'''
Data manager for PermaSense data. Initially created for the paper "A decade of detailed observations (2008--2018) in steep
bedrock permafrost at Matterhorn Hoernligrat (Zermatt, CH)"
Copyright (c) 2020, Swiss Federal Institute of Technology (ETH Zurich)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
@date: October 31, 2020
@author: Samuel Weber, Jan Beutel and Matthias Meyer
usage: manage_GSNdata.py [-h] [--gsn2raw GSN2RAW] [--load LOAD]
[--filter FILTER] [--clean CLEAN]
[--aggregationInterval AGGREGATIONINTERVAL]
[--export2csv EXPORT2CSV]
[--sanityPlot SANITYPLOT] [--gsn2img GSN2IMG]
[--nImg NIMG] [-p PATH] [-y1 YEARBEGIN]
[-m1 MONTHBEGIN] [-d1 DAYBEGIN] [-y2 YEAREND]
[-m2 MONTHEND] [-d2 DAYEND]
optional arguments:
-h, --help show this help message and exit
--gsn2raw GSN2RAW Get raw data from GSN and store locally (step1,
default: True)
--load LOAD Load locally stored data (step2, default: True)
--filter FILTER Filter data (according reference values, step3,
default: True)
--clean CLEAN Clean data manually (step4, default: True)
--aggregationInterval AGGREGATIONINTERVAL
Aggregate data over timewindows of X minutes (step5,
default: 60)
--export2csv EXPORT2CSV
Export data in yearly csv files (step6, default: True)
--sanityPlot SANITYPLOT
Plot for sanity check (step7, default: True)
--gsn2img GSN2IMG Downlod images from GSN and convert NEF to JPEG
(default: True)
--nImg NIMG Number of images to download and convert (default: 10)
-p PATH, --path PATH Relative path for the data output (default: ./data)
-y1 YEARBEGIN, --yearBegin YEARBEGIN
Year begin (default: 2008)
-m1 MONTHBEGIN, --monthBegin MONTHBEGIN
Month begin (default: 1)
-d1 DAYBEGIN, --dayBegin DAYBEGIN
Day begin (default: 1)
-y2 YEAREND, --yearEnd YEAREND
Year end (default: today)
-m2 MONTHEND, --monthEnd MONTHEND
Month end (default: today)
-d2 DAYEND, --dayEnd DAYEND
Day end (default: today)
'''
def main():
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
## SETUP PYTHON ENVIRONMENT
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
# Load functions
import sys
import socket
import argparse
import os as os
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib as mpl
# For proper plotting on server
if socket.gethostname() == 'tik43x':
mpl.use('Agg')
import matplotlib.pyplot as plt
# faster plotting
import matplotlib.style as mplstyle
mplstyle.use('fast')
# remove matplotlib warning
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
mpl.rc('figure', max_open_warning = 0)
#adjust display width for variables
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# Load functions in relative path
sys.path.append('~/permasense/')
from permasense.GSNdata import get_GSNdata, save_data, load_data, filter_data, clean_data, get_GSNimg
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
## ASSIGN ARGUMENTS TO VARIABLES
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
args = parseArguments()
# Define path to data
PATH_DATA = args.path
# Define time range
TBEG = dt.datetime(args.yearBegin,args.monthBegin,args.dayBegin)
TEND = dt.datetime(args.yearEnd,args.monthEnd,args.dayEnd)
# Get raw data from GSN (step1) and produce derived data product (step2 - step7)
GSN2RAW = args.gsn2raw
LOAD = args.load
FILTER = args.filter
CLEAN = args.clean
AGGREGATION_INTERVAL = args.aggregationInterval
EXPORT2CSV = args.export2csv
SANITY_PLOT = args.sanityPlot
if FILTER | CLEAN | EXPORT2CSV | SANITY_PLOT:
LOAD = True
