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a/_images/p_signal_surrogate1.png b/_images/p_signal_surrogate1.png index 3c74cef3e1..35934e3fc1 100644 Binary files a/_images/p_signal_surrogate1.png and b/_images/p_signal_surrogate1.png differ diff --git a/_images/p_signal_surrogate2.png b/_images/p_signal_surrogate2.png index 5ab03ac760..49332670b6 100644 Binary files a/_images/p_signal_surrogate2.png and b/_images/p_signal_surrogate2.png differ diff --git a/_images/p_signal_surrogate3.png b/_images/p_signal_surrogate3.png index 22cfc91e34..75744cd9cc 100644 Binary files a/_images/p_signal_surrogate3.png and b/_images/p_signal_surrogate3.png differ diff --git a/_images/p_summary1.png b/_images/p_summary1.png index 36be62df48..ff41d0f104 100644 Binary files a/_images/p_summary1.png and b/_images/p_summary1.png differ diff --git a/examples/bio_custom/bio_custom.html b/examples/bio_custom/bio_custom.html index 10dace00ed..512af4edb9 100644 --- a/examples/bio_custom/bio_custom.html +++ b/examples/bio_custom/bio_custom.html @@ -469,6 +469,11 @@

Customize your Processing Pipeline +
Matplotlib is building the font cache; this may take a moment.
+
+
+
@@ -490,7 +495,7 @@

The Default NeuroKit processing pipeline -../../_images/6d389455c535fa4cbb21150e33f84258fd3410396a41c47131bccfcdc207db9c.png +../../_images/3217df1f178cfbf53ea750d0e71e421f3fc5fee7396242652a3979198898ebfa.png @@ -538,7 +543,7 @@

Building your own -../../_images/a1df997e6c2a5dd77c63ce8a6bccdf6d8eb59b67024f5d79bcc5c2436ad86cb3.png +../../_images/d6e71e897255974471205bc0355a3087e02428c76dfdb84093e9a7ca971863ef.png @@ -630,7 +635,7 @@

Customize even more!
-../../_images/5bc7d67109d0670364743fbb0b85914920221860ef86e2f8302bfb11d026d1c1.png +../../_images/cca178807ade3aba4111ab501fba41c71a28ad8144492735188e79d8344c43ea.png

This doesn’t look bad :) Can you do better?

diff --git a/examples/bio_eventrelated/bio_eventrelated.html b/examples/bio_eventrelated/bio_eventrelated.html index cffc02d188..e3d9201369 100644 --- a/examples/bio_eventrelated/bio_eventrelated.html +++ b/examples/bio_eventrelated/bio_eventrelated.html @@ -530,7 +530,7 @@

Find Events -../../_images/4f6361f3a1bdac5a161b1f0f97065defa1514812a52eed9367a5a5a035e46181.png +../../_images/9d36a4e7989dc9be23e00f43be2bf1236ac552fffcec4463921015427846e247.png

The output of events_plot() shows the corresponding events in the signal, with the blue dashed line representing a Negative event and red dashed line representing a Neutral event.

@@ -641,11 +641,11 @@

Manually Extract Event Related Features -
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7080\4246861920.py:12: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_3668\4246861920.py:12: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   ecg_mean = epoch["ECG_Rate"][0:4].mean()  # Mean heart rate in the 0-4 seconds
-C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7080\4246861920.py:18: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+C:\Users\runneradmin\AppData\Local\Temp\ipykernel_3668\4246861920.py:18: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   rsp_rate = epoch["RSP_Rate"][0:6].mean()  # Longer window for RSP that has a slower dynamic
-C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7080\4246861920.py:24: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+C:\Users\runneradmin\AppData\Local\Temp\ipykernel_3668\4246861920.py:24: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   scr_max = epoch["SCR_Amplitude"][0:6].max()  # Maximum SCR peak
 

 

diff --git a/examples/ecg_generate_12leads/ecg_generate_12leads.html b/examples/ecg_generate_12leads/ecg_generate_12leads.html
index fa00235e3a..4f0292c11c 100644
--- a/examples/ecg_generate_12leads/ecg_generate_12leads.html
+++ b/examples/ecg_generate_12leads/ecg_generate_12leads.html
@@ -481,7 +481,7 @@ 
Normal Multi-lead ECG
-../../_images/77fba950a70805d05786377d40adc0b87a29b3efdd0549c9b9183e744c8ad66e.png +../../_images/4b65c7b2e9b8bc61149a9958a4db7b072b49a2a3f65ae2864cb81c406c6f2fd3.png
@@ -513,7 +513,7 @@

Abnormal Multi-lead ECG
-../../_images/98087ffb8dde657e0361b107ec69b5e805257488a10ccf6cca2031598198c098.png +../../_images/8b89b368588dc0b8ea842103089cfbb3f3bf619e9f42c5e910ddaf8fb15f1a6d.png
diff --git a/examples/ecg_heartbeats/ecg_heartbeats.html b/examples/ecg_heartbeats/ecg_heartbeats.html index 323c18557b..f531fd4ca3 100644 --- a/examples/ecg_heartbeats/ecg_heartbeats.html +++ b/examples/ecg_heartbeats/ecg_heartbeats.html @@ -520,7 +520,7 @@

Extract R-peaks location -../../_images/5be9652f5bf08969e1111669f1d669a4325cb1be1710b8071209bd98cf5776c0.png +../../_images/d5cb6b8e8c435e8b11e50c0c1dd0b051931322da7359b536b9827f3e74383f53.png

Once that we know where the R-peaks are located, we can create windows of signal around them (of a length of for instance 1 second, ranging from 400 ms before the R-peak), which we can refer to as epochs.

@@ -536,7 +536,7 @@

Segment the signal around the heart beats -../../_images/4325a4d17df837a732b535f0e650f89f74931b29cd5b7922122356c837bbc893.png +../../_images/3eee64bf8b9b1082d4b621b65374bcfd0afcaee3d10f332e1026953376134920.png

This create a dictionary of dataframes for each ‘epoch’ (in this case, each heart beat).

@@ -592,36 +592,36 @@

Custom colors and legend -../../_images/4281a2d1b832cee22fc0d2ae16bbca7343c2a4682cb05a1f5e7610e64d06a86c.png +../../_images/b7542d27cd37fab8dc81dda2416d55f7a7879d70718956e16d34b8fd372bfd01.png diff --git a/examples/eda_peaks/eda_peaks.html b/examples/eda_peaks/eda_peaks.html index 463a2f3f6c..5dfb8481bb 100644 --- a/examples/eda_peaks/eda_peaks.html +++ b/examples/eda_peaks/eda_peaks.html @@ -514,7 +514,7 @@

Locate Skin Conductance Response (SCR) features -../../_images/bfe47d584416959ee30b97adbd806f81d1b9ac14c5cc21e70d5ae51c4a469185.png +../../_images/2f8b73457f21179b2b814bbe04313c560962e6c85575e8e4419dc4b3b4f273d1.png @@ -543,7 +543,7 @@

Decompose EDA into Phasic and Tonic components
<Axes: >
-../../_images/64636fc0781b8c871b50c2d6171392f44de15a18992700871ab0d96068812227.png +../../_images/0b9a9390deb15a90022377762fac4025e420ee19d664ea7377838fa71a9e831b.png @@ -562,7 +562,7 @@

