In [1]:

    

import numpy
import matplotlib.pyplot
%matplotlib inline


    

In [2]:

    

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


    

In [3]:

    

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

#create a placeholder 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 [4]:

    

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


    

    




b

In [5]:

    

for char in word:
    print(char)


    

    




n
o
t
e
b
o
o
k

In [6]:

    

import glob


    

In [7]:

    

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 [8]:

    

filenames = sorted(glob.glob('data/weather*.csv')) 


#filenames = filenames[0:3]


for f in filenames: 
    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")
    elif numpy.sum(numpy.min(data, axis=0)) == 0:
        print("Minima equal zero!")
    else:
        print("Data looks ok")
        
    # create a figure to hold subplots
    fig = matplotlib.pyplot.figure(figsize= (10.0, 3.0))

    #create a placeholder 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 equal 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 equal zero!






    













    




data\weather-09.csv
Suspicious looking maxima






    













    




data\weather-10.csv
Suspicious looking maxima






    













    




data\weather-11.csv
Minima equal zero!






    













    




data\weather-12.csv
Suspicious looking maxima






    

In [9]:

    

num = 107
if num > 100:
    print ('Greater')
else:
    print ('Not Greater')
    print ('Done')


    

    




Greater

In [10]:

    

num = 3

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


    

    




3 is positive

In [11]:

    

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


    

In [12]:

    

print(fahr_to_kelvin(110))


    

    




316.4833333333333

In [21]:

    

def analyse (filename):
    """ This function plots the average, minima and maxima for a weather station dataset.
    
        It takes a comma-delimited file and ploats each row.
    """
    data = numpy.loadtxt(fname=filename, delimiter=',')
    # create a figure to hold subplots
    fig = matplotlib.pyplot.figure(figsize= (10.0, 3.0))

    #create a placeholder 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 [20]:

    

def detect_problems(filename):
    """Sum of our temperature files have problems, check for these
    
       This function reads a comma-delimited file and reports on odd maxima and minia 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")
    elif numpy.sum(numpy.min(data, axis=0)) == 0:
        print("Minima equal zero!")
    else:
        print("Data looks ok")


    

In [15]:

    

for f in filenames [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 equal zero!
data\weather-04.csv






    













    




Suspicious looking maxima
data\weather-05.csv






    













    




Suspicious looking maxima

In [16]:

    

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 [19]:

    

help(detect_problems)


    

    




Help on function detect_problems in module __main__:

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

In [23]:

    

help(analyse)


    

    




Help on function analyse in module __main__:

analyse(filename)
    This function plots the average, minima and maxima for a weather station dataset.
    
    It takes a comma-delimited file and ploats each row.

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