def set_initial_map():
global d_v, h5, k, dateList, inps, data_lim
d_v = h5['timeseries'][inps.epoch_num][:] * inps.unit_fac
# Initial Map
print(str(dateList))
d_v = readfile.read(inps.timeseries_file, datasetName=dateList[inps.epoch_num])[0] * inps.unit_fac
#d_v = h5[k].get(dateList[inps.epoch_num])[:]*inps.unit_fac
if inps.ref_date:
inps.ref_d_v = readfile.read(inps.timeseries_file, datasetName=inps.ref_date)[0]*inps.unit_fac
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
data_lim = [np.nanmin(d_v), np.nanmax(d_v)]
if not inps.ylim_mat:
inps.ylim_mat = data_lim
print(('Initial data range: '+str(data_lim)))
print(('Display data range: '+str(inps.ylim_mat)))
print(('Initial data range: ' + str(data_lim)))
print(('Display data range: ' + str(inps.ylim)))
def time_slider_update(val):
'''Update Displacement Map using Slider'''
global inps.tims, tslider, ax_v, d_v, inps, img, fig_v, h5, k, dateList
timein = tslider.val
idx_nearest = np.argmin(np.abs(np.array(inps.tims) - timein))
ax_v.set_title('N = %d, Time = %s' % (idx_nearest, inps.dates[idx_nearest].strftime('%Y-%m-%d')))
d_v = h5[k][idx_nearest][:] * inps.unit_fac
if inps.ref_date:
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
img.set_data(d_v)
fig_v.canvas.draw()
def time_slider_update(val):
"""Update Displacement Map using Slider"""
timein = tslider.val
idx_nearest = np.argmin(np.abs(np.array(inps.yearList) - timein))
ax_v.set_title(
'N = %d, Time = %s' %
(idx_nearest, inps.dates[idx_nearest].strftime('%Y-%m-%d')))
d_v = h5[dateList[idx_nearest]][:] * inps.unit_fac
if inps.ref_date:
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
img.set_data(d_v)
fig_v.canvas.draw()
def main(iargs=None):
# Actual code.
inps = cmd_line_parse(iargs)
# Time Series Info
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
print('input file is ' + k + ': ' + inps.timeseries_file)
if not k in ['timeseries', 'GIANT_TS']:
raise ValueError('Only timeseries file is supported!')
obj = timeseries(inps.timeseries_file)
obj.open()
h5 = h5py.File(inps.timeseries_file, 'r')
if k in ['GIANT_TS']:
dateList = [
dt.fromordinal(int(i)).strftime('%Y%m%d')
for i in h5['dates'][:].tolist()
]
else:
dateList = obj.dateList
date_num = len(dateList)
inps.dates, inps.yearList = ptime.date_list2vector(dateList)
# Read exclude dates
if inps.ex_date_list:
input_ex_date = list(inps.ex_date_list)
inps.ex_date_list = []
if input_ex_date:
for ex_date in input_ex_date:
if os.path.isfile(ex_date):
ex_date = ptime.read_date_list(ex_date)
else:
ex_date = [ptime.yyyymmdd(ex_date)]
inps.ex_date_list += list(
set(ex_date) - set(inps.ex_date_list))
# delete dates not existed in input file
inps.ex_date_list = sorted(
list(set(inps.ex_date_list).intersection(dateList)))
inps.ex_dates = ptime.date_list2vector(inps.ex_date_list)[0]
