ppcpy.calibration

ppcpy.calibration.lidarconstant

Testtext for documentation

contains get_best_LC()

ppcpy.calibration.lidarconstant.lc_for_cldFreeGrps(data_cube, retrieval: str) → list

Estimate the lidar constant from the optical profiles.

Parameters:

data_cubeobject

Main PicassoProc object.

retrievalstr

Retrieval type. ‘klett’ or ‘raman’.

Returns:

LCslist

Lidar constant for retrieval type per channel per cloud free period.

Notes

• For NR, done directly form the optical profiles, whereas in the matlab version, the LC*olAttri387.sigRatio is taken.

Todo

Change back to Picasso version to check if lidar calibration constants get more similar.

Todo

Check if LC’s are normalized with respect to the mean of the profiles.

ppcpy.calibration.lidarconstant.get_best_LC(LCs: list) → dict

Get lidar constant with the lowest standard deviation.

Parameters:

LCslist

Lidar constant for each channel per cloud free period.

Returns:

LCuseddict

Lidar constants with lowest standard deviation per channel.

Notes

• Since LC = LC_stable and LCStd = LC_stable * LC_Std so will any negative LC also have a negative LCStd, and thus be chosen as the best LC.

History

• 2026-02-16: Added additional checks to hinder negative LCs to be chosen.

ppcpy.calibration.polarization

ppcpy.calibration.polarization.onemx_onepx(x)

calculate the fraction of (1-x)/(1+x)

ppcpy.calibration.polarization.smooth_signal(signal: ndarray, window_len: int) → ndarray

Uniformly smooth the input signal

Parameters:

singalndarray

Signal to be smooth

window_lenint

Width of the applied uniform filter

Returns:

ndarray

Smoothed signal

Notes

History

• 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter

ppcpy.calibration.polarization.loadGHK(data_cube)

prepare the GHK parameters, especially if given in TR convert them into GHK

ppcpy.calibration.polarization.calibrateGHK(data_cube)

estimate the polarization calibration from the delta 90 Method [1]

Parameters:

data_cube

the input data cube

Returns:

pol_calidict

polarization factors from delta 90 for each wavelength containing sub-dicts with ‘eta’, ‘eta_std’, ‘time_start’, ‘time_end’, ‘status’

Notes

History

Function is called here PollyNET/Pollynet_Processing_Chain The two most relevant functions here are PollyNET/Pollynet_Processing_Chain which also calls PollyNET/Pollynet_Processing_Chain

References

[1]

Freudenthaler 2016

ppcpy.calibration.polarization.depol_cali_ghk(signal_t, bg_t, signal_x, bg_x, time, pol_cali_pang_start_time, pol_cali_pang_stop_time, pol_cali_nang_start_time, pol_cali_nang_stop_time, K, cali_h_indx_range, SNRmin, sig_max, rel_std_dplus, rel_std_dminus, segment_len, smooth_win, collect_debug=False)

Polarization calibration for PollyXT lidar system.

Parameters:

signal_tndarray

Background-removed photon count signal at the total channel. Shape: (n_bins, n_profiles)

bg_tndarray

Background at the total channel. Shape: (n_bins, n_profiles)

signal_xndarray

Background-removed photon count signal at the cross channel. Shape: (n_bins, n_profiles)

bg_xndarray

Background at the cross channel. Shape: (n_bins, n_profiles)

timendarray

Datetime array representing the measurement time of each profile.

pol_cali_pang_start_time, pol_cali_pang_stop_timendarray

Start and stop times when the polarizer rotates to the positive angle.

pol_cali_nang_start_time, pol_cali_nang_stop_timendarray

Start and stop times when the polarizer rotates to the negative angle.

Kfloat

Parameter from GHK to correct the calibration.

cali_h_indx_rangelist or tuple

Range of height indexes to use for polarization calibration.

SNRminlist

Minimum SNR for calibration. Length: 4

sig_maxlist

Maximum signal allowed for calibration to prevent pulse pileup.

rel_std_dplus, rel_std_dminusfloat

Maximum relative uncertainty of dplus and dminus allowed.

segment_lenint

Segment length for testing the variability of calibration results.

smooth_winint

Width of the sliding window for smoothing the signal.

collect_debugbool, default=False

store and return the intermediate results

Returns:

pol_cali_etalist

Eta values from polarization calibration.

pol_cali_eta_stdlist

Uncertainty of eta values from calibration.

pol_cali_start_time, pol_cali_stop_timelist

Start and stop times for successful calibration.

cali_statusint

1 if calibration is successful, 0 otherwise.

global_attridict, optional

Information about the depolarization calibration.

ppcpy.calibration.polarization.analyze_segments(dplus, dminus, segment_len, rel_std_dplus, rel_std_dminus)

ppcpy.calibration.polarization.default_to_regular(d)

ppcpy.calibration.polarization.calibrateMol(data_cube)

calibrate the polarization with the molecular signal

Converted from the matlab code to the best knowledge, but not cross-validated yet

ppcpy.calibration.polarization.depol_cali_mol(signal_t, background_t, signal_c, background_c, TR_t, TR_t_std, TR_c, TR_c_std, minSNR, mdr, mdrStd)

Molecular polarization calibration.

Parameters:

signal_t: numeric

Total signal (photon count).

background_t: numeric

Background at total channel (photon count).

signal_c: numeric

Cross signal (photon count).

background_c: numeric

Background at cross channel (photon count).

TR_t: scalar

Transmission ratio at total channel.

TR_t_std: scalar

Uncertainty of the transmission ratio at total channel.

TR_c: scalar

Transmission ratio at cross channel.

