Matplotlib; Teilkräfte von zehn; Wissenschaftliche Notation

Question

Ich beschäftige mich mit Simulationsdaten und habe in letzter Zeit viel Matplotlib benutzt und bin auf etwas gestoßen (ein Bug?), das nervt.

Ich habe Matplotlib erlaubt, die Tick-Etiketten und ihren Typ automatisch zu setzen (wissenschaftliche, etc) und mit einigen Daten bekomme ich seltsame wissenschaftliche Ticker-Etiketten.

Bei der Suche nach einer Auflösung zu diesem fand ich, dass Sie set_powerlimits ((n, m)) aufrufen können, um die Grenzen der Daten festzulegen, die mit wissenschaftlicher Notation angezeigt werden. Aber ich habe dieses Problem (wenn ich mich richtig erinnere) mit Daten gefunden, die mehrere Größenordnungen überspannen, auch meine Daten sind überall, so dass ich eine programmatische Lösung irgendeiner Art brauche, keine feste Lösung. siehe: http://matplotlib.org/api/ticker_api.html

Im Folgenden habe ich Beispieldaten, Code und einen Screenshot enthalten.

#! /usr/bin/env python 
from matplotlib import pyplot as plt 

data = [ 
[1.83186088e-08,0.03275], 
[1.07139009e-07,0.03275], 
[2.06376627e-07,0.03275], 
[3.03918517e-07,0.03275], 
[4.06032883e-07,0.03275], 
[5.01194017e-07,0.03275], 
[6.02195723e-07,0.03275], 
[7.03536925e-07,0.03275], 
[8.04625154e-07,0.03275], 
[9.06401951e-07,0.03275], 
[1.00041895e-06,0.03275], 
[1.10230745e-06,0.03275], 
[1.2042525e-06,0.03275], 
[1.30647822e-06,0.03275], 
[1.40109887e-06,0.03275], 
[1.50380097e-06,0.03275], 
[1.60683242e-06,0.03275], 
[1.70208505e-06,0.03275], 
[1.80545692e-06,0.03275], 
[1.90090648e-06,0.03275], 
[2.00453092e-06,0.03275], 
[2.10018627e-06,0.03275], 
[2.20401747e-06,0.03275], 
[2.30009359e-06,0.03275], 
[2.4043033e-06,0.03275], 
[2.50066449e-06,0.03275], 
[2.60513728e-06,0.03275], 
[2.70165405e-06,0.03275], 
[2.80635938e-06,0.03275], 
[2.90331342e-06,0.03275], 
[3.00021199e-06,0.03275], 
[3.10546819e-06,0.03275], 
[3.20257899e-06,0.03275], 
[3.30032923e-06,0.0327499999], 
[3.40612833e-06,0.0327499999], 
[3.50401732e-06,0.0327499997], 
[3.60153069e-06,0.0327499996], 
[3.70700708e-06,0.0327499993], 
[3.80456907e-06,0.0327499988], 
[3.90259984e-06,0.0327499982], 
[4.00084149e-06,0.0327499973], 
[4.10700266e-06,0.0327499959], 
[4.2047462e-06,0.0327499942], 
[4.30209468e-06,0.0327499918], 
[4.40018204e-06,0.0327499886], 
[4.50712875e-06,0.032749984], 
[4.60630591e-06,0.0327499785], 
[4.70519881e-06,0.0327499715], 
[4.80398305e-06,0.0327499628], 
[4.90251297e-06,0.0327499521], 
[5.00182752e-06,0.032749939], 
[5.10157551e-06,0.0327499232], 
[5.20157575e-06,0.0327499043], 
[5.30145192e-06,0.0327498822], 
[5.40127044e-06,0.0327498565], 
[5.500537e-06,0.0327498272], 
[5.60773155e-06,0.0327497911], 
[5.70660709e-06,0.0327497534], 
[5.80610521e-06,0.0327497112], 
[5.90651786e-06,0.0327496642], 
[6.00749437e-06,0.0327496124], 
[6.10822094e-06,0.0327495566], 
[6.20042255e-06,0.0327495018], 
[6.30049028e-06,0.0327494386], 
[6.40035803e-06,0.0327493715], 
[6.50035477e-06,0.0327493004], 
[6.60056805e-06,0.0327492251], 
[6.70029936e-06,0.0327491461], 
[6.80054193e-06,0.0327490625], 
[6.90130872e-06,0.0327489743], 
[7.00202598e-06,0.0327488818], 
[7.10217348e-06,0.0327487855], 
[7.20243015e-06,0.0327486847], 
[7.30199609e-06,0.0327485801], 
[7.40193254e-06,0.0327484707], 
[7.50188319e-06,0.0327483567], 
[7.60306205e-06,0.0327482367], 
[7.70357184e-06,0.0327481129], 
[7.80343389e-06,0.0327479853], 
[7.90330165e-06,0.0327478532], 
[8.00348513e-06,0.0327477162], 
[8.10167039e-06,0.0327475777], 
[8.206328e-06,0.0327474253], 
[8.3020567e-06,0.0327472819], 
[8.40527826e-06,0.0327471228], 
[8.50095898e-06,0.0327469714], 
[8.60536828e-06,0.0327468019], 
[8.70106059e-06,0.0327466426], 
[8.80396558e-06,0.032746467], 
[8.90727378e-06,0.0327462865], 
[9.00225164e-06,0.0327461166], 
[9.10359892e-06,0.0327459311], 
[9.20470894e-06,0.0327457418], 
[9.30582982e-06,0.0327455481], 
[9.40750123e-06,0.0327453488], 
[9.50134495e-06,0.0327451608], 
[9.60358199e-06,0.0327449513], 
[9.70705637e-06,0.0327447344], 
[9.80377546e-06,0.0327445269], 
[9.90091941e-06,0.032744314], 
] 

