def iter_sites_w_company(directory_or_file):
contents = iter_files_content(directory_or_file)
for swc in imap(SITES_W_COMPANY_PARSER, contents):
ranks = map(operator.attrgetter('rank'), swc.ranking)
index = pandas.DatetimeIndex(map(operator.attrgetter('tstamp'),
swc.ranking))
ts = pandas.Series(ranks, index=index)
tstamp = pandas.Timestamp(swc.tstamp)
yield (swc.site, ts, swc.company, swc.code, tstamp)
def make_fr_per_date_plot(companies, plot_file=None):
contents = iter_files_content(companies)
d = collections.defaultdict(list)
min_date = datetime.date(2011, 3, 1)
months = set()
for c in imap(FLATTENED_PARSER, contents):
if c.tstamp >= min_date:
d[c.code].append(matplotlib.dates.date2num(c.tstamp))
months.add(datetime.date(c.tstamp.year, c.tstamp.month, 1))
months = sorted(months)
right_border = months[-1] + datetime.timedelta(31)
right_border = datetime.date(right_border.year, right_border.month, 1)
months.append(right_border)
fig = plt.figure(figsize=(4*1.4, 3*1.4))
ax = fig.add_subplot(111)
ax.hist(d.values(), label=map(str.title, d.keys()),
bins=matplotlib.dates.date2num(months))
ax.set_xlim(matplotlib.dates.date2num(months[0]),
matplotlib.dates.date2num(months[-1]))
ax.legend()
ax.xaxis.set_major_locator(
matplotlib.dates.MonthLocator(bymonthday=15, interval=2)
)
ax.xaxis.set_major_formatter(
matplotlib.ticker.FuncFormatter(
lambda d, _: matplotlib.dates.num2date(d).strftime('%B %Y')
)
)
fig.autofmt_xdate()
ax.set_ylabel('Number of Funding Rounds')
ax.grid(True, axis='y')
if plot_file:
fig.savefig(plot_file)
return fig