def plot_bar4date_rms(inps):
inps.figName = os.path.splitext(inps.rmsFile)[0] + '.pdf'
if ut.update_file(inps.figName,
[inps.exDateFile, inps.refDateFile, inps.template_file],
check_readable=False):
if inps.fig_size:
fig = plt.figure(figsize=inps.fig_size)
else:
fig = plt.figure()
ax = fig.add_subplot(111)
font_size = 12
dates, datevector = ptime.date_list2vector(inps.dateList)
try:
bar_width = ut.most_common(np.diff(dates).tolist()) * 3 / 4
except:
bar_width = np.min(np.diff(dates).tolist()) * 3 / 4
x_list = [i - bar_width / 2 for i in dates]
inps.rmsList = [i * 1000. for i in inps.rmsList]
min_rms = inps.min_rms * 1000.
# Plot all dates
ax.bar(x_list, inps.rmsList, bar_width.days)
# Plot reference date
ax.bar(x_list[inps.refDateIndex],
inps.rmsList[inps.refDateIndex],
bar_width.days,
label='Reference date')
# Plot exclude dates
if inps.exIdxList:
ex_x_list = [x_list[i] for i in inps.exIdxList]
inps.exRmsList = [inps.rmsList[i] for i in inps.exIdxList]
ax.bar(ex_x_list,
inps.exRmsList,
bar_width.days,
color='darkgray',
label='Exclude date(s)')
# Plot min_rms line
ax, xmin, xmax = pp.auto_adjust_xaxis_date(
ax, datevector, font_size, every_year=inps.tick_year_num)
ax.plot(np.array([xmin, xmax]), np.array([min_rms, min_rms]), '--k')
# axis format
ax = pp.auto_adjust_yaxis(ax,
inps.rmsList + [min_rms],
font_size,
ymin=0.0)
ax.set_xlabel('Time [years]', fontsize=font_size)
ax.set_ylabel('Root Mean Square [mm]', fontsize=font_size)
ax.yaxis.set_ticks_position('both')
ax.tick_params(labelsize=font_size)
plt.legend(fontsize=font_size)
# save figure
fig.savefig(inps.figName, bbox_inches='tight', transparent=True)
print('save figure to file: ' + inps.figName)
return inps
def check_exist_grib_file(gfile_list, print_msg=True):
"""Check input list of grib files, and return the existing ones with right size."""
gfile_exist = ut.get_file_list(gfile_list)
if gfile_exist:
file_sizes = [
os.path.getsize(i) for i in gfile_exist
if os.path.getsize(i) > 10e6
]
if file_sizes:
comm_size = ut.most_common([i for i in file_sizes])
if print_msg:
print('common file size: {} bytes'.format(comm_size))
print('number of grib files existed : {}'.format(
len(gfile_exist)))
gfile_corrupt = []
for gfile in gfile_exist:
if os.path.getsize(gfile) < comm_size * 0.9:
gfile_corrupt.append(gfile)
else:
gfile_corrupt = gfile_exist
if gfile_corrupt:
if print_msg:
print(
'------------------------------------------------------------------------------'
)
print(
'corrupted grib files detected! Delete them and re-download...'
