Stats

Stats#

Utilities#

correlation()#

cor(x, y, method='pearson', show=False)[source]#

Density estimation

Computes kernel density estimates.

Parameters:

  • x (Union[list, np.array, pd.Series]) – Vectors of values.

  • y (Union[list, np.array, pd.Series]) – Vectors of values.

  • method (str) – Correlation method. Can be one of "pearson", "spearman", "kendall".

  • show (bool) – Draw a scatterplot with a regression line.

Returns:

r – The correlation coefficient.

Examples

In [1]: import neurokit2 as nk

In [2]: x = [1, 2, 3, 4, 5]

In [3]: y = [3, 1, 5, 6, 6]

In [4]: corr = nk.cor(x, y, method="pearson", show=True)

In [5]: corr
Out[5]: 0.8022574532384201
../_images/p_cor1.png

density()#

density(x, desired_length=100, bandwidth='Scott', show=False, **kwargs)[source]#

Density estimation.

Computes kernel density estimates.

Parameters:

  • x (Union[list, np.array, pd.Series]) – A vector of values.

  • desired_length (int) – The amount of values in the returned density estimation.

  • bandwidth (float) – Passed to the method argument from the density_bandwidth() function.

  • show (bool) – Display the density plot.

  • **kwargs – Additional arguments to be passed to density_bandwidth().

Returns:

  • x – The x axis of the density estimation.

  • y – The y axis of the density estimation.

See also

density_bandwidth

Examples

In [1]: import neurokit2 as nk

In [2]: signal = nk.ecg_simulate(duration=20)

In [3]: x, y = nk.density(signal, bandwidth=0.5, show=True)
../_images/p_density1.png 
# Bandwidth comparison
In [4]: _, y2 = nk.density(signal, bandwidth=1)

In [5]: _, y3 = nk.density(signal, bandwidth=2)

In [6]: _, y4 = nk.density(signal, bandwidth="scott")

In [7]: _, y5 = nk.density(signal, bandwidth="silverman")

In [8]: _, y6 = nk.density(signal, bandwidth="kernsmooth")

In [9]: nk.signal_plot([y, y2, y3, y4, y5, y6],
   ...:                labels=["0.5", "1", "2", "Scott", "Silverman", "KernSmooth"])
   ...:
../_images/p_density2.png

distance()#

distance(X=None, method='mahalanobis')[source]#

Distance

Compute distance using different metrics.

Parameters:

  • X (array or DataFrame) – A dataframe of values.

  • method (str) – The method to use. One of "mahalanobis" or "mean" for the average distance from the mean.

Returns:

array – Vector containing the distance values.

Examples

In [1]: import neurokit2 as nk

# Load the iris dataset
In [2]: data = nk.data("iris").drop("Species", axis=1)

In [3]: data["Distance"] = nk.distance(data, method="mahalanobis")

In [4]: fig = data.plot(x="Petal.Length", y="Petal.Width", s="Distance", c="Distance", kind="scatter")
../_images/p_distance1.png 
In [5]: data["DistanceZ"] = np.abs(nk.distance(data.drop("Distance", axis=1), method="mean"))

In [6]: fig = data.plot(x="Petal.Length", y="Sepal.Length", s="DistanceZ", c="DistanceZ", kind="scatter")
../_images/p_distance2.png

hdi()#

hdi(x, ci=0.95, show=False, **kwargs)[source]#

Highest Density Interval (HDI)

Compute the Highest Density Interval (HDI) of a distribution. All points within this interval have a higher probability density than points outside the interval. The HDI can be used in the context of uncertainty characterisation of posterior distributions (in the Bayesian farmework) as Credible Interval (CI). Unlike equal-tailed intervals that typically exclude 2.5% from each tail of the distribution and always include the median, the HDI is not equal-tailed and therefore always includes the mode(s) of posterior distributions.

Parameters:

  • x (Union[list, np.array, pd.Series]) – A vector of values.

  • ci (float) – Value of probability of the (credible) interval - CI (between 0 and 1) to be estimated. Default to .95 (95%).

  • show (bool) – If True, the function will produce a figure.

  • **kwargs (Line2D properties) – Other arguments to be passed to nk.density().

See also

density

Returns:

  • float(s) – The HDI low and high limits.

