pytesmo package¶

Subpackages¶

Submodules¶

pytesmo.df_metrics module¶

Module contains wrappers for methods in pytesmo.metrics which can be given pandas.DataFrames instead of single numpy.arrays . If the DataFrame has more columns than the function has input parameters the function will be applied pairwise

Created on Aug 14, 2013

@author: Christoph Paulik Christoph.Paulik@geo.tuwien.ac.at

exception pytesmo.df_metrics.DataFrameDimensionError[source]¶

Bases: Exception

args¶

with_traceback()¶: Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

pytesmo.df_metrics.RSS(df)[source]¶

Redidual sum of squares

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

See also

pytesmo.metrics.RSS()

pytesmo.df_metrics.bias(df)[source]¶

Bias

Returns:	bias – of shape (len(df.columns),len(df.columns))
Return type:	pandas.Dataframe

See also

pytesmo.metrics.bias()

pytesmo.df_metrics.kendalltau(df)[source]¶

Wrapper for scipy.stats.kendalltau

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

pytesmo.df_metrics.mse(df)[source]¶

Mean square error (MSE) as a decomposition of the RMSD into individual error components

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

See also

pytesmo.metrics.mse()

pytesmo.df_metrics.nash_sutcliffe(df)[source]¶

Nash Sutcliffe model efficiency coefficient

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

See also

pytesmo.metrics.nash_sutcliffe()

pytesmo.df_metrics.nrmsd(df)[source]¶

Normalized root-mean-square deviation

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

See also

pytesmo.metrics.nrmsd()

pytesmo.df_metrics.pairwise_apply(df, method, comm=False)[source]¶

Compute given method pairwise for all columns, excluding NA/null values

Parameters:	df (pandas.DataFrame) – input data, method will be applied to each column pair method (function) – method to apply to each column pair. has to take 2 input arguments of type numpy.array and return one value or tuple of values
Returns:	results
Return type:	pandas.DataFrame

pytesmo.df_metrics.pearsonr(df)[source]¶

Wrapper for scipy.stats.pearsonr

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

pytesmo.df_metrics.rmsd(df)[source]¶

Root-mean-square deviation

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

See also

pytesmo.metrics.rmsd()

pytesmo.df_metrics.spearmanr(df)[source]¶

Wrapper for scipy.stats.spearmanr

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

See also

pytesmo.metrics.spearmenr(), scipy.stats.spearmenr()

pytesmo.df_metrics.tcol_error(df)[source]¶

Triple collocation error estimate In this case df has to have exactly 3 columns, since triple wise application of a function is not yet implemented and would probably return a complicated structure

Returns:	result – with column names of df
Return type:	namedtuple

See also

pytesmo.metrics.tcol_error()

pytesmo.df_metrics.ubrmsd(df)[source]¶

Unbiased root-mean-square deviation

Returns:	result – with column names of df for which the calculation was done as name of the element separated by ‘_and_’
Return type:	namedtuple

See also

pytesmo.metrics.ubrmsd()

pytesmo.metrics module¶

pytesmo.metrics.RSS(o, p)[source]¶

Residual sum of squares.

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predictions.
Returns:	res – Residual sum of squares.
Return type:	float

pytesmo.metrics.aad(o, p)[source]¶

Average (=mean) absolute deviation (AAD).

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predicitions.
Returns:	d – Mean absolute deviation.
Return type:	float

pytesmo.metrics.bias(o, p)[source]¶

Difference of the mean values.

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predictions.
Returns:	bias – Bias between observations and predictions.
Return type:	float

pytesmo.metrics.check_if_biased(combs, correlated)[source]¶: Supporting function for extended collocation Checks whether the estimators are biased by checking of not too manny data sets (are assumed to have cross-correlated errors)

pytesmo.metrics.ecol(data, correlated=None, err_cov=None, abs_est=True)[source]¶

Extended collocation analysis to obtain estimates of:

signal variances
error variances
signal-to-noise ratios [dB]
error cross-covariances (and -correlations)

based on an arbitrary number of N>3 data sets.

!!! EACH DATA SET MUST BE MEMBER OF >= 1 TRIPLET THAT FULFILLS THE CLASSICAL TRIPLE COLLOCATION ASSUMPTIONS !!!

