如何可靠地縮放大數據集的matplotlib pcolormesh圖？

Question

我試圖從matplotlib.pyplot使用pcolormesh來繪製一些數據，但在保存輸出時（特別是在適當縮放圖像時）我遇到了一些困難。

我使用Python v3.4和matplotlib v1.51，如果這有所作爲。

這是我的代碼目前的樣子：

import numpy as np 
import pandas as pd 
from matplotlib import pyplot as plt 

def GetData(data_entries, num_of_channels): 
    data_dict = {'timestamp' : np.linspace(1, data_entries*21, data_entries, endpoint=True)} 

    for chan in range(0, num_of_channels, 1): 
     data_dict['random%03d'%chan] = np.random.rand(data_entries, 1).flatten() 
     num_at_each_end_to_highlight = 10 
     data_dict['random%03d'%chan][0:num_at_each_end_to_highlight] = 1.5 
     data_dict['random%03d'%chan][-num_at_each_end_to_highlight:] = 1.5 

    for chan in range(0, num_of_channels, 1): 
     data_dict['periodic%03d' % chan] = np.zeros(data_entries)#.flatten() 
     data_dict['periodic%03d' % chan][::65] = 5000 

    return pd.DataFrame(data_dict) 

def GetSubPlotIndex(totalRows, totalCols, row): 
    return totalRows*100+totalCols*10+row 

def PlotData(df, num_of_channels, field_names): 
    # Calculate the range of data to plot 
    data_entries = len(df.index) 

    # Create the x/y mesh that the data will be plotted on 
    x = df['timestamp'] 
    y = np.linspace(0, num_of_channels - 1, num_of_channels) 
    X,Y = np.meshgrid(x,y) 

    # Iterate through all of the field types and produce one plot for each but share the X axis 
    for idx, field_name in enumerate(field_names): 

     # Create this sub-plot 
     subPlotIndex = GetSubPlotIndex(len(field_names), 1, idx + 1) 
     ax = plt.subplot(subPlotIndex) 
     if idx is 0: 
      ax.set_title('Raw Data Time Series') 
     # Set the axis scale to exactly meet the limits of the data set. 
     ax.set_autoscale_on(False) 
     plt.axis([x[0], x[data_entries-1], 0, num_of_channels - 1]) 
     # Set up the colour palette used to render the data. 
     # Make bad results (those that are masked) invisible so the background shows instead. 
     palette = plt.cm.get_cmap('autumn') 
     palette.set_bad(alpha=0.0) 
     ax.set_axis_bgcolor('black') # Set the background to zero 

     # Grab the data and transpose it so we can stick it in the time series running along the X axis. 
     firstFftCol = df.columns.get_loc(field_name + "%03d"%(0)) 
     lastFftCol = df.columns.get_loc(field_name + "%03d"%(num_of_channels - 1)) 
     data = df.ix[:,firstFftCol:lastFftCol] 
     data = data.T # Transpose so that time runs along the X axis and bin index is on the Y 

     # Mask off data with zero's so that it doesn't obscure the data we're actually interested in. 
     data = np.ma.masked_where(data == 0.0, data) 

     # Actually create the data mesh so we can plot it 
     z_min, z_max = data.min().min(), data.max().max() 
     p = ax.pcolormesh(X,Y, data, cmap=palette, vmin=z_min, vmax=z_max) 

     # Render it 
     plt.plot() 

     # Label the plot and add a key 
     plt.ylabel(field_name) 
     plt.colorbar(p) 

    # Label the plot 
    plt.xlabel('Time (ms)') 

    # Record the result 
    plt.savefig('test.png', edgecolor='none', transparent=False) 

if __name__ == '__main__': 
    data_entries = 30000 # Large values here cause issues 
    num_of_channels = 255 

    fields_to_plot = ('random', 'periodic') 

    data = GetData(data_entries, num_of_channels) 

    width_in_pixels = len(data.index)+200 
    additional_vertical_space_per_plot = 50 
    num_of_plots = len(fields_to_plot) 
    height_in_pixels = (num_of_channels+additional_vertical_space_per_plot)*num_of_plots 
    dpi = 80 # The default according to the documentation. 
    fig = plt.figure(1,figsize=(width_in_pixels/dpi, height_in_pixels/dpi), dpi=dpi) 

    PlotData(data, num_of_channels, fields_to_plot) 

