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"""Calculate statistics on the digits of pi in parallel.
This program uses the functions in :file:`pidigits.py` to calculate
the frequencies of 2 digit sequences in the digits of pi. The
results are plotted using matplotlib.
To run, text files from https://www.super-computing.org/
must be installed in the working directory of the IPython engines.
The actual filenames to be used can be set with the ``filestring``
variable below.
The dataset we have been using for this is the 200 million digit one here:
ftp://pi.super-computing.org/.2/pi200m/
and the files used will be downloaded if they are not in the working directory
of the IPython engines.
"""
from timeit import default_timer as clock
from matplotlib import pyplot as plt
from pidigits import (
compute_two_digit_freqs,
fetch_pi_file,
plot_two_digit_freqs,
reduce_freqs,
)
import ipyparallel as ipp
# Files with digits of pi (10m digits each)
filestring = 'pi200m.ascii.%(i)02dof20'
files = [filestring % {'i': i} for i in range(1, 21)]
# Connect to the IPython cluster
c = ipp.Client()
c[:].run('pidigits.py')
# the number of engines
n = len(c)
id0 = c.ids[0]
v = c[:]
v.block = True
# fetch the pi-files
print("downloading %i files of pi" % n)
v.map(fetch_pi_file, files[:n]) # noqa: F821
print("done")
# Run 10m digits on 1 engine
t1 = clock()
freqs10m = c[id0].apply_sync(compute_two_digit_freqs, files[0])
t2 = clock()
digits_per_second1 = 10.0e6 / (t2 - t1)
print("Digits per second (1 core, 10m digits): ", digits_per_second1)
# Run n*10m digits on all engines
t1 = clock()
freqs_all = v.map(compute_two_digit_freqs, files[:n])
freqs150m = reduce_freqs(freqs_all)
t2 = clock()
digits_per_second8 = n * 10.0e6 / (t2 - t1)
print("Digits per second (%i engines, %i0m digits): " % (n, n), digits_per_second8)
print("Speedup: ", digits_per_second8 / digits_per_second1)
plot_two_digit_freqs(freqs150m)
plt.title("2 digit sequences in %i0m digits of pi" % n)
plt.show()
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