Parameters:

data (pd.DataFrame) – Temporally matched input data sets in each column
correlated (tuple of tuples (string)) – A tuple containing tuples of data set names (column names), between which the error cross-correlation shall be estimated. e.g. [[‘AMSR-E’,’SMOS’],[‘GLDAS’,’ERA’]] estimates error cross-correlations between (AMSR-E and SMOS), and (GLDAS and ERA), respectively.
err_cov – A priori known error cross-covariances that shall be included in the estimation (to obtain unbiased estimates)
abs_est – Force absolute values for signal and error variance estimates (to mitiate the issue of estimation uncertainties)

Returns:

A dictionary with the following entries (<name> correspond to data set (df column’s) names
- sig_<name> (signal variance of <name>)
- err_<name> (error variance of <name>)
- snr_<name> (SNR (in dB) of <name>)
- err_cov_<name1>_<name2> (error covariance between <name1> and <name2>)
- err_corr_<name1>_<name2> (error correlation between <name1> and <name2>)

Notes

Rescaling parameters can be derived from the signal variances e.g., scaling <src> against <ref>: beta = np.sqrt(sig_<ref> / sig_<src>) rescaled = (data[<src>] - data[<src>].mean()) * beta + data[<ref>].mean()

References

[Gruber2016]

Gruber, A., Su, C. H., Crow, W. T., Zwieback, S., Dorigo, W. A., & Wagner, W. (2016). Estimating error

cross-correlations in soil moisture data sets using extended collocation analysis. Journal of Geophysical Research: Atmospheres, 121(3), 1208-1219.

pytesmo.metrics.index_of_agreement(o, p)[source]¶

Index of agreement was proposed by Willmot (1981), to overcome the insenstivity of Nash-Sutcliffe efficiency E and R^2 to differences in the observed and predicted means and variances (Legates and McCabe, 1999). The index of agreement represents the ratio of the mean square error and the potential error (Willmot, 1984). The potential error in the denominator represents the largest value that the squared difference of each pair can attain. The range of d is similar to that of R^2 and lies between 0 (no correlation) and 1 (perfect fit).

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predictions.
Returns:	d – Index of agreement.
Return type:	float

pytesmo.metrics.kendalltau(o, p)[source]¶

Wrapper for scipy.stats.kendalltau

Parameters:

o (numpy.array) – Observations.
p (numpy.array) – Predictions.

Returns:

Kendall’s tau (float) – The tau statistic
p-value (float) – The two-sided p-palue for a hypothesis test whose null hypothesis is an absence of association, tau = 0.

See also

scipy.stats.kendalltau()

pytesmo.metrics.mad(o, p)[source]¶

Median absolute deviation (MAD).

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predicitions.
Returns:	d – Median absolute deviation.
Return type:	float

pytesmo.metrics.mse(o, p, ddof=0)[source]¶

Mean square error (MSE) as a decomposition of the RMSD into individual error components. The MSE is the second moment (about the origin) of the error, and thus incorporates both the variance of the estimator and its bias. For an unbiased estimator, the MSE is the variance of the estimator. Like the variance, MSE has the same units of measurement as the square of the quantity being estimated. The delta degree of freedom keyword (ddof) can be used to correct for the case the true variance is unknown and estimated from the population. Concretely, the naive sample variance estimator sums the squared deviations and divides by n, which is biased. Dividing instead by n - 1 yields an unbiased estimator.

Parameters:

o (numpy.ndarray) – Observations.
p (numpy.ndarray) – Predictions.
ddof (int, optional) – Delta degree of freedom.The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.

Returns:

mse (float) – Mean square error (MSE).
mse_corr (float) – Correlation component of MSE.
mse_bias (float) – Bias component of the MSE.
mse_var (float) – Variance component of the MSE.

pytesmo.metrics.nash_sutcliffe(o, p)[source]¶

Nash Sutcliffe model efficiency coefficient E. The range of E lies between 1.0 (perfect fit) and -inf.

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predictions.
Returns:	E – Nash Sutcliffe model efficiency coefficient E.
Return type:	float

pytesmo.metrics.nrmsd(o, p, ddof=0)[source]¶

Normalized root-mean-square deviation (nRMSD).

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predictions. ddof (int, optional) – Delta degree of freedom.The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.
Returns:	nrmsd – Normalized root-mean-square deviation (nRMSD).
Return type:	float

pytesmo.metrics.pearson_conf(r, n, c=95)[source]¶

Calcalates the confidence interval of a given pearson correlation coefficient using a fisher z-transform, only valid for correlation coefficients calculated from a bivariate normal distribution

Parameters:

r (float or numpy.ndarray) – Correlation coefficient
n (int or numpy.ndarray) – Number of observations used in determining the correlation coefficient
c (float) – Level of confidence in percent, from 0-100.

