def read_csv(fname, temperature_limits=(-20, -0.5)):
"""
Arguments
---------
temerature_limits: tuple.
The temperature reading has false readings in it which can cause porblems later"""
df = pd.read_csv(fname, sep='\t')
pandas_tools.ensure_column_exists(df, 'DateTime', _date_time_alts)
pandas_tools.ensure_column_exists(df, 'Pressure_Pa', _pressure_alt)
pandas_tools.ensure_column_exists(df, 'Temperature', _temp_alt)
pandas_tools.ensure_column_exists(df, 'Relative_humidity', _RH_alt)
# return df
df.index = pd.Series(
pd.to_datetime(df.DateTime, format='%Y-%m-%d %H:%M:%S'))
# df['Pressure_Pa'] = df.PRESS
# df['Temperature'] = df.AT
# df['Relative_humidity'] = df.RH
# df = df.drop('PRESS', axis=1)
# df = df.drop('AT', axis=1)
# df = df.drop('RH', axis=1)
df = df.drop('DateTime', axis=1)
df = df.sort_index()
if temperature_limits:
df = df[df.Temperature > temperature_limits[0]]
df = df[temperature_limits[1] > df.Temperature]
hk = timeseries.TimeSeries(df)
return hk
def read_csv(fname, temperature_limits=(-20, -0.5)):
"""
Arguments
---------
temerature_limits: tuple.
The temperature reading has false readings in it which can cause porblems later"""
df = pd.read_csv(fname, sep="\t")
pandas_tools.ensure_column_exists(df, "DateTime", _date_time_alts)
pandas_tools.ensure_column_exists(df, "Pressure_Pa", _pressure_alt)
pandas_tools.ensure_column_exists(df, "Temperature", _temp_alt)
pandas_tools.ensure_column_exists(df, "Relative_humidity", _RH_alt)
# return df
df.index = pd.Series(pd.to_datetime(df.DateTime, format="%Y-%m-%d %H:%M:%S"))
# df['Pressure_Pa'] = df.PRESS
# df['Temperature'] = df.AT
# df['Relative_humidity'] = df.RH
# df = df.drop('PRESS', axis=1)
# df = df.drop('AT', axis=1)
# df = df.drop('RH', axis=1)
df = df.drop("DateTime", axis=1)
df = df.sort_index()
if temperature_limits:
df = df[df.Temperature > temperature_limits[0]]
df = df[temperature_limits[1] > df.Temperature]
hk = timeseries.TimeSeries(df)
return hk
def convert2verticalprofile(self, alt_label = None, alt_timeseries = None):
ts_tmp = self.copy()
# hk_tmp.data['Time'] = hk_tmp.data.index
# if alt_label:
# label = alt_label
# else:
# label = 'Altitude'
if alt_timeseries:
alt_timeseries = alt_timeseries.align_to(ts_tmp)
_pandas_tools.ensure_column_exists(alt_timeseries.data, 'Altitude', col_alt=alt_label)
ts_tmp.data.index = alt_timeseries.data['Altitude']
else:
_pandas_tools.ensure_column_exists(ts_tmp.data, 'Altitude', col_alt=alt_label)
ts_tmp.data.index = ts_tmp.data['Altitude']
out = atmPy.general.vertical_profile.VerticalProfile(ts_tmp.data)
out._x_label = self._y_label
return out
def convert2verticalprofile(self, alt_label=None, alt_timeseries=None):
ts_tmp = self.copy()
# hk_tmp.data['Time'] = hk_tmp.data.index
# if alt_label:
# label = alt_label
# else:
# label = 'Altitude'
if alt_timeseries:
alt_timeseries = alt_timeseries.align_to(ts_tmp)
_pandas_tools.ensure_column_exists(alt_timeseries.data,
'Altitude',
col_alt=alt_label)
ts_tmp.data.index = alt_timeseries.data['Altitude']
else:
_pandas_tools.ensure_column_exists(ts_tmp.data,
'Altitude',
col_alt=alt_label)
ts_tmp.data.index = ts_tmp.data['Altitude']
out = atmPy.general.vertical_profile.VerticalProfile(ts_tmp.data)
out._x_label = self._y_label
return out
def zdanovskii_stokes_robinson(data, which = 'refractive_Index'):
"""(Stokes and Robinson,1966)
Arguments
---------
data: pandas dataframe
containing chemical composition data