times=[] 
vals=[] 
for elem in data: 
    times.append(elem[0]) 
    vals.append(elem[1]) 

plt.plot(times,vals) 
plt.show() 

screen_shot

Answer 1

Dies ist ein known problem. Sie sollten besser die Daten für ihre Grenzen manuell analysieren, wie Sie es in dem Screenshot getan haben, und ax.set_ylim(min, max) selbst nach dem Plotten verwenden. Sie können auch den Offset mit deaktivieren:

import matplotlib.ticker as mticker 

# plot some stuff 
# ... 

y_formatter = mticker.ScalarFormatter(useOffset=False) 
ax.yaxis.set_major_formatter(y_formatter) 

Answer 2

Sie könnten versuchen, mit dem Engineering-Formatter:

times=[] 
vals=[] 
for elem in data: 
    times.append(elem[0]) 
    vals.append(elem[1]) 

plt.plot(times,vals) 
plt.show() 

formatter = matplotlib.ticker.EngFormatter(unit='S', places=3) 
formatter.ENG_PREFIXES[-6] = 'u' 
plt.axes().yaxis.set_major_formatter(formatter) 

, die wie folgt aussehen:

Answer 3

Ich denke, dass Sie die beste Wahl ist logarithmische Achse zu verwenden, aber wenn Sie die Grafik mit linearer Achse erstellen müssen, müssen Sie die Leistungsgrenzen selbst festlegen. Sie können die Leistungsgrenzen mit math.log10 berechnen:

import math 
from matplotlib import ticker 

# Compute the span of the data 
pow_min = math.floor(math.log10(min(vals))) 
pow_max = math.ceil(math.log10(max(vals))) 

# Create a scalar formatter without offset, in order to have 
# the right exponent over the yaxis 
fmt = ticker.ScalarFormatter(useOffset=False) 
fmt.set_powerlimits((pow_min, pow_max)) 

fig = plt.figure() 
ax1 = fig.add_subplot(1, 1, 1) 
ax1.plot(times, vals) 
ax1.yaxis.set_major_formatter(fmt) # Set the formatter