inps.ex_idx_list = sorted(
[dateList.index(i) for i in inps.ex_date_list])
print('exclude date:' + str(inps.ex_date_list))
# Zero displacement for 1st acquisition
if inps.zero_first:
if inps.ex_date_list:
inps.zero_idx = min(
list(set(range(date_num)) - set(inps.ex_idx_list)))
else:
inps.zero_idx = 0
# File Size
length = int(atr['LENGTH'])
width = int(atr['WIDTH'])
print('data size in [y0,y1,x0,x1]: [%d, %d, %d, %d]' %
(0, length, 0, width))
try:
ullon = float(atr['X_FIRST'])
ullat = float(atr['Y_FIRST'])
lon_step = float(atr['X_STEP'])
lat_step = float(atr['Y_STEP'])
lrlon = ullon + width * lon_step
lrlat = ullat + length * lat_step
print('data size in [lat0,lat1,lon0,lon1]: [%.4f, %.4f, %.4f, %.4f]' %
(lrlat, ullat, ullon, lrlon))
except:
pass
# Initial Pixel Coord
if inps.lalo and 'Y_FIRST' in atr.keys():
y = int((inps.lalo[0] - ullat) / lat_step + 0.5)
x = int((inps.lalo[1] - ullon) / lon_step + 0.5)
inps.yx = [y, x]
if inps.ref_lalo and 'Y_FIRST' in atr.keys():
y = int((inps.ref_lalo[0] - ullat) / lat_step + 0.5)
x = int((inps.ref_lalo[1] - ullon) / lon_step + 0.5)
inps.ref_yx = [y, x]
# Display Unit
if inps.disp_unit == 'cm':
inps.unit_fac = 100.0
elif inps.disp_unit == 'm':
inps.unit_fac = 1.0
elif inps.disp_unit == 'dm':
inps.unit_fac = 10.0
elif inps.disp_unit == 'mm':
inps.unit_fac = 1000.0
elif inps.disp_unit == 'km':
inps.unit_fac = 0.001
else:
raise ValueError('Un-recognized unit: ' + inps.disp_unit)
if k in ['GIANT_TS']:
print('data unit: mm')
inps.unit_fac *= 0.001
else:
print('data unit: m')
print('display unit: ' + inps.disp_unit)
# Flip up-down / left-right
if inps.auto_flip:
inps.flip_lr, inps.flip_ud = pp.auto_flip_direction(atr)
else:
inps.flip_ud = False
inps.left_lr = False
# Mask file
if not inps.mask_file:
if os.path.basename(inps.timeseries_file).startswith('geo_'):
file_list = ['geo_maskTempCoh.h5']
else:
file_list = ['maskTempCoh.h5', 'mask.h5']
try:
inps.mask_file = ut.get_file_list(file_list)[0]
except:
inps.mask_file = None
try:
mask = readfile.read(inps.mask_file, datasetName='mask')[0]
mask[mask != 0] = 1
print('load mask from file: ' + inps.mask_file)
except:
mask = None
print('No mask used.')
# Initial Map
d_v = readfile.read(
inps.timeseries_file,
datasetName=dateList[inps.epoch_num])[0] * inps.unit_fac
if inps.ref_date:
inps.ref_d_v = readfile.read(
inps.timeseries_file, datasetName=inps.ref_date)[0] * inps.unit_fac
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
data_lim = [np.nanmin(d_v), np.nanmax(d_v)]
if not inps.ylim_mat:
inps.ylim_mat = data_lim
print('Initial data range: ' + str(data_lim))
print('Display data range: ' + str(inps.ylim_mat))
# Fig 1 - Cumulative Displacement Map
if not inps.disp_fig:
plt.switch_backend('Agg')
fig_v = plt.figure('Cumulative Displacement')
# Axes 1
#ax_v = fig_v.add_subplot(111)
