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| id: org.psychopy.PsychoPy | |
| runtime: org.freedesktop.Platform | |
| runtime-version: '22.08' | |
| sdk: org.freedesktop.Sdk | |
| command: psychopy | |
| finish-args: | |
| - --device=dri | |
| - --device=input | |
| - --filesystem=home | |
| - --filesystem=host-os |
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| pkgbase = rstudio-desktop-bin | |
| pkgdesc = An integrated development environment (IDE) for R (binary from RStudio official repository) | |
| pkgver = 2025.09.2.418 | |
| pkgrel = 2 | |
| url = https://posit.co/products/open-source/rstudio/ | |
| arch = x86_64 | |
| license = AGPL | |
| depends = r>=3.3.0 | |
| depends = ttf-dejavu | |
| provides = rstudio-desktop=2025.09.2.418 |
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| import numpy as np | |
| import pandas as pd | |
| import mne | |
| files = ["data/black-white_gaming1.bdf", "data/black-white_standard3.bdf"] | |
| rows = [] | |
| n = 100 # expected number of events | |
| mne.set_log_level("ERROR") |
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| from pathlib import Path | |
| import mne | |
| fpath = Path(mne.datasets.sample.data_path()) / "MEG" / "sample" | |
| raw = mne.io.read_raw_fif(fpath / "sample_audvis_filt-0-40_raw.fif") |
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| import matplotlib.pyplot as plt | |
| from matplotlib.animation import FuncAnimation | |
| import numpy as np | |
| from scipy.stats import norm | |
| fps = 25 | |
| fig, ax = plt.subplots(figsize=(8, 6)) | |
| x = np.linspace(0, 20, 1000) |
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| package_versions <- function(packages=NULL, base=FALSE, sort=FALSE) { | |
| #' Determine package versions. | |
| #' | |
| #' @param packages Packages to query; if not specified (or \code{NULL}), | |
| #' all currently attached packages are queried. | |
| #' @param base If \code{TRUE}, include the base package (R version). | |
| #' @param sort If \code{TRUE}, sort package names alphabetically. | |
| #' @return Data frame with package names and corresponding versions. | |
| if (is.null(packages)) | |
| packages <- names(sessionInfo()$otherPkgs) |
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| import numpy as np | |
| def dependence_factor(a, b, norm=True): | |
| """Compute measure of dependence between a and b. | |
| Independent means that Pab(i,j) - Pa(i) * Pa(j) == 0 for all i and j, where | |
| i and j are the individual values that the variables a and b can take. | |
| If norm=True, this function returns a value between 0 and 1, where 1 means |