A request for a Matplotlib extension to hist

Virgil_Stokes · April 24, 2015, 10:33am

I have some Python (2.7.9) code that processes some rather large
data sets to determine the curvatures along 2D curves. One feature
of these data that I like to look at is the distribution of the
curvatures. I use NumPy to to determine histograms for each set, and
save the histogram parameters returned from numpy.histogram
in a file.

I would like to use Matplotlib to plot histograms **      from the

parameters returned in NumPy** and stored in a file — why?
Because the size of my data sets does not allow for the use of the
histogram plot function in Matplotlib (1.4.3); i.e., it needs the
data sets to calculate the histogram, before doing the plot. I would
like to have a histogram plot function in Matplotlib that could
bypass the actual calculation of the bin counts and edges from the
data, and use values of these found a priori . Of course, an
obvious question is – Why not write code to plot the rectangles
yourself? Yes, I could do this; but, why not extend the Matplotlib
histogram class to allow for this option? If I better understood
Matplotlib, I would try this myself. Maybe it is possible to get
this into the next planned release (1.5).
If this request is inappropriate for this list, then please accept
my apology and direct me to where I should send this request.
Best regards.

···

Christian_Alis · April 24, 2015, 10:58am

I had the same problem some time ago and what I did is to use bar() to
plot the histogram, which can be done in one line:

hist, bin_edges = np.histogram(data)
plt.bar(bin_edges[:-1], hist)

Perhaps this trick can be added in the documentation?

I am willing to code Virgil's request if many will find this useful.

···

On Fri, Apr 24, 2015 at 11:33 AM, Virgil Stokes <vs@...2650...> wrote:

I have some Python (2.7.9) code that processes some rather large data sets
to determine the curvatures along 2D curves. One feature of these data that
I like to look at is the distribution of the curvatures. I use NumPy to to
determine histograms for each set, and save the histogram parameters
returned from numpy.histogram in a file.

I would like to use Matplotlib to plot histograms from the parameters
returned in NumPy and stored in a file --- why? Because the size of my data
sets does not allow for the use of the histogram plot function in Matplotlib
(1.4.3); i.e., it needs the data sets to calculate the histogram, before
doing the plot. I would like to have a histogram plot function in Matplotlib
that could bypass the actual calculation of the bin counts and edges from
the data, and use values of these found a priori. Of course, an obvious
question is -- Why not write code to