def deconvolution(dataframe, **kwargs):
"""
Calculates pollutant concentration for convolved metrics, such as NO2+O3.
Needs convolved metric, and target pollutant sensitivities
Parameters
----------
source: string
Name of convolved metric containing both pollutants (such as NO2+O3)
base: string
Name of one of the already deconvolved pollutants (for instance NO2)
id: int
Sensor ID
pollutant: string
Pollutant name. Must be included in the corresponding LUTs for unit convertion and additional parameters:
MOLECULAR_WEIGHTS, config._background_conc, CHANNEL_LUT
Returns
-------
calculation of pollutant based on: 6.36 * sensitivity(working - zero_working)/(auxiliary - zero_auxiliary)
"""
result = Series()
baseline = Series()
flag_error = False
if 'source' not in kwargs: flag_error = True
if 'base' not in kwargs: flag_error = True
if 'id' not in kwargs: flag_error = True
if 'pollutant' not in kwargs: flag_error = True
if flag_error:
std_out('Problem with input data', 'ERROR')
return None
sensitivity_1 = config.calibrations.loc[kwargs['id'], 'sensitivity_1']
sensitivity_2 = config.calibrations.loc[kwargs['id'], 'sensitivity_2']
target_1 = config.calibrations.loc[kwargs['id'], 'target_1']
target_2 = config.calibrations.loc[kwargs['id'], 'target_2']
nWA = config.calibrations.loc[kwargs['id'],
'w_zero_current'] / config.calibrations.loc[
kwargs['id'], 'aux_zero_current']
if target_1 != kwargs['pollutant']:
std_out(
f"Sensor {kwargs['id']} doesn't coincide with calibration data",
'ERROR')
return None
factor_unit_1 = get_units_convf(kwargs['pollutant'], from_units='ppm')
factor_unit_2 = get_units_convf(kwargs['base'], from_units='ppm')
result = factor_unit_1 * (
config._alphadelta_pcb * dataframe[kwargs['source']] -
dataframe[kwargs['base']] / factor_unit_2 *
abs(sensitivity_2)) / abs(sensitivity_1)
# Add Background concentration
result += config._background_conc[kwargs['pollutant']]
return result
def basic_4electrode_alg(dataframe, **kwargs):
"""
Calculates pollutant concentration based on 4 electrode sensor readings (mV)
and calibration ID. It adds a configurable background concentration.
Parameters
----------
working: string
Name of working electrode found in dataframe
auxiliary: string
Name of auxiliary electrode found in dataframe
id: int
Sensor ID
pollutant: string
Pollutant name. Must be included in the corresponding LUTs for unit convertion and additional parameters:
MOLECULAR_WEIGHTS, config.background_conc, CHANNEL_LUT
Returns
-------
calculation of pollutant based on: 6.36*sensitivity(working - zero_working)/(auxiliary - zero_auxiliary)
"""
flag_error = False
if 'working' not in kwargs: flag_error = True
if 'auxiliary' not in kwargs: flag_error = True
if 'id' not in kwargs: flag_error = True
if 'pollutant' not in kwargs: flag_error = True
if flag_error:
std_out('Problem with input data', 'ERROR')
return None
# Get Sensor data
if kwargs['id'] not in config.calibrations.index:
std_out(f"Sensor {kwargs['id']} not in calibration data", 'ERROR')
return None
sensitivity_1 = config.calibrations.loc[kwargs['id'],'sensitivity_1']
sensitivity_2 = config.calibrations.loc[kwargs['id'],'sensitivity_2']
target_1 = config.calibrations.loc[kwargs['id'],'target_1']
target_2 = config.calibrations.loc[kwargs['id'],'target_2']
nWA = config.calibrations.loc[kwargs['id'],'w_zero_current']/config.calibrations.loc[kwargs['id'],'aux_zero_current']
if target_1 != kwargs['pollutant']:
std_out(f"Sensor {kwargs['id']} doesn't coincide with calibration data", 'ERROR')
return None
# This is always in ppm since the calibration data is in signal/ppm
result = config.alphadelta_pcb*(dataframe[kwargs['working']] - nWA*dataframe[kwargs['auxiliary']])/abs(sensitivity_1)
# Convert units
result *= get_units_convf(kwargs['pollutant'], from_units = 'ppm')
# Add Background concentration
result += config.background_conc[kwargs['pollutant']]
return result
def __convert_units__(self):
'''
Convert the units based on the UNIT_LUT and blueprint
NB: what is read/written from/to the cache is not converted.
