime as dt
from dateutil.relativedelta import relativedelta
-from matplotlib import pyplot as plt
from mintpy.objects import timeseries, giantTimeseries
-from mintpy.utils import ptime, readfile, plot as pp, utils as ut
+from mintpy.utils import readfile, plot as pp, utils as ut
from mintpy.objects.gps import GPS
from mintpy.defaults.plot import *
-############################## utilities functions ##########################################
-
-def plot_insar_vs_gps_scatter(vel_file, csv_file='gps_enu2los.csv', msk_file=None, ref_gps_site=None,
- xname='InSAR', vlim=None, ex_gps_sites=[], display=True):
- """Scatter plot to compare the velocities between SAR/InSAR and GPS.
-
- Parameters: vel_file - str, path of InSAR LOS velocity HDF5 file..
- ref_gps_site - str, reference GNSS site name
- csv_file - str, path of GNSS CSV file, generated after running view.py --gps-comp
- msk_file - str, path of InSAR mask file.
- xname - str, xaxis label
- vlim - list of 2 float, display value range in the unit of cm/yr
- Default is None to grab from data
- If set, the range will be used to prune the SAR and GPS observations
- ex_gps_sites - list of str, exclude GNSS sites for analysis and plotting.
- Example:
- from mintpy.objects.insar_vs_gps import plot_insar_vs_gps_scatter
- csv_file = os.path.join(work_dir, 'geo/gps_enu2los.csv')
- vel_file = os.path.join(work_dir, 'geo/geo_velocity.h5')
- msk_file = os.path.join(work_dir, 'geo/geo_maskTempCoh.h5')
- plot_insar_vs_gps_scatter(vel_file, ref_gps_site='CACT', csv_file=csv_file, msk_file=msk_file, vlim=[-2.5, 2])
- """
-
- disp_unit = 'cm/yr'
- unit_fac = 100.
-
- # read GPS velocity from CSV file (generated by gps.get_gps_los_obs())
- col_names = ['Site', 'Lon', 'Lat', 'Displacement', 'Velocity']
- num_col = len(col_names)
- col_types = ['U10'] + ['f8'] * (num_col - 1)
-
- print('read GPS velocity from file: {}'.format(csv_file))
- fc = np.genfromtxt(csv_file, dtype=col_types, delimiter=',', names=True)
- sites = fc['Site']
- lats = fc['Lat']
- lons = fc['Lon']
- gps_obs = fc[col_names[-1]] * unit_fac
-
- if ex_gps_sites:
- ex_flag = np.array([x in ex_gps_sites for x in sites], dtype=np.bool_)
- if np.sum(ex_flag) > 0:
- sites = sites[~ex_flag]
- lats = lats[~ex_flag]
- lons = lons[~ex_flag]
- gps_obs = gps_obs[~ex_flag]
-
- # read InSAR velocity
- print('read InSAR velocity from file: {}'.format(vel_file))
- atr = readfile.read_attribute(vel_file)
- length, width = int(atr['LENGTH']), int(atr['WIDTH'])
- coord = ut.coordinate(atr)
- ys, xs = coord.geo2radar(lats, lons)[:2]
-
- msk = readfile.read(msk_file)[0] if msk_file else np.ones((length, width), dtype=np.bool_)
-
- num_site = sites.size
- insar_obs = np.zeros(num_site, dtype=np.float32) * np.nan
- prog_bar = ptime.progressBar(maxValue=num_site)
- for i in range(num_site):
- x, y = xs[i], ys[i]
- if (0 <= x < width) and (0 <= y < length) and msk[y, x]:
- box = (x, y, x+1, y+1)
- insar_obs[i] = readfile.read(vel_file, datasetName='velocity', box=box)[0] * unit_fac
- prog_bar.update(i+1, suffix='{}/{} {}'.format(i+1, num_site, sites[i]))
- prog_bar.close()
-
- off_med = np.nanmedian(insar_obs - gps_obs)
- print(f'median offset between InSAR and GPS [before common referencing]: {off_med:.2f} cm/year')
-
- # reference site
- if ref_gps_site:
- print(f'referencing both InSAR and GPS data to site: {ref_gps_site}')
- ref_ind = sites.tolist().index(ref_gps_site)
- gps_obs -= gps_obs[ref_ind]
- insar_obs -= insar_obs[ref_ind]
-
- # remove NaN value
- print('removing sites with NaN values in GPS or {}'.format(xname))
- flag = np.multiply(~np.isnan(insar_obs), ~np.isnan(gps_obs))
- if vlim is not None:
- print('pruning sites with value range: {} {}'.format(vlim, disp_unit))
- flag *= gps_obs >= vlim[0]
- flag *= gps_obs <= vlim[1]
- flag *= insar_obs >= vlim[0]
- flag *= insar_obs <= vlim[1]
-
- gps_obs = gps_obs[flag]
- insar_obs = insar_obs[flag]
- sites = sites[flag]
-
- # stats
- print('GPS min/max: {:.2f} / {:.2f}'.format(np.nanmin(gps_obs), np.nanmax(gps_obs)))
- print('InSAR min/max: {:.2f} / {:.2f}'.format(np.nanmin(insar_obs), np.nanmax(insar_obs)))
-
- rmse = np.sqrt(np.sum((insar_obs - gps_obs)**2) / (gps_obs.size - 1))
