def statistics(self):
#get the request parameters
s = request.forms.get('start_time', type=str)
e = request.forms.get('end_time', type=str)
dst = request.forms.get('daylight_savings')
timezone = request.forms.get('timezone')
sea_file = request.forms.get('sea_file')
baro_file = request.forms.get('baro_file')
#add a response header so the service understands it is json
response.headers['Content-type'] = 'application/json'
if sea_file is None:
file_name = './data/SSS.true.nc'
air_file_name = './data/SSS.hobo.nc'
else:
file_name = ny_wv_data[int(sea_file)]
air_file_name = ny_bp_data[int(baro_file)]
#process data
so = StormOptions()
so.sea_fname = file_name
so.air_fname = air_file_name
so.high_cut = 1.0
so.low_cut = 0.045
#temp deferring implementation of type of filter
self.process_data(so, s, e, dst, timezone, 25, data_type="stat")
stat_data = {}
ignore_list = ['PSD Contour', 'time', 'Spectrum', 'HighSpectrum', 'LowSpectrum', 'Frequency']
for i in so.stat_dictionary:
if i not in ignore_list:
stat = []
upper_ci = []
lower_ci = []
for x in range(0, len(so.stat_dictionary['time'])):
stat.append({"x": so.stat_dictionary['time'][x], 'y':so.stat_dictionary[i][x]})
upper_ci.append({"x": so.stat_dictionary['time'][x], 'y':so.upper_stat_dictionary[i][x]})
lower_ci.append({"x": so.stat_dictionary['time'][x], 'y':so.lower_stat_dictionary[i][x]})
stat_data[i] = stat
stat_data[''.join(['upper_', i])] = upper_ci
stat_data[''.join(['lower_', i])] = lower_ci
#add land surface elevation to water level so statistics can be superimposed
# wave_data = []
# scale = 0 - np.min(so.wave_water_level)
# for x in range(0, len(adjusted_times)):
# wave_data.append({"x": adjusted_times[x], "y": (so.wave_water_level[x] + scale) * uc.METER_TO_FEET})
#
# stat_data['wave_wl'] = wave_data
self.stat_data = stat_data
return self.stat_data
def __init__(self, val):
self.val = val
def get(self):
return self.val
if __name__ == '__main__':
so = StormOptions()
so.clip = False
so.air_fname = 'NCDAR00003_1511478_stormtide_unfiltered.nc'
so.sea_fname = 'NCDAR00003_1511478_stormtide_unfiltered.nc'
so.int_units = False
so.high_cut = 1.0
so.low_cut = 0.045
so.from_water_level_file = True
so.timezone = 'GMT'
so.daylight_savings = False
ss = StormStatistics()
for y in so.statistics:
so.statistics[y] = Bool(False)
so.statistics['H1/3'] = Bool(True)
so.statistics['Average Z Cross'] = Bool(True)
so.statistics['PSD Contour'] = Bool(True)
so.statistics['Peak Wave'] = Bool(True)
so.format_output_fname('FEVTest2'.replace('/','-'))
ss.process_graphs(so)
def process_storm_files(args):
so = StormOptions()
so.air_fname = args['air_fname']
so.sea_fname = args['sea_fname']
#check to see if the correct type of files were uploaded
if so.check_file_types() == False:
return 2
so.wind_fname = None
so.format_output_fname(args['out_fname'])
so.timezone = args['tz_info']
so.daylight_savings = args['daylight_savings']
if 'baro_y_min' in args and args['baro_y_min'] is not None:
so.baroYLims = []
so.baroYLims.append(args['baro_y_min'])
so.baroYLims.append(args['baro_y_max'])
if 'wl_y_min' in args and args['wl_y_min'] is not None:
so.wlYLims = []
so.wlYLims.append(args['wl_y_min'])
so.wlYLims.append(args['wl_y_max'])
#check to see if the time series of the water and air file overlap
overlap = so.time_comparison()
#if there is no overlap
if overlap == 2:
return 4
try:
snc = Storm_netCDF()
so.netCDF['Storm Tide with Unfiltered Water Level'] = Bool(True)
so.netCDF['Storm Tide Water Level'] = Bool(True)
snc.process_netCDFs(so)
sg = StormGraph()
so.graph['Storm Tide with Wind Data'] = Bool(False)
so.graph['Storm Tide with Unfiltered Water Level'] = Bool(True)
so.graph['Storm Tide Water Level'] = Bool(True)
so.graph['Atmospheric Pressure'] = Bool(True)
sg.process_graphs(so)
if args['sea_4hz'].lower() == 'true':
so.int_units = False
so.high_cut = 1.0
so.low_cut = 0.045
so.from_water_level_file = False
ss = StormStatistics()
for y in so.statistics:
so.statistics[y] = Bool(False)
so.statistics['H1/3'] = Bool(True)
so.statistics['Average Z Cross'] = Bool(True)
so.statistics['PSD Contour'] = Bool(True)
ss.process_graphs(so)
return 0
except:
return 5
def __init__(self, val):
self.val = val
def get(self):
return self.val
if __name__ == '__main__':
so = StormOptions()
so.clip = False
so.air_fname = 'NCDAR00003_1511478_stormtide_unfiltered.nc'
so.sea_fname = 'NCDAR00003_1511478_stormtide_unfiltered.nc'
so.int_units = False
so.high_cut = 1.0
so.low_cut = 0.045
so.from_water_level_file = True
so.timezone = 'GMT'
so.daylight_savings = False
ss = StormStatistics()
for y in so.statistics:
so.statistics[y] = Bool(False)
so.statistics['H1/3'] = Bool(True)
so.statistics['Average Z Cross'] = Bool(True)
so.statistics['PSD Contour'] = Bool(True)
so.statistics['Peak Wave'] = Bool(True)
so.format_output_fname('FEVTest2'.replace('/','-'))
ss.process_graphs(so)
def psd_contour(self):
#get the request parameters
s = request.forms.get('start_time', type=str)
e = request.forms.get('end_time', type=str)
dst = request.forms.get('daylight_savings')
timezone = request.forms.get('timezone')
sea_file = request.forms.get('sea_file')
baro_file = request.forms.get('baro_file')
#keep this number static for now, will make dynamic if necessary
num_colors = 11
#add a response header so the service understands it is json
response.headers['Content-type'] = 'application/json'
if sea_file is None:
file_name = './data/SSS.true.nc'
air_file_name = './data/SSS.hobo.nc'
else:
file_name = ny_wv_data[int(sea_file)]
air_file_name = ny_bp_data[int(baro_file)]
#process data
so = StormOptions()
so.sea_fname = file_name
so.air_fname = air_file_name
so.high_cut = 1.0
so.low_cut = 0.045
#temp deferring implementation of type of filter
self.process_data(so, s, e, dst, timezone, 25, data_type="stat")
psd_data = {}
#Get the min and max for the PSD since it is easier to compute on the server
print('time len', len(so.stat_dictionary['time']))
x_max = so.stat_dictionary['time'][-1]
x_min = so.stat_dictionary['time'][0]
freqs = [x for x in so.stat_dictionary['Frequency'][0] if x > .033333333]
y_min = 1.0/np.max(freqs)
y_max = 1.0/np.min(freqs)
z_max = np.max(np.max(so.stat_dictionary['Spectrum'], axis = 1))
z_min = np.min(np.min(so.stat_dictionary['Spectrum'], axis = 1))
z_range = np.linspace(z_min, z_max, num_colors)
print(z_range[1] - z_range[0], x_max, x_min)
print(len(so.stat_dictionary['Spectrum']))
psd_data['x'] = list(so.stat_dictionary['time'])
# psd_data['y'] = list(so.stat_dictionary['Frequency'][0])
psd_data['z'] = list([list(x) for x in so.stat_dictionary['Spectrum']])
psd_data['x_range'] = list([x_min,x_max])
psd_data['y_range'] = list([y_min,y_max])
psd_data['z_range'] = list(z_range)
self.psd_data = psd_data
self.stat_so = so.stat_dictionary
return self.psd_data
def single(self):
'''This displays the single waterlevel and atmospheric pressure'''
#get the request parameters
s = request.forms.get('start_time', type=str)
e = request.forms.get('end_time', type=str)
dst = request.forms.get('daylight_savings')
timezone = request.forms.get('timezone')
sea_file = request.forms.get('sea_file')
baro_file = request.forms.get('baro_file')
multi = request.forms.get('multi')
event = request.forms.get('event')
self.fs = 4
if sea_file is None:
file_name = './data/SSS.true.nc'
air_file_name = './data/SSS.hobo.nc'
from_wl = False
else:
if event is None or event == "ny":
file_name = ny_wv_data[int(sea_file)]
air_file_name = ny_bp_data[int(baro_file)]
if int(sea_file) in [2,3]:
self.fs = 1/30
from_wl = False
else:
file_name = nc_wv_data[int(sea_file)]
air_file_name = nc_bp_data[int(baro_file)]
from_wl = True
# filter = request.forms.get('filter')
#add a response header so the service understands it is json
response.headers['Content-type'] = 'application/json'
#process data
so = StormOptions()
so.from_water_level_file = from_wl
so.sea_fname = file_name
so.air_fname = air_file_name
so.high_cut = 1.0
so.low_cut = 0.045
#temp deferring implementation of type of filter
if multi == 'True':
step = 100
else:
step = 25
adjusted_times = self.process_data(so, s, e, dst, timezone, step=step)
#convert in format for javascript
raw_final = []
for x in range(0, len(adjusted_times)):
raw_final.append({"x": adjusted_times[x], 'y':so.raw_water_level[x] * uc.METER_TO_FEET})
surge_final = []
for x in range(0, len(adjusted_times)):
surge_final.append({"x": adjusted_times[x], 'y':so.surge_water_level[x] * uc.METER_TO_FEET})
wave_final = []
for x in range(0, len(adjusted_times)):
wave_final.append({"x": adjusted_times[x], 'y':so.wave_water_level[x] * uc.METER_TO_FEET})
air_final = []
for x in range(0, len(adjusted_times)):
air_final.append({"x": adjusted_times[x], 'y':so.interpolated_air_pressure[x] * uc.DBAR_TO_INCHES_OF_MERCURY})
return {'raw_data': raw_final ,
'surge_data': surge_final,
'wave_data': wave_final,
'air_data': air_final ,
'latitude': float(so.latitude), 'longitude': float(so.longitude),
'air_latitude': float(so.air_latitude), 'air_longitude': float(so.air_longitude),
'sea_stn': [so.stn_station_number,so.stn_instrument_id],
'air_stn': [so.air_stn_station_number,so.air_stn_instrument_id]}