# Get high-resolution images (.NEF) and convert to .JPG
GSN2IMG = args.gsn2img
N_IMG = args.nImg
KEEP_NEF = False
# max number of images to download
DOWNLOAD_LIMIT = 30000
#DOWNLOAD_MAX_SIZE = 20000
DOWNLOAD_MAX_SIZE = 1000000
RESIZE = False
#resize_w, resize_h = 356, 536
RESIZE_WIDTH = 712
RESIZE_HEIGHT = 1072
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
## DICTIONARIES ASSIGNING 'VIRTUAL_SENSOR' TO 'POSITION'
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
DEPO_VS = {
# 'AD01': {'AD01': {'displacement', 'temperature_rock'}}, #OK
# 'AD02': {'AD02': {'displacement'}}, #OK
# 'AD03': {'AD03': {'displacement'}}, #OK
# 'AD04': {'AD04': {'displacement', 'temperature_rock'}}, #OK
# 'AD05': {'AD05': {'displacement'}}, #OK
# 'AD06': {'AD06': {'displacement', 'temperature_rock'}}, #OK
# 'AD07': {'AD07': {'temperature_rock'}}, #OK
# 'AD08': {'AD08': {'temperature_rock'}}, #OK
# 'AD09': {'AD09': {'temperature_rock'}}, #OK
'DH00': {'RD01': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, # OK
'DH05': {'DI55': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, # OK
'DH06': {'RA01': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #000deg, OK
'DH07': {'DI57': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, # OK
'DH09': {'RA02': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #000deg, OK
'DH12': {'BH03': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, # OK
'DH15': {'DI02': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #225/131/281/246deg, OK
'DH17': {'DI07': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #105/277/321/334deg, OK
'DH21': {'LS01': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #110deg, OK
'DH23': {'LS04': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #225deg, OK
'DH25': {'BH07': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #240deg, OK
'DH27': {'BH09': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #130deg, OK
'DH29': {'ST02': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #004deg, OK
'DH31': {'ST05': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #124deg, OK
'DH33': {'GU02': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #335deg, OK
'DH35': {'GU03': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #010deg, OK
'DH39': {'RG01': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, # OK
'DH41': {'GG52': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, # OK
'DH43': {'GG01': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #325deg, OK
'DH44': {'GG02': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #205/041deg OK
'DH55': {'BH10': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #280/270/294/312deg OK
'DH56': {'RL01': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #309deg OK
'DH57': {'GU04': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #014deg, OK
'DH62': {'BH12': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #140deg, OK
'DH63': {'BH13': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #090/000deg, OK
'DH64': {'LS05': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #318/330deg, OK
'DH66': {'GG66': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, #, OK
'DH67': {'GG67': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, #, OK
'DH70': {'RA03': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #000deg, OK
'DH81': {'RAND': {'gps_differential__batch__daily','gps_differential__rtklib__daily'}}, #, OK
'DH82': {'WYS1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #170deg, OK
