BLANK IMAGE WHY!!!所以我尝试在Pycharm上运行我的代码，蜘蛛根本没有显示情节。我是一个非常新手的新手。需要你的帮助。字图实际上出现了。其他人都很空。我会附上一张照片。
import pandas as pd
import numpy as np
import word_StudentID as ws
import preprocessor_StudentID as pre
import character_Student as cha
import matplotlib.pyplot as plt
class AnalysisVisualiser:
def __init__(self, df1, df2, df3, df4, df5, df6):
self.df_rich = open('Richard_II_Shakespeare.tok')
self.df_Edw = open('Edward_II_Marlowe.tok')
self.df_ham = open('Hamlet_Shakespeare.tok')
self.df_hen1 = open('Henry_VI_Part1_Shakespeare.tok')
self.df_hen2 = open('Henry_VI_Part2_Shakespeare.tok')
self.df_jew = open('Jew_of_Malta_Marlowe.tok')
def visualise_character_frequency(self):
ob1 = pre.Preprocessor()
ob2 = pre.Preprocessor()
ob3 = pre.Preprocessor()
ob4 = pre.Preprocessor()
ob5 = pre.Preprocessor()
ob6 = pre.Preprocessor()
''' Processing the file'''
print("part 1")
ob1.tokenise(self.df_rich)
ob2.tokenise(self.df_Edw)
ob3.tokenise(self.df_ham)
ob4.tokenise(self.df_hen1)
ob5.tokenise(self.df_hen2)
ob6.tokenise(self.df_jew)
''' converting file into a line'''
print("part 2")
token1 = ob1._tokenised_list()
token2 = ob2._tokenised_list()
token3 = ob3._tokenised_list()
token4 = ob4._tokenised_list()
token5 = ob5._tokenised_list()
token6 = ob6._tokenised_list()
''''getting file in the form of list'''
print("part 5")
ob_cha1 = cha.CharacterAnalyser()
ob_cha2 = cha.CharacterAnalyser()
ob_cha3 = cha.CharacterAnalyser()
ob_cha4 = cha.CharacterAnalyser()
ob_cha5 = cha.CharacterAnalyser()
ob_cha6 = cha.CharacterAnalyser()
''' Counting number of characters'''
print("part 6")
ac1 = ob_cha1.analyse_characters(token1)
ac2 = ob_cha2.analyse_characters(token2)
ac3 = ob_cha3.analyse_characters(token3)
ac4 = ob_cha4.analyse_characters(token4)
ac5 = ob_cha5.analyse_characters(token5)
ac6 = ob_cha6.analyse_characters(token6)
plt.plot(ac1['val'], ac1['frq'], label = 'Richard_II_Shakespeare')
plt.plot(ac2['val'], ac2['frq'], label = 'Edward_II_Marlowe')
plt.plot(ac3['val'], ac3['frq'], label = 'Hamlet_Shakespeare')
plt.plot(ac4['val'], ac4['frq'], label = 'Henry_VI_Part1_Shakespeare')
plt.plot(ac5['val'], ac5['frq'], label = 'Henry_VI_Part2_Shakespeare')
plt.plot(ac6['val'], ac6['frq'], label = 'Jew_of_Malta_Marlowe')
plt.title('character_frequency')
plt.show()
def visualise_punctuation_frequency(self):
ob1 = pre.Preprocessor()
ob2 = pre.Preprocessor()
ob3 = pre.Preprocessor()
ob4 = pre.Preprocessor()
ob5 = pre.Preprocessor()
ob6 = pre.Preprocessor()
print("part 1")
ob1.tokenise(self.df_rich)
ob2.tokenise(self.df_Edw)
ob3.tokenise(self.df_ham)
ob4.tokenise(self.df_hen1)
ob5.tokenise(self.df_hen2)
ob6.tokenise(self.df_jew)
print("part 2")
