我想使用辅助线来绘制数据。对于每张图，我尝试绘制线性回归线，置信度和预测度。我的代码可以正常工作而无需子图。但是，后者是强制性的。我尝试在同一子图中绘制数据，线性回归，置信区间，预测区间。这是我的代码：
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from pylab import *
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from scipy import stats
try:
import uncertainties.unumpy as unp
import uncertainties as unc
except:
import pip
pip.main(['install','uncertainties'])
import uncertainties.unumpy as unp
import uncertainties as unc
def f(x, a, b):
return a * x + b
def Convert_ASN_Decimal(asn_hex):
asn_dec = asn_hex[5] + asn_hex[2:4] + asn_hex[0:2]
reversed = int(asn_dec, 16)
return (reversed)
def predband(x, xd, yd, p, func, conf=0.95):
# x = requested points
# xd = x data
# yd = y data
# p = parameters
# func = function name
alpha = 1.0 - conf # significance
N = xd.size # data sample size
var_n = len(p) # number of parameters
# Quantile of Student's t distribution for p=(1-alpha/2)
q = stats.t.ppf(1.0 - alpha / 2.0, N - var_n)
# Stdev of an individual measurement
se = np.sqrt(1. / (N - var_n) * np.sum((yd - func(xd, *p)) ** 2))
# Auxiliary definitions
sx = (x - xd.mean()) ** 2
sxd = np.sum((xd - xd.mean()) ** 2)
# Predicted values (best-fit model)
yp = func(x, *p)
# Prediction band
dy = q * se * np.sqrt(1.0+ (1.0/N) + (sx/sxd))
# Upper & lower prediction bands.
lpb, upb = yp - dy, yp + dy
return lpb, upb
plots = {'02141592cc00000004': {
'dest': ['1', '2', '3', '4', '5', '6', '7', '1', '2', '3', '4', '5', '6', '7', '1', '2', '3', '4', '5', '6', '7',
'1', '2', '3', '4', '5', '6', '7', '1', '2', '3', '4', '5', '6', '7'],
'data': [39794, 39996, 40400, 42218, 42420, 43026, 43632, 45450, 45652, 46258, 46460, 46662, 47066, 49086, 50096,
50298, 50500, 50904, 51106, 52116, 52318, 53328, 53732, 55146, 55954, 56560, 56762, 56964, 57166, 57772,
58984, 59388, 60196, 63024, 64438],
'timestamp': [1549033439, 1549033441, 1549033443, 1549033453, 1549033454, 1549033458, 1549033462, 1549033477,
1549033479, 1549033483, 1549033484, 1549033485, 1549033488, 1549033502, 1549033510, 1549033512,
1549033513, 1549033516, 1549033517, 1549033524, 1549033526, 1549033533, 1549033535, 1549033546,
1549033552, 1549033557, 1549033558, 1549033559, 1549033561, 1549033564, 1549033571, 1549033573,
1549033578, 1549033599, 1549033610]}, '02141592cc00000003': {
'dest': ['1', '2', '3', '4', '5', '6', '7', '1', '2', '3', '4', '5', '6', '7', '1', '2', '3', '4', '5', '6', '7',
'1', '2', '3', '4', '5', '6', '7', '1', '2', '3', '4', '5', '6', '7'],
'data': [39794, 39996, 40400, 42218, 42420, 43026, 43632, 45450, 45652, 46258, 46460, 46662, 47066, 49086, 50096,
50298, 50500, 50904, 51106, 52116, 52318, 53328, 53732, 55146, 55954, 56560, 56762, 56964, 57166, 57772,
58984, 59388, 60196, 63024, 64438],
'timestamp': [1549033439, 1549033441, 1549033443, 1549033453, 1549033454, 1549033458, 1549033462, 1549033477,
1549033479, 1549033483, 1549033484, 1549033485, 1549033488, 1549033502, 1549033510, 1549033512,
1549033513, 1549033516, 1549033517, 1549033524, 1549033526, 1549033533, 1549033535, 1549033546,
1549033552, 1549033557, 1549033558, 1549033559, 1549033561, 1549033564, 1549033571, 1549033573,
1549033578, 1549033599, 1549033610]}}
f, ax = plt.subplots(len(plots), 1, figsize=(50, 30))
plt.suptitle('Data')
plt.subplots_adjust(hspace=2)
for i, item in enumerate(plots):
print(i)
print(item)
y = [x for _, x in sorted(zip(plots[item]['timestamp'], plots[item]['data']))]
x = sorted(plots[item]['timestamp'])
ax[i].scatter(x, y, s=20)
ax[i].set_ylabel('data')
ax[i].set_xlabel('timestamp')
ax[i].set_title(str(item))
x = np.array(x)
y = np.array(y)
n=len(y)
popt, pcov = curve_fit(f, x, y)
# retrieve parameter values
a = popt[0]
b = popt[1]
print('Optimal Values')
print('a: ' + str(a))
print('b: ' + str(b))
# compute r^2
r2 = 1.0 - (sum((y - f(x, a, b)) ** 2) / ((n - 1.0) * np.var(y, ddof=1)))
print('R^2: ' + str(r2))
# calculate parameter confidence interval
a, b = unc.correlated_values(popt, pcov)
print('Uncertainty')
print('a: ' + str(a))
print('b: ' + str(b))
# plot data
plt.scatter(x, y, s=3, label='Data')
# calculate regression confidence interval
px = np.linspace(14, 24, n)
print(px)
py = a * px + b
print(py)
nom = unp.nominal_values(py)
print(nom)
std = unp.std_devs(py)
print(std)
lpb, upb = predband(px, x, y, popt, f, conf=0.95)
# plot the regression
plt.plot(px, nom, c='black', label='y=a x + b')
# uncertainty lines (95% confidence)
plt.plot(px, nom - 1.96 * std, c='orange', label='95% Confidence Region')
plt.plot(px, nom + 1.96 * std, c='orange')
# prediction band (95% confidence)
plt.plot(px, lpb, 'k--', label='95% Prediction Band')
plt.plot(px, upb, 'k--')
plt.ylabel('y')
plt.xlabel('x')
plt.legend(loc='best')
plt.show()
但是它给了我这个错误：
Traceback (most recent call last):
File "Test.py", line 92, in <module>
popt, pcov = curve_fit(f, x, y)
File "path/lib/python3.5/site-packages/scipy/optimize/minpack.py", line 678, in curve_fit
args, varargs, varkw, defaults = _getargspec(f)
File "path/lib/python3.5/site-packages/scipy/_lib/_util.py", line 291, in getargspec_no_self
sig = inspect.signature(func)
File "/usr/lib/python3.5/inspect.py", line 2987, in signature
return Signature.from_callable(obj, follow_wrapped=follow_wrapped)
File "/usr/lib/python3.5/inspect.py", line 2737, in from_callable
follow_wrapper_chains=follow_wrapped)
File "/usr/lib/python3.5/inspect.py", line 2156, in _signature_from_callable
raise TypeError('{!r} is not a callable object'.format(obj))
TypeError: <Figure size 5000x3000 with 2 Axes> is not a callable object
答案 0 :(得分：1)
错误消息是说参数f不是curve_fit所期望的。检查完代码后，我发现您首先创建了一个名为f的函数，然后在调用f时又创建了一个f, ax = plt.subplots(len(plots), 1, figsize=(50, 30))。
您只需将第二个f重命名为其他名称即可解决问题。