def applyPlotOptions(fig, ax, options):
if options.xlim: xlim(float(options.xlim.split(",")[0]), float(options.xlim.split(",")[1]))
if options.xticks:
vals = options.xticks.split(",")
if len(vals)==1: ax.tick_params(axis='x', which='major', labelsize=float(vals[0]))
elif len(vals)==4: xticks(arange(float(vals[0]), float(vals[1]), float(vals[2])), size=float(vals[3]))
else:
print "Error: ticks must be given as one float (<SIZE>) or four floats (<MIN>,<MAX>,<STEP>,<SIZE>)"
sys.exit()
if options.xtime1: ax.xaxis.set_major_formatter(ff(m2hm1))
if options.xtime2: ax.xaxis.set_major_formatter(ff(m2hm2))
if options.xgrid: ax.xaxis.grid(True)
if options.xlabel: xlabel(options.xlabel, size=options.xlabelsize)
if options.ylim: ylim(float(options.ylim.split(",")[0]), float(options.ylim.split(",")[1]))
if options.yticks:
vals = options.yticks.split(",")
if len(vals)==1: ax.tick_params(axis='y', which='major', labelsize=float(vals[0]))
elif len(vals)==4: yticks(arange(float(vals[0]), float(vals[1]), float(vals[2])), size=float(vals[3]))
else:
print "Error: ticks must be given as one float (<SIZE>) or four floats (<MIN>,<MAX>,<STEP>,<SIZE>)"
sys.exit()
if options.ytime1: ax.yaxis.set_major_formatter(ff(m2hm1))
if options.ytime2: ax.yaxis.set_major_formatter(ff(m2hm2))
if options.ygrid: ax.yaxis.grid(True)
if options.ylabel: ylabel(options.ylabel, size=options.ylabelsize)
if options.title: title(options.title, size=options.titlesize)
if options.adjust:
vals = options.adjust.split(",")
if len(vals)==2: fig.subplots_adjust(left=float(vals[0]), bottom=float(vals[1]))
elif len(vals)==4: fig.subplots_adjust(left=float(vals[0]), bottom=float(vals[1]), right=float(vals[2]), top=float(vals[3]))
else:
print "Error: adjust must be given as two floats (<LEFT>,<BOTTOM>) or four floats (<LEFT>,<BOTTOM>,<RIGHT>,<TOP>)"
sys.exit()
def show_figure(self):
# some cosmetics on the figure
current_axes = self.fig.gca()
current_axes.xaxis.set_minor_locator(matplotlib.dates.HourLocator())
current_axes.xaxis.set_major_locator(matplotlib.dates.DayLocator())
current_axes.xaxis.set_major_formatter(ff(m2yyyymmdd))
current_axes.xaxis.set_minor_formatter(ff(m2hm))
current_axes.set_xlabel("UTC Time")
current_axes.set_ylabel("Signal Strength")
for label in current_axes.xaxis.get_majorticklabels():
label.set_fontsize(8)
label.set_rotation(30) # 'vertical')
# label.set_horizontalalignment='left'
for label in current_axes.xaxis.get_minorticklabels():
label.set_fontsize(12 if len(self.daysList.keys()) == 1 else 8)
# specific drawings for linked to each sid_file: flares and sunrise/sunset
bottom_max, top_max = current_axes.get_ylim()
for sid_file in self.sid_files:
# for each flare from NOAA, draw the lines and box with flares intensity
for eventName, BeginTime, MaxTime, EndTime, Particulars in sid_file.XRAlist:
self.graph.vlines([BeginTime, MaxTime, EndTime],
0,
self.max_data,
color=['g', 'r', 'y'],
linestyles='dotted')
self.graph.text(MaxTime,
self.max_data +
(top_max - self.max_data) / 4.0,
Particulars,
horizontalalignment='center',
bbox={
'facecolor': 'w',
'alpha': 0.5,
'fill': True
})
# draw the rectangles for rising and setting of the sun with astronomical twilight
if sid_file.rising < sid_file.setting:
self.graph.axvspan(sid_file.startTime,
sid_file.rising.datetime(),
facecolor='blue',
alpha=0.1)
self.graph.axvspan(sid_file.setting.datetime(),
max(sid_file.timestamp),
facecolor='blue',
alpha=0.1)
else:
self.graph.axvspan(sid_file.setting.datetime(),
sid_file.rising.datetime(),
facecolor='blue',
alpha=0.1)
self.canvas.show()
def show_figure(self):
# some cosmetics on the figure
current_axes = self.fig.gca()
current_axes.xaxis.set_minor_locator(matplotlib.dates.HourLocator())
current_axes.xaxis.set_major_locator(matplotlib.dates.DayLocator())
current_axes.xaxis.set_major_formatter(ff(m2yyyymmdd))
current_axes.xaxis.set_minor_formatter(ff(m2hm))
current_axes.set_xlabel("UTC Time")
current_axes.set_ylabel("Signal Strength")
for label in current_axes.xaxis.get_majorticklabels():
label.set_fontsize(8)
label.set_rotation(30) # 'vertical')
# label.set_horizontalalignment='left'
for label in current_axes.xaxis.get_minorticklabels():
label.set_fontsize(12 if len(self.daysList.keys()) == 1 else 8)
# specific drawings for linked to each sid_file: flares and sunrise/sunset
bottom_max, top_max = current_axes.get_ylim()
for sid_file in self.sid_files:
# for each flare from NOAA, draw the lines and box with flares intensity
for eventName, BeginTime, MaxTime, EndTime, Particulars in sid_file.XRAlist:
self.graph.vlines([BeginTime, MaxTime, EndTime], 0, self.max_data,
color=['g', 'r', 'y'], linestyles='dotted')
self.graph.text(MaxTime, self.max_data + (top_max - self.max_data) / 4.0,
Particulars, horizontalalignment='center',
bbox={'facecolor': 'w', 'alpha': 0.5, 'fill': True})
# draw the rectangles for rising and setting of the sun with astronomical twilight
if sid_file.rising < sid_file.setting:
self.graph.axvspan(sid_file.startTime, sid_file.rising.datetime(),
facecolor='blue', alpha=0.1)
self.graph.axvspan(sid_file.setting.datetime(), max(sid_file.timestamp),
facecolor='blue', alpha=0.1)
else:
self.graph.axvspan(sid_file.setting.datetime(), sid_file.rising.datetime(),
facecolor='blue', alpha=0.1)
self.canvas.show()
def plot_probability(times, smoothfit=True):
# dates is a list of strings where each element takes form "mm yy"
times = [roundval(x) for x in times]
# Convert to unique list
time_count = Counter(times)
time_count = sorted(time_count.items())
times = [x[0] for x in time_count]
quantity = np.array([float(x[1]) for x in time_count])
quantity = quantity / sum(quantity)
if smoothfit:
times_new = np.linspace(min(times), max(times), 300)
quantity = spline(times, quantity, times_new)
times = times_new.tolist()
fig, ax = plt.subplots()
plt.plot(
times,
quantity,
'b',
linewidth=2.0,
)
# Remove some x-labels to reduce clutter in the plot
label_spacing = 60 # 1 label per hour
for ndx, item in enumerate(times):
if ndx % label_spacing != 0:
times[ndx] = ''
ax.set_xlabel('Time')
ax.set_xticklabels(times)
# Rotate x axis labels slightly
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(30)
ax.set_xlim(0, 24 * 60) # 0 to 24 hours
ax.xaxis.set_major_locator(pylab.MultipleLocator(60))
ax.xaxis.set_major_formatter(ff(min_to_hourmin))
ax.set_ylabel('Probability')
ax.set_ylim(0, max(quantity))
titlestr = 'Probability Over Time of ' + config.MAIL_TO_ANALYSE + ' Mail'
plt.title(titlestr)
plt.draw()
def plot_dates_sent(dates, times):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot_date(dates, times)
# Rotate x axis labels slightly
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(30)
ax.set_ylim(0, 24 * 60) # 0 to 24 hours
ax.yaxis.set_major_locator(pylab.MultipleLocator(60))
ax.yaxis.set_major_formatter(ff(min_to_hourmin))
plt.title('Times of Message Deliveries')
plt.xlabel('Date')
plt.ylabel('Delivery Time (24hr)')
plt.draw()
def plot_dates_sent(dates, times):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot_date(dates, times)
# Rotate x axis labels slightly
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(30)
ax.set_ylim(0,24*60) # 0 to 24 hours
ax.yaxis.set_major_locator(pylab.MultipleLocator(60))
ax.yaxis.set_major_formatter(ff(min_to_hourmin))
plt.title('Times of Message Deliveries')
plt.xlabel('Date')
plt.ylabel('Delivery Time (24hr)')
plt.draw()
def plot_timestamps(timestamps: pd.Series, user: GHUser, timezone=None):
"""Plot datetimes with matplotlib."""
if timezone is not None and timestamps is not None:
timestamps = timestamps.dt.tz_convert(timezone)
# color points distant from midday (1pm)
dist_from_13h = abs(timestamps.dt.hour - 13)
times = [dt2m(x) for x in timestamps]
times = [x + 1440 if x < 240 else x for x in times] # Looping
dates = [x.date() for x in timestamps.dt.to_pydatetime()]
fig, ax = plt.subplots(figsize=(8, 6))
ax.set_ylim(240, 1440 + 240) # 4am
ax.set_ylabel(timezone or 'UTC' + f' ({user.location})')
ax.yaxis.set_major_locator(ticker.MultipleLocator(60))
# ax.yaxis.set_minor_locator(ticker.MultipleLocator(15))
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.yaxis.set_major_formatter(ff(m2hm))
ax.yaxis.grid(color='k', linestyle='--', linewidth=0.5, alpha=.3)
ax.set_title(user.username)
ax.set_xlim([min(dates)-timedelta(days=1), max(dates) + timedelta(days=1)])
# ax.scatter(dates, times, c=dist_from_13h, cmap=discrete_cmap(24, 'autumn_r'))
# sns.lineplot(date2num(dates), times, ci=99, alpha =0.3)
sns.scatterplot(date2num(dates), times, alpha=0.7)
weekends = get_weekends(dates)
for start, end in weekends:
ax.axvspan(start - timedelta(hours=12), end + timedelta(hours=12), alpha=0.05, color='gray')
plt.autoscale(True, axis='x', tight=True)
plt.gcf().autofmt_xdate()
plot_filename = create_plot_filename(user.username)
fig.savefig(to_local_path(plot_filename))
add_plot(user, plot_filename)
return plot_filename
def plot_probability(times, smoothfit=True):
# dates is a list of strings where each element takes form "mm yy"
times = [roundval(x) for x in times]
# Convert to unique list
time_count = Counter(times)
time_count = sorted(time_count.items())
times = [x[0] for x in time_count]
quantity = np.array([float(x[1]) for x in time_count])
quantity = quantity/sum(quantity)
if smoothfit:
times_new = np.linspace(min(times), max(times), 300)
quantity = spline(times,quantity,times_new)
times = times_new.tolist()
fig, ax = plt.subplots()
plt.plot(times, quantity, 'b', linewidth=2.0, )
# Remove some x-labels to reduce clutter in the plot
label_spacing = 60 # 1 label per hour
for ndx, item in enumerate(times):
if ndx%label_spacing != 0:
times[ndx] = ''
ax.set_xlabel('Time')
ax.set_xticklabels(times)
# Rotate x axis labels slightly
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(30)
ax.set_xlim(0,24*60) # 0 to 24 hours
ax.xaxis.set_major_locator(pylab.MultipleLocator(60))
ax.xaxis.set_major_formatter(ff(min_to_hourmin))
ax.set_ylabel('Probability')
ax.set_ylim(0, max(quantity))
titlestr = 'Probability Over Time of ' + config.MAIL_TO_ANALYSE + ' Mail'
plt.title(titlestr)
plt.draw()
def f(activity, vehicle, dropoff, dw, dw_start, dw_end, tw, tw_start, tw_end,
far_dc, personel_daily_cost, cargo_daily_cost, lcv_daily_cost,
truck_daily_cost, cargo_emission, lcv_emission, truck_emission):
#first_leg_time = math.ceil(b.avg_first_leg*60/50000)
first_leg_time = 6000 * 60 / 20000
#tour_time = math.ceil(b.avg_tour*60/20000)*6
tour_time = time_sim_results[vehicle][activity] * (
200 / vehicle_sim_results[vehicle][activity])
time_dropoff = dropoff * (200 / vehicle_sim_results[vehicle][activity])
#return_leg_time = math.ceil(b.avg_return_leg*60/50000)
return_leg_time = 6000 * 60 / 20000
time_rungis = 30000 * 60 / 70000
# Print the type of vehicle
print('The fleet is composed of ' +
str(vehicle_sim_results[vehicle][activity]) + ' ' +
list_vehicle[vehicle] + ".")
print('The vehicles have a capacity of ' +
str(vehicle_capacity[vehicle][activity]) +
' client(s), each vehicle delivers in average ' +
str(round(200 / vehicle_sim_results[vehicle][activity], 1)) +
" client(s), each tour has an average length of " +
str(distance_sim_results[vehicle][activity]) + " km.")
print('The average driving time between two deliveries is ' +
str(time_sim_results[vehicle][activity]) + " min.")
#print('Each vehicle delivers in average ' + str(round(hot_fix_clients[clients]/vehicle_sim_results[clients][capacity])) + " clients.")
#print("Therefore, average total driving time is " + str(tour_time) + " min." )
city_line_width = 3
line_width = 3
if far_dc:
fig2, ax2 = plt.subplots()
"""ax2.bar(0, 30, bottom=b.avg_first_leg, color = r_color)
ax2.bar(1, 0, bottom=b.avg_tour, color = r_color)
ax2.bar(2, 30, bottom=b.avg_return_leg+b.avg_first_leg + b.avg_tour, color = r_color)"""
ax2.bar(0, first_leg_time, bottom=dw_start, color=driving_color)
ax2.bar(0,
time_rungis,
bottom=dw_start + first_leg_time,
color=rungis_color,
label="Rungis")
ax2.axhline(y=dw_start + first_leg_time + time_rungis,
linewidth=city_line_width,
color='#CDC7C6',
linestyle='-',
label="In city",
zorder=2)
ax2.bar(1,
tour_time,
bottom=dw_start + first_leg_time + time_rungis,
color=driving_color,
label="Driving")
ax2.bar(1,
time_dropoff,
bottom=dw_start + first_leg_time + time_rungis + tour_time,
color=delivering_color,
label="Delivering ")
ax2.axhline(y=dw_start + first_leg_time + time_rungis + tour_time +
time_dropoff,
color='#CDC7C6',
linestyle='-',
linewidth=city_line_width,
zorder=2)
ax2.bar(2,
return_leg_time,
bottom=dw_start + first_leg_time + time_rungis + tour_time +
time_dropoff,
color=driving_color)
ax2.bar(2,
time_rungis,
bottom=dw_start + first_leg_time + time_rungis + tour_time +
time_dropoff + return_leg_time,
color=rungis_color)
#ax2.set_ylim(dw_start-100, dw_start + first_leg_time + time_rungis + tour_time + time_dropoff + return_leg_time + time_rungis + 100)
ax2.set_ylim(dw_start - 100, dw_end + 100)
else:
fig2, ax2 = plt.subplots()
ax2.bar(0, first_leg_time, bottom=dw_start, color=driving_color)
ax2.axhline(y=dw_start + first_leg_time,
color='#CDC7C6',
linestyle='-',
linewidth=city_line_width,
zorder=2)
ax2.bar(1,
tour_time,
bottom=first_leg_time + dw_start,
color=driving_color,
label="Driving")
ax2.bar(1,
time_dropoff,
bottom=first_leg_time + tour_time + dw_start,
color=delivering_color,
label="Delivering ")
ax2.axhline(y=dw_start + first_leg_time + tour_time + time_dropoff,
color='#CDC7C6',
linestyle='-',
label="Within city",
linewidth=city_line_width,
zorder=2)
ax2.bar(2,
return_leg_time,
bottom=first_leg_time + tour_time + dw_start + time_dropoff,
color=driving_color)
#ax2.set_ylim(dw_start-100, dw_start + first_leg_time + time_rungis + tour_time + time_dropoff + return_leg_time + time_rungis + 100)
if dw_start + first_leg_time + time_rungis + tour_time + time_dropoff + return_leg_time + time_rungis + 100 > dw_end + 100:
ax2.set_ylim(
dw_start - 100, dw_start + first_leg_time + time_rungis +
tour_time + time_dropoff + return_leg_time + time_rungis + 100)
else:
ax2.set_ylim(dw_start - 100, dw_end + 100)
ax2.yaxis.set_major_formatter(ff(m2hm))
# Working day
dw_color = "#008240"
if dw:
ax2.axhline(y=dw_start,
color=dw_color,
linestyle='-',
label="Time shift",
linewidth=line_width,
zorder=2)
ax2.axhline(y=dw_end,
color=dw_color,
linestyle='-',
linewidth=line_width,
zorder=2)
# Time window from clients or city
tw_color = "#A0211B"
if tw:
ax2.axhline(y=tw_start,
color=tw_color,
linestyle='--',
label="Time Window",
linewidth=line_width,
zorder=2)
ax2.axhline(y=tw_end,
color=tw_color,
linestyle='--',
linewidth=line_width,
zorder=2)
ax2.set_title("Average working shift of one " + list_vehicle[vehicle] +
" delivering " + list_activity[activity])
ax2.set_xticklabels(["", "", "First leg", "", "Tour", "", "Return leg"])
ax2.tick_params(bottom=False)
ax2.legend(bbox_to_anchor=(1.05, 1), loc=2)
fig2.set_size_inches(15.5, 9.5)
# Plot cost per delivery
cargo_cost = vehicle_sim_results[0][activity] * (personel_daily_cost +
cargo_daily_cost)
lcv_cost = vehicle_sim_results[1][activity] * (personel_daily_cost +
lcv_daily_cost)
truck_cost = vehicle_sim_results[2][activity] * (personel_daily_cost +
truck_daily_cost)
fig3, ax3 = plt.subplots()
ax3.bar(0, cargo_cost, color=driving_color)
ax3.bar(1, lcv_cost, color=driving_color)
ax3.bar(2, truck_cost, color=driving_color)
ax3.set_title("Total cost of delivery for " + list_activity[activity] +
" (in €)")
ax3.set_xticklabels([
"", "",
str(vehicle_sim_results[0][activity]) + " cargo bikes", "",
str(vehicle_sim_results[1][activity]) + " LCVs", "",
str(vehicle_sim_results[2][activity]) + " trucks"
])
ax3.yaxis.set_major_formatter(
matplotlib.ticker.FormatStrFormatter('€%1.2f'))
fig3.set_size_inches(13.55, 8)
# Plot C02 emissions
if far_dc:
fig4, ax4 = plt.subplots()
ax4.bar(0,
cargo_emission * (distance_sim_results[0][activity] *
vehicle_sim_results[0][activity]),
color=driving_color,
label="Tour")
ax4.bar(0,
cargo_emission * 15 * vehicle_sim_results[0][activity],
bottom=cargo_emission * (distance_sim_results[0][activity] *
