I have a pandas dataframe, where there are two columns: time and value. The column time has a timestep from 0 ... to 200. Where in each column value, there's a numpy array with shape (100, 3). Every element of the array is a 3-value tuple (left boundary, right boundary, count). Where left/right boundary is a range, in which histogram's bin is counted. And count is number of counts in a given histogram.
I want to produce a plot, where x axis corresponds to time, y axis corresponds to bins in value and counts corresponds to transparency.
In the plot below, every "less transparent" spot, signifies higher density of the histogram. Where every point on the x axis is a time step, for which one histogram for values on y axis is produced.
I have tried to set transparency to counts/max(all_count) and use fill_between. But still can't reproduce graph above, but I get this one below.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# df - it is my dataframe
# here is example of data, of first timestep
# for the first 5 bins:
# array([[-2.77325630e+00, -2.75546048e+00, 3.90000000e+01],
# [-2.75546048e+00, -2.73766467e+00, 1.75000000e+02],
# [-2.73766467e+00, -2.71986885e+00, 3.41000000e+02],
# [-2.71986885e+00, -2.70207303e+00, 9.55000000e+02],
# [-2.70207303e+00, -2.68427721e+00, 2.80700000e+03]])
fig, ax = plt.subplots()
in df.iterrows()])
for i, row in df.iterrows():
left = np.array(row['value'])[:, 0]
right = np.array(row['value'])[:, 1]
count = np.array(row['value'])[:, 2]
# normalize each timestep
transparency = count / count.max()
ax.fill_between(i, left, right, alpha=transparency, color='blue')
ax.set_xlabel("Time")
ax.set_ylabel("Bins in Value")
plt.show()
Related
I am trying to plot multiple different plots on a single matplotlib figure with in a for loop. At the moment it is all good in matlab as shown in the picture below and then am able to save the figure as a video frame. Here is a link of a sample video generated in matlab for 10 frames
In python, tried it as below
import matplotlib.pyplot as plt
for frame in range(FrameStart,FrameEnd):#loop1
# data generation code within a for loop for n frames from source video
array1 = np.zeros((200, 3800))
array2 = np.zeros((19,2))
array3 = np.zeros((60,60))
for i in range(len(array2)):#loop2
#generate data for arrays 1 to 3 from the frame data
#end loop2
plt.subplot(6,1,1)
plt.imshow(DataArray,cmap='gray')
plt.subplot(6, 1, 2)
plt.bar(data2D[:,0], data2D[:,1])
plt.subplot(2, 2, 3)
plt.contourf(mapData)
# for fourth plot, use array2[3] and array2[5], plot it as shown and keep the\is #plot without erasing for next frame
not sure how to do the 4th axes with line plots. This needs to be there (done using hold on for this axis in matlab) for the entire sequence of frames processing in the for loop while the other 3 axes needs to be erased and updated with new data for each frame in the movie. The contour plot needs to be square all the time with color bar on the side. At the end of each frame processing, once all the axes are updated, it needs to be saved as a frame of a movie. Again this is easily done in matlab, but not sure in python.
Any suggestions
thanks
I guess you need something like this format.
I have used comments # in code to answer your queries. Please check the snippet
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(6,6))
ax1=fig.add_subplot(311) #3rows 1 column 1st plot
ax2=fig.add_subplot(312) #3rows 1 column 2nd plot
ax3=fig.add_subplot(325) #3rows 2 column 5th plot
ax4=fig.add_subplot(326) #3rows 2 column 6th plot
plt.show()
To turn off ticks you can use plt.axis('off'). I dont know how to interpolate your format so left it blank . You can adjust your figsize based on your requirements.
