def read_radiosonde_csv(fname, cal):
"""reads a csv file and returns a TimeSeries
Parameters
----------
fname: str
Name of file to be opend
calibration: str or calibration instance
Either pass the name of the file containing the calibration data, or a calibration instance.
"""
df = pd.read_csv(fname, header=15)
fkt = lambda x: x.lstrip(' ').replace(' ', '_')
col_new = [fkt(i) for i in df.columns.values]
df.columns = col_new
time = df['date_[y-m-d_GMT]'] + df['time_[h:m:s_GMT]'] + '.' + df[
'milliseconds'].astype(str)
df.index = pd.Series(
pd.to_datetime(time, format=time_tools.get_time_formate()))
df[df == 99999.000] = np.nan
alt = df['GPS_altitude_[km]'].copy()
df['Altitude'] = alt * 1e3
df.rename(columns={
'GPS_latitude': 'Lat',
'GPS_longitude': 'Lon'
},
inplace=True)
bins = []
for k in df.keys():
if 'Bin' in k:
bins.append(k)
# print(k)
# print(bins)
sd = df.loc[:, bins]
hk = df.drop(bins, axis=1)
hk = timeseries.TimeSeries(hk)
hk.data.sort_index(inplace=True)
hk.data.Altitude.interpolate(inplace=True)
hk.data['temperature_K'] = hk.data[
'iMet_air_temperature_(corrected)_[deg_C]'] + 273.15
hk.data['pressure_Pa'] = hk.data['iMet_pressure_[mb]'] * 100
# fname_cal = '/Users/htelg/data/POPS_calibrations/150622_china_UAV.csv'
cal = calibration.read_csv(cal)
ib = cal.get_interface_bins(20)
sd = sizedistribution.SizeDist_TS(
sd, ib['binedges_v_int'].values.transpose()[0], 'numberConcentration')
return sd, hk
def read_radiosonde_csv(fname, cal):
"""reads a csv file and returns a TimeSeries
Parameters
----------
fname: str
Name of file to be opend
calibration: str or calibration instance
Either pass the name of the file containing the calibration data, or a calibration instance.
"""
df = pd.read_csv(fname,header = 15)
fkt = lambda x: x.lstrip(' ').replace(' ', '_')
col_new = [fkt(i) for i in df.columns.values]
df.columns = col_new
time = df['date_[y-m-d_GMT]'] + df['time_[h:m:s_GMT]'] + '.' + df['milliseconds'].astype(str)
df.index = pd.Series(pd.to_datetime(time, format = time_tools.get_time_formate()))
df[df == 99999.000] = np.nan
alt = df['GPS_altitude_[km]'].copy()
df['Altitude'] = alt * 1e3
df.rename(columns={'GPS_latitude':'Lat', 'GPS_longitude': 'Lon'}, inplace=True)
bins = []
for k in df.keys():
if 'Bin' in k:
bins.append(k)
# print(k)
# print(bins)
sd = df.loc[:,bins]
hk = df.drop(bins, axis=1)
hk = timeseries.TimeSeries(hk)
hk.data.sort_index(inplace=True)
hk.data.Altitude.interpolate(inplace=True)
hk.data['temperature_K'] = hk.data['iMet_air_temperature_(corrected)_[deg_C]'] + 273.15
hk.data['pressure_Pa'] = hk.data['iMet_pressure_[mb]'] * 100
# fname_cal = '/Users/htelg/data/POPS_calibrations/150622_china_UAV.csv'
cal = calibration.read_csv(cal)
ib = cal.get_interface_bins(20)
sd = sizedistribution.SizeDist_TS(sd, ib['binedges_v_int'].values.transpose()[0], 'numberConcentration')
return sd,hk
def _read_file(fname):
picof = open(fname, 'r')
header = picof.readline()
picof.close()
header = header.split(' ')
header_cleaned = []
for head in header:
bla = head.replace('<', '').replace('>', '')
where = bla.find('[')
if where != -1:
bla = bla[:where]
header_cleaned.append(bla)
data = pd.read_csv(fname,
names=header_cleaned,
sep=' ',
skiprows=1,
header=0)
data.drop(range(20), inplace=True
) # dropping the first x lines, since the time is often dwrong
time_series = data.Year.astype(str) + '-' + data.Month.apply(
lambda x: '%02i' % x) + '-' + data.Day.apply(
lambda x: '%02i' % x) + ' ' + data.Hours.apply(
lambda x: '%02i' % x) + ':' + data.Minutes.apply(
lambda x: '%02i' % x) + ':' + data.Seconds.apply(
lambda x: '%05.2f' % x)
data.index = pd.Series(
pd.to_datetime(time_series, format=time_tools.get_time_formate()))
_drop_some_columns(data)
# convert from rad to deg
data.Lat.values[:] = np.rad2deg(data.Lat.values)
data.Lon.values[:] = np.rad2deg(data.Lon.values)
data['Altitude'] = data['Height']
data = data.drop('Height', axis=1)
data.sort_index(inplace=True)
return timeseries.TimeSeries(data, {'original header': header})
def _read_file(fname):
picof = open(fname, 'r')
header = picof.readline()
picof.close()
header = header.split(' ')
header_cleaned = []
for head in header:
bla = head.replace('<', '').replace('>', '')
where = bla.find('[')
if where != -1:
bla = bla[:where]
header_cleaned.append(bla)
data = pd.read_csv(fname,
names=header_cleaned,
sep=' ',
skiprows=1,
header=0)
data.drop(range(20), inplace=True) # dropping the first x lines, since the time is often dwrong
time_series = data.Year.astype(str) + '-' + data.Month.apply(lambda x: '%02i' % x) + '-' + data.Day.apply(
lambda x: '%02i' % x) + ' ' + data.Hours.apply(lambda x: '%02i' % x) + ':' + data.Minutes.apply(
lambda x: '%02i' % x) + ':' + data.Seconds.apply(lambda x: '%05.2f' % x)
data.index = pd.Series(pd.to_datetime(time_series, format=time_tools.get_time_formate()))
_drop_some_columns(data)
# convert from rad to deg
data.Lat.values[:] = np.rad2deg(data.Lat.values)
data.Lon.values[:] = np.rad2deg(data.Lon.values)
data['Altitude'] = data['Height']
data = data.drop('Height', axis=1)
data.sort_index(inplace=True)
return timeseries.TimeSeries(data, {'original header': header})