vehicle_sim_results[0][activity]),
color=rungis_color,
label="Rungis")
ax4.bar(1,
lcv_emission * (distance_sim_results[1][activity] *
vehicle_sim_results[1][activity]),
color=driving_color)
ax4.bar(1,
lcv_emission * 15 * vehicle_sim_results[1][activity],
bottom=lcv_emission * (distance_sim_results[1][activity] *
vehicle_sim_results[1][activity]),
color=rungis_color)
ax4.bar(2,
truck_emission * (distance_sim_results[2][activity] *
vehicle_sim_results[2][activity]),
color=driving_color)
ax4.bar(2,
truck_emission * 15 * vehicle_sim_results[2][activity],
bottom=truck_emission * (distance_sim_results[2][activity] *
vehicle_sim_results[2][activity]),
color=rungis_color)
else:
fig4, ax4 = plt.subplots()
ax4.bar(0,
cargo_emission * (distance_sim_results[0][activity] *
vehicle_sim_results[0][activity]),
color=driving_color,
label="Tour")
ax4.bar(1,
lcv_emission * (distance_sim_results[1][activity] *
vehicle_sim_results[1][activity]),
color=driving_color)
ax4.bar(2,
truck_emission * (distance_sim_results[2][activity] *
vehicle_sim_results[2][activity]),
color=driving_color)
ax4.set_title("Total amount of C02 emissions to deliver " +
list_activity[activity] + " (in g of CO2)")
ax4.set_xticklabels([
"", "",
str(vehicle_sim_results[0][activity]) + " cargo bikes", "",
str(vehicle_sim_results[1][activity]) + " LCVs", "",
str(vehicle_sim_results[2][activity]) + " trucks"
])
ax4.legend(bbox_to_anchor=(1.05, 1), loc=2)
fig4.set_size_inches(13.95, 8)
h = int(x/60)
m = int(x%60)
return '%(h)02d:%(m)02d' % {'h':h,'m':m}
fig, ax = plt.subplots(figsize=(12,6))
dates = new_table.index
dates = dates.date
dates = dates.tolist()
wake_time_as_y = new_table['wake_time']
wake_time_as_y = map(lambda x:dt2m(x), wake_time_as_y)
ax.plot_date(dates, wake_time_as_y, color = 'red', label = 'wake_time')
ax.set_xlim([dates[0],dates[len(dates)-1]])
ax.xaxis.set_major_locator(mdates.AutoDateLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
fmt = mdates.DateFormatter('%Y/%m')
ax.xaxis.set_major_formatter(fmt)
ax.xaxis.grid(color='#ffcf06', linestyle=':', linewidth=0.5)
ax.set_ylim(180,900)
ax.yaxis.set_major_locator(MultipleLocator(30))
ax.yaxis.set_major_formatter(ff(m2hm))
ax.yaxis.grid(color='black', linestyle=':', linewidth=0.5)
plt.legend(loc='best')
plt.xlabel('Date' )
plt.ylabel('Hour (24hr)' )
plt.title('wake time figure')
plt.show()
def plot_filelist(self, filelist, showPlot = True, eMail=None, pdf=None, web=False, config=None):
"""Read the files in the filelist parameters.
Each data are combine in one plot.
That plot can be displayed or not (showPlot), sent by email (eMail provided), saved as pdf (pdf provided).
Connection for the given days to NOAA website is possible (web) in order to draw vetical lines for XRA data."""
emailText = []
Tstamp = lambda HHMM: datetime.datetime(year=int(day[:4]), month=int(day[4:6]), day=int(day[6:8]),
hour=int(HHMM[:2]), minute=int(HHMM[2:]))
## Sunrise and sunset shade
#sun_rise = 6.0
#sun_set = 18.0
#plt.axvspan(0.0, sun_rise, facecolor='blue', alpha=0.2)
#plt.axvspan(sun_set, 24.0, facecolor='blue', alpha=0.2)
if type(filelist) is str:
if filelist.find(',') >= 0: # file1,file2,...,fileN given as script argument
filelist = filelist.split(",")
else:
filelist = (filelist, )
filenames = []
# use glob for one or more files
filenames.extend([a for a in itertools.chain.from_iterable([glob.glob(f) for f in filelist])])
#print (filenames)
# plot's figure and axis
fig = plt.figure()
current_axes = fig.gca()
current_axes.xaxis.set_minor_locator(matplotlib.dates.HourLocator())
current_axes.xaxis.set_major_locator(matplotlib.dates.DayLocator())
current_axes.xaxis.set_major_formatter(ff(self.m2yyyymmdd))
current_axes.xaxis.set_minor_formatter(ff(self.m2hm))
current_axes.set_xlabel("UTC Time")
current_axes.set_ylabel("Signal Strength")
## Get data from files
maxData, data_length = -1, -1; # impossible values
XRAlist = [] # flare list from NOAA
daysList = set() # date of NOAA's pages already retrieved, prevent multiple fetch
figTitle = [] # list of file names (w/o path and extension) as figure's title
# one color per station
colorList = "brgcmy"
colorStation = {}
colorIdx = 0
time.clock()
for filename in sorted(filenames):
figTitle.append(os.path.basename(filename)[:-4]) # extension .csv assumed
sFile = SidFile(filename)
for station in sFile.stations:
# Does this station already have a color? if not, reserve one
if station not in colorStation:
colorStation[station] = colorList[colorIdx % len(colorList)] + '-' # format like 'b-'
colorIdx += 1
# Add points to the plot
plt.plot_date(sFile.timestamp, sFile.get_station_data(station), colorStation[station])
# Extra housekeeping
maxData = max(max(sFile.get_station_data(station)), maxData) # maxData will be used later to put the XRA labels up
#msg = str(len(sFile.get_station_data(station))) + " points plotted after reading " + os.path.basename(filename)
msg = "[{}] {} points plotted after reading {}".format(station, len(sFile.get_station_data(station)), os.path.basename(filename))
print (msg)
emailText.append(msg)
if web and sFile.startTime not in daysList:
# get the XRA data from NOAA website to draw corresponding lines on the plot
# fetch that day's flares on NOAA as not previously accessed
day = sFile.sid_params["utc_starttime"][:10].replace("-","")
#NOAA_URL = 'http://www.swpc.noaa.gov/ftpdir/warehouse/%s/%s_events/%sevents.txt' % (day[:4], day[:4], day)
#ftp://ftp.swpc.noaa.gov/pub/indices/events/20141030events.txt
NOAA_URL = 'ftp://ftp.swpc.noaa.gov/pub/indices/events/%sevents.txt' % (day)
response = None
if sys.version[0]<'3': # python 2.7 vs. Python 3.3
try:
response = urllib2.urlopen(NOAA_URL)
except urllib2.HTTPError as err:
print (err,"\n",NOAA_URL)
else:
try:
response = urllib.request.urlopen(NOAA_URL)
except urllib.error.HTTPError as err:
print (err,"\n",NOAA_URL)
lastXRAlen = len(XRAlist) # save temporarly current number of XRA events in memory
if response:
for webline in response.read().splitlines():
if sys.version[0]>='3': webline = str(webline, 'utf-8') # Python 3: cast bytes to str
fields = webline.split()
if len(fields) >= 9 and not fields[0].startswith("#"):
if fields[1] == '+': fields.remove('+')
if fields[6] in ('XRA', ): # maybe other event types could be of interrest
# eventName, BeginTime, MaxTime, EndTime, Particulars
msg = fields[0]+" "+fields[1]+" "+fields[2]+" "+fields[3]+" "+fields[8]
emailText.append(msg)
print (msg)
try:
btime = Tstamp(fields[1]) # 'try' necessary as few occurences of --:-- instead of HH:MM exist
except:
pass
try:
mtime = Tstamp(fields[2])
except:
mtime = btime
try:
etime = Tstamp(fields[3])
except:
etime = mtime
XRAlist.append( (fields[0], btime, mtime, etime, fields[8]) ) # as a tuple
msg = str(len(XRAlist) - lastXRAlen) + " XRA events recorded by NOAA on " + day
emailText.append(msg)
print (msg)
# keep track of the days
daysList.add(sFile.startTime)
print ("All files read in", time.clock(), "sec.")
if web: # add the lines marking the retrieved flares from NOAA
alternate = 0
for eventName, BeginTime, MaxTime, EndTime, Particulars in XRAlist:
plt.vlines( [BeginTime, MaxTime, EndTime], 0, maxData,
color=['g','r','y'], linestyles='dotted')
plt.text(MaxTime, alternate * maxData, Particulars, horizontalalignment='center',
bbox=dict(fill=True, alpha=0.5, facecolor='w'))
alternate = 0 if alternate==1 else 1
# plot/page size / figure size with on A4 paper
if len(daysList) == 1:
fig.set_size_inches(29.7 / 2.54, 21.0 / 2.54, forward=True)
else: # allow PDF poster for many days (monthly graph) --> use Adobe PDF Reader --> Print --> Poster mode
fig.set_size_inches((29.7 / 2.54) * (len(daysList)/2.0), (21.0 / 2.54) / 2.0, forward=True)
fig.subplots_adjust(bottom=0.08, left = 0.05, right = 0.98, top=0.95)
# some cosmetics on the figure
for label in current_axes.xaxis.get_majorticklabels():
label.set_fontsize(8)
label.set_rotation(30) # 'vertical')
#label.set_horizontalalignment='left'
for label in current_axes.xaxis.get_minorticklabels():
label.set_fontsize(12 if len(daysList) == 1 else 8)
fig.suptitle(", ".join(figTitle))
xLegend = 0.03
for station, color in colorStation.items():
fig.text(xLegend, 0.93, station, color=color[0], fontsize=12, bbox={'fc':"w", 'pad':10, 'ec':color[0]})
xLegend += 0.05
# actions requested by user
if pdf or eMail:
pp = PdfPages(pdf or 'Image.pdf') # in case option eMail is given but not pdf
plt.savefig(pp, format='pdf')
pp.close()
if showPlot: plt.show()
if eMail: sendMail(config, eMail, "\n".join(emailText), pdf or 'Image.pdf')
datetime.datetime.strptime(foo, '%m/%d/%Y %H:%M:%S') for foo in elapsedtime
]
# [1] "Then format the Y axis as Hour:Minute using a custom formatter:"
def m2hm(x, i):
h = int(x / 60)
m = int(x % 60)
return '%(h)02d:%(m)02d' % {'h': h, 'm': m}
inittime_mins = [dt2m(foo) for foo in inittime]
elaptime_mins = [dt2m(foo) for foo in elapsedtime]
#heights = [60,120,240]
print inittime_mins
fig = plt.figure(figsize=(12, 7)) #figsize in inches
ax = fig.add_subplot(1, 1, 1)
ax.bar(inittime, elaptime_mins, bottom=inittime_mins)
plt.xticks(rotation='vertical')
# matplotlib date format object
hfmt = dates.DateFormatter('%b %d')
ax.xaxis.set_major_formatter(hfmt)
ax.xaxis.set_major_locator(MultipleLocator(1.0)) #a tick mark a day
ax.set_ylim([0, 24 * 60])
ax.yaxis.set_major_formatter(ff(m2hm))
ax.yaxis.set_major_locator(pylab.MultipleLocator(60)) #a tick mark an hour
times = races[race]
winners.append(numpy.min(times))
means.append(numpy.average(times))
medians.append(numpy.median(times))
spreads.append(numpy.std(times))
dates.append(race.date())
x = mpl.dates.date2num(dates)
winners = numpy.array(winners)
medians = numpy.array(medians)
spreads = numpy.array(spreads)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.yaxis.set_major_formatter(ff(sec2minsec))
plt.xlabel("Date of Parkrun")
plt.ylabel("Time (min:sec)")
ax.plot_date(x, winners, 'ro', label="Winning time")
ax.errorbar(x, medians, yerr=spreads, label="Median time (and spread)")
fig.autofmt_xdate()
legend = plt.legend()
format = "png"
imagedata = StringIO()
plt.savefig(imagedata, format=format, dpi=96)
utils.httpresponseheader("Content-Type", "image/%s" % format)
def plot_filelist(self, filelist, showPlot = True, eMail=None, pdf=None, web=False, config=None):
"""Read the files in the filelist parameters.