Returns:

r_lower (float or numpy.ndarray) – Lower confidence boundary.
r_upper (float or numpy.ndarray) – Upper confidence boundary.

pytesmo.metrics.pearsonr(o, p)[source]¶

Wrapper for scipy.stats.pearsonr. Calculates a Pearson correlation coefficient and the p-value for testing non-correlation.

Parameters:

o (numpy.ndarray) – Observations.
p (numpy.ndarray) – Predictions.

Returns:

r (float) – Pearson’s correlation coefficent.
p-value (float) – 2 tailed p-value.

See also

scipy.stats.pearsonr()

pytesmo.metrics.pearsonr_recursive(x, y, n_old=0, sum_xi_yi=0, sum_xi=0, sum_yi=0, sum_x2=0, sum_y2=0)[source]¶

Calculate pearson correlation in a recursive manner based on

$r_{xy} = \frac{n\sum x_iy_i-\sum x_i\sum y_i} {\sqrt{n\sum x_i^2-(\sum x_i)^2}~\sqrt{n\sum y_i^2-(\sum y_i)^2}}$

Parameters:

x (numpy.ndarray) – New values for x
y (numpy.ndarray) – New values for y
n_old (float, optional) – number of observations from previous pass
sum_xi_yi (float, optional) –

$\sum x_iy_i$

from previous pass
sum_xi (float, optional) –

$\sum x_i$

from previous pass
sum_yi (float, optional) –

$\sum y_i$

from previous pass
sum_x2 (float, optional) –

$\sum x_i^2$

from previous pass
sum_y2 (float, optional) –

$\sum y_i^2$

from previous pass

Returns:

r (float) – Pearson correlation coefficient
params (tuple) – tuple of (n_new, sum_xi_yi, sum_xi, sum_yi, sum_x2, sum_y2) . Can be used when calling the next iteration as *params.

pytesmo.metrics.rmsd(o, p, ddof=0)[source]¶

Root-mean-square deviation (RMSD). It is implemented for an unbiased estimator, which means the RMSD is the square root of the variance, also known as the standard error. The delta degree of freedom keyword (ddof) can be used to correct for the case the true variance is unknown and estimated from the population. Concretely, the naive sample variance estimator sums the squared deviations and divides by n, which is biased. Dividing instead by n -1 yields an unbiased estimator

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predictions. ddof (int, optional) – Delta degree of freedom.The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.
Returns:	rmsd – Root-mean-square deviation.
Return type:	float

pytesmo.metrics.spearmanr(o, p)[source]¶

Wrapper for scipy.stats.spearmanr. Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation.

Parameters:

o (numpy.ndarray) – Observations.
p (numpy.ndarray) – Predictions.

Returns:

rho (float) – Spearman correlation coefficient
p-value (float) – The two-sided p-value for a hypothesis test whose null hypothesis is that two sets of data are uncorrelated

See also

scipy.stats.spearmenr()

pytesmo.metrics.tcol_error(x, y, z)[source]¶

Triple collocation error estimate of three calibrated/scaled datasets.

Parameters:

x (numpy.ndarray) – 1D numpy array to calculate the errors
y (numpy.ndarray) – 1D numpy array to calculate the errors
z (numpy.ndarray) – 1D numpy array to calculate the errors

Returns:

e_x (float) – Triple collocation error for x.
e_y (float) – Triple collocation error for y.
e_z (float) – Triple collocation error for z.

Notes

This function estimates the triple collocation error based on already scaled/calibrated input data. It follows formula 4 given in [Scipal2008].

$\sigma_{\varepsilon_x}^2 = \langle (x-y)(x-z) \rangle$

$\sigma_{\varepsilon_y}^2 = \langle (y-x)(y-z) \rangle$

$\sigma_{\varepsilon_z}^2 = \langle (z-x)(z-y) \rangle$

where the $\langle\rangle$ brackets mean the temporal mean.

References

[Scipal2008]

Scipal, K., Holmes, T., De Jeu, R., Naeimi, V., & Wagner, W. (2008). A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophysical Research Letters, 35(24), .

pytesmo.metrics.tcol_snr(x, y, z, ref_ind=0)[source]¶

triple collocation based estimation of signal-to-noise ratio, absolute errors, and rescaling coefficients

Parameters:

x (1D numpy.ndarray) – first input dataset
y (1D numpy.ndarray) – second input dataset
z (1D numpy.ndarray) – third input dataset
ref_ind (int) – index of reference data set for estimating scaling coefficients. default: 0 (x)

Returns:

snr (numpy.ndarray) – signal-to-noise (variance) ratio [dB]
err_std (numpy.ndarray) – SCALED error standard deviation
beta (numpy.ndarray) – scaling coefficients (i_scaled = i * beta_i)

Notes

This function estimates the triple collocation errors, the scaling parameter $\beta$ and the signal to noise ratio directly from the covariances of the dataset. For a general overview and how this function and pytesmo.metrics.tcol_error() are related please see [Gruber2015].

