Functions
bin_up(data_bin_by, data_for_percentile[, …]) |
bins “data_for_percentile” into “nbins” using “data_bin_by” to decide how indices are assigned to bins returns bin center,N,q25,50,75 for each bin |
create_confusion_matrix(answer_type, …[, …]) |
compares classifications of matched objects, returns 2D array which is conf matrix and xylabels return 5x5 confusion matrix and colum/row names answer_type,predict_type – arrays of same length with reference and prediction types |
eboss_ts(gmag, rz, gr[, region]) |
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flux2mag(nmgy) |
return ABmag |
mag2flux(ABmag) |
return nmgy |
myannot(ax, xarr, yarr, sarr[, ha, va, fontsize]) |
x,y,s are iterable |
myerrorbar(ax, x, y[, yerr, xerr, color, …]) |
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myhist(ax, data[, bins, color, normed, ls, …]) |
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myhist2D(ax, x, y[, xlim, ylim, nbins]) |
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myhist_step(ax, data[, bins, color, normed, …]) |
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myscatter(ax, x, y[, color, m, s, alpha, label]) |
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myscatter_open(ax, x, y[, color, m, s, …]) |
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mytext(ax, x, y, text[, ha, va, fontsize, …]) |
adds text in x,y units of fraction axis |
obiwan.qa.plots_common.bin_up(data_bin_by, data_for_percentile, bin_minmax=(18.0, 26.0), nbins=20)[source]¶bins “data_for_percentile” into “nbins” using “data_bin_by” to decide how indices are assigned to bins returns bin center,N,q25,50,75 for each bin
obiwan.qa.plots_common.create_confusion_matrix(answer_type, predict_type, poss_ans_types=['PSF', 'SIMP', 'EXP', 'DEV', 'COMP', 'REX'], poss_pred_types=['PSF', 'SIMP', 'EXP', 'DEV', 'COMP', 'REX'])[source]¶compares classifications of matched objects, returns 2D array which is conf matrix and xylabels return 5x5 confusion matrix and colum/row names answer_type,predict_type – arrays of same length with reference and prediction types