Quick Plot +../../_images/5c5f948501e5cdd4c1f128be62d39c37ba7da86387107c185d92b85a51b5899f.png diff --git a/examples/eeg_complexity/eeg_complexity.html b/examples/eeg_complexity/eeg_complexity.html index f0472cb9f3..924fc719c2 100644 --- a/examples/eeg_complexity/eeg_complexity.html +++ b/examples/eeg_complexity/eeg_complexity.html @@ -771,9 +771,9 @@

Compute complexity indices per epoch -
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_6148\1026332364.py:11: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_6844\1026332364.py:11: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
   df.groupby(["condition", "epoch"], as_index=False).apply(get_sdvden),
-C:\Users\runneradmin\AppData\Local\Temp\ipykernel_6148\1026332364.py:12: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
+C:\Users\runneradmin\AppData\Local\Temp\ipykernel_6844\1026332364.py:12: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
   df.groupby(["condition", "epoch"], as_index=False).apply(get_hjorth)
 

 

diff --git a/examples/eeg_microstates/eeg_microstates.html b/examples/eeg_microstates/eeg_microstates.html
index 4a8d04637b..a24aa39cd4 100644
--- a/examples/eeg_microstates/eeg_microstates.html
+++ b/examples/eeg_microstates/eeg_microstates.html
@@ -525,7 +525,7 @@ 
Minimal Example
-../../_images/f5286ec450503e9be0cf23fc3e93372bad7c0e1c011406bb37b40d456b57a2bd.png
+../../_images/ec47fb3c57d8bacff582fc06f7cfeec61323d9e10e0ccc485405c2fe583856aa.png

This shows the aspect of the microstates and their sequence in the Global Field Power (GFP; see below). We can then proceed to a statistical analysis.

@@ -577,28 +577,28 @@

Minimal Example +../../_images/64d05e1f55641e3e6784e6b28f9b1a7a007608b9613e344ed54ec3b337c07400.png

This shows computes static statistics, such as the prevalence of each microstates and the median duration time (also shown in the graph).

@@ -648,26 +648,26 @@

Minimal Example +../../_images/370bb21a3c44fd7224e1ea17c16c5a5f5bfe3af0c7ffccc9f60e1c3395c2071a.png

Finally, one can compute complexity features of the sequence of microstates, such as the entropy.

@@ -702,11 +702,11 @@

Minimal Example +../../_images/a37e0ba209c91919da98ea36ae7c218a4bc42d58aa311a57195c722061eb3f2a.png @@ -743,7 +743,7 @@

Global Field Power (GFP)
-../../_images/eff9561e51038591c76914503ea448b2b8282ee3c041a4e4e3e08513a590971e.png +../../_images/d975e9b0338137da89bf83ec37ccf2bf5ce3e1831f21b1b7aa19738b52a1a699.png
@@ -787,10 +787,10 @@

How many microstates?
-
Optimal number of microstates:  5.0
+
Optimal number of microstates:  4.0
 

 

-../../_images/09e7a1f1285f866d1bfa4aac2421709cbb0f86ef7332ad75c3b6e0a3c69f9fca.png
+../../_images/628b8c1536cb4f0006feedc7b38914ac74ed73ffeb2df4b00538b79992ee957b.png
@@ -817,12 +817,12 @@

Microstates clustering algorithms -
 Using conventional Kmeans,  GEV = 61.26%
- Using modified Kmeans,  GEV = 71.36%
+
 Using conventional Kmeans,  GEV = 61.84%
+ Using modified Kmeans,  GEV = 72.09%
 

 

-../../_images/01e1a3b898a9255823cd71821aa34c776eb59acdbd3f2c2ccee94b03800b63cd.png
-../../_images/724088b767a05e2722347ca08fccfdccccceb545b20d36296cd9a3dc35fb06ba.png
+../../_images/787c4203b88236aa88a5fac47fe123369ae551c126bb55dbc3d33d04341a12f6.png
+../../_images/caae0d32569dd530d825a53d5313110d60b3152d55652f098a0f42a3b8f10641.png
diff --git a/examples/eeg_power/eeg_power.html b/examples/eeg_power/eeg_power.html index 6b2459bb1c..bee6ae8ca8 100644 --- a/examples/eeg_power/eeg_power.html +++ b/examples/eeg_power/eeg_power.html @@ -534,7 +534,7 @@

Preprocessing
Plotting power spectral density (dB=True).
-
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7772\3777469918.py:3: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.
+
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_5360\3777469918.py:3: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.
   fig = spectrum.plot()
 C:\hostedtoolcache\windows\Python\3.10.11\x64\lib\site-packages\mne\viz\utils.py:167: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
   (fig or plt).show(**kwargs)
@@ -574,7 +574,7 @@ 
Frontal Alpha Asymmetry (FAA)
Subtracting Evoked from Epochs
 

 

-
    The following channels are not included in the subtraction: STI 015, STI 004, STI 014, STI 016, STI 002, STI 001, STI 005, EOG 061, STI 006, STI 003
+
    The following channels are not included in the subtraction: EOG 061, STI 016, STI 003, STI 004, STI 005, STI 015, STI 002, STI 006, STI 014, STI 001
 

 

 
[done]
diff --git a/examples/eog_analyze/eog_analyze.html b/examples/eog_analyze/eog_analyze.html
index c9ac63abbd..ef0e1a3e3f 100644
--- a/examples/eog_analyze/eog_analyze.html
+++ b/examples/eog_analyze/eog_analyze.html
@@ -562,7 +562,7 @@ 
Detect and visualize eye blinks
-
[<matplotlib.lines.Line2D at 0x24637258df0>]
+
[<matplotlib.lines.Line2D at 0x15a02268df0>]
 

 

 ../../_images/44ee34df138bd364b80f25a80e1afcd0ca369d5fb0d463c30d8e925e0004cb14.png
diff --git a/examples/misc_epochs_create/misc_epochs_create.html b/examples/misc_epochs_create/misc_epochs_create.html
index b53dd652c7..aabe3f2f44 100644
--- a/examples/misc_epochs_create/misc_epochs_create.html
+++ b/examples/misc_epochs_create/misc_epochs_create.html
@@ -534,7 +534,7 @@ 
One signal with multiple event markings
-../../_images/9b5e8086d6f83e6bf7f624d35e55cf5236b70204d28e348513e430bbc54e362f.png
+../../_images/09ad272058e357ee118ccb0d95768aa69b9dc8d9d45657e8fc46555d26f893e8.png
 

 

 
Or you can visualize the events together with the all other signals.

@@ -545,7 +545,7 @@ 
One signal with multiple event markings
-../../_images/9d36a4e7989dc9be23e00f43be2bf1236ac552fffcec4463921015427846e247.png
+../../_images/4f6361f3a1bdac5a161b1f0f97065defa1514812a52eed9367a5a5a035e46181.png
 

 

 
After you have located the events, you can now create epochs using the NeuroKit epochs_create() function. However, we recommend to process your signal first before cutting them to smaller epochs. You can read more about processing of physiological signals using NeuroKit in Custom your Processing Pipeline Example.

diff --git a/examples/misc_fit_function/misc_fit_function.html b/examples/misc_fit_function/misc_fit_function.html
index a605b78c51..68b76a68d5 100644
--- a/examples/misc_fit_function/misc_fit_function.html
+++ b/examples/misc_fit_function/misc_fit_function.html
@@ -485,7 +485,7 @@ 
Fit a linear function
 

 

-../../_images/84901f1f8c95862c47ea8d50c76e550a997e22599a98b688d2c1c01bc0905173.png
+../../_images/a33a706c2abb32ad5fead9af225fedcd221a885d7444b0b2c6d53ea09c2e1e6f.png
 

 

 
In this case we know that the best fitting line will be a linear function (i.e., a straight line), and we want to find its parameters. A linear function has two parameters, the intercept and the slope.