'DH83': {'LS11': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #262deg, OK
'DH84': {'LS12': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #006deg, OK
'DH86': {'DI03': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #000deg, OK
'DH87': {'DI04': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #000deg, OK
'DH88': {'LS06': {'gps_differential__rtklib__daily','gps_inclinometer'}}, #000deg, OK
'DH89': {'SA01': {'gps_differential__rtklib__daily','gps_inclinometer'}}, #000deg
'DH91': {'SATT': {'gps_differential__rtklib__daily'}}, #
'DH92': {'SA92': {'temperature_rock'}}, #
'DH13': {'DH13': {'vaisalawxt520prec', 'vaisalawxt520windpth'}}, #
'DH42': {'DH42': {'vaisalawxt520prec', 'vaisalawxt520windpth'}}, #
'DH68': {'DH68': {'vaisalawxt520prec', 'vaisalawxt520windpth'}}, #
'DH69': {'DH69': {'vaisalawxt520prec', 'vaisalawxt520windpth'}}, #
'DH73': {'DH73': {'radiometer__conv'}}, #
'PE01': {'DIS1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE02': {'DIS2': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE03': {'RIT1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE04': {'GRU1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE05': {'JAE1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE06': {'SCH1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE07': {'MUA1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE08': {'LAR1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE09': {'LAR2': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
'PE10': {'COR1': {'gps_differential__batch__daily','gps_differential__rtklib__daily','gps_inclinometer'}}, #, OK
# # 'JJ01': {'JJ01': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ02': {'JJ02': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ03': {'JJ03': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ04': {'JJ04': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ05': {'JJ05': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ06': {'JJ06': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ07': {'JJ07': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ08': {'JJ08': {'temperature_fracture'}}, #OK
# # 'JJ09': {'JJ09': {'resistivity_rock', 'temperature_rock'}}, #OK
# # 'JJ10': {'JJ10': {'temperature_fracture'}}, #OK
# # 'JJ13': {'JJ13': {'temperature_rock'}}, #
# # 'JJ14': {'JJ14': {'temperature_rock'}}, #
# # 'JJ18': {'JJ18': {'temperature_rock'}}, #
# # 'JJ22': {'JJ22': {'temperature_rock'}}, #
# # 'JJ25': {'JJ25': {'temperature_rock'}}, #
'MH01': {'MH01': {'displacement', 'temperature_fracture', 'temperature_rock'}}, #ok
'MH02': {'MH02': {'displacement', 'temperature_fracture', 'temperature_rock'}}, #ok
'MH03': {'MH03': {'displacement', 'temperature_fracture', 'temperature_rock'}}, #ok
'MH04': {'MH04': {'displacement', 'temperature_fracture', 'temperature_rock'}}, #ok
'MH05': {'MH05': {'resistivity_fracture', 'temperature_fracture'}}, #ok but resistivity
'MH06': {'MH06': {'displacement', 'temperature_rock'}}, #ok
'MH07': {'MH07': {'resistivity_fracture', 'temperature_fracture'}}, #ok but resistivity
'MH08': {'MH08': {'displacement', 'temperature_rock'}}, #ok
'MH09': {'MH09': {'displacement', 'temperature_fracture'}}, #ok
'MH10': {'MH10': {'resistivity_rock', 'temperature_rock'}}, #ok but resistivity
'MH11': {'MH11': {'resistivity_rock', 'temperature_rock'}}, #ok but resistivity_rock
'MH12': {'MH12': {'resistivity_rock', 'temperature_rock'}}, #ok but resistivity