# ax_v.set_position([0.125,0.25,0.75,0.65])
# This works on OSX. Original worked on Linux.
# rect[left, bottom, width, height]
ax_v = fig_v.add_axes([0.125, 0.25, 0.75, 0.65])
if inps.dem_file:
dem = readfile.read(inps.dem_file, datasetName='height')[0]
ax_v = pp.plot_dem_yx(ax_v, dem)
img = ax_v.imshow(d_v,
cmap=inps.colormap,
clim=inps.ylim_mat,
interpolation='nearest')
# Reference Pixel
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
ax_v.plot(inps.ref_yx[1], inps.ref_yx[0], 'ks', ms=6)
else:
try:
ax_v.plot(int(atr['REF_X']), int(atr['REF_Y']), 'ks', ms=6)
except:
pass
# Initial Pixel
if inps.yx:
ax_v.plot(inps.yx[1], inps.yx[0], 'ro', markeredgecolor='black')
ax_v.set_xlim(0, np.shape(d_v)[1])
ax_v.set_ylim(np.shape(d_v)[0], 0)
# Status Bar
def format_coord(x, y):
col = int(x + 0.5)
row = int(y + 0.5)
if 0 <= col < width and 0 <= row < length:
z = d_v[row, col]
try:
lon = ullon + x * lon_step
lat = ullat + y * lat_step
return 'x=%.0f, y=%.0f, value=%.4f, lon=%.4f, lat=%.4f' % (
x, y, z, lon, lat)
except:
return 'x=%.0f, y=%.0f, value=%.4f' % (x, y, z)
ax_v.format_coord = format_coord
# Title and Axis Label
ax_v.set_title(
'N = %d, Time = %s' %
(inps.epoch_num, inps.dates[inps.epoch_num].strftime('%Y-%m-%d')))
if not 'Y_FIRST' in atr.keys():
ax_v.set_xlabel('Range')
ax_v.set_ylabel('Azimuth')
# Flip axis
if inps.flip_lr:
ax_v.invert_xaxis()
print('flip map left and right')
if inps.flip_ud:
ax_v.invert_yaxis()
print('flip map up and down')
# Colorbar
cbar = fig_v.colorbar(img, orientation='vertical')
cbar.set_label('Displacement [%s]' % inps.disp_unit)
# Axes 2 - Time Slider
ax_time = fig_v.add_axes([0.125, 0.1, 0.6, 0.07],
facecolor='lightgoldenrodyellow',
yticks=[])
tslider = Slider(ax_time,
'Years',
inps.yearList[0],
inps.yearList[-1],
valinit=inps.yearList[inps.epoch_num])
tslider.ax.bar(inps.yearList,
np.ones(len(inps.yearList)),
facecolor='black',
width=0.01,
ecolor=None)
tslider.ax.set_xticks(
np.round(
np.linspace(inps.yearList[0], inps.yearList[-1], num=5) * 100) /
100)
def time_slider_update(val):
"""Update Displacement Map using Slider"""
timein = tslider.val
idx_nearest = np.argmin(np.abs(np.array(inps.yearList) - timein))
ax_v.set_title(
'N = %d, Time = %s' %
(idx_nearest, inps.dates[idx_nearest].strftime('%Y-%m-%d')))
d_v = h5[dateList[idx_nearest]][:] * inps.unit_fac
if inps.ref_date:
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
img.set_data(d_v)
fig_v.canvas.draw()
tslider.on_changed(time_slider_update)
# Fig 2 - Time Series Displacement - Point
fig_ts = plt.figure('Time series - point', figsize=inps.fig_size)
ax_ts = fig_ts.add_subplot(111)
# Read Error List
inps.error_ts = None
if inps.error_file:
error_fileContent = np.loadtxt(inps.error_file,
dtype=bytes).astype(str)
inps.error_ts = error_fileContent[:, 1].astype(
np.float) * inps.unit_fac
if inps.ex_date_list:
e_ts = inps.error_ts[:]
inps.ex_error_ts = np.array([e_ts[i] for i in inps.ex_idx_list])
inps.error_ts = np.array([
e_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
])
def plot_timeseries_errorbar(ax, dis_ts, inps):
dates = list(inps.dates)
d_ts = dis_ts[:]
if inps.ex_date_list:
# Update displacement time-series
dates = sorted(list(set(inps.dates) - set(inps.ex_dates)))
