matplotlib; on kesirli güçler; bilimsel gösterim

Question

Simülasyon verileriyle uğraşıyorum ve son zamanlarda matplotlib'i çok kullanıyorum ve rahatsız edici olan bir şeyle karşılaşıyorum (bir hata?).

Matplotlib'in onay etiketlerini ve türlerini (bilimsel, vb.) Otomatik olarak ayarlamasına izin veriyorum ve bazı verilerle garip bilimsel etiketler alıyorum.

Bunun için bir çözüm ararken, bilimsel gösterimi kullanarak görüntülenecek verilerin sınırlarını ayarlamak için set_powerlimits ((n, m)) öğesini çağırabilirsiniz. Ama ben bu problemle karşılaştım (eğer doğru bir şekilde hatırlıyorum) veriyi birkaç büyüklük büyüklüğünü kapsayan verilerle birleştirdim, ayrıca verilerim her yerde olduğu için, bir çeşit programlı bir çözüm, zor bir çözüm değil. bkz .: http://matplotlib.org/api/ticker_api.html

Aşağıdaki örnek verileri, kodu ve ekran görüntüsünü ekledim.

#! /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

Bu known problem olduğunu. Ekran görüntüsünde yaptığınız gibi, verileri kendi sınırları için manuel olarak analiz etmeniz ve çizim işleminden sonra kendiniz ax.set_ylim(min, max) kullanın.

import matplotlib.ticker as mticker 

# plot some stuff 
# ... 

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

Answer 2

kullanarak Mühendislik Formatter deneyebilirsiniz: aşağıdaki gibi görünecektir

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) 

:

Answer 3

Sana iyi seçenek olduğunu düşünüyorum Ayrıca kapatmak mahsup edebilir logaritmic eksenini kullanmak için, ancak grafiği lineer eksenle oluşturmanız gerekiyorsa, güç sınırlarını kendiniz ayarlamanız gerekir. Sen math.log10 kullanarak güç sınırlarını hesaplayabiliriz:

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