TR_c_std: scalar

Uncertainty of the transmission ratio at cross channel.

minSNR: float

The SNR constraint for the signal strength at reference height.

mdr: float

Default molecular depolarization ratio.

mdrStd: float

Default standard deviation of molecular depolarization ratio.

Returns:

polCaliEta: array

Polarization calibration eta.

polCaliEtaStd: array

Uncertainty of polarization calibration eta.

polCaliFac: array

Polarization calibration factor.

polCaliFacStd: array

Uncertainty of polarization calibration factor.

Notes

History

• 2021-07-06: First edition by Zhenping

• 2024-12-23: converted to python

References

Baars, H., et al., Aerosol profiling with lidar in the Amazon Basin during the wet and dry season, J Geophys Res-Atmos, 117, 10.1029/2012jd018338, 2012.

ppcpy.calibration.rayleighfit

ppcpy.calibration.rayleighfit.getVal(array: ndarray, indices: list) → list

Get values of array for a set of indices, including robustness to NaN indices.

Parameters:

heightndarray

Array to be searched.

indicesarry_like

Indeces of the array, may include NaN values.

Returns:

outlist

Values of the array at given indices. If indx is NaN value is also NaN.

ppcpy.calibration.rayleighfit.rayleighfit(data_cube) → list

Rayleighfit …

Parameters:

data_cubeobject

Main PicassoProc object.

Returns:

refHlist of dicts

Reference heights per channel per cloud free period.

Notes

Todo

Write docstring.

ppcpy.calibration.rayleighfit.smooth_signal(signal: ndarray, window_len: int) → ndarray

Uniformly smooth the input signal

Parameters:

singalndarray

Signal to be smooth

window_lenint

Width of the applied uniform filter

Returns:

ndarray

Smoothed signal

Notes

History

• 2026-02-04: Changed from scipy.ndimage.uniform_filter1d to ppcpy.misc.helper.uniform_filter

ppcpy.calibration.rayleighfit.DouglasPeucker(signal: ndarray, height: ndarray, epsilon: float, heightBase: float, heightTop: float, maxHThick: float, window_size: int = 1) → ndarray

Simplify signal according to Douglas-Peucker algorithm.

Parameters:

signalndarray

Molecule corrected signal. [MHz]

heightndarray

Height. [m]

epsilonfloat

Maximum distance.

heightBasefloat

Minimum height for the algorithm. [m]

heightTopfloat

Maximum height for the algorithm. [m]

maxHThickfloat

Maximum spatial thickness of each segment. [m]

window_sizeint

Size of the average smooth window.

Returns:

sigIndxarray

Index of the signal that stands for different segments of the signal.

Notes

History

• 2017-12-29: First edition by Zhenping.

• 2018-07-29: Add the height range for the searching instead of SNR restriction.

• 2018-07-31: Add the maxHThick argument to control the maximum thickness of each output segment.

• 2024-12-20: Direct translated from matlab with ai

References

https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm

ppcpy.calibration.rayleighfit.DP_algorithm(pointList: list, epsilon: float, maxHThick: float) → list

Recursive implementation of the Douglas-Peucker algorithm.

Parameters:

pointListlist

List of points [[x1, y1], [x2, y2], …].

epsilonfloat

Maximum distance.

maxHThickfloat

Maximum thickness for each segment.

Returns:

sigIndxlist

Indices of simplified points.

ppcpy.calibration.rayleighfit.my_dist(pointM: list, pointS: list, pointE: list) → float

Calculate the perpendicular distance between pointM and the line connecting pointS and pointE.

Parameters:

pointMlist

Middle point [x, y].

pointSlist

Start point [x, y].

pointElist

End point [x, y].

Returns:

dfloat

Distance.

ppcpy.calibration.rayleighfit.chi2fit(x: ndarray, y: ndarray, measure_error: ndarray) → tuple

CHI2FIT Chi-2 fitting. All the code are translated from the exemplified code in Numerical Recipes in C (2nd Edition). Great help comes from Birgit Heese.

Parameters:

xndarray

The length of x should be larger than 1.

yndarray

The measured signal.

measure_errorndarray

Measurement errors for the y values.

Returns:

afloat

Intercept of the linear regression.

bfloat

Slope of the linear regression.

sigmaAfloat

Uncertainty of the intercept.

sigmaBfloat

Uncertainty of the slope.

chi2float

Chi-square value.

Qfloat

Goodness of fit.

Notes

History

• 2018-08-03: First edition by Zhenping.

Examples

a, b, sigmaA, sigmaB, chi2, Q = chi2fit(x, y, measure_error)

ppcpy.calibration.rayleighfit.fit_profile(height: ndarray, sig_aer: ndarray, pc: ndarray, bg: ndarray, sig_mol: ndarray, dpIndx: ndarray, layerThickConstrain: float, slopeConstrain: float, SNRConstrain: float, flagShowDetail: bool = False) → tuple

Search the clean region with rayleigh fit algorithm.

Parameters:

heightarray_like

height [m]

sig_aerarray_like

range corrected signal

pcarray_like

photon count signal

bgarray_like

background

sig_molarray_like

range corrected molecular signal

dpIndxarray_like

index of the region calculated by Douglas-Peucker algorithm

layerThickConstrainfloat

constrain for the reference layer thickness [m]

slopeConstrainfloat

constrain for the uncertainty of the regressed extinction coefficient

SNRConstrainfloat

minimum SNR for the signal at the reference height

flagShowDetailbool, optional

if True, calculation information will be printed (default: False)

Returns:

tuple

(hBIndx, hTIndx) - indices of bottom and top of searched region Returns (nan, nan) if region not found