ex_d_ts = np.array([dis_ts[i] for i in inps.ex_idx_list])
d_ts = np.array([
dis_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
])
# Plot excluded dates
(_, caps, _) = ax.errorbar(inps.ex_dates,
ex_d_ts,
yerr=inps.ex_error_ts,
fmt='-o',
color='gray',
ms=inps.marker_size,
lw=0,
alpha=1,
mfc='gray',
elinewidth=inps.edge_width,
ecolor='black',
capsize=inps.marker_size * 0.5)
for cap in caps:
cap.set_markeredgewidth(inps.edge_width)
# Plot kept dates
(_, caps, _) = ax.errorbar(dates,
d_ts,
yerr=inps.error_ts,
fmt='-o',
ms=inps.marker_size,
lw=0,
alpha=1,
elinewidth=inps.edge_width,
ecolor='black',
capsize=inps.marker_size * 0.5)
for cap in caps:
cap.set_markeredgewidth(inps.edge_width)
return ax
def plot_timeseries_scatter(ax, dis_ts, inps):
dates = list(inps.dates)
d_ts = dis_ts[:]
if inps.ex_date_list:
# Update displacement time-series
dates = sorted(list(set(inps.dates) - set(inps.ex_dates)))
ex_d_ts = np.array([dis_ts[i] for i in inps.ex_idx_list])
d_ts = np.array([
dis_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
])
# Plot excluded dates
ax.scatter(inps.ex_dates,
ex_d_ts,
s=inps.marker_size**2,
color='gray') # color='crimson'
# Plot kept dates
ax.scatter(dates, d_ts, s=inps.marker_size**2)
return ax
def update_timeseries(ax_ts, y, x):
"""Plot point time series displacement at pixel [y, x]"""
d_ts = read_timeseries_yx(
inps.timeseries_file, y, x, ref_yx=inps.ref_yx) * inps.unit_fac
# for date in dateList:
# d = h5[k].get(date)[y,x]
# if inps.ref_yx:
# d -= h5[k].get(date)[inps.ref_yx[0], inps.ref_yx[1]]
# d_ts.append(d*inps.unit_fac)
if inps.zero_first:
d_ts -= d_ts[inps.zero_idx]
ax_ts.cla()
if inps.error_file:
ax_ts = plot_timeseries_errorbar(ax_ts, d_ts, inps)
else:
ax_ts = plot_timeseries_scatter(ax_ts, d_ts, inps)
if inps.ylim:
ax_ts.set_ylim(inps.ylim)
for tick in ax_ts.yaxis.get_major_ticks():
tick.label.set_fontsize(inps.font_size)
# Title
title_ts = 'Y = %d, X = %d' % (y, x)
try:
lat = ullat + y * lat_step
lon = ullon + x * lon_step
title_ts += ', lat = %.4f, lon = %.4f' % (lat, lon)
except:
pass
if inps.disp_title:
ax_ts.set_title(title_ts)
ax_ts = pp.auto_adjust_xaxis_date(ax_ts,
inps.yearList,
fontSize=inps.font_size)[0]
ax_ts.set_xlabel('Time', fontsize=inps.font_size)
ax_ts.set_ylabel('Displacement [%s]' % inps.disp_unit,
fontsize=inps.font_size)
fig_ts.canvas.draw()
# Print to terminal
print('\n---------------------------------------')
print(title_ts)
print(d_ts)
# Slope estimation
if inps.ex_date_list:
inps.yearList_kept = [
inps.yearList[i] for i in range(date_num)
if i not in inps.ex_idx_list
]
d_ts_kept = [
d_ts[i] for i in range(date_num) if i not in inps.ex_idx_list
]
d_slope = stats.linregress(np.array(inps.yearList_kept),
np.array(d_ts_kept))
else:
d_slope = stats.linregress(np.array(inps.yearList), np.array(d_ts))
print('linear velocity: %.2f +/- %.2f [%s/yr]' %
(d_slope[0], d_slope[4], inps.disp_unit))
return d_ts
# Initial point time series plot
if inps.yx:
d_ts = update_timeseries(ax_ts, inps.yx[0], inps.yx[1])
else:
d_ts = np.zeros(len(inps.yearList))
ax_ts = plot_timeseries_scatter(ax_ts, d_ts, inps)
def plot_timeseries_event(event):
"""Event function to get y/x from button press"""
if event.inaxes != ax_v:
return