  • fig – Distribution plot.

Examples

In [1]: import numpy as np

In [2]: import neurokit2 as nk

In [3]: x = np.random.normal(loc=0, scale=1, size=100000)

In [4]: ci_min, ci_high = nk.hdi(x, ci=0.95, show=True)
../_images/p_hdi1.png

mad()#

mad(x, constant=1.4826, **kwargs)[source]#

Median Absolute Deviation: a “robust” version of standard deviation

Parameters:

  • x (Union[list, np.array, pd.Series]) – A vector of values.

  • constant (float) – Scale factor. Use 1.4826 for results similar to default R.

Returns:

float – The MAD.

Examples

In [1]: import neurokit2 as nk

In [2]: nk.mad([2, 8, 7, 5, 4, 12, 5, 1])
Out[2]: 3.7064999999999997

References

rescale()#

rescale(data, to=[0, 1], scale=None)[source]#

Rescale data

Rescale a numeric variable to a new range.

Parameters:

  • data (Union[list, np.array, pd.Series]) – Raw data.

  • to (list) – New range of values of the data after rescaling. Must be a list or tuple of two values. If more values, the function will assume it is another signal and will derive the min and max from it.

  • scale (list) – A list or tuple of two values specifying the actual range of the data. If None, the minimum and the maximum of the provided data will be used.

Returns:

list – The rescaled values.

Examples

In [1]: import neurokit2 as nk

# Normalize to 0-1
In [2]: nk.rescale([3, 1, 2, 4, 6], to=[0, 1])
Out[2]: [0.4, 0.0, 0.2, 0.6000000000000001, 1.0]

# Rescale to 0-1, but specify that 0 corresponds to another value that the
# minimum of the data.
In [3]: nk.rescale([3, 1, 2, 4, 6], to=[0, 1], scale=[0, 6])
Out[3]: [0.5, 0.16666666666666666, 0.3333333333333333, 0.6666666666666666, 1.0]

# Rescale to 0-4 (the min-max of another signal)
In [4]: nk.rescale([3, 1, 2, 4, 6], to=[0, 1, 3, 4])
Out[4]: [1.6, 0.0, 0.8, 2.4000000000000004, 4.0]

standardize()#

standardize(data, robust=False, window=None, **kwargs)[source]#

Standardization of data

Performs a standardization of data (Z-scoring), i.e., centering and scaling, so that the data is expressed in terms of standard deviation (i.e., mean = 0, SD = 1) or Median Absolute Deviance (median = 0, MAD = 1).

Parameters:

  • data (Union[list, np.array, pd.Series]) – Raw data.

  • robust (bool) – If True, centering is done by substracting the median from the variables and dividing it by the median absolute deviation (MAD). If False, variables are standardized by substracting the mean and dividing it by the standard deviation (SD).

  • window (int) – Perform a rolling window standardization, i.e., apply a standardization on a window of the specified number of samples that rolls along the main axis of the signal. Can be used for complex detrending.

  • **kwargs (optional) – Other arguments to be passed to pandas.rolling().

Returns:

list – The standardized values.

Examples

In [1]: import neurokit2 as nk

In [2]: import pandas as pd

# Simple example
In [3]: nk.standardize([3, 1, 2, 4, 6, np.nan])
Out[3]: 
[-0.10397504898200735,
 -1.14372553880208,
 -0.6238502938920437,
 0.41590019592802896,
 1.4556506857481015,
 nan]

In [4]: nk.standardize([3, 1, 2, 4, 6, np.nan], robust=True)
Out[4]: 
[0.0,
 -1.3489815189531904,
 -0.6744907594765952,
 0.6744907594765952,
 2.023472278429786,
 nan]

In [5]: nk.standardize(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]).T)
Out[5]: 
array([[-1.161895  , -1.161895  ],
       [-0.38729833, -0.38729833],
       [ 0.38729833,  0.38729833],
       [ 1.161895  ,  1.161895  ]])

In [6]: nk.standardize(pd.DataFrame({"A": [3, 1, 2, 4, 6, np.nan],
   ...:                              "B": [3, 1, 2, 4, 6, 5]}))
   ...: 
Out[6]: 
          A         B
0 -0.103975 -0.267261
1 -1.143726 -1.336306
2 -0.623850 -0.801784
3  0.415900  0.267261
4  1.455651  1.336306
5       NaN  0.801784

# Rolling standardization of a signal
In [7]: signal = nk.signal_simulate(frequency=[0.1, 2], sampling_rate=200)

In [8]: z = nk.standardize(signal, window=200)

In [9]: nk.signal_plot([signal, z], standardize=True)
../_images/p_standardize1.png

summary()#

summary_plot(x, errorbars=0, **kwargs)[source]#

Descriptive plot

Visualize a distribution with density, histogram, boxplot and rugs plots all at once.

