matplotlib; ułamkowe moce dziesięciu; notacja naukowa

Question

Mam do czynienia z danymi symulacyjnymi i ostatnio często korzystam z matplotlib i spotykam się z czymś (bug?), który jest denerwujący.

Pozwoliłem matplotlib na automatyczne ustawianie znaczników tyknięć i ich typów (naukowych, itp.), A przy niektórych danych dostaję dziwne etykiety naukowych tickerów.

Szukając rozwiązania tego problemu, stwierdziłem, że można wywołać set_powerlimits ((n, m)), aby ustawić granice danych, które będą wyświetlane za pomocą notacji naukowej. Ale spotkałem się z tym problemem (jeśli dobrze pamiętam) z danymi obejmującymi kilka rzędów wielkości, również moje dane są wszędzie, więc potrzebuję jakiegoś programowego rozwiązania, a nie zestawu trudnych ustawień. patrz: http://matplotlib.org/api/ticker_api.html

Poniżej zamieściłem przykładowe dane, kod i zrzut ekranu.

#! /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

To known problem. Lepiej będzie ręcznie przeanalizować dane pod kątem limitów, tak jak zrobiliście to na ekranie, i po wydrukowaniu użyć ax.set_ylim(min, max). Można również wyłączyć przesunięcie z:

import matplotlib.ticker as mticker 

# plot some stuff 
# ... 

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

Answer 2

Można spróbować użyć inżynierii 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) 

Która będzie wyglądać następująco:

Answer 3

Myślę, że najlepszym rozwiązaniem jest aby użyć osi logarytmicznej, ale jeśli chcesz utworzyć grafikę z osią liniową, musisz samodzielnie ustawić granice mocy. Można obliczyć limity zasilania za pomocą math.log10:

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