def drawCity(self):
"""
作图
:return:
"""
pl.title("pm25 / time " + str(self.numMonitors) + "_monitors")# give plot a title
pl.xlabel('time')# make axis labels
pl.ylabel('pm2.5')
self.fill_cityPm25List()
for monitorStr in self.cityPm25List:
data = np.loadtxt(StringIO(monitorStr), dtype=np.dtype([("t", "S13"),("v", float)]))
datestr = np.char.replace(data["t"], "T", " ")
t = pl.datestr2num(datestr)
v = data["v"]
pl.plot_date(t, v, fmt="-o")
pl.subplots_adjust(bottom=0.3)
# pl.legend(loc=4)#指定legend的位置,读者可以自己help它的用法
ax = pl.gca()
ax.fmt_xdata = pl.DateFormatter('%Y-%m-%d %H:%M:%S')
pl.xticks(rotation=70)
# pl.xticks(t, datestr) # 如果以数据点为刻度，则注释掉这一行
ax.xaxis.set_major_formatter(pl.DateFormatter('%Y-%m-%d %H:%M'))
pl.grid()
pl.show()# show the plot on the screen
def plot_diurnal(headers):
"""
Diurnal plot of the emails, with years running along the x axis and times of
day on the y axis.
"""
xday = []
ytime = []
print 'making diurnal plot...'
for h in iterview(headers):
if len(h) > 1:
try:
s = h[1][5:].strip()
x = dateutil.parser.parse(s)
except ValueError:
print
print marquee(' ERROR: skipping ')
print h
print marquee()
continue
timestamp = mktime(
x.timetuple()) # convert datetime into floating point number
mailstamp = datetime.fromtimestamp(timestamp)
xday.append(mailstamp)
# Time the email is arrived
# Note that years, month and day are not important here.
y = datetime(2010, 10, 14, mailstamp.hour, mailstamp.minute,
mailstamp.second)
ytime.append(y)
plot_date(xday, ytime, '.', alpha=.7)
xticks(rotation=30)
return xday, ytime
def globtime(self):
mat = np.load('globcummat.npz')
jd1 = pl.datestr2num('2003-01-01')
jd2 = pl.datestr2num('2012-12-31')
jdvec = np.arange(jd1, jd2)
figpref.current()
pl.close(1)
pl.figure(1)
pl.plot_date(jdvec,
mat['northmat'][10, :],
'r-',
alpha=0.5,
label="Northern hemisphere")
pl.plot_date(jdvec,
mat['southmat'][10, :],
'b-',
alpha=0.5,
label="Southern hemisphere")
pl.ylim(0, 1)
pl.legend(loc='lower right')
pl.savefig('figs/liege/glob_time.pdf',
transparent=True,
bbox_inches='tight')
def draw_graph():
# Создание графика:
# Преобразуем даты в числовой формат (для дальнейшего форматирования тикеров осей)
register_matplotlib_converters()
x_data_float = matplotlib.dates.date2num(
report['date'].astype(dtype='datetime64'))
# Вызовем subplot явно, чтобы получить экземпляр класса AxesSubplot,
# из которого будем иметь доступ к осям
axes = pylab.subplot(1, 1, 1)
# Пусть в качестве меток по оси X выводится только месяц.год
axes.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%m.%y"))
# Изменим локатор, используемый по умолчанию
locator = matplotlib.dates.AutoDateLocator(minticks=5, maxticks=12)
axes.xaxis.set_major_locator(locator)
# Отобразим данные
pylab.plot_date(x_data_float, report['value'], fmt="b-")
plt.title(f'{category_name}\n{region}\nс {date_min} по {date_max}')
plt.plot(x_data_float, report['value'])
plt.show()
def get_spot_history(self, instance_type, start=None, end=None, plot=False):
if not utils.is_iso_time(start):
raise exception.InvalidIsoDate(start)
if not utils.is_iso_time(end):
raise exception.InvalidIsoDate(end)
hist = self.conn.get_spot_price_history(start_time=start,
end_time=end,
instance_type=instance_type,
product_description="Linux/UNIX")
if not hist:
raise exception.SpotHistoryError(start,end)
dates = [ utils.iso_to_datetime_tuple(i.timestamp) for i in hist]
prices = [ i.price for i in hist ]
maximum = max(prices)
avg = sum(prices)/len(prices)
log.info("Current price: $%.2f" % hist[-1].price)
log.info("Max price: $%.2f" % maximum)
log.info("Average price: $%.2f" % avg)
if plot:
try:
import pylab
pylab.plot_date(pylab.date2num(dates), prices, linestyle='-')
pylab.xlabel('date')
pylab.ylabel('price (cents)')
pylab.title('%s Price vs Date (%s - %s)' % (instance_type, start, end))
pylab.grid(True)
pylab.show()
except ImportError,e:
log.error("Error importing pylab:")
log.error(str(e))
log.error("please check that matplotlib is installed and that:")
log.error(" $ python -c 'import pylab'")
log.error("completes without error")
def mains_energy():
import psycopg2
conn = psycopg2.connect('dbname=gateway')
cursor = conn.cursor()
meter_name = 'ug04'
date_start = '20111001'
date_end = '20111201'
query = """select *
from midnight_rider
where name = '%s' and
ip_address = '192.168.1.200' and
date >= '%s' and
date <= '%s'
order by date
""" % (meter_name, date_start, date_end)
shniz = cursor.execute(query)
shniz = cursor.fetchall()
dates = []
watthours = []
for s in shniz:
dates.append(s[0])
watthours.append(s[2])
import pylab
pylab.plot_date(dates,watthours)
pylab.grid()
pylab.show()
def plotdepthtime(self, FontSize):
""" plot depth against time """
pylab.plot_date(vector_epoch2datetime(self['etime']), self['depth'],'o')
#pylab.title('Depth-time view')
pylab.xlabel('time', fontsize=FontSize)
pylab.ylabel('depth', fontsize=FontSize)
pylab.gca().invert_yaxis()
def diurnalplot(headers):
""" diurnal plot of the emails,
with years running along the x axis
and times of day on the y axis.