@@ -508,7 +508,7 @@ 
Fit a linear function
 

 

-
array([2.11383376, 3.10039158])
+
array([4.19071353, 2.69141784])
 

 

 

@@ -525,7 +525,7 @@ 
Fit a linear function
 

 

-../../_images/96705d447ba1b59a0ee082753e598f2fe3fd007ec02b69812cf9565d10a3f3e9.png
+../../_images/345aff4755b160551b808b7a4529736cc859cfa5f48d459f4a3dbdd843839754.png
 

 

 
@@ -540,7 +540,7 @@ 
Non-linear curves
-../../_images/c1cf8d83a27f120c9ad163f9bdd5757e04894a14126aa7f5942d4f5a7a01c689.png
+../../_images/e8f31c0176c57ffc44f266e526cf20550d11ac4ba4f11e611ab67604560d0442.png

In this example, we will try to approximate this Skin Conductance Response (SCR) using a gamma distribution, which is quite a flexible distribution defined by 3 parameters (a, loc and scale).

@@ -578,7 +578,7 @@

Non-linear curves -
array([0.94750446, 2.28455258, 1.02441018, 0.76152304, 2.11634213])
+
array([0.94329221, 2.36397182, 1.04335257, 0.68136267, 2.16797685])
@@ -594,7 +594,7 @@

Non-linear curves -../../_images/69e356f32f77c7d0afe8f65fa7ed45c3fdc74dbf2a335844a55547eabacbb481.png +../../_images/f74747e20a32dbf9279245c12339d05a01271ec79382efdc4e67e61d7bc2a449.png

diff --git a/examples/signal_simulation/signal_simulation.html b/examples/signal_simulation/signal_simulation.html index 839737acef..4c0ba205b8 100644 --- a/examples/signal_simulation/signal_simulation.html +++ b/examples/signal_simulation/signal_simulation.html @@ -490,7 +490,7 @@

Cardiac Activity (ECG)
-../../_images/13d1f9ff6dacb9b7bd1d74c5ab651fcf52274a007e6e52585099a02162a85fbb.png +../../_images/63be78e9c74e6140ebc14051eefe19329c8650d9c0e0a5bd1cb86c57475ebe66.png

You can also choose to generate the default, simple simulation based on Daubechies wavelets, which roughly approximates one cardiac cycle, or a more complex one by specifiying method="ecgsyn".

@@ -507,7 +507,7 @@

Cardiac Activity (ECG)
-../../_images/9b13a244048f899c0c077ec8af7d7334faf0f7de2c86fdf780595ae9531e5874.png +../../_images/9a5cfc57cd0a554da3b6b6121fa665f5976e4352297a2a7e96e45d190775bf0f.png
@@ -530,7 +530,7 @@

Respiration (RSP) -../../_images/ffa46ea61d1cf3cede519823af1dd1682356da5f7d3ef5ff566c8f0c414daa42.png +../../_images/d2b90b1ba5f515390b7fc46d7828c3cb27c8dbc96e948dca3f9cf90995d25b31.png @@ -553,7 +553,7 @@

Electromyography (EMG)
-../../_images/9dd5d8ef52375c074a03e0d51af88209f3a1e880f49d9860c836d44df479851d.png +../../_images/9f22d71ea99f4041dc82558da228301b79ee93f14662089c40e761b41cccc02d.png
@@ -576,7 +576,7 @@

Electrodermal Activity (EDA) -../../_images/c574b7224d93043465d01e8275c17e25f0f800471a1cf2d1900adcb402363fbd.png +../../_images/17db12235fd6bb0af30114d59ff396bb21d03a45469443267b87952dc25d2bbd.png diff --git a/functions/benchmark.html b/functions/benchmark.html index d3bcf4c576..b25599a4ba 100644 --- a/functions/benchmark.html +++ b/functions/benchmark.html @@ -498,7 +498,7 @@

benchmark_ecg()In [5]: nk.benchmark_ecg_preprocessing(function, ecg, true_rpeaks, sampling_rate=200) Out[5]: Sampling_Rate Duration Score Recording_Length Error -0 200 0.031249 0.019123 0.333333 None +0 200 0.031916 0.019036 0.333333 None # Example using database (commented-out) # nk.benchmark_ecg_preprocessing(function, r"path/to/GUDB_database") diff --git a/functions/bio.html b/functions/bio.html index d86839e77f..408c362da9 100644 --- a/functions/bio.html +++ b/functions/bio.html @@ -544,11 +544,11 @@

bio_process()In [7]: bio_df.head() Out[7]: ECG_Raw ECG_Clean ECG_Rate ... EMG_Offsets RSA_P2T RSA_Gates -0 1.084603 -0.068494 69.991705 ... 0 0.0 7.95585 -1 1.031722 -0.192039 69.991705 ... 0 0.0 7.95585 -2 0.894749 -0.328305 69.991705 ... 0 0.0 7.95585 -3 0.700171 -0.481670 69.991705 ... 0 0.0 7.95585 -4 0.476217 -0.648945 69.991705 ... 0 0.0 7.95585 +0 1.042678 -0.073564 70.064355 ... 0 0.0 7.921069 +1 0.993462 -0.189293 70.064355 ... 0 0.0 7.921069 +2 0.865599 -0.317579 70.064355 ... 0 0.0 7.921069 +3 0.682748 -0.463115 70.064355 ... 0 0.0 7.921069 +4 0.470257 -0.623382 70.064355 ... 0 0.0 7.921069 [5 rows x 49 columns] diff --git a/functions/complexity.html b/functions/complexity.html index a368af8bff..a07b3c9695 100644 --- a/functions/complexity.html +++ b/functions/complexity.html @@ -1473,7 +1473,7 @@

complexity_k()../_images/p_complexity_k1.png 
In [4]: k_max
-Out[4]: 34
+Out[4]: 23

References

@@ -1559,7 +1559,7 @@

fractal_katz()In [11]: KFD, _ = nk.fractal_katz(random) In [12]: KFD -Out[12]: 1.7929191759397172 +Out[12]: 2.088476871656432 In [13]: KFD, _ = nk.fractal_katz(simple) @@ -1569,7 +1569,7 @@

fractal_katz()In [15]: KFD, _ = nk.fractal_katz(complex) In [16]: KFD -Out[16]: 3.6337125220644055 +Out[16]: 4.026102388833012

References

@@ -1827,7 +1827,7 @@

fractal_nld()In [9]: nld = [nk.fractal_nld(i, corrected=False)[0] for i in windows] In [10]: np.mean(nld) # Get average -Out[10]: 0.5945502220572204 +Out[10]: 0.5972971539831926

Example 3: Calculate FD-NLD on sliding windows

@@ -1906,7 +1906,7 @@

fractal_psdslope()../_images/p_fractal_psdslope1.png 
In [4]: psdslope
-Out[4]: 2.9359800992956124
+Out[4]: 2.761268706040873

