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b/_images/p_summary1.png index 345df361d9..fd2dbcdd77 100644 Binary files a/_images/p_summary1.png and b/_images/p_summary1.png differ diff --git a/_images/p_transition_matrix1.png b/_images/p_transition_matrix1.png index b1e42245ef..68e4b85e6c 100644 Binary files a/_images/p_transition_matrix1.png and b/_images/p_transition_matrix1.png differ diff --git a/_images/signal_detrend1.png b/_images/signal_detrend1.png index ab2aca0b22..001477346e 100644 Binary files a/_images/signal_detrend1.png and b/_images/signal_detrend1.png differ diff --git a/_modules/neurokit2/signal/signal_interpolate.html b/_modules/neurokit2/signal/signal_interpolate.html index 029073a9be..b0fc5eafbd 100644 --- a/_modules/neurokit2/signal/signal_interpolate.html +++ b/_modules/neurokit2/signal/signal_interpolate.html @@ -466,6 +466,8 @@

Source code for neurokit2.signal.signal_interpolate

x_values = np.squeeze(x_values.values) if isinstance(x_new, pd.Series): x_new = np.squeeze(x_new.values) + if isinstance(y_values, pd.Series): + y_values = np.squeeze(y_values.values) if len(x_values) != len(y_values): raise ValueError("x_values and y_values must be of the same length.") @@ -523,7 +525,7 @@

Source code for neurokit2.signal.signal_interpolate

# scipy.interpolate.PchipInterpolator for constant extrapolation akin to the behavior of # scipy.interpolate.interp1d with fill_value=([y_values[0]], [y_values[-1]]. fill_value = ([interpolated[first_index]], [interpolated[last_index]]) - elif isinstance(fill_value, float) or isinstance(fill_value, int): + elif isinstance(fill_value, (float, int)): # if only a single integer or float is provided as a fill value, format as a tuple fill_value = ([fill_value], [fill_value]) diff --git a/examples/bio_custom/bio_custom.html b/examples/bio_custom/bio_custom.html index 65526d75bc..8fd33f745b 100644 --- a/examples/bio_custom/bio_custom.html +++ b/examples/bio_custom/bio_custom.html @@ -473,7 +473,7 @@

The Default NeuroKit processing pipeline -../../_images/0cc77df558cdacaf436b4417bcbd6c1fd065e05c8da504a9891e50ff28074497.png +../../_images/91eac185f60d0d443bef4fca9d0e15cd599a185af649bc066963a2c3099eca54.png

@@ -521,7 +521,7 @@

Building your own -../../_images/27441eafad79d3ce95b8840e81f0fdd79ba8877593f4f7f05e78b8742c13c1d2.png +../../_images/c000adf4c00749a1bdd6385a8daf1d280f3422a9bcf5822e8f2b143cd9973b3e.png @@ -613,7 +613,7 @@

Customize even more!
-../../_images/614c5dfdf784b2b39d8c95a0928b02d72c7408a22333900d35199c73d999bd0b.png +../../_images/630dbc76770498d1eb523e511a9bb8f62b05a029803adb25ca37eb97caab2953.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 41a86db853..2413d35928 100644 --- a/examples/bio_eventrelated/bio_eventrelated.html +++ b/examples/bio_eventrelated/bio_eventrelated.html @@ -513,7 +513,7 @@

Find Events -../../_images/79370bc3c42578447e039d137e7ad0cfac0fc81c2c4e71419f6d7162b9951850.png +../../_images/b6c54b59c48e5fefd2992a8edcfcc573a62ba41182d0d70995d6079a909ec992.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.

@@ -540,7 +540,7 @@

Process the Signals
array([<Axes: >, <Axes: >, <Axes: >, <Axes: >], dtype=object)
-../../_images/ed155a5854ccfaf5e9f46b97841a7689e83f8e7c041780f9bbb487dbe0345f1c.png +../../_images/2d2878f98eaab0eb9144e7397d902939ec89f9dbf254f4d72f912895e52875d9.png @@ -569,10 +569,10 @@

Create Epochs -../../_images/0335e07638d2517dda5f7ebd176624a87ea01f43b80a54540a2906db750ce9a2.png -../../_images/924cf154c84145fa21fc9e7cfffaa8423c9a15d1d9ee9eefce0f2080daac4c18.png -../../_images/78ce842a0cc2e46f6040e768bdea5fe360dfd837bfacafc22624b1ba00bb2c86.png -../../_images/75eed28facd9142136e25887f3c953faacdd4f3c5b150a4687dc92244566dac1.png +../../_images/a5d099f5a27e669a0d3990f2c736fc273cbe26b88a1dd98512dd0c5fb844a560.png +../../_images/e58e6de011c795c45162ccd8c7b25a6bfd933f5611caccb9db924785393d8950.png +../../_images/303cfc33ce1c5d024ea378f88d264055a7fff7a75a6661a91cc9b68e2f02a461.png +../../_images/bb6244d0536fb3366efac4f5fd4b615b6e9465c574cfd0227c0c2c8a60ce7e75.png @@ -624,11 +624,11 @@

Manually Extract Event Related Features -
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_3204\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_4932\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_3204\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_4932\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_3204\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_4932\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
 

 

@@ -856,7 +856,7 @@ 
Plot Event-Related Features
-../../_images/25ee250b3193423e5f45374c8e95a5c1f308d880a2f861426131abc0c640d8e2.png
+../../_images/dc46df3c646d48412302b6dba8664ccda667edffa72dd0bd251f4fa90b06fa2a.png
@@ -866,7 +866,7 @@

Plot Event-Related Features -../../_images/80af38b0e44ed155b93b0e255e2941a1f92b99093ab9da5b8cae80cac9e1153b.png +../../_images/3f896dd1930cf41e71631fbae432a3f5d1ea0dfbe9ece2b2c58a638f462b5819.png

@@ -876,7 +876,7 @@

Plot Event-Related Features -../../_images/9905fc2c1b09400b860cad8ed76592deca868382e11e733852c863dce1d4f4e4.png +../../_images/9d9dfc5d915aa809fe349bac3d81db8acb732d589340e0857fe737ec51f9ab61.png

Interpretation: As we can see, there seems to be a difference between the negative and the neutral pictures. Negative stimuli, as compared to neutral stimuli, were related to a stronger cardiac deceleration (i.e., higher heart rate variability), an accelerated breathing rate, and higher SCR magnitude.

diff --git a/examples/bio_intervalrelated/bio_intervalrelated.html b/examples/bio_intervalrelated/bio_intervalrelated.html index 227e6dcab1..96320095f8 100644 --- a/examples/bio_intervalrelated/bio_intervalrelated.html +++ b/examples/bio_intervalrelated/bio_intervalrelated.html @@ -484,7 +484,7 @@

Process the Signals -../../_images/2c5a35fcdd66e6836c9d2e9d973650dd9afdeb68c59ab4e45e68708f43d2d9d1.png +../../_images/7a88e3781fc7558506254d54469a667becc521debc39977161d534cd022ade27.png
@@ -497,7 +497,7 @@

Process the Signals -../../_images/001a6ea0adb378df6dbab76fe71aee2f0f204011ec339a002d274f41ae5a1f2c.png +../../_images/2461b24044dea6991cca037ca32af88cb25335c76da9b044eaec75d8879f3d33.png

diff --git a/examples/ecg_delineate/ecg_delineate.html b/examples/ecg_delineate/ecg_delineate.html index fea8d4a1ad..4cf5acda0c 100644 --- a/examples/ecg_delineate/ecg_delineate.html +++ b/examples/ecg_delineate/ecg_delineate.html @@ -481,7 +481,7 @@

Find the R peaks -../../_images/76808ef5c15fe17289b18f622111ac4de6fc308b56a7492f1a0cd328edc205ff.png +../../_images/cf854beead3f241822cb01a1e2ebe95f77ba2b2ee6c279525bdd77e973dbabeb.png
@@ -492,7 +492,7 @@

Find the R peaks -../../_images/53baae0a7ad6144b683b867bd3333207880548a6f70a268dd47350b314697b23.png +../../_images/01c1a067ae172211c3cd06bab0547b441f387c9ebf1478249ee273c8e8a6168e.png

Visually, the R-peaks seem to have been correctly identified. You can also explore searching for R-peaks using different methods provided by NeuroKit ecg_peaks().

@@ -522,7 +522,7 @@

Peak method -../../_images/f4e986de52b3a6fd17702b6d6496b72bd3d4cbc8b7720c5aefc63727d6791589.png +../../_images/d4d0c54474b089f98c0fc4940b96f174ba959122225d5f2f950da1bce7735aff.png

Visually, the ‘peak’ method seems to have correctly identified the P-peaks, Q-peaks, S-peaks and T-peaks for this signal, at least, for the first few complexes. Well done, peak!

@@ -540,7 +540,7 @@

Peak method -../../_images/dd397d1f6765a13d9feb1e05f2951176deb92a240c751b641ede21d1fe4f61f1.png +../../_images/f9baffb54eac82946f530bae16c8678d6c84cee00f285370173e99038499898d.png

The ‘peak’ method is doing a glamorous job with identifying all the ECG peaks for this piece of ECG signal.

@@ -559,7 +559,7 @@

Peak method -../../_images/eb5b3126637860a1e17810c573995fc8f33960c23b7d4783183f9eb45d62b21e.png +../../_images/7e26da04027ed61475a2517bbfd04ac3e5d52af456938614822cbf2114dec1b8.png
@@ -575,7 +575,7 @@

Peak method -../../_images/1653fcdd149acfe0b444273dc23fd953701cb71dccbca23904fd2cf9444155b8.png +../../_images/bd2b06c1c204bd95779747fd03b2630b0dc7b6ed68ffda2559619d4b1a405be6.png

Both the onsets of P-peaks and the offsets of T-peaks appears to have been correctly identified here. This information will be used to delineate cardiac phases in ecg_phase().

@@ -596,7 +596,7 @@

Continuous Wavelet Method (CWT) -../../_images/d72f58b5333d1541932a0583e5e2f885bdcec9a858bb52d518932ab378f73b65.png +../../_images/4e87bf972fc6e8984e38b864f9ffa620b5ef5a085170c1a3ab9b03cdf2d3723a.png

By specifying ‘all’ in the show_type argument, you can plot all delineated information output by the cwt method. However, it could be hard to evaluate the accuracy of the delineated information with everyhing plotted together. Let’s tease them apart!