有了1000個條目，結果看起來不錯：

如果我增加樣本數量來排序的大小我實際上想繪製（30000），圖像是正確的尺寸（30200像素寬），但是我看到很多死角。這是我看到的問題縮小的總結：

有沒有一種方法能夠更準確的數據填充的形象呢？

Answer 1

感謝來自@Dusch提示，這似乎解決的事情而整齊：

import numpy as np 
import pandas as pd 
from matplotlib import pyplot as plt 

def GetData(data_entries, num_of_channels): 
    data_dict = {'timestamp' : np.linspace(1, data_entries*21, data_entries, endpoint=True)} 

    for chan in range(0, num_of_channels, 1): 
     data_dict['random%03d'%chan] = np.random.rand(data_entries, 1).flatten() 
     num_at_each_end_to_highlight = 10 
     data_dict['random%03d'%chan][0:num_at_each_end_to_highlight] = 1.5 
     data_dict['random%03d'%chan][-num_at_each_end_to_highlight:] = 1.5 

    for chan in range(0, num_of_channels, 1): 
     data_dict['periodic%03d' % chan] = np.zeros(data_entries)#.flatten() 
     data_dict['periodic%03d' % chan][::65] = 5000 

    return pd.DataFrame(data_dict) 

def GetSubPlotIndex(totalRows, totalCols, row): 
    return totalRows*100+totalCols*10+row 

def PlotData(df, num_of_channels, field_names): 
    # Calculate the range of data to plot 
    data_entries = len(df.index) 

    # Create the x/y mesh that the data will be plotted on 
    x = df['timestamp'] 
    y = np.linspace(0, num_of_channels - 1, num_of_channels) 
    X,Y = np.meshgrid(x,y) 

    # Iterate through all of the field types and produce one plot for each but share the X axis 
    for idx, field_name in enumerate(field_names): 

     # Create this sub-plot 
     subPlotIndex = GetSubPlotIndex(len(field_names), 1, idx + 1) 
     ax = plt.subplot(subPlotIndex) 
     if idx is 0: 
      ax.set_title('Raw Data Time Series') 
     # Set the axis scale to exactly meet the limits of the data set. 
     ax.set_autoscale_on(False) 
     plt.axis([x[0], x[data_entries-1], 0, num_of_channels - 1]) 
     # Set up the colour palette used to render the data. 
     # Make bad results (those that are masked) invisible so the background shows instead. 
     palette = plt.cm.get_cmap('autumn') 
     palette.set_bad(alpha=0.0) 
     ax.set_axis_bgcolor('black') # Set the background to zero 

     # Grab the data and transpose it so we can stick it in the time series running along the X axis. 
     firstFftCol = df.columns.get_loc(field_name + "%03d"%(0)) 
     lastFftCol = df.columns.get_loc(field_name + "%03d"%(num_of_channels - 1)) 
     data = df.ix[:,firstFftCol:lastFftCol] 
     data = data.T # Transpose so that time runs along the X axis and bin index is on the Y 

     # Mask off data with zero's so that it doesn't obscure the data we're actually interested in. 
     data = np.ma.masked_where(data == 0.0, data) 

     # Actually create the data mesh so we can plot it 
     z_min, z_max = data.min().min(), data.max().max() 
     p = ax.pcolormesh(X,Y, data, cmap=palette, vmin=z_min, vmax=z_max) 

     # Render it 
     plt.plot() 

     # Label this sub-plot 
     plt.ylabel(field_name) 

     # Sort out the color bar 
     fig = plt.gcf() 
     image_width = fig.get_size_inches()[0] * fig.dpi # size in pixels 
     colorbar_padding_width_in_pixels = 20 
     colorbar_padding = colorbar_padding_width_in_pixels/image_width 
     plt.colorbar(p, pad=colorbar_padding) 

    # Label the plot 
    plt.xlabel('Time (ms)') 

    # Record the result 
    plt.savefig('test.png', edgecolor='none', transparent=False, bbox_inches='tight') 

    plt.tight_layout() 

if __name__ == '__main__': 
    data_entries = 30000 # Large values here cause issues 
    num_of_channels = 255 

    fields_to_plot = ('random', 'periodic') 

    data = GetData(data_entries, num_of_channels) 

    width_in_pixels = len(data.index)+200 
    additional_vertical_space_per_plot = 50 
    num_of_plots = len(fields_to_plot) 
    height_in_pixels = (num_of_channels+additional_vertical_space_per_plot)*num_of_plots 
    dpi = 80 # The default according to the documentation. 
    fig = plt.figure(1,figsize=(width_in_pixels/dpi, height_in_pixels/dpi), dpi=dpi) 

    PlotData(data, num_of_channels, fields_to_plot) 

到底祕訣是：

1. 立即plt.savefig調用之前添加plt.tight_layout()。
2. 將bbox_inches='tight'添加到plt.savefig呼叫。
3. 添加, pad=colorbar_padding在計算colorbar_padding之後，通過檢查20像素填充等於的整個圖像寬度的比例。