References

@@ -1972,7 +1972,7 @@

fractal_higuchi()../_images/p_fractal_higuchi2.png 
In [5]: hfd
-Out[5]: 1.9778924204132198
+Out[5]: 1.9326181659695203

References

@@ -2059,7 +2059,7 @@

fractal_density()In [11]: D = info1["Average"] - info2["Average"] In [12]: plt.imshow(nk.standardize(D), cmap='RdBu') -Out[12]: <matplotlib.image.AxesImage at 0x1a73a7589a0> +Out[12]: <matplotlib.image.AxesImage at 0x2012138c7c0> ../_images/p_fractal_density3.png @@ -2322,7 +2322,7 @@

fractal_dfa()../_images/p_fractal_dfa1.png 
In [4]: dfa
-Out[4]: 0.6514149107084047
+Out[4]: 0.6508288426124976

As we can see from the plot, the final value, corresponding to the slope of the red line, @@ -2344,7 +2344,7 @@

fractal_dfa()
In [8]: mfdfa
 Out[8]: 
       Width      Peak      Mean  ...  Asymmetry  Fluctuation  Increment
-0  0.804719  0.774356  0.874036  ...  -0.376131     0.000223   0.038267
+0  0.808201  0.775488  0.876203  ...  -0.375384     0.000242   0.039074
 
 [1 rows x 8 columns]
@@ -2437,10 +2437,10 @@

fractal_tmf()../_images/p_fractal_tmf.png 
In [4]: tMF  # t-value
-Out[4]: 15.356659471283072
+Out[4]: -0.6785161230787953
 
 In [5]: info["p"]  # p-value
-Out[5]: 6.062915716604909e-28
+Out[5]: 0.4990275125578799

References

@@ -2531,7 +2531,7 @@

entropy_shannon()../_images/p_entropy_shannon1.png 
In [7]: shanen
-Out[7]: 1.5549937707946866
+Out[7]: 1.553007180630181

Compare with scipy (using the same base).

@@ -2541,12 +2541,12 @@

entropy_shannon()In [9]: binned = pd.cut(signal, bins=3, labels=False) In [10]: scipy.stats.entropy(pd.Series(binned).value_counts()) -Out[10]: 1.0778395480146148 +Out[10]: 1.0764625486431596 In [11]: shanen, info = nk.entropy_shannon(binned, base=np.e) In [12]: shanen -Out[12]: 1.0778395480146148 +Out[12]: 1.0764625486431596

References

@@ -2641,7 +2641,7 @@

entropy_differential()In [3]: diffen, info = nk.entropy_differential(signal) In [4]: diffen -Out[4]: 0.5213911526773847 +Out[4]: 0.5163509575676162

References

@@ -2694,11 +2694,11 @@

entropy_power()In [4]: powen, info = nk.entropy_power(signal) In [5]: powen -Out[5]: 0.058559159412952734 +Out[5]: 0.05855915852519631 # Visualize the distribution that the entropy power is based on In [6]: plt.plot(info["Values"], info["Density"]) -Out[6]: [<matplotlib.lines.Line2D at 0x1a73cece8f0>] +Out[6]: [<matplotlib.lines.Line2D at 0x201213ed960>] ../_images/entropy_power2.png @@ -2707,7 +2707,7 @@

entropy_power()
In [7]: powen, info = nk.entropy_power(signal, bandwidth=0.01)
 
 In [8]: powen
-Out[8]: 0.05855891128854484
+Out[8]: 0.05855890393915596

References

@@ -3287,7 +3287,7 @@

entropy_kl()In [3]: klen, info = nk.entropy_kl(signal, delay=1, dimension=3) In [4]: klen -Out[4]: 1.669639470699082 +Out[4]: 1.6600577343528697

References

@@ -3352,7 +3352,7 @@

entropy_spectral()../_images/p_entropy_spectral1.png 
In [4]: SpEn
-Out[4]: 0.6055991761391418
+Out[4]: 0.5942877803001523

Bin the frequency spectrum.

@@ -3423,7 +3423,7 @@

entropy_phase()../_images/p_entropy_phase1.png 
In [4]: phasen
-Out[4]: 1.169225266760233
+Out[4]: 1.1758103351771272
 
In [5]: phasen, info = nk.entropy_phase(signal, k=8, show=True)
@@ -3490,7 +3490,7 @@ 
entropy_grid()../_images/p_entropy_grid1.png
 
 
In [4]: phasen
-Out[4]: 2.0467140903308536
+Out[4]: 1.4478553388570126
 
 In [5]: phasen, info = nk.entropy_grid(signal, k=10, show=True)
 

@@ -3498,7 +3498,7 @@ 
entropy_grid()../_images/p_entropy_grid2.png
 
 
In [6]: info["GDR"]
-Out[6]: 0.46
+Out[6]: 0.41
 

 

 
References

@@ -3565,7 +3565,7 @@ 
entropy_attention()../_images/p_entropy_attention1.png
 
 
In [4]: atten
-Out[4]: 1.115474933090878
+Out[4]: 1.1832382278172346
 

 

 
References

@@ -3633,7 +3633,7 @@ 
entropy_increment()In [3]: incren, _ = nk.entropy_increment(signal, dimension=3, q=2)
 
 In [4]: incren
-Out[4]: 2.8238936190637403
+Out[4]: 2.7637143234957313
 
 # Multiscale IncrEn (MSIncrEn)
 In [5]: msincren, _ = nk.entropy_multiscale(signal, method="MSIncrEn", show=True)
@@ -3702,12 +3702,12 @@ 
entropy_slope()In [3]: slopen, info = nk.entropy_slope(signal, dimension=3, thresholds=[0.1, 45])
 
 In [4]: slopen
-Out[4]: 3.82006373965447
+Out[4]: 3.8471753826968653
 
 In [5]: slopen, info = nk.entropy_slope(signal, dimension=3, thresholds=[5, 45, 60, 90])
 
 In [6]: slopen
-Out[6]: 5.057342112474435
+Out[6]: 5.120748719604175
 
 # Compute Multiscale Slope Entropy (MSSlopEn)
 In [7]: msslopen, info = nk.entropy_multiscale(signal, method="MSSlopEn", show=True)
@@ -3768,12 +3768,12 @@ 
entropy_symbolicdynamic()In [4]: sydyen, info = nk.entropy_symbolicdynamic(signal, c=3, symbolize="MEP")
 
 In [5]: sydyen
-Out[5]: 3.7328955331851827
+Out[5]: 3.7145628876406196
 
 In [6]: sydyen, info = nk.entropy_symbolicdynamic(signal, c=3, symbolize="kmeans")
 
 In [7]: sydyen
-Out[7]: 3.273403975943795
+Out[7]: 3.4140238543972745
 
 # Compute Multiscale Symbolic Dynamic Entropy (MSSyDyEn)
 In [8]: mssydyen, info = nk.entropy_multiscale(signal, method="MSSyDyEn", show=True)
@@ -3846,17 +3846,17 @@ 
entropy_dispersion()In [3]: dispen, info = nk.entropy_dispersion(signal, c=3)
 
 In [4]: dispen
-Out[4]: 1.4253704000914291
+Out[4]: 1.3992386764411027
 
 # Get Reverse Dispersion Entropy (RDEn)
 In [5]: info["RDEn"]
-Out[5]: 0.0028923123541946295
+Out[5]: 0.007625584435513476
 
