def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.extinction_coeff_per_bin = timeseries.TimeSeries_2D(self.extinction_coeff_per_bin)
self.extinction_coeff_per_bin._data_period = self.parent_sizedist._data_period
self.angular_scatt_func = timeseries.TimeSeries_2D(self.angular_scatt_func.transpose())
self.angular_scatt_func._data_period = self.parent_sizedist._data_period
self.__hemispheric_forwardscattering = None
self.__hemispheric_backscattering = None
self.__hemispheric_backscattering_ratio = None
self.__hemispheric_forwardscattering_ratio = None
def _parse_netCDF(self):
super(ArmDatasetSub, self)._parse_netCDF()
mass_concentrations = _pd.DataFrame(index=self.time_stamps)
mass_conc_keys = [
'total_organics', 'ammonium', 'sulfate', 'nitrate', 'chloride'
]
for k in mass_conc_keys:
mass_concentrations[k] = _pd.Series(self._read_variable(
k, reverse_qc_flag=4),
index=self.time_stamps)
mass_concentrations.columns.name = 'Mass conc. ug/m^3'
mass_concentrations.index.name = 'Time'
org_mx = self._read_variable('org_mx')
org_mx = _pd.DataFrame(org_mx, index=self.time_stamps)
org_mx.columns = self._read_variable('amus')
org_mx.columns.name = 'amus (m/z)'
self.mass_concentrations = _AMS.AMS_Timeseries_lev01(
mass_concentrations)
self.mass_concentrations.data[
'total'] = self.mass_concentrations.data.sum(axis=1)
self.mass_concentrations.data.rename(
columns={'total_organics': 'organic_aerosol'}, inplace=True)
self.mass_concentrations._data_period = self._data_period
self.organic_mass_spectral_matrix = _timeseries.TimeSeries_2D(org_mx)
self.organic_mass_spectral_matrix._data_period = self._data_period
return
def _concat(self, arm_data_objs, close_gaps=True):
for att in self._concatable:
first_object = getattr(arm_data_objs[0], att)
which_type = type(first_object).__name__
data_period = first_object._data_period
if which_type == 'TimeSeries_2D':
value = _timeseries.TimeSeries_2D(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
elif which_type == 'TimeSeries':
value = _timeseries.TimeSeries(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
elif which_type == 'AMS_Timeseries_lev01':
value = _AMS.AMS_Timeseries_lev01(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
elif which_type == 'SizeDist_TS':
# value = _AMS.AMS_Timeseries_lev01(pd.concat([getattr(i, att).data for i in arm_data_objs]))
data = _pd.concat(
[getattr(i, att).data for i in arm_data_objs])
value = _sizedistribution.SizeDist_TS(
data,
getattr(arm_data_objs[0], att).bins, 'dNdlogDp')
elif which_type == 'TimeSeries_3D':
value = _timeseries.TimeSeries_3D(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
else:
raise TypeError(
'%s is not an allowed type here (TimeSeries_2D, TimeSeries)'
% which_type)
value._data_period = data_period
if close_gaps:
setattr(self, att, value.close_gaps())
else:
setattr(self, att, value)
def kappa_values(self):
if not self.__kappa_values:
# RH =
kappa_values = hg.kappa_simple(self.mean_growth_factor.data.values[:,:,0],self.RH_interDMA.data.values, inverse = True)
kappa_values = pd.DataFrame(kappa_values,columns=self.mean_growth_factor.data.major_axis, index = self.mean_growth_factor.data.items)
self.__kappa_values = timeseries.TimeSeries_2D(kappa_values)
# self.plottable.append('kappa_values')
self.__kappa_values._data_period = self._data_period
return self.__kappa_values
def _concat(self, arm_data_objs, close_gaps=True):
for att in self._concatable:
first_object = getattr(arm_data_objs[0], att)
which_type = type(first_object).__name__
data_period = first_object._data_period
if which_type == 'TimeSeries_2D':
value = _timeseries.TimeSeries_2D(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
elif which_type == 'TimeSeries':
value = _timeseries.TimeSeries(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
elif which_type == 'AMS_Timeseries_lev01':
value = _AMS.AMS_Timeseries_lev01(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
elif which_type == 'SizeDist_TS':
# value = _AMS.AMS_Timeseries_lev01(pd.concat([getattr(i, att).data for i in arm_data_objs]))
data = _pd.concat(
[getattr(i, att).data for i in arm_data_objs])
value = _sizedistribution.SizeDist_TS(
data,
getattr(arm_data_objs[0], att).bins,
'dNdlogDp',
ignore_data_gap_error=True,
)
elif which_type == 'TimeSeries_3D':
value = _timeseries.TimeSeries_3D(
_pd.concat([getattr(i, att).data for i in arm_data_objs]))
else:
raise TypeError(
'%s is not an allowed type here (TimeSeries_2D, TimeSeries)'
% which_type)
if hasattr(first_object, 'availability'):
try:
avail_concat = _pd.concat([
getattr(i, att).availability.availability
for i in arm_data_objs
])
avail = Data_Quality(None, avail_concat, None,
first_object.flag_info)
value.availability = avail
except:
_warnings.warn(
'availability could not be concatinated make sure you converted it to a pandas frame at some point!'
)
value._data_period = data_period
if close_gaps:
setattr(self, att, value.close_gaps())
else:
setattr(self, att, value)
def signal2noise_ratio(self, value, **kwargs):
if type(value).__name__ == 'TimeSeries_2D':
self._signal2noise_ratio = value
else:
self._signal2noise_ratio = _timeseries.TimeSeries_2D(
value, **kwargs)