import numpy as np
from numpy import random
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(6,6)) #First is width Second is height
ax1=fig.add_subplot(311)
ax2=fig.add_subplot(312)
ax3=fig.add_subplot(325)
ax4=fig.add_subplot(326)
#Bar Plot
langs = ['C', 'C++', 'Java', 'Python', 'PHP']
students = [23,17,35,29,12]
ax2.bar(langs,students)
#Contour Plot
xlist = np.linspace(-3.0, 3.0, 100)
ylist = np.linspace(-3.0, 3.0, 100)
X, Y = np.meshgrid(xlist, ylist)
Z = np.sqrt(X**2 + Y**2)
cp = ax3.contourf(X, Y, Z)
fig.colorbar(cp,ax=ax3) #Add a colorbar to a plot
#Multiple line plot
x = np.linspace(-1, 1, 50)
y1 = 2*x + 1
y2 = 2**x + 1
ax4.plot(x, y2)
ax4.plot(x, y1, color='red',linewidth=1.0)
plt.tight_layout() #Make sures plots dont overlap
plt.show()
I'm having trouble understanding Pandas subplots - and how to create axes so that all subplots are shown (not over-written by subsequent plot).
For each "Site", I want to make a time-series plot of all columns in the dataframe.
The "Sites" here are 'shark' and 'unicorn', both with 2 variables. The output should be be 4 plotted lines - the time-indexed plot for Var 1 and Var2 at each site.
Make Time-Indexed Data with Nans:
df = pd.DataFrame({
# some ways to create random data
'Var1':pd.np.random.randn(100),
'Var2':pd.np.random.randn(100),
'Site':pd.np.random.choice( ['unicorn','shark'], 100),
# a date range and set of random dates
'Date':pd.date_range('1/1/2011', periods=100, freq='D'),
# 'f':pd.np.random.choice( pd.date_range('1/1/2011', periods=365,
# freq='D'), 100, replace=False)
})
df.set_index('Date', inplace=True)
df['Var2']=df.Var2.cumsum()
df.loc['2011-01-31' :'2011-04-01', 'Var1']=pd.np.nan
Make a figure with a sub-plot for each site:
fig, ax = plt.subplots(len(df.Site.unique()), 1)
counter=0
for site in df.Site.unique():
print(site)
sitedat=df[df.Site==site]
sitedat.plot(subplots=True, ax=ax[counter], sharex=True)
ax[0].title=site #Set title of the plot to the name of the site
counter=counter+1
plt.show()
However, this is not working as written. The second sub-plot ends up overwriting the first. In my actual use case, I have 14 variable number of sites in each dataframe, as well as a variable number of 'Var1, 2, ...'. Thus, I need a solution that does not require creating each axis (ax0, ax1, ...) by hand.
As a bonus, I would love a title of each 'site' above that set of plots.
The current code over-writes the first 'Site' plot with the second. What I missing with the axes here?!
When you are using DataFrame.plot(..., subplot=True) you need to provide the correct number of axes that will be used for each column (and with the right geometry, if using layout=). In your example, you have 2 columns, so plot() needs two axes, but you are only passing one in ax=, therefore pandas has no choice but to delete all the axes and create the appropriate number of axes itself.
Therefore, you need to pass an array of axes of length corresponding to the number of columns you have in your dataframe.
# the grouper function is from itertools' cookbook
from itertools import zip_longest
def grouper(iterable, n, fillvalue=None):
"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
fig, axs = plt.subplots(len(df.Site.unique())*(len(df.columns)-1),1, sharex=True)
for (site,sitedat),axList in zip(df.groupby('Site'),grouper(axs,len(df.columns)-1)):
sitedat.plot(subplots=True, ax=axList)
axList[0].set_title(site)
plt.tight_layout()
I need to draw many such rows (for a0 .. a128) in a single window. I've searched in FacetGrid, PairGrid and all over around but couldn't find. Only regplot has similar argument ax but it doesn't plot histograms. My data is 128 real valued features with label column [0, 1]. I need the graphs to be shown from my Python code as a separate application on Linux.
Also, it there a way to scale this histogram to show relative values on Y such that the right curve is not skewed?
g = sns.FacetGrid(df, col="Result")
g.map(plt.hist, "a0", bins=20)
plt.show()
Just a simple example using matplotlib. The code is not optimized (ugly, but simple plot-indexing):
import numpy as np
import matplotlib.pyplot as plt
N = 5
data = np.random.normal(size=(N*N, 1000))
f, axarr = plt.subplots(N, N) # maybe you want sharex=True, sharey=True
pi = [0,0]
for i in range(data.shape[0]):
if pi[1] == N:
pi[0] += 1 # next row
pi[1] = 0 # first column again
axarr[pi[0], pi[1]].hist(data[i], normed=True) # i was wrong with density;
# normed=True should be used
pi[1] += 1
plt.show()
Output:
I have a simple stacked line plot that has exactly the date format I want magically set when using the following code.