@@ -613,7 +613,7 @@

Continuous Wavelet Method (CWT) -../../_images/520a3d103c8af1fbc426a5a488f46ce4fd2d50d65fee898e55d55835ac3c8327.png +../../_images/4438c3c179ede147cc0ece321707f4708f1527b800de39e7486fce4177f671b8.png
@@ -629,7 +629,7 @@

Continuous Wavelet Method (CWT) -../../_images/4330e59e8f7eec4cb7edb3464aec2dd232d56a7ae8722798a77db11c9f2ab85c.png +../../_images/bb4ccef9f91ea7633fb583f8e7b92fdb38ce300abf3a2dcf57a16f86f8cfbc0c.png

@@ -645,7 +645,7 @@

Continuous Wavelet Method (CWT) -../../_images/3abfa0c25fa3de811f1145b389dbac0c20e0a4687460103124d3cc3794e8db0e.png +../../_images/53a4c6985c5200b81c16f1f61e20bca501cf63c94bb1044ed8e468c9131f59d7.png

@@ -661,7 +661,7 @@

Continuous Wavelet Method (CWT) -../../_images/987300da8c1e5a6199b376f0928b103e3b79630eae4f32159ee528a9e0fd70e1.png +../../_images/652d9012006311d3f1f4faf0806c6a5e56fa2bde8b8db7a8990ab8f3e77d58eb.png

Unlike the peak method, the continuous wavelet method does not idenfity the Q-peaks and S-peaks. However, it provides more information regarding the boundaries of the waves

@@ -683,7 +683,7 @@

Discrete Wavelet Method (DWT) - default method -../../_images/7f936d0f66bb35613c35a71bd4a8bc83888109850846e8dc5105c47dca6b3a54.png +../../_images/bb180658a7b7584b8a6b5087e1f3bb6832cbd38ceef8d3b1cd3101e1f53c55d3.png
@@ -699,7 +699,7 @@

Discrete Wavelet Method (DWT) - default method -../../_images/66106b21bfa574de8d8f16ade3445668ee1ff2787c2def0e6fbd2fc53c1fcfb7.png +../../_images/d68352513f4ca2dacfe5e80b4a49753b0409ff5951df40e899f965627cb71620.png

@@ -715,7 +715,7 @@

Discrete Wavelet Method (DWT) - default method -../../_images/8e4d26a336fabf1b0692ddbfa3cd656475c44f2488ffb222dd1c15bff29668fb.png +../../_images/e9aa9d83e3d067fa3d088c1f5413d607c2ea9e689ac973805eb5c4577313caad.png

@@ -731,7 +731,7 @@

Discrete Wavelet Method (DWT) - default method -../../_images/7553d0a2194793b0df83cd52a5a8c1b575d802ad429c977374ce148bc5a94f35.png +../../_images/aa1fd8ded73f8ffc6b3ef5e8f04a6eb72436a0c7382a40474c224af3a45f84a8.png

@@ -747,7 +747,7 @@

Discrete Wavelet Method (DWT) - default method -../../_images/629dc10b24a11cc0c4f19fe74c936cd173e2858caa2de9f841b1c073aade26a7.png +../../_images/7a428bd7eb19887bdde33c8043d29c1fd516d392def3532cdfacad118cd3d0fa.png

Visually, from the plots above, the delineated outputs of DWT appear to be more accurate than CWT, especially for the P-peaks and P-wave boundaries.

diff --git a/examples/ecg_edr/ecg_edr.html b/examples/ecg_edr/ecg_edr.html index 744cb488e4..4c623934cc 100644 --- a/examples/ecg_edr/ecg_edr.html +++ b/examples/ecg_edr/ecg_edr.html @@ -468,7 +468,7 @@

Download ECG Data -../../_images/23d9a407791a60c99557c707331f9c34efd954dac36753cc60ef0013fafddd97.png +../../_images/5fd2676fe4cf7e5393c2e7ec9d1c0283de47414f1bdd3ab33e1bea3544071073.png @@ -501,7 +501,7 @@

Analyse EDR -../../_images/8c6c96f69abba26d063d784ec9dfa64733b97b8cd1b569f126555741921593af.png +../../_images/518e1211936c59d95d90f7909a906d92683ab7252950fa10c398d82316a6a526.png

The default method used is the one by Van Gent et al. (2019), but the function ecg_rsp() incorporates different methods to compute EDR. For a visual comparison of the different methods, we can create a dataframe of EDR columns each of which are produced by different methods, and then plot it, like so:

@@ -524,7 +524,7 @@

Analyse EDR -../../_images/ddcbb0224826ae64f28ee755679e8a49de60d8a6739159e1a911514cc55752f0.png +../../_images/9fd824d7edf9ad74adaf0eecb18bd3b8771c3163587d8c45539a712d1066d6c7.png diff --git a/examples/ecg_generate_12leads/ecg_generate_12leads.html b/examples/ecg_generate_12leads/ecg_generate_12leads.html index 83e522f98b..7099425fc6 100644 --- a/examples/ecg_generate_12leads/ecg_generate_12leads.html +++ b/examples/ecg_generate_12leads/ecg_generate_12leads.html @@ -464,7 +464,7 @@

Normal Multi-lead ECG
-../../_images/a8f21ad8004bb87537974d4e1979634d596a85688c88a03c162d811c9a6ee5cd.png +../../_images/08118619703a82600ad0dca00e4e7dc3e23b6a1f3b0bfef74da8a5d41bec02f8.png
@@ -496,7 +496,7 @@

Abnormal Multi-lead ECG
-../../_images/bb2c3657a55323da4769b5192f6756ebbbe5e76d1cd8f713f6e9266b460529f4.png +../../_images/e1e1ed03111027ac6fcaedc6f34e9abfbaf1875b0b0317c6fac54cda006844b1.png
diff --git a/examples/ecg_heartbeats/ecg_heartbeats.html b/examples/ecg_heartbeats/ecg_heartbeats.html index 4f293340e2..6730d37cc8 100644 --- a/examples/ecg_heartbeats/ecg_heartbeats.html +++ b/examples/ecg_heartbeats/ecg_heartbeats.html @@ -503,7 +503,7 @@

Extract R-peaks location -../../_images/f3b07e768f414ca0dc803cfa59933aab6b146771d3c1b218183b407b640d59bb.png +../../_images/fa5e52b4c3b2233c50271df151a2c8be37bd154b44aec3ed8d0e470a93019d5b.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.

@@ -519,7 +519,7 @@

Segment the signal around the heart beats -../../_images/65d079a7290810d7a2afd84e089681a6ea5e015db36e1acb0eea55bd4d53ab42.png +../../_images/84dcd983cc2066d24222a95632a7a01e13bbe86efe7d63800e49115256a45b7e.png

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

@@ -575,36 +575,36 @@

Custom colors and legend -../../_images/97fd63250e6a6cc4b9279d4485fff4765656b62280e58f06c8e49d185ddcfe4b.png +../../_images/6e23bd5c0905f5251324a019d02d83f882aa898484850f240301fb08a8730a30.png diff --git a/examples/ecg_hrv/ecg_hrv.html b/examples/ecg_hrv/ecg_hrv.html index 98214d5326..5d93f485c9 100644 --- a/examples/ecg_hrv/ecg_hrv.html +++ b/examples/ecg_hrv/ecg_hrv.html @@ -621,7 +621,7 @@

Time-Domain Analysis

1 rows × 25 columns

-../../_images/37515f518b4d2eb0b667ac05fd8661526afc26c6b748ad3642046dc9b14a837e.png +../../_images/a0ed3a3d03fbbf3161d619b4a945b253a3dd4175f66e81d77c6ef8b8f3382298.png

These features include the RMSSD (square root of the mean of the sum of successive differences between adjacent RR intervals), MeanNN (mean of RR intervals) so on and so forth. You can also visualize the distribution of R-R intervals by specifying show=True in hrv_time().

@@ -683,7 +683,7 @@

Frequency-Domain Analysis +../../_images/7dddf79860dd68b4d1ae61e25405d90769c120a7ee07c60b0460adc350da0dc5.png @@ -767,7 +767,7 @@

Non-Linear Domain Analysis +../../_images/e71053533bb3388689b80d371051c762d2cad61e0a7b733181c8db82aa074726.png

This will produce a Poincaré plot which plots each RR interval against the next successive one.

@@ -853,7 +853,7 @@

All Domains +../../_images/872d652d23b8bded6e9b4b59afc77a9992ba296df67141b4701e815f37a1d6ba.png diff --git a/examples/eda_peaks/eda_peaks.html b/examples/eda_peaks/eda_peaks.html index 31855c55c4..f80ac24903 100644 --- a/examples/eda_peaks/eda_peaks.html +++ b/examples/eda_peaks/eda_peaks.html @@ -497,7 +497,7 @@

Locate Skin Conductance Response (SCR) features -../../_images/9c2e4742d9a5beea1cdf4ab75834fc7874c48936d0515fe39694bd61a0bf6e8e.png +../../_images/d423745f58f54189ca3a08fae3557cf02b01e1bfe716834d33d5716da2b710d4.png @@ -526,7 +526,7 @@

Decompose EDA into Phasic and Tonic components
<Axes: >
-../../_images/a826b85c65b90869daa4f26cf6842b4939cd02b69639495fa8048eb3820a7466.png +../../_images/a9c325ca66f55741fc409b41518eb15848997a956b900d4448653dbaa60d8610.png @@ -545,7 +545,7 @@

Quick Plot +../../_images/c44b4996e7ced32aa48ac20ff5244fac6ee38d85517c17fc3139c5f5e67b9fe8.png diff --git a/examples/eeg_complexity/eeg_complexity.html b/examples/eeg_complexity/eeg_complexity.html index 6e4e174dcd..4cf51aa4b4 100644 --- a/examples/eeg_complexity/eeg_complexity.html +++ b/examples/eeg_complexity/eeg_complexity.html @@ -499,7 +499,7 @@

Data Preprocessing -../../_images/c802c82f6d602ff6cb747f699cf2b1a9fb536f8a10fe9f96b411180d219f8e9d.png +../../_images/c5a7835aae85342b8413eb20635d4582b878907d009c54dfe626758427123542.png @@ -524,7 +524,7 @@

Interval-related Complexity Analysis
NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).
-../../_images/9dd5f150a8103aeda16b5e89fb8bfbb758782be3248bec65b80cefcd5f3768d1.png +../../_images/330e241b9a6561ae0f1267c3a2c6966e7ca1dd26922fff1b271c429b2b76bb63.png

Now, we can compute the complexity metrics of this signal.

