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#!/usr/bin/env python
"""Graphs the progress of various technologies."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
__author__="Geert Barentsen"
importos
importnumpyasnp
importpylabasplt
importmatplotlib.tickerasticker
fromastropy.tableimportTable
fromastropyimportlog
###########
# Constants
###########
DATADIR="data"
RED="#e74c3c"
BLUE="#2c3e50"
#########
# Classes
#########
classDataSet(object):
labelcolumn=None
xlim, ylim=None, None
def__init__(self, filename=None):
# Read the data
iffilename==None:
filename=os.path.join(DATADIR, self.prefix, self.prefix+'.csv')
self.table=Table.read(filename, format='ascii')
self.xdata=self.table[self.xcolumn]
self.ydata=self.table[self.ycolumn]
deftrendfit(self):
# Fit the exponential trend
returnnp.polyfit(self.xdata, np.log10(self.ydata), 1)
defplot(self, trendfit=True, title=True):
self.fig=plt.figure(figsize=(8, 5))
self.ax=plt.subplot(111)
self.ax.set_yscale("log")
self.ax.scatter(self.xdata,
self.ydata,
facecolor=RED,
s=70,
linewidth=1,
edgecolor='black')
# Show labels next to the data points
ifself.labelcolumn:
labels=self.table[self.labelcolumn]
foriinrange(len(labels)):
plt.text(self.xdata[i] +0.6, self.ydata[i], labels[i],
ha="left",
va="center",
fontsize=16,
backgroundcolor="#f6f6f6")
iftrendfit:
self.ax.plot(self.xdata, 10**np.polyval(self.trendfit(), self.xdata),
color=BLUE, lw=2, alpha=0.5, zorder=-10)
iftitle:
"""
self.ax.text(0.05, 0.95,
'{0}\n{1}'.format(self.title,
self.get_doubling_text()),
va='top',
transform=self.ax.transAxes,
fontsize=18)
"""
if'ranial'inself.ylabel:
self.ax.text(0.05, 0.95,
"+{:.5f}% per year".format(self.get_annual_increase()),
va='top',
ha='left',
transform=self.ax.transAxes,
fontsize=18)
else:
self.ax.text(0.05, 0.95,
"+{:.0f}% per year".format(self.get_annual_increase()),
va='top',
ha='left',
transform=self.ax.transAxes,
fontsize=18)
self.ax.set_xlabel(self.xlabel, fontsize=18)
self.ax.set_ylabel(self.ylabel, fontsize=18)
ifself.xlim:
self.ax.set_xlim(self.xlim)
ifself.ylim:
self.ax.set_ylim(self.ylim)
self.ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f'))
# Aesthetics
self.ax.spines["right"].set_visible(False)
self.ax.spines["top"].set_visible(False)
self.ax.get_xaxis().tick_bottom()
self.ax.get_yaxis().tick_left()
self.fig.tight_layout()
returnself.fig
defget_doubling_time(self):
"""Returns number of months it takes for the y-axis data to double."""
doubling_time=12*np.log10(2) /self.trendfit()[0]
returndoubling_time
defget_doubling_text(self):
return"double every {:.0f} months".format(self.get_doubling_time())
defget_annual_increase(self):
"""Returns the percentage increase per year."""
annual_fractional_increase=100* (10**self.trendfit()[0]) -100
log.info("{0} increases by {1:.2f} percent each year".format(self.prefix, annual_fractional_increase))
returnannual_fractional_increase
defget_prediction(self):
"""Returns the increase after 22 years."""