token1 = ob1._tokenised_list()
token2 = ob2._tokenised_list()
token3 = ob3._tokenised_list()
token4 = ob4._tokenised_list()
token5 = ob5._tokenised_list()
token6 = ob6._tokenised_list()
print("part 3")
ob_ws1 = ws.WordAnalyser()
ob_ws2 = ws.WordAnalyser()
ob_ws3 = ws.WordAnalyser()
ob_ws4 = ws.WordAnalyser()
ob_ws5 = ws.WordAnalyser()
ob_ws6 = ws.WordAnalyser()
print("part 4")
token1 = ob1._tokenised_list()
token2 = ob2._tokenised_list()
token3 = ob3._tokenised_list()
token4 = ob4._tokenised_list()
token5 = ob5._tokenised_list()
token6 = ob6._tokenised_list()
print("part 5")
ob_cha1 = cha.CharacterAnalyser()
ob_cha2 = cha.CharacterAnalyser()
ob_cha3 = cha.CharacterAnalyser()
ob_cha4 = cha.CharacterAnalyser()
ob_cha5 = cha.CharacterAnalyser()
ob_cha6 = cha.CharacterAnalyser()
print("part 6")
ob_cha1.analyse_characters(token1)
ob_cha2.analyse_characters(token2)
ob_cha3.analyse_characters(token3)
ob_cha4.analyse_characters(token4)
ob_cha5.analyse_characters(token5)
ob_cha6.analyse_characters(token6)
print("part 7")
pun1 = ob_cha1.get_punctuation_frequency()
pun2 = ob_cha2.get_punctuation_frequency()
pun3 = ob_cha3.get_punctuation_frequency()
pun4 = ob_cha4.get_punctuation_frequency()
pun5 = ob_cha5.get_punctuation_frequency()
pun6 = ob_cha6.get_punctuation_frequency()
print("part 8")
plt.plot(pun1['val'], pun1['frq'], label = 'Richard_II_Shakespeare')
plt.plot(pun2['val'], pun2['frq'], label = 'Edward_II_Marlowe')
plt.plot(pun3['val'], pun3['frq'], label = 'Hamlet_Shakespeare')
plt.plot(pun4['val'], pun4['frq'], label = 'Henry_VI_Part1_Shakespeare')
plt.plot(pun5['val'], pun5['frq'], label = 'Henry_VI_Part2_Shakespeare')
plt.plot(pun6['val'], pun6['frq'], label = 'Jew_of_Malta_Marlowe')
plt.title('punctuation_frequency')
plt.show()
def visualise_stopword_frequency(self):
ob1 = pre.Preprocessor()
ob2 = pre.Preprocessor()
ob3 = pre.Preprocessor()
ob4 = pre.Preprocessor()
ob5 = pre.Preprocessor()
ob6 = pre.Preprocessor()
print("part 1")
ob1.tokenise(self.df_rich)
ob2.tokenise(self.df_Edw)
ob3.tokenise(self.df_ham)
ob4.tokenise(self.df_hen1)
ob5.tokenise(self.df_hen2)
ob6.tokenise(self.df_jew)
print("part 2")
token1 = ob1._tokenised_list()
token2 = ob2._tokenised_list()
token3 = ob3._tokenised_list()
token4 = ob4._tokenised_list()
token5 = ob5._tokenised_list()
token6 = ob6._tokenised_list()
print("part 3")
ob_ws1 = ws.WordAnalyser()
ob_ws2 = ws.WordAnalyser()
ob_ws3 = ws.WordAnalyser()
ob_ws4 = ws.WordAnalyser()
ob_ws5 = ws.WordAnalyser()
ob_ws6 = ws.WordAnalyser()
print("part 4")
ob_ws1.analyse_word(token1)
ob_ws2.analyse_word(token2)
ob_ws3.analyse_word(token3)
ob_ws4.analyse_word(token4)