@@ -550,7 +550,7 @@

Interval-related Complexity Analysis
SVDEn score: 1.22
-../../_images/fc4d05012e4c28103088c84ef87f3a44191e169d4512d982a3b3e093be35270f.png +../../_images/8d39be93f3734a508cc85194c91ff007aeafdc8b09c4c9b0ea667f98c4f7668b.png @@ -596,11 +596,11 @@

Event-related Preprocessing (Epoching)
No projector specified for this dataset. Please consider the method self.add_proj.
-../../_images/e01775b47785dd50861540c54b068924921c43bc4e200c21a3a3117a32684853.png +../../_images/36d53bf26af34a8a6836b131759c406cde31fb38927fa1ce0de663473cad486e.png 
No projector specified for this dataset. Please consider the method self.add_proj.
-../../_images/9e264387a8361efc939b56357f080c2c15e6525db45cb5e00319e81a7d63bf76.png +../../_images/af9d901d008eface1535745d39e4769df0e08f316b10d176a2905742f267c3b6.png

Now we are going to convert the data to a Pandas dataframe and concatenate the two groups of epochs. Next, we are going to average together the EEG channels.

@@ -727,7 +727,7 @@

Event-related Attractors
Text(0.5, 1.0, 'Event-related Attractors')
-../../_images/c54cc9a1bcadccd92dca74742d8f3b57571304d231a7ce0b7de329eb5897802e.png +../../_images/8806825200664cfbc9e5dee12f951df64166f67ead37e5cc7dacd8df92465d47.png @@ -754,9 +754,9 @@

Compute complexity indices per epoch -
C:\Users\runneradmin\AppData\Local\Temp\ipykernel_3732\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_8036\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_3732\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_8036\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)
 

 

@@ -861,7 +861,7 @@ 
Compute complexity indices per epoch
<Axes: xlabel='variable', ylabel='value'>
-../../_images/46f498926721580d5db943321f445d3a2faf233902c2c1eea7114d4f72963730.png +../../_images/50e14dab1806bbf46b4152063875254d2c7d35aaa69ee14f266a20fcb4309562.png
diff --git a/examples/eeg_microstates/eeg_microstates.html b/examples/eeg_microstates/eeg_microstates.html index b2eb758a60..c8fb9d7449 100644 --- a/examples/eeg_microstates/eeg_microstates.html +++ b/examples/eeg_microstates/eeg_microstates.html @@ -488,7 +488,7 @@

EEG Preprocessing -../../_images/34ef9f864622fe221ce14e9e15b82cb0582ca96ffdad60fcf6da314df07de4b4.png +../../_images/408a7812df107934dad6e08b4bd9ac8c4b49bb9b8f5e325b25038ad2f7c3c295.png @@ -508,7 +508,7 @@

Minimal Example -../../_images/c495ae8b84481edf9fa51ab7720ca2eb38c74585ec8f40e84378d6c0cad5273a.png +../../_images/f671f3570f8b379d876f0448ad5520dce9c73336c343109a77a8b871ace0ca72.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.

@@ -560,28 +560,28 @@

Minimal Example +../../_images/991ba97fdee28d314e6e4025e8a9b84ab68584f1eeaa06050729dd6754f8a4ef.png

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

@@ -631,26 +631,26 @@

Minimal Example +../../_images/521dd24e0fdf8f2ad547a7b48bb3cc0229f66f0384c47402729ff70469284caf.png

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

@@ -685,11 +685,11 @@

Minimal Example +../../_images/9d0619dd2aadd032406c7c2d1bc57c4169954ff611eb2a765d888db7baf13416.png @@ -714,7 +714,7 @@

Global Field Power (GFP)
-../../_images/6ae3f5d75761cd2873bbdf98b31b5793d2594c6718bbbeaaaa6891d9a874bcf0.png +../../_images/098984af9e14b554c7e2e52ba2d43615984670f6776094dc4f17dfc4de6711c0.png

By default, the microstates clustering algorithm is trained on the EEG activity at these peaks, and then applied to back-predict the state at all data points. However, this behaviour can be changed. For instance, one can decide to train the algorithm directly on all data points.

@@ -726,7 +726,7 @@

Global Field Power (GFP)
-../../_images/930d1299ddd4511d731059af43ac4ad1788b073179e4e9292458ddaf934e5df7.png +../../_images/d2bc65e95989773c5bc0a19472f4a83abe1e80eb5c5d18184d4c19add58916c0.png
@@ -767,6 +767,9 @@

How many microstates?
[██████████████████████████████████......] 6/7
+

+
+
[████████████████████████████████████████] 7/7
@@ -776,7 +779,7 @@

How many microstates?
Optimal number of microstates:  4.0
-../../_images/39abbee8761965238c950f369069ed6cbf10d2d8287320cf3f27551becfbede3.png +../../_images/7912380232e3a605e24305059c475c79a5cd02dfa6dc6fae1d5f50e82c118c7d.png @@ -803,12 +806,12 @@

Microstates clustering algorithms -
 Using conventional Kmeans,  GEV = 61.40%
- Using modified Kmeans,  GEV = 71.38%
+
 Using conventional Kmeans,  GEV = 61.14%
+ Using modified Kmeans,  GEV = 72.24%
 

 

-../../_images/75a8b2f9e1019f1c56ef25b193f8c750794c5063dbedd0b0a2f8244a4b9455b3.png
-../../_images/28e8f9c388c7ff37a9f355512b59baa98c40ce4872ece62c41e8cd6cbc0ee644.png
+../../_images/4a33c68e8304983c4929d071e1ef815857c901fcdb755362f8ee64da4677e535.png
+../../_images/e8abee7ba4159cae7bb05937041c8af844f6fb0d040fb2f70a09b8f6940bf4df.png
diff --git a/examples/eeg_power/eeg_power.html b/examples/eeg_power/eeg_power.html index d9cc5783a9..2ea81b8c27 100644 --- a/examples/eeg_power/eeg_power.html +++ b/examples/eeg_power/eeg_power.html @@ -521,7 +521,7 @@

Preprocessing +../../_images/cbaedbaf6a44632e3776f6bd9f5621474bdee65fd6fbd8f2aed906d8d0d24743.png @@ -556,7 +556,7 @@

Frontal Alpha Asymmetry (FAA)
Subtracting Evoked from Epochs
-
    The following channels are not included in the subtraction: STI 014, STI 003, STI 002, EOG 061, STI 005, STI 016, STI 006, STI 001, STI 015, STI 004
+
    The following channels are not included in the subtraction: STI 016, EOG 061, STI 002, STI 001, STI 003, STI 004, STI 014, STI 006, STI 015, STI 005
 

 

 
[done]
@@ -736,7 +736,7 @@ 
Frontal Alpha Asymmetry (FAA)
-../../_images/c802c82f6d602ff6cb747f699cf2b1a9fb536f8a10fe9f96b411180d219f8e9d.png
+../../_images/c5a7835aae85342b8413eb20635d4582b878907d009c54dfe626758427123542.png
 

 

 
This is work in progress for now. Help is needed to finish this example!

diff --git a/examples/eog_analyze/eog_analyze.html b/examples/eog_analyze/eog_analyze.html
index 81b8910b29..bbe5dcf2d3 100644
--- a/examples/eog_analyze/eog_analyze.html
+++ b/examples/eog_analyze/eog_analyze.html
@@ -467,7 +467,7 @@ 
Explore the EOG signal
-../../_images/0bdcf91d3dc40da3a84463d35d6e138b3e4f927e9482d0c6676506529f66785a.png +../../_images/1193696361bc1fb317221ffeb761b7b62bf80faba1cbdec87746fc486deaeec8.png

Let’s zoom in to some areas where clear blinks are present.

@@ -478,7 +478,7 @@

Explore the EOG signal
-../../_images/9d2e553e420c111bc9b3e87d2155a76560ec207bc53abb12e97e3ae89c1fdf79.png +../../_images/092dcfd81a6c4e9cb79ff7bc09ee0ca198c711c395e66c898a0480ede21cf33c.png
@@ -501,7 +501,7 @@

Clean the signal -../../_images/91da7996b5f7efb15730a7200b57dfacb1e3f673c39ac8ade7f192158a8ea36c.png +../../_images/62032d8ec3620af9adb12d8f51f7c3de74068011fd1a7b38f0b406d90c06310a.png @@ -545,10 +545,10 @@

Detect and visualize eye blinks -
[<matplotlib.lines.Line2D at 0x13235dab850>]
+
[<matplotlib.lines.Line2D at 0x244452d7c40>]
 

 

-../../_images/9675126eb1b3a9eccfa883bf3426e1b72cb2532086139404924aed09d131eec4.png
+../../_images/ee3aba31d9041cefc908594b863ed616993bf12d81746970edb198ad336785ea.png
diff --git a/examples/misc_epochs_create/misc_epochs_create.html b/examples/misc_epochs_create/misc_epochs_create.html index 497bf6b3f4..ade9711a36 100644 --- a/examples/misc_epochs_create/misc_epochs_create.html +++ b/examples/misc_epochs_create/misc_epochs_create.html @@ -473,7 +473,7 @@

One signal with multiple event markings -../../_images/c69defecf8d093444fc5259927bb7419b013f4f769f58d3984196cbd28becdb1.png +../../_images/731d3468c1b37d31b003875a38d7a6a5cf095974100e4de509701e04d0363a8b.png

Depending on how you set up your experiment, the onset of the event can either be marked by signal going from 0 to 5 or vice versa. Specific to this data, the onsets of the events are marked where the signal in the event-marking channel goes from 5 to 0 and the offsets of the events are marked where the signal goes from 0 to 5.

@@ -517,7 +517,7 @@

One signal with multiple event markings -../../_images/72a199e0ffad9961b014544a1d06ffb191cfcb4a7b4f4b9ccb3a85fa0d30a352.png +../../_images/05edc656e6d40df6b5abd10641f65eeddcf5d3f1dd786404f433e3ceb870cf04.png

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

@@ -528,7 +528,7 @@

One signal with multiple event markings -../../_images/f1ed439591daae9181271f07e5855aa12500651bd6dd567ad5b594d6a8b67423.png +../../_images/f50fb9f76e12912b0cb0653e4585ddf265a7e96060feb5311f5bdda776116171.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 559693fff2..fc81e88c18 100644 --- a/examples/misc_fit_function/misc_fit_function.html +++ b/examples/misc_fit_function/misc_fit_function.html @@ -468,7 +468,7 @@

Fit a linear function
-../../_images/f98e59f25376b30f6c1d743754eaa84b8f5785d3dd62b0355f81119aa3c5d13f.png +../../_images/63dbdfc6b2f184ce5d6b80d1d3e7bc8a8b8042bcbf5fae50ad7fe939f0c4b7e5.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.

