Question

我正在运行一个简单的脚本，在一个绘图上绘制两种不同的数据类型，并且收到此错误（下面的完整错误文本）。我怀疑它与索引此行上的列表有关，但还没弄清楚为什么

`modelresp1 *= -f1.attrs['dx, dy, dz'][1]`

此外，我已经运行了好几天，直到今天下午才有这个问题。 HDF5文件是相同的，上面显示的行是相同的。具体是什么导致了这个错误，可以做些什么改变来避免它？我使用的是Python 3.5.2，Scipy 0.18.1，Numpy 1.11.2

感谢!!!

错误：

C:\MHD_Models\MHDsimple_plot.py:82: VisibleDeprecationWarning: using a non-integer number instead of
 an integer will result in an error in the future
  modelresp1 *= -f1.attrs['dx, dy, dz'][1]
Traceback (most recent call last):
  File "C:\Anaconda3\lib\runpy.py", line 174, in _run_module_as_main
    mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
  File "C:\Anaconda3\lib\runpy.py", line 109, in _get_module_details
    __import__(pkg_name)
  File "C:\MHD_Models\MHDsimple_plot.py", line 120, in <module>
    ax1.plot(modeltime1, modelresp1, 'g-', label='Modeled Data')
  File "C:\Anaconda3\lib\site-packages\matplotlib\__init__.py", line 1818, in inner
    return func(ax, *args, **kwargs)
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py", line 1382, in plot
    for line in self._get_lines(*args, **kwargs):
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py", line 381, in _grab_next_args
    for seg in self._plot_args(remaining, kwargs):
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py", line 359, in _plot_args
    x, y = self._xy_from_xy(x, y)
  File "C:\Anaconda3\lib\site-packages\matplotlib\axes\_base.py", line 219, in _xy_from_xy
    raise ValueError("x and y must have same first dimension")
ValueError: x and y must have same first dimension

这是脚本

import os
import sys

import h5py
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal 


#datafile 1 is the MODEL data file
datafile1 = "C:\MHD_Models\GSSI_400MHz_Opt_final6.out"
#define what output field to use from the modeled data
outputs = "Ey"
#datafile 2 is the measured REAL data file
datafile2 = "C:\MHD_Models\GSSI_400MHz_realprn(Amplitude).txt"  


#REAL DATA
# open and read/load measured REAL data file row by row.  
with open(datafile2, 'r') as f2:
    refdata2 = np.loadtxt(f2)

# set reftime and refresp as the arrays of time and amplitude values in the reference file, respectively
reftime2 = refdata2[:,0] * 1e-9
refresp2 = refdata2[:,1] 


#MODEL DATA
# open and read/load model data.
f1 = h5py.File(datafile1, 'r')
nrx = f1.attrs['nrx']
dt = f1.attrs['dt']
iterations = f1.attrs['Iterations']

# Check that there are any receivers
if nrx == 0:
    raise CmdInputError('No receivers found in {}'.format(datafile1))

# set modeltime as the array of time values in the model file
modeltime1 = np.linspace(0, iterations * dt, iterations)

# set modelresp as the array of amplitude values in the model file
for rx in range(1, nrx + 1):
    outputpath = f1['/rxs/rx' + str(rx) + '/']
    outputname = list(outputpath.keys())[0]
    modelresp1 = outputpath[outputname]

    # Convert electric field value (V/m) to voltage (V)
    if outputname == 'Ex':
        modelresp1 *= -f1.attrs['dx, dy, dz'][0]
    elif outputname == 'Ey':
        modelresp1 *= -f1.attrs['dx, dy, dz'][1]
    elif outputname == 'Ez':
        modelresp1 *= -f1.attrs['dx, dy, dz'][2]


# DATA fitting
#Manage Data sets to plot on same instance

#Normalise reference respose and response from output file
refresp2 /= np.amax(np.abs(refresp2))
modelresp1 /= np.amax(np.abs(modelresp1))

#Make both responses the same length in time
if reftime2[-1] > modeltime1[-1]:
    reftime2 = np.arange(0, dt * iterations, reftime2[-1] / len(reftime2))
    refresp2 = refresp2[0:len(reftime2)]
elif modeltime1[-1] > reftime2[-1]:
    modeltime1 = np.arange(0, reftime2[-1], f1.attrs['dt'])
    modelresp1 = modelresp1[0:len(modeltime1)]

#Downsample the response with the higher sampling rate
if len(modeltime1) < len(reftime2):
    refresp2 = signal.resample(refresp2, len(modelresp1))
elif len(reftime2) < len(modeltime1):
    modelresp1 = signal.resample(modelresp1, len(refresp2))


# PLOTTING 
#create figure and plot
fig = plt.figure()

ax1 = fig.add_subplot(111)

ax1.set_title("A-scan Comparison")    
ax1.set_xlabel("Time (ns)")
ax1.set_ylabel("Normalized Amplitude")

ax1.plot(reftime2, refresp2, 'r-', label='Real Data')
ax1.plot(modeltime1, modelresp1, 'g-', label='Modeled Data')

leg = ax1.legend()

plt.show()

VisibleDeprecationWarning：使用非整数而不是整数

0 个答案: