Example #1
0
def parse_data():
    sd = parse_get_data("../Test/Data/hemsedal_hollekolten_jan2016.xml")
    ff_s = Series(sd["25112"]["FF"]["val"], index=sd["25112"]["index"])

    # replace fill values to NaN
    ff_s.replace(-99999.0, np.nan, inplace=True)
    ff24_s = ff_s.resample("D")

    sd = parse_get_data("../Test/Data/hemsedal_hoelto_jan2016.xml")
    ts = Series(sd["25100"]["RR_24"]["val"], index=sd["25100"]["index"])

    # select only measurements at 06 every day
    ts = ts[ts.index.hour == 6]
    # ts.replace(-99999.0, np.nan, inplace=True)
    # ts.replace(-1.0, 0.0, inplace=True)
    print(ff24_s)

    plt.bar(ts.index, ts.values)
    plt.hold(True)

    plt.plot(ff_s.index, ff_s.values)
    plt.plot(ff24_s.index, ff24_s.values)
    # plt.bar(sd['25100']['index'], sd['25100']['RR_24']['val'])
    plt.show()
def parse_data():
    sd = parse_get_data('../Test/Data/hemsedal_hollekolten_jan2016.xml')
    ff_s = Series(sd['25112']['FF']['val'], index=sd['25112']['index'])

    # replace fill values to NaN
    ff_s.replace(-99999.0, np.nan, inplace=True)
    ff24_s = ff_s.resample('D')

    sd = parse_get_data('../Test/Data/hemsedal_hoelto_jan2016.xml')
    ts = Series(sd['25100']['RR_24']['val'], index=sd['25100']['index'])

    # select only measurements at 06 every day
    ts = ts[ts.index.hour == 6]
    # ts.replace(-99999.0, np.nan, inplace=True)
    # ts.replace(-1.0, 0.0, inplace=True)
    print(ff24_s)

    plt.bar(ts.index, ts.values)
    plt.hold(True)

    plt.plot(ff_s.index, ff_s.values)
    plt.plot(ff24_s.index, ff24_s.values)
    # plt.bar(sd['25100']['index'], sd['25100']['RR_24']['val'])
    plt.show()
Example #3
0
    if dt.hour == 6 and dt.minute == 0:
        return True
    else:
        return False


def removeNull(rr):
    if rr == -99999.0:
        return False
    else:
        return True


if __name__ == '__main__':

    sd = parse_get_data('../Test/Data/eklima_data.xml')

    station_id = '15890'  # TODO: some stations measure TA with a 10 min interval, which causes the time-axis to be wrong.

    dates = sd[station_id]['index']
    temperature = sd[station_id]['TA']['val']
    precip = sd[station_id]['RR_1']['val']

    precip_24 = list(filter(removeNull, sd[station_id]['RR_24']['val']))
    dates_24 = list(filter(isMetDay, dates))

    ta_rr_plotly(station_id, dates, dates_24, temperature, precip, precip_24)
'''
dates = [datetime.datetime.today() - datetime.timedelta(days=i) for i in range(4)]

temperature = np.array([4, 1, -3, -17])
Example #4
0
                    'RR_1', 'RR_6', 'RRINTENS', 'DAGRRRR00', 'DAGRRSS00',
                    'X1UU', 'X1UM', 'UU',
                    'FX_6', 'FX_12', 'X1FX_1', 'X1FF', 'DD', 'FF',
                    'SAM', 'SA', 'RTS_1', 'SS_1', 'SS_24',
                    'X1QO', 'QNET', 'QSI', 'QSO', 'QRA', 'QOB', 'QLI', 'QLO', 'QD', 'QR', 'QT']

print(st_elems)

st_sensors = [sensor for sensor in relevant_sensors if sensor in st_elems.keys()]


'''
STEP 2
'''
rsp = wsKlimaRequest('getMetData', {'timeserietypeID': tt_id, 'format': "", 'from': t_from, 'to': t_to,
                                   'stations': stations, 'elements': st_sensors,
                                   'hours': range(0, 24), 'months': "", 'username': ""}).get()

sd = parse_get_data(rsp.content)

# pylab.plot(sd[str(stnr)]['index'], sd[str(stnr)]['TA']['val'])
# pylab.show()

'''
STEP 3
'''

sites = {st_props[str(stnr)]['name']: [st_props[str(stnr)]['latDec'], st_props[str(stnr)]['lonDec']]}

forcing_from_thredds(sites)
Example #5
0
def isMetDay(dt):
    if dt.hour == 6 and dt.minute == 0:
        return True
    else:
        return False

def removeNull(rr):
    if rr == -99999.0:
        return False
    else:
        return True


if __name__ == '__main__':

    sd = parse_get_data('../Test/Data/eklima_data.xml')

    station_id = '15890' # TODO: some stations measure TA with a 10 min interval, which causes the time-axis to be wrong.

    dates = sd[station_id]['index']
    temperature = sd[station_id]['TA']['val']
    precip = sd[station_id]['RR_1']['val']

    precip_24 = list(filter(removeNull, sd[station_id]['RR_24']['val']))
    dates_24 = list(filter(isMetDay, dates))

    ta_rr_plotly(station_id, dates, dates_24, temperature, precip, precip_24)

'''
dates = [datetime.datetime.today() - datetime.timedelta(days=i) for i in range(4)]