Estimation of the error variances from the covariances of the datasets (e.g. $\sigma_{XY}$ for the covariance between $x$ and $y$ ) is done using the following formula:

$\sigma_{\varepsilon_x}^2 = \sigma_{X}^2 - \frac{\sigma_{XY}\sigma_{XZ}}{\sigma_{YZ}}$

$\sigma_{\varepsilon_y}^2 = \sigma_{Y}^2 - \frac{\sigma_{YX}\sigma_{YZ}}{\sigma_{XZ}}$

$\sigma_{\varepsilon_z}^2 = \sigma_{Z}^2 - \frac{\sigma_{ZY}\sigma_{ZX}}{\sigma_{YX}}$

$\beta$ can also be estimated from the covariances:

$\beta_x = 1$

$\beta_y = \frac{\sigma_{XZ}}{\sigma_{YZ}}$

$\beta_z=\frac{\sigma_{XY}}{\sigma_{ZY}}$

The signal to noise ratio (SNR) is also calculated from the variances and covariances:

$\text{SNR}_X[dB] = -10\log\left(\frac{\sigma_{X}^2\sigma_{YZ}}{\sigma_{XY}\sigma_{XZ}}-1\right)$

$\text{SNR}_Y[dB] = -10\log\left(\frac{\sigma_{Y}^2\sigma_{XZ}}{\sigma_{YX}\sigma_{YZ}}-1\right)$

$\text{SNR}_Z[dB] = -10\log\left(\frac{\sigma_{Z}^2\sigma_{XY}}{\sigma_{ZX}\sigma_{ZY}}-1\right)$

It is given in dB to make it symmetric around zero. If the value is zero it means that the signal variance and the noise variance are equal. +3dB means that the signal variance is twice as high as the noise variance.

References

[Gruber2015]

Gruber, A., Su, C., Zwieback, S., Crow, W., Dorigo, W., Wagner, W. (2015). Recent advances in (soil moisture) triple collocation analysis. International Journal of Applied Earth Observation and Geoinformation, in review

pytesmo.metrics.ubrmsd(o, p, ddof=0)[source]¶

Unbiased root-mean-square deviation (uRMSD).

Parameters:	o (numpy.ndarray) – Observations. p (numpy.ndarray) – Predictions. ddof (int, optional) – Delta degree of freedom.The divisor used in calculations is N - ddof, where N represents the number of elements. By default ddof is zero.
Returns:	urmsd – Unbiased root-mean-square deviation (uRMSD).
Return type:	float

pytesmo.scaling module¶

Created on Apr 17, 2013

@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at

pytesmo.scaling.add_scaled(df, method='linreg', label_in=None, label_scale=None)[source]¶

takes a dataframe and appends a scaled time series to it. If no labels are given the first column will be scaled to the second column of the DataFrame

Parameters:	df (pandas.DataFrame) – input dataframe method (string) – scaling method label_in (string, optional) – the column of the dataframe that should be scaled to that with label_scale default is the first column label_scale (string, optional) – the column of the dataframe the label_in column should be scaled to default is the second column
Returns:	df – input dataframe with new column labeled label_in+’_scaled_’+method
Return type:	pandas.DataFrame

pytesmo.scaling.cdf_match(src, ref, min_val=None, max_val=None, nbins=100)[source]¶

computes cumulative density functions of src and ref at their respective bin-edges by 5th order spline interpolation; then matches CDF of src to CDF of ref.

This function does not make sure that the percentiles are unique so it can happen that multiple measurements are scaled to one point or that there are NaN values in the output array.