- r2 = stats.linregress(insar_obs, gps_obs)[2]
- print('RMSE = {:.1f} cm'.format(rmse))
- print('R^2 = {:.2f}'.format(r2))
-
- # plot
- if display:
- plt.rcParams.update({'font.size': 12})
- if vlim is None:
- vlim = [np.min(insar_obs), np.max(insar_obs)]
- buffer = (vlim[1] - vlim[0]) * 0.1
- vlim = [vlim[0] - buffer, vlim[1] + buffer]
-
- fig, ax = plt.subplots(figsize=[4, 4])
- ax.plot((vlim[0], vlim[1]), (vlim[0], vlim[1]), 'k--')
- ax.plot(insar_obs, gps_obs, '.', ms=15)
-
- # axis format
- ax.set_xlim(vlim)
- ax.set_ylim(vlim)
- ax.set_xlabel(f'{xname} [{disp_unit}]')
- ax.set_ylabel(f'GNSS [{disp_unit}]')
- ax.set_aspect('equal', 'box')
- fig.tight_layout()
-
- # output
- out_fig = '{}_vs_gps_scatter.pdf'.format(xname.lower())
- plt.savefig(out_fig, bbox_inches='tight', transparent=True, dpi=300)
- print('save figure to file', out_fig)
- plt.show()
-
- return sites, insar_obs, gps_obs
-
-
-
############################## beginning of insar_vs_gps class ##############################
class insar_vs_gps:
""" Comparing InSAR time-series with GPS time-series in LOS direction
=====================================
mintpy/reference_point.py
=====================================
@@ -464,7 +464,9 @@ def read_reference_input(inps):
# Do not use ref_y/x in masked out area
if inps.maskFile and os.path.isfile(inps.maskFile):
print('mask: '+inps.maskFile)
- mask = readfile.read(inps.maskFile, datasetName='mask')[0]
+ ds_names = readfile.get_dataset_list(inps.maskFile)
+ ds_name = [x for x in ds_names if x in ['mask', 'waterMask']][0]
+ mask = readfile.read(inps.maskFile, datasetName=ds_name)[0]
if mask[inps.ref_y, inps.ref_x] == 0:
inps.ref_y, inps.ref_x = None, None
msg = 'input reference point is in masked OUT area defined by {}!'.format(inps.maskFile)
=====================================
mintpy/smallbaselineApp.py
=====================================
@@ -49,7 +49,7 @@ EXAMPLE = """example:
smallbaselineApp.py -H #print default template options
smallbaselineApp.py -g #generate default template if it does not exist
smallbaselineApp.py -g <custom_template> #generate/update default template based on custom template
- smallbaselineApp.py --plot #plot results without run
+ smallbaselineApp.py --plot #plot results w/o run [to populate the 'pic' folder after failed runs]
# Run with --start/stop/dostep options
smallbaselineApp.py GalapagosSenDT128.template --dostep velocity #run at step 'velocity' only
@@ -617,6 +617,12 @@ class TimeSeriesAnalysis:
msg += "Try the following:\n"
msg += "1) Check the reference pixel and make sure it's not in areas with unwrapping errors\n"
msg += "2) Check the network and make sure it's fully connected without subsets"
+ print(f'ERROR: {msg}')
+
+ # populate the pic folder to facilate the trouble shooting
+ self.plot_result(print_aux=False)
+
+ # terminate the program
raise RuntimeError(msg)
return
=====================================
mintpy/timeseries2velocity.py
=====================================
@@ -487,7 +487,7 @@ def run_timeseries2time_func(inps):
# Bootstrapping is a resampling method which can be used to estimate properties
# of an estimator. The method relies on independently sampling the data set with
# replacement.
- print('estimating time function STD with bootstrap resampling ({} times) ...'.format(
+ print('estimating time functions STD with bootstrap resampling ({} times) ...'.format(
inps.bootstrapCount))
# calc model of all bootstrap sampling
@@ -559,7 +559,7 @@ def run_timeseries2time_func(inps):
# TO DO: save the full covariance matrix of the time function parameters
# only the STD is saved right now
covar_flag = True if len(ts_cov.shape) == 3 else False
- msg = 'estimating time function STD from time-serries '
+ msg = 'estimating time functions STD from time-serries '
msg += 'covariance pixel-by-pixel ...' if covar_flag else 'variance pixel-by-pixel ...'
print(msg)
@@ -583,7 +583,7 @@ def run_timeseries2time_func(inps):
elif inps.uncertaintyQuantification == 'residue':
# option 2.3 - assume obs errors following normal dist. in time
- print('estimating time function STD from time-series fitting residual ...')
+ print('estimating time functions STD from time-series fitting residual ...')
