def draw_graph(dates, counts):
###########################################################
# Drawing takes place here.
pylab.figure(1)
ax = pylab.subplot(111)
pylab.plot_date(dates,
counts,
color='r',
linestyle='-',
marker='o',
markersize=3)
ax.xaxis.set_major_formatter(pylab.DateFormatter('%Y'))
ax.xaxis.set_major_locator(pylab.YearLocator())
ax.xaxis.set_minor_locator(pylab.MonthLocator())
ax.set_xlim((dates[0] - 92, dates[len(dates) - 1] + 92))
ax.yaxis.set_major_formatter(pylab.FormatStrFormatter('%d'))
pylab.ylabel('Total # of Public DAV Servers')
lastdate = datetime.datetime.fromordinal(dates[len(dates) -
1]).strftime("%B %Y")
pylab.xlabel("Data as of " + lastdate)
pylab.title('Security Space Survey of\nPublic Subversion DAV Servers')
# End drawing
###########################################################
png = open(OUTPUT_FILE, 'w')
pylab.savefig(png)
png.close()
os.rename(OUTPUT_FILE, OUTPUT_FILE + ".tmp.png")
try:
im = Image.open(OUTPUT_FILE + ".tmp.png", 'r')
(width, height) = im.size
print("Original size: %d x %d pixels" % (width, height))
scale = float(OUTPUT_IMAGE_WIDTH) / float(width)
width = OUTPUT_IMAGE_WIDTH
height = int(float(height) * scale)
print("Final size: %d x %d pixels" % (width, height))
im = im.resize((width, height), Image.ANTIALIAS)
im.save(OUTPUT_FILE, im.format)
os.unlink(OUTPUT_FILE + ".tmp.png")
except Exception as e:
sys.stderr.write("Error attempting to resize the graphic: %s\n" %
(str(e)))
os.rename(OUTPUT_FILE + ".tmp.png", OUTPUT_FILE)
raise
pylab.close()
def plot_rh(self, df_nsa_aotd, a):
outs = []
returns = {}
i = 0
for e, meas in enumerate(df_nsa_aotd.data):
# test and skip bad quality values
if meas.based_on_file_pops in self.inst_perform_pops.fname.values:
qualtest = self.inst_perform_pops[self.inst_perform_pops.fname
== meas.based_on_file_pops]
if qualtest.quality.loc[0] == 'bad':
print('bad quality measurement skiped ({})'.format(
meas.based_on_file_pops))
continue
# resample
# if resample:
# meas = meas.resample(datetime=resample).mean()
colorbar = False
values = meas.relative_humidity.to_pandas()
values[values < 0] = np.nan
values[values > 110] = np.nan
a, lc, cm = plt_tools.plot.plot_gradiant_color(
meas.time.values + np.timedelta64(self.timezone, 'h'),
meas.altitude.values,
values,
ax=a,
colorbar=colorbar)
out = dict(lc=lc, cm=cm)
out['mean'] = float(meas.relative_humidity.median().values)
out['std'] = float(meas.relative_humidity.std().values)
out['cmax'] = meas.relative_humidity.max()
out['cmin'] = meas.relative_humidity.min(
) # out['mean'] - (2 * out['std'])
# print(out['cmin'])
out['alt_max'] = meas.altitude.max()
outs.append(out)
i += 1
cmax = max([out['cmax'] for out in outs])
cmin = min([out['cmin'] for out in outs])
returns['clim'] = (cmin, cmax)
lcs = [out['lc'] for out in outs]
# returns['zobjects'] = lcs
a.zobjects = lcs
for lc in lcs:
lc.set_clim(cmin, cmax)
lc.set_cmap(self.cm_meins)
lc.set_linewidth(self.lw_pgc)
a.set_ylim(-10, max([out['alt_max'] for out in outs]) * 1.2)
a.xaxis.set_major_formatter(plt.DateFormatter("%H:%M:%S"))
# a.set_xlabel('')
f = a.get_figure()
f.autofmt_xdate()
a.set_ylabel('Altitude (m)')
# colorbar
cb, cax = plt_tools.colorbar.colorbar_axis_split_off(lc, a)
cax.set_ylabel('RH (%)', labelpad=0.5)
cb.locator = plt.MaxNLocator(5, prune='both')
cb.update_ticks()
a.cax = cax
returns['a'] = a
# returns['a'].cax.set_label('buba')
return returns
def plot_pops_NC(self, df_nsa_aotd, a, resample='1min'):
outs = []
returns = {}
i = 0
for e, meas in enumerate(df_nsa_aotd.data):
# test and skip bad quality values
if meas.based_on_file_pops in self.inst_perform_pops.fname.values:
qualtest = self.inst_perform_pops[self.inst_perform_pops.fname
== meas.based_on_file_pops]
if qualtest.quality.loc[0] == 'bad':
self.send_message(
'bad quality measurement skiped ({})'.format(
meas.based_on_file_pops))
continue
# if resample:
# meas = meas.resample(datetime=resample).mean()
colorbar = False
a, lc, cm = plt_tools.plot.plot_gradiant_color(
meas.time.values + np.timedelta64(self.timezone, 'h'),
meas.altitude.values,
meas.pops_particle_number_concentration.values,
ax=a,
colorbar=colorbar)
out = dict(lc=lc, cm=cm)
out['mean'] = float(
meas.pops_particle_number_concentration.median().values)
out['std'] = float(
meas.pops_particle_number_concentration.std().values)
# out['cmax'] = out['mean'] + (1 * out['std'])
# meast = meas.resample(datetime = '10min').mean()
# trying to get rid of those plumes close to the ground
meast = meas.copy(deep=True)
meast.pops_particle_number_concentration[
meast.altitude < 20] = np.nan
meast.pops_particle_number_concentration[np.isnan(
meast.altitude)] = np.nan
out['cmax'] = meast.pops_particle_number_concentration.max()
out['cmin'] = meast.pops_particle_number_concentration.min()
out['alt_max'] = meas.altitude.max()
outs.append(out)
i += 1
cmax = max([out['cmax'] for out in outs])
cmin = min([out['cmin'] for out in outs])
returns['clim'] = (cmin, cmax)
lcs = [out['lc'] for out in outs]
# returns['zobjects'] = lcs
a.zobjects = lcs
for lc in lcs:
lc.set_clim(cmin, cmax)
# lc.set_clim(0,25)
lc.set_cmap(self.cm_meins)
lc.set_linewidth(self.lw_pgc)
a.set_ylim(-10, max([out['alt_max'] for out in outs]) * 1.2)
a.xaxis.set_major_formatter(plt.DateFormatter("%H:%M:%S"))
# a.set_xlabel('')
f = a.get_figure()
f.autofmt_xdate()
a.set_ylabel('Altitude (m)')
# colorbar
cb, cax = plt_tools.colorbar.colorbar_axis_split_off(lc, a)
# self.lc = lc
# self.cb, self.cax = cb, cax
a.cax = cax
cb.locator = plt.MaxNLocator(5, prune='both')
cb.update_ticks()
cax.set_ylabel('NC$_{POPS}$ (#/cm$^3$)', labelpad=0.5)
returns['a'] = a
return returns