def get_descript(campaign_name, system, parameter):
""" """
app.logger.info("got request fori description {} {} {}".format(
campaign_name, system, parameter))
larda = pyLARDA.LARDA().connect(campaign_name, build_lists=False)
#if "rformat" in request.args and request.args['rformat'] == 'bin':
# rformat = 'bin'
#elif "rformat" in request.args and request.args['rformat'] == 'msgpack':
# rformat = 'msgpack'
#else:
# rformat = 'json'
app.logger.warning('request.args {}'.format(dict(request.args)))
text_string = larda.description(system, parameter)
#if rformat == 'bin':
# resp = Response(cbor.dumps(data_container), status=200, mimetype='application/cbor')
#elif rformat == 'msgpack':
# resp = Response(msgpack.packb(data_container), status=200, mimetype='application/msgpack')
#elif rformat == 'json':
# resp = Response(json.dumps(data_container), status=200, mimetype='application/json')
resp = Response(text_string.encode('utf-8'),
status=200,
mimetype='text/plain')
return resp
def api_entry():
"""
Returns:
campaign list
"""
larda = pyLARDA.LARDA()
return jsonify(campaign_list=larda.campaign_list)
def get_campaign_info(campaign_name):
"""
Returns:
json object with all the campaign info
"""
campaign_info = {}
larda = pyLARDA.LARDA().connect(campaign_name, build_lists=False)
campaign_info['config_file'] = larda.camp.info_dict
#print("Parameters in stock: ",[(k, larda.connectors[k].params_list) for k in larda.connectors.keys()])
campaign_info['connectors'] = {
system: conn.get_as_plain_dict()
for system, conn in larda.connectors.items()
}
return jsonify(**campaign_info)
sys.path.append('.')
import matplotlib
matplotlib.use('Agg')
import pyLARDA
import datetime
import logging
log = logging.getLogger('pyLARDA')
log.setLevel(logging.INFO)
log.addHandler(logging.StreamHandler())
# Load LARDA
larda = pyLARDA.LARDA().connect('lacros_dacapo')
#case_prefix = 'plots/scatter_case_studies/comparison_cloud_'
case_prefix = 'plots/'
c_info = [larda.camp.LOCATION, larda.camp.VALID_DATES]
print(larda.days_with_data())
# begin_dt=datetime.datetime(2018,12,6,0,1)
begin_dt = datetime.datetime(2018, 12, 6, 0, 0, 0)
end_dt = datetime.datetime(2018, 12, 6, 0, 30, 0)
plot_range = [0, 12000]
# load the reflectivity data
MIRA_Zg = larda.read("MIRA", "Zg", [begin_dt, end_dt], [0, 'max'])
def get_param(campaign_name, system, param):
""" """
app.logger.info("got request for {} {} {}".format(campaign_name, system,
param))
starttime = time.time()
larda = pyLARDA.LARDA().connect(campaign_name, build_lists=False)
app.logger.debug("{:5.3f}s load larda".format(time.time() - starttime))
if "rformat" in request.args and request.args['rformat'] == 'bin':
rformat = 'bin'
elif "rformat" in request.args and request.args['rformat'] == 'msgpack':
rformat = 'msgpack'
else:
rformat = 'json'
intervals = request.args.get('interval').split(',')
time_interval = [h.ts_to_dt(float(t)) for t in intervals[0].split('-')]
further_slices = [[
float(e) if e not in ['max'] else e for e in s.split('-')
] for s in intervals[1:]]
app.logger.warning('request.args {}'.format(dict(request.args)))
app.logger.info("time request {}".format(time_interval))
starttime = time.time()
data_container = larda.read(system, param, time_interval, *further_slices,
**dict(request.args))
app.logger.debug("{:5.3f}s read data".format(time.time() - starttime))
starttime = time.time()
for k in ['ts', 'rg', 'vel', 'var', 'mask']:
if k in data_container and hasattr(data_container[k], 'tolist'):
if data_container[k].dtype is not np.dtype('object'):
data_container[k][~np.isfinite(data_container[k])] = 0
data_container[k] = data_container[k].tolist()
app.logger.debug("{:5.3f}s convert data".format(time.time() - starttime))
#import io
#test_datacont = {**data_container}
#for k in ['ts', 'rg', 'var', 'mask']:
# if k in test_datacont:
# print(k,type(test_datacont[k]))
# start = time.time()
# test_datacont[k][~np.isfinite(test_datacont[k])] = 0
# memfile = io.BytesIO()
# np.save(memfile, test_datacont[k])
# memfile.seek(0)
# test_datacont[k] = memfile.read().decode('latin-1')
#resp = Response(json.dumps(test_datacont), status=200, mimetype='application/json')
starttime = time.time()
if rformat == 'bin':
resp = Response(cbor.dumps(data_container),
status=200,
mimetype='application/cbor')
elif rformat == 'msgpack':
resp = Response(msgpack.packb(data_container),
status=200,
mimetype='application/msgpack')
elif rformat == 'json':
resp = Response(json.dumps(data_container),
status=200,
mimetype='application/json')
app.logger.debug("{:5.3f}s dumps {}".format(time.time() - starttime,
rformat))
#for some reason the manual response is faster...