"""
xday = []
ytime = []
for header in headers:
#print(header['Date'], header['From'], email.header.decode_header(header['Subject']))
if len(header['Date']) > 1:
#print('Message %s\n' % (h))
#timestamp = mktime(dateutil.parser.parse(h))
#mailstamp = datetime.fromtimestamp(timestamp)
mailstamp = dateutil.parser.parse(header['Date'])
xday.append(mailstamp)
# Time the email is arrived
# Note that years, month and day are not important here.
yyy = datetime(2010, 10, 14, mailstamp.hour, mailstamp.minute, mailstamp.second)
ytime.append(yyy)
#print("%s" % (mailstamp))
#print("%s" % (y))
plot_date(xday, ytime, '.', alpha=.7)
xticks(rotation=30)
return xday, ytime
def load_graph():
pylab.figure(num = 1, figsize=(6, 3))
ax = pylab.gca() #get the current graphics region
#Set the axes position with pos = [left, bottom, width, height]
ax.set_position([.1,.15,.8,.75])
if zoom == False:
print "zoom = False"
pylab.plot_date(date, kiln_temp, 'r-', linewidth = 1)
if zoom == True:
print "zoom = True"
zoom_date = date[(len(date)-zoom_level):]
zoom_kiln_temp = kiln_temp[(len(kiln_temp)-zoom_level):]
pylab.plot(zoom_date, zoom_kiln_temp, 'r-', linewidth = 1)
pylab.title(r"Kiln Temperature")
ax.xaxis.set_major_formatter(DateFormatter('%H:%M'))
xlabels = ax.get_xticklabels()
#pylab.setp(xlabels,'rotation', 45, fontsize=10)
ylabels = ax.get_yticklabels()
#pylab.setp(ylabels,fontsize=10)
pylab.savefig('graph.png')
pylab.close(1)
graph = os.path.join("","graph.png")
graph_surface = pygame.image.load(graph).convert()
screen.blit(graph_surface, (100,30))
"""
def plotmagtime(self, FontSize):
""" plot magnitude versus time """
import pylab as plt
plt.plot_date(self.time, self.mag)
#plt.title('Mag-time view')
plt.xlabel('time', fontsize=FontSize)
plt.ylabel('magnitude', fontsize=FontSize)
def get_spot_history(self, instance_type,
start=None, end=None, plot=False):
if not utils.is_iso_time(start):
raise exception.InvalidIsoDate(start)
if not utils.is_iso_time(end):
raise exception.InvalidIsoDate(end)
hist = self.conn.get_spot_price_history(start_time=start,
end_time=end,
instance_type=instance_type,
product_description="Linux/UNIX")
if not hist:
raise exception.SpotHistoryError(start, end)
dates = [utils.iso_to_datetime_tuple(i.timestamp) for i in hist]
prices = [i.price for i in hist]
maximum = max(prices)
avg = sum(prices) / len(prices)
log.info("Current price: $%.2f" % hist[-1].price)
log.info("Max price: $%.2f" % maximum)
log.info("Average price: $%.2f" % avg)
if plot:
try:
import pylab
pylab.plot_date(pylab.date2num(dates), prices, linestyle='-')
pylab.xlabel('date')
pylab.ylabel('price (cents)')
pylab.title('%s Price vs Date (%s - %s)' % (instance_type,
start, end))
pylab.grid(True)
pylab.show()
except ImportError, e:
log.error("Error importing pylab:")
log.error(str(e))
log.error("please ensure matplotlib is installed and that:")
log.error(" $ python -c 'import pylab'")
log.error("completes without error")
def main():
Global.logdir = sys.argv[1]
Global.account = sys.argv[2]
Global.parameter = sys.argv[3]
Global.t_start = datetime.strptime(sys.argv[4], "%Y%m%d%H%M")
Global.t_end = datetime.strptime(sys.argv[5], "%Y%m%d%H%M")
lines = lines_from_dir("192_168_1_2%s.log" % (Global.account,), Global.logdir)
logs = meter_logs(lines)
print "Site:", Global.logdir
print "Meter:", Global.account
print "Parameter:", Global.parameter
print "Time Range:", Global.t_start.isoformat(' '), "to", Global.t_end.isoformat(' ')
values = sorted([(el['Time Stamp'], el[Global.parameter])
for el in logs], key=lambda t: t[0])
print len(values)
print "plotting..."
pylab.figure()
pylab.title("Site:%s, Meter: %s, Parameter: %s, Time Scale: %s to %s" % (
Global.logdir, Global.account, Global.parameter,
Global.t_start, Global.t_end))
pylab.xlabel("Time")
pylab.ylabel(Global.parameter)
pylab.plot_date(*zip(*values), fmt='-')
pylab.grid(True)
pylab.savefig('%s_%s_%s_%s_%s' % (
Global.logdir, Global.account, Global.parameter,
Global.t_start.strftime("%Y%m%d%H%M"), Global.t_end.strftime("%Y%m%d%H%M")))
pylab.show()
print "done."
def plot_diurnal(headers):
"""
Diurnal plot of the emails, with years running along the x axis and times of
day on the y axis.
"""
xday = []
ytime = []
print 'making diurnal plot...'
for h in iterview(headers):
if len(h) > 1:
try:
s = h[1][5:].strip()
x = dateutil.parser.parse(s)
except ValueError:
print
print marquee(' ERROR: skipping ')
print h
print marquee()
continue
timestamp = mktime(x.timetuple()) # convert datetime into floating point number
mailstamp = datetime.fromtimestamp(timestamp)
xday.append(mailstamp)
# Time the email is arrived
# Note that years, month and day are not important here.
y = datetime(2010, 10, 14, mailstamp.hour, mailstamp.minute, mailstamp.second)
ytime.append(y)
plot_date(xday,ytime,'.',alpha=.7)
xticks(rotation=30)
return xday,ytime
def ForecastDraw(self):
"""
at the day-level
:return:
"""
pl.title("aqi/time(day)")# give plot a title
pl.xlabel('time')# make axis labels
pl.ylabel('aqi')
data = np.loadtxt(StringIO(self._xyArrayStr), dtype=np.dtype([("t", "S13"), ("v", float)]))
datestr = np.char.replace(data["t"], "T", " ")
t = dt.datestr2num(datestr)
# k = pl.num2date(t)
# k2 = dt.num2date(t)
v = data["v"]
if len(t) > 30:
t = t[-30:]
v = v[-30:]
pl.plot_date(t, v, fmt="-o")
self.polyfit(t, v)
pl.subplots_adjust(bottom=0.3)
# pl.legend(loc=4)#指定legend的位置,读者可以自己help它的用法
ax = pl.gca()
ax.fmt_xdata = pl.DateFormatter('%Y-%m-%d %H:%M:%S')
pl.xticks(rotation=70)
# pl.xticks(t, datestr) # 如果以数据点为刻度，则注释掉这一行
ax.xaxis.set_major_formatter(pl.DateFormatter('%Y-%m-%d %H:%M'))
# pl.xlim(('2016-03-09 00:00', '2016-03-12 00:00'))
pl.grid() # 有格子
pl.show()# show the plot on the screen
return self._forecast_Value
def plotStock(symbol, startDate, endDate):
"""returns a plot of stock (symbol) between startDate and endDate
symbol: String, stock ticker symbol
startDate, endDate = tuple(YYYY, MM, DD)
"""
company = symbol
sYear, sMonth, sDay = startDate
eYear, eMonth, eDay = endDate
start = datetime(sYear, sMonth, sDay)
end = datetime(eYear, eMonth, eDay)
data = DataReader(company, "yahoo", start, end)
closingVals = data["Adj Close"]
#convert to matplotlib format
dates = [i for i in data.index]
dates = matplotlib.dates.date2num(dates)
pylab.ylim([0, int(max(closingVals)*1.2)])
pylab.plot_date(dates, closingVals, label = symbol, xdate = True,
ydate=False, linestyle = '-')
pylab.legend()
pylab.show()
def line_plot(data, out):
"""Turning ([key, ...], [value, ...]) into line graphs"""
pylab.clf()
pylab.plot_date(data[0], data[1], '-')
pylab.savefig(out)
def plot_save(X, Y, title, filename):
clf()
xticks(rotation=90)
suptitle(title)
subplots_adjust(bottom=0.2)
plot_date(X, Y, "k.")