G_inv = linalg.inv(np.dot(G.T, G))
m_var = e2.reshape(1, -1) / (num_date - num_param)
m_std[:, mask] = np.sqrt(np.dot(np.diag(G_inv).reshape(-1, 1), m_var))
=====================================
mintpy/timeseries_rms.py
=====================================
@@ -9,10 +9,9 @@
import os
import sys
import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import make_axes_locatable
+
from mintpy.defaults.template import get_template_content
-from mintpy.utils import readfile, ptime, utils as ut, plot as pp
+from mintpy.utils import readfile, utils as ut, plot as pp
from mintpy.utils.arg_utils import create_argument_parser
@@ -103,15 +102,14 @@ def analyze_rms(date_list, rms_list, inps):
print('save date to file: '+ref_date_file)
# exclude date(s) - outliers
- try:
- rms_threshold = ut.median_abs_deviation_threshold(rms_list, center=0., cutoff=inps.cutoff)
- except:
- # equivalent calculation using numpy assuming Gaussian distribution
- rms_threshold = np.median(rms_list) / .6745 * inps.cutoff
+ # equivalent calculation using numpy assuming Gaussian distribution as:
+ # rms_threshold = np.median(rms_list) / .6745 * inps.cutoff
+ rms_threshold = ut.median_abs_deviation_threshold(rms_list, center=0., cutoff=inps.cutoff)
ex_idx = [rms_list.index(i) for i in rms_list if i > rms_threshold]
- print(('-'*50+'\ndate(s) with RMS > {} * median RMS'
- ' ({:.4f})'.format(inps.cutoff, rms_threshold)))
+ print('-'*50)
+ print(f'date(s) with RMS > {inps.cutoff} * median RMS ({rms_threshold:.4f})')
+
ex_date_file = 'exclude_date.txt'
if ex_idx:
# print
@@ -127,110 +125,37 @@ def analyze_rms(date_list, rms_list, inps):
if os.path.isfile(ex_date_file):
os.remove(ex_date_file)
- # plot bar figure and save
- fig_file = os.path.splitext(inps.rms_file)[0]+'.pdf'
- fig, ax = plt.subplots(figsize=inps.fig_size)
- print('create figure in size:', inps.fig_size)
- ax = plot_rms_bar(ax, date_list, np.array(rms_list)*1000., cutoff=inps.cutoff)
- fig.savefig(fig_file, bbox_inches='tight', transparent=True)
- print('save figure to file: '+fig_file)
return inps
-def plot_rms_bar(ax, date_list, rms, cutoff=3., font_size=12,
- tick_year_num=1, legend_loc='best',
- disp_legend=True, disp_side_plot=True, disp_thres_text=False,
- ylabel='Residual phase RMS [mm]'):
- """ Bar plot Phase Residual RMS
- Parameters: ax : Axes object
- date_list : list of string in YYYYMMDD format
- rms : 1D np.array of float for RMS value in mm
- cutoff : cutoff value of MAD outlier detection
- tick_year_num : int, number of years per major tick
- legend_loc : 'upper right' or (0.5, 0.5)
- Returns: ax : Axes object
- """
- dates, datevector = ptime.date_list2vector(date_list)
- dates = np.array(dates)
- try:
- bar_width = min(ut.most_common(np.diff(dates).tolist(), k=2))*3/4
- except:
- bar_width = np.min(np.diff(dates).tolist())*3/4
- rms = np.array(rms)
-
- # Plot all dates
- ax.bar(dates, rms, bar_width.days, color=pp.mplColors[0])
-
- # Plot reference date
- ref_idx = np.argmin(rms)
- ax.bar(dates[ref_idx], rms[ref_idx], bar_width.days, color=pp.mplColors[1], label='Reference date')
-
- # Plot exclude dates
- rms_threshold = ut.median_abs_deviation_threshold(rms, center=0., cutoff=cutoff)
- ex_idx = rms > rms_threshold
- if not np.all(ex_idx==False):
- ax.bar(dates[ex_idx], rms[ex_idx], bar_width.days, color='darkgray', label='Exclude date')
-
- # Plot rms_threshold line
- (ax, xmin, xmax) = pp.auto_adjust_xaxis_date(ax, datevector, font_size, every_year=tick_year_num)
- ax.plot(np.array([xmin, xmax]), np.array([rms_threshold, rms_threshold]), '--k',
- label='Median Abs Dev * {}'.format(cutoff))
-
- # axis format
- ax = pp.auto_adjust_yaxis(ax, np.append(rms, rms_threshold), font_size, ymin=0.0)
- #ax.set_xlabel('Time [years]', fontsize=font_size)
- ax.set_ylabel(ylabel, fontsize=font_size)
- ax.tick_params(which='both', direction='in', labelsize=font_size,
- bottom=True, top=True, left=True, right=True)
-
- # 2nd axes for circles
- if disp_side_plot:
- divider = make_axes_locatable(ax)
- ax2 = divider.append_axes("right", "10%", pad="2%")
- ax2.plot(np.ones(rms.shape, np.float32) * 0.5, rms, 'o', mfc='none', color=pp.mplColors[0])
- ax2.plot(np.ones(rms.shape, np.float32)[ref_idx] * 0.5, rms[ref_idx], 'o', mfc='none', color=pp.mplColors[1])
- if not np.all(ex_idx==False):
- ax2.plot(np.ones(rms.shape, np.float32)[ex_idx] * 0.5, rms[ex_idx], 'o', mfc='none', color='darkgray')
- ax2.plot(np.array([0, 1]), np.array([rms_threshold, rms_threshold]), '--k')
-
- ax2.set_ylim(ax.get_ylim())
- ax2.set_xlim([0, 1])
- ax2.tick_params(which='both', direction='in', labelsize=font_size,
- bottom=True, top=True, left=True, right=True)
- ax2.get_xaxis().set_ticks([])
- ax2.get_yaxis().set_ticklabels([])
-
- if disp_legend:
- ax.legend(loc=legend_loc, frameon=False, fontsize=font_size)
-
- # rms_threshold text
- if disp_thres_text:
- ymin, ymax = ax.get_ylim()
- yoff = (ymax - ymin) * 0.1
- if (rms_threshold - ymin) > 0.5 * (ymax - ymin):
- yoff *= -1.