df_ts = df.resample("W", how='max')
df_ts.plot(figsize=(12,8), stacked=True)
However, the dates mysteriously transform themselves to an ugly and unreadable format when plotting the same data as a bar plot.
df_ts = df.resample("W", how='max')
df_ts.plot(kind='bar', figsize=(12,8), stacked=True)
The original data was transformed a bit to have the weekly max. Why is this radical change in automatically set dates happening? How can I have the nicely formatted dates as above?
Here is some dummy data
start = pd.to_datetime("1-1-2012")
idx = pd.date_range(start, periods= 365).tolist()
df=pd.DataFrame({'A':np.random.random(365), 'B':np.random.random(365)})
df.index = idx
df_ts = df.resample('W', how= 'max')
df_ts.plot(kind='bar', stacked=True)
The plotting code assumes that each bar in a bar plot deserves its own label.
You could override this assumption by specifying your own formatter:
ax.xaxis.set_major_formatter(formatter)
The pandas.tseries.converter.TimeSeries_DateFormatter that Pandas uses to format the dates in the "good" plot works well with line plots when the x-values are dates. However, with a bar plot the x-values (at least those received by TimeSeries_DateFormatter.__call__) are merely integers starting at zero. If you try to use TimeSeries_DateFormatter with a bar plot, all the labels thus start at the Epoch, 1970-1-1 UTC, since this is the date which corresponds to zero. So the formatter used for line plots is unfortunately useless for bar plots (at least as far as I can see).
The easiest way I see to produce the desired formatting is to generate and set the labels explicitly:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as ticker
start = pd.to_datetime("5-1-2012")
idx = pd.date_range(start, periods=365)
df = pd.DataFrame({'A': np.random.random(365), 'B': np.random.random(365)})
df.index = idx
df_ts = df.resample('W').max()
ax = df_ts.plot(kind='bar', stacked=True)
# Make most of the ticklabels empty so the labels don't get too crowded
ticklabels = ['']*len(df_ts.index)
# Every 4th ticklable shows the month and day
ticklabels[::4] = [item.strftime('%b %d') for item in df_ts.index[::4]]
# Every 12th ticklabel includes the year
ticklabels[::12] = [item.strftime('%b %d\n%Y') for item in df_ts.index[::12]]
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
plt.gcf().autofmt_xdate()
plt.show()
yields
For those looking for a simple example of a bar plot with dates:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
dates = pd.date_range('2012-1-1', '2017-1-1', freq='M')
df = pd.DataFrame({'A':np.random.random(len(dates)), 'Date':dates})
fig, ax = plt.subplots()
df.plot.bar(x='Date', y='A', ax=ax)
ticklabels = ['']*len(df)
skip = len(df)//12
ticklabels[::skip] = df['Date'].iloc[::skip].dt.strftime('%Y-%m-%d')
ax.xaxis.set_major_formatter(mticker.FixedFormatter(ticklabels))
fig.autofmt_xdate()
# fixes the tracker
# https://matplotlib.org/users/recipes.html
def fmt(x, pos=0, max_i=len(ticklabels)-1):
i = int(x)
i = 0 if i < 0 else max_i if i > max_i else i
return dates[i]
ax.fmt_xdata = fmt
plt.show()
I've struggled with this problem too, and after reading several posts came up with the following solution, which seems to me slightly clearer than matplotlib.dates approach.