'MH18': {'MH18': {'displacement'}}, #ok
'MH20': {'MH20': {'displacement'}}, #ok
'MH21': {'MH21': {'displacement'}}, #ok
'MH22': {'MH22': {'displacement'}}, #ok
'MH27': {'MH27': {'temperature_rock'}}, #ok
'MH30': {'MH30': {'temperature_rock'}}, #ok
'MH46': {'MH46': {'temperature_rock'}}, #ok
'MH47': {'MH47': {'temperature_rock'}}, #ok
'MH15': {'MH15': {'radiometer__conv'}}, #ok
'MH25': {'MH25': {'vaisalawxt520prec', 'vaisalawxt520windpth'}}, #ok
'MH51': {'MH51': {'vaisalawxt520prec', 'vaisalawxt520windpth'}}, #ok
'MH33': {'MH33': {'gps_differential__batch__daily', 'gps_differential__rtklib__daily', 'gps_inclinometer'}}, #ok
'MH34': {'MH34': {'gps_differential__batch__daily', 'gps_differential__rtklib__daily', 'gps_inclinometer'}}, #ok
'MH35': {'MH35': {'gps_differential__batch__daily', 'gps_differential__rtklib__daily', 'gps_inclinometer'}}, #ok
'MH40': {'MH40': {'gps_differential__batch__daily', 'gps_differential__rtklib__daily'}}, #ok
'MH42': {'MH42': {'gps_differential__batch__daily', 'gps_differential__rtklib__daily'}}, #ok
'MH43': {'MH43': {'gps_differential__rtklib__daily'}},
}
DEPO_GPS = ['MH33', 'MH34', 'MH35', 'MH40', 'MH42', 'MH43']
DEPO_IMG = {
# 'MH19': {'binary__mapped'}
}
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
## Create PATH_DATA if it does not exist yet
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
os.makedirs(PATH_DATA, exist_ok=True)
os.makedirs(PATH_DATA + '/gnss_data_raw', exist_ok=True)
os.makedirs(PATH_DATA + '/gnss_derived_data_products', exist_ok=True)
os.makedirs(PATH_DATA + '/timelapse_images', exist_ok=True)
os.makedirs(PATH_DATA + '/timeseries_data_raw', exist_ok=True)
os.makedirs(PATH_DATA + '/timeseries_derived_data_products', exist_ok=True)
os.makedirs(PATH_DATA + '/timeseries_sanity_plots', exist_ok=True)
os.makedirs(PATH_DATA + '/timeseries_sanity_plots/' + 'channelwise', exist_ok=True)
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
## GSN timeseries data
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
# Loop through keys (depo) in dictionary DEPO_VS
for depo, depo_line in DEPO_VS.items():
# print(depo)
# print(depo_line)
for label in depo_line:
print('Working on data of label {:s} and depo {:s}.'.format(label, depo))
if depo[0:2] == 'JJ':
deployment = 'jungfraujoch'
elif depo[0:2] == 'AD':
deployment = 'adm'
elif depo[0:2] == 'MH':
deployment = 'matterhorn'
elif depo[0:2] == 'DH':
deployment = 'dirruhorn'
elif depo[0:2] == 'PE':
deployment = 'permos'
position = int(depo[2:4])
# Step 1: Query data from GSN server and save it locally
# Loop through values for a given key of dictionary DEPO_VS
for vsensor in sorted(list(depo_line[label])):
# print(label, depo, deployment, vsensor, position)
if GSN2RAW:
print('Data from label {:s}, position {:s} and virtual sensor {:s} is imported.'.format(label, depo, vsensor))
print('Time interval: ',TBEG, ' to ', TEND)
df = get_GSNdata(deployment, position, vsensor, TBEG, TEND, download_max_size = DOWNLOAD_MAX_SIZE)
if len(df) > 1:
print('Saving raw data')
if vsensor == 'gps_differential__batch__daily':
save_data(df, PATH_DATA + '/gnss_data_raw/', label, vsensor, deployment, file_type='csv')
save_data(df, PATH_DATA + '/gnss_data_raw/', label, vsensor, deployment, yearly=True, iso=True, file_type='csv')
elif vsensor == 'gps_differential__rtklib__daily':
save_data(df, PATH_DATA + '/gnss_data_raw/', label, vsensor, deployment, file_type='csv')