 # Fluctuation-based DispEn with "finesort"
 In [6]: dispen, info = nk.entropy_dispersion(signal, c=3, symbolize="finesort", fluctuation=True)
 
 In [7]: dispen
-Out[7]: 1.4253704000914291
+Out[7]: 1.399238676441103

References

@@ -3910,7 +3910,7 @@

entropy_ofentropy()In [3]: enofen, _ = nk.entropy_ofentropy(signal, scale=10, bins=10) In [4]: enofen -Out[4]: 4.139822782008755 +Out[4]: 3.7209505944546697

References

@@ -3981,31 +3981,31 @@

entropy_permutation()In [2]: pen, info = nk.entropy_permutation(signal, corrected=False) In [3]: pen -Out[3]: 2.5815982077668775 +Out[3]: 2.5689221788480774 # Weighted Permutation Entropy (WPEn) In [4]: wpen, info = nk.entropy_permutation(signal, weighted=True) In [5]: wpen -Out[5]: 0.992743834140565 +Out[5]: 0.994216652717069 # Conditional Permutation Entropy (CPEn) In [6]: cpen, info = nk.entropy_permutation(signal, conditional=True) In [7]: cpen -Out[7]: 0.42339739660174047 +Out[7]: 0.421250066798127 # Conditional Weighted Permutation Entropy (CWPEn) In [8]: cwpen, info = nk.entropy_permutation(signal, weighted=True, conditional=True) In [9]: cwpen -Out[9]: 0.4073845673915319 +Out[9]: 0.4175403738553019 # Conditional Renyi Permutation Entropy (CRPEn) In [10]: crpen, info = nk.entropy_permutation(signal, conditional=True, algorithm=nk.entropy_renyi, alpha=2) In [11]: crpen -Out[11]: 0.2859972171647614 +Out[11]: 0.2845828603353124

References

@@ -4586,7 +4586,7 @@

fishershannon_information()In [3]: fsi, info = nk.fishershannon_information(signal, method=0.01) In [4]: fsi -Out[4]: 0.00030316123223497575 +Out[4]: 0.0002743304438036069

References

@@ -4840,7 +4840,7 @@

complexity_decorrelation()In [3]: dt, _ = nk.complexity_decorrelation(signal, show=True) In [4]: dt -Out[4]: 4 +Out[4]: 5 ../_images/p_complexity_decorrelation1.png @@ -4915,13 +4915,13 @@

complexity_lempelziv()In [3]: lzc, info = nk.complexity_lempelziv(signal) In [4]: lzc -Out[4]: 1.1020916641962775 +Out[4]: 1.0588723832474038 # PLZC In [5]: plzc, info = nk.complexity_lempelziv(signal, delay=1, dimension=3, permutation=True) In [6]: plzc -Out[6]: 0.6799731176847983 +Out[6]: 0.7051573072286799 
# MSLZC
@@ -5075,7 +5075,7 @@ 
complexity_lyapunov()In [3]: lle, info = nk.complexity_lyapunov(signal, method="rosenstein", show=True)
 
 In [4]: lle
-Out[4]: 0.06539126815792774
+Out[4]: 0.06158640708173858
 
 # Makowski's change-point method
 In [5]: lle, info = nk.complexity_lyapunov(signal, method="makowski", show=True)
@@ -5181,7 +5181,7 @@ 
complexity_rqa()
In [4]: results
 Out[4]: 
    RecurrenceRate   DiagRec  Determinism  ...         W  WMax       WEn
-0        0.209005  0.009946     0.838082  ...  9.832283   369  3.006661
+0        0.220132 -0.041402     0.825967  ...  8.706259   388  2.851844
 
 [1 rows x 14 columns]
 
@@ -5253,7 +5253,7 @@ 
fractal_mandelbrot()In [5]: m = nk.fractal_mandelbrot(real_range=(-2, 0.75), imaginary_range=(-1.25, 1.25))
 
 In [6]: plt.imshow(m.T, cmap="viridis")
-Out[6]: <matplotlib.image.AxesImage at 0x1a742d56320>
+Out[6]: <matplotlib.image.AxesImage at 0x201216b7a90>
 
 In [7]: plt.axis("off")
 Out[7]: (-0.5, 908.5, 999.5, -0.5)
@@ -5267,7 +5267,7 @@ 
fractal_mandelbrot()   ...: 
 
 In [9]: plt.imshow(b.T, cmap="gray")
-Out[9]: <matplotlib.image.AxesImage at 0x1a742d542e0>
+Out[9]: <matplotlib.image.AxesImage at 0x201216b7c10>
 
 In [10]: plt.axis("off")
 Out[10]: (-0.5, 1363.5, 1499.5, -0.5)
@@ -5283,7 +5283,7 @@ 
fractal_mandelbrot()In [13]: mixed = m - b
 
 In [14]: plt.imshow(mixed.T, cmap="gray")
-Out[14]: <matplotlib.image.AxesImage at 0x1a73abba110>
+Out[14]: <matplotlib.image.AxesImage at 0x20124f6c430>
 
 In [15]: plt.axis("off")
 Out[15]: (-0.5, 999.5, 999.5, -0.5)
diff --git a/functions/ecg.html b/functions/ecg.html
index a325295b47..ec86b993ee 100644
--- a/functions/ecg.html
+++ b/functions/ecg.html
@@ -1498,32 +1498,32 @@ 
ecg_phase()In [6]: _, ax = plt.subplots(nrows=2)
 
 In [7]: ax[0].plot(nk.rescale(ecg), label="ECG", color="red", alpha=0.3)
-Out[7]: [<matplotlib.lines.Line2D at 0x1a73b3119c0>]
+Out[7]: [<matplotlib.lines.Line2D at 0x20121082b30>]
 
 In [8]: ax[0].plot(cardiac_phase["ECG_Phase_Atrial"], label="Atrial Phase", color="orange")
-Out[8]: [<matplotlib.lines.Line2D at 0x1a73b391690>]
+Out[8]: [<matplotlib.lines.Line2D at 0x20122bc7d60>]
 
 In [9]: ax[0].plot(cardiac_phase["ECG_Phase_Completion_Atrial"],
    ...:            label="Atrial Phase Completion", linestyle="dotted")
    ...: 
-Out[9]: [<matplotlib.lines.Line2D at 0x1a73b390c40>]
+Out[9]: [<matplotlib.lines.Line2D at 0x20122bc4940>]
 
 In [10]: ax[0].legend(loc="upper right")
-Out[10]: <matplotlib.legend.Legend at 0x1a73f274b20>
+Out[10]: <matplotlib.legend.Legend at 0x201210a9030>
 
 In [11]: ax[1].plot(nk.rescale(ecg), label="ECG", color="red", alpha=0.3)
-Out[11]: [<matplotlib.lines.Line2D at 0x1a73b3935e0>]
+Out[11]: [<matplotlib.lines.Line2D at 0x20122bc5ff0>]
 
 In [12]: ax[1].plot(cardiac_phase["ECG_Phase_Ventricular"], label="Ventricular Phase", color="green")
-Out[12]: [<matplotlib.lines.Line2D at 0x1a73b393880>]
+Out[12]: [<matplotlib.lines.Line2D at 0x20122bc56f0>]
 
