The Big Programming Thread - Page 175

def getData( url ) :
    """
    Retrieves a CSV file from the Interwebs and returns the
    columns as a series of vectors.
    """
    from urllib import urlretrieve          # get a file from a URL
    urlretrieve( url , 'tempfile.csv') 
    from numpy import loadtxt               # load a CSV file
    return loadtxt( 'tempfile.csv', skiprows=1, unpack=True, delimiter=',' )

def getRow( data, row ) : 
    """
    Accepts data, a 2D array, and row, a list of one or more row numbers.
    
    Returns the requested row(s) of the data set, omitting the first column
    which contains the year rather than a temperature.
    """
    # the first column, offset zero, containing the year, is
    # *purposefully* excluded
    return data[ 1:13 , row ]               # colon operator means columns with 
                                            # offsets 1-12 inclusive

def getCol( data, column ) : 
    """
    Accepts data, a 2D array, and column, a list of one or more column numbers.
    
    Returns the requested column(s) of the data set.
    """
    return data[ column, : ]                # colon operator means 'all' rows

def annualAverage( data ) : 
    """
    Accepts data, a 2D array.
    
    Returns the average temperature for each row (excluding the year
    column, column zero, from the calculation).
    """
    from numpy import mean 
    
    months      = range( 1, 13 )            # month values, 1-12 inclusive 
    newData     = getCol( data, months )    # get the subset of data 
    return mean( newData, axis=0 )          # average over each rows => axis=0 

def plotHotCold( data ) : 
    from matplotlib.pyplot import plot
    from scipy.stats import linregress

    url ='http://gnh.uwaterloo.ca/toronto.csv'
    Year, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec = getData(url)
    
    plot(Year,Jan, 'b-')
    plot(Year,Jul, 'r-')
    
    a = linregress(Year,Jan)                                # set slope/intercept to the stats of linregress
    slopeJan = a[0]
    interceptJan = a[1]                                     # rename them to values for a specific month
    b = linregress(Year,Jul)
    slopeJul = b[0]
    interceptJul = b[1]

    plot( Year , slopeJan*Year + interceptJan , 'k-')
    plot( Year , slopeJul*Year + interceptJul , 'k-')

    report = """
    (a) The average annual January temperature increase is %5.3f C / year.
    The average annual July temperature increase is %5.3f C / year.
    """ % ( slopeJan, slopeJul ) 
    print report 

def plotAverage( data ) : 
    from matplotlib.pyplot import plot
    from scipy.stats import linregress

    url = 'http://gnh.uwaterloo.ca/toronto.csv'
    Year, Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec = getData(url)     
    y1 = annualAverage(toronto)                                                         #find the average over each row

    plot( Year , y1 )      
    a = linregress(Year,y1)
    slope = a[0]
    intercept = a[1]
    plot( Year , slope*Year + intercept)
    
    report = """
    (b) The average annual temperature increase is %5.3f C / year.
    """ % ( slope ) 
    print report 

#=========================================================================
from matplotlib.pyplot import clf, savefig, xlabel, ylabel, grid 
# Get the data from the supplied url.
toronto = getData( 'http://gnh.uwaterloo.ca/toronto.csv' ) 

# (a) Plot the January and July temperatures as a function of year.
clf() 
plotHotCold( toronto )
xlabel( 'Year', fontsize=16 )
ylabel( 'Temperature', fontsize=16 )
grid( 'on' )

savefig( 'JanJul.png' ) 

# (b) Plot the mean annual temperature as a function of year.
clf()
plotAverage( toronto )
xlabel( 'Year', fontsize=16 )
ylabel( 'Average Temperature', fontsize=16 )
grid( 'on' )
savefig( 'AnnualAverage.png' ) 

# (c) Report the boiling and freezing years.

print slope

print """
    (c) Extrapolating, Toronto was frozen in the year %-4.0f and will
    boil in year %-6.0f.
""" % ( -6000, 2011 ) # not the right answer