Labels without modification:
# Use DatetimeIndex instead of date_range for pandas earlier than 1.0.0 version
timeline = pd.date_range(start='2018, November', freq='M', periods=15)
df = pd.DataFrame({'date': timeline, 'value': np.random.randn(15)})
df.set_index('date', inplace=True)
df.plot(kind='bar', figsize=(12, 8), color='#2ecc71')
Labels with modification:
def line_format(label):
"""
Convert time label to the format of pandas line plot
"""
month = label.month_name()[:3]
if month == 'Jan':
month += f'\n{label.year}'
return month
# Note that we specify rot here
ax = df.plot(kind='bar', figsize=(12, 8), color='#2ecc71', rot=0)
ax.set_xticklabels(map(line_format, df.index))
This approach will add year to the label only if it is January
Here's an easy approach with pandas plot() and without using matplotlib dates:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# generate sample data
start = pd.to_datetime("1-1-2012")
index = pd.date_range(start, periods= 365)
df = pd.DataFrame({'A' : np.random.random(365), 'B' : np.random.random(365)}, index=index)
# resample to any timeframe you need, e.g. months
df_months = df.resample("M").sum()
# plot
fig, ax = plt.subplots()
df_months.plot(kind="bar", figsize=(16,5), stacked=True, ax=ax)
# format xtick-labels with list comprehension
ax.set_xticklabels([x.strftime("%Y-%m") for x in df_months.index], rotation=45)
plt.show()
How to get nicely formatted dates like the pandas line plot
The issue is that the pandas bar plot processes the date variable as a categorical variable where each date is considered to be a unique category, so the x-axis units are set to integers starting at 0 (like the default DataFrame index when none is assigned) and the full string of each date is shown without any automatic formatting.
Here are two solutions to format the date tick labels of a pandas (stacked) bar chart of a time series:
The first is a variation of the answer by unutbu and is made to better fit the data shown in the question;
The second is a generalized solution that lets you use matplotlib date tick locators and formatters which produces appropriate date labels for time series of any type of frequency.
But first, let's see what the nicely formatted tick labels look like when the sample data is plotted with a pandas line plot.
Default pandas line plot date formatting
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.dates as mdates # v 3.3.2
# Create sample dataset with a daily frequency and resample it to a weekly frequency
rng = np.random.default_rng(seed=123) # random number generator
idx = pd.date_range(start='2012-01-01', end='2013-12-31', freq='D')
df_raw = pd.DataFrame(rng.random(size=(idx.size, 3)),
index=idx, columns=list('ABC'))
df = df_raw.resample('W').sum() # default is 'W-SUN'
# Create pandas stacked line plot
ax = df.plot(stacked=True, figsize=(10,5))
Because the data is grouped by week with timestamps for Sundays (frequency W-SUN), the monthly tick labels are not necessarily placed on the first day of the month and there can be 3 or 4 weeks between each first week of the month so the minor ticks are unevenly spaced (noticeable if you look closely). Here are the exact dates of the major ticks:
# Convert major x ticks to date labels
np.array([mdates.num2date(tick*7-4).strftime('%Y-%b-%d') for tick in ax.get_xticks()])
"""
array(['2012-Jan-01', '2012-Apr-01', '2012-Jul-01', '2012-Oct-07',
'2013-Jan-06', '2013-Apr-07', '2013-Jul-07', '2013-Oct-06',
'2014-Jan-05'], dtype='<U11')
"""
The challenge lies in selecting the ticks for each first week of the month seeing as they are unequally spaced. Other answers have provided simple solutions based on a fixed tick frequency which produces oddly spaced labels in terms of dates where the months can be sometimes repeated (for example the month of July in unutbu's answer). Or they have provided solutions based on a monthly time series instead of a weekly time series, which is simpler to format seeing as there are always 12 months per year. So here is a solution that gives nicely formatted tick labels like in the pandas line plot and that works for any frequency of data.
Solution 1: pandas bar plot with tick labels based on the DatetimeIndex
# Create pandas stacked bar chart
ax = df.plot.bar(stacked=True, figsize=(10,5))
# Create list of monthly timestamps by selecting the first weekly timestamp of each
# month (in this example, the first Sunday of each month)
monthly_timestamps = [timestamp for idx, timestamp in enumerate(df.index)
if (timestamp.month != df.index[idx-1].month) | (idx == 0)]
# Automatically select appropriate number of timestamps so that x-axis does
# not get overcrowded with tick labels
step = 1
while len(monthly_timestamps[::step]) > 10: # increase number if time range >3 years
step += 1
timestamps = monthly_timestamps[::step]
# Create tick labels from timestamps
labels = [ts.strftime('%b\n%Y') if ts.year != timestamps[idx-1].year
else ts.strftime('%b') for idx, ts in enumerate(timestamps)]
# Set major ticks and labels
ax.set_xticks([df.index.get_loc(ts) for ts in timestamps])
ax.set_xticklabels(labels)
# Set minor ticks without labels
ax.set_xticks([df.index.get_loc(ts) for ts in monthly_timestamps], minor=True)
# Rotate and center labels
ax.figure.autofmt_xdate(rotation=0, ha='center')
To my knowledge, there is no way of getting this exact label formatting with the matplotlib.dates (mdates) tick locators and formatters. Nevertheless, combining mdates functionalities with a pandas stacked bar plot can come in handy if you prefer using tick locators/formatters or if you want to have dynamic ticks when using the interactive interface of matplotlib (to pan/zoom in and out).