Each data are combine in one plot.
That plot can be displayed or not (showPlot), sent by email (eMail provided), saved as pdf (pdf provided).
Connection for the given days to NOAA website is possible (web) in order to draw vetical lines for XRA data."""
emailText = []
Tstamp = lambda HHMM: datetime.datetime(year=int(day[:4]), month=int(day[4:6]), day=int(day[6:8]),
hour=int(HHMM[:2]), minute=int(HHMM[2:]))
## Sunrise and sunset shade
#sun_rise = 6.0
#sun_set = 18.0
#plt.axvspan(0.0, sun_rise, facecolor='blue', alpha=0.2)
#plt.axvspan(sun_set, 24.0, facecolor='blue', alpha=0.2)
if type(filelist) is str:
if filelist.find(',') >= 0: # file1,file2,...,fileN given as script argument
filelist = filelist.split(",")
else:
filelist = (filelist, )
filenames = []
# use glob for one or more files
filenames.extend([a for a in itertools.chain.from_iterable(
[glob.glob(os.path.expanduser(f)) for f in filelist]) ])
#print (filenames)
# plot's figure and axis
fig = plt.figure()
current_axes = fig.gca()
current_axes.xaxis.set_minor_locator(matplotlib.dates.HourLocator())
current_axes.xaxis.set_major_locator(matplotlib.dates.DayLocator())
current_axes.xaxis.set_major_formatter(ff(self.m2yyyymmdd))
current_axes.xaxis.set_minor_formatter(ff(self.m2hm))
current_axes.set_xlabel("UTC Time")
current_axes.set_ylabel("Signal Strength")
## Get data from files
maxData, data_length = -1, -1; # impossible values
XRAlist = [] # flare list from NOAA
daysList = set() # date of NOAA's pages already retrieved, prevent multiple fetch
figTitle = [] # list of file names (w/o path and extension) as figure's title
# one color per station
colorList = "brgcmy"
colorStation = {}
colorIdx = 0
time.clock()
for filename in sorted(filenames):
figTitle.append(os.path.basename(filename)[:-4]) # extension .csv assumed
sFile = SidFile(filename)
for station in sFile.stations:
# Does this station already have a color? if not, reserve one
if station not in colorStation:
colorStation[station] = colorList[colorIdx % len(colorList)] + '-' # format like 'b-'
colorIdx += 1
# Add points to the plot
plt.plot_date(sFile.timestamp, sFile.get_station_data(station), colorStation[station])
# Extra housekeeping
maxData = max(max(sFile.get_station_data(station)), maxData) # maxData will be used later to put the XRA labels up
#msg = str(len(sFile.get_station_data(station))) + " points plotted after reading " + os.path.basename(filename)
msg = "[{}] {} points plotted after reading {}".format(station, len(sFile.get_station_data(station)), os.path.basename(filename))
print (msg)
emailText.append(msg)
if web and sFile.startTime not in daysList:
# get the XRA data from NOAA website to draw corresponding lines on the plot
# fetch that day's flares on NOAA as not previously accessed
day = sFile.sid_params["utc_starttime"][:10].replace("-","")
#NOAA_URL = 'http://www.swpc.noaa.gov/ftpdir/warehouse/%s/%s_events/%sevents.txt' % (day[:4], day[:4], day)
#ftp://ftp.swpc.noaa.gov/pub/indices/events/20141030events.txt
NOAA_URL = 'ftp://ftp.swpc.noaa.gov/pub/indices/events/%sevents.txt' % (day)
response = None
if sys.version[0]<'3': # python 2.7 vs. Python 3.3
try:
response = urllib2.urlopen(NOAA_URL)
except urllib2.HTTPError as err:
print (err,"\n",NOAA_URL)
else:
try:
response = urllib.request.urlopen(NOAA_URL)
except urllib.error.HTTPError as err:
print (err,"\n",NOAA_URL)
lastXRAlen = len(XRAlist) # save temporarly current number of XRA events in memory
if response:
for webline in response.read().splitlines():
if sys.version[0]>='3': webline = str(webline, 'utf-8') # Python 3: cast bytes to str
fields = webline.split()
if len(fields) >= 9 and not fields[0].startswith("#"):
if fields[1] == '+': fields.remove('+')
if fields[6] in ('XRA', ): # maybe other event types could be of interrest
# eventName, BeginTime, MaxTime, EndTime, Particulars
msg = fields[0]+" "+fields[1]+" "+fields[2]+" "+fields[3]+" "+fields[8]
emailText.append(msg)
print (msg)
try:
btime = Tstamp(fields[1]) # 'try' necessary as few occurences of --:-- instead of HH:MM exist
except:
pass
try:
mtime = Tstamp(fields[2])
except:
mtime = btime
try:
etime = Tstamp(fields[3])
except:
etime = mtime
XRAlist.append( (fields[0], btime, mtime, etime, fields[8]) ) # as a tuple
msg = str(len(XRAlist) - lastXRAlen) + " XRA events recorded by NOAA on " + day
emailText.append(msg)
print (msg)
# keep track of the days
daysList.add(sFile.startTime)
print ("All files read in", time.clock(), "sec.")