ii = int(event.ydata + 0.5)
jj = int(event.xdata + 0.5)
d_ts = update_timeseries(ax_ts, ii, jj)
# Output
if inps.save_fig and inps.yx:
print('save info for pixel ' + str(inps.yx))
if not inps.fig_base:
inps.fig_base = 'y%d_x%d' % (inps.yx[0], inps.yx[1])
# TXT - point time series
outName = inps.fig_base + '_ts.txt'
header_info = 'timeseries_file=' + inps.timeseries_file
header_info += '\ny=%d, x=%d' % (inps.yx[0], inps.yx[1])
try:
lat = ullat + inps.yx[0] * lat_step
lon = ullon + inps.yx[1] * lon_step
header_info += '\nlat=%.6f, lon=%.6f' % (lat, lon)
except:
pass
if inps.ref_yx:
header_info += '\nreference pixel: y=%d, x=%d' % (inps.ref_yx[0],
inps.ref_yx[1])
else:
header_info += '\nreference pixel: y=%s, x=%s' % (atr['REF_Y'],
atr['REF_X'])
header_info += '\nunit=m/yr'
np.savetxt(outName,
list(zip(np.array(dateList),
np.array(d_ts) / inps.unit_fac)),
fmt='%s',
delimiter=' ',
header=header_info)
print('save time series displacement in meter to ' + outName)
# Figure - point time series
outName = inps.fig_base + '_ts.pdf'
fig_ts.savefig(outName,
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save time series plot to ' + outName)
# Figure - map
outName = inps.fig_base + '_' + dateList[inps.epoch_num] + '.png'
fig_v.savefig(outName,
bbox_inches='tight',
transparent=True,
dpi=inps.fig_dpi)
print('save map plot to ' + outName)
# Final linking of the canvas to the plots.
cid = fig_v.canvas.mpl_connect('button_press_event', plot_timeseries_event)
if inps.disp_fig:
plt.show()
fig_v.canvas.mpl_disconnect(cid)
def estimate_phase_elevation_ratio(dem, ts_data, inps):
"""Estimate phase/elevation ratio for each acquisition of timeseries
Parameters: dem : 2D array in size of ( length, width)
ts_data : 3D array in size of (num_date, length, width)
inps : Namespace
Returns: X : 2D array in size of (poly_num+1, num_date)
"""
num_date = ts_data.shape[0]
# prepare phase and elevation data
print('reading mask from file: ' + inps.mask_file)
mask = readfile.read(inps.mask_file, datasetName='mask')[0]
dem = mask_matrix(np.array(dem), mask)
ts_data = mask_matrix(np.array(ts_data), mask)
# display
# 1. effect of multilooking --> narrow phase range --> better ratio estimation
debug_mode = False
if debug_mode:
import matplotlib.pyplot as plt
#d_index = np.argmax(topo_trop_corr)
d_index = 47
data = ts_data[d_index, :, :]
title = inps.date_list[d_index]
fig = plt.figure()
plt.plot(dem[~np.isnan(dem)],
data[~np.isnan(dem)],
'.',
label='Number of Looks = 1')
mli_dem = multilook_data(dem, 8, 8)
mli_data = multilook_data(data, 8, 8)
plt.plot(mli_dem[~np.isnan(mli_dem)],
mli_data[~np.isnan(mli_dem)],
'.',
label='Number of Looks = 8')
plt.legend()
plt.xlabel('Elevation (m)')
plt.ylabel('Range Change (m)')
plt.title(title)
out_file = 'phase_elevation_ratio_{}.png'.format(title)
plt.savefig(out_file, bbox_inches='tight', transparent=True, dpi=300)
print('save to {}'.format(out_file))
#plt.show()
print('----------------------------------------------------------')
print(
'Empirical tropospheric delay correction based on phase/elevation ratio (Doin et al., 2009)'
)
print('polynomial order: {}'.format(inps.poly_order))
if inps.num_multilook > 1:
print('number of multilook: {} (multilook data for estimation only)'.