)
print('number of grib files corrupted : {}'.format(
len(gfile_corrupt)))
for i in gfile_corrupt:
rmCmd = 'rm ' + i
print(rmCmd)
os.system(rmCmd)
gfile_exist.remove(i)
if print_msg:
print(
'------------------------------------------------------------------------------'
)
return gfile_exist
def plot_coherence_history(ax, date12List, cohList, plot_dict={}):
"""Plot min/max Coherence of all interferograms for each date"""
# Figure Setting
if not 'fontsize' in plot_dict.keys(): plot_dict['fontsize'] = 12
if not 'linewidth' in plot_dict.keys(): plot_dict['linewidth'] = 2
if not 'markercolor' in plot_dict.keys(): plot_dict['markercolor'] = 'orange'
if not 'markersize' in plot_dict.keys(): plot_dict['markersize'] = 16
if not 'disp_title' in plot_dict.keys(): plot_dict['disp_title'] = True
if not 'every_year' in plot_dict.keys(): plot_dict['every_year'] = 1
# Get date list
date12List = ptime.yyyymmdd_date12(date12List)
m_dates = [date12.split('_')[0] for date12 in date12List]
s_dates = [date12.split('_')[1] for date12 in date12List]
dateList = sorted(ptime.yyyymmdd(list(set(m_dates + s_dates))))
dates, datevector = ptime.date_list2vector(dateList)
bar_width = ut.most_common(np.diff(dates).tolist())*3/4
x_list = [i-bar_width/2 for i in dates]
coh_mat = pnet.coherence_matrix(date12List, cohList)
ax.bar(x_list, np.nanmax(coh_mat, axis=0), bar_width.days, label='Max Coherence')
ax.bar(x_list, np.nanmin(coh_mat, axis=0), bar_width.days, label='Min Coherence')
if plot_dict['disp_title']:
ax.set_title('Coherence History of All Related Interferograms')
ax = auto_adjust_xaxis_date(ax, datevector, plot_dict['fontsize'],
every_year=plot_dict['every_year'])[0]
ax.set_ylim([0.0, 1.0])
ax.set_xlabel('Time [years]', fontsize=plot_dict['fontsize'])
ax.set_ylabel('Coherence', fontsize=plot_dict['fontsize'])
ax.legend(loc='lower right')
return ax
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=True,
ylabel=r'Residual Phase $\hat \phi_{resid}$ 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)
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='RMS threshold')
# 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 dload_grib_pyaps(date_list, hour, trop_model='ECMWF', weather_dir='./'):
"""Download weather re-analysis grib files using PyAPS
Inputs:
date_list : list of string in YYYYMMDD format
hour : string in HH:MM or HH format
trop_model : string,
weather_dir : string,
Output:
grib_file_list : list of string
"""
print(
'*' * 50 +
'\nDownloading weather model data using PyAPS (Jolivet et al., 2011, GRL) ...'
)
# Grib data directory
grib_dir = weather_dir + '/' + trop_model
if not os.path.isdir(grib_dir):
os.makedirs(grib_dir)
print('making directory: ' + grib_dir)
# Date list to grib file list
grib_file_list = date_list2grib_file(date_list, hour, trop_model, grib_dir)
# Get date list to download (skip already downloaded files)
grib_file_existed = ut.get_file_list(grib_file_list)
if grib_file_existed:
grib_filesize_digit = ut.most_common(
[len(str(os.path.getsize(i))) for i in grib_file_existed])
grib_filesize_max2 = ut.most_common(
[str(os.path.getsize(i))[0:2] for i in grib_file_existed])
grib_file_corrupted = [
i for i in grib_file_existed
if (len(str(os.path.getsize(i))) != grib_filesize_digit
or str(os.path.getsize(i))[0:2] != grib_filesize_max2)
]
print('file size mode: %se%d bytes' %
(grib_filesize_max2, grib_filesize_digit - 2))
print('number of grib files existed : %d' % len(grib_file_existed))
if grib_file_corrupted:
print(
'------------------------------------------------------------------------------'
)
print(
'corrupted grib files detected! Delete them and re-download...'
)
print('number of grib files corrupted : %d' %
len(grib_file_corrupted))
for i in grib_file_corrupted:
rmCmd = 'rm ' + i
print(rmCmd)
os.system(rmCmd)
grib_file_existed.remove(i)
print(
'------------------------------------------------------------------------------'
)
grib_file2download = sorted(
list(set(grib_file_list) - set(grib_file_existed)))
date_list2download = [
str(re.findall('\d{8}', i)[0]) for i in grib_file2download
]
print('number of grib files to download: %d' % len(date_list2download))
print(
'------------------------------------------------------------------------------\n'
)
# Download grib file using PyAPS
if trop_model == 'ECMWF': pa.ECMWFdload(date_list2download, hour, grib_dir)
elif trop_model == 'MERRA':
pa.MERRAdload(date_list2download, hour, grib_dir)
elif trop_model == 'NARR':
pa.NARRdload(date_list2download, hour, grib_dir)
elif trop_model == 'ERA':
pa.ERAdload(date_list2download, hour, grib_dir)
elif trop_model == 'MERRA1':
pa.MERRA1dload(date_list2download, hour, grib_dir)
return grib_file_list