if web: # add the lines marking the retrieved flares from NOAA
alternate = 0
for eventName, BeginTime, MaxTime, EndTime, Particulars in XRAlist:
plt.vlines( [BeginTime, MaxTime, EndTime], 0, maxData,
color=['g','r','y'], linestyles='dotted')
plt.text(MaxTime, alternate * maxData, Particulars, horizontalalignment='center',
bbox={'facecolor': 'w', 'alpha': 0.5, 'fill': True})
alternate = 0 if alternate==1 else 1
# plot/page size / figure size with on A4 paper
if len(daysList) == 1:
fig.set_size_inches(29.7 / 2.54, 21.0 / 2.54, forward=True)
else: # allow PDF poster for many days (monthly graph) --> use Adobe PDF Reader --> Print --> Poster mode
fig.set_size_inches((29.7 / 2.54) * (len(daysList)/2.0), (21.0 / 2.54) / 2.0, forward=True)
fig.subplots_adjust(bottom=0.08, left = 0.05, right = 0.98, top=0.95)
# some cosmetics on the figure
for label in current_axes.xaxis.get_majorticklabels():
label.set_fontsize(8)
label.set_rotation(30) # 'vertical')
#label.set_horizontalalignment='left'
for label in current_axes.xaxis.get_minorticklabels():
label.set_fontsize(12 if len(daysList) == 1 else 8)
fig.suptitle(", ".join(figTitle))
xLegend = 0.03
for station, color in colorStation.items():
fig.text(xLegend, 0.93, station, color=color[0], fontsize=12, bbox={'fc':"w", 'pad':10, 'ec':color[0]})
xLegend += 0.05
# actions requested by user
if pdf or eMail:
pp = PdfPages(pdf or 'Image.pdf') # in case option eMail is given but not pdf
plt.savefig(pp, format='pdf')
pp.close()
if showPlot: plt.show()
if eMail: sendMail(config, eMail, "\n".join(emailText), pdf or 'Image.pdf')
mn = [yy[:i].mean() for i in xrange(len(yy))]
x2 = [0, 0.3, 0.5, 0.75, 1, 1.25,1.5,1.75, 2]
y2 =[2.75, 2., 2.1,2.2,2.3,2.4,2.5, 2.6, 2.7]
s2 = UnivariateSpline(x2, y2, k=5)
yy2 = s2(t)
mn2 = [yy2[:i].mean() for i in xrange(len(yy2))]
n = len(yy)
k=0
for i in xrange(1, n-1, 10):
pylab.figure()
pylab.plot(t[:i+1], yy[:i+1], 'g-', linewidth=4)
pylab.plot(t[:i+1], mn[:i+1], 'g--', linewidth=4)
pylab.plot(t[:i+1], yy2[:i+1], 'r-', linewidth=4)
pylab.plot(t[:i+1], mn2[:i+1], 'r--', linewidth=4)
pylab.gca().yaxis.set_major_formatter(ff(m2hm))
pylab.ylabel("500m Split", fontsize=15, weight="bold")
pylab.xlabel("Km", fontsize=15, weight="bold")
pylab.xlim(0,2)
pylab.ylim(1,3)
pylab.gcf().savefig("plotframes/plot_%04d.png" % k)
k+=1
# pylab.show()
def init():
mpl_fig = plt.figure()
ax = mpl_fig.add_subplot(111)
ax.xaxis.set_major_formatter(ff(m2hm))
def makeBarPlot(data,
plotname="barplot.png",
plot_size=(9, 7),
invert_time=True):
"""
data: 3-tuple of lists
start_times: list of datetime instances for event start
end_times: list of datetime instances for event end
intervals: list of timedelta instances for event duration
Makes a bar plot, date vs. time of the computer activity.
It's easy to put in the date data as datetime instances, but
the time data needs a workaround. We turn the datetime intances
into integers and then format the time axis accordingly.
Places were there are a lot of lines as opposed to one block
shows I was very attentive! [ironic]
"""
start_times, end_times, intervals = data # we won't be using end_times
## x axis date data
days = [dt.date() for dt in start_times]
## y axis workaround
def dt2m(dt):
return (dt.hour * 60) + dt.minute
interval_mins = [i.seconds / 60 for i in intervals] # height of the bars
starttimes_mins = [dt2m(dt)
for dt in start_times] # bottom point of the bars
## Set up the plot ##
## Decide colors
bgcolor = "#DBFFFD" #FDF6E3"
plot_color = "#FDF6E3"
label_color = "#D33582"
spine_color = "#A0A1A1"
bar_color = "#224596" #279186"
fig = plt.figure(figsize=plot_size, dpi=96) # figsize in inches
ax = fig.add_subplot(1, 1, 1)
## bar is (x, height of bar, bottom of bar)
plt.bar(days,
interval_mins,
bottom=starttimes_mins,
align="center",
color=bar_color,
alpha=0.8,
edgecolor='none',
linewidth=0)
## Customize the plot ##
ax.spines['top'].set_position(('outward', 4))
## Show or hide the spines, ticks and labels
# ax.axes.get_yaxis().set_visible(False) # hides targeted axis labels & ticks
plt.tick_params(
size=0
) # affects only the major ticks, can add kwarg "axis=x" to affect only one axis
for position, spine in ax.spines.iteritems(
): # spines is a dict, position is the key
# if position == 'top': continue # doesn't affect top spine
spine.set_visible(False) # hides spine
## Customize the x axis
# set limits for the x axis
first_day = days[0]
last_day = days[-1]
# ax.set_xlim([first_day, last_day + datetime.timedelta(days=1)])
ax.xaxis.set_major_locator(mdates.DayLocator(bymonthday=range(1, 32)))
ax.xaxis.set_major_formatter(
mdates.DateFormatter('%a %e')) # format x axis tick labels as "mmm dd"
ax.xaxis.tick_top() # put the x axis date labels on top
for label in ax.get_xticklabels():
label.set_horizontalalignment('center')
## Customize the y axis
ax.set_ylim([0, 24 * 60])
if invert_time:
ax.invert_yaxis(
) # time axis is from 00:00 (top) to 23:59 (bottom) like in iCal
ax.yaxis.set_minor_locator(
plt.MultipleLocator(60)) # a tick mark every hour
ax.yaxis.set_major_locator(plt.MultipleLocator(
4 * 60)) # a tick label every 4 hours
# format y axis tick labels as Hour:Minute using a custom formatter
def m2hm(x, i):
h = int(x / 60)
m = int(x % 60)
return '%(h)2d:%(m)02d' % {
'h': h,
'm': m
} # hours don't show a leading 0 zero
ax.yaxis.set_major_formatter(ff(m2hm)) # the custom formatter
## Set colors, sizes and transparency
fig.patch.set_facecolor(bgcolor)
fig.patch.set_alpha(0.5)
ax.patch.set_facecolor(plot_color)
ax.patch.set_alpha(0.0)
ax.spines['top'].set_color(spine_color)
ax.spines['top'].set_linewidth(1)
ax.spines['top'].set_alpha(1.0)
# set colors for the x axis date labels # for some reason, this doesn't work if I put it above in the colour section
for label in ax.get_xticklabels():
label.set_color(label_color)
label.set_fontsize(15) # default is 12
label.set_fontweight("1000")
for label in ax.xaxis.get_majorticklabels():
label.set_family("SimHei") # Trebuchet MS works well enough too
plt.savefig(plotname, dpi=96, bbox_inches="tight",
transparent=False) #, facecolor=fig.get_facecolor())
# If we don't specify the edgecolor and facecolor for the figure when
# saving with savefig, it will override the value we set earlier
return
def makeBarPlot(data, plotname="barplot.png", plot_size=(9,7), invert_time=True):
"""
data: 3-tuple of lists
start_times: list of datetime instances for event start
end_times: list of datetime instances for event end
intervals: list of timedelta instances for event duration
Makes a bar plot, date vs. time of the computer activity.