- ax.annotate('Median Abs Dev * {}'.format(cutoff),
- xy=(xmin + (xmax-xmin)*0.05, rms_threshold + yoff ),
- color='k', xycoords='data', fontsize=font_size)
- return ax
-
-
######################################################################################################
def main(iargs=None):
- plt.switch_backend('Agg') # Backend setting
+ # read inputs
inps = cmd_line_parse(iargs)
if inps.template_file:
inps = read_template2inps(inps.template_file, inps)
# calculate timeseries of residual Root Mean Square
- (inps.rms_list,
- inps.date_list,
- inps.rms_file) = ut.get_residual_rms(inps.timeseries_file,
- mask_file=inps.maskFile,
- ramp_type=inps.deramp)
+ inps.rms_list, inps.date_list, inps.rms_file = ut.get_residual_rms(
+ inps.timeseries_file,
+ mask_file=inps.maskFile,
+ ramp_type=inps.deramp,
+ )
+ # analyze RMS: generate reference/exclude_date.txt files
analyze_rms(inps.date_list, inps.rms_list, inps)
+
+ # plot RMS
+ pp.plot_timeseries_rms(
+ rms_file=inps.rms_file,
+ cutoff=inps.cutoff,
+ out_fig=os.path.splitext(inps.rms_file)[0]+'.pdf',
+ disp_fig=False,
+ fig_size=inps.fig_size,
+ tick_year_num=inps.tick_year_num,
+ )
+
return
=====================================
mintpy/unwrap_error_phase_closure.py
=====================================
@@ -11,7 +11,7 @@ import sys
import time
import h5py
import numpy as np
-from matplotlib import pyplot as plt, ticker
+from matplotlib import pyplot as plt
try:
from cvxopt import matrix
@@ -306,69 +306,11 @@ def calc_num_triplet_with_nonzero_integer_ambiguity(ifgram_file, mask_file=None,
writefile.write(num_nonzero_closure, out_file, meta)
# plot
- plot_num_triplet_with_nonzero_integer_ambiguity(out_file)
+ pp.plot_num_triplet_with_nonzero_integer_ambiguity(out_file)
return out_file
-def plot_num_triplet_with_nonzero_integer_ambiguity(fname, display=False, font_size=12, fig_size=[9,3]):
- """Plot the histogram for the number of triplets with non-zero integer ambiguity
-
- Fig. 3d-e in Yunjun et al. (2019, CAGEO).
- """
-
- # read data
- data, atr = readfile.read(fname)
- vmax = int(np.nanmax(data))
-
- # plot
- fig, axs = plt.subplots(nrows=1, ncols=2, figsize=fig_size)
-
- # subplot 1 - map
- ax = axs[0]
- im = ax.imshow(data, cmap='RdBu_r', interpolation='nearest')
-
- # reference point
- if all(key in atr.keys() for key in ['REF_Y','REF_X']):
- ax.plot(int(atr['REF_X']), int(atr['REF_Y']), 's', color='white', ms=3)
-
- # format
- pp.auto_flip_direction(atr, ax=ax, print_msg=False)
- fig.colorbar(im, ax=ax)
- ax.set_title(r'$T_{int}$', fontsize=font_size)
-
- # subplot 2 - histogram
- ax = axs[1]
- ax.hist(data[~np.isnan(data)].flatten(), range=(0, vmax), log=True, bins=vmax)
-
- # axis format
- ax.set_xlabel(r'# of triplets w non-zero int ambiguity $T_{int}$', fontsize=font_size)
- ax.set_ylabel('# of pixels', fontsize=font_size)
- ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
- ax.yaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=15))
- ax.yaxis.set_minor_locator(ticker.LogLocator(base=10.0, numticks=15,
- subs=(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9)))
- ax.yaxis.set_minor_formatter(ticker.NullFormatter())
-
- for ax in axs:
- ax.tick_params(which='both', direction='in', labelsize=font_size,
- bottom=True, top=True, left=True, right=True)
-
- fig.tight_layout()
-
- # output
- out_fig = '{}.png'.format(os.path.splitext(fname)[0])
- print('plot and save figure to file', out_fig)
- fig.savefig(out_fig, bbox_inches='tight', transparent=True, dpi=300)
-
- if display:
- plt.show()
- else:
- plt.close(fig)
-
- return
-
-
##########################################################################################
def get_common_region_int_ambiguity(ifgram_file, cc_mask_file, water_mask_file=None, num_sample=100,
dsNameIn='unwrapPhase', cc_min_area=2.5e3):
@@ -584,7 +526,7 @@ def main(iargs=None):
# for debug
debug_mode = False
if debug_mode:
- plot_num_triplet_with_nonzero_integer_ambiguity(out_file)
+ pp.plot_num_triplet_with_nonzero_integer_ambiguity(out_file)
m, s = divmod(time.time()-start_time, 60)
print('time used: {:02.0f} mins {:02.1f} secs\nDone.'.format(m, s))
=====================================
mintpy/utils/plot.py
=====================================
@@ -17,6 +17,7 @@ import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt, ticker, dates as mdates
from mpl_toolkits.axes_grid1 import make_axes_locatable
+from scipy import stats
from mintpy.objects.coord import coordinate
from mintpy.objects.colors import ColormapExt
@@ -788,6 +789,7 @@ def plot_perp_baseline_hist(ax, dateList, pbaseList, p_dict={}, dateList_drop=[]
return ax
+
def plot_rotate_diag_coherence_matrix(ax, coh_list, date12_list, date12_list_drop=[],
rotate_deg=-45., cmap='RdBu', disp_half=False, disp_min=0.2):
"""Plot Rotated Coherence Matrix, suitable for Sentinel-1 data with sequential network"""
@@ -927,9 +929,173 @@ def plot_coherence_matrix(ax, date12List, cohList, date12List_drop=[], p_dict={}
return ax, coh_mat, im
+def plot_num_triplet_with_nonzero_integer_ambiguity(fname, display=False, font_size=12, fig_size=[9,3]):
+ """Plot the histogram for the number of triplets with non-zero integer ambiguity.
+
+ Fig. 3d-e in Yunjun et al. (2019, CAGEO).
+
+ Parameters: fname - str, path to the numTriNonzeroIntAmbiguity.h5 file.
