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January 7, 2018 07:58
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ipython startup (deprecated)
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| # coding: utf-8 | |
| """ | |
| This is the ipython startup script that I used to use in university. | |
| (but not anymore) | |
| Place it in ~/.ipython/profile_default/startup/startup.py for it to work | |
| """ | |
| import numpy as np | |
| import math | |
| import matplotlib.pyplot as plt | |
| import matplotlib | |
| import itertools as itr | |
| def pairwise(iterable): | |
| "s -> (s0,s1), (s1,s2), (s2, s3), ..." | |
| a, b = itr.tee(iterable) | |
| next(b, None) | |
| return itr.izip(a, b) | |
| def homogeneous(arr): | |
| """ | |
| Create 4xN homogeneous representation matrix from 3xN matrix. | |
| Do NOT pass Nx3 matrix. | |
| """ | |
| arr = np.asarray(arr) | |
| arr = np.reshape(arr, (3, -1)) | |
| return np.vstack((arr, np.ones((1, arr.shape[1])))) | |
| def unhomogeneous(arr): | |
| """ | |
| From 4xN homogeneous, create 3xN normal representation. | |
| Will NOT warn even if 4th element is non-one. | |
| """ | |
| arr = np.asarray(arr) | |
| arr = np.reshape(arr, (4,-1)) | |
| return arr[0:3, :] | |
| def hide_spines(): | |
| """ | |
| Hides the top and rightmost axis spines from view for all active | |
| figures and their respective axes. | |
| Copyright Stackoverflow http://stackoverflow.com/questions/3439344/setting-spines-in-matplotlibrc | |
| Modified 2015/1/24 by Yuta Koreeda | |
| """ | |
| # Retrieve a list of all current figures. | |
| figures = [x for x in matplotlib._pylab_helpers.Gcf.get_all_fig_managers()] | |
| for figure in figures: | |
| # Get all Axis instances related to the figure. | |
| for ax in figure.canvas.figure.get_axes(): | |
| # Disable spines. | |
| ax.spines['right'].set_color('none') | |
| ax.spines['top'].set_color('none') | |
| # Disable ticks. | |
| ax.xaxis.set_ticks_position('bottom') | |
| ax.yaxis.set_ticks_position('left') | |
| # Change spine position | |
| ax.spines['bottom'].set_position('zero') | |
| ax.spines['bottom'].set_smart_bounds(False) | |
| ax.spines['left'].set_smart_bounds(False) | |
| def boxplot_kikai(x, vert_=True, positions_=None, widths_=None, usermedians_=None, conf_intervals_=None, showmeans_=False, labels_=None): | |
| """ | |
| Draw a boxplot in Kikai-ka style | |
| Whisker shows min-max. | |
| Based on matplotlib.pyplot.boxplot | |
| """ | |
| bp = plt.boxplot(x,notch=False, sym='', vert=vert_, whis=200, positions=positions_, widths=widths_, patch_artist=True, bootstrap=None, usermedians=usermedians_, conf_intervals=conf_intervals_, meanline=True, showmeans=showmeans_, showcaps=True, showbox=True, showfliers=False, boxprops=dict(linewidth=1.5), labels=labels_, flierprops=None) | |
| for med_artist in bp['medians']: | |
| plt.setp(med_artist, color='black', linewidth=1.5, solid_capstyle="projecting") | |
| for caps_artist in bp['caps']: | |
| plt.setp(caps_artist, color='black',linewidth=1.5) | |
| for whisk_artist in bp['whiskers']: | |
| plt.setp(whisk_artist,color='black', linestyle='-', linewidth=1.5, solid_capstyle="projecting") | |
| return bp | |
| def freedmanDiaconisRule(x): | |
| """ | |
| Calculate appropriate number of bins for histogram using Freedman-Diaconis' Rule | |
| """ | |
| iqr = np.subtract(*np.percentile(x, [75, 25])) | |
| h = 2 * iqr / math.pow(len(x), 1.0/ 3.0) | |
| return math.ceil((np.max(x) - np.min(x)) / h) | |
| def roundToSignificant(val, dig): | |
| assert dig >=1, "Significant digits must at least 1 digit" | |
| if val != 0: | |
| return round(val, -int(math.floor(math.log10(abs(val))))+ (dig - 1)) | |
| else: | |
| itms = list("0.") + ["0" for i in range(dig - 1)] | |
| return "".join(itms) | |
| def boxplotWithValues(arr_of_arr, significant_digits=2): | |
| """ | |
| Draw boxplot with value annotations | |
| Whiskers show min-max | |
| """ | |
| plt.boxplot(arr_of_arr, showmeans=True, whis=200) | |
| medians = np.median(arr_of_arr, axis=1) | |
| means = np.mean(arr_of_arr, axis=1) | |
| for i in range(len(arr_of_arr)): | |
| plt.annotate(str(roundToSignificant(medians[i], significant_digits)), xy=(i + 0.9, medians[i]), horizontalalignment='right', verticalalignment='center') | |
| plt.annotate(str(roundToSignificant(means[i], significant_digits)), xy=(i + 0.9, means[i]), horizontalalignment='right', verticalalignment='center') | |
| def pause(): | |
| """ | |
| Let matplotlib draw graph. | |
| """ | |
| plt.pause(1.e-6) | |
| def toMillisec(hour, minute, second): | |
| return ((hour * 60 + minute) * 60 + second) * 1000 | |
| def in2px(inches, fig=None): | |
| import matplotlib.pyplot as plt | |
| if fig is None: | |
| fig = plt.gcf() | |
| return inches * fig.get_dpi() | |
| def pt2in(point): return point/72.0 | |
| def pt2px(point, fig=None): | |
| return in2px(inches(point), fig) | |
| def mm2inch(millimeters): | |
| return 0.0393700787 * millimeters | |
| def inch2mm(inches): | |
| return 25.4 * inches |
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