Examples

In [1]: import neurokit2 as nk

In [2]: import numpy as np

In [3]: x = np.random.normal(size=100)

In [4]: fig = nk.summary_plot(x)
../_images/p_summary1.png

Clustering#

cluster()#

cluster(data, method='kmeans', n_clusters=2, random_state=None, optimize=False, **kwargs)[source]#

Data Clustering

Performs clustering of data using different algorithms.

  • kmod: Modified k-means algorithm.

  • kmeans: Normal k-means.

  • kmedoids: k-medoids clustering, a more stable version of k-means.

  • pca: Principal Component Analysis.

  • ica: Independent Component Analysis.

  • aahc: Atomize and Agglomerate Hierarchical Clustering. Computationally heavy.

  • hierarchical

  • spectral

  • mixture

  • mixturebayesian

See sklearn for methods details.

Parameters:

  • data (np.ndarray) – Matrix array of data (E.g., an array (channels, times) of M/EEG data).

  • method (str) – The algorithm for clustering. Can be one of "kmeans" (default), "kmod", "kmedoids", "pca", "ica", "aahc", "hierarchical", "spectral", "mixture", "mixturebayesian".

  • n_clusters (int) – The desired number of clusters.

  • random_state (Union[int, numpy.random.RandomState]) – The RandomState for the random number generator. Defaults to None, in which case a different random state is chosen each time this function is called.

  • optimize (bool) – Optimized method in Poulsen et al. (2018) for the k-means modified method.

  • **kwargs – Other arguments to be passed into sklearn functions.

Returns:

  • clustering (DataFrame) – Information about the distance of samples from their respective clusters.

  • clusters (np.ndarray) – Coordinates of cluster centers, which has a shape of n_clusters x n_features.

  • info (dict) – Information about the number of clusters, the function and model used for clustering.

Examples

In [1]: import neurokit2 as nk

In [2]: import matplotlib.pyplot as plt

# Load the iris dataset
In [3]: data = nk.data("iris").drop("Species", axis=1)

# Cluster using different methods
In [4]: clustering_kmeans, clusters_kmeans, info = nk.cluster(data, method="kmeans", n_clusters=3)

In [5]: clustering_spectral, clusters_spectral, info = nk.cluster(data, method="spectral", n_clusters=3)

In [6]: clustering_hierarchical, clusters_hierarchical, info = nk.cluster(data, method="hierarchical", n_clusters=3)

In [7]: clustering_agglomerative, clusters_agglomerative, info= nk.cluster(data, method="agglomerative", n_clusters=3)

In [8]: clustering_mixture, clusters_mixture, info = nk.cluster(data, method="mixture", n_clusters=3)

In [9]: clustering_bayes, clusters_bayes, info = nk.cluster(data, method="mixturebayesian", n_clusters=3)

In [10]: clustering_pca, clusters_pca, info = nk.cluster(data, method="pca", n_clusters=3)

In [11]: clustering_ica, clusters_ica, info = nk.cluster(data, method="ica", n_clusters=3)

In [12]: clustering_kmod, clusters_kmod, info = nk.cluster(data, method="kmod", n_clusters=3)

In [13]: clustering_kmedoids, clusters_kmedoids, info = nk.cluster(data, method="kmedoids", n_clusters=3)

In [14]: clustering_aahc, clusters_aahc, info = nk.cluster(data, method='aahc_frederic', n_clusters=3)

# Visualize classification and 'average cluster'
In [15]: fig, axes = plt.subplots(ncols=2, nrows=5)

In [16]: axes[0, 0].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_kmeans['Cluster'])
Out[16]: <matplotlib.collections.PathCollection at 0x1a742b39de0>