+ """
+
+ # read data
+ data, atr = readfile.read(fname)
+ vmax = int(np.nanmax(data))
+
+ # plot
+ fig, axs = plt.subplots(nrows=1, ncols=2, figsize=fig_size)
+
+ # subplot 1 - map
+ ax = axs[0]
+ im = ax.imshow(data, cmap='RdBu_r', interpolation='nearest')
+
+ # reference point
+ if all(key in atr.keys() for key in ['REF_Y','REF_X']):
+ ax.plot(int(atr['REF_X']), int(atr['REF_Y']), 's', color='white', ms=3)
+
+ # format
+ auto_flip_direction(atr, ax=ax, print_msg=False)
+ fig.colorbar(im, ax=ax)
+ ax.set_title(r'$T_{int}$', fontsize=font_size)
+
+ # subplot 2 - histogram
+ ax = axs[1]
+ ax.hist(data[~np.isnan(data)].flatten(), range=(0, vmax), log=True, bins=vmax)
+
+ # axis format
+ ax.set_xlabel(r'# of triplets w non-zero int ambiguity $T_{int}$', fontsize=font_size)
+ ax.set_ylabel('# of pixels', fontsize=font_size)
+ ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
+ ax.yaxis.set_major_locator(ticker.LogLocator(base=10.0, numticks=15))
+ ax.yaxis.set_minor_locator(ticker.LogLocator(base=10.0, numticks=15,
+ subs=(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9)))
+ ax.yaxis.set_minor_formatter(ticker.NullFormatter())
+
+ for ax in axs:
+ ax.tick_params(which='both', direction='in', labelsize=font_size,
+ bottom=True, top=True, left=True, right=True)
+
+ fig.tight_layout()
+
+ # output
+ out_fig = '{}.png'.format(os.path.splitext(fname)[0])
+ print('plot and save figure to file', out_fig)
+ fig.savefig(out_fig, bbox_inches='tight', transparent=True, dpi=300)
+
+ if display:
+ plt.show()
+ else:
+ plt.close(fig)
+
+ return
+
+
+def plot_timeseries_rms(rms_file, cutoff=3, out_fig=None, disp_fig=True,
+ fig_size=[5, 3], font_size=12, tick_year_num=1, legend_loc='best',
+ disp_legend=True, disp_side_plot=True, disp_thres_text=False,
+ ylabel='Residual phase RMS [mm]'):
+ """ Bar plot for the phase residual RMS time series.
+
+ Parameters: rms_file - str, path to the time series RMS text file
+ Generated by utils1.get_residual_rms().
+ cutoff - float, cutoff value of MAD outlier detection
+ fig_size - list of 2 float, figure size in inch
+ """
+
+ # read date / RMS info from text file
+ fc = np.loadtxt(rms_file, dtype=bytes).astype(str)
+ rms_list = fc[:, 1].astype(np.float32) * 1000.
+ date_list = list(fc[:, 0])
+
+ dates, datevector = ptime.date_list2vector(date_list)
+ dates = np.array(dates)
+ try:
+ bar_width = min(ut0.most_common(np.diff(dates).tolist(), k=2))*3/4
+ except:
+ bar_width = np.min(np.diff(dates).tolist())*3/4
+ rms = np.array(rms_list)
+
+ # Plot all dates
+ fig, ax = plt.subplots(figsize=fig_size)
+ ax.bar(dates, rms, bar_width.days, color='C0')
+
+ # Plot reference date
+ ref_idx = np.argmin(rms)
+ ax.bar(dates[ref_idx], rms[ref_idx], bar_width.days, color='C1', label='Reference date')
+
+ # Plot exclude dates
+ rms_threshold = ut0.median_abs_deviation_threshold(rms, center=0., cutoff=cutoff)
+ ex_idx = rms > rms_threshold
+ if np.any(ex_idx==True):
+ ax.bar(dates[ex_idx], rms[ex_idx], bar_width.days, color='darkgray', label='Exclude date')
+
+ # Plot rms_threshold line
+ (ax, xmin, xmax) = auto_adjust_xaxis_date(ax, datevector, font_size, every_year=tick_year_num)
+ ax.plot(np.array([xmin, xmax]), np.array([rms_threshold, rms_threshold]), '--k',
+ label='Median Abs Dev * {}'.format(cutoff))
+
+ # axis format
+ ax = auto_adjust_yaxis(ax, np.append(rms, rms_threshold), font_size, ymin=0.0)
+ #ax.set_xlabel('Time [years]', fontsize=font_size)
+ ax.set_ylabel(ylabel, fontsize=font_size)
+ ax.tick_params(which='both', direction='in', labelsize=font_size,
+ bottom=True, top=True, left=True, right=True)
+
+ # 2nd axes for circles
+ if disp_side_plot:
+ divider = make_axes_locatable(ax)
+ ax2 = divider.append_axes("right", "10%", pad="2%")
+ ax2.plot(np.ones(rms.shape, np.float32) * 0.5, rms, 'o', mfc='none', color='C0')
+ ax2.plot(np.ones(rms.shape, np.float32)[ref_idx] * 0.5, rms[ref_idx], 'o', mfc='none', color='C1')
+ if np.any(ex_idx==True):
+ ax2.plot(np.ones(rms.shape, np.float32)[ex_idx] * 0.5, rms[ex_idx], 'o', mfc='none', color='darkgray')
+ ax2.plot(np.array([0, 1]), np.array([rms_threshold, rms_threshold]), '--k')
+
+ ax2.set_ylim(ax.get_ylim())
+ ax2.set_xlim([0, 1])
+ ax2.tick_params(which='both', direction='in', labelsize=font_size,
+ bottom=True, top=True, left=True, right=True)
+ ax2.get_xaxis().set_ticks([])
+ ax2.get_yaxis().set_ticklabels([])
+
+ if disp_legend:
+ ax.legend(loc=legend_loc, frameon=False, fontsize=font_size)
+
+ # rms_threshold text
+ if disp_thres_text:
+ ymin, ymax = ax.get_ylim()
+ yoff = (ymax - ymin) * 0.1
+ if (rms_threshold - ymin) > 0.5 * (ymax - ymin):
+ yoff *= -1.
