old_contents: '''
  using matplotlib to viz dates.txt
  '''
  from collections import Counter
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib import dates as pltdates
  import pandas as pd
  
  datefile = input('please enter datefile name: ')
  dates = eval(open(datefile,'r+').read())
  datescount = Counter(dates)
  datescount[20080627] = 500
  # this value skews the graph too much, original value is approx 23K
  
  def matplot_bar(datescount):
      labels, values = zip(*datescount.items())
  
      indexes = np.arange(len(labels))
      width = 1
  
      plt.bar(indexes, values, width)
      plt.xticks(indexes + width * 0.5, labels)
      plt.show()
  
  def pandas_histogram(dates):
      datesdf = pd.DataFrame(dates)
      plt.figure()
      datesdf.plot(kind='hist', alpha=0.5)
      plt.show()
  
  def plotdates(datelist, valuelist):
  
  
      yerp=dataserv.Leadtime()
      newdatelist = []
      for i in datelist:
          if not isinstance(i, str):
              i=i[0]
          j = yerp.convert_datestring(i)
          newdatelist.append(j)
      dates = pltdates.date2num(newdatelist)
      plt.plot_date(dates, valuelist)
  
new_contents: '''
  using matplotlib to viz dates.txt
  '''
  from collections import Counter
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib import dates as pltdates
  import pandas as pd
  import sqlite3
  
  def getdatesfromfile():
      datefile = input('please enter datefile name: ')
      dates = eval(open(datefile,'r+').read())
      datescount = Counter(dates)
      datescount[20080627] = 500
  
  def matplot_bar(datescount):
      labels, values = zip(*datescount.items())
  
      indexes = np.arange(len(labels))
      width = 1
  
      plt.bar(indexes, values, width)
      plt.xticks(indexes + width * 0.5, labels)
      plt.show()
  
  def pandas_histogram(dates):
      datesdf = pd.DataFrame(dates)
      plt.figure()
      datesdf.plot(kind='hist', alpha=0.5)
      plt.show()
  
  def plotdates(datelist, valuelist):
      yerp=dataserv.Leadtime()
      newdatelist = []
      for i in datelist:
          if not isinstance(i, str):
              i=i[0]
          j = yerp.convert_datestring(i)
          newdatelist.append(j)
      dates = pltdates.date2num(newdatelist)
      plt.plot_date(dates, valuelist)
  
  def sqltopandas():
      db = input('please enter DB name: ')
      sqlquery = input('please enter SQL query to execute: ')
      with sqlite3.connect(db) as con:
          dbquery = pd.read_sql(sqlquery, con)
  
      return dbquery
  
subject: define getdatesfromfile(), sqltopandas() in matplotchart.py