|
A bit of searching gave me this much
simpler solution:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import datetime
import matplotlib.dates as m_dates
import matplotlib.ticker as ticker
def make_month_axis(dates, y, ax, fig):
newax = fig.add_axes(ax.get_position())
newax.spines['bottom'].set_position(('outward', 25))
newax.patch.set_visible(False)
newax.yaxis.set_visible(False)
newax.plot_date(dates, y, visible=False)
newax.xaxis.set_major_locator(m_dates.MonthLocator())
newax.xaxis.set_minor_locator(m_dates.MonthLocator(bymonthday=15))
newax.xaxis.set_major_formatter(ticker.NullFormatter())
newax.xaxis.set_minor_formatter(m_dates.DateFormatter('%b'))
for tick in newax.xaxis.get_minor_ticks():
tick.tick1line.set_markersize(0)
tick.tick2line.set_markersize(0)
tick.label1.set_horizontalalignment('center')
start = datetime.datetime(2012, 8, 26)
dates = [start]
for i in range(1,10):
dates.append(dates[-1] + datetime.timedelta(days=7))
y = np.random.normal(10, 5, len(dates))
fig = plt.figure()
fig.subplots_adjust(bottom=0.2, right=0.85, wspace=.8, hspace=.8)
ax = fig.add_subplot(1,1,1)
ax.plot(dates, y)
ax.xaxis.set_major_formatter(
matplotlib.dates.DateFormatter('%W'))
make_month_axis(dates = dates, y = y, ax = ax, fig = fig)
plt.show()
Paul
On 10/8/12 11:03 PM, Paul Tremblay wrote:
I often have to make graphs with time series, with each point
being the start of a week. Below is the result I wish:
However, in order to make the secondary x axis the the month
labels, I need something like 40 lines of code. My strategy
consists in first drawing the rates in the normal way and using
matplotlib's dateFormatter to set the week numbers. That part is
easy.
In order to draw the x axis below to show when the weeks occur, I
draw two more lines, and makes these lines invisible. The first
line has as the x points the start of each month, and the y values
as the corresponding rate. The second line has as its x value the
middle of each month, with the corresponding y value.
Below is my code. I have to use such a graph quite often--so
often, in fact, that if I don't find an easier way to make the
secondary x axis, I will store the code in my own library for
re-use. Does anyone know of a simpler, more elegant way?
Thanks!
Paul
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates
from datetime import datetime
from datetime import timedelta
def unique_months(dates, rates):
"""
returns two lists, one of the first time a month occurs, the
second, the
rates representing that month
"""
y = [rates[0]]
temp = dates[0] - timedelta(days=dates[0].day)
months = [temp]
found = [dates[0].month]
counter = 0
for d in dates:
month = d.month
if month not in found:
new_date = d - timedelta(days=d.day)
months.append(new_date)
y.append(rates[counter])
found.append(month)
counter += 1
return (months, y)
def half_months(dates, defects):
"""
returns two lists, one the middle of the month occurs, the
second, the
rates representing that month
"""
months, y = unique_months(dates, defects)
new_months =[]
new_y = []
counter = 0
for m in months:
new_date = m - timedelta(days=m.day) + timedelta(days=15)
if new_date <= months[-1]:
new_months.append(new_date)
new_y.append(y[counter])
counter += 1
return new_months, new_y
dates = [datetime(2012,8,19), datetime(2012,8,26),datetime(2012,
9, 3), datetime(2012,9,10), datetime(2012,9,17),
datetime(2012,9,24),
datetime(2012,10,1), datetime(2012,10,8)]
rates = [2,3,4,2,5,3,7,2]
fig = plt.figure()
fig.set_size_inches(3,3)
x1, y1 = unique_months(dates, rates)
ax = fig.add_subplot(1,1,1)
ax.yaxis.grid(True, linestyle='-', which='major',
color='lightgrey', alpha=0.5)
ax.set_axisbelow(True)
fig.subplots_adjust(bottom=0.2, right=0.85, wspace=.8, hspace=.8)
# plot dates and rates
ax.plot(dates, rates)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%W'))
# start with bottom axis
x2, y2 = half_months(dates, rates)
# add tick marks for start of month
# x1 starts at first of each mont; y1 doesn't matter
newax = fig.add_axes(ax.get_position())
newax.spines['bottom'].set_position(('outward', 25))
newax.patch.set_visible(False)
newax.yaxis.set_visible(False)
newax.plot_date(x1, y1, visible=False)
newax.xaxis.set_major_locator( matplotlib.dates.MonthLocator())
newax.set_xticklabels([])
# set labels for months, inbetween tick marsk
# x2 is 15th of each month
axmlab = fig.add_axes(ax.get_position())
axmlab.spines['bottom'].set_position(('outward', 25))
axmlab.patch.set_visible(False)
axmlab.yaxis.set_visible(False)
axmlab.plot_date(x2, y2, visible=False)
axmlab.xaxis.set_major_formatter(
matplotlib.dates.DateFormatter('%b'))
axmlab.xaxis.set_major_locator(matplotlib.dates.DayLocator(bymonthday=1))
# get rid of tick marks for this axis
for i, line in enumerate(axmlab.get_xticklines() +
newax.get_yticklines()):
line.set_visible(False)
axmlab.plot_date(x2, y2, visible=False)
axmlab.xaxis.set_major_locator( matplotlib.dates.MonthLocator())
plt.xlabel('Week in Year')
ax.set_ylabel('Rates')
plt.savefig('temp.png', dpi=100)
|