Parameters:	src (numpy.array) – input dataset which will be scaled ref (numpy.array) – src will be scaled to this dataset min_val (float, optional) – Minimum allowed value, output data is capped at this value max_val (float, optional) – Maximum allowed value, output data is capped at this value nbins (int, optional) – Number of bins to use for estimation of the CDF
Returns:	CDF matched values – dataset src with CDF as ref
Return type:	numpy.array

pytesmo.scaling.gen_cdf_match(src, perc_src, perc_ref, min_val=None, max_val=None, k=1)[source]¶

General cdf matching:

computes discrete cumulative density functions of src- and ref at the given percentiles
computes continuous CDFs by k-th order spline fitting
CDF of src is matched to CDF of ref

Parameters:	src (numpy.array) – input dataset which will be scaled perc_src (numpy.array) – percentiles of src perc_ref (numpy.array) – percentiles of reference data estimated through method of choice, must be same size as perc_src min_val (float, optional) – Minimum allowed value, output data is capped at this value max_val (float, optional) – Maximum allowed value, output data is capped at this value k (int, optional) – Order of spline to fit
Returns:	CDF matched values – dataset src with CDF as ref
Return type:	numpy.array

pytesmo.scaling.get_scaling_function(method)[source]¶

Get scaling function based on method name.

Parameters:	method (string) – method name as string
Returns:	scaling_func – function(src:numpy.ndarray, ref:numpy.ndarray) > scaled_src:np.ndarray
Return type:	function
Raises:	KeyError: – if method is not found

pytesmo.scaling.get_scaling_method_lut()[source]¶

Get all defined scaling methods and their function names.

Returns:	lut – key: scaling method name value: function
Return type:	dictionary

pytesmo.scaling.lin_cdf_match(src, ref, min_val=None, max_val=None, percentiles=[0, 5, 10, 30, 50, 70, 90, 95, 100])[source]¶

computes cumulative density functions of src and ref at their respective bin-edges by linear interpolation; then matches CDF of src to CDF of ref.

This function does not make sure that the percentiles are unique so it can happen that multiple measurements are scaled to one point or that there are NaN values in the output array.

Parameters:	src (numpy.array) – input dataset which will be scaled ref (numpy.array) – src will be scaled to this dataset min_val (float, optional) – Minimum allowed value, output data is capped at this value max_val (float, optional) – Maximum allowed value, output data is capped at this value percentiles (list or numpy.ndarray) – Percentiles to use for CDF matching
Returns:	CDF matched values – dataset src with CDF as ref
Return type:	numpy.array

pytesmo.scaling.lin_cdf_match_stored_params(src, perc_src, perc_ref, min_val=None, max_val=None)[source]¶

Performs cdf matching using given percentiles.

Parameters:

src (numpy.array) – input data to scale
perc_src (numpy.array) – percentiles of src estimated through method of choice
perc_ref (numpy.array) – percentiles of reference data estimated through method of choice, must be same size as perc_src
min_val (float, optional) – Minimum allowed value, output data is capped at this value
max_val (float, optional) – Maximum allowed value, output data is capped at this value

pytesmo.scaling.linreg(src, ref)[source]¶

scales the input datasets using linear regression

Parameters:	src (numpy.array) – input dataset which will be scaled ref (numpy.array) – src will be scaled to this dataset
Returns:	scaled dataset – dataset scaled using linear regression
Return type:	numpy.array

pytesmo.scaling.mean_std(src, ref)[source]¶

scales the input datasets so that they have the same mean and standard deviation afterwards

Parameters:	src (numpy.array) – input dataset which will be scaled ref (numpy.array) – src will be scaled to this dataset
Returns:	scaled dataset – dataset src with same mean and standard deviation as ref
Return type:	numpy.array

pytesmo.scaling.min_max(src, ref)[source]¶

scales the input datasets so that they have the same minimum and maximum afterwards

Parameters:	src (numpy.array) – input dataset which will be scaled ref (numpy.array) – src will be scaled to this dataset
Returns:	scaled dataset – dataset src with same maximum and minimum as ref
Return type:	numpy.array

pytesmo.scaling.scale(df, method='linreg', reference_index=0)[source]¶

takes pandas.DataFrame and scales all columns to the column specified by reference_index with the chosen method

Parameters:	df (pandas.DataFrame) – containing matched time series that should be scaled method (string, optional) – method definition, has to be a function in globals() that takes 2 numpy.array as input and returns one numpy.array of same length reference_index (int, optional) – default 0, column index of reference dataset in dataframe
Returns:	scaled data – all time series of the input DataFrame scaled to the one specified by reference_index
Return type:	pandas.DataFrame

pytesmo.temporal_matching module¶

Provides a temporal matching function

pytesmo.temporal_matching.df_match(reference, *args, **kwds)[source]¶

Finds temporal match between the reference pandas.DataFrame (index has to be datetime) and n other pandas.DataFrame (index has to be datetime).