+ ax.annotate('Median Abs Dev * {}'.format(cutoff),
+ xy=(xmin + (xmax-xmin)*0.05, rms_threshold + yoff ),
+ color='k', xycoords='data', fontsize=font_size)
+
+ # figure output
+ if out_fig:
+ print('save figure to file:', out_fig)
+ fig.savefig(out_fig, bbox_inches='tight', transparent=True)
+
+ if disp_fig:
+ plt.show()
+ else:
+ plt.close()
+
+ return
+
+############################################### GNSS ###############################################
+
def plot_gps(ax, SNWE, inps, metadata=dict(), print_msg=True):
+ """Plot GNSS as scatters on top of the input matplotlib.axes.
+
+ Parameters: ax - matplotlib.axes object
+ SNWE - tuple of 4 float, for south, north, west and east
+ inps - Namespace object, from view.py
+ metadata - dict, mintpy metadata
+ Returns: ax - matplotlib.axes object
+ """
+
from mintpy.objects import gps
vprint = print if print_msg else lambda *args, **kwargs: None
@@ -1047,6 +1213,143 @@ def plot_gps(ax, SNWE, inps, metadata=dict(), print_msg=True):
return ax
+def plot_insar_vs_gps_scatter(vel_file, csv_file='gps_enu2los.csv', msk_file=None, ref_gps_site=None, cutoff=5,
+ fig_size=[4, 4], xname='InSAR', vlim=None, ex_gps_sites=[], display=True):
+ """Scatter plot to compare the velocities between SAR/InSAR and GPS.
+
+ Parameters: vel_file - str, path of InSAR LOS velocity HDF5 file..
+ csv_file - str, path of GNSS CSV file, generated after running view.py --gps-comp
+ msk_file - str, path of InSAR mask file.
+ ref_gps_site - str, reference GNSS site name
+ cutoff - float, threshold in terms of med abs dev (MAD) for outlier detection
+ xname - str, xaxis label
+ vlim - list of 2 float, display value range in the unit of cm/yr
+ Default is None to grab from data
+ If set, the range will be used to prune the SAR and GPS observations
+ ex_gps_sites - list of str, exclude GNSS sites for analysis and plotting.
+ Returns: sites - list of str, GNSS site names used for comparison
+ insar_obs - 1D np.ndarray in float32, InSAR velocity in cm/yr
+ gps_obs - 1D np.ndarray in float32, GNSS velocity in cm/yr
+ Example:
+ from mintpy.utils import plot as pp
+ csv_file = os.path.join(work_dir, 'geo/gps_enu2los.csv')
+ vel_file = os.path.join(work_dir, 'geo/geo_velocity.h5')
+ msk_file = os.path.join(work_dir, 'geo/geo_maskTempCoh.h5')
+ pp.plot_insar_vs_gps_scatter(vel_file, ref_gps_site='CACT', csv_file=csv_file, msk_file=msk_file, vlim=[-2.5, 2])
+ """
+
+ disp_unit = 'cm/yr'
+ unit_fac = 100.
+
+ # read GPS velocity from CSV file (generated by gps.get_gps_los_obs())
+ col_names = ['Site', 'Lon', 'Lat', 'Displacement', 'Velocity']
+ num_col = len(col_names)
+ col_types = ['U10'] + ['f8'] * (num_col - 1)
+
+ print('read GPS velocity from file: {}'.format(csv_file))
+ fc = np.genfromtxt(csv_file, dtype=col_types, delimiter=',', names=True)
+ sites = fc['Site']
+ lats = fc['Lat']
+ lons = fc['Lon']
+ gps_obs = fc[col_names[-1]] * unit_fac
+
+ if ex_gps_sites:
+ ex_flag = np.array([x in ex_gps_sites for x in sites], dtype=np.bool_)
+ if np.sum(ex_flag) > 0:
+ sites = sites[~ex_flag]
+ lats = lats[~ex_flag]
+ lons = lons[~ex_flag]
+ gps_obs = gps_obs[~ex_flag]
+
+ # read InSAR velocity
+ print('read InSAR velocity from file: {}'.format(vel_file))
+ atr = readfile.read_attribute(vel_file)
+ length, width = int(atr['LENGTH']), int(atr['WIDTH'])
+ ys, xs = coordinate(atr).geo2radar(lats, lons)[:2]
+
+ msk = readfile.read(msk_file)[0] if msk_file else np.ones((length, width), dtype=np.bool_)
+
+ num_site = sites.size
+ insar_obs = np.zeros(num_site, dtype=np.float32) * np.nan
+ prog_bar = ptime.progressBar(maxValue=num_site)
+ for i in range(num_site):
+ x, y = xs[i], ys[i]
+ if (0 <= x < width) and (0 <= y < length) and msk[y, x]:
+ box = (x, y, x+1, y+1)