savefig(filename, dpi=600)
def plot_session_timeline(sessions,prefix=fname_prefix):
offset = {'behop':1,'lwapp':2 }
annot = {'behop':'b-*','lwapp':'r-*'}
pylab.figure()
for s in sessions:
pylab.plot_date(matplotlib.dates.date2num([s['start'],s['end']]),[offset[s['type']],offset[s['type']]],
annot[s['type']])
#pylab.plot([s['start'],s['end']],[offset[s['type']],offset[s['type']]], annot[s['type']])
pylab.xlim((STARTING_DAY,END_DAY))
pylab.ylim([0,3])
timespan = END_DAY - STARTING_DAY
print "Total Seconds %d" % timespan.total_seconds()
timeticks = [STARTING_DAY + timedelta(seconds=x) for x in range(0,int(timespan.total_seconds()) + 1, int(timespan.total_seconds()/4))]
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d-%H'))
plt.gca().set_xticks(timeticks)
#start = sessions[0]['start']
#dates = [(s['end'] - start).days for s in sessions]
#print dates
#plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))
#tick_dates = [start + timedelta(days=i) for i in range(0,max(dates))]
#plt.gca().set_xticks(tick_dates)
pylab.savefig('%s_timeline' % (prefix))
def mains_energy():
import psycopg2
conn = psycopg2.connect('dbname=gateway')
cursor = conn.cursor()
meter_name = 'ug04'
date_start = '20111001'
date_end = '20111201'
query = """select *
from midnight_rider
where name = '%s' and
ip_address = '192.168.1.200' and
date >= '%s' and
date <= '%s'
order by date
""" % (meter_name, date_start, date_end)
shniz = cursor.execute(query)
shniz = cursor.fetchall()
dates = []
watthours = []
for s in shniz:
dates.append(s[0])
watthours.append(s[2])
import pylab
pylab.plot_date(dates, watthours)
pylab.grid()
pylab.show()
def drawCity(self):
"""
作图
:return:
"""
pl.title("pm25 / time " + str(self.numMonitors) +
"_monitors") # give plot a title
pl.xlabel('time') # make axis labels
pl.ylabel('pm2.5')
self.fill_cityPm25List()
for monitorStr in self.cityPm25List:
data = np.loadtxt(StringIO(monitorStr),
dtype=np.dtype([("t", "S13"), ("v", float)]))
datestr = np.char.replace(data["t"], "T", " ")
t = pl.datestr2num(datestr)
v = data["v"]
pl.plot_date(t, v, fmt="-o")
pl.subplots_adjust(bottom=0.3)
# pl.legend(loc=4)#指定legend的位置,读者可以自己help它的用法
ax = pl.gca()
ax.fmt_xdata = pl.DateFormatter('%Y-%m-%d %H:%M:%S')
pl.xticks(rotation=70)
# pl.xticks(t, datestr) # 如果以数据点为刻度，则注释掉这一行
ax.xaxis.set_major_formatter(pl.DateFormatter('%Y-%m-%d %H:%M'))
pl.grid()
pl.show() # show the plot on the screen
def plot_dwaf_data(realtime, file_name='data_plot.png', gauge=True):
x = pl.date2num(realtime.date)
y = realtime.q
pl.clf()
pl.figure(figsize=(7.5, 4.5))
pl.rc('text', usetex=True)# TEX fonts
pl.plot_date(x,y,'b-',linewidth=1)
pl.grid(which='major')
pl.grid(which='minor')
if gauge:
pl.ylabel(r'Flow rate (m$^3$s$^{-1}$)')
title = 'Real-time flow -- %s [%s]' % (realtime.station_id[0:6], realtime.station_desc)
else:
title = 'Real-time capacity -- %s [%s]' % (realtime.station_id[0:6], realtime.station_desc)
pl.ylabel('Percentage of F.S.C')
labeled_days = DayLocator(interval=3)
ticked_days = DayLocator()
dayfmt = DateFormatter('%d/%m/%Y')
ax = pl.gca()
ax.xaxis.set_major_locator(labeled_days)
ax.xaxis.set_major_formatter(dayfmt)
ax.xaxis.set_minor_locator(ticked_days)
pl.xticks(fontsize=10)
pl.yticks(fontsize=10)
pl.title(title, fontsize=14)
pl.savefig(file_name, dpi=100)
def work_1():
data_in = datetime(2010,6,24,8,00,0)
data_fin = datetime(2010,6,24,22,00,0)
#np.concatenate((dati,dati2))
dati = df.query_db('greenhouse.db','data',data_in,data_fin)
Is = dati['rad_int_sup_solar']
lista_to_filter = df.smooht_Is(Is)
Is_2 = df.smooth_value(Is,lista_to_filter)
tra_P_M = mf.transpiration_P_M(Is_2,dati['rad_int_inf_solar'],0.64,2.96,((dati['temp_1']+dati['temp_2'])/2)+273.15,(dati['RH_1']+dati['RH_2'])/200)
tra_weight = mf.transpiration_from_balance(dati['peso_balanca'],300,2260000)
delta_peso = np.diff(dati['peso_balanca'])
fr,lista_irr,lista_irr_free = mf.find_irrigation_point(delta_peso,dati['data'])
lista_night = mf.remove_no_solar_point(dati['rad_int_sup_solar'],50)
lista_no = list(set(lista_irr+ lista_night))
tran_weight,lista_yes = mf.transpiration_from_balance_irr(dati['peso_balanca'],300,2260000,lista_no)
min_avg = 6
tra_weigh_avg,time_weight = df.avg2(tran_weight,lista_yes,min_avg)
tra_P_M_avg,time_P_M = df.avg2(tra_P_M,lista_yes,min_avg)
data_plot.plot_time_data_2_y_same_axis(dati['data'][time_P_M], tra_P_M_avg, 'tra Penman', tra_weigh_avg, 'trans weight')
RMSE = df.RMSE(tra_P_M_avg, tra_weigh_avg)
print "RMSE is", RMSE
print "RRMSE is", df.RRMSE(RMSE, tra_weigh_avg)
date = dati['data'][time_P_M].astype(object)
dates= pylab.date2num(date)
pylab.plot_date(dates,tra_weigh_avg,'rx')
def draw_domain(dom):
n1 = "Wikisource_-_pages_%s.png"%dom
fig = pylab.figure(1, figsize=(12,12))
ax = fig.add_subplot(111)
pylab.clf()
pylab.hold(True)
pylab.grid(True)
count = count_array[dom]
pylab.fill_between(count[0][1], 0, count[0][0], facecolor="#ffa0a0")#red
pylab.fill_between(count[4][1], 0, count[4][0], facecolor="#b0b0ff")#blue
pylab.fill_between(count[3][1], 0, count[3][0], facecolor="#dddddd")#gray
pylab.fill_between(count[2][1], 0, count[2][0], facecolor="#ffe867")#yellow
pylab.fill_between(count[1][1], 0, count[1][0], facecolor="#90ff90")#green
x = range(1)
b1 = pylab.bar(x, x, color='#ffa0a0')
b0 = pylab.bar(x, x, color='#dddddd')
b2 = pylab.bar(x, x, color='#b0b0ff')