save_data(df, PATH_DATA + '/gnss_data_raw/', label, vsensor, deployment, yearly=True, iso=True, file_type='csv')
else:
save_data(df, PATH_DATA + '/timeseries_data_raw/', label, vsensor, deployment, file_type='csv')
save_data(df, PATH_DATA + '/timeseries_data_raw/', label, vsensor, deployment, yearly=True, iso=True, file_type='csv')
print()
# Loop through values for a given key of dictionary DEPO_VS
for vsensor in list(vs for vs in sorted(list(depo_line[label]))):
df={}
# Step 2: Load data
if LOAD:
print('Loading data', label, depo, deployment, vsensor)
if vsensor == 'gps_differential__batch__daily':
df = load_data(PATH_DATA + '/gnss_data_raw/', label, vsensor, file_type='csv')
df_raw = df.copy()
elif vsensor == 'gps_differential__rtklib__daily':
df = load_data(PATH_DATA + '/gnss_data_raw/', label, vsensor, file_type='csv')
df_raw = df.copy()
else:
df = load_data(PATH_DATA + '/timeseries_data_raw/', label, vsensor, file_type='csv')
df_raw = df.copy()
# Step 3 (optional): Filter data (according to reference values)
if FILTER:
print('Filtering data', label, depo, deployment, vsensor)
df = filter_data(df, depo)
df_filt = df.copy()
# Step 4 (optional): Clean data manually
if CLEAN:
print('Cleaning data', label, depo, deployment, vsensor)
try:
df = clean_data(df, depo, vsensor)
except Exception as i:
print('An issue with data cleaning raised - please check the data', i)
pass
df_filt_clean = df.copy()
# Step 5: Correct GPS data for moving reference HOGR
if vsensor == 'gps_differential__rtklib__daily':
# print(df)
# print(label)
print(df.index[0])
print(df.index[-1])
t1 = df.index[0]
t2 = df.index[-1]
trng_reindex = pd.date_range(t1, t2, freq='24h')
label_list = ['MH33', 'MH34', 'MH35', 'MH40', 'MH43']
if label in label_list:
print("Correcting GPS for moving reference HOGR")
year_list = list(range(2008, dt.datetime.today().year + 1))
depo_HOGR = 'MH42'
vsensor = 'gps_differential__rtklib__daily'
# #vsensor = 'gps_differential__batch__daily'
df_HOGR = load_data(PATH_DATA + '/gnss_derived_data_products/', depo_HOGR, vsensor, year=year_list, file_type='csv')
# print(df_HOGR)
df_HOGR = df_HOGR.reindex(trng_reindex)
df['e'] = df['e'] + df_HOGR['e [m]'] - df_HOGR['e [m]'][0]
df['n'] = df['n'] + df_HOGR['n [m]'] - df_HOGR['n [m]'][0]
df['h'] = df['h'] + df_HOGR['h [m]'] - df_HOGR['h [m]'][0]
df_gps_corr = df.copy
# Step 6 (optional): Aggregate data
if AGGREGATION_INTERVAL > 0:
print('Aggregating data', label, depo, deployment, vsensor)
try:
if vsensor == 'vaisalawxt520prec':
f = {'position':'mean', 'device_id':'mean', 'rain_accumulation':'sum', 'rain_duration':'sum', 'rain_intensity':'mean', 'rain_peak_intensity':'max', 'hail_accumulation':'sum', 'hail_duration':'sum', 'hail_intensity':'mean', 'hail_peak_intensity':'mean'}
df = df.groupby(pd.Grouper(freq=str(AGGREGATION_INTERVAL) + 'Min')).agg(f)
elif vsensor == 'vaisalawxt520windpth':
f = {'position':'mean', 'device_id':'mean', 'wind_direction_minimum':'min', 'wind_direction_average':'mean', 'wind_direction_maximum':'max', 'wind_speed_minimum':'min', 'wind_speed_average':'mean', 'wind_speed_maximum':'max', 'temp_air':'mean', 'temp_internal':'mean', 'relative_humidity':'mean', 'air_pressure':'mean'}
df = df.groupby(pd.Grouper(freq=str(AGGREGATION_INTERVAL) + 'Min')).agg(f)
elif vsensor == 'gps_differential__batch__daily':
print('Copying GPS data, no aggregation.')
# df = df
elif vsensor == 'gps_differential__rtklib__daily':
print('Copying GPS data, no aggregation.')