In [17]: axes[0, 0].scatter(clusters_kmeans[:, 2], clusters_kmeans[:, 3], c='red')
Out[17]: <matplotlib.collections.PathCollection at 0x1a7425df490>

In [18]: axes[0, 0].set_title("k-means")
Out[18]: Text(0.5, 1.0, 'k-means')

In [19]: axes[0, 1].scatter(data.iloc[:,[2]], data.iloc[:, [3]], c=clustering_spectral['Cluster'])
Out[19]: <matplotlib.collections.PathCollection at 0x1a742b5c970>

In [20]: axes[0, 1].scatter(clusters_spectral[:, 2], clusters_spectral[:, 3], c='red')
Out[20]: <matplotlib.collections.PathCollection at 0x1a7425df460>

In [21]: axes[0, 1].set_title("Spectral")
Out[21]: Text(0.5, 1.0, 'Spectral')

In [22]: axes[1, 0].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_hierarchical['Cluster'])
Out[22]: <matplotlib.collections.PathCollection at 0x1a74ab79270>

In [23]: axes[1, 0].scatter(clusters_hierarchical[:, 2], clusters_hierarchical[:, 3], c='red')
Out[23]: <matplotlib.collections.PathCollection at 0x1a73b77ae00>

In [24]: axes[1, 0].set_title("Hierarchical")
Out[24]: Text(0.5, 1.0, 'Hierarchical')

In [25]: axes[1, 1].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_agglomerative['Cluster'])
Out[25]: <matplotlib.collections.PathCollection at 0x1a742b3bca0>

In [26]: axes[1, 1].scatter(clusters_agglomerative[:, 2], clusters_agglomerative[:, 3], c='red')
Out[26]: <matplotlib.collections.PathCollection at 0x1a73b779030>

In [27]: axes[1, 1].set_title("Agglomerative")
Out[27]: Text(0.5, 1.0, 'Agglomerative')

In [28]: axes[2, 0].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_mixture['Cluster'])
Out[28]: <matplotlib.collections.PathCollection at 0x1a742b5d5d0>

In [29]: axes[2, 0].scatter(clusters_mixture[:, 2], clusters_mixture[:, 3], c='red')
Out[29]: <matplotlib.collections.PathCollection at 0x1a742b5d690>

In [30]: axes[2, 0].set_title("Mixture")
Out[30]: Text(0.5, 1.0, 'Mixture')

In [31]: axes[2, 1].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_bayes['Cluster'])
Out[31]: <matplotlib.collections.PathCollection at 0x1a742b5df60>

In [32]: axes[2, 1].scatter(clusters_bayes[:, 2], clusters_bayes[:, 3], c='red')
Out[32]: <matplotlib.collections.PathCollection at 0x1a742b5dff0>

In [33]: axes[2, 1].set_title("Bayesian Mixture")
Out[33]: Text(0.5, 1.0, 'Bayesian Mixture')

In [34]: axes[3, 0].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_pca['Cluster'])
Out[34]: <matplotlib.collections.PathCollection at 0x1a742b5e5c0>

In [35]: axes[3, 0].scatter(clusters_pca[:, 2], clusters_pca[:, 3], c='red')
Out[35]: <matplotlib.collections.PathCollection at 0x1a742b5e920>

In [36]: axes[3, 0].set_title("PCA")
Out[36]: Text(0.5, 1.0, 'PCA')

In [37]: axes[3, 1].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_ica['Cluster'])
Out[37]: <matplotlib.collections.PathCollection at 0x1a742b5ee60>

In [38]: axes[3, 1].scatter(clusters_ica[:, 2], clusters_ica[:, 3], c='red')
Out[38]: <matplotlib.collections.PathCollection at 0x1a742b5f1c0>

In [39]: axes[3, 1].set_title("ICA")
Out[39]: Text(0.5, 1.0, 'ICA')

In [40]: axes[4, 0].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_kmod['Cluster'])
Out[40]: <matplotlib.collections.PathCollection at 0x1a742b5ee00>

In [41]: axes[4, 0].scatter(clusters_kmod[:, 2], clusters_kmod[:, 3], c='red')
Out[41]: <matplotlib.collections.PathCollection at 0x1a742b5f880>