which: str
which property to mix ['refractive_Index', 'density', 'kappa_chem']
"""
materials = _properties.get_commen()
materials.index = materials.species_name
essential_elcts = ['ammonium_sulfate',
'ammonium_nitrate',
'ammonium_chloride',
'sodium_chloride',
'sodium_sulfate',
'sodium_nitrate',
'calcium_nitrate',
'calcium_chloride',
'organic_aerosol'
]
electrolytes = materials.loc[essential_elcts]
electrolytes = electrolytes[['refractive_Index', 'density', 'kappa_chem']]
_pandas_tools.ensure_column_exists(data, 'organic_aerosol', col_alt = ['total_organics'] )
for e in essential_elcts:
_pandas_tools.ensure_column_exists(data, e)
tobemixed = electrolytes[which]
# _pdb.set_trace()
numerator = (data * tobemixed / electrolytes.density).sum(axis=1)
denominator = (data / electrolytes.density).sum(axis=1)
mixed = numerator/denominator
df = _pd.DataFrame(mixed, columns=[which])
ts = _timeseries.TimeSeries(df)
return ts
def read_csv(fname, temperature_limits=(-20, -0.5)):
"""
Arguments
---------
temerature_limits: tuple.
The temperature reading has false readings in it which can cause porblems later"""
df = _pd.read_csv(fname, sep='\t')
_pandas_tools.ensure_column_exists(df, 'DateTime', _date_time_alts)
_pandas_tools.ensure_column_exists(df, 'Pressure_Pa', _pressure_alt)
_pandas_tools.ensure_column_exists(df, 'Temperature', _temp_alt)
_pandas_tools.ensure_column_exists(df, 'Relative_humidity', _RH_alt)
_pandas_tools.ensure_column_exists(df, 'Temperature_instrument', _temp_payload_alt, raise_error=False)
try:
df.Temperature_instrument = _pd.to_numeric(df.Temperature_instrument, errors='coerce')
except AttributeError:
pass
# return df
df.index = _pd.Series(_pd.to_datetime(df.DateTime, format='%Y-%m-%d %H:%M:%S'))
df = df.drop('DateTime', axis=1)
df = df.sort_index()
if temperature_limits:
df = df[df.Temperature > temperature_limits[0]]
df = df[temperature_limits[1] > df.Temperature]
# df.Temperature_payload = df.Temperature_payload.astype(float)
hk = _timeseries.TimeSeries(df)
hk._data_period = 2
return hk
def read_csv(fname, temperature_limits=(-20, -0.5)):
"""
Arguments
---------
temerature_limits: tuple.
The temperature reading has false readings in it which can cause porblems later"""
df = _pd.read_csv(fname, sep='\t')
_pandas_tools.ensure_column_exists(df, 'DateTime', _date_time_alts)
_pandas_tools.ensure_column_exists(df, 'Pressure_Pa', _pressure_alt)
_pandas_tools.ensure_column_exists(df, 'Temperature', _temp_alt)
_pandas_tools.ensure_column_exists(df, 'Relative_humidity', _RH_alt)
_pandas_tools.ensure_column_exists(df, 'Temperature_instrument', _temp_payload_alt, raise_error=False)
_pandas_tools.ensure_column_exists(df, 'CN_concentration', _cn_concentration_alt, raise_error=False)
try:
# df.Temperature_payload = df.Temperature_payload.astype(float)
df.Temperature_instrument = _pd.to_numeric(df.Temperature_instrument, errors='coerce')
df.CN_concentration = _pd.to_numeric(df.CN_concentration, errors='coerce')
df.CONCN = _pd.to_numeric(df.CONCN, errors='coerce')
df.COUNT = _pd.to_numeric(df.COUNT, errors='coerce')
except AttributeError:
pass
# return df
df.index = _pd.Series(_pd.to_datetime(df.DateTime, format='%Y-%m-%d %H:%M:%S'))
df = df.drop('DateTime', axis=1)
df = df.sort_index()
if temperature_limits:
df = df[df.Temperature > temperature_limits[0]]
df = df[temperature_limits[1] > df.Temperature]
hk = _timeseries.TimeSeries(df)
hk._data_period = 2
return hk