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plot_network: check nan values of coh/bperp in kept ifgrams (#1108)
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+ `plot_network`: raise error for pairs with invalid coh/Bperp value, probably due to upstream insar processing issues. This is rarely seen, as reported in the [google group](https://groups.google.com/g/mintpy/c/l1oOaHhqa-o).

+ `utils.plot.auto_adjust_yaxis`: ignore nan value in input data
  - use `np.nanmin/nanmax()` to replace `min/max()` to ignore nan values in the input data list
  - light refactoring to improve the readability

+ `utils.plot.py`: use consistent parameters/returns comment styles for all functions
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@yunjunz
yunjunz committed Oct 13, 2023
1 parent caac2df commit 2adeb67
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Showing 2 changed files with 104 additions and 98 deletions.
13 changes: 13 additions & 0 deletions src/mintpy/plot_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ def read_network_info(inps):
inps.date12List = stack_obj.get_date12_list(dropIfgram=False)
inps.dateList = stack_obj.get_date_list(dropIfgram=False)
inps.pbaseList = stack_obj.get_perp_baseline_timeseries(dropIfgram=False)
pbase12List = stack_obj.pbaseIfgram

if inps.dsetName in readfile.get_dataset_list(inps.file):
inps.cohList = ut.spatial_average(
Expand Down Expand Up @@ -93,6 +94,18 @@ def read_network_info(inps):
if len(inps.dateList_drop) > 0:
print(inps.dateList_drop)

# check: pairs with invalid coherence / Bperp values (nan)
# due to processing issues in the upstream insar processing
flag_nan = np.logical_or(np.isnan(inps.cohList), np.isnan(pbase12List))
if np.sum(flag_nan) > 0:
date12_nan = np.array(inps.date12List)[flag_nan].tolist()
date12_nan = [x for x in date12_nan if x not in inps.date12List_drop]
if len(date12_nan) > 0:
msg = 'Invalid NaN value found in the following kept pairs for Bperp or coherence! '
msg += '\n\tThey likely have issues, check them and maybe exclude them in your network.'
msg += f'\n\t{date12_nan}'
raise ValueError(msg)

return inps


Expand Down
189 changes: 91 additions & 98 deletions src/mintpy/utils/plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -487,30 +487,32 @@ def auto_adjust_xaxis_date(ax, datevector, fontsize=12, every_year=None, buffer_
return ax, xmin, xmax


def auto_adjust_yaxis(ax, dataList, fontsize=12, ymin=None, ymax=None):
"""Adjust Y axis
Input:
ax : matplot figure axes object
dataList : list of float, value in y axis
fontsize : float, font size
ymin : float, lower y axis limit
ymax : float, upper y axis limit
Output:
ax
def auto_adjust_yaxis(ax, data_list, fontsize=12, ymin=None, ymax=None):
"""Adjust Y axis lower/upper limit.
Parameters: ax - matplotlib figure axes object
data_list - list(float), Y-axis values
fontsize - float, font size
ymin/max - float, lower/upper Y-axis limit
Returns: ax - matplotlib figure axes object
"""
# Min/Max
dataRange = max(dataList) - min(dataList)
if ymin is None:
ymin = min(dataList) - 0.1*dataRange
if ymax is None:
ymax = max(dataList) + 0.1*dataRange
# check
if np.all(np.isnan(data_list)):
raise ValueError('All NaN values detected in the input data_list!')

# calc ymin/max
dmin = np.nanmin(data_list)
dmax = np.nanmax(data_list)
drange = dmax - dmin
ymin = ymin if ymin is not None else dmin - 0.1 * drange
ymax = ymax if ymax is not None else dmax + 0.1 * drange
# set ymin/max
ax.set_ylim([ymin, ymax])
# Tick/Label setting
#xticklabels = plt.getp(ax, 'xticklabels')
#yticklabels = plt.getp(ax, 'yticklabels')
#plt.setp(yticklabels, 'color', 'k', fontsize=fontsize)
#plt.setp(xticklabels, 'color', 'k', fontsize=fontsize)

return ax


Expand Down Expand Up @@ -558,23 +560,20 @@ def plot_coherence_history(ax, date12List, cohList, p_dict={}):