 In [13]: ax[1].plot(cardiac_phase["ECG_Phase_Completion_Ventricular"],
    ....:            label="Ventricular Phase Completion", linestyle="dotted")
    ....: 
-Out[13]: [<matplotlib.lines.Line2D at 0x1a73b393d00>]
+Out[13]: [<matplotlib.lines.Line2D at 0x20122bc5de0>]
 
 In [14]: ax[1].legend(loc="upper right")
-Out[14]: <matplotlib.legend.Legend at 0x1a73f287c70>
+Out[14]: <matplotlib.legend.Legend at 0x201210a9bd0>
../_images/p_ecg_phase.png @@ -1727,9 +1727,9 @@

ecg_eventrelated()In [5]: nk.ecg_eventrelated(epochs) Out[5]: Label Event_Onset ... ECG_Phase_Completion_Ventricular ECG_Quality_Mean -1 1 5000 ... 0.626283 0.957787 -2 2 10000 ... 0.409369 0.959537 -3 3 15000 ... 0.037037 0.947208 +1 1 5000 ... 0.679592 0.978627 +2 2 10000 ... 0.361746 0.962606 +3 3 15000 ... 0.117172 0.973550 [3 rows x 17 columns]

diff --git a/functions/eda.html b/functions/eda.html index 209997b335..a69ed4791c 100644 --- a/functions/eda.html +++ b/functions/eda.html @@ -1141,8 +1141,8 @@

eda_eventrelated()In [5]: nk.eda_eventrelated(epochs) Out[5]: Label Event_Onset ... SCR_RiseTime SCR_RecoveryTime -1 1 5000 ... 0.365 0.510 -2 2 10000 ... 0.367 0.561 +1 1 5000 ... 0.361 0.515 +2 2 10000 ... 0.361 0.569 3 3 15000 ... NaN NaN [3 rows x 8 columns] @@ -1351,7 +1351,7 @@

eda_autocor()In [4]: cor = nk.eda_autocor(eda_cleaned) In [5]: cor -Out[5]: -0.121252233557716 +Out[5]: -0.034592253632890604

References

@@ -1403,7 +1403,7 @@

eda_changepoints()In [3]: eda_cleaned = nk.eda_clean(eda_signal, sampling_rate=100) In [4]: nk.eda_changepoints(eda_cleaned, penalty = 100, show=True) -Out[4]: 11 +Out[4]: 8 ../_images/p_eda_changepoints1.png diff --git a/functions/emg.html b/functions/emg.html index 93a50e4beb..d09707542e 100644 --- a/functions/emg.html +++ b/functions/emg.html @@ -610,9 +610,9 @@

emg_analyze()In [7]: analyze_epochs Out[7]: Label Event_Onset ... EMG_Amplitude_Max_Time EMG_Bursts -1 1 3000 ... 1.414757 1.0 +1 1 3000 ... 1.327714 1.0 2 2 6000 ... NaN NaN -3 3 9000 ... 1.133617 1.0 +3 3 9000 ... 0.790445 1.0 [3 rows x 8 columns] @@ -622,7 +622,7 @@

emg_analyze()In [9]: analyze_df Out[9]: EMG_Activation_N EMG_Amplitude_Mean -0 3.0 0.242652 +0 3.0 0.252452 @@ -978,9 +978,9 @@

emg_eventrelated()In [5]: nk.emg_eventrelated(epochs) Out[5]: Label Event_Onset ... EMG_Amplitude_Max_Time EMG_Bursts -1 1 3000 ... 1.510805 1.0 +1 1 3000 ... 1.857979 1.0 2 2 6000 ... NaN NaN -3 3 9000 ... 0.575338 1.0 +3 3 9000 ... 1.361731 1.0 [3 rows x 8 columns] @@ -1023,15 +1023,15 @@

emg_intervalrelated()In [4]: nk.emg_intervalrelated(emg_signals) Out[4]: EMG_Activation_N EMG_Amplitude_Mean -0 3.0 0.247996 +0 3.0 0.248364 In [5]: epochs = nk.epochs_create(emg_signals, events=[0, 20000], sampling_rate=1000, epochs_end=20) In [6]: nk.emg_intervalrelated(epochs) Out[6]: Label EMG_Activation_N EMG_Amplitude_Mean -1 1 2 0.242355 -2 2 1 0.253580 +1 1 2 0.255609 +2 2 1 0.241141 diff --git a/functions/events.html b/functions/events.html index 4ad9c77a12..df9f4ba8b0 100644 --- a/functions/events.html +++ b/functions/events.html @@ -819,7 +819,7 @@

events_to_mne()In [8]: events, event_id = nk.events_to_mne(events) In [9]: event_id -Out[9]: {'A': 0, 'B': 1} +Out[9]: {'B': 0, 'A': 1} diff --git a/functions/markov.html b/functions/markov.html index aa78f8e9b3..c9245d254f 100644 --- a/functions/markov.html +++ b/functions/markov.html @@ -603,7 +603,7 @@

markov_simulate()In [4]: x = nk.markov_simulate(tm, n=15) In [5]: x -Out[5]: array([3, 2, 1, 0, 0, 3, 3, 3, 1, 2, 1, 2, 1, 0, 1]) +Out[5]: array([3, 2, 2, 2, 2, 2, 1, 0, 1, 0, 3, 1, 2, 2, 2]) diff --git a/functions/misc.html b/functions/misc.html index b96a59584b..1b1281755c 100644 --- a/functions/misc.html +++ b/functions/misc.html @@ -788,7 +788,7 @@

find_plateau()In [5]: plateau = nk.find_plateau(y, show=True) In [6]: plateau -Out[6]: 47 +Out[6]: 35 ../_images/p_find_plateau1.png @@ -1109,7 +1109,7 @@

as_vector()In [2]: import matplotlib.pyplot as plt In [3]: plt.plot([1, 2, 3, 4, 5]) # Make plot -Out[3]: [<matplotlib.lines.Line2D at 0x1a703ebdff0>] +Out[3]: [<matplotlib.lines.Line2D at 0x20123b52260>] In [4]: fig = plt.gcf() # Get current figure diff --git a/functions/ppg.html b/functions/ppg.html index 09f083b9c8..f48de98167 100644 --- a/functions/ppg.html +++ b/functions/ppg.html @@ -618,9 +618,9 @@

ppg_analyze()In [6]: analyze_epochs Out[6]: Label Event_Onset ... PPG_Rate_Trend_Quadratic PPG_Rate_Trend_R2 -1 1 5000 ... 0.386051 0.957536 -2 2 10000 ... 0.123544 0.985500 -3 3 15000 ... -0.895468 0.862935 +1 1 5000 ... -0.560269 0.916556 +2 2 10000 ... -0.743554 0.969809 +3 3 15000 ... -0.371831 0.807862 [3 rows x 12 columns] @@ -867,8 +867,8 @@

ppg_peaks()In [6]: info["PPG_Peaks"] Out[6]: -array([ 46, 89, 133, 173, 217, 263, 307, 351, 407, 476, 515, 557, 598, - 633, 671, 706, 742, 779, 816, 850, 890, 927, 966]) +array([ 46, 88, 132, 173, 217, 264, 305, 352, 405, 443, 480, 531, 597, + 632, 670, 707, 742, 779, 813, 851, 888, 925, 964]) # Method by Bishop et al., (2018) In [7]: peaks, info = nk.ppg_peaks(ppg, sampling_rate=100, method="bishop", show=True) @@ -955,9 +955,9 @@