format(inps.num_multilook))
mask = multilook_data(mask, inps.num_multilook, inps.num_multilook)
dem = multilook_data(dem, inps.num_multilook, inps.num_multilook)
ts_data = multilook_data(ts_data, inps.num_multilook,
inps.num_multilook)
if inps.threshold > 0.:
print('correlation threshold: {}'.format(inps.threshold))
mask_nan = ~np.isnan(dem)
dem = dem[mask_nan]
ts_data = ts_data[:, mask_nan]
# calculate correlation coefficient
print('----------------------------------------------------------')
print('calculate correlation of DEM with each acquisition')
topo_trop_corr = np.zeros(num_date, np.float32)
for i in range(num_date):
phase = ts_data[i, :]
cc = 0.
if np.count_nonzero(phase) > 0:
comp_data = np.vstack((dem, phase))
cc = np.corrcoef(comp_data)[0, 1]
topo_trop_corr[i] = cc
print('{}: {:>5.2f}'.format(inps.date_list[i], cc))
topo_trop_corr = np.abs(topo_trop_corr)
print('average correlation magnitude: {:>5.2f}'.format(
np.nanmean(topo_trop_corr)))
# estimate ratio parameter
print('----------------------------------------------------------')
print('estimate phase/elevation ratio')
A = design_matrix(dem=dem, poly_order=inps.poly_order)
X = np.dot(np.linalg.pinv(A), ts_data.T)
X = np.array(X, dtype=np.float32)
X[:, topo_trop_corr < inps.threshold] = 0.
return X
def main():
parser = build_parser()
parseArgs = parser.parse_args()
file_name = parseArgs.file
output_folder = parseArgs.outputDir
should_mask = True
path_name_and_extension = os.path.basename(file_name).split(".")
path_name = path_name_and_extension[0]
extension = path_name_and_extension[1]
# ---------------------------------------------------------------------------------------
# start clock to track how long conversion process takes
start_time = time.clock()
# use h5py to open specified group(s) in the h5 file
# then read datasets from h5 file into memory for faster reading of data
file = h5py.File(file_name, "r")
timeseries_group = file["HDFEOS"]["GRIDS"]["timeseries"]
displacement_3d_matrix = timeseries_group["observation"]["displacement"]
# get attributes (stored at root) of UNAVCO timeseries file
attributes = dict(file.attrs)
# in timeseries displacement_3d_matrix, there are datasets
# need to get datasets with dates - strings that can be converted to integers
dates = displacement_3d_matrix.attrs["DATE_TIMESERIES"].split(" ")
# array that stores dates from dates that have been converted to decimal
decimal_dates = []
# read datasets in the group into a dictionary of 2d arrays and intialize decimal dates
timeseries_datasets = {}
i = 0
for displacement_2d_matrix in displacement_3d_matrix:
dataset = displacement_2d_matrix[:]
if should_mask:
print "Masking " + dates[i]
mask = timeseries_group["quality"]["mask"][:]
dataset = mask_matrix(dataset, mask)
timeseries_datasets[dates[i]] = dataset
d = get_date(dates[i])
decimal = get_decimal_date(d)
decimal_dates.append(decimal)
i += 1
# close h5 file
file.close()
path_list = path_name.split("/")
folder_name = path_name.split("/")[len(path_list) - 1]
try: # create path for output
os.mkdir(output_folder)
except:
print output_folder + " already exists"
# read and convert the datasets, then write them into json files and insert into database
convert_data(attributes, decimal_dates, timeseries_datasets, dates,
output_folder, folder_name)
# run tippecanoe command to get mbtiles file
os.chdir(os.path.abspath(output_folder))
os.system(
"tippecanoe *.json -l chunk_1 -x d -pf -pk -Bg -d9 -D12 -g12 -r0 -o " +
folder_name + ".mbtiles")
# ---------------------------------------------------------------------------------------
# check how long it took to read h5 file data and create json files
end_time = time.clock()
print("time elapsed: " + str(end_time - start_time))
inps.mask_file = None
try:
mask = readfile.read(inps.mask_file)[0]
mask[mask != 0] = 1
print 'load mask from file: ' + inps.mask_file
except:
mask = None
print 'No mask used.'
