else:
this_line = f'{this_hour}{param.separator}{X}{param.separator}{int(round(float(V)))}'
key = f'{X}:{int(round(float(V)))}'
h_file.append(this_line)
if key in d_file.keys():
d_file[key] += 1
else:
d_file[key] = 1
file.close()
# Open the output file.
pdf_dir_tag, pdf_file_tag = file_lib.get_dir(param.dataDirectory,
param.pdfDirectory, network,
station, location, channel)
file_lib.make_path(pdf_dir_tag)
this_path = os.path.join(pdf_dir_tag, f'Y{this_year}')
file_lib.make_path(this_path)
output_file = os.path.join(this_path, f'D{this_doy}.bin')
msg_lib.info(f'DAILY OUTPUT FILE: {output_file}')
with open(output_file, 'w') as output_file:
for key in sorted(d_file.keys()):
day, db = key.split(':')
output_file.write(
f'{day}{param.separator}{db}{param.separator}{d_file[key]}\n')
output_file.close()
if param.pdfHourlySave > 0:
this_path = os.path.join(this_path, param.pdfHourlyDirectory)
file_lib.make_path(this_path)
output_file = os.path.join(this_path, f'H{this_doy}.bin')
# get the response information
msg_lib.info(tr.stats.response)
# Convert to dB.
power = 10.0 * np.log10(power)
smooth_psd = 10.0 * np.log10(smooth_psd)
# Create output paths if they do not exist.
if utils_lib.param(param, 'outputValues').outputValues > 0:
filePath, psd_file_tag = file_lib.get_dir(data_directory,
psd_db_directory,
network,
station, location, channel)
filePath = os.path.join(filePath, segment_start_year, segment_start_doy)
file_lib.make_path(filePath)
# Output is based on the xtype.
if verbose:
msg_lib.info(f'trChannel.stats: {tr.stats} '
f'REQUEST: {segment_start} '
f'TRACE: {tr.stats.starttime.strftime("%Y-%m-%dT%H:%M:%S")} '
f'DELTA: {tr.stats.delta} '
f'SAMPLES: '
f'{int(window_length / float(tr.stats.delta) + 1)} ')
time_label = tr.stats.starttime.strftime('%Y-%m-%dT%H:%M:%S')
tagList = [psd_file_tag, time_label,
f'{window_length}', xtype]
output_file_name = file_lib.get_file_name(utils_lib.param(
param, 'namingConvention').namingConvention, filePath, tagList)
elif date_range < 45:
ax.xaxis.set_major_locator(weeks)
ax.xaxis.set_major_formatter(daysFmt)
elif date_range < 90:
ax.xaxis.set_major_locator(weeks)
ax.xaxis.set_major_formatter(monthsFmt)
elif date_range < 400:
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthsFmt)
else:
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
#
# rotate the x labels by 60 degrees
#
for xlab in ax.get_xticklabels():
xlab.set_rotation(60)
fig.subplots_adjust(top=0.95, right=0.95, bottom=0.2, hspace=0)
image_directory = os.path.join(param.ntkDirectory, param.imageDirectory)
file_lib.make_path(image_directory)
image_file = os.path.join(
image_directory,
"_".join([file_name.replace('.txt', ''), image_tag, window_tag]))
plt.savefig(f'{image_file}.eps', format="eps", dpi=300)
msg_lib.info(f'image file: {image_file}.eps')
plt.savefig(f'{image_file}.png', format="png", dpi=150)
msg_lib.info(f'image file: {image_file}.png')
plt.show()
window_width_second = float(window_width_hour) * 3600.0
window_shift_second = param.windowShiftSecond
msg_lib.info(
f'Wind length and shift in seconds {window_width_second}, {window_shift_second}'
)
start_time = UTCDateTime(start) - (window_width_second / 2.0
) # we want the first sample at start_time
end_time = UTCDateTime(end) + (window_width_second / 2.0)
duration = end_time - start_time # seconds to process
n_shift = int(float(duration / window_shift_second))
# Place the median directory under the power directory.
if verbose:
msg_lib.info(f'POWER PATH: {power_directory}')
if not file_lib.make_path(power_directory):
code = msg_lib.error(f'Error, failed to access {power_directory}', 2)
sys.exit(code)
# Out_power_file_name = file_lib.getFileName(param.namingConvention, power_directory,
out_power_file_name = file_lib.get_file_name(
param.namingConvention, power_directory,
[power_file.replace('.txt', ''), window_tag])
# Open the output file.
with open(os.path.join(power_directory, out_power_file_name),
'w') as output_file:
# w\Write the output header.
output_file.write(f'Period\n')
output_file.write(f'{"Date-Time":20s}')
for k in range(len(bin_start)):
NM_SLM_--_BH_2009-01-06
"""
psd_db_dir_tag, psd_db_file_tag = file_lib.get_dir(param.dataDirectory,
param.psdDbDirectory,
network, station, location,
channel)
msg_lib.info(f'PSD DIR TAG: {psd_db_dir_tag}')
if verbose > 0:
msg_lib.info(f'PSD FILE TAG: {psd_db_file_tag}')
# Open the output file.
psd_dir_tag, psd_file_tag = file_lib.get_dir(param.dataDirectory,
param.psdDirectory, network,
station, location, channel)
file_lib.make_path(psd_dir_tag)
tag_list = [
psd_file_tag,
start_date_time.split('.')[0],
end_date_time.split('.')[0], xtype
]
output_file_name = file_lib.get_file_name(param.namingConvention, psd_dir_tag,
tag_list)
with open(output_file_name, 'w') as output_file:
# Loop through the windows.
for n in range(len(data_days_list)):
thisFile = os.path.join(psd_db_dir_tag, data_days_list[n],
psd_db_file_tag + '*' + xtype + '.txt')
msg_lib.info(f'Day: {data_days_list[n]}')
this_file_list = sorted(glob.glob(thisFile))