b3 = pylab.bar(x, x, color='#ffe867')
b4 = pylab.bar(x, x, color='#90ff90')
pylab.legend([b1[0], b3[0], b4[0], b0[0], b2[0]],['not proofread','proofread','validated','without text','problematic'],loc=2, prop={'size':'medium'})
pylab.plot_date(count[0][1],pylab.zeros(len(count[0][1])),'k-' )
ax.xaxis.set_major_locator( pylab.YearLocator() )
ax.xaxis.set_major_formatter( pylab.DateFormatter('%Y-%m-%d') )
fig.autofmt_xdate()
pylab.title("%s.wikisource.org"%dom, fontdict = { 'fontsize' : 'xx-large' } )
pylab.ylim(0)
pylab.savefig(savepath+n1)
n1 = "Wikisource_-_texts_%s.png"%dom
pylab.figure(1, figsize=(12,12))
pylab.clf()
pylab.hold(True)
pylab.grid(True)
count = count_array[dom]
pylab.fill_between( rm29(dom,count[8][1]), 0,rm29(dom,count[8][0]), facecolor="#b0b0ff")
pylab.fill_between( rm29(dom,count[7][1]), 0,rm29(dom,count[7][0]), facecolor="#ffa0a0")
pylab.fill_between( rm29(dom,count[9][1]), 0,rm29(dom,count[9][0]), facecolor="#dddddd")
x = range(1)
b1 = pylab.bar(x, x, color='#b0b0ff')
b2 = pylab.bar(x, x, color='#ffa0a0')
if dom!='de':
pylab.legend([b1[0],b2[0]],['with scans','naked'],loc=3, prop={'size':'medium'})
else:
pylab.legend([b1[0],b2[0]],['with transclusion (PR2)','older system (PR1)'],loc=3, prop={'size':'medium'})
pylab.plot_date( rm29(dom,count[8][1]),pylab.zeros(len( rm29(dom,count[8][1]) )),'k-' )
ax.xaxis.set_major_locator( pylab.YearLocator() )
ax.xaxis.set_major_formatter( pylab.DateFormatter('%Y-%m-%d') )
fig.autofmt_xdate()
pylab.title("%s.wikisource.org"%dom, fontdict = { 'fontsize' : 'xx-large' } )
pylab.ylim(0)
pylab.savefig(savepath+n1)
def run():
# PLOT 1
##############
members = Attendee.objects.filter(membership_started__isnull=False,
membership_ended__isnull=False)
# PLOT 1
##############
members = Attendee.objects.filter(membership_started__isnull=False,
membership_ended__isnull=False)
print members.count()
membership_durations = [
(member.membership_ended - member.membership_started).days / 356.0
for member in members]
membership_durations.sort()
for member in members:
print member, (member.membership_ended -
member.membership_started).days / 356.0
#pylab.hist(membership_durations, cumulative=True, normed=True)
pylab.hist(membership_durations)
pylab.title('Histogram of membership duration (years)')
pylab.ylabel('Number of members')
pylab.xlabel('Years a member')
pylab.savefig('membership_duration_histogram.pdf')
# PLOT 2
##############
guest_attendees = []
member_attendees = []
total_attendees = []
for meeting in Meeting.objects.all():
num_member_attendees = meeting.attendees.filter(
membership_started__isnull=False,
membership_ended__isnull=False).count()
num_attendees_total = meeting.attendees.count()
guest_attendees.append(
(meeting.date, num_attendees_total - num_member_attendees))
member_attendees.append((meeting.date, num_member_attendees))
total_attendees.append((meeting.date, num_attendees_total))
pylab.figure()
#
pylab.plot_date(guest_attendees[0], guest_attendees[1])
# member_attendees, 'g-',
# total_attendees, 'b-')
pylab.title('Attendence rates over time')
pylab.ylabel('Number of attendees')
pylab.xlabel('Year')
pylab.savefig('attendence_over_time.pdf')
def plotdepthtime(self, FontSize):
""" plot depth against time """
import pylab as plt
plt.plot_date(self.time, self.depth,'o')
#plt.title('Depth-time view')
plt.xlabel('time', fontsize=FontSize)
plt.ylabel('depth', fontsize=FontSize)
plt.gca().invert_yaxis()
def plot_sma(rpc,symbol,n=5, range=20 ):
data = rpc.exec_tool('tools.ClientTools.simple_moving_average',symbol, n)
sma,price, dates =zip(*data[:range])
#price = map(lambda x:x[1],data)
dates = map(lambda x: datetime.strptime(str(x),DATEFORMAT),dates)
ndates = pylab.date2num(dates)
for ydata in [sma,price]:
pylab.plot_date(ndates,ydata,'-')
def plotdepthtime(self, FontSize):
""" plot depth against time """
pylab.plot_date(vector_epoch2datetime(self['etime']), self['depth'],
'o')
#pylab.title('Depth-time view')
pylab.xlabel('time', fontsize=FontSize)
pylab.ylabel('depth', fontsize=FontSize)
pylab.gca().invert_yaxis()
def test_single_date():
time1=[ 721964.0 ]
data1=[ -65.54 ]
fig = pylab.figure()
pylab.subplot( 211 )
pylab.plot_date( time1, data1, 'o', color='r' )
pylab.subplot( 212 )
pylab.plot( time1, data1, 'o', color='r' )
fig.savefig( 'single_date' )
def plot_temp(self, tempVal, aver_temp, dateVal, title, xlabel="Days", ylabel="Temperature", label="Temperature"):
pylab.title(title)
pylab.xlabel(xlabel)
pylab.ylabel(ylabel)
pylab.plot_date(dateVal, tempVal, 'b-', label=label)
pylab.plot_date([min(dateVal), max(dateVal)], [aver_temp, aver_temp], 'r-', label="Average t="+str(round(aver_temp,2)))
pylab.grid(True)
pylab.legend(loc=0)
pylab.show()
def draw(domlist, index, func, max, tick, name, log=False):
if not log:
n1 = "Wikisource_-_%s.png" % name
else:
n1 = "Wikisource_-_%s.log.png" % name
fig = pylab.figure(1, figsize=(7, 7))
ax = fig.add_subplot(111)
pylab.clf()
pylab.hold(True)
pylab.grid(True)
for dom in domlist:
count = count_array[dom][index]
if func:
count = func(count, dom)
if count:
if not log:
pylab.plot(count[1],
count[0],
'-',
color=colors[dom],
label=dom,
linewidth=1.5)
else:
pylab.semilogy(count[1],
count[0],
'-',
color=colors[dom],
label=dom,
linewidth=1.5)
#pylab.xlabel("Days (since 9-24-2007)")
ymin, ymax = pylab.ylim()
