NumPy - files

https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html

https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html

https://docs.python.org/3/library/io.html

LOAD DATA FROM A TEXT FILE

numpy.loadtxt() is a fast reader for simple text files. Each row in the text file must have the same number of values.

# numpy.loadtxt(fname, dtype=float, comments='#', delimiter=None,
#     converters=None, skiprows=0, usecols=None, unpack=False,
#     ndmin=0, encoding='bytes', max_rows=None)
# dtype : data-type of the resulting array (default: float).
# The default delimiter is whitespace.
# delimiter=',' for CSV files.
# converters={0: datestr2num} if column 0 is a date string.
# skiprows : skip the first skiprows lines, including comments.
# usecols=(1, 4, 5) will extract the 2nd, 5th and 6th columns.

data = np.loadtxt("two_columns.txt")   # points in the plane; positions in time
print(data.shape)   # (100, 2)
t = data[:,0]   # first column
x = data[:,1]   # second column

import io   # StringIO behaves like a file object

fname = io.StringIO(u"1, 2, 3\n4, 5, 6")   # Py2 and Py3
data = np.loadtxt(fname, delimiter =', ', usecols =(0, 1, 2))   # dtype=float
#data = np.loadtxt(fname, dtype=int, delimiter =', ', usecols =(0, 1, 2))
print(data.shape)   # (2, 3)
x = data[:,0]
y = data[:,1]
z = data[:,2]

# https://scipython.com/book/chapter-6-numpy/examples/using-numpys-loadtxt-method/

fname = io.StringIO(u"M 21 72\nF 35 58") 
# Structured type
dtype1 = np.dtype([('gender','S1'), ('age','i4'), ('weight','f4')])
data = np.loadtxt(fname, dtype=dtype1)
print(data.shape)   # (2,) two rows only
print(data)
# [(b'M', 21, 72.) (b'F', 35, 58.)] Py3
# [('M', 21, 72.) ('F', 35, 58.)] Py2

male = data['gender'] == b'M'
print(male)   # [ True False ]
print(data["age"][male] )   # [21] selecting items
male_av = data['weight'][male].mean()
female_av = data['weight'][~male].mean()

SAVE DATA TO A TEXT FILE

# numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n',
#    header='', footer='', comments='# ', encoding=None)
# fname : filename or file handle
#    If the filename ends in .gz, the file is automatically saved in compressed
#    gzip format. loadtxt() understands gzipped files transparently.
# X : 1D or 2D array_like
# header : string that will be written at the beginning of the file
# footer : string that will be written at the end of the file
# comments : string that will be prepended to the header and footer strings,
#     to mark them as comments

x = np.arange(0,1,0.1),reshape(2,5)
# array([[0. , 0.1, 0.2, 0.3, 0.4],
#        [0.5, 0.6, 0.7, 0.8, 0.9]])

np.savetxt('x.out', x)   # delimiter=' '
# np.savetxt('x.out', x, delimiter=',')
# np.savetxt('x.out', x, fmt='%1.4e')   # use exponential notation

y = np.loadtxt('x.out')
np.array_equal(x, y)   # True

data = np.random.randn(5,3)

np.savetxt("data.csv", data, delimiter=",", comments="# ", 
    header="x, y, z", footer="end of data")

# The content of the file "data.csv".

# x, y, z
-2.653479360505936469e-01,1.716230058430250338e-01,2.831800738912415016e-01
-1.030493245302025684e+00,1.127211883360050626e+00,-7.984555882272663663e-01
7.069631944228116360e-01,-5.481806388086940540e-01,4.934060973542733208e-01
-1.037116411318822040e+00,-1.603295439838508252e-01,-6.553986822288586367e-02
4.523746268916202795e-01,1.416743281175641966e+00,-1.391845318345590998e+00
# end of data