The files are with original units, and then converted in the device only
for the readings but never chached like so.
'''
std_out('Checking if units need to be converted')
for sensor in self.sensors:
factor = get_units_convf(sensor,
from_units=self.sensors[sensor]['units'])
if factor != 1:
self.readings.rename(columns={sensor: sensor + '_RAW'},
inplace=True)
self.readings.loc[:,
sensor] = self.readings.loc[:, sensor +
'_RAW'] * factor
def baseline_4electrode_alg(dataframe, **kwargs):
"""
Calculates pollutant concentration based on 4 electrode sensor readings (mV), but using
one of the metrics (baseline) as a baseline of the others. It uses the baseline correction algorithm
explained here:
https://docs.smartcitizen.me/Components/sensors/Electrochemical%20Sensors/#baseline-correction-based-on-temperature
and the calibration ID. It adds a configurable background concentration.
Parameters
----------
target: string
Name of working electrode found in dataframe
baseline: string
Name of auxiliary electrode found in dataframe
id: int
Sensor ID
pollutant: string
Pollutant name. Must be included in the corresponding LUTs for unit convertion and additional parameters:
MOLECULAR_WEIGHTS, config._background_conc, CHANNEL_LUT
regression_type: 'string'
'best'
Use a 'linear' or 'exponential' regression for the calculation of the baseline
period: pd.offset_alias or 'full'
1D
The period at which the baseline is calculated. If full, the whole index will be used.
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases
store_baseline: boolean
True
Whether or not to store the baseline in the dataframe
resample: str
'1Min'
Resample frequency for the target dataframe
pcb_factor: int
config._alphadelta_pcb (6.36)
Factor converting mV to nA due to the board configuration
Returns
-------
calculation of pollutant based on: pcb_factor*sensitivity(working - zero_working)/(auxiliary - zero_auxiliary)
"""
result = Series()
baseline = Series()
flag_error = False
if 'target' not in kwargs: flag_error = True
if 'baseline' not in kwargs: flag_error = True
if 'id' not in kwargs: flag_error = True
if 'pollutant' not in kwargs: flag_error = True
if 'regression_type' in kwargs:
if kwargs['regression_type'] not in ['best', 'exponential', 'linear']:
flag_error = True
else:
reg_type = kwargs['regression_type']
else:
reg_type = 'best'
if 'period' in kwargs:
if kwargs['period'] not in ['best', 'exponential', 'linear']:
flag_error = True
else:
period = kwargs['period']
else:
period = '1D'
if 'store_baseline' in kwargs: store_baseline = kwargs['store_baseline']
else: store_baseline = True
if 'resample' in kwargs: resample = kwargs['resample']
else: resample = '1Min'
if 'pcb_factor' in kwargs: pcb_factor = kwargs['pcb_factor']
else: pcb_factor = config._alphadelta_pcb
if 'baseline_type' in kwargs: baseline_type = kwargs['baseline_type']
else: baseline_type = 'deltas'
if 'deltas' in kwargs: deltas = kwargs['deltas']
else: deltas = config._baseline_deltas
if flag_error:
std_out('Problem with input data', 'ERROR')
return None
min_date, max_date, _ = find_dates(dataframe)
pdates = date_range(start=min_date, end=max_date, freq=period)
for pos in range(0, len(pdates) - 1):
chunk = dataframe.loc[pdates[pos]:pdates[pos + 1],
[kwargs['target'], kwargs['baseline']]]
bchunk = baseline_calc(chunk,
reg_type=reg_type,
resample=resample,
baseline_type=baseline_type,
deltas=deltas)