In [42]: axes[4, 0].set_title("modified K-means")
Out[42]: Text(0.5, 1.0, 'modified K-means')

In [43]: axes[4, 1].scatter(data.iloc[:,[2]], data.iloc[:,[3]], c=clustering_aahc['Cluster'])
Out[43]: <matplotlib.collections.PathCollection at 0x1a742b5fee0>

In [44]: axes[4, 1].scatter(clusters_aahc[:, 2], clusters_aahc[:, 3], c='red')
Out[44]: <matplotlib.collections.PathCollection at 0x1a742b5ff70>

In [45]: axes[4, 1].set_title("AAHC (Frederic's method)")
Out[45]: Text(0.5, 1.0, "AAHC (Frederic's method)")
../_images/p_cluster1.png

References

  • Park, H. S., & Jun, C. H. (2009). A simple and fast algorithm for K-medoids clustering. Expert systems with applications, 36(2), 3336-3341.

cluster_findnumber()#

cluster_findnumber(data, method='kmeans', n_max=10, show=False, **kwargs)[source]#

Optimal Number of Clusters

Find the optimal number of clusters based on different indices of quality of fit.

Parameters:

  • data (np.ndarray) – An array (channels, times) of M/EEG data.

  • method (str) – The clustering algorithm to be passed into nk.cluster().

  • n_max (int) – Runs the clustering alogrithm from 1 to n_max desired clusters in nk.cluster() with quality metrices produced for each cluster number.

  • show (bool) – Plot indices normalized on the same scale.

  • **kwargs – Other arguments to be passed into nk.cluster() and nk.cluster_quality().

Returns:

DataFrame – The different quality scores for each number of clusters:

  • Score_Silhouette

  • Score_Calinski

  • Score_Bouldin

  • Score_VarianceExplained

  • Score_GAP

  • Score_GAPmod

  • Score_GAP_diff

  • Score_GAPmod_diff

See also

cluster, cluster_quality

Examples

In [1]: import neurokit2 as nk

# Load the iris dataset
In [2]: data = nk.data("iris").drop("Species", axis=1)

# How many clusters
In [3]: results = nk.cluster_findnumber(data, method="kmeans", show=True)
../_images/p_cluster_findnumber1.png

cluster_quality()#

cluster_quality(data, clustering, clusters=None, info=None, n_random=10, random_state=None, **kwargs)[source]#

Assess Clustering Quality

Compute quality of the clustering using several metrics.

Parameters:

  • data (np.ndarray) – A matrix array of data (e.g., channels, sample points of M/EEG data)

  • clustering (DataFrame) – Information about the distance of samples from their respective clusters, generated from cluster().

  • clusters (np.ndarray) – Coordinates of cluster centers, which has a shape of n_clusters x n_features, generated from cluster().

  • info (dict) – Information about the number of clusters, the function and model used for clustering, generated from cluster().

  • n_random (int) – The number of random initializations to cluster random data for calculating the GAP statistic.

  • random_state (None, int, numpy.random.RandomState or numpy.random.Generator) – Seed for the random number generator. See for misc.check_random_state for further information.

  • **kwargs – Other argument to be passed on, for instance GFP as 'sd' in microstates.

Returns:

  • individual (DataFrame) – Indices of cluster quality scores for each sample point.

  • general (DataFrame) – Indices of cluster quality scores for all clusters.

Examples

In [1]: import neurokit2 as nk

# Load the iris dataset
In [2]: data = nk.data("iris").drop("Species", axis=1)

# Cluster
In [3]: clustering, clusters, info = nk.cluster(data, method="kmeans", n_clusters=3)

# Compute indices of clustering quality
In [4]: individual, general = nk.cluster_quality(data, clustering, clusters, info)

In [5]: general
Out[5]: 
   n_Clusters  Score_Silhouette  ...  Score_GAP_sk  Score_GAPmod_sk
0         3.0          0.552819  ...      0.312362      1155.431941

[1 rows x 12 columns]

References

  • Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.

  • Mohajer, M., Englmeier, K. H., & Schmid, V. J. (2011). A comparison of Gap statistic definitions with and without logarithm function. arXiv preprint arXiv:1103.4767.

Indices of fit#

fit_error()#

fit_error(y, y_predicted, n_parameters=2)[source]#

Calculate the fit error for a model

Also specific and direct access functions can be used, such as fit_mse(), fit_rmse() and fit_r2().