# df = df
else:
df = df.groupby(pd.Grouper(freq=str(AGGREGATION_INTERVAL) + 'Min')).aggregate(np.mean)
#df = df.groupby(pd.TimeGrouper(freq=str(AGGREGATION_INTERVAL) + 'Min')).aggregate(np.median)
except:
print('There was an issue with time aggregation')
pass
df_agg = df.copy()
# Step 7: Export to csv
if EXPORT2CSV:
print('Exporting data', label, depo, deployment, vsensor)
df_csv = df.copy()
for col in ['device_id', 'ref1', 'ref2', 'ref3', 'ref4', 'ref5', 'ref6']:
try:
df_csv.drop([col], axis=1, inplace=True)
except:
pass
try:
if vsensor == 'gps_differential__batch__daily':
save_data(df_csv, PATH_DATA + '/gnss_derived_data_products/', label, vsensor, deployment, yearly=True, iso=True, file_type='csv', float_format=4)
elif vsensor == 'gps_differential__rtklib__daily':
save_data(df_csv, PATH_DATA + '/gnss_derived_data_products/', label, vsensor, deployment, yearly=True, iso=True, file_type='csv', float_format=4)
else:
save_data(df_csv, PATH_DATA + '/timeseries_derived_data_products/', label, vsensor, deployment, yearly=True, iso=True, file_type='csv', float_format=4)
except:
pass
# Step 8: Plot for sanity check
if SANITY_PLOT:
print('Plotting data', label, depo, deployment, vsensor)
for i, col in enumerate(df_agg):
if col not in ['device_id', 'device_type', 'position', 'label', 'ref1', 'ref2', 'ref3', 'ref4', 'ref5', 'ref6', 'sd_e', 'sd_n', 'sd_h', 'version', 'reference_label', 'processing_time', 'ratio_of_fixed_ambiguities']:
#print(col)
#plt.figure(figsize=(24/2.54, 18/2.54), facecolor='w', edgecolor='k')
plt.figure(figsize=(40/2.54, 18/2.54), facecolor='w', edgecolor='k')
plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.85) # left=0.125, right=0.9, bottom=0.1, top=0.9, wspace=0.2, hspace=0.2
plt.axhline(y=0, linestyle='dotted', linewidth=1, color='grey')
plt.plot(df_raw[col][~df_raw[col].isnull()] , color='C0', label='raw')
plt.plot(df_filt[col], color='C1', label='filtered')
plt.plot(df_filt_clean[col], color='C2', label='cleaned')
plt.plot(df_agg[col], color='C3', label='aggregated')
diff = df_agg[col].max() - df_agg[col].min()
try:
plt.ylim([df_agg[col].min() - diff/10, df_agg[col].max() + diff/10])
except:
pass
plt.title(' Label: {:s} \n Deployment: {:s} \n Position: {:s} \n VSENSOR: {:s}\n VARIABLE: {:s}'.format(label,deployment,depo[2:4],vsensor,col), loc='left')
#plt.legend(['raw','+ filtered', '+ cleaned', '+ aggregated'], loc="upper left")
plt.legend(loc="upper left")
#print(mpl.rcParams['agg.path.chunksize'])
mpl.rcParams['agg.path.chunksize'] = 10000
# start from fixed date
if deployment == 'dirruhorn' or deployment == 'permos':
plt.xlim([TBEG, TEND])
# plt.xlim([dt.datetime(2011,1,1), TEND])
else :
plt.xlim([TBEG, TEND])
plt.savefig('{:s}/timeseries_sanity_plots/channelwise/{:s}_{:s}_{:s}.png'.format(PATH_DATA , label, vsensor, col), dpi=300)
plt.close('all')
mpl.rcParams['agg.path.chunksize'] = 0
#plt.figure(figsize=(24/2.54, 18/2.54), facecolor='w', edgecolor='k')
fig = plt.figure(figsize=(40/2.54, 18/2.54), facecolor='w', edgecolor='k')
plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.85) # left=0.125, right=0.9, bottom=0.1, top=0.9, wspace=0.2, hspace=0.2
for i, col in enumerate(df_agg):
if col not in ['device_id', 'device_type', 'position', 'label', 'ref1', 'ref2', 'ref3', 'ref4', 'ref5', 'ref6', 'sd_e', 'sd_n', 'sd_h', 'version', 'reference_label', 'processing_time', 'ratio_of_fixed_ambiguities']:
#print(df_agg[col])
if ~df_agg[col].isnull().all():
# print(col)
if col == 'e':
col_label = 'East'
elif col == 'n':
col_label = 'North'
elif col == 'h':
col_label = 'Altitude'
else:
col_label = col
plt.axhline(y=0, linestyle='dotted', linewidth=1, color='grey')
plt.plot(df_agg[col], label=col_label)
#print(p1)
#print(col_label)
plt.title(' Label: {:s} \n Deployment: {:s} \n Position: {:s} \n VSENSOR: {:s}\n All channels'.format(label,deployment,depo[2:4],vsensor), loc='left')
# any legend location is too slow
# #plt.legend()
plt.legend(loc="upper left")
# start from fixed date
if deployment == 'dirruhorn' or deployment == 'permos':
plt.xlim([TBEG, TEND])
# plt.xlim([dt.datetime(2011,1,1), TEND])
else :
plt.xlim([TBEG, TEND])
#plt.tight_layout()
#if fig.get_axes(): # Do stuff when the figure isn't empty.
