def __init__(self):
wx.Frame.__init__(self,
None,
wx.ID_ANY,
title='First Chart',
size=(800, 700))
datafile = matplotlib.get_example_data('goog.npy')
r = np.load(datafile).view(np.recarray)
datesFloat = matplotlib.dates.date2num(r.date)
figure = Figure()
xMaxDatetime = r.date[len(r.date) - 1]
xMinDatetime = r.date[0]
xMaxFloat = datesFloat[len(datesFloat) - 1]
xMinFloat = datesFloat[0]
yMin = min(r.adj_close) // 5 * 5
yMax = (1 + max(r.adj_close) // 5) * 5
master = figure.add_subplot(211)
master.plot(datesFloat, r.adj_close)
master.xaxis.set_minor_locator(mdates.MonthLocator())
master.xaxis.set_major_locator(
mdates.MonthLocator(bymonth=(1, 4, 7, 10)))
master.xaxis.set_major_formatter(mdates.DateFormatter('%b-%y'))
master.set_xlim(datesFloat[120], datesFloat[120] + 92)
master.yaxis.set_minor_locator(mtickers.MultipleLocator(50))
master.yaxis.set_major_locator(mtickers.MultipleLocator(100))
master.set_ylim(yMin, yMax)
master.set_position([0.05, 0.20, 0.92, 0.75])
master.xaxis.grid(True, which='minor')
master.yaxis.grid(True, which='minor')
slave = figure.add_subplot(212, yticks=[])
slave.plot(datesFloat, r.adj_close)
slave.xaxis.set_minor_locator(mdates.MonthLocator())
slave.xaxis.set_major_locator(mdates.YearLocator())
slave.xaxis.set_major_formatter(mdates.DateFormatter('%b-%y'))
slave.set_xlim(xMinDatetime, xMaxDatetime)
slave.set_ylim(yMin, yMax)
slave.set_position([0.05, 0.05, 0.92, 0.10])
rectangle = mpatches.Rectangle((datesFloat[120], yMin),
92,
yMax - yMin,
facecolor='yellow',
alpha=0.4)
slave.add_patch(rectangle)
canvas = FigureCanvas(self, -1, figure)
drag = DraggableRectangle(rectangle, master, xMinFloat, xMaxFloat - 92)
drag.connect()
def __init__(self):
wx.Frame.__init__(self, None, wx.ID_ANY, title='First Chart', size=(800, 700))
datafile = matplotlib.get_example_data('goog.npy')
r = np.load(datafile).view(np.recarray)
datesFloat = matplotlib.dates.date2num(r.date)
figure = Figure()
xMaxDatetime = r.date[len(r.date)-1]
xMinDatetime = r.date[0]
xMaxFloat = datesFloat[len(datesFloat)-1]
xMinFloat = datesFloat[0]
yMin = min(r.adj_close) // 5 * 5
yMax = (1 + max(r.adj_close) // 5) * 5
master = figure.add_subplot(211)
master.plot(datesFloat, r.adj_close)
master.xaxis.set_minor_locator(mdates.MonthLocator())
master.xaxis.set_major_locator(mdates.MonthLocator(bymonth=(1,4,7,10)))
master.xaxis.set_major_formatter(mdates.DateFormatter('%b-%y'))
master.set_xlim(datesFloat[120], datesFloat[120]+92)
master.yaxis.set_minor_locator(mtickers.MultipleLocator(50))
master.yaxis.set_major_locator(mtickers.MultipleLocator(100))
master.set_ylim(yMin, yMax)
master.set_position([0.05,0.20,0.92,0.75])
master.xaxis.grid(True, which='minor')
master.yaxis.grid(True, which='minor')
slave = figure.add_subplot(212, yticks=[])
slave.plot(datesFloat, r.adj_close)
slave.xaxis.set_minor_locator(mdates.MonthLocator())
slave.xaxis.set_major_locator(mdates.YearLocator())
slave.xaxis.set_major_formatter(mdates.DateFormatter('%b-%y'))
slave.set_xlim(xMinDatetime, xMaxDatetime)
slave.set_ylim(yMin, yMax)
slave.set_position([0.05,0.05,0.92,0.10])
rectangle = mpatches.Rectangle((datesFloat[120], yMin), 92, yMax-yMin, facecolor='yellow', alpha = 0.4)
slave.add_patch(rectangle)
canvas = FigureCanvas(self, -1, figure)
drag = DraggableRectangle(rectangle, master, xMinFloat, xMaxFloat - 92)
drag.connect()
import datetime
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.mlab as mlab
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
# load a numpy record array from yahoo csv data with fields date,
# open, close, volume, adj_close from the mpl-data/example directory.
# The record array stores python datetime.date as an object array in
# the date column
datafile = matplotlib.get_example_data('goog.npy')
r = np.load(datafile).view(np.recarray)
#print r
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(r.date, r.adj_close)
#line = matplotlib.lines.Line2D([2009,2009], [100,400], color='red', linestyle='-.')
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
datemin = datetime.date(r.date.min().year, 1, 1)
datemax = datetime.date(r.date.max().year+1, 1, 1)
import datetime
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.mlab as mlab
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter('%Y')
# load a numpy record array from yahoo csv data with fields date,
# open, close, volume, adj_close from the mpl-data/example directory.
# The record array stores python datetime.date as an object array in
# the date column
datafile = matplotlib.get_example_data('goog.npy')
r = np.load(datafile).view(np.recarray)
#print r
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(r.date, r.adj_close)
#line = matplotlib.lines.Line2D([2009,2009], [100,400], color='red', linestyle='-.')
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
datemin = datetime.date(r.date.min().year, 1, 1)
datemax = datetime.date(r.date.max().year + 1, 1, 1)
import datetime
import numpy as np
import matplotlib
import matplotlib.dates as dates
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
fh = matplotlib.get_example_data("aapl.npy")
r = np.load(fh)
fh.close()
r = r[-250:] # get the last 250 days
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(r.date, r.adj_close)
ax.xaxis.set_major_locator(dates.MonthLocator())
ax.xaxis.set_minor_locator(dates.MonthLocator(bymonthday=15))
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.xaxis.set_minor_formatter(dates.DateFormatter("%b"))
for tick in ax.xaxis.get_minor_ticks():
tick.tick1line.set_markersize(0)
tick.tick2line.set_markersize(0)
tick.label1.set_horizontalalignment("center")
imid = len(r) / 2
ax.set_xlabel(str(r.date[imid].year))
plt.show()
#
#
# but this doesn't help center the label between ticks. One solution
# is to "face it". Use the minor ticks to place a tick centered
# between the major ticks. Here is an example that labels the months,
# centered between the ticks
import datetime
import numpy as np
import matplotlib
import matplotlib.dates as dates
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
# load some financial data; apple's stock price
fh = matplotlib.get_example_data('aapl.npy')
r = np.load(fh); fh.close()
r = r[-250:] # get the last 250 days
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(r.date, r.adj_close)
ax.xaxis.set_major_locator(dates.MonthLocator())
ax.xaxis.set_minor_locator(dates.MonthLocator(bymonthday=15))
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.xaxis.set_minor_formatter(dates.DateFormatter('%b'))
for tick in ax.xaxis.get_minor_ticks():
tick.tick1line.set_markersize(0)