ppg_eventrelated()In [4]: nk.ppg_eventrelated(epochs) Out[4]: Label Event_Onset ... PPG_Rate_Trend_Quadratic PPG_Rate_Trend_R2 -1 1 5000 ... 0.257877 0.913436 -2 2 10000 ... -0.745813 0.997436 -3 3 15000 ... -0.232874 0.630803 +1 1 5000 ... -1.232985 0.838864 +2 2 10000 ... 0.193102 0.991985 +3 3 15000 ... 2.085251 0.720999 [3 rows x 12 columns] diff --git a/functions/rsp.html b/functions/rsp.html index d64c73fe92..0879b3bc73 100644 --- a/functions/rsp.html +++ b/functions/rsp.html @@ -603,7 +603,7 @@

rsp_process()| RSP_Rate_Mean | RSP_Rate_SD | |----------------:|--------------:| -| 15.1781 | 2.11007 | +| 14.8238 | 1.58018 | References - Harrison, S. J., Bianchi, S., Heinzle, J., Stephan, K. E., Iglesias, S., & Kasper, L. (2021). @@ -1362,8 +1362,8 @@

rsp_rrv()In [4]: nk.rsp_rrv(rsp, show=True) Out[4]: - RRV_RMSSD RRV_MeanBB RRV_SDBB ... RRV_SD2SD1 RRV_ApEn RRV_SampEn -0 1172.862961 4009.85 671.509319 ... 0.492385 0.018896 inf + RRV_RMSSD RRV_MeanBB RRV_SDBB ... RRV_SD2SD1 RRV_ApEn RRV_SampEn +0 887.988946 4000.05 586.351114 ... 0.807746 0.054067 -inf [1 rows x 20 columns] @@ -1510,9 +1510,9 @@

rsp_eventrelated()In [4]: nk.rsp_eventrelated(epochs) Out[4]: Label Event_Onset ... RSP_RVT_Baseline RSP_RVT_Mean -1 1 5000 ... 0.257665 -0.008878 -2 2 10000 ... 0.270371 0.001712 -3 3 15000 ... 0.285331 -0.033076 +1 1 5000 ... 0.244131 -0.016618 +2 2 10000 ... 0.241125 -0.005391 +3 3 15000 ... 0.253203 -0.009929 [3 rows x 22 columns] @@ -1716,7 +1716,7 @@

rsp_intervalrelated()In [7]: rav Out[7]: RAV_Mean RAV_SD RAV_RMSSD RAV_CVSD -0 0.939315 0.066375 0.075193 0.080051 +0 0.941502 0.063399 0.080812 0.085833 diff --git a/functions/signal.html b/functions/signal.html index e9519f7fc5..e924f9f725 100644 --- a/functions/signal.html +++ b/functions/signal.html @@ -1363,7 +1363,7 @@

signal_interpolate()# Add original data points In [8]: plt.scatter(x_values, signal, label="original datapoints", zorder=3) -Out[8]: <matplotlib.collections.PathCollection at 0x1a73a75b550> +Out[8]: <matplotlib.collections.PathCollection at 0x201276d77c0> ../_images/p_signal_interpolate1.png @@ -1484,19 +1484,19 @@

signal_noise()In [13]: psd_brown = nk.signal_psd(brown, sampling_rate=200, method="fft") In [14]: plt.loglog(psd_violet["Frequency"], psd_violet["Power"], c="violet") -Out[14]: [<matplotlib.lines.Line2D at 0x1a756066b30>] +Out[14]: [<matplotlib.lines.Line2D at 0x2013e2601f0>] In [15]: plt.loglog(psd_blue["Frequency"], psd_blue["Power"], c="blue") -Out[15]: [<matplotlib.lines.Line2D at 0x1a702f18a60>] +Out[15]: [<matplotlib.lines.Line2D at 0x20142e27670>] In [16]: plt.loglog(psd_white["Frequency"], psd_white["Power"], c="grey") -Out[16]: [<matplotlib.lines.Line2D at 0x1a702f18f40>] +Out[16]: [<matplotlib.lines.Line2D at 0x20142e27b50>] In [17]: plt.loglog(psd_pink["Frequency"], psd_pink["Power"], c="pink") -Out[17]: [<matplotlib.lines.Line2D at 0x1a702f193c0>] +Out[17]: [<matplotlib.lines.Line2D at 0x20142e27fd0>] In [18]: plt.loglog(psd_brown["Frequency"], psd_brown["Power"], c="brown") -Out[18]: [<matplotlib.lines.Line2D at 0x1a702f19870>] +Out[18]: [<matplotlib.lines.Line2D at 0x20142e24dc0>] ../_images/p_signal_noise2.png @@ -1545,16 +1545,16 @@

signal_surrogate()In [5]: surrogate_random = nk.signal_surrogate(signal, method = "random") In [6]: plt.plot(surrogate_random, label = "Random Surrogate") -Out[6]: [<matplotlib.lines.Line2D at 0x1a702f1b2e0>] +Out[6]: [<matplotlib.lines.Line2D at 0x20126c7de70>] In [7]: plt.plot(surrogate_iaaft, label = "IAAFT Surrogate") -Out[7]: [<matplotlib.lines.Line2D at 0x1a702f1b0a0>] +Out[7]: [<matplotlib.lines.Line2D at 0x20144338160>] In [8]: plt.plot(signal, label = "Original") -Out[8]: [<matplotlib.lines.Line2D at 0x1a73b56ca30>] +Out[8]: [<matplotlib.lines.Line2D at 0x20144338490>] In [9]: plt.legend() -Out[9]: <matplotlib.legend.Legend at 0x1a743ea3100> +Out[9]: <matplotlib.legend.Legend at 0x20120bdcd30> ../_images/p_signal_surrogate1.png @@ -1562,16 +1562,16 @@

signal_surrogate()As we can see, the signal pattern is destroyed by random surrogates, but not in the IAAFT one. And their distributions are identical:

In [10]: plt.plot(*nk.density(signal), label = "Original")
-Out[10]: [<matplotlib.lines.Line2D at 0x1a70349a2f0>]
+Out[10]: [<matplotlib.lines.Line2D at 0x2014437bd90>]
 
 In [11]: plt.plot(*nk.density(surrogate_iaaft), label = "IAAFT Surrogate")
-Out[11]: [<matplotlib.lines.Line2D at 0x1a70349a470>]
+Out[11]: [<matplotlib.lines.Line2D at 0x20144363100>]
 
 In [12]: plt.plot(*nk.density(surrogate_random), label = "Random Surrogate")
-Out[12]: [<matplotlib.lines.Line2D at 0x1a70349a7a0>]
+Out[12]: [<matplotlib.lines.Line2D at 0x20144fe0c10>]
 
 In [13]: plt.legend()
-Out[13]: <matplotlib.legend.Legend at 0x1a702e7e560>
+Out[13]: <matplotlib.legend.Legend at 0x201443c3d00>
../_images/p_signal_surrogate2.png @@ -1898,7 +1898,9 @@

signal_changepoints()In [2]: signal = nk.emg_simulate(burst_number=3) In [3]: nk.signal_changepoints(signal, change="var", show=True) -Out[3]: array([1750, 2748, 3480, 4500, 5500, 7250, 8250]) +Out[3]: +array([1750, 2750, 3413, 4500, 5500, 6157, 7250, 7400, 7403, 8011, 8250, + 8523]) ../_images/p_signal_changepoints1.png diff --git a/functions/stats.html b/functions/stats.html index ea58bacb94..abe2072144 100644 --- a/functions/stats.html +++ b/functions/stats.html @@ -980,91 +980,91 @@