Parameters:

  • y (Union[list, np.array, pd.Series]) – The response variable (the y axis).

  • y_predicted (Union[list, np.array, pd.Series]) – The fitted data generated by a model.

  • n_parameters (int) – Number of model parameters (for the degrees of freedom used in R2).

Returns:

dict – A dictionary containing different indices of fit error.

See also

fit_mse, fit_rmse, fit_r2

Examples

In [1]: import neurokit2 as nk

In [2]: y = np.array([-1.0, -0.5, 0, 0.5, 1])

In [3]: y_predicted = np.array([0.0, 0, 0, 0, 0])

# Master function
In [4]: x = nk.fit_error(y, y_predicted)

In [5]: x
Out[5]: 
{'SSE': 2.5,
 'MSE': 0.5,
 'RMSE': 0.7071067811865476,
 'R2': 0.7071067811865475,
 'R2_adjusted': 0.057190958417936755}

# Direct access
In [6]: nk.fit_mse(y, y_predicted)
Out[6]: 0.5

In [7]: nk.fit_rmse(y, y_predicted)
Out[7]: 0.7071067811865476

In [8]: nk.fit_r2(y, y_predicted, adjusted=False)
Out[8]: 0.7071067811865475

In [9]: nk.fit_r2(y, y_predicted, adjusted=True, n_parameters=2)
Out[9]: 0.057190958417936755

fit_loess()#

fit_loess(y, X=None, alpha=0.75, order=2)[source]#

Local Polynomial Regression (LOESS)

Performs a LOWESS (LOcally WEighted Scatter-plot Smoother) regression.

Parameters:

  • y (Union[list, np.array, pd.Series]) – The response variable (the y axis).

  • X (Union[list, np.array, pd.Series]) – Explanatory variable (the x axis). If None, will treat y as a continuous signal (useful for smoothing).

  • alpha (float) – The parameter which controls the degree of smoothing, which corresponds to the proportion of the samples to include in local regression.

  • order (int) – Degree of the polynomial to fit. Can be 1 or 2 (default).

Returns:

  • array – Prediction of the LOESS algorithm.

  • dict – Dictionary containing additional information such as the parameters (order and alpha).

See also

signal_smooth, signal_detrend, fit_error

Examples

In [1]: import pandas as pd

In [2]: import neurokit2 as nk

# Simulate Signal
In [3]: signal = np.cos(np.linspace(start=0, stop=10, num=1000))

# Add noise to signal
In [4]: distorted = nk.signal_distort(signal,
   ...:                               noise_amplitude=[0.3, 0.2, 0.1],
   ...:                               noise_frequency=[5, 10, 50])
   ...: 

# Smooth signal using local regression
In [5]: pd.DataFrame({ "Raw": distorted, "Loess_1": nk.fit_loess(distorted, order=1)[0],
   ...:                "Loess_2": nk.fit_loess(distorted, order=2)[0]}).plot()
   ...: 
Out[5]: <Axes: >
../_images/p_fit_loess1.png

References

fit_mixture()#

fit_mixture(X=None, n_clusters=2)[source]#

Gaussian Mixture Model

Performs a polynomial regression of given order.

Parameters:

  • X (Union[list, np.array, pd.Series]) – The values to classify.

  • n_clusters (int) – Number of components to look for.

Returns:

  • pd.DataFrame – DataFrame containing the probability of belonging to each cluster.

  • dict – Dictionary containing additional information such as the parameters (n_clusters()).

See also

signal_detrend, fit_error

Examples

In [1]: import pandas as pd

In [2]: import neurokit2 as nk

In [3]: x = nk.signal_simulate()

In [4]: probs, info = nk.fit_mixture(x, n_clusters=2)  # Rmb to merge with main to return ``info``

In [5]: fig = nk.signal_plot([x, probs["Cluster_0"], probs["Cluster_1"]], standardize=True)
../_images/p_fit_mixture.png

fit_polynomial()#

fit_polynomial(y, X=None, order=2, method='raw')[source]#

Polynomial Regression

Performs a polynomial regression of given order.

Parameters:

  • y (Union[list, np.array, pd.Series]) – The response variable (the y axis).