@@ -491,7 +491,7 @@

Fit a linear function
-
array([1.61975388, 3.17497804])
+
array([2.65493712, 2.87239043])
@@ -508,7 +508,7 @@

Fit a linear function

-../../_images/01a68a8041d4f9a1a6ffc75e1fe952d97a2d357a6f7270aaba283d21d31236f6.png +../../_images/0d62fa29b5edaa129bb29957ed64ab8643453a36d8d591977ba50eeb55cc965d.png
@@ -523,7 +523,7 @@

Non-linear curves -../../_images/8582fc38b8aed852fabd51ac1ada714734c45f278ae80a31b36bf5f5d8295ab6.png +../../_images/492989d9f6658a7f1b844820d39847d56475ba265acd6ffe5fb80dbb4625d6f5.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).

@@ -549,7 +549,7 @@

Non-linear curves -../../_images/f2b689bc4f7e39a25ae3658e7756a3d3990583c3c3f7a54675bd269630b7e8b0.png +../../_images/a861a75f4f2882afe7e3edcd9ffcca99c9ac2e8cedb4d83c2f3061d79691abc0.png

Since these values are already a good start, we will use them as “starting point” (through the p0 argument), to help the estimation algorithm converge (otherwise it could never find the right combination of parameters).

@@ -561,7 +561,7 @@

Non-linear curves -
array([0.95043251, 2.21754751, 1.03304596, 2.04408816, 2.04744737])
+
array([0.95170814, 2.20086335, 1.13427525, 2.24431469, 1.79006045])
@@ -577,7 +577,7 @@

Non-linear curves -../../_images/9394f530055fd4cfa40581f433eb59ea06b16c5253667e2cc7b29a3b785ddd95.png +../../_images/018099eb538b4252317e71a6660aa6c77fea9b9c052ac4fd9ee67d8aa3b220b2.png

diff --git a/examples/rsp_rrv/rsp_rrv.html b/examples/rsp_rrv/rsp_rrv.html index 8042736dc2..5acc673a15 100644 --- a/examples/rsp_rrv/rsp_rrv.html +++ b/examples/rsp_rrv/rsp_rrv.html @@ -468,7 +468,7 @@

Download Data and Extract Relevant Signals -../../_images/a44e338ce41f9548eb56b944051ef03706dff1a7576f5ec2b928971a1cfccad6.png +../../_images/50158a8a7edda358ae005011fc6fc8fb51a10e1a19c14a051c366c5bca12f5f7.png

You now have the raw RSP signal in the shape of a vector (i.e., a one-dimensional array). You can then clean it using rsp_clean() and extract the inhalation peaks of the signal using rsp_peaks(). This will output 1) a dataframe indicating the occurrences of inhalation peaks and exhalation troughs (“1” marked in a list of zeros), and 2) a dictionary showing the samples of peaks and troughs.

@@ -493,7 +493,7 @@

Download Data and Extract Relevant Signals -../../_images/202eb61bbb2bc0a13fabab53680c4e83d5c084ec2452c726fba95eed375fd535.png +../../_images/8ef71767f857132e414c20855e07d959ebd3651b53f7c3a0aadf089eab6e85e4.png
@@ -503,7 +503,7 @@

Download Data and Extract Relevant Signals -../../_images/3822b899ab067d87400ad719da936777c199bdf267267101788fc43b8c29970a.png +../../_images/24716b753a862147132215b2601e0560f9d36f56080628748fecf96e68a2579f.png

@@ -513,7 +513,7 @@

Download Data and Extract Relevant Signals -../../_images/3822b899ab067d87400ad719da936777c199bdf267267101788fc43b8c29970a.png +../../_images/24716b753a862147132215b2601e0560f9d36f56080628748fecf96e68a2579f.png

@@ -531,7 +531,7 @@

Download Data and Extract Relevant Signals
Text(0, 0.5, 'Breaths Per Minute')

-../../_images/f28cddae0099a9524adf5fff98c0dd35882d56f91e88429a2e813ff1cddf7d65.png +../../_images/f4978cfae56b8334d2d2e7e241efa28f955dcd8e1033eca0a2623a898daaa0c6.png @@ -613,8 +613,8 @@

Analyse RRV -../../_images/5fb0cfd02dce2dca52ad519b576635cde762fdd9a86d6451b6544f193155e8ba.png +../../_images/56acc80835f7e9bda6d51796b4c5bef7a1f681ebea5c05d66b7275f6efd2d9c5.png +../../_images/2ba67b17d3e63b0bffe211185c1a704115e03f6755402dfa49075937bfa5a639.png

This is a simple visualization tool for short-term (SD1) and long-term variability (SD2) in respiratory rhythm.

diff --git a/examples/signal_simulation/signal_simulation.html b/examples/signal_simulation/signal_simulation.html index acd6262c2e..6b393be2a2 100644 --- a/examples/signal_simulation/signal_simulation.html +++ b/examples/signal_simulation/signal_simulation.html @@ -473,7 +473,7 @@

Cardiac Activity (ECG)
-../../_images/c1f1d5ebcd6bca3fe460b4e579f381a54c2c764419bc7b7a60573a228fb3536e.png +../../_images/2812d61e201294179151ed134a82a67c37cdb3157f470501a0609dbbbcd33029.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".

@@ -490,7 +490,7 @@

Cardiac Activity (ECG)
-../../_images/bec2dc8f31b3c2551c1ab2783698189c047f2067451b3635bed96fde384795f9.png +../../_images/9005f330283562c845f29b6a287627ae239da49c0d4e3a05b548ca8dda304039.png
@@ -513,7 +513,7 @@

Respiration (RSP) -../../_images/3c02f70b5fecac07ff10a60501584af807ea43fcc8da4915e64fdd487a45954e.png +../../_images/b815d499239f2381590482b5dd76bd7d4089e75d67142d79ce378f33b6f60932.png @@ -536,7 +536,7 @@

Electromyography (EMG)
-../../_images/fefb3c08ec695db01e334e4a6291f86ef305c8713ab4932d29d6fa253748788e.png +../../_images/4bd9be45e607bda2fa6a759c992d5690d557c56cc672ee34080656468b2f9620.png
@@ -559,7 +559,7 @@

Electrodermal Activity (EDA) -../../_images/e70f1a4943147d1dab40c272cd321284cc52e5104172c5568e5956ccbdfd4b7a.png +../../_images/2d957a98cb1f5a771214434dbda97950c6162a8595d9515f6a400eb35a57c8f7.png diff --git a/functions/benchmark.html b/functions/benchmark.html index c164b3c6d7..2d661d209f 100644 --- a/functions/benchmark.html +++ b/functions/benchmark.html @@ -481,7 +481,7 @@

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 0.015694 0.018861 0.333333 None +0 200 0.031242 0.019175 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 584c87b09d..4c04bb4f01 100644 --- a/functions/bio.html +++ b/functions/bio.html @@ -527,11 +527,11 @@

bio_process()In [7]: bio_df.head() Out[7]: ECG_Raw ECG_Clean ECG_Rate ... EMG_Offsets RSA_P2T RSA_Gates -0 1.089700 -0.067656 70.028011 ... 0 32.0 7.933478 -1 1.035764 -0.193407 70.028011 ... 0 32.0 7.933478 -2 0.896185 -0.331899 70.028011 ... 0 32.0 7.933478 -3 0.698307 -0.487400 70.028011 ... 0 32.0 7.933478 -4 0.471227 -0.656518 70.028011 ... 0 32.0 7.933478 +0 1.072194 -0.069229 70.042544 ... 0 36.0 7.976297 +1 1.019847 -0.191889 70.042544 ... 0 36.0 7.976297 +2 0.884072 -0.327482 70.042544 ... 0 36.0 7.976297 +3 0.690618 -0.480635 70.042544 ... 0 36.0 7.976297 +4 0.466986 -0.648399 70.042544 ... 0 36.0 7.976297 [5 rows x 49 columns] diff --git a/functions/complexity.html b/functions/complexity.html index 4346567f33..f3cd7a0da2 100644 --- a/functions/complexity.html +++ b/functions/complexity.html @@ -1456,7 +1456,7 @@

complexity_k()../_images/p_complexity_k1.png 
In [4]: k_max
-Out[4]: 66
+Out[4]: 20

References

@@ -1542,7 +1542,7 @@

fractal_katz()In [11]: KFD, _ = nk.fractal_katz(random) In [12]: KFD -Out[12]: 1.7602799811233942 +Out[12]: 2.0721836004084992 In [13]: KFD, _ = nk.fractal_katz(simple) @@ -1552,7 +1552,7 @@

fractal_katz()In [15]: KFD, _ = nk.fractal_katz(complex) In [16]: KFD -Out[16]: 3.789619556076831 +Out[16]: 4.0105571208269355

References

@@ -1810,7 +1810,7 @@

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]: 0.5978264240269113 +Out[10]: 0.5877112747999839

Example 3: Calculate FD-NLD on sliding windows

@@ -1889,7 +1889,7 @@

fractal_psdslope()../_images/p_fractal_psdslope1.png 
In [4]: psdslope
-Out[4]: 2.6752304660380433
+Out[4]: 2.643716909953294

References

@@ -1955,7 +1955,7 @@

fractal_higuchi()../_images/p_fractal_higuchi2.png 
In [5]: hfd
-Out[5]: 1.9442044574492716
+Out[5]: 1.9645077876434114

References

@@ -2042,7 +2042,7 @@

fractal_density()In [11]: D = info1["Average"] - info2["Average"] In [12]: plt.imshow(nk.standardize(D), cmap='RdBu') -Out[12]: <matplotlib.image.AxesImage at 0x2773c5e1360> +Out[12]: <matplotlib.image.AxesImage at 0x1e107c255d0> ../_images/p_fractal_density3.png @@ -2305,7 +2305,7 @@

fractal_dfa()../_images/p_fractal_dfa1.png 
In [4]: dfa
-Out[4]: 0.6498413195179513
+Out[4]: 0.650789745992934

As we can see from the plot, the final value, corresponding to the slope of the red line, @@ -2326,8 +2326,8 @@

fractal_dfa()
In [8]: mfdfa
 Out[8]: 
-      Width     Peak     Mean  ...  Asymmetry  Fluctuation  Increment
-0  0.834274  0.77431  0.88877  ...  -0.362803     0.000253   0.041397
+      Width      Peak      Mean  ...  Asymmetry  Fluctuation  Increment
+0  0.824115  0.775839  0.882532  ...  -0.370536     0.000241   0.040338
 
 [1 rows x 8 columns]
@@ -2420,10 +2420,10 @@

fractal_tmf()../_images/p_fractal_tmf.png 
In [4]: tMF  # t-value
-Out[4]: 6.878213026619276
+Out[4]: 9.108084234426563
 
 In [5]: info["p"]  # p-value
-Out[5]: 5.527270194854978e-10
+Out[5]: 9.639553704174184e-15

References

@@ -2514,7 +2514,7 @@

entropy_shannon()../_images/p_entropy_shannon1.png 
In [7]: shanen
-Out[7]: 1.5535621768481935
+Out[7]: 1.5498418576695772

Compare with scipy (using the same base).