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> +Out[16]: <matplotlib.collections.PathCollection at 0x20146b64d90> In [17]: axes[0, 0].scatter(clusters_kmeans[:, 2], clusters_kmeans[:, 3], c='red') -Out[17]: <matplotlib.collections.PathCollection at 0x1a7425df490> +Out[17]: <matplotlib.collections.PathCollection at 0x2014700d000> 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> +Out[19]: <matplotlib.collections.PathCollection at 0x20146b65de0> In [20]: axes[0, 1].scatter(clusters_spectral[:, 2], clusters_spectral[:, 3], c='red') -Out[20]: <matplotlib.collections.PathCollection at 0x1a7425df460> +Out[20]: <matplotlib.collections.PathCollection at 0x20146b64e50> 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> +Out[22]: <matplotlib.collections.PathCollection at 0x20146b12b30> In [23]: axes[1, 0].scatter(clusters_hierarchical[:, 2], clusters_hierarchical[:, 3], c='red') -Out[23]: <matplotlib.collections.PathCollection at 0x1a73b77ae00> +Out[23]: <matplotlib.collections.PathCollection at 0x20146b13220> 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> +Out[25]: <matplotlib.collections.PathCollection at 0x2014700f6d0> In [26]: axes[1, 1].scatter(clusters_agglomerative[:, 2], clusters_agglomerative[:, 3], c='red') -Out[26]: <matplotlib.collections.PathCollection at 0x1a73b779030> +Out[26]: <matplotlib.collections.PathCollection at 0x20146b10370> 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> +Out[28]: <matplotlib.collections.PathCollection at 0x20146b10e50> In [29]: axes[2, 0].scatter(clusters_mixture[:, 2], clusters_mixture[:, 3], c='red') -Out[29]: <matplotlib.collections.PathCollection at 0x1a742b5d690> +Out[29]: <matplotlib.collections.PathCollection at 0x20146b100d0> 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> +Out[31]: <matplotlib.collections.PathCollection at 0x20146b11a50> In [32]: axes[2, 1].scatter(clusters_bayes[:, 2], clusters_bayes[:, 3], c='red') -Out[32]: <matplotlib.collections.PathCollection at 0x1a742b5dff0> +Out[32]: <matplotlib.collections.PathCollection at 0x20146b11d80> 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> +Out[34]: <matplotlib.collections.PathCollection at 0x20146b11a80> In [35]: axes[3, 0].scatter(clusters_pca[:, 2], clusters_pca[:, 3], c='red') -Out[35]: <matplotlib.collections.PathCollection at 0x1a742b5e920> +Out[35]: <matplotlib.collections.PathCollection at 0x20146b125f0> 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> +Out[37]: <matplotlib.collections.PathCollection at 0x20146b12d70> In [38]: axes[3, 1].scatter(clusters_ica[:, 2], clusters_ica[:, 3], c='red') -Out[38]: <matplotlib.collections.PathCollection at 0x1a742b5f1c0> +Out[38]: <matplotlib.collections.PathCollection at 0x20146b11420> 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> +Out[40]: <matplotlib.collections.PathCollection at 0x20146b137f0> In [41]: axes[4, 0].scatter(clusters_kmod[:, 2], clusters_kmod[:, 3], c='red') -Out[41]: <matplotlib.collections.PathCollection at 0x1a742b5f880> +Out[41]: <matplotlib.collections.PathCollection at 0x20146b12f50> 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> +Out[43]: <matplotlib.collections.PathCollection at 0x20146b10820> In [44]: axes[4, 1].scatter(clusters_aahc[:, 2], clusters_aahc[:, 3], c='red') -Out[44]: <matplotlib.collections.PathCollection at 0x1a742b5ff70> +Out[44]: <matplotlib.collections.PathCollection at 0x20146b105b0> In [45]: axes[4, 1].set_title("AAHC (Frederic's method)") Out[45]: Text(0.5, 1.0, "AAHC (Frederic's method)") @@ -1178,7 +1178,7 @@

cluster_quality()In [5]: general Out[5]: n_Clusters Score_Silhouette ... Score_GAP_sk Score_GAPmod_sk -0 3.0 0.552819 ... 0.312362 1155.431941 +0 3.0 0.552819 ... 0.289881 1055.169311 [1 rows x 12 columns] @@ -1450,7 +1450,7 @@

fit_polynomial()In [3]: bw = nk.density_bandwidth(x) In [4]: bw -Out[4]: 0.4713234541565577 +Out[4]: 0.3478780604364814 In [5]: nk.density_bandwidth(x, method="scott") Out[5]: 0.3981071705534972 diff --git a/searchindex.js b/searchindex.js index 2040290951..879da04bb1 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Documentation website": [[45, "documentation-website"]], "1/f Electrophysiological Noise": [[13, "f-electrophysiological-noise"]], "2. The source on Github": [[45, "the-source-on-github"]], "Abnormal Multi-lead ECG": [[7, "abnormal-multi-lead-ecg"]], "Accuracy": [[51, "accuracy"]], "Adding examples and tutorials": [[45, "adding-examples-and-tutorials"]], "Adding tests": [[45, "adding-tests"]], "Additional Resources": [[49, null]], "Advanced Plotting": [[8, "advanced-plotting"]], "Algorithm Comparison Conclusion": [[51, "algorithm-comparison-conclusion"]], "Algorithm Comparison Procedure": [[51, "algorithm-comparison-procedure"]], "Algorithm Comparison Results": [[51, "algorithm-comparison-results"]], "Algorithm Comparison Setup Functions": [[51, "algorithm-comparison-setup-functions"]], "All Domains": [[9, "all-domains"]], "An GAM-based Approach to EEG/ERP Analysis using Python and R": [[53, null]], "Analyse EDR": [[6, "analyse-edr"]], "Analyse RRV": [[19, "analyse-rrv"]], "Analysis": [[25, "analysis"], [26, "analysis"], [28, "analysis"], [29, "analysis"], [37, "analysis"], [38, "analysis"], [39, "analysis"]], 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module neurokit2.hrv)": [[32, "neurokit2.hrv.hrv_rsa", false]], "hrv_time() (in module neurokit2.hrv)": [[32, "neurokit2.hrv.hrv_time", false]], "intervals_process() (in module neurokit2.hrv)": [[32, "neurokit2.hrv.intervals_process", false]], "intervals_to_peaks() (in module neurokit2.hrv)": [[32, "neurokit2.hrv.intervals_to_peaks", false]], "listify() (in module neurokit2.misc)": [[36, "neurokit2.misc.listify", false]], "mad() (in module neurokit2)": [[40, "neurokit2.mad", false]], "markov_mixingtime() (in module neurokit2.markov)": [[34, "neurokit2.markov.markov_mixingtime", false]], "markov_simulate() (in module neurokit2.markov)": [[34, "neurokit2.markov.markov_simulate", false]], "markov_test_homogeneity() (in module neurokit2.markov)": [[34, "neurokit2.markov.markov_test_homogeneity", false]], "markov_test_random() (in module neurokit2.markov)": [[34, "neurokit2.markov.markov_test_random", false]], "markov_test_symmetry() (in module neurokit2.markov)": [[34, 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