myfit=self.trendfit()
predict=10**np.polyval(self.trendfit(), [2000, 2022])
increase=predict[1] /predict[0]
return"{0}: increased {1:.0f}x between 2000 and 2022".format(self.prefix, increase)
classTransistorCountData(DataSet):
title="CPU transistor counts"
prefix="transistor-counts"
xcolumn="year"
xlabel="Year"
ycolumn="transistors"
ylabel="Transistors"
xlim= [1965, 2020]
defplot(self, **kwargs):
super(TransistorCountData, self).plot(**kwargs)
# Annotate the era of multi-core processors
self.ax.plot([2006, 2018], [5e6, 5e6], lw=2.5, color='black')
self.ax.text(2012, 1.7e6, "Multi-core era", fontsize=15, ha="center")
returnself.fig
classDiskDrivePriceData(DataSet):
title="Storage per dollar ratios"
prefix="disk-drive-price"
xcolumn="year"
xlabel="Year"
ycolumn="size_mb"
ylabel="MB per dollar"
xlim= [1999, 2019]
def__init__(self):
super(DiskDrivePriceData, self).__init__()
self.ydata=self.table['size_mb'] /self.table['cost_usd']
classSupercomputerSpeedData(DataSet):
title="Supercomputer speeds"
prefix="fastest-supercomputer"
xcolumn="year"
xlabel="Year"
ycolumn="flops"
ylabel="FLOPS"
xlim= [1991, 2018]
classResearchInternetSpeedData(DataSet):
title="Internet speeds"
prefix="research-internet-speed"
xcolumn="year"
xlabel="Year"
ycolumn="bps"
ylabel="Bits/s"
classStorageBusSpeedData(DataSet):
title="Storage bus speeds"
prefix="storage-bus-speed"
xcolumn="year"
xlabel="Year"
ycolumn="bps"
ylabel="Bits/s"
labelcolumn="name"
xlim= [1980, 2020]
classTelescopePixelCountsData(DataSet):
title="Pixel rates of large optical surveys"
prefix="telescope-pixel-counts"
xcolumn="year"
xlabel="Start of science"
ycolumn="pixels"
ylabel="Pixels/s"
labelcolumn="name"
xlim= [1998, 2026]
def__init__(self):
super(TelescopePixelCountsData, self).__init__()
self.ydata=self.table['pixels'] /self.table['cycle_time']
classTelescopePixelCountsInfraredData(DataSet):
title="Pixel rates of near-infrared surveys"
prefix="telescope-pixel-counts-near-infrared"
xcolumn="year"
xlabel="Start of science"
ycolumn="pixels"
ylabel="Pixels/s"
labelcolumn="name"
#xlim = [1998, 2025]
def__init__(self):
super(TelescopePixelCountsInfraredData, self).__init__()
self.ydata=self.table['pixels'] /self.table['cycle_time']
classSpacePhotometryData(DataSet):
title="Pixel rates of NASA's photometry missions"
prefix="space-photometry-missions"
xcolumn="year"
xlabel="Launch"
ycolumn="pixels_per_second"
ylabel="Pixels/s"
labelcolumn="name"
xlim= [2006, 2029]
classIAUMembers(DataSet):
title="Number of IAU members"
prefix="iau-members"
xcolumn="year"
xlabel="Year"
ycolumn="iau_members"
ylabel="Number of IAU Members"
classCranialCapacityData(DataSet):
title="The cranial capacity of humans"
prefix="cranial-capacity"
xcolumn="year"
xlabel="Million years BC"
ycolumn="brain_cc"
ylabel="Cranial capacity [cm³]"
xlim= [-3.5, 0.1]
def__init__(self):
super(CranialCapacityData, self).__init__()
self.xdata=self.table['year'] /1e6
defget_doubling_time(self):
"""Returns number of months it takes for the y-axis data to double."""
doubling_time=np.log10(2) /self.trendfit()[0]
returndoubling_time
defget_doubling_text(self):
return"doubles every {:.1f} million years".format(self.get_doubling_time())
defget_annual_increase(self):
"""Returns the percentage increase per year."""
annual_fractional_increase=100* (10**self.trendfit()[0]) -100
annual_fractional_increase=annual_fractional_increase/1e6
log.info("{0} increases by {1:.2f} percent each year".format(self.prefix, annual_fractional_increase))
returnannual_fractional_increase
if__name__=='__main__':
"""Create graphs for all datasets in the repository."""
DESTINATION_DIR='graphs'
datasets= [DiskDrivePriceData(),
SupercomputerSpeedData(),
ResearchInternetSpeedData(),
StorageBusSpeedData(),
TelescopePixelCountsData(),
TelescopePixelCountsInfraredData(),
SpacePhotometryData(),
IAUMembers(),
TransistorCountData(),
CranialCapacityData()]
fordsindatasets:
forextensionin ['png', 'pdf']:
output_filename=os.path.join(DESTINATION_DIR,
ds.prefix+'.'+extension)
log.info("Writing {}".format(output_filename))
ds.plot(title=True).savefig(output_filename, dpi=200)
print("{} {}".format(ds.title, ds.get_doubling_text()))
#print(ds.get_prediction())