@@ -2524,12 +2524,12 @@

entropy_shannon()In [9]: binned = pd.cut(signal, bins=3, labels=False) In [10]: scipy.stats.entropy(pd.Series(binned).value_counts()) -Out[10]: 1.0768472427068965 +Out[10]: 1.0742685139574555 In [11]: shanen, info = nk.entropy_shannon(binned, base=np.e) In [12]: shanen -Out[12]: 1.0768472427068965 +Out[12]: 1.0742685139574555

References

@@ -2624,7 +2624,7 @@

entropy_differential()In [3]: diffen, info = nk.entropy_differential(signal) In [4]: diffen -Out[4]: 0.5265499114516373 +Out[4]: 0.493761847390952

References

@@ -2677,11 +2677,11 @@

entropy_power()In [4]: powen, info = nk.entropy_power(signal) In [5]: powen -Out[5]: 0.05855913133249168 +Out[5]: 0.05855918916092749 # Visualize the distribution that the entropy power is based on In [6]: plt.plot(info["Values"], info["Density"]) -Out[6]: [<matplotlib.lines.Line2D at 0x2773dd8edd0>] +Out[6]: [<matplotlib.lines.Line2D at 0x1e1063bf1f0>] ../_images/entropy_power2.png @@ -2690,7 +2690,7 @@

entropy_power()
In [7]: powen, info = nk.entropy_power(signal, bandwidth=0.01)
 
 In [8]: powen
-Out[8]: 0.058558777638393865
+Out[8]: 0.058558913228957385

References

@@ -3270,7 +3270,7 @@

entropy_kl()In [3]: klen, info = nk.entropy_kl(signal, delay=1, dimension=3) In [4]: klen -Out[4]: 1.6368516888655722 +Out[4]: 1.6156183670957067

References

@@ -3335,7 +3335,7 @@

entropy_spectral()../_images/p_entropy_spectral1.png 
In [4]: SpEn
-Out[4]: 0.6039410068251649
+Out[4]: 0.6298309788317512

Bin the frequency spectrum.

@@ -3406,7 +3406,7 @@

entropy_phase()../_images/p_entropy_phase1.png 
In [4]: phasen
-Out[4]: 1.160468905223419
+Out[4]: 1.1660245433083012
 
In [5]: phasen, info = nk.entropy_phase(signal, k=8, show=True)
@@ -3473,7 +3473,7 @@ 
entropy_grid()../_images/p_entropy_grid1.png
 
 
In [4]: phasen
-Out[4]: 2.098137718700253
+Out[4]: 1.9220404351684415
 
 In [5]: phasen, info = nk.entropy_grid(signal, k=10, show=True)
 

@@ -3481,7 +3481,7 @@ 
entropy_grid()../_images/p_entropy_grid2.png
 
 
In [6]: info["GDR"]
-Out[6]: 0.53
+Out[6]: 0.55
 

 

 
References

@@ -3548,7 +3548,7 @@ 
entropy_attention()../_images/p_entropy_attention1.png
 
 
In [4]: atten
-Out[4]: 1.1980763999424202
+Out[4]: 1.142638045581421
 

 

 
References

@@ -3616,7 +3616,7 @@ 
entropy_increment()In [3]: incren, _ = nk.entropy_increment(signal, dimension=3, q=2)
 
 In [4]: incren
-Out[4]: 2.8290729964801247
+Out[4]: 2.7192214198212348
 
 # Multiscale IncrEn (MSIncrEn)
 In [5]: msincren, _ = nk.entropy_multiscale(signal, method="MSIncrEn", show=True)
@@ -3685,12 +3685,12 @@ 
entropy_slope()In [3]: slopen, info = nk.entropy_slope(signal, dimension=3, thresholds=[0.1, 45])
 
 In [4]: slopen
-Out[4]: 3.852428435588282
+Out[4]: 3.840743150669669
 
 In [5]: slopen, info = nk.entropy_slope(signal, dimension=3, thresholds=[5, 45, 60, 90])
 
 In [6]: slopen
-Out[6]: 5.040078180224091
+Out[6]: 5.044041521410757
 
 # Compute Multiscale Slope Entropy (MSSlopEn)
 In [7]: msslopen, info = nk.entropy_multiscale(signal, method="MSSlopEn", show=True)
@@ -3751,12 +3751,12 @@ 
entropy_symbolicdynamic()In [4]: sydyen, info = nk.entropy_symbolicdynamic(signal, c=3, symbolize="MEP")
 
 In [5]: sydyen
-Out[5]: 3.7599306978172216
+Out[5]: 3.5937765813641005
 
 In [6]: sydyen, info = nk.entropy_symbolicdynamic(signal, c=3, symbolize="kmeans")
 
 In [7]: sydyen
-Out[7]: 3.212311480065756
+Out[7]: 3.362184514531246
 
 # Compute Multiscale Symbolic Dynamic Entropy (MSSyDyEn)
 In [8]: mssydyen, info = nk.entropy_multiscale(signal, method="MSSyDyEn", show=True)
@@ -3829,17 +3829,17 @@ 
entropy_dispersion()In [3]: dispen, info = nk.entropy_dispersion(signal, c=3)
 
 In [4]: dispen
-Out[4]: 1.411338507169183
+Out[4]: 1.4106885238634852
 
 # Get Reverse Dispersion Entropy (RDEn)
 In [5]: info["RDEn"]
-Out[5]: 0.0063930980763889
+Out[5]: 0.005868635795910359
 
 # Fluctuation-based DispEn with "finesort"
 In [6]: dispen, info = nk.entropy_dispersion(signal, c=3, symbolize="finesort", fluctuation=True)
 
 In [7]: dispen
-Out[7]: 1.4113385071691829
+Out[7]: 1.4106885238634852

References

@@ -3893,7 +3893,7 @@

entropy_ofentropy()In [3]: enofen, _ = nk.entropy_ofentropy(signal, scale=10, bins=10) In [4]: enofen -Out[4]: 4.106198332810095 +Out[4]: 3.806198332810096

References

@@ -3964,31 +3964,31 @@

entropy_permutation()In [2]: pen, info = nk.entropy_permutation(signal, corrected=False) In [3]: pen -Out[3]: 2.571015917929897 +Out[3]: 2.568776154756591 # Weighted Permutation Entropy (WPEn) In [4]: wpen, info = nk.entropy_permutation(signal, weighted=True) In [5]: wpen -Out[5]: 0.9983944918858431 +Out[5]: 0.9912398479868824 # Conditional Permutation Entropy (CPEn) In [6]: cpen, info = nk.entropy_permutation(signal, conditional=True) In [7]: cpen -Out[7]: 0.41960411935048963 +Out[7]: 0.4243723518902717 # Conditional Weighted Permutation Entropy (CWPEn) In [8]: cwpen, info = nk.entropy_permutation(signal, weighted=True, conditional=True) In [9]: cwpen -Out[9]: 0.4151597997807082 +Out[9]: 0.40897017968291827 # 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]: 0.28328504204283944 +Out[11]: 0.2860474721646164

References

@@ -4569,7 +4569,7 @@

fishershannon_information()In [3]: fsi, info = nk.fishershannon_information(signal, method=0.01) In [4]: fsi -Out[4]: 0.00033384686815827774 +Out[4]: 0.00027718898722075533

References

@@ -4823,7 +4823,7 @@

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 @@ -4904,7 +4904,7 @@

complexity_lempelziv()In [5]: plzc, info = nk.complexity_lempelziv(signal, delay=1, dimension=3, permutation=True) In [6]: plzc -Out[6]: 0.6547889281409168 +Out[6]: 0.6631836579888775 
# MSLZC
@@ -5058,7 +5058,7 @@ 
complexity_lyapunov()In [3]: lle, info = nk.complexity_lyapunov(signal, method="rosenstein", show=True)
 
 In [4]: lle
-Out[4]: 0.06568747279416336
+Out[4]: 0.06635598721155835
 
 # Makowski's change-point method
 In [5]: lle, info = nk.complexity_lyapunov(signal, method="makowski", show=True)
@@ -5163,8 +5163,8 @@ 
complexity_rqa()
 
In [4]: results
 Out[4]: 
-   RecurrenceRate   DiagRec  Determinism  ...         W  WMax       WEn
-0        0.232083 -0.011641     0.845792  ...  9.530802   295  2.928854
+   RecurrenceRate   DiagRec  Determinism  ...        W  WMax       WEn
+0        0.204819 -0.016467     0.838048  ...  9.69788   303  3.037245
 
 [1 rows x 14 columns]
 
@@ -5236,7 +5236,7 @@ 
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 0x2773dc58040>
+Out[6]: <matplotlib.image.AxesImage at 0x1e10b9dc5b0>
 
 In [7]: plt.axis("off")
 Out[7]: (-0.5, 908.5, 999.5, -0.5)
@@ -5250,7 +5250,7 @@ 
fractal_mandelbrot()   ...: 
 
 In [9]: plt.imshow(b.T, cmap="gray")
-Out[9]: <matplotlib.image.AxesImage at 0x2773dc59ba0>
+Out[9]: <matplotlib.image.AxesImage at 0x1e10ba1e620>
 
 In [10]: plt.axis("off")
 Out[10]: (-0.5, 1363.5, 1499.5, -0.5)
@@ -5266,7 +5266,7 @@ 
fractal_mandelbrot()In [13]: mixed = m - b
 
 In [14]: plt.imshow(mixed.T, cmap="gray")
-Out[14]: <matplotlib.image.AxesImage at 0x2773dd431f0>
+Out[14]: <matplotlib.image.AxesImage at 0x1e10b9c7cd0>
 
 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 f6a8f3d140..fda98b5059 100644
--- a/functions/ecg.html
+++ b/functions/ecg.html
@@ -1495,32 +1495,32 @@ 
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 0x2773be61e70>]
+Out[7]: [<matplotlib.lines.Line2D at 0x1e117043f40>]
 
 In [8]: ax[0].plot(cardiac_phase["ECG_Phase_Atrial"], label="Atrial Phase", color="orange")
-Out[8]: [<matplotlib.lines.Line2D at 0x2773bf39090>]
+Out[8]: [<matplotlib.lines.Line2D at 0x1e1159d4f10>]
 