It's easy to put in the date data as datetime instances, but
the time data needs a workaround. We turn the datetime intances
into integers and then format the time axis accordingly.
Places were there are a lot of lines as opposed to one block
shows I was very attentive! [ironic]
"""
start_times, end_times, intervals = data # we won't be using end_times
## x axis date data
days = [ dt.date() for dt in start_times ]
## y axis workaround
def dt2m(dt):
return (dt.hour*60) + dt.minute
interval_mins = [ i.seconds/60 for i in intervals ] # height of the bars
starttimes_mins = [ dt2m(dt) for dt in start_times ] # bottom point of the bars
## Set up the plot ##
## Decide colors
bgcolor = "#DBFFFD" #FDF6E3"
plot_color = "#FDF6E3"
label_color = "#D33582"
spine_color = "#A0A1A1"
bar_color = "#224596" #279186"
fig = plt.figure(figsize=plot_size, dpi=96) # figsize in inches
ax = fig.add_subplot(1, 1, 1)
## bar is (x, height of bar, bottom of bar)
plt.bar(days, interval_mins, bottom=starttimes_mins,
align="center", color=bar_color, alpha=0.8, edgecolor='none', linewidth=0)
## Customize the plot ##
ax.spines['top'].set_position(('outward', 4))
## Show or hide the spines, ticks and labels
# ax.axes.get_yaxis().set_visible(False) # hides targeted axis labels & ticks
plt.tick_params(size=0) # affects only the major ticks, can add kwarg "axis=x" to affect only one axis
for position, spine in ax.spines.iteritems(): # spines is a dict, position is the key
# if position == 'top': continue # doesn't affect top spine
spine.set_visible(False) # hides spine
## Customize the x axis
# set limits for the x axis
first_day = days[0]
last_day = days[-1]
# ax.set_xlim([first_day, last_day + datetime.timedelta(days=1)])
ax.xaxis.set_major_locator(mdates.DayLocator(bymonthday=range(1,32)))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%a %e')) # format x axis tick labels as "mmm dd"
ax.xaxis.tick_top() # put the x axis date labels on top
for label in ax.get_xticklabels():
label.set_horizontalalignment('center')
## Customize the y axis
ax.set_ylim([0, 24*60])
if invert_time:
ax.invert_yaxis() # time axis is from 00:00 (top) to 23:59 (bottom) like in iCal
ax.yaxis.set_minor_locator(plt.MultipleLocator(60)) # a tick mark every hour
ax.yaxis.set_major_locator(plt.MultipleLocator(4*60)) # a tick label every 4 hours
# format y axis tick labels as Hour:Minute using a custom formatter
def m2hm(x, i):
h = int(x/60)
m = int(x%60)
return '%(h)2d:%(m)02d' % {'h':h,'m':m} # hours don't show a leading 0 zero
ax.yaxis.set_major_formatter(ff(m2hm)) # the custom formatter
## Set colors, sizes and transparency
fig.patch.set_facecolor(bgcolor)
fig.patch.set_alpha(0.5)
ax.patch.set_facecolor(plot_color)
ax.patch.set_alpha(0.0)
ax.spines['top'].set_color(spine_color)
ax.spines['top'].set_linewidth(1)
ax.spines['top'].set_alpha(1.0)
# set colors for the x axis date labels # for some reason, this doesn't work if I put it above in the colour section
for label in ax.get_xticklabels():
label.set_color(label_color)
label.set_fontsize(15) # default is 12
label.set_fontweight("1000")
for label in ax.xaxis.get_majorticklabels():
label.set_family("SimHei") # Trebuchet MS works well enough too
plt.savefig(plotname, dpi=96, bbox_inches="tight",
transparent=False)#, facecolor=fig.get_facecolor())
# If we don't specify the edgecolor and facecolor for the figure when
# saving with savefig, it will override the value we set earlier
return
times = races[race]
winners.append(numpy.min(times))
means.append(numpy.average(times))
medians.append(numpy.median(times))
spreads.append(numpy.std(times))
dates.append(race.date())
x = mpl.dates.date2num(dates)
winners = numpy.array(winners)
medians = numpy.array(medians)
spreads = numpy.array(spreads)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.yaxis.set_major_formatter(ff(sec2minsec))
plt.xlabel("Date of Parkrun")
plt.ylabel("Time (min:sec)")
ax.plot_date(x, winners, 'ro', label="Winning time")
ax.errorbar(x, medians, yerr=spreads, label="Median time (and spread)")
fig.autofmt_xdate()
legend = plt.legend()
format = "png"
imagedata = StringIO()
plt.savefig(imagedata, format=format, dpi=96)
utils.httpresponseheader("Content-Type", "image/%s" % format)
dumpMessage({