pylab.plot_date(count[1], pylab.zeros(len(count[1])), 'k-')
ax.xaxis.set_major_locator(pylab.YearLocator())
ax.xaxis.set_major_formatter(pylab.DateFormatter('%Y-%m-%d'))
fig.autofmt_xdate()
if not log:
if max:
ymax = max
xmax = int(pylab.xlim()[1] / 7)
pylab.yticks(pylab.arange(0, max + 1, tick))
pylab.ylim(ymin, ymax)
else:
pylab.ylim(100, ymax)
pylab.legend(loc=2, ncol=2, prop={'size': 'small'})
pylab.title(name.replace('_', ' '))
pylab.savefig(savepath + n1)
return
def draw(self):
x = matplotlib.dates.date2num([e['time'] for e in self.history])
y = [e['entropy'] for e in self.history]
pylab.figure()
fig, ax = pylab.subplots()
pylab.setp(pylab.xticks()[1], rotation=30, ha='right')
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H:%M'))
pylab.plot_date(x, y)
pylab.savefig('%s.png' % self.name, bbox_inches='tight')
pylab.close()
def plot_field(self, field, suffix="", out_dir=None):
"""
Построение графика изменения параметра field
"""
tempVal = map(float, self.table[field])
aver_temp = sum(tempVal) / float(len(tempVal))
dateVal = self.get_date_month()
maxField, maxDateIdx = self.get_extremum_field(field)
maxDate = dateVal[maxDateIdx]
minField, minDateIdx = self.get_extremum_field(field, min)
minDate = dateVal[minDateIdx]
title = 'Day by Day ' + field.upper(
) + ' in ' + self.city + ' in ' + self.date.strftime(
"%B") + ' ' + self.date.strftime("%Y")
#self.plot_temp(tempVal, aver_temp, dateVal, title)
pylab.title(title)
pylab.xlabel(r"Days")
pylab.ylabel(field)
pylab.plot_date(dateVal, tempVal, 'b-', label=field)
pylab.plot_date([min(dateVal), max(dateVal)], [aver_temp, aver_temp],
'r-',
label="Average " + str(round(aver_temp, 2)))
pylab.grid(True)
pylab.legend(loc=0)
dt = dateVal[-1] - dateVal[0]
df = maxField - minField
#print dt.seconds
offset = (datetime.timedelta(days=dt.days / 20), df / 50)
#print offset
arrowprops = dict(width=0.2, facecolor='black', shrink=0.05)
posMax = (maxDate + offset[0], maxField + offset[1])
pylab.annotate(
'max = ' + str(round(maxField, 2)),
xy=(maxDate, maxField),
xytext=posMax,
arrowprops=arrowprops,
horizontalalignment='left',
verticalalignment='bottom',
)
posMin = (minDate - offset[0], minField - offset[1])
pylab.annotate(
'min = ' + str(round(minField, 2)),
xy=(minDate, minField),
xytext=posMin,
arrowprops=arrowprops,
horizontalalignment='right',
verticalalignment='top',
)
if out_dir is not None:
pylab.savefig(os.path.join(out_dir, field + "_" + suffix + ".png"))
pylab.close()
else:
pylab.show()
def csv2png(p):
print p
title, axis, data = get_data(p)
dates = data[0]
release_title, release_axis, release_data = get_data( py.path.local("release_dates.dat") )
release_dates, release_names = release_data
sprint_title, sprint_axis, sprint_data = get_data( py.path.local("sprint_dates.dat") )
sprint_locations, sprint_begin_dates, sprint_end_dates = sprint_data
ax = pylab.subplot(111)
for i, d in enumerate(data[1:]):
args = [dates, d, colors[i]]
pylab.plot_date(linewidth=0.8, *args)
ymax = max(pylab.yticks()[0]) #just below the legend
for i, release_date in enumerate(release_dates):
release_name = release_names[i]
if greyscale:
color = 0.3
else:
color = "g"
pylab.axvline(release_date, linewidth=0.8, color=color, alpha=0.5)
ax.text(release_date, ymax * 0.4, release_name,
fontsize=10,
horizontalalignment='right',
verticalalignment='top',
rotation='vertical')
for i, location in enumerate(sprint_locations):
begin = sprint_begin_dates[i]
end = sprint_end_dates[i]
if float(begin) >= float(min(dates[0],dates[-1])):
if greyscale:
color = 0.8
else:
color = "y"
pylab.axvspan(begin, end, linewidth=0, facecolor=color, alpha=0.5)
ax.text(begin, ymax * 0.85, location,
fontsize=10,
horizontalalignment='right',
verticalalignment='top',
rotation='vertical')
pylab.legend(axis[1:], "upper left")
pylab.ylabel(axis[0])
pylab.xlabel("")
ticklabels = ax.get_xticklabels()
pylab.setp(ticklabels, 'rotation', 45, size=9)
# ax.autoscale_view()
ax.grid(True)
pylab.title(title)
pylab.savefig(p.purebasename + ".png")
pylab.savefig(p.purebasename + ".eps")
py.process.cmdexec("epstopdf %s" % (p.purebasename + ".eps", ))
def newusersEvolution(cursor=None, title=''):
result = cursor.execute("SELECT STRFTIME('%Y-%m-%d', rev_timestamp) AS date, rev_user_text FROM revision WHERE 1 ORDER BY date ASC")
newusers = {}
for row in result:
if not newusers.has_key(row[1]):
newusers[row[1]] = datetime.date(year=int(row[0][0:4]), month=int(row[0][5:7]), day=int(row[0][8:10]))
newusers2 = {}
for newuser, date in newusers.items():
if newusers2.has_key(date):
newusers2[date] += 1
else:
newusers2[date] = 1
newusers_list = [[x, y] for x, y in newusers2.items()]
newusers_list.sort()
startdate = newusers_list[0][0]
enddate = newusers_list[-1:][0][0]
delta = datetime.timedelta(days=1)
newusers_list = [] #reset, adding all days between startdate and enddate
d = startdate
while d < enddate:
if newusers2.has_key(d):
newusers_list.append([d, newusers2[d]])
else:
newusers_list.append([d, 0])
d += delta
import pylab
from matplotlib.dates import DateFormatter, rrulewrapper, RRuleLocator, drange
loc = pylab.MonthLocator(bymonth=(1,6))
formatter = DateFormatter('%Y-%m-%d')
dates = drange(startdate, enddate, delta)
fig = pylab.figure()
ax = fig.add_subplot(1,1,1)
ax.set_ylabel('Newusers')
ax.set_xlabel('Date (YYYY-MM-DD)')
print '#'*100
print len(dates)
print dates