 In [9]: ax[0].plot(cardiac_phase["ECG_Phase_Completion_Atrial"],
    ...:            label="Atrial Phase Completion", linestyle="dotted")
    ...: 
-Out[9]: [<matplotlib.lines.Line2D at 0x2773bf39f90>]
+Out[9]: [<matplotlib.lines.Line2D at 0x1e1159d6260>]
 
 In [10]: ax[0].legend(loc="upper right")
-Out[10]: <matplotlib.legend.Legend at 0x27743bcc400>
+Out[10]: <matplotlib.legend.Legend at 0x1e122229060>
 
 In [11]: ax[1].plot(nk.rescale(ecg), label="ECG", color="red", alpha=0.3)
-Out[11]: [<matplotlib.lines.Line2D at 0x2773bf3a290>]
+Out[11]: [<matplotlib.lines.Line2D at 0x1e115a430a0>]
 
 In [12]: ax[1].plot(cardiac_phase["ECG_Phase_Ventricular"], label="Ventricular Phase", color="green")
-Out[12]: [<matplotlib.lines.Line2D at 0x2773bf3b4f0>]
+Out[12]: [<matplotlib.lines.Line2D at 0x1e115a40c70>]
 
 In [13]: ax[1].plot(cardiac_phase["ECG_Phase_Completion_Ventricular"],
    ....:            label="Ventricular Phase Completion", linestyle="dotted")
    ....: 
-Out[13]: [<matplotlib.lines.Line2D at 0x2773bf38e50>]
+Out[13]: [<matplotlib.lines.Line2D at 0x1e115a403d0>]
 
 In [14]: ax[1].legend(loc="upper right")
-Out[14]: <matplotlib.legend.Legend at 0x27743bcfa00>
+Out[14]: <matplotlib.legend.Legend at 0x1e122228580>
 

 

 ../_images/p_ecg_phase.png
@@ -1719,9 +1719,9 @@ 
ecg_eventrelated()In [5]: nk.ecg_eventrelated(epochs)
 Out[5]: 
   Label  Event_Onset  ...  ECG_Phase_Completion_Ventricular  ECG_Quality_Mean
-1     1         5000  ...                          0.661191          0.977886
-2     2        10000  ...                          0.425703          0.960208
-3     3        15000  ...                          0.058212          0.928629
+1     1         5000  ...                          0.761711          0.956242
+2     2        10000  ...                          0.434511          0.927347
+3     3        15000  ...                          0.163561          0.968443
 
 [3 rows x 17 columns]
diff --git a/functions/eda.html b/functions/eda.html index bc8f1b9101..0618506356 100644 --- a/functions/eda.html +++ b/functions/eda.html @@ -1113,8 +1113,8 @@

eda_eventrelated()In [5]: nk.eda_eventrelated(epochs) Out[5]: Label Event_Onset ... SCR_RiseTime SCR_RecoveryTime -1 1 5000 ... 0.360 0.506 -2 2 10000 ... 0.365 0.550 +1 1 5000 ... 0.362 0.511 +2 2 10000 ... 0.361 0.561 3 3 15000 ... NaN NaN [3 rows x 8 columns] @@ -1330,7 +1330,7 @@

eda_autocor()In [4]: cor = nk.eda_autocor(eda_cleaned) In [5]: cor -Out[5]: -0.22546669122297958 +Out[5]: -0.23194892164071715

References

@@ -1382,7 +1382,7 @@

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]: 15 ../_images/p_eda_changepoints1.png diff --git a/functions/emg.html b/functions/emg.html index efd755217b..48456d4bb8 100644 --- a/functions/emg.html +++ b/functions/emg.html @@ -592,9 +592,9 @@

emg_analyze()In [7]: analyze_epochs Out[7]: Label Event_Onset ... EMG_Amplitude_Max_Time EMG_Bursts -1 1 3000 ... 1.461781 1.0 +1 1 3000 ... 1.324712 1.0 2 2 6000 ... NaN NaN -3 3 9000 ... 0.807454 1.0 +3 3 9000 ... 0.635368 1.0 [3 rows x 8 columns] @@ -604,7 +604,7 @@

emg_analyze()In [9]: analyze_df Out[9]: EMG_Activation_N EMG_Amplitude_Mean -0 3.0 0.233737 +0 3.0 0.248417 @@ -955,9 +955,9 @@

emg_eventrelated()In [5]: nk.emg_eventrelated(epochs) Out[5]: Label Event_Onset ... EMG_Amplitude_Max_Time EMG_Bursts -1 1 3000 ... 1.457779 1.0 +1 1 3000 ... 1.784942 1.0 2 2 6000 ... NaN NaN -3 3 9000 ... 0.559330 1.0 +3 3 9000 ... 0.844472 1.0 [3 rows x 8 columns] @@ -1003,15 +1003,15 @@

emg_intervalrelated()In [4]: nk.emg_intervalrelated(emg_signals) Out[4]: EMG_Activation_N EMG_Amplitude_Mean -0 3.0 0.234831 +0 3.0 0.240771 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 0.233358 -2 2 1 0.236293 +1 1 2 0.236811 +2 2 1 0.244797 diff --git a/functions/markov.html b/functions/markov.html index 53a788ff21..a266aecd57 100644 --- a/functions/markov.html +++ b/functions/markov.html @@ -586,7 +586,7 @@

markov_simulate()In [4]: x = nk.markov_simulate(tm, n=15) In [5]: x -Out[5]: array([3, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1]) +Out[5]: array([3, 1, 2, 1, 0, 0, 3, 1, 2, 2, 2, 2, 2, 1, 2]) diff --git a/functions/misc.html b/functions/misc.html index 6d156a0914..b1a14c20dd 100644 --- a/functions/misc.html +++ b/functions/misc.html @@ -771,7 +771,7 @@

find_plateau()In [5]: plateau = nk.find_plateau(y, show=True) In [6]: plateau -Out[6]: 41 +Out[6]: 42 ../_images/p_find_plateau1.png @@ -804,7 +804,7 @@

find_knee()In [3]: y += np.random.normal(0, 0.2, len(y)) In [4]: nk.find_knee(y, show=True) -Out[4]: 21 +Out[4]: 20

References

@@ -1092,7 +1092,7 @@

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 0x2774775c4c0>] +Out[3]: [<matplotlib.lines.Line2D at 0x1e10d565060>] In [4]: fig = plt.gcf() # Get current figure diff --git a/functions/ppg.html b/functions/ppg.html index 976ae2c0be..a59cf5571c 100644 --- a/functions/ppg.html +++ b/functions/ppg.html @@ -601,9 +601,9 @@

ppg_analyze()In [6]: analyze_epochs Out[6]: Label Event_Onset ... PPG_Rate_Trend_Quadratic PPG_Rate_Trend_R2 -1 1 5000 ... -0.438471 0.965475 -2 2 10000 ... -0.828726 0.981771 -3 3 15000 ... 0.236967 0.879532 +1 1 5000 ... -1.082171 0.834789 +2 2 10000 ... 1.010333 0.934836 +3 3 15000 ... 0.176961 0.638617 [3 rows x 12 columns] @@ -850,8 +850,8 @@

ppg_peaks()In [6]: info["PPG_Peaks"] Out[6]: -array([ 46, 88, 133, 177, 217, 264, 305, 351, 425, 482, 519, 559, 596, - 632, 670, 704, 742, 778, 813, 852, 888, 927, 966]) +array([ 47, 88, 133, 177, 218, 262, 306, 348, 411, 451, 490, 565, 631, + 670, 705, 742, 777, 815, 889, 927, 966]) # Method by Bishop et al., (2018) In [7]: peaks, info = nk.ppg_peaks(ppg, sampling_rate=100, method="bishop", show=True) @@ -937,9 +937,9 @@

ppg_eventrelated()In [4]: nk.ppg_eventrelated(epochs) Out[4]: Label Event_Onset ... PPG_Rate_Trend_Quadratic PPG_Rate_Trend_R2 -1 1 5000 ... -0.264315 0.953901 -2 2 10000 ... -0.745969 0.944476 -3 3 15000 ... 0.341248 0.925435 +1 1 5000 ... -0.352364 0.934166 +2 2 10000 ... -0.845666 0.988034 +3 3 15000 ... 0.745659 0.738939 [3 rows x 12 columns] diff --git a/functions/rsp.html b/functions/rsp.html index 8ea8046dba..aba8395d19 100644 --- a/functions/rsp.html +++ b/functions/rsp.html @@ -583,7 +583,7 @@

rsp_process()| RSP_Rate_Mean | RSP_Rate_SD | |----------------:|--------------:| -| 15.2663 | 1.72975 | +| 14.9819 | 1.88031 | References - Harrison, S. J., Bianchi, S., Heinzle, J., Stephan, K. E., Iglesias, S., & Kasper, L. (2021). @@ -1325,8 +1325,8 @@

rsp_rrv()In [4]: nk.rsp_rrv(rsp, show=True) Out[4]: - RRV_RMSSD RRV_MeanBB RRV_SDBB ... RRV_SD2SD1 RRV_ApEn RRV_SampEn -0 565.033953 4041.25 523.948257 ... 1.516457 0.091859 inf + RRV_RMSSD RRV_MeanBB RRV_SDBB ... RRV_SD2SD1 RRV_ApEn RRV_SampEn +0 729.34738 3961.238095 519.560382 ... 0.970306 0.018021 inf [1 rows x 20 columns] @@ -1467,9 +1467,9 @@

rsp_eventrelated()In [4]: nk.rsp_eventrelated(epochs) Out[4]: Label Event_Onset ... RSP_RVT_Baseline RSP_RVT_Mean -1 1 5000 ... 0.188396 0.010823 -2 2 10000 ... 0.211681 0.006838 -3 3 15000 ... 0.257663 -0.021700 +1 1 5000 ... 0.243456 0.011733 +2 2 10000 ... 0.281702 -0.002063 +3 3 15000 ... 0.101996 0.019739 [3 rows x 22 columns] @@ -1675,7 +1675,7 @@

rsp_intervalrelated()In [7]: rav Out[7]: RAV_Mean RAV_SD RAV_RMSSD RAV_CVSD -0 0.929702 0.066899 0.08061 0.086706 +0 0.995793 0.042181 0.040455 0.040626 diff --git a/functions/signal.html b/functions/signal.html index 9635149240..32b43b01e7 100644 --- a/functions/signal.html +++ b/functions/signal.html @@ -1346,7 +1346,7 @@

signal_interpolate()# Add original data points In [8]: plt.scatter(x_values, signal, label="original datapoints", zorder=3) -Out[8]: <matplotlib.collections.PathCollection at 0x27744bd8ee0> +Out[8]: <matplotlib.collections.PathCollection at 0x1e10c271240> ../_images/p_signal_interpolate1.png @@ -1467,19 +1467,19 @@