At this point, it may be useful to consider creating the stacked bar plot in matplotlib directly, where you need to loop through the variables to create the stacked bar. The pandas-based solution shown below works by looping through the patches of the bars to relocate them according to matplotlib date units. So it is basically one loop instead of another, up to you to see which is more convenient.
Solution 2: pandas bar plot with matplotlib tick locators and formatters
This generalized solution uses the mdates AutoDateLocator which places ticks at the beginning of months/years. If you generate data and timestamps with pd.date_range in pandas (like in this example), you should keep in mind that the commonly used 'M' and 'Y' frequencies produce timestamps for the end date of the periods. The code given in the following example aligns monthly/yearly tick marks with 'MS' and 'YS' frequencies.
If you import a dataset using end-of-period dates (or some other type of pandas frequency not aligned with AutoDateLocator ticks), I am not aware of any convenient way to shift the AutoDateLocator accordingly so that the labels become correctly aligned with the bars. I see two options: i) resample the data using df.resample('MS').sum() if that does not cause any issue regarding the meaning of the underlying data; ii) or else use another date locator.
This issue causes no problem in the following example seeing as the data has a week end frequency 'W-SUN' so the monthly/yearly labels placed at a month/year start frequency are fine.
# Create pandas stacked bar chart with the default bar width = 0.5
ax = df.plot.bar(stacked=True, figsize=(10,5))
# Compute width of bars in matplotlib date units, 'md' (in days) and adjust it if
# the bar width in df.plot.bar has been set to something else than the default 0.5
bar_width_md_default, = np.diff(mdates.date2num(df.index[:2]))/2
bar_width = ax.patches[0].get_width()
bar_width_md = bar_width*bar_width_md_default/0.5
# Compute new x values in matplotlib date units for the patches (rectangles) that
# make up the stacked bars, adjusting the positions according to the bar width:
# if the frequency is in months (or years), the bars may not always be perfectly
# centered over the tick marks depending on the number of days difference between
# the months (or years) given by df.index[0] and [1] used to compute the bar
# width, this should not be noticeable if the bars are wide enough.
x_bars_md = mdates.date2num(df.index) - bar_width_md/2
nvar = len(ax.get_legend_handles_labels()[1])
x_patches_md = np.ravel(nvar*[x_bars_md])
# Set bars to new x positions and adjust width: this loop works fine with NaN
# values as well because in bar plot NaNs are drawn with a rectangle of 0 height
# located at the foot of the bar, you can verify this with patch.get_bbox()
for patch, x_md in zip(ax.patches, x_patches_md):
patch.set_x(x_md)
patch.set_width(bar_width_md)
# Set major ticks
maj_loc = mdates.AutoDateLocator()
ax.xaxis.set_major_locator(maj_loc)
# Show minor tick under each bar (instead of each month) to highlight
# discrepancy between major tick locator and bar positions seeing as no tick
# locator is available for first-week-of-the-month frequency
ax.set_xticks(x_bars_md + bar_width_md/2, minor=True)
# Set major tick formatter
zfmts = ['', '%b\n%Y', '%b', '%b-%d', '%H:%M', '%H:%M']
fmt = mdates.ConciseDateFormatter(maj_loc, zero_formats=zfmts, show_offset=False)
ax.xaxis.set_major_formatter(fmt)
# Shift the plot frame to where the bars are now located
xmin = min(x_bars_md) - bar_width_md
xmax = max(x_bars_md) + 2*bar_width_md
ax.set_xlim(xmin, xmax)
# Adjust tick label format last, else it may sometimes not be applied correctly
ax.figure.autofmt_xdate(rotation=0, ha='center')
Minor ticks a displayed under each bar to highlight the fact that the timestamps of the bars often do not coincide with a month/year start marked by the labels of the AutoDateLocator ticks. I am not aware of any date locator that can be used to select ticks for the first week of each month and reproduce exactly the result shown in solution 1.