print '#'*100
print len(pylab.array([y for x, y in newusers_list]))
print pylab.array([y for x, y in newusers_list])
print '#'*100
pylab.plot_date(dates, pylab.array([y for x, y in newusers_list]), 'o', color='green')
ax.xaxis.set_major_locator(loc)
ax.xaxis.set_major_formatter(formatter)
ax.set_title(title)
ax.grid(True)
ax.set_yscale('log')
labels = ax.get_xticklabels()
pylab.setp(labels, rotation=30, fontsize=10)
def line_plot(data, out):
"""Turning ([key, ...], [value, ...]) into line graphs"""
import matplotlib as mpl
mpl.use('Agg')
import pylab
pylab.clf()
pylab.plot_date(data[0], data[1], '-')
pylab.savefig(out)
def test_single_date():
time1 = [721964.0]
data1 = [-65.54]
fig = pylab.figure()
pylab.subplot(211)
pylab.plot_date(time1, data1, 'o', color='r')
pylab.subplot(212)
pylab.plot(time1, data1, 'o', color='r')
fig.savefig('single_date')
def drawBacktest(benchmarkReturn, portReturn, dailyDate):
#pylab.plot_date(pylab.date2num(dailyDate),portReturn,'r',linewidth=0.8,linestyle='-')
#pylab.plot_date(pylab.date2num(dailyDate),benchmarkReturn,'g',linewidth=0.8,linestyle='-')
pylab.plot_date(dailyDate, portReturn,'r',linewidth=0.8,linestyle='-')
xtext = pylab.xlabel('Out-Of-Sample Date')
ytext = pylab.ylabel('Cumulative Return')
ttext = pylab.title('Portfolio Return Vs Benchmark Return')
pylab.grid(True)
pylab.setp(ttext, size='large', color='r')
pylab.setp(xtext, size='large', weight='bold', color='g')
pylab.setp(ytext, size='large', weight='light', color='b')
pylab.savefig('backtest.png')
def newpagesEvolution(cursor=None, title=''):
result = cursor.execute(
"SELECT STRFTIME('%Y-%m-%d', page_creation_timestamp) AS date, COUNT(*) AS count FROM page WHERE 1 GROUP BY date ORDER BY date ASC"
)
newpages = {}
for row in result:
d = datetime.date(year=int(row[0][0:4]),
month=int(row[0][5:7]),
day=int(row[0][8:10]))
newpages[d] = row[1]
newpages_list = [[x, y] for x, y in newpages.items()]
newpages_list.sort()
startdate = newpages_list[0][0]
enddate = newpages_list[-1:][0][0]
delta = datetime.timedelta(days=1)
newpages_list = [] #reset, adding all days between startdate and enddate
d = startdate
while d < enddate:
if newpages.has_key(d):
newpages_list.append([d, newpages[d]])
else:
newpages_list.append([d, 0])
d += delta
import pylab
from matplotlib.dates import DateFormatter, rrulewrapper, RRuleLocator, drange
loc = pylab.MonthLocator(bymonth=(1, 6))
formatter = DateFormatter('%Y-%m-%d')
dates = drange(startdate, enddate, delta)
fig = pylab.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_ylabel('Newpages')
ax.set_xlabel('Date (YYYY-MM-DD)')
print '#' * 100
print len(dates)
print dates
print '#' * 100
print len(pylab.array([y for x, y in newpages_list]))
print pylab.array([y for x, y in newpages_list])
print '#' * 100
pylab.plot_date(dates, pylab.array([y for x, y in newpages_list]), 'o')
ax.xaxis.set_major_locator(loc)
ax.xaxis.set_major_formatter(formatter)
ax.set_title(title)
ax.grid(True)
ax.set_yscale('log')
labels = ax.get_xticklabels()
pylab.setp(labels, rotation=30, fontsize=10)
def CloseCurve(df):
daytime = list(df["Date"])
closelist = list(df["Close"])
pylab.plot_date(pylab.date2num(daytime),
closelist,
marker='.',
mfc='darkblue',
linestyle='-')
plt.subplots_adjust(bottom=0.15)
plt.xlabel("date") #X轴标签
plt.ylabel("close") #Y轴标签
plt.title("hs300 close curve") #标题
plt.show()
def do_plot(x_axis, y_axis, title, xlabel, ylable):
import pylab, setting
pylab.plot_date(x_axis, y_axis, linestyle='dashed')
pylab.xlabel(xlabel)
pylab.ylabel(ylable)
pylab.title(title)
pylab.grid(True)
if setting.PIC_SAVE_PATH_AND_PREFIX:
title__png_ = setting.PIC_SAVE_PATH_AND_PREFIX + title + ".png"
pylab.savefig(title__png_)
print "draw:", title__png_
pylab.figure()
else:
pylab.show()
def test2():
today = datetime.now()
dates = [today + timedelta(days=i) for i in range(10)]
print(dates)
values = [3, 2, 8, 4, 5, 6, 7, 8, 11, 2]
pylab.plot_date(pylab.date2num(dates), values, linestyle='-')
#text(17, 277, '瞬时流量示意')
xt = [d.strftime('%m-%d') for d in dates]
xticks(dates, xt)
xlabel('时间time (s)')
ylabel('单位 (m3)')
title('供应商日销售汇总折线图')
grid(True)
show()
def n_lines_charts(labels, file_path, unit, type, y_max, y_title):
plt.style.use('ggplot')
plt.figure(figsize=(14, 4))
for x1, x2, x3, x4, x5 in labels:
pl.plot_date(x1, x2, label=x3, linestyle=x4, linewidth=x5)
ax = pl.gca()
xfmt = dt.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(xfmt)
pl.gcf().autofmt_xdate()
# Y轴单位，例如50%，100%
if 'SAMPLE' == type:
# 如果是原始变量，直接根据单位设置Y轴单位后缀
if 'percent' == unit:
y_tick = y_max
suffix = '%' # 单位后缀
elif 'bytes' == unit:
if y_max > 1024 * 1024 * 1024 * 1024:
y_tick = y_max / 1024 / 1024 / 1024 / 1024
suffix = 'T'
if y_max > 1024 * 1024 * 1024:
y_tick = y_max / 1024 / 1024 / 1024
suffix = 'G'
elif y_max > 1024 * 1024:
y_tick = y_max / 1024 / 1024
suffix = 'M'
elif y_max > 1024:
y_tick = y_max / 1024
suffix = 'K' # 网络流量单位转换
else:
y_tick = y_max
suffix = 'b'
else:
# 如果是计算变量，默认设置
y_tick = y_max
suffix = '%'
y_ticks = [0, 0.5 * y_max, y_max]
y_ticks_labels = [
'0', "%.1f" % (0.5 * y_tick) + suffix,
"%.1f" % y_tick + suffix
]
ax.set_yticks(y_ticks)
ax.set_yticklabels(y_ticks_labels)
pl.legend(loc='best', fontsize=6)
pl.grid(True)
pl.ylabel(y_title)
pl.savefig(file_path)
pl.cla()
def stat_day(messages):
import collections
from pylab import plot_date, show