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 0x277466edc60>] +Out[14]: [<matplotlib.lines.Line2D at 0x1e115794700>] In [15]: plt.loglog(psd_blue["Frequency"], psd_blue["Power"], c="blue") -Out[15]: [<matplotlib.lines.Line2D at 0x2774f1ebfd0>] +Out[15]: [<matplotlib.lines.Line2D at 0x1e111c77670>] In [16]: plt.loglog(psd_white["Frequency"], psd_white["Power"], c="grey") -Out[16]: [<matplotlib.lines.Line2D at 0x2774f230490>] +Out[16]: [<matplotlib.lines.Line2D at 0x1e111c77b50>] In [17]: plt.loglog(psd_pink["Frequency"], psd_pink["Power"], c="pink") -Out[17]: [<matplotlib.lines.Line2D at 0x2774f230910>] +Out[17]: [<matplotlib.lines.Line2D at 0x1e111c77220>] In [18]: plt.loglog(psd_brown["Frequency"], psd_brown["Power"], c="brown") -Out[18]: [<matplotlib.lines.Line2D at 0x2774f230e20>] +Out[18]: [<matplotlib.lines.Line2D at 0x1e111c984c0>] ../_images/p_signal_noise2.png @@ -1528,16 +1528,16 @@

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 0x2774f2517e0>] +Out[6]: [<matplotlib.lines.Line2D at 0x1e111c4b070>] In [7]: plt.plot(surrogate_iaaft, label = "IAAFT Surrogate") -Out[7]: [<matplotlib.lines.Line2D at 0x2774f251a20>] +Out[7]: [<matplotlib.lines.Line2D at 0x1e111c4a770>] In [8]: plt.plot(signal, label = "Original") -Out[8]: [<matplotlib.lines.Line2D at 0x2773def5f60>] +Out[8]: [<matplotlib.lines.Line2D at 0x1e1063684f0>] In [9]: plt.legend() -Out[9]: <matplotlib.legend.Legend at 0x27754922da0> +Out[9]: <matplotlib.legend.Legend at 0x1e105695270> ../_images/p_signal_surrogate1.png @@ -1545,16 +1545,16 @@

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 0x2774f2c4e20>]
+Out[10]: [<matplotlib.lines.Line2D at 0x1e111ccbac0>]
 
 In [11]: plt.plot(*nk.density(surrogate_iaaft), label = "IAAFT Surrogate")
-Out[11]: [<matplotlib.lines.Line2D at 0x2774f233040>]
+Out[11]: [<matplotlib.lines.Line2D at 0x1e111ccba60>]
 
 In [12]: plt.plot(*nk.density(surrogate_random), label = "Random Surrogate")
-Out[12]: [<matplotlib.lines.Line2D at 0x277509ad420>]
+Out[12]: [<matplotlib.lines.Line2D at 0x1e111cefcd0>]
 
 In [13]: plt.legend()
-Out[13]: <matplotlib.legend.Legend at 0x2774f2c4fa0>
+Out[13]: <matplotlib.legend.Legend at 0x1e111ce00a0>
../_images/p_signal_surrogate2.png @@ -1881,7 +1881,7 @@

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, 2750, 4500, 5500, 7250, 8250]) +Out[3]: array([ 322, 1750, 2749, 4501, 5500, 7251, 8250, 9222, 9224]) ../_images/p_signal_changepoints1.png diff --git a/functions/stats.html b/functions/stats.html index 5453086d4f..2247af6b5c 100644 --- a/functions/stats.html +++ b/functions/stats.html @@ -963,91 +963,91 @@

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 0x27779cb17b0> +Out[16]: <matplotlib.collections.PathCollection at 0x1e11c4a8670> In [17]: axes[0, 0].scatter(clusters_kmeans[:, 2], clusters_kmeans[:, 3], c='red') -Out[17]: <matplotlib.collections.PathCollection at 0x277662d7a00> +Out[17]: <matplotlib.collections.PathCollection at 0x1e125ee84c0> 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 0x27779cb0ca0> +Out[19]: <matplotlib.collections.PathCollection at 0x1e125207f40> In [20]: axes[0, 1].scatter(clusters_spectral[:, 2], clusters_spectral[:, 3], c='red') -Out[20]: <matplotlib.collections.PathCollection at 0x277662d78e0> +Out[20]: <matplotlib.collections.PathCollection at 0x1e125ee93c0> 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 0x27741dd4b20> +Out[22]: <matplotlib.collections.PathCollection at 0x1e1252066e0> In [23]: axes[1, 0].scatter(clusters_hierarchical[:, 2], clusters_hierarchical[:, 3], c='red') -Out[23]: <matplotlib.collections.PathCollection at 0x277662d7e80> +Out[23]: <matplotlib.collections.PathCollection at 0x1e125206bf0> 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 0x27779cb2140> +Out[25]: <matplotlib.collections.PathCollection at 0x1e125205c90> In [26]: axes[1, 1].scatter(clusters_agglomerative[:, 2], clusters_agglomerative[:, 3], c='red') -Out[26]: <matplotlib.collections.PathCollection at 0x27779cb2200> +Out[26]: <matplotlib.collections.PathCollection at 0x1e1252042b0> 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 0x27779cb2770> +Out[28]: <matplotlib.collections.PathCollection at 0x1e125204400> In [29]: axes[2, 0].scatter(clusters_mixture[:, 2], clusters_mixture[:, 3], c='red') -Out[29]: <matplotlib.collections.PathCollection at 0x27777183730> +Out[29]: <matplotlib.collections.PathCollection at 0x1e125ee9090> 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 0x27779cb3100> +Out[31]: <matplotlib.collections.PathCollection at 0x1e125206950> In [32]: axes[2, 1].scatter(clusters_bayes[:, 2], clusters_bayes[:, 3], c='red') -Out[32]: <matplotlib.collections.PathCollection at 0x27779cb31c0> +Out[32]: <matplotlib.collections.PathCollection at 0x1e125206020> 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 0x27779cb39a0> +Out[34]: <matplotlib.collections.PathCollection at 0x1e1251e0640> In [35]: axes[3, 0].scatter(clusters_pca[:, 2], clusters_pca[:, 3], c='red') -Out[35]: <matplotlib.collections.PathCollection at 0x27779cb2da0> +Out[35]: <matplotlib.collections.PathCollection at 0x1e125206920> 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 0x27779cb3ee0> +Out[37]: <matplotlib.collections.PathCollection at 0x1e11d9fa110> In [38]: axes[3, 1].scatter(clusters_ica[:, 2], clusters_ica[:, 3], c='red') -Out[38]: <matplotlib.collections.PathCollection at 0x2776638c7c0> +Out[38]: <matplotlib.collections.PathCollection at 0x1e11da60040> 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 0x27779cd89d0> +Out[40]: <matplotlib.collections.PathCollection at 0x1e1251e0970> In [41]: axes[4, 0].scatter(clusters_kmod[:, 2], clusters_kmod[:, 3], c='red') -Out[41]: <matplotlib.collections.PathCollection at 0x27779cd8ac0> +Out[41]: <matplotlib.collections.PathCollection at 0x1e1251e1e40> 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 0x27779cb3610> +Out[43]: <matplotlib.collections.PathCollection at 0x1e11d9fb910> In [44]: axes[4, 1].scatter(clusters_aahc[:, 2], clusters_aahc[:, 3], c='red') -Out[44]: <matplotlib.collections.PathCollection at 0x27779cd9420> +Out[44]: <matplotlib.collections.PathCollection at 0x1e1251e0fd0> In [45]: axes[4, 1].set_title("AAHC (Frederic's method)") Out[45]: Text(0.5, 1.0, "AAHC (Frederic's method)") @@ -1161,7 +1161,7 @@

cluster_quality()In [5]: general Out[5]: n_Clusters Score_Silhouette ... Score_GAP_sk Score_GAPmod_sk -0 3.0 0.551192 ... 0.274685 1022.422406 +0 3.0 0.552819 ... 0.385113 1005.593612 [1 rows x 12 columns] @@ -1433,7 +1433,7 @@

