

In [2]:

    
import numpy
import matplotlib.pyplot
%matplotlib inline



In [3]:

    
data =numpy.loadtxt (fname = 'data/weather-01.csv', delimiter=',')



In [4]:

    
#create a wide figure to hold the subplots
fig = matplotlib.pyplot.figure (figsize=(10.0, 3.0))

#create placeholders for plots
subplot1= fig.add_subplot(1,3,1
                        )
subplot2= fig.add_subplot(1,3,2)
subplot3= fig.add_subplot(1,3,3)

subplot1.set_ylabel('average')
subplot1.plot(numpy.mean(data, axis=0))

subplot2.set_ylabel('min')
subplot2.plot(numpy.min(data, axis=0))

subplot3.set_ylabel('max')
subplot3.plot(numpy.max(data, axis=0))

fig.tight_layout()
matplotlib.pyplot.show()



In [ ]:

loops



In [5]:

    
#repeat same operations again and again



In [6]:

    
word= 'notebook'
print (word [4])



In [7]:

    
print (word [0])



In [8]:

    
#a loop 
for char in word:
    print (char)









    



n
o
t
e
b
o
o
k



In [9]:

    
# get a list of all the filenames from disk
import glob



In [10]:

    
print (glob.glob('data/weather*.csv'))









    



['data\\weather-01.csv', 'data\\weather-02.csv', 'data\\weather-03.csv', 'data\\weather-04.csv', 'data\\weather-05.csv', 'data\\weather-06.csv', 'data\\weather-07.csv', 'data\\weather-08.csv', 'data\\weather-09.csv', 'data\\weather-10.csv', 'data\\weather-11.csv', 'data\\weather-12.csv']



In [11]:

    
#produce series of plots for each datafile



In [12]:

    
filename= sorted (glob.glob('data/weather*.csv'))
filename= filename [0:3] #takes 01, 02, 03 files from data/weather only

for f in filename:
    print (f)
    
    data= numpy.loadtxt (fname=f, delimiter= ',')

    #create a wide figure to hold the subplots
    fig = matplotlib.pyplot.figure (figsize=(10.0, 3.0))

    #create placeholders for plots
    subplot1= fig.add_subplot(1,3,1
                        )
    subplot2= fig.add_subplot(1,3,2)
    subplot3= fig.add_subplot(1,3,3)

    subplot1.set_ylabel('average')
    subplot1.plot(numpy.mean(data, axis=0))

    subplot2.set_ylabel('min')
    subplot2.plot(numpy.min(data, axis=0))

    subplot3.set_ylabel('max')
    subplot3.plot(numpy.max(data, axis=0))

    fig.tight_layout()
    matplotlib.pyplot.show()









    



data\weather-01.csv






    












    



data\weather-02.csv






    












    



data\weather-03.csv

making decisions



In [13]:

    
num =37
if num>100:
    print ('greater')
else:
    print ('not greater')
print ('Done')









    



not greater
Done



In [14]:

    
num =-3
if num>0:
    print (num, "is positive")
elif num==0:
    print (num, "is zero")
else:
    print (num, "is negative")









    



-3 is negative



In [15]:

    
filename= sorted (glob.glob('data/weather*.csv'))
#filename= filename [0:3] #takes 01, 02, 03 files from data/weather only

for f in filename:
    print (f)
    
    data= numpy.loadtxt (fname=f, delimiter= ',')
    
    if numpy.max (data, axis=0)[0] ==0 and numpy.max (data, axis=0)[20]==20:
        print ("suspicious looking maxima") #if the first max value is 0 and the 20th one is 20c
    elif numpy.sum (numpy.min(data, axis=0))==0:
        print ("minima add up to zero")
    else:
        print ("data looks ok")

    #create a wide figure to hold the subplots
    fig = matplotlib.pyplot.figure (figsize=(10.0, 3.0))

    #create placeholders for plots
    subplot1= fig.add_subplot(1,3,1
                        )
    subplot2= fig.add_subplot(1,3,2)
    subplot3= fig.add_subplot(1,3,3)

    subplot1.set_ylabel('average')
    subplot1.plot(numpy.mean(data, axis=0))

    subplot2.set_ylabel('min')
    subplot2.plot(numpy.min(data, axis=0))

    subplot3.set_ylabel('max')
    subplot3.plot(numpy.max(data, axis=0))

    fig.tight_layout()
    matplotlib.pyplot.show()









    



data\weather-01.csv
suspicious looking maxima






    












    



data\weather-02.csv
suspicious looking maxima






    












    



data\weather-03.csv
minima add up to zero






    












    



data\weather-04.csv
suspicious looking maxima






    












    



data\weather-05.csv
suspicious looking maxima






    












    



data\weather-06.csv
suspicious looking maxima






    












    



data\weather-07.csv
suspicious looking maxima






    












    



data\weather-08.csv
minima add up to zero






    












    



data\weather-09.csv
suspicious looking maxima






    












    



data\weather-10.csv
suspicious looking maxima






    












    



data\weather-11.csv
minima add up to zero






    












    



data\weather-12.csv
suspicious looking maxima

FUNCTIONS



In [16]:

    
def fahr_to_kelvin (temp):
    return((temp-32)*(5/9)+ 273.15)



In [17]:

    
print ('freezing point of water', fahr_to_kelvin(32))









    



freezing point of water 273.15



In [18]:

    
print ('boiling point of water', fahr_to_kelvin(212))