Parameters:	reference (pandas.DataFrame or pandas.TimeSeries) – The index of this dataframe will be the reference. args (pandas.DataFrame* or pandas.TimeSeries) – The index of this dataframe(s) will be matched. If it is a pandas.Series then it has to have a name. Otherwise no column name can be assigned to the matched DataFrame. window (float) – Fraction of days of the maximum pos./neg. distance allowed, i.e. the value of window represents the half-winow size (e.g. window=0.5, will search for matches between -12 and +12 hours) (default: None) dropna (boolean) – Drop rows containing only NaNs (default: False) dropduplicates (boolean) – Drop duplicated temporal matched (default: False) asym_window (string, optional) – `<=` stands for using a smaller and equal only for the left/smaller side of the window comparison `>=` stands for using a larger and equal only for the right/larger side of the window comparison The default is to use <= and >= for both sides of the search window
Returns:	temporal_matched_args – Dataframe with index from matched reference index
Return type:	pandas.DataFrame or tuple of pandas.DataFrame

pytesmo.temporal_matching.matching(reference, *args, **kwargs)[source]¶

Finds temporal match between the reference pandas.TimeSeries (index has to be datetime) and n other pandas.TimeSeries (index has to be datetime).

Parameters:	reference (pandas.TimeSeries) – The index of this Series will be the reference. args (pandas.TimeSeries) – The index of these Series(s) will be matched. window* (float) – Fraction of days of the maximum pos./neg. distance allowed, i.e. the value of window represents the half-winow size (e.g. window=0.5, will search for matches between -12 and +12 hours) (default: None)
Returns:	temporal_match – containing the index of the reference Series and a column for each of the other input Series
Return type:	pandas.DataFrame

pytesmo.utils module¶

Module containing utility functions that do not fit into other modules

pytesmo.utils.betainc(x, a, b)[source]¶

pytesmo.utils.element_iterable(el)[source]¶

Test if a element is iterable

Parameters:	el (object) –
Returns:	iterable – if True then then el is iterable if Fales then not
Return type:	boolean

pytesmo.utils.ensure_iterable(el)[source]¶

Ensure that an object is iterable by putting it into a list. Strings are handled slightly differently. They are technically iterable but we want to keep the whole.

Parameters:	el (object) –
Returns:	iterable – [el]
Return type:	list

pytesmo.utils.interp_uniq(src)[source]¶

replace non unique values by their linear interpolated value This method interpolates iteratively like it is done in IDL.

Parameters:	src (numpy.array) – array to ensure uniqueness of
Returns:	src – interpolated unique values in array of same size as src
Return type:	numpy.array

pytesmo.utils.ml_percentile(in_data, percentiles)[source]¶

Calculate percentiles in the way Matlab and IDL do it.

By using interpolation between the lowest an highest rank and the minimum and maximum outside.

Parameters:	in_data (numpy.ndarray) – input data percentiles (numpy.ndarray) – percentiles at which to calculate the values
Returns:	perc – values of the percentiles
Return type:	numpy.ndarray

pytesmo.utils.unique_percentiles_beta(perc_values, percentiles)[source]¶

Compute unique percentile values by fitting the CDF of a beta distribution to the percentiles.

Parameters:	perc_values (list or numpy.ndarray) – calculated values for the given percentiles percentiles (list or numpy.ndarray) – Percentiles to use for CDF matching
Returns:	uniq_perc_values – Unique percentile values generated through fitting the CDF of a beta distribution.
Return type:	numpy.ndarray
Raises:	`RuntimeError` – If no fit could be found.

pytesmo.utils.unique_percentiles_interpolate(perc_values, percentiles=[0, 5, 10, 30, 50, 70, 90, 95, 100], k=1)[source]¶

Try to ensure that percentile values are unique and have values for the given percentiles.

If only all the values in perc_values are the same. The array is unchanged.

Parameters:	perc_values (list or numpy.ndarray) – calculated values for the given percentiles percentiles (list or numpy.ndarray) – Percentiles to use for CDF matching k (int) – Degree of spline interpolation to use for filling duplicate percentile values
Returns:	uniq_perc_values – Unique percentile values generated through linear interpolation over removed duplicate percentile values
Return type:	numpy.ndarray

pytesmo package¶

Subpackages¶

Submodules¶

pytesmo.df_metrics module¶

pytesmo.metrics module¶

pytesmo.scaling module¶

pytesmo.temporal_matching module¶

pytesmo.utils module¶

Module contents¶