#return jsonify(data_container)
return resp
log = logging.getLogger('pyLARDA')
log.setLevel(logging.INFO)
log.addHandler(logging.StreamHandler())
ROOT_DIR = Path(__file__).resolve().parent
parser = argparse.ArgumentParser(description='''
Example `python3 ListCollector.py -c oceanet_pascal lacros_accept`.
As default all campaigns are collected.''')
parser.add_argument('-c',
'--campaign',
nargs='+',
help='just run for a defined campaign(s)')
args = parser.parse_args()
camp = pyLARDA.LARDA_campaign(ROOT_DIR.parent / "larda-cfg", "campaigns.toml")
camp_list = camp.get_campaign_list()
if args.campaign:
assert set(args.campaign).issubset(camp_list), 'campaign not in list'
camp_list = args.campaign
#larda=pyLARDA.LARDA().connect('test_filepatterns', build_lists=True)
for cname in camp_list:
larda = pyLARDA.LARDA().connect(cname, build_lists=True)
print(larda.connectors.keys())
#larda=pyLARDA.LARDA('COLRAWI')
#larda=pyLARDA.LARDA('LACROS_at_Leipzig', build_lists=True)
*range_interval, ",".join(params))
return s
if __name__ == '__main__':
case_name = '20190801-01'
config_case_studies = toml.load('dacapo_case_studies.toml')
case_study = config_case_studies['case'][case_name]
dt_interval = [
datetime.datetime.strptime(t, '%Y%m%d-%H%M')
for t in case_study['time_interval']
]
# pprint.pprint(config_case_studies)
larda = pyLARDA.LARDA().connect('lacros_dacapo_gpu', build_lists=True)
data, savenames = plot_case_study(case_study, contour_lines='T')
case_study['link'] = get_explorer_link(
'lacros_dacapo', dt_interval, case_study['range_interval'],
["CLOUDNET|CLASS", "CLOUDNET|Z", "POLLY|attbsc1064", "POLLY|depol"])
case_study['location'] = larda.camp.LOCATION
case_study['coordinates'] = larda.camp.COORDINATES
make_html_overview(case_name, case_study, data, savenames)
print('\n ...Done...\n')
# | | | |_____| | | \ | |_____] |_____/ | | | ____ |_____/ |_____| | | |
# | | | | | __|__ | \_| | | \_ |_____| |_____| | \_ | | | | |
#
#
if __name__ == '__main__':
start_time = time.time()
log = logging.getLogger('pyLARDA')
log.setLevel(logging.INFO)
log.addHandler(logging.StreamHandler())
# Load LARDA
# larda = pyLARDA.LARDA('remote', uri='http://larda.tropos.de/larda3').connect('lacros_dacapo', build_lists=False)
larda = pyLARDA.LARDA().connect('lacros_dacapo_catalpa')
c_info = [larda.camp.LOCATION, larda.camp.VALID_DATES]
# print('available systems:', larda.connectors.keys())
# print("available parameters: ", [(k, larda.connectors[k].params_list) for k in larda.connectors.keys()])
print('days with data', larda.days_with_data())
# gather command line arguments
method_name, args, kwargs = h._method_info_from_argv(sys.argv)
# gather argument
if 'date' in kwargs:
date = str(kwargs['date'])
begin_dt = datetime.datetime.strptime(date + ' 00:00:05',
'%Y%m%d %H:%M:%S')
end_dt = datetime.datetime.strptime(date + ' 23:59:55',
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pyLARDA
import pyLARDA.helpers as h
import datetime
import numpy as np
import scipy.ndimage as spn
from scipy import stats
#Load LARDA
#larda=pyLARDA.LARDA('lacros_dacapo')
larda = pyLARDA.LARDA().connect_local('lacros_dacapo')
#c_info = [larda.camp.LOCATION, larda.camp.VALID_DATES]
print(larda.days_with_data())
#print("array_avail()", larda.array_avail(2015, 6))
#print("single month with new interface ", larda.instr_status(2015, 6))
#begin_dt=datetime.datetime(2018,12,6,0,1)
begin_dt = datetime.datetime(2018, 12, 6, 1, 40)
end_dt = datetime.datetime(2018, 12, 6, 4, 0, 0)
plot_range = [300, 10000]
case_prefix = 'plots/scatter_case_studies/comparison_cloud_'
# load the reflectivity data
MIRA_Zg = larda.read("MIRA", "Zg", [begin_dt, end_dt], [0, 'max'])