date2num = collections.defaultdict(int)
for time, author, body in messages:
date2num[time.strftime('%y-%m-%d')] += 1
dates, nums = [], []
for k in sorted(date2num.iterkeys()):
y, m, d = k.split('-')
dates.append(datetime(int(y), int(m), int(d)))
nums.append(date2num[k])
plot_date(dates, nums, 'go')
show()
def newpagesEvolution(cursor=None, title=""):
result = cursor.execute(
"SELECT STRFTIME('%Y-%m-%d', page_creation_timestamp) AS date, COUNT(*) AS count FROM page WHERE 1 GROUP BY date ORDER BY date ASC"
)
newpages = {}
for row in result:
d = datetime.date(year=int(row[0][0:4]), month=int(row[0][5:7]), day=int(row[0][8:10]))
newpages[d] = row[1]
newpages_list = [[x, y] for x, y in newpages.items()]
newpages_list.sort()
startdate = newpages_list[0][0]
enddate = newpages_list[-1:][0][0]
delta = datetime.timedelta(days=1)
newpages_list = [] # reset, adding all days between startdate and enddate
d = startdate
while d < enddate:
if newpages.has_key(d):
newpages_list.append([d, newpages[d]])
else:
newpages_list.append([d, 0])
d += delta
import pylab
from matplotlib.dates import DateFormatter, rrulewrapper, RRuleLocator, drange
loc = pylab.MonthLocator(bymonth=(1, 6))
formatter = DateFormatter("%Y-%m-%d")
dates = drange(startdate, enddate, delta)
fig = pylab.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_ylabel("Newpages")
ax.set_xlabel("Date (YYYY-MM-DD)")
print "#" * 100
print len(dates)
print dates
print "#" * 100
print len(pylab.array([y for x, y in newpages_list]))
print pylab.array([y for x, y in newpages_list])
print "#" * 100
pylab.plot_date(dates, pylab.array([y for x, y in newpages_list]), "o")
ax.xaxis.set_major_locator(loc)
ax.xaxis.set_major_formatter(formatter)
ax.set_title(title)
ax.grid(True)
ax.set_yscale("log")
labels = ax.get_xticklabels()
pylab.setp(labels, rotation=30, fontsize=10)
def NavCurve(fileaddress):
df = pd.read_csv(fileaddress, parse_dates=['NavDate'])
daytime = list(df["NavDate"])
navlist = list(df["Nav"])
pylab.plot_date(pylab.date2num(daytime),
navlist,
marker='.',
mfc='darkblue',
linestyle='-')
plt.subplots_adjust(bottom=0.15)
plt.xlabel("date") #X轴标签
plt.ylabel("nav") #Y轴标签
plt.title("baseline nav curve") #标题
plt.show()
def plotData(inputFile, oriThick):
timestamp, metalloss = calcData(inputFile, oriThick)
timestamp = pylab.array(timestamp)
metalloss = pylab.array(metalloss)
# pylab.date2num(date),将date 类型转换为pylab的时间类型，
# 使用pylab.plot_date()画图
pylab.plot_date(pylab.date2num(timestamp),
metalloss,
'bo',
label='Metal Loss(mm)')
pylab.title('Metal Loss to Time')
pylab.xlabel('Time')
pylab.ylabel('Metal loss(mm)')
pylab.show()
def do_plot(x_axis, y_axis,title,xlabel,ylable):
import pylab,setting
pylab.plot_date(x_axis, y_axis, linestyle='dashed')
pylab.xlabel(xlabel)
pylab.ylabel(ylable)
pylab.title(title)
pylab.grid(True)
if setting.PIC_SAVE_PATH_AND_PREFIX:
title__png_ = setting.PIC_SAVE_PATH_AND_PREFIX + title + ".png"
pylab.savefig(title__png_)
print "draw:",title__png_
pylab.figure()
else:
pylab.show()
def draw_lsjz(data):
x, y = [], []
for i in data:
x.append(i[0])
y.append(float(i[1]))
x.reverse()
y.reverse()
pl.xticks(rotation='vertical')
pl.plot_date(x, y, 'r-')
index = 0
xl = len(x)
while index < xl:
pl.axvline(x=x[index], ymin=0, ymax=1)
index += 1
pl.show()
def fig_plot():
lj_hs_rd = open('lj_hs.txt', 'r')
wj_hs_rd = open('wj_hs.txt', 'r')
date_rd = open('date.txt', 'r')
lj_ay = []
wj_ay = []
dt_ay = []
for lj_line in lj_hs_rd.readlines():
lj_list = lj_line.strip().split(' ')
for i in range(len(lj_list)):
aa = int(lj_list[i])
lj_ay.append(aa)
for wj_line in wj_hs_rd.readlines():
wj_list = wj_line.strip().split(' ')
for j in range(len(wj_list)):
bb = int(wj_list[j])
wj_ay.append(bb)
for date_line in date_rd.readlines():
date_list = date_line.strip().split(' ')
for k in range(len(date_list)):
dt = datetime.datetime.strptime(date_list[k], '%m/%d')
dt_ay.append(dt)
dt_fl = pl.date2num(dt_ay)
plt.figure(1)
pl.gca().xaxis.set_major_formatter(mdate.DateFormatter('%m/%d'))
pl.gca().xaxis.set_major_locator(mdate.DayLocator())
pl.plot_date(dt_fl, lj_ay, label='lj hosue number', linestyle='-')
pl.plot_date(dt_fl, wj_ay, label='wawj house number', linestyle='-')
plt.xlabel('date')
plt.ylabel('house number')
plt.grid(True)
plt.legend(loc='upper left')
#plt.show()
plt.savefig('line_plot.png')
plt.figure(2)
total_width, n = 0.8, 2
width = total_width / n
plt.bar(dt_fl, lj_ay, width=width, label='lj')
plt.bar(dt_fl + width, wj_ay, width=width, label='wj')
plt.legend()
#plt.show()
plt.savefig('bar_plot.png')
def plotcounts(self, binsize, pngfile):
""" bin the data based on a bin size of binsize days """
#vector_datetime2epoch = scipy.vectorize(converttime.datetime2epoch)
#etime = vector_d2e(self['etime'])
etime = self['etime']
etimemin = scipy.floor(min(etime) / 86400) * 86400
etimemax = scipy.ceil(max(etime) / 86400) * 86400
r = [etimemin, etimemax]
nbins = (etimemax - etimemin) / (binsize * 86400)
h = scipy.histogram(etime, nbins, r)
ecount = h.__getitem__(0)
ebin = h.__getitem__(1)
#vector_e2d = scipy.vectorize(converttime.epoch2datetime)
edt = vector_epoch2datetime(ebin)
pylab.plot_date(edt, ecount, linestyle='steps-mid')
pylab.savefig(pngfile)
def group(factorX,factorY,groupNum):#Q是factorX,mv或动量是factorY
factorGroup=np.zeros([factorY.shape[0],groupNum])
