In [1]:

    

import numpy
import matplotlib.pyplot
%matplotlib inline


    

In [2]:

    

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


    

In [3]:

    

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

# create place holders 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('max')
subplot2.plot(numpy.max(data, axis = 0))

subplot3.set_ylabel('min')
subplot3.plot(numpy.min(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[0:5]

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 maximum')
    elif numpy.sum(numpy.min(data, axis = 0)) == 0:
        print ('Suspicious looking minimum')
    else :
        print ('Data looks fantastic')

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

    # create place holders 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('max')
    subplot2.plot(numpy.max(data, axis = 0))

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

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


    

    




data/weather-01.csv
Suspicious looking maximum






    













    




data/weather-02.csv
Suspicious looking maximum






    













    




data/weather-03.csv
Suspicious looking minimum






    













    




data/weather-04.csv
Suspicious looking maximum






    













    




data/weather-05.csv
Suspicious looking maximum






    

In [ ]:

    

num = 37
if num > 100:
    print ('Greater')
else:
    print ('Not Greater')
    
print ('Done')


    

In [ ]:

    

num = 2

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


    

In [9]:

    

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


    

In [10]:

    

print ('Freezing point of water: ', fahr_to_kelvin(0), 'degrees kelvin')


    

    




Freezing point of water:  255.3722222222222 degrees kelvin

In [11]:

    

print ('Boiling point of water: ', fahr_to_kelvin(212), 'degrees kelvin')


    

    




Boiling point of water:  373.15 degrees kelvin

In [20]:

    

def analyse (filename):
    
    """ Analyses the mean, max and min of given data along x = 0, then
        creates a plot containing subplots of the mean, max and min.
    
    """
    data = numpy.loadtxt(fname = filename, delimiter = ',')
    
    # create a wide figure to hold the subplots
    fig = matplotlib.pyplot.figure (figsize = (10.0,3.0))

    # create place holders 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('max')
    subplot2.plot(numpy.max(data, axis = 0))

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

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


    

In [21]:

    

def detect_problems (filename):
    """Some of our temperature files have problems, check for these
    
       This function reads a file (filename argument) and reports on odd 
       looking maxima and minia that add up to zero. This seems to happen
       when 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 maximum')
    elif numpy.sum(numpy.min(data, axis = 0)) == 0:
        print ('Suspicious looking minimum')
    else :
        print ('Data looks fantastic')


    

In [22]:

    

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


    

    




data/weather-01.csv






    













    




Suspicious looking maximum
data/weather-02.csv






    













    




Suspicious looking maximum
data/weather-03.csv






    













    




Suspicious looking minimum
data/weather-04.csv






    













    




Suspicious looking maximum
data/weather-05.csv






    













    




Suspicious looking maximum

In [15]:

    

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 [23]:

    

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 argument) and reports on odd 
    looking maxima and minia that add up to zero. This seems to happen
    when sensors break.
    The function does not return any data

In [24]:

    

help(analyse)


    

    




Help on function analyse in module __main__:

analyse(filename)
    Analyses the mean, max and min of given data along x = 0, then
    creates a plot containing subplots of the mean, max and min.

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