fit_polynomial()In [3]: bw = nk.density_bandwidth(x) In [4]: bw -Out[4]: 0.37137075032150113 +Out[4]: 0.31454120601392416 In [5]: nk.density_bandwidth(x, method="scott") Out[5]: 0.3981071705534972 diff --git a/searchindex.js b/searchindex.js index d23b28a4b2..2cb269aa72 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Documentation website": [[46, "documentation-website"]], "1/f Electrophysiological Noise": [[14, "f-electrophysiological-noise"]], "2. The source on Github": [[46, "the-source-on-github"]], "Abnormal Multi-lead ECG": [[8, "abnormal-multi-lead-ecg"]], "Accuracy": [[52, "accuracy"]], "Adding examples and tutorials": [[46, "adding-examples-and-tutorials"]], "Adding tests": [[46, "adding-tests"]], "Additional Resources": [[50, null]], "Advanced Plotting": [[9, "advanced-plotting"]], "Algorithm Comparison Conclusion": [[52, "algorithm-comparison-conclusion"]], "Algorithm Comparison Procedure": [[52, "algorithm-comparison-procedure"]], "Algorithm Comparison Results": [[52, "algorithm-comparison-results"]], "Algorithm Comparison Setup Functions": [[52, "algorithm-comparison-setup-functions"]], "All Domains": [[10, "all-domains"]], "An GAM-based Approach to EEG/ERP Analysis using Python and R": [[54, null]], "Analyse EDR": [[7, "analyse-edr"]], "Analyse RRV": [[20, "analyse-rrv"]], "Analysis": [[26, "analysis"], [27, "analysis"], [29, "analysis"], [30, "analysis"], [38, "analysis"], [39, "analysis"], [40, "analysis"]], 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neurokit2.data)": [[25, "neurokit2.data.data", false]], "density() (in module neurokit2)": [[41, "neurokit2.density", false]], "density_bandwidth() (in module neurokit2.stats)": [[41, "neurokit2.stats.density_bandwidth", false]], "distance() (in module neurokit2)": [[41, "neurokit2.distance", false]], "download_from_url() (in module neurokit2.data)": [[25, "neurokit2.data.download_from_url", false]], "download_zip() (in module neurokit2.data)": [[25, "neurokit2.data.download_zip", false]], "ecg_analyze() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_analyze", false]], "ecg_clean() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_clean", false]], "ecg_delineate() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_delineate", false]], "ecg_eventrelated() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_eventrelated", false]], "ecg_findpeaks() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_findpeaks", false]], "ecg_intervalrelated() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_intervalrelated", false]], "ecg_invert() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_invert", false]], "ecg_peaks() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_peaks", false]], "ecg_phase() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_phase", false]], "ecg_plot() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_plot", false]], "ecg_process() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_process", false]], "ecg_quality() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_quality", false]], "ecg_rate() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_rate", false]], "ecg_rsp() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_rsp", false]], "ecg_segment() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_segment", false]], "ecg_simulate() (in module neurokit2.ecg)": [[26, "neurokit2.ecg.ecg_simulate", false]], "eda_analyze() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_analyze", false]], "eda_autocor() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_autocor", false]], "eda_changepoints() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_changepoints", false]], "eda_clean() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_clean", false]], "eda_eventrelated() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_eventrelated", false]], "eda_findpeaks() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_findpeaks", false]], "eda_fixpeaks() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_fixpeaks", false]], "eda_intervalrelated() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_intervalrelated", false]], "eda_peaks() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_peaks", false]], "eda_phasic() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_phasic", false]], "eda_plot() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_plot", false]], "eda_process() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_process", false]], "eda_simulate() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_simulate", false]], "eda_sympathetic() (in module neurokit2.eda)": [[27, "neurokit2.eda.eda_sympathetic", false]], "eeg_badchannels() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_badchannels", false]], "eeg_diss() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_diss", false]], "eeg_gfp() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_gfp", false]], "eeg_power() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_power", false]], "eeg_rereference() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_rereference", false]], "eeg_simulate() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_simulate", false]], "eeg_source() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_source", false]], "eeg_source_extract() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.eeg_source_extract", false]], "emg_activation() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_activation", false]], "emg_amplitude() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_amplitude", false]], "emg_analyze() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_analyze", false]], "emg_clean() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_clean", false]], "emg_eventrelated() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_eventrelated", false]], "emg_intervalrelated() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_intervalrelated", false]], "emg_plot() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_plot", false]], "emg_process() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_process", false]], "emg_simulate() (in module neurokit2.emg)": [[29, "neurokit2.emg.emg_simulate", false]], "entropy_approximate() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_approximate", false]], "entropy_attention() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_attention", false]], "entropy_bubble() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_bubble", false]], "entropy_cumulativeresidual() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_cumulativeresidual", false]], "entropy_differential() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_differential", false]], "entropy_dispersion() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_dispersion", false]], "entropy_fuzzy() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_fuzzy", false]], "entropy_grid() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_grid", false]], "entropy_hierarchical() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_hierarchical", false]], "entropy_increment() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_increment", false]], "entropy_kl() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_kl", false]], "entropy_maximum() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_maximum", false]], "entropy_multiscale() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_multiscale", false]], "entropy_ofentropy() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_ofentropy", false]], "entropy_permutation() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_permutation", false]], "entropy_phase() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_phase", false]], "entropy_power() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_power", false]], "entropy_quadratic() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_quadratic", false]], "entropy_range() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_range", false]], "entropy_rate() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_rate", false]], "entropy_renyi() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_renyi", false]], "entropy_sample() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_sample", false]], "entropy_shannon() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_shannon", false]], "entropy_shannon_joint() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_shannon_joint", false]], "entropy_slope() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_slope", false]], "entropy_spectral() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_spectral", false]], "entropy_svd() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_svd", false]], "entropy_symbolicdynamic() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_symbolicdynamic", false]], "entropy_tsallis() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.entropy_tsallis", false]], "eog_analyze() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_analyze", false]], "eog_clean() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_clean", false]], "eog_eventrelated() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_eventrelated", false]], "eog_features() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_features", false]], "eog_findpeaks() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_findpeaks", false]], "eog_intervalrelated() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_intervalrelated", false]], "eog_peaks() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_peaks", false]], "eog_plot() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_plot", false]], "eog_process() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_process", false]], "eog_rate() (in module neurokit2.eog)": [[30, "neurokit2.eog.eog_rate", false]], "epochs_average() (in module neurokit2.epochs)": [[31, "neurokit2.epochs.epochs_average", false]], "epochs_create() (in module neurokit2.epochs)": [[31, "neurokit2.epochs.epochs_create", false]], "epochs_plot() (in module neurokit2.epochs)": [[31, "neurokit2.epochs.epochs_plot", false]], "epochs_to_array() (in module neurokit2.epochs)": [[31, "neurokit2.epochs.epochs_to_array", false]], "epochs_to_df() (in module neurokit2.epochs)": [[31, "neurokit2.epochs.epochs_to_df", false]], "events_create() (in module neurokit2.events)": [[32, "neurokit2.events.events_create", false]], "events_find() (in module neurokit2.events)": [[32, "neurokit2.events.events_find", false]], "events_plot() (in module neurokit2.events)": [[32, "neurokit2.events.events_plot", false]], "events_to_mne() (in module neurokit2.events)": [[32, "neurokit2.events.events_to_mne", false]], "expspace() (in module neurokit2.misc)": [[37, "neurokit2.misc.expspace", false]], "fig2img() (in module neurokit2.misc)": [[37, "neurokit2.misc.fig2img", false]], "find_closest() (in module neurokit2.misc)": [[37, "neurokit2.misc.find_closest", false]], "find_consecutive() (in module neurokit2.misc)": [[37, "neurokit2.misc.find_consecutive", false]], "find_groups() (in module neurokit2.misc)": [[37, "neurokit2.misc.find_groups", false]], "find_knee() (in module neurokit2.misc)": [[37, "neurokit2.misc.find_knee", false]], "find_outliers() (in module neurokit2.misc)": [[37, "neurokit2.misc.find_outliers", false]], "find_plateau() (in module neurokit2.misc)": [[37, "neurokit2.misc.find_plateau", false]], "fisher_information() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fisher_information", false]], "fishershannon_information() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fishershannon_information", false]], "fit_error() (in module neurokit2)": [[41, "neurokit2.fit_error", false]], "fit_loess() (in module neurokit2)": [[41, "neurokit2.fit_loess", false]], "fit_mixture() (in module neurokit2)": [[41, "neurokit2.fit_mixture", false]], "fit_mse() (in module neurokit2.stats)": [[41, "neurokit2.stats.fit_mse", false]], "fit_polynomial() (in module neurokit2)": [[41, "neurokit2.fit_polynomial", false]], "fit_polynomial_findorder() (in module neurokit2.stats)": [[41, "neurokit2.stats.fit_polynomial_findorder", false]], "fit_r2() (in module neurokit2.stats)": [[41, "neurokit2.stats.fit_r2", false]], "fit_rmse() (in module neurokit2.stats)": [[41, "neurokit2.stats.fit_rmse", false]], "fractal_correlation() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_correlation", false]], "fractal_density() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_density", false]], "fractal_dfa() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_dfa", false]], "fractal_higuchi() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_higuchi", false]], "fractal_hurst() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_hurst", false]], "fractal_katz() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_katz", false]], "fractal_linelength() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_linelength", false]], "fractal_mandelbrot() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_mandelbrot", false]], "fractal_nld() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_nld", false]], "fractal_petrosian() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_petrosian", false]], "fractal_psdslope() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_psdslope", false]], "fractal_sevcik() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_sevcik", false]], "fractal_tmf() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.fractal_tmf", false]], "hdi() (in module neurokit2)": [[41, "neurokit2.hdi", false]], "hrv() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.hrv", false]], "hrv_frequency() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.hrv_frequency", false]], "hrv_nonlinear() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.hrv_nonlinear", false]], "hrv_rqa() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.hrv_rqa", false]], "hrv_rsa() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.hrv_rsa", false]], "hrv_time() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.hrv_time", false]], "intervals_process() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.intervals_process", false]], "intervals_to_peaks() (in module neurokit2.hrv)": [[33, "neurokit2.hrv.intervals_to_peaks", false]], "listify() (in module neurokit2.misc)": [[37, "neurokit2.misc.listify", false]], "mad() (in module neurokit2)": [[41, "neurokit2.mad", false]], "markov_mixingtime() (in module neurokit2.markov)": [[35, "neurokit2.markov.markov_mixingtime", false]], "markov_simulate() (in module neurokit2.markov)": [[35, "neurokit2.markov.markov_simulate", false]], "markov_test_homogeneity() (in module neurokit2.markov)": [[35, "neurokit2.markov.markov_test_homogeneity", false]], "markov_test_random() (in module neurokit2.markov)": [[35, "neurokit2.markov.markov_test_random", false]], "markov_test_symmetry() (in module neurokit2.markov)": [[35, "neurokit2.markov.markov_test_symmetry", false]], "microstates_classify() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_classify", false]], "microstates_clean() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_clean", false]], "microstates_dynamic() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_dynamic", false]], "microstates_findnumber() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_findnumber", false]], "microstates_peaks() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_peaks", false]], "microstates_plot() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_plot", false]], "microstates_segment() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_segment", false]], "microstates_static() (in module neurokit2.microstates)": [[36, "neurokit2.microstates.microstates_static", false]], "mne_channel_add() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.mne_channel_add", false]], "mne_channel_extract() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.mne_channel_extract", false]], "mne_crop() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.mne_crop", false]], "mne_data() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.mne_data", false]], "mne_templatemri() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.mne_templateMRI", false]], "mne_to_df() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.mne_to_df", false]], "mne_to_dict() (in module neurokit2.eeg)": [[28, "neurokit2.eeg.mne_to_dict", false]], "module": [[25, "module-neurokit2.data", false], [26, "module-neurokit2.ecg", false], [27, "module-neurokit2.eda", false], [28, "module-neurokit2.eeg", false], [29, "module-neurokit2.emg", false], [30, "module-neurokit2.eog", false], [33, "module-neurokit2.hrv", false], [37, "module-neurokit2.misc", false], [38, "module-neurokit2.ppg", false], [39, "module-neurokit2.rsp", false], [40, "module-neurokit2.signal", false], [41, "module-neurokit2.stats", false]], "mutual_information() (in module neurokit2.complexity)": [[24, "neurokit2.complexity.mutual_information", false]], "neurokit2.data": [[25, "module-neurokit2.data", false]], "neurokit2.ecg": [[26, "module-neurokit2.ecg", false]], "neurokit2.eda": [[27, "module-neurokit2.eda", false]], "neurokit2.eeg": [[28, "module-neurokit2.eeg", false]], "neurokit2.emg": [[29, "module-neurokit2.emg", false]], "neurokit2.eog": [[30, "module-neurokit2.eog", false]], "neurokit2.hrv": [[33, "module-neurokit2.hrv", false]], "neurokit2.misc": [[37, "module-neurokit2.misc", false]], "neurokit2.ppg": [[38, "module-neurokit2.ppg", false]], "neurokit2.rsp": [[39, "module-neurokit2.rsp", false]], "neurokit2.signal": [[40, "module-neurokit2.signal", 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