ob_ws5.analyse_word(token5)
ob_ws6.analyse_word(token6)
stop1 = ob_ws1.get_stopword_frequency()
stop2 = ob_ws2.get_stopword_frequency()
stop3 = ob_ws3.get_stopword_frequency()
stop4 = ob_ws4.get_stopword_frequency()
stop5 = ob_ws5.get_stopword_frequency()
stop6 = ob_ws6.get_stopword_frequency()
plt.plot(stop1['val'], stop1['frq'], label = 'Richard_II_Shakespeare')
plt.plot(stop2['val'], stop2['frq'], label = 'Edward_II_Marlowe')
plt.plot(stop3['val'], stop3['frq'], label = 'Hamlet_Shakespeare')
plt.plot(stop4['val'], stop4['frq'], label = 'Henry_VI_Part1_Shakespeare')
plt.plot(stop5['val'], stop5['frq'], label = 'Henry_VI_Part2_Shakespeare')
plt.plot(stop6['val'], stop6['frq'], label = 'Jew_of_Malta_Marlowe')
plt.title('stopword_frequency')
plt.show()
def visualise_word_length_frequency(self):
ob1 = pre.Preprocessor()
ob2 = pre.Preprocessor()
ob3 = pre.Preprocessor()
ob4 = pre.Preprocessor()
ob5 = pre.Preprocessor()
ob6 = pre.Preprocessor()
print("part 1")
ob1.tokenise(self.df_rich)
ob2.tokenise(self.df_Edw)
ob3.tokenise(self.df_ham)
ob4.tokenise(self.df_hen1)
ob5.tokenise(self.df_hen2)
ob6.tokenise(self.df_jew)
print("part 2")
token1 = ob1._tokenised_list()
token2 = ob2._tokenised_list()
token3 = ob3._tokenised_list()
token4 = ob4._tokenised_list()
token5 = ob5._tokenised_list()
token6 = ob6._tokenised_list()
print("part 3")
ob_ws1 = ws.WordAnalyser()
ob_ws2 = ws.WordAnalyser()
ob_ws3 = ws.WordAnalyser()
ob_ws4 = ws.WordAnalyser()
ob_ws5 = ws.WordAnalyser()
ob_ws6 = ws.WordAnalyser()
print("part 4")
ob_ws1.analyse_word(token1)
ob_ws2.analyse_word(token2)
ob_ws3.analyse_word(token3)
ob_ws4.analyse_word(token4)
ob_ws5.analyse_word(token5)
ob_ws6.analyse_word(token6)
print("part 5")
le_frq1 = ob_ws1.get_word_length_frequency()
le_frq2 = ob_ws2.get_word_length_frequency()
le_frq3 = ob_ws3.get_word_length_frequency()
le_frq4 = ob_ws4.get_word_length_frequency()
le_frq5 = ob_ws5.get_word_length_frequency()
le_frq6 = ob_ws6.get_word_length_frequency()
print("part 6")
plt.plot(le_frq1['val'], le_frq1['frq'], label = 'Richard_II_Shakespeare')
plt.plot(le_frq2['val'], le_frq2['frq'], label = 'Edward_II_Marlowe' )
plt.plot(le_frq3['val'], le_frq3['frq'], label = 'Hamlet_Shakespeare' )
plt.plot(le_frq4['val'], le_frq4['frq'], label = 'Henry_VI_Part1_Shakespeare')
plt.plot(le_frq5['val'], le_frq5['frq'], label = 'Henry_VI_Part2_Shakespeare')
plt.plot(le_frq6['val'], le_frq6['frq'], label = 'Jew_of_Malta_Marlowe')
plt.title('word_length_frequency')
plt.show()
答案 0 :(得分：0)
我怀疑问题是您在每种方法中都在阅读文件，但是在您拨打第一个文件后，您就会在文件的末尾。如果您要再次使用该文件（而不是说，关闭并重新打开它），您需要seek回到起点。
尝试在您从self.df_rich.seek(0)文件中读取的每个地方之前添加self.df_rich（以及您阅读它们的其他文件的类似行）。