+ insar_obs[i] = readfile.read(vel_file, datasetName='velocity', box=box)[0] * unit_fac
+ prog_bar.update(i+1, suffix='{}/{} {}'.format(i+1, num_site, sites[i]))
+ prog_bar.close()
+
+ off_med = np.nanmedian(insar_obs - gps_obs)
+ print(f'median offset between InSAR and GPS [before common referencing]: {off_med:.2f} cm/year')
+
+ # reference site
+ if ref_gps_site:
+ print(f'referencing both InSAR and GPS data to site: {ref_gps_site}')
+ ref_ind = sites.tolist().index(ref_gps_site)
+ gps_obs -= gps_obs[ref_ind]
+ insar_obs -= insar_obs[ref_ind]
+
+ # remove NaN value
+ print('removing sites with NaN values in GPS or {}'.format(xname))
+ flag = np.multiply(~np.isnan(insar_obs), ~np.isnan(gps_obs))
+ if vlim is not None:
+ print('pruning sites with value range: {} {}'.format(vlim, disp_unit))
+ flag *= gps_obs >= vlim[0]
+ flag *= gps_obs <= vlim[1]
+ flag *= insar_obs >= vlim[0]
+ flag *= insar_obs <= vlim[1]
+
+ gps_obs = gps_obs[flag]
+ insar_obs = insar_obs[flag]
+ sites = sites[flag]
+
+ # stats
+ print('GPS min/max: {:.2f} / {:.2f}'.format(np.nanmin(gps_obs), np.nanmax(gps_obs)))
+ print('InSAR min/max: {:.2f} / {:.2f}'.format(np.nanmin(insar_obs), np.nanmax(insar_obs)))
+
+ rmse = np.sqrt(np.sum((insar_obs - gps_obs)**2) / (gps_obs.size - 1))
+ r2 = stats.linregress(insar_obs, gps_obs)[2]
+ print('RMSE = {:.2f} {}'.format(rmse, disp_unit))
+ print('R^2 = {:.2f}'.format(r2))
+
+ # preliminary outlier detection
+ diff_mad = ut0.median_abs_deviation(abs(insar_obs - gps_obs), center=0)
+ print(f'Preliminary outliers detection: abs(InSAR - GNSS) > med abs dev ({diff_mad:.2f}) * {cutoff}')
+ print('Site: InSAR GNSS')
+ for site_name, insar_val, gps_val in zip(sites, insar_obs, gps_obs):
+ if abs(insar_val - gps_val) > diff_mad * cutoff:
+ print(f'{site_name:s}: {insar_val:5.1f}, {gps_val:5.1f} {disp_unit}')
+
+ # plot
+ if display:
+ plt.rcParams.update({'font.size': 12})
+ if vlim is None:
+ vlim = [np.min(insar_obs), np.max(insar_obs)]
+ vbuffer = (vlim[1] - vlim[0]) * 0.2
+ vlim = [vlim[0] - vbuffer, vlim[1] + vbuffer]
+
+ fig, ax = plt.subplots(figsize=fig_size)
+ ax.plot((vlim[0], vlim[1]), (vlim[0], vlim[1]), 'k--')
+ ax.plot(insar_obs, gps_obs, '.', ms=15)
+
+ # axis format
+ ax.set_xlim(vlim)
+ ax.set_ylim(vlim)
+ ax.set_xlabel(f'{xname} [{disp_unit}]')
+ ax.set_ylabel(f'GNSS [{disp_unit}]')
+ ax.set_aspect('equal', 'box')
+ fig.tight_layout()
+
+ # output
+ out_fig = '{}_vs_gps_scatter.pdf'.format(xname.lower())
+ plt.savefig(out_fig, bbox_inches='tight', transparent=True, dpi=300)
+ print('save figure to file', out_fig)
+ plt.show()
+
+ return sites, insar_obs, gps_obs
+
+
def plot_colorbar(inps, im, cax):
# extend
if not inps.cbar_ext:
=====================================
mintpy/utils/readfile.py
=====================================
@@ -395,26 +395,35 @@ def read_hdf5_file(fname, datasetName=None, box=None, xstep=1, ystep=1, print_ms
slice_flag[date_list.index(d)] = True
# read data
+ num_slice = np.sum(slice_flag)
+ inds = np.where(slice_flag)[0].tolist()
+
if xstep * ystep == 1:
- data = ds[:,
- box[1]:box[3],
- box[0]:box[2]][slice_flag]
+ if num_slice / slice_flag.size < 0.05:
+ # single indexing if only a small fraction is read
+ data = np.zeros((num_slice, ysize, xsize), dtype=ds.dtype)
+ for i, ind in enumerate(inds):
+ data[i] = ds[ind,
+ box[1]:box[3],
+ box[0]:box[2]]
+ else:
+ data = ds[:,
+ box[1]:box[3],
+ box[0]:box[2]][slice_flag]
else:
# sampling / nearest interplation in y/xstep
# use for loop to save memory
- num_slice = np.sum(slice_flag)
data = np.zeros((num_slice, ysize, xsize), ds.dtype)
- inds = np.where(slice_flag)[0]
- for i in range(num_slice):
+ for i, ind in enumerate(inds):
# print out msg
if print_msg:
sys.stdout.write('\r' + f'reading 2D slices {i+1}/{num_slice}...')