if bchunk is None: continue
baseline = baseline.combine_first(bchunk)
if kwargs['pollutant'] not in config.convolved_metrics:
sensitivity_1 = config.calibrations.loc[kwargs['id'], 'sensitivity_1']
sensitivity_2 = config.calibrations.loc[kwargs['id'], 'sensitivity_2']
target_1 = config.calibrations.loc[kwargs['id'], 'target_1']
target_2 = config.calibrations.loc[kwargs['id'], 'target_2']
nWA = config.calibrations.loc[
kwargs['id'],
'w_zero_current'] / config.calibrations.loc[kwargs['id'],
'aux_zero_current']
if target_1 != kwargs['pollutant']:
std_out(
f"Sensor {kwargs['id']} doesn't coincide with calibration data",
'ERROR')
return None
result = pcb_factor * (dataframe[kwargs['target']] -
baseline) / abs(sensitivity_1)
# Convert units
result *= get_units_convf(kwargs['pollutant'], from_units='ppm')
# Add Background concentration
result += config._background_conc[kwargs['pollutant']]
else:
# Calculate non convolved part
result = dataframe[kwargs['target']] - baseline
# Make use of DataFrame inmutable properties to store in it the baseline
if store_baseline:
dataframe[kwargs['target'] + '_BASELINE'] = baseline
return result
def basic_4electrode_alg(dataframe, **kwargs):
"""
Calculates pollutant concentration based on 4 electrode sensor readings (mV)
and calibration ID. It adds a configurable background concentration.
Parameters
----------
working: string
Name of working electrode found in dataframe
auxiliary: string
Name of auxiliary electrode found in dataframe
id: int
Sensor ID
pollutant: string
Pollutant name. Must be included in the corresponding LUTs for unit convertion and additional parameters:
MOLECULAR_WEIGHTS, config._background_conc, CHANNEL_LUT
hardware: alphadelta or isb
Returns
-------
calculation of pollutant based on: 6.36 * sensitivity(working - zero_working)/(auxiliary - zero_auxiliary)
"""
flag_error = False
if 'working' not in kwargs: flag_error = True
if 'auxiliary' not in kwargs: flag_error = True
if 'id' not in kwargs: flag_error = True
if 'pollutant' not in kwargs: flag_error = True
if flag_error:
std_out('Problem with input data', 'ERROR')
return None
# Get Sensor data
if kwargs['id'] not in config.calibrations:
std_out(f"Sensor {kwargs['id']} not in calibration data", 'ERROR')
return None
we_sensitivity_na_ppb = config.calibrations[
kwargs['id']]['we_sensitivity_na_ppb']
we_cross_sensitivity_no2_na_ppb = config.calibrations[
kwargs['id']]['we_cross_sensitivity_no2_na_ppb']
sensor_type = config.calibrations[kwargs['id']]['sensor_type']
nWA = config.calibrations[
kwargs['id']]['we_sensor_zero_mv'] / config.calibrations[
kwargs['id']]['ae_sensor_zero_mv']
if sensor_type != kwargs['pollutant']:
std_out(
f"Sensor {kwargs['id']} doesn't coincide with calibration data",
'ERROR')
return None
# This is always in ppm since the calibration data is in signal/ppm
if kwargs['hardware'] == 'alphadelta':
current_factor = config._alphadelta_pcb
elif kwargs['hardware'] == 'isb':
current_factor = 1 #TODO make it so we talk in mV
else:
std_out(f"Measurement hardware {kwargs['hardware']} not supported",
'ERROR')
return None
result = current_factor * (
dataframe[kwargs['working']] -
nWA * dataframe[kwargs['auxiliary']]) / abs(we_sensitivity_na_ppb)
# Convert units
result *= get_units_convf(kwargs['pollutant'], from_units='ppm')
# Add Background concentration
result += config._background_conc[kwargs['pollutant']]
return result