1  %matplotlib inline
   2  
   3  import matplotlib.pyplot as plt
   4  import matplotlib.dates as dates
   5  import matplotlib.ticker as ticker
   6  
   7  import pandas as pd
   8  import numpy as np
   9  
  10  data = pd.read_csv('data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')
  11  
  12  # Convert all data to degrees Celsius:
  13  data['Data_Value'] = data['Data_Value']*0.1
  14  
  15  data['Date'] = pd.to_datetime(data['Date'])
  16  
  17  data['Year'] = data['Date'].dt.year
  18  
  19  data['Month_Day'] = data['Date'].dt.strftime('%m-%d')
  20  
  21  # Remove February 29:
  22  data = data[data['Month_Day']!='02-29']
  23  
  24  # Max and min data after grouping by each day:
  25  max_temp = data[(data.Year >= 2005) & (data.Year < 2015) & (data['Element'] == 'TMAX')].groupby(['Month_Day'])['Data_Value'].max()
  26  min_temp = data[(data.Year >= 2005) & (data.Year < 2015) & (data['Element'] == 'TMIN')].groupby(['Month_Day'])['Data_Value'].min()
  27  
  28  # Record breaking data:
  29  data = data.merge(max_temp.reset_index(drop=False).rename(columns={'Data_Value':'Max_temp'}), on='Month_Day', how='left')
  30  data = data.merge(min_temp.reset_index(drop=False).rename(columns={'Data_Value':'Min_temp'}), on='Month_Day', how='left')
  31  
  32  record_high = data[(data.Year==2015)&(data.Data_Value > data.Max_temp)]
  33  record_low = data[(data.Year==2015)&(data.Data_Value < data.Min_temp)]
  34  
  35  # X Axis spanning 2015 only:
  36  date_index = np.arange('2015-01-01','2016-01-01', dtype='datetime64[D]')
  37  
  38  plt.figure()
  39  
  40  plt.plot(date_index,max_temp,color='lightcoral', linewidth=1) # Max in red.
  41  plt.plot(date_index,min_temp,color='skyblue', linewidth=1) # Min in blue.
  42  
  43  plt.scatter(record_high.Date.values, record_high.Data_Value.values, color='red', s=8)
  44  plt.scatter(record_low.Date.values, record_low.Data_Value.values, color='blue', s=8)
  45  
  46  # Set x and y limits.
  47  ax = plt.gca()
  48  ax.axis(['2015/01/01','2015/12/31',-50,50])
  49  
  50  # Set axis names and title:
  51  plt.xlabel('Date', fontsize=10)
  52  plt.ylabel('° Celsius', fontsize=10)
  53  plt.title('Temperature in Ann Arbour, Michigan (2005-2015)', fontsize=12)
  54  
  55  # Create legend and title
  56  # loc=0 provides the best position for the legend
  57  plt.legend(['Record high (2005-2014)','Record low (2005-2014)','Record breaking high in 2015','Record breaking low in 2015'],loc=0,frameon=False)
  58  
  59  # Fill colour between highs and lows:
  60  # alpha adjusts darkness of the shade.
  61  ax.fill_between(date_index, max_temp, min_temp, facecolor='grey', alpha=0.25)
  62  
  63  # Where you locate the major and minor ticks:
  64  ax.xaxis.set_major_locator(dates.MonthLocator())
  65  ax.xaxis.set_minor_locator(dates.MonthLocator(bymonthday=15)) # Put the label at the minor tick so it's in the center.
  66  #ax.yaxis.set_minor_locator()
  67  
  68  # What you put at the ticks:
  69  ax.xaxis.set_major_formatter(ticker.NullFormatter())
  70  ax.xaxis.set_minor_formatter(dates.DateFormatter('%b'))
  71  
  72  # 1 refers to the bottom of the plot for xticks and the left for yticks
  73  # 2 refers to the top of the plot for xticks and the right for yticks
  74  for tick in ax.xaxis.get_minor_ticks():
  75      tick.tick1line.set_markersize(0) # Make small ticker disappear
  76      tick.label1.set_horizontalalignment('center')