Package: anaties
anatiesAnaties (contraction of 'analysis utilities'). A place for common operations like signal smoothing that are useful across all my data analysis projects.Installation and usageInstall with pip:pip install anatiesWhen a new release is made, upgrade with:pip install anaties --upgradeUsage is simple. In your code:import anaties as ana
ana.function_name()You can test it out with:import anaties as ana
print(ana.datetime_string())

plt.plot([0, 1], [0,1], color='k', linewidth=0.6)
ana.rect_highlight([0.25, 0.5])All other functions are listed below.Brief summary of all utilitiessignals.py (for 1d data arrays, or arrays of such arrays)
        - smooth: smooth a signal with a window (gaussian, etc)
        - smooth_rows: smooth each row of a 2d array using smooth()
        - power_spec: get the power spectral density or power spectrum
        - spectrogram: calculate/plot spectrogram of a signal
        - notch_filter: notch filter to attenuate specific frequency (e.g. 60hz)
        - bandpass_filter: allow through frequencies within low- and high-cutoff

    plots.py (basic plotting)
        - error_shade: plot line with shaded error region
        - freqhist: calculate/plot a relative frequency histogram
        - paired_bar: bar plot for paired data
        - plot_with_events: plot with vertical lines to indicate events
        - rect_highlight: overlay rectangular highlight to figure
        - vlines: add vertical lines to figure

    stats (basic statistical things)
        - collective_correlation: collective correlation coefficient
        - med_semed: median and std error of median of an array
        - mean_sem: mean and std error of the mean of an array
        - mean_std: mean and standard deviation of an array
        - se_mean: std err of mean of array
        - se_median: std error of median of array
        - cramers_v: cramers v for effect size for chi-square test

    helpers.py (generic utility functions for use everywhere)
        - datetime_string : return date_time string to use for naming files etc
        - file_exists: check to see if file exists
        - get_bins: get bin edges and centers, given limits and bin width
        - get_offdiag_vals: get lower off-diagonal values of a symmetric matrix
        - ind_limits: return indices that contain array data within range
        - is_symmetric: check if 2d array is symmetric
        - rand_rgb: returns random array of rgb valuesAcknowledgmentsSongbird wav is open source from:https://freesound.org/people/Sonic-ranger/sounds/243677/Developed with the support from NIH Bioinformatics and the Neurobehavioral Core at NIEHS.To do: More importantfinish adding tests.plots.rect_highlight should just use axvspan/axhspan!use median instead of mean in spectrogramadd proper documentation and tests to stats module.integrate vlines into pypi and version up (maybe good test for ci)add ax return for all plot functions, when possible.finish plots.twinx and make sure it worksadd test for plots.error_shade.Add return object for plots.rect_highlight()consider adding directory_exists to helperspaired_bar and mean_sem/std need to handle one point better (throws warning)Add a proper suptitle fix in aplots it is a pita to add manually/remember:
f.suptitle(..., fontsize=16)
f.tight_layout()
f.subplots_adjust(top=0.9)For freqhist should I guarantee it sums to 1 even when bin widths don't match data limits? Probably not. Something to think about though.In smoother, consider switching from filtfilt() to sosfiltfilt() for reasons laid out here:https://dsp.stackexchange.com/a/17255/51564Convert notch filter to sos?For spectral density estimation consider adding multitaper option. Good discussions:https://github.com/cokelaer/spectrumhttps://pyspectrum.readthedocs.io/en/latest/https://mark-kramer.github.io/Case-Studies-Python/04.htmladd ability to control event colors in spectrogram.ind_limits: add checks for data, data_limits, clarify description and docsAdd numerical tests with random seed set not just graphical eyeball tests.To do: longer termAdd audio playback of signals (see notes in audio_playback_workspace), incorporate this into some tests of filtering, etc.. simpleaudio package is too simple I think.autodocs (sphinx?)CI/CD with github actionsconsider adding wavelets.Add 3d array support for stat functions like mn_semUseful sourcesSmoothinghttps://scipy-cookbook.readthedocs.io/items/FiltFilt.htmlhttps://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.filtfilt.htmlWhat about wavelets?I may add wavelets at some point, but it isn't plug-and-play enough for this repo. If you want to get started with wavelets in Python, I recommendhttp://ataspinar.com/2018/12/21/a-guide-for-using-the-wavelet-transform-in-machine-learning/Tolerance valuesFor a discussion of the difference between relative and absolute tolerance values when testing floats for equality (for instance as used inhelpers.is_symmetric()) see:https://stackoverflow.com/questions/65909842/what-is-rtol-for-in-numpys-allclose-functionSuggestions?If there is something you'd like to see, please open an issue.