Documentation: date format codes, mdates.ConciseDateFormatter
Here's a possibly easier approach using mdates, though requires you to loop over your columns, calling bar plot from matplotlib. Here's an example where I plot just one column and use mdates for customized ticks and labels (EDIT Added looping function to plot all columns stacked):
import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def format_x_date_month_day(ax):
# Standard date x-axis formatting block, labels each month and ticks each day
days = mdates.DayLocator()
months = mdates.MonthLocator() # every month
dayFmt = mdates.DateFormatter('%D')
monthFmt = mdates.DateFormatter('%Y-%m')
ax.figure.autofmt_xdate()
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(monthFmt)
ax.xaxis.set_minor_locator(days)
def df_stacked_bar_formattable(df, ax, **kwargs):
P = []
lastBar = None
for col in df.columns:
X = df.index
Y = df[col]
if lastBar is not None:
P.append(ax.bar(X, Y, bottom=lastBar, **kwargs))
else:
P.append(ax.bar(X, Y, **kwargs))
lastBar = Y
plt.legend([p[0] for p in P], df.columns)
span_days = 90
start = pd.to_datetime("1-1-2012")
idx = pd.date_range(start, periods=span_days).tolist()
df=pd.DataFrame(index=idx, data={'A':np.random.random(span_days), 'B':np.random.random(span_days)})
plt.close('all')
fig, ax = plt.subplots(1)
df_stacked_bar_formattable(df, ax)
format_x_date_month_day(ax)
plt.show()
(Referencing matplotlib.org for example of looping to create a stacked bar plot.) This gives us
Another approach that should work and be much easier is to use df.plot.bar(ax=ax, stacked=True), however it does not admit date axis formatting with mdates and is the subject of my question.
Maybe not the most elegant, but hopefully easy way:
fig = plt.figure()
ax = fig.add_subplot(111)
df_ts.plot(kind='bar', figsize=(12,8), stacked=True,ax=ax)
ax.set_xticklabels(''*len(df_ts.index))
df_ts.plot(linewidth=0, ax=ax) # This sets the nice x_ticks automatically
[EDIT]: ax=ax neede in df_ts.plot()
I am trying to vary the color of a line plotted from data in two arrays (eg. ax.plot(x,y)). The color should vary as the index into x and yincreases. I am essentially trying to capture the natural 'time' parameterization of the data in arrays x and y.
In a perfect world, I want something like:
fig = pyplot.figure()
ax = fig.add_subplot(111)
x = myXdata
y = myYdata
# length of x and y is 100
ax.plot(x,y,color=[i/100,0,0]) # where i is the index into x (and y)
to produce a line with color varying from black to dark red and on into bright red.
I have seen examples that work well for plotting a function explicitly parameterized by some 'time' array, but I can't get it to work with raw data...
The second example is the one you want... I've edited it to fit your example, but more importantly read my comments to understand what is going on:
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
x = myXdata
y = myYdata
t = np.linspace(0,1,x.shape[0]) # your "time" variable
# set up a list of (x,y) points
points = np.array([x,y]).transpose().reshape(-1,1,2)
print points.shape # Out: (len(x),1,2)
# set up a list of segments
segs = np.concatenate([points[:-1],points[1:]],axis=1)
print segs.shape # Out: ( len(x)-1, 2, 2 )
# see what we've done here -- we've mapped our (x,y)
# points to an array of segment start/end coordinates.
# segs[i,0,:] == segs[i-1,1,:]
# make the collection of segments
lc = LineCollection(segs, cmap=plt.get_cmap('jet'))
lc.set_array(t) # color the segments by our parameter
# plot the collection
plt.gca().add_collection(lc) # add the collection to the plot
plt.xlim(x.min(), x.max()) # line collections don't auto-scale the plot
plt.ylim(y.min(), y.max())