plt.savefig('{:s}/timeseries_sanity_plots/{:s}_{:s}_all.png'.format(PATH_DATA , label, vsensor), dpi=300)
plt.close('all')
print()
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
## GSN images
## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ##
print(GSN2IMG)
if GSN2IMG:
# Loop through keys (depo) in dictionary DEPO_IMG
for depo in DEPO_IMG:
if depo[0:2] == 'MH':
deployment = 'matterhorn'
position = int(depo[2:4])
# Loop through values for a given key of dictionary DEPO_IMG
for vsensor in list(DEPO_IMG[depo]):
print(deployment, position, vsensor, TBEG, TEND)
df = get_GSNimg(PATH_DATA + '/timelapse_images/', deployment, position, vsensor, TBEG, TEND, n_img = N_IMG, keep_nef = KEEP_NEF, download_limit = DOWNLOAD_LIMIT, download_max_size = DOWNLOAD_MAX_SIZE, resize = RESIZE, resize_w = RESIZE_WIDTH, resize_h = RESIZE_HEIGHT)
def parseArguments():
import argparse
import datetime as dt
def true_or_false(arg):
ua = str(arg).upper()
if 'TRUE'.startswith(ua):
return True
elif 'FALSE'.startswith(ua):
return False
else:
pass #error condition maybe?
# Create argument parser
parser = argparse.ArgumentParser()
# Positional (mandatory) arguments
#parser.add_argument("positionalArgument1", help="Positional argument 1", type=float)
# Optional arguments
parser.add_argument("--gsn2raw", help="Get raw data from GSN and store locally (step1, default: %(default)s)", type=true_or_false, default=True)
parser.add_argument("--load", help="Load locally stored data (step2, default: %(default)s)", type=true_or_false, default=True)
parser.add_argument("--filter", help="Filter data (according reference values, step3, default: %(default)s)", type=true_or_false, default=True)
parser.add_argument("--clean", help="Clean data manually (step4, default: %(default)s)", type=true_or_false, default=True)
parser.add_argument("--aggregationInterval", help="Aggregate data over timewindows of X minutes (step5, default: %(default)s)", type=int, default=60)
parser.add_argument("--export2csv", help="Export data in yearly csv files (step6, default: %(default)s)", type=true_or_false, default=True)
parser.add_argument("--sanityPlot", help="Plot for sanity check (step7, default: %(default)s)", type=true_or_false, default=True)
parser.add_argument("--gsn2img", help="Downlod images from GSN and convert NEF to JPEG (default: %(default)s)", type=true_or_false, default=True)
parser.add_argument("--nImg", help="Number of images to download and convert (default: %(default)s)", type=int, default=10)
parser.add_argument("-p", "--path", help="Relative path for the data output (default: %(default)s)", type=str, default='./data')
parser.add_argument("-y1", "--yearBegin", help="Year begin (default: %(default)s)", type=int, default=2008)
parser.add_argument("-m1", "--monthBegin", help="Month begin (default: %(default)s)", type=int, default=1)
parser.add_argument("-d1", "--dayBegin", help="Day begin (default: %(default)s)", type=int, default=1)
parser.add_argument("-y2", "--yearEnd", help="Year end (default: today)", type=int, default=dt.datetime.now().year)
parser.add_argument("-m2", "--monthEnd", help="Month end (default: today)", type=int, default=dt.datetime.now().month)
parser.add_argument("-d2", "--dayEnd", help="Day end (default: today)", type=int, default=dt.datetime.now().day)
# Print version
#parser.add_argument("--version", action="version", version='%(prog)s - Version 1.0')
# Parse argument
return parser.parse_args()
if __name__ == "__main__":
main()