# Initial Map
d_v = h5[k].get(dateList[inps.epoch_num])[:] * inps.unit_fac
if inps.ref_date:
inps.ref_d_v = h5[k].get(inps.ref_date)[:] * inps.unit_fac
d_v -= inps.ref_d_v
if mask is not None:
d_v = mask_matrix(d_v, mask)
if inps.ref_yx:
d_v -= d_v[inps.ref_yx[0], inps.ref_yx[1]]
data_lim = [np.nanmin(d_v), np.nanmax(d_v)]
if not inps.ylim:
inps.ylim = data_lim
print 'Initial data range: ' + str(data_lim)
print 'Display data range: ' + str(inps.ylim)
########## Fig 1 - Cumulative Displacement Map
if not inps.disp_fig:
plt.switch_backend('Agg')
fig_v = plt.figure('Cumulative Displacement')
def main():
parser = build_parser()
parseArgs = parser.parse_args()
file_name = parseArgs.file
output_folder = parseArgs.outputDir
should_mask = True
path_name_and_extension = os.path.basename(file_name).split(".")
path_name = path_name_and_extension[0]
extension = path_name_and_extension[1]
# ---------------------------------------------------------------------------------------
# start clock to track how long conversion process takes
start_time = time.clock()
# use h5py to open specified group(s) in the h5 file
# then read datasets from h5 file into memory for faster reading of data
file = h5py.File(file_name, "r")
group = file['timeseries']
# get attributes (stored at root) of UNAVCO timeseries file
attributes = dict(group.attrs)
# in timeseries group, there are datasets
# need to get datasets with dates - strings that can be converted to integers
dataset_keys = []
for k in group["GRIDS"].keys():
if k.isdigit():
dataset_keys.append(k)
dataset_keys.sort()
# array that stores dates from dataset_keys that have been converted to decimal
decimal_dates = []
# read datasets in the group into a dictionary of 2d arrays and intialize decimal dates
timeseries_datasets = {}
for key in dataset_keys:
dataset = group["GRIDS"][key][:]
if should_mask:
print "Masking " + str(key)
mask = group["GRIDS"].get('mask')[:]
dataset = mask_matrix(dataset, mask)
timeseries_datasets[key] = dataset
d = get_date(key)
decimal = get_decimal_date(d)
decimal_dates.append(decimal)
# close h5 file
file.close()
path_list = path_name.split("/")
folder_name = path_name.split("/")[len(path_list)-1]
try: # create path for output
os.mkdir(output_folder)
except:
print output_folder + " already exists"
# read and convert the datasets, then write them into json files and insert into database
convert_data(attributes, decimal_dates, timeseries_datasets, dataset_keys, output_folder, folder_name)
# run tippecanoe command to get mbtiles file
os.chdir(os.path.abspath(output_folder))
os.system("tippecanoe *.json -l chunk_1 -x d -pf -pk -Bg -d9 -D12 -g12 -r0 -o " + folder_name + ".mbtiles")
# ---------------------------------------------------------------------------------------
# check how long it took to read h5 file data and create json files
end_time = time.clock()
print ("time elapsed: " + str(end_time - start_time))