for i in range(factorY.shape[0]):
factorSort=np.sort(factorX[i,])#np.sort 默认升序排列，nan在最后
factorSortIdx=np.argsort(factorX[i,])
firstNanIdx=np.where(np.isnan(factorSort)==True)[0][0]
factorSortIdx=factorSortIdx[:firstNanIdx]
inGroupNum=int(round(len(factorSortIdx)/groupNum))#每组股票数
for j in range(groupNum-1):
factorGroup[i,j]=np.nanmean(factorY[i,factorSortIdx[inGroupNum*j:inGroupNum*(j+1)]])
factorGroup[i,groupNum-1]=np.nanmean(factorY[i,factorSortIdx[inGroupNum*(groupNum-1):]])
for j in range(groupNum):
pylab.plot_date(date,mvGroup[:,j],linestyle='-')
label = [u'第1组',u'第2组',u'第3组',u'第4组',u'第5组']
plt.legend(label)
return factorGroup
def plotcounts(self, binsize, pngfile):
""" bin the data based on a bin size of binsize days """
#vector_datetime2epoch = scipy.vectorize(converttime.datetime2epoch)
#etime = vector_d2e(self['etime'])
etime = self['etime']
etimemin = scipy.floor(min(etime)/86400) * 86400
etimemax = scipy.ceil(max(etime)/86400) * 86400
r = [etimemin, etimemax]
nbins = (etimemax - etimemin) / (binsize * 86400)
h = scipy.histogram(etime, nbins, r)
ecount = h.__getitem__(0)
ebin = h.__getitem__(1)
#vector_e2d = scipy.vectorize(converttime.epoch2datetime)
edt = vector_epoch2datetime(ebin)
pylab.plot_date(edt, ecount, linestyle='steps-mid')
pylab.savefig(pngfile)
def specframeconvert(val='344.84e9Hz', inframe='TOPO', indoppler='RADIO', restfreq='1.410GHz', timebeg='2011/5/28/01h00m00', timeend='', direction='J2000 12h01m51 -18d52m00', observatory='VLA', outframe='LSRK', npoints=100, doplot=False):
"""
val= frequency or velocity quantity (e.g '113GHz' or '-25km/s')
inframe=frame of input value (e.g 'TOPO', 'GEO', BARY')
indoppler=doppler velocity definition if val is velocity ('RADIO', 'OPTICAL', 'Z' etc)
restfrequency=line rest frequency if val is in velocity units
timebeg=start of time range in UTC e.g '2012/12/25/00h00m00'
timeend=end of time range in UTC or '' if only one value is needed
direction=Source direction of interest (e.g 'J2000 12h01m51 -18d52m00')
observatory=name of observatory to know where on earth this calculation is for
outframe=frame of the output frequencies for the time range requested
npoints= number of points to have values calculated in time range
doplot=True or False (if Frequecy v/s Time plot is needed)
"""
me.doframe(me.observatory(observatory))
me.doframe(me.source(direction))
me.doframe(me.epoch('utc', timebeg))
epnow=me.epoch('utc', timebeg)
interval=0
xday=[]
yday=[]
if(timeend==''):
npoints=1
else:
interval=qa.convert(qa.sub(qa.quantity(timeend), qa.quantity(timebeg)), 's')['value']
interval=interval/npoints
if ((qa.quantity(val)['unit'].find('Hz')) <0 and (qa.quantity(val)['unit'].find('m/s') >=0)):
vel=me.doppler(indoppler, val)
freq=me.tofrequency(inframe, vel, qa.quantity(restfreq))['m0']
else:
freq=qa.quantity(val)
freqval=me.frequency(inframe, freq)
for k in range(npoints):
me.doframe(epnow)
xday.append(pl.date2num(dateutil.parser.parse(qa.time(epnow['m0'], form='ymd')[0])))
yday.append(me.measure(freqval, 'LSRK')['m0']['value'])
epnow['m0']=qa.add(epnow['m0'], qa.quantity(interval, 's'))
if(doplot):
pl.figure(1)
pl.clf()
pl.plot_date(xday, np.array(yday), 'o')
pl.xlabel('Time')
pl.ylabel('Frequency in Hz')
pl.title('Variation of frequency in '+outframe)
return np.array(yday)
def test_single_date(self):
"""Test single-point date plots."""
fname = self.outFile("single_date.png")
time1 = [721964.0]
data1 = [-65.54]
fig = pylab.figure()
pylab.subplot(211)
pylab.plot_date(time1, data1, 'o', color='r')
pylab.subplot(212)
pylab.plot(time1, data1, 'o', color='r')
fig.savefig(fname)
self.checkImage(fname)
def dataplot(self, *var, **adict):
"""
Plotting the data with variable names
"""
# calling convenience functions to clean-up input parameters
var, sel = self.__var_and_sel_clean(var, adict)
dates, nobs = self.__dates_and_nobs_clean(var, sel)
for i in var:
pylab.plot_date(dates, self.data[i][sel], 'o-')
pylab.xlabel("Time (n = " + str(nobs) + ")")
pylab.title("Data plot of " + self.DBname)
pylab.legend(var)
if adict.has_key('file'):
pylab.savefig(adict['file'], dpi=600)
pylab.show()
def diurnalPlot(headers):
""" diurnal plot of the emails, with years running along the x axis and times of day on the y axis."""
xday = []
ytime = []
for h in headers:
if len(h) > 1:
timestamp = mktime(parsedate(h[1][5:].replace('.', ':')))
mailstamp = datetime.fromtimestamp(timestamp)
xday.append(mailstamp)
# Time the email is arrived
# Note that years, month and day are not important here.
y = datetime(2010, 10, 14, mailstamp.hour, mailstamp.minute,
mailstamp.second)
ytime.append(y)
plot_date(xday, ytime, '.', alpha=.7)
xticks(rotation=30)
return xday, ytime
def test_graph():
today = datetime.now()
dates = [today + timedelta(days=i) for i in range(10)]
#values = [random.randint(1, 20) for i in range(10)]
values = [3,2,8,4,5,6,7,8,11,2]
pylab.plot_date(pylab.date2num(dates), values, linestyle='-')
text(17, 277, u'瞬时流量示意')
xtext = xlabel(u'时间time (s)')
ytext = ylabel(u'单位 (m3)')
ttext = title(u'xx示意图')
grid(True)
setp(ttext, size='large', color='r')
#setp(text, size='medium', name='courier', weight='bold',color='b')
setp(xtext, size='medium', name='courier', weight='bold', color='g')
setp(ytext, size='medium', name='helvetica', weight='light', color='b')
#savefig('simple_plot.png')
savefig('simple_plot')
show()