    



boiling point of water 373.15



In [19]:

    
# as you go through your work you can create functions and put them in your own library



In [20]:

    
def analyse (filename):
    data= numpy.loadtxt (fname=filename, delimiter= ',')
     #create a wide figure to hold the subplots
    fig = matplotlib.pyplot.figure (figsize=(10.0, 3.0))

    #create placeholders for plots
    subplot1= fig.add_subplot(1,3,1
                        )
    subplot2= fig.add_subplot(1,3,2)
    subplot3= fig.add_subplot(1,3,3)

    subplot1.set_ylabel('average')
    subplot1.plot(numpy.mean(data, axis=0))

    subplot2.set_ylabel('min')
    subplot2.plot(numpy.min(data, axis=0))

    subplot3.set_ylabel('max')
    subplot3.plot(numpy.max(data, axis=0))

    fig.tight_layout()
    matplotlib.pyplot.show()



In [26]:

    
def detect_problems (filename):
    """Some of our temperature files have problems, check for these
       
       This function reads a file (filename arguent) and reports on 
       odd looking maxima and minima that add up to zero. this seems
       to happen when the sensors break.
       The function does not return any data.
    """
    data= numpy.loadtxt (fname=filename, delimiter= ',')
    if numpy.max (data, axis=0)[0] ==0 and numpy.max (data, axis=0)[20]==20:
        print ("suspicious looking maxima") #if the first max value is 0 and the 20th one is 20c
    elif numpy.sum (numpy.min(data, axis=0))==0:
        print ("minima add up to zero")
    else:
        print ("data looks ok")



In [22]:

    
for f in filename [0:5]:
    print (f)
    analyse (f)
    detect_problems (f)









    



data\weather-01.csv






    












    



suspicious looking maxima
data\weather-02.csv






    












    



suspicious looking maxima
data\weather-03.csv






    












    



minima add up to zero
data\weather-04.csv






    












    



suspicious looking maxima
data\weather-05.csv






    












    



suspicious looking maxima



In [23]:

    
help (numpy.loadtxt
     )









    



Help on function loadtxt in module numpy.lib.npyio:

loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)
    Load data from a text file.
    
    Each row in the text file must have the same number of values.
    
    Parameters
    ----------
    fname : file or str
        File, filename, or generator to read.  If the filename extension is
        ``.gz`` or ``.bz2``, the file is first decompressed. Note that
        generators should return byte strings for Python 3k.
    dtype : data-type, optional
        Data-type of the resulting array; default: float.  If this is a
        structured data-type, the resulting array will be 1-dimensional, and
        each row will be interpreted as an element of the array.  In this
        case, the number of columns used must match the number of fields in
        the data-type.
    comments : str or sequence, optional
        The characters or list of characters used to indicate the start of a
        comment;
        default: '#'.
    delimiter : str, optional
        The string used to separate values.  By default, this is any
        whitespace.
    converters : dict, optional
        A dictionary mapping column number to a function that will convert
        that column to a float.  E.g., if column 0 is a date string:
        ``converters = {0: datestr2num}``.  Converters can also be used to
        provide a default value for missing data (but see also `genfromtxt`):
        ``converters = {3: lambda s: float(s.strip() or 0)}``.  Default: None.
    skiprows : int, optional
        Skip the first `skiprows` lines; default: 0.
    usecols : sequence, optional
        Which columns to read, with 0 being the first.  For example,
        ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
        The default, None, results in all columns being read.
    unpack : bool, optional
        If True, the returned array is transposed, so that arguments may be
        unpacked using ``x, y, z = loadtxt(...)``.  When used with a structured
        data-type, arrays are returned for each field.  Default is False.
    ndmin : int, optional
        The returned array will have at least `ndmin` dimensions.
        Otherwise mono-dimensional axes will be squeezed.
        Legal values: 0 (default), 1 or 2.
    
        .. versionadded:: 1.6.0
    
    Returns
    -------
    out : ndarray
        Data read from the text file.
    
    See Also
    --------
    load, fromstring, fromregex
    genfromtxt : Load data with missing values handled as specified.
    scipy.io.loadmat : reads MATLAB data files
    
    Notes
    -----
    This function aims to be a fast reader for simply formatted files.  The
    `genfromtxt` function provides more sophisticated handling of, e.g.,
    lines with missing values.
    
    .. versionadded:: 1.10.0
    
    The strings produced by the Python float.hex method can be used as
    input for floats.
    
    Examples
    --------
    >>> from io import StringIO   # StringIO behaves like a file object
    >>> c = StringIO("0 1\n2 3")
    >>> np.loadtxt(c)
    array([[ 0.,  1.],
           [ 2.,  3.]])
    
    >>> d = StringIO("M 21 72\nF 35 58")
    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
    ...                      'formats': ('S1', 'i4', 'f4')})
    array([('M', 21, 72.0), ('F', 35, 58.0)],
          dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])
    
    >>> c = StringIO("1,0,2\n3,0,4")
    >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
    >>> x
    array([ 1.,  3.])
    >>> y
    array([ 2.,  4.])



In [27]:

    
help (detect_problems)









    



Help on function detect_problems in module __main__:

detect_problems(filename)
    Some of our temperature files have problems, check for these
    
    This function reads a file (filename arguent) and reports on 
    odd looking maxima and minima that add up to zero. this seems
    to happen when the sensors break.
    The function does not return any data.



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