sys.stdout.flush()
# read and index
- d2 = ds[inds[i],
+ d2 = ds[ind,
box[1]:box[3],
box[0]:box[2]]
d2 = d2[int(ystep/2)::ystep,
=====================================
mintpy/version.py
=====================================
@@ -10,6 +10,7 @@ import collections
###########################################################################
Tag = collections.namedtuple('Tag', 'version date')
release_history = (
+ Tag('1.4.1', '2022-08-15'),
Tag('1.4.0', '2022-08-04'),
Tag('1.3.3', '2022-04-14'),
Tag('1.3.2', '2021-11-21'),
@@ -34,19 +35,19 @@ release_history = (
release_version = release_history[0].version
release_date = release_history[0].date
-def get_version_info(version='v{}'.format(release_version), date=release_date):
+def get_version_info():
"""Grab version and date of the latest commit from a git repository"""
# go to the repository directory
dir_orig = os.getcwd()
os.chdir(os.path.dirname(os.path.dirname(__file__)))
- # grab git info into string
try:
+ # grab from git cmd
cmd = "git describe --tags"
version = subprocess.check_output(cmd.split(), stderr=subprocess.DEVNULL)
- version = version.decode('utf-8').strip()
+ version = version.decode('utf-8').strip()[1:]
- #if there are new commits after the latest release
+ # if there are new commits after the latest release
if '-' in version:
version, num_commit = version.split('-')[:2]
version += '-{}'.format(num_commit)
@@ -54,8 +55,11 @@ def get_version_info(version='v{}'.format(release_version), date=release_date):
cmd = "git log -1 --date=short --format=%cd"
date = subprocess.check_output(cmd.split(), stderr=subprocess.DEVNULL)
date = date.decode('utf-8').strip()
+
except:
- pass
+ # use the latest release version/date
+ version = release_version
+ date = release_date
# go back to the original directory
os.chdir(dir_orig)
@@ -64,9 +68,9 @@ def get_version_info(version='v{}'.format(release_version), date=release_date):
###########################################################################
-version_num, version_date = get_version_info()
+version, version_date = get_version_info()
version_description = """MintPy version {v}, date {d}""".format(
- v=version_num,
+ v=version,
d=version_date,
)
@@ -87,7 +91,7 @@ ___________________________________________________________
Miami InSAR Time-series software in Python \______/
MintPy {v}, {d}
___________________________________________________________
-""".format(v=version_num, d=version_date)
+""".format(v=version, d=version_date)
website = 'https://github.com/insarlab/MintPy'
=====================================
mintpy/view.py
=====================================
@@ -407,11 +407,8 @@ def update_data_with_plot_inps(data, metadata, inps):
inps.ref_yx[1] + 1, inps.ref_yx[0] + 1]
# update ref_y/x to subset
- try:
- ref_y = inps.ref_yx[0] - inps.pix_box[1]
- ref_x = inps.ref_yx[1] - inps.pix_box[0]
- except:
- pass
+ ref_y = inps.ref_yx[0] - inps.pix_box[1]
+ ref_x = inps.ref_yx[1] - inps.pix_box[0]
# update ref_y/x for multilooking
if inps.multilook_num > 1:
@@ -567,8 +564,8 @@ def plot_slice(ax, data, metadata, inps=None):
y, x = coord.geo2radar(ref_site_lalo[0], ref_site_lalo[1])[0:2]
ref_data = data[y - inps.pix_box[1], x - inps.pix_box[0]]
data -= ref_data
- vprint(('referencing InSAR data to the pixel nearest to '
- f'GPS station: {inps.ref_gps_site} at {ref_site_lalo} '
+ vprint(('referencing InSAR data to the pixel nearest to GNSS station: '
+ f'{inps.ref_gps_site} at [{ref_site_lalo[0]:.6f}, {ref_site_lalo[1]:.6f}] '
f'by substrating {ref_data:.3f} {inps.disp_unit}'))
# do not show the original InSAR reference point
inps.disp_ref_pixel = False
@@ -1107,6 +1104,10 @@ def read_data4figure(i_start, i_end, inps, metadata):
xstep=inps.multilook_num,
ystep=inps.multilook_num,
print_msg=False)[0]
+ # reference pixel info in unwrapPhase
+ if inps.dset[i].startswith('unwrapPhase') and inps.file_ref_yx:
+ ref_y, ref_x = inps.file_ref_yx
+ d[d!=0] -= d[ref_y, ref_x]
data[i - i_start, :, :] = d
prog_bar.update(i - i_start + 1, suffix=inps.dset[i].split('/')[-1])
prog_bar.close()
=====================================
requirements.txt
=====================================
@@ -18,11 +18,9 @@ numpy
pyaps3>=0.3
pykml>=0.2
pyproj
+pyresample
pysolid
scikit-image
scipy
-# for ARIA, FRInGE, HyP3, GMTSAR
+# for ISCE, ARIA, FRInGE, HyP3, GMTSAR
# gdal>=3
-# for pyresample
-pyresample
-openmp
=====================================
setup.py
=====================================
@@ -5,26 +5,20 @@
# Author: Zhang Yunjun, Nov 2020 #
############################################################
-
+import os
+import sys
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
+# Grab version and description from version.py
+# link: https://stackoverflow.com/questions/53648900
+sys.path.append(os.path.dirname(__file__))
+from mintpy.version import version, description
-# Grab from README file: long_description
+# Grab long_description from README.md
with open("docs/README.md", "r") as f:
long_description = f.read()
-# Grab from version.py file: version and description
-with open("mintpy/version.py", "r") as f:
- lines = f.readlines()
- # version
- line = [line for line in lines if line.strip().startswith("Tag(")][0].strip()
- version = line.replace("'",'"').split('"')[1]
- # description
- line = [line for line in lines if line.startswith("description")][0].strip()
- description = line.replace("'",'"').split('"')[1]
-
-
setup(
name="mintpy",
version=version,
@@ -70,11 +64,10 @@ setup(
"pyaps3>=0.3",
"pykml>=0.2",
"pyproj",
+ "pyresample", # pip installed version does not work
"setuptools",
"scikit-image",
"scipy",
- "pyresample",
- # "openmp",
],
extras_require={
"cli": ["argcomplete"],
View it on GitLab: https://salsa.debian.org/debian-gis-team/mintpy/-/commit/2aede91191d1b5c6837d2d9eb14aa86c9b83853a
--
View it on GitLab: https://salsa.debian.org/debian-gis-team/mintpy/-/commit/2aede91191d1b5c6837d2d9eb14aa86c9b83853a
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