  • X (Union[list, np.array, pd.Series]) – Explanatory variable (the x axis). If None, will treat y as a continuous signal.

  • order (int) – The order of the polynomial. 0, 1 or > 1 for a baseline, linear or polynomial fit, respectively. Can also be "auto", in which case it will attempt to find the optimal order to minimize the RMSE.

  • method (str) – If "raw" (default), compute standard polynomial coefficients. If "orthogonal", compute orthogonal polynomials (and is equivalent to R’s poly default behavior).

Returns:

  • array – Prediction of the regression.

  • dict – Dictionary containing additional information such as the parameters (order) used and the coefficients (coefs).

See also

signal_detrend, fit_error, fit_polynomial_findorder

Examples

In [1]: import pandas as pd

In [2]: import neurokit2 as nk

In [3]: y = np.cos(np.linspace(start=0, stop=10, num=100))

In [4]: pd.DataFrame({"y": y,
   ...:               "Poly_0": nk.fit_polynomial(y, order=0)[0],
   ...:               "Poly_1": nk.fit_polynomial(y, order=1)[0],
   ...:               "Poly_2": nk.fit_polynomial(y, order=2)[0],
   ...:               "Poly_3": nk.fit_polynomial(y, order=3)[0],
   ...:               "Poly_5": nk.fit_polynomial(y, order=5)[0],
   ...:               "Poly_auto": nk.fit_polynomial(y, order='auto')[0]}).plot()
   ...: 
Out[4]: <Axes: >
../_images/p_fit_polynomial1.png

Any function appearing below this point is not explicitly part of the documentation and should be added. Please open an issue if there is one.

Submodule for NeuroKit.

density_bandwidth(x, method='KernSmooth', resolution=401)[source]#

Bandwidth Selection for Density Estimation

Bandwidth selector for density() estimation. See bw_method argument in scipy.stats.gaussian_kde().

The "KernSmooth" method is adapted from the dpik() function from the KernSmooth R package. In this case, it estimates the optimal AMISE bandwidth using the direct plug-in method with 2 levels for the Parzen-Rosenblatt estimator with Gaussian kernel.

Parameters:

  • x (Union[list, np.array, pd.Series]) – A vector of values.

  • method (float) – The bandwidth of the kernel. The larger the values, the smoother the estimation. Can be a number, or "scott" or "silverman" (see bw_method argument in scipy.stats.gaussian_kde()), or "KernSmooth".

  • resolution (int) – Only when method="KernSmooth". The number of equally-spaced points over which binning is performed to obtain kernel functional approximation (see gridsize argument in KernSmooth::dpik()).

Returns:

float – Bandwidth value.

See also

density

Examples

In [1]: import neurokit2 as nk

In [2]: x = np.random.normal(0, 1, size=100)

In [3]: bw = nk.density_bandwidth(x)

In [4]: bw
Out[4]: 0.4713234541565577

In [5]: nk.density_bandwidth(x, method="scott")
Out[5]: 0.3981071705534972

In [6]: nk.density_bandwidth(x, method=1)
Out[6]: 1

In [7]: x, y = nk.density(signal, bandwidth=bw, show=True)
../_images/p_density_bandwidth.png

References

  • Jones, W. M. (1995). Kernel Smoothing, Chapman & Hall.

fit_mse(y, y_predicted)[source]#

Compute Mean Square Error (MSE).

fit_polynomial_findorder(y, X=None, max_order=6)[source]#

Polynomial Regression.

Find the optimal order for polynomial fitting. Currently, the only method implemented is RMSE minimization.

Parameters:

  • y (Union[list, np.array, pd.Series]) – The response variable (the y axis).

  • X (Union[list, np.array, pd.Series]) – Explanatory variable (the x axis). If ‘None’, will treat y as a continuous signal.

  • max_order (int) – The maximum order to test.

Returns:

int – Optimal order.

See also

fit_polynomial

Examples

import neurokit2 as nk

y = np.cos(np.linspace(start=0, stop=10, num=100))

nk.fit_polynomial_findorder(y, max_order=10)

9

fit_r2(y, y_predicted, adjusted=True, n_parameters=2)[source]#

Compute R2.

fit_rmse(y, y_predicted)[source]#

Compute Root Mean Square Error (RMSE).

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