def plot_network(ax, date12List, dateList, pbaseList, p_dict={}, date12List_drop=[], print_msg=True):
"""Plot Temporal-Perp baseline Network
Inputs
ax : matplotlib axes object
date12List : list of string for date12 in YYYYMMDD_YYYYMMDD format
dateList : list of string, for date in YYYYMMDD format
pbaseList : list of float, perp baseline, len=number of acquisition
p_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
cohList : list of float, coherence value of each interferogram, len = number of ifgrams
colormap : string, colormap name
disp_title : bool, show figure title or not, default: True
disp_drop: bool, show dropped interferograms or not, default: True
Output
ax : matplotlib axes object
Parameters: ax - matplotlib axes object
date12List - list(str) for date12 in YYYYMMDD_YYYYMMDD format
dateList - list(str), for date in YYYYMMDD format
pbaseList - list(float), perp baseline, len=number of acquisition
p_dict - dictionary with the following items:
fontsize
linewidth
markercolor
markersize
cohList - list(float), coherence value of each interferogram, len = number of ifgrams
colormap - str, colormap name
disp_title - bool, show figure title or not, default: True
disp_drop - bool, show dropped interferograms or not, default: True
Returns: ax - matplotlib axes object
"""

# Figure Setting
Expand Down Expand Up @@ -731,21 +730,19 @@ def plot_network(ax, date12List, dateList, pbaseList, p_dict={}, date12List_drop

def plot_perp_baseline_hist(ax, dateList, pbaseList, p_dict={}, dateList_drop=[]):
""" Plot Perpendicular Spatial Baseline History
Inputs
ax : matplotlib axes object
dateList : list of string, date in YYYYMMDD format
pbaseList : list of float, perp baseline
p_dict : dictionary with the following items:
fontsize
linewidth
markercolor
markersize
disp_title : bool, show figure title or not, default: True
every_year : int, number of years for the major tick on xaxis
dateList_drop : list of string, date dropped in YYYYMMDD format
e.g. ['20080711', '20081011']
Output:
ax : matplotlib axes object
Parameters: ax - matplotlib axes object
dateList - list(str), date in YYYYMMDD format
pbaseList - list(float), perp baseline
p_dict - dictionary with the following items:
fontsize
linewidth
markercolor
markersize
disp_title : bool, show figure title or not, default: True
every_year : int, number of years for the major tick on xaxis
dateList_drop - list(str), date dropped in YYYYMMDD format
e.g. ['20080711', '20081011']
Returns: ax - matplotlib axes object
"""
# Figure Setting
if 'fontsize' not in p_dict.keys(): p_dict['fontsize'] = 12
Expand Down Expand Up @@ -851,7 +848,7 @@ def plot_rotate_diag_coherence_matrix(ax, coh_list, date12_list, date12_list_dro
def plot_coherence_matrix(ax, date12List, cohList, date12List_drop=[], p_dict={}):
"""Plot Coherence Matrix of input network
if date12List_drop is not empty, plot KEPT pairs in the upper triangle and
ALL pairs in the lower triangle.
ALL pairs in the lower triangle.
Parameters: ax : matplotlib.pyplot.Axes,
date12List : list of date12 in YYYYMMDD_YYYYMMDD format
cohList : list of float, coherence value
Expand Down Expand Up @@ -1603,14 +1600,12 @@ def check_disp_unit_and_wrap(metadata, disp_unit=None, wrap=False, wrap_range=[-

def scale_data2disp_unit(data=None, metadata=dict(), disp_unit=None):
"""Scale data based on data unit and display unit
Inputs:
data : 2D np.array
metadata : dictionary, meta data
disp_unit : str, display unit
Outputs:
data : 2D np.array, data after scaling
disp_unit : str, display unit
Default data file units in MintPy are: m, m/yr, radian, 1
Parameters: data - 2D np.ndarray
metadata - dictionary, meta data
disp_unit - str, display unit
Returns: data - 2D np.ndarray, data after scaling
disp_unit - str, display unit
Default data file units in MintPy are: m, m/yr, radian, 1
"""
if not metadata:
metadata['UNIT'] = 'm'
Expand Down Expand Up @@ -1710,18 +1705,16 @@ def scale_data2disp_unit(data=None, metadata=dict(), disp_unit=None):
def scale_data4disp_unit_and_rewrap(data, metadata, disp_unit=None, wrap=False, wrap_range=[-1.*np.pi, np.pi],
print_msg=True):
"""Scale 2D matrix value according to display unit and re-wrapping flag
Inputs:
data - 2D np.array
metadata - dict, including the following attributes:
UNIT
FILE_TYPE
WAVELENGTH
disp_unit - string, optional
wrap - bool, optional
Outputs:
data
disp_unit
wrap
Parameters: data - 2D np.ndarray
metadata - dict, including the following attributes:
UNIT
FILE_TYPE
WAVELENGTH
disp_unit - str, optional
wrap - bool, optional
Returns: data - 2D np.ndarray, scaled data matrix
disp_unit - str
wrap - bool
"""
if not disp_unit:
disp_unit, wrap = check_disp_unit_and_wrap(
Expand Down Expand Up @@ -1752,12 +1745,12 @@ def scale_data4disp_unit_and_rewrap(data, metadata, disp_unit=None, wrap=False,
def read_mask(fname, mask_file=None, datasetName=None, box=None, xstep=1, ystep=1,
vmin=None, vmax=None, print_msg=True):
"""Find and read mask for input data file fname
Parameters: fname : string, data file name/path
mask_file : string, optional, mask file name
datasetName : string, optional, dataset name for HDFEOS file type
box : tuple of 4 int, for reading part of data
Returns: mask : 2D np.ndarray in bool, mask data
mask_file : string, file name of mask data
Parameters: fname - str, data file name/path
mask_file - str, optional, mask file name
datasetName - str, optional, dataset name for HDFEOS file type
box - tuple of 4 int, for reading part of data
Returns: mask - 2D np.ndarray in bool, mask data
mask_file - str, file name of mask data
"""
vprint = print if print_msg else lambda *args, **kwargs: None
atr = readfile.read_attribute(fname)
Expand Down Expand Up @@ -1918,15 +1911,15 @@ def read_dem(dem_file, pix_box=None, geo_box=None, print_msg=True):

def prep_dem_background(dem, inps, print_msg=True):
"""Prepare to plot DEM on background
Parameters: dem : 2D np.int16 matrix, dem data
inps : Namespace with the following 4 items:
'disp_dem_shade' : bool, True/False
'disp_dem_contour' : bool, True/False
'dem_contour_step' : float, 200.0
'dem_contour_smooth': float, 3.0
Returns: dem_shade : 3D np.array in size of (length, width, 4)
dem_contour : 2D np.array in size of (length, width)
dem_contour_seq : 1D np.array
Parameters: dem - 2D np.int16 matrix, dem data
inps - Namespace with the following 4 items:
'disp_dem_shade' : bool, True/False
'disp_dem_contour' : bool, True/False
'dem_contour_step' : float, 200.0
'dem_contour_smooth': float, 3.0
Returns: dem_shade - 3D np.ndarray in size of (length, width, 4)
dem_contour - 2D np.ndarray in size of (length, width)
dem_contour_seq - 1D np.ndarray
Examples:
from mintpy.cli import view
from mintpy.utils import plot as pp
Expand Down Expand Up @@ -2002,19 +1995,19 @@ def prep_dem_background(dem, inps, print_msg=True):
def plot_dem_background(ax, geo_box=None, dem_shade=None, dem_contour=None, dem_contour_seq=None,
dem=None, inps=None, print_msg=True):
"""Plot DEM as background.
Parameters: ax : matplotlib.pyplot.Axes or BasemapExt object
geo_box : tuple of 4 float in order of (E, N, W, S), geo bounding box
dem_shade : 3D np.array in size of (length, width, 4)
dem_contour : 2D np.array in size of (length, width)
dem_contour_seq : 1D np.array
dem : 2D np.array of DEM data
inps : Namespace with the following 4 items:
'disp_dem_shade' : bool, True/False
'disp_dem_contour' : bool, True/False
'dem_contour_step' : float, 200.0
'dem_contour_smooth': float, 3.0
'pix_box' : 4-tuple of int, (x0, y0, x1, y1)
Returns: ax : matplotlib.pyplot.Axes or BasemapExt object
Parameters: ax - matplotlib.pyplot.Axes or BasemapExt object
geo_box - tuple of 4 float in order of (E, N, W, S), geo bounding box
dem_shade - 3D np.ndarray in size of (length, width, 4)
dem_contour - 2D np.ndarray in size of (length, width)
dem_contour_seq - 1D np.ndarray
dem - 2D np.array of DEM data
inps - Namespace with the following 4 items:
'disp_dem_shade' : bool, True/False
'disp_dem_contour' : bool, True/False
'dem_contour_step' : float, 200.0
'dem_contour_smooth': float, 3.0
'pix_box' : 4-tuple of int, (x0, y0, x1, y1)
Returns: ax - matplotlib.pyplot.Axes or BasemapExt object
Examples: ax = pp.plot_dem_background(ax, geo_box=inps.geo_box, dem=dem, inps=inps)
ax = pp.plot_dem_background(ax, geo_box=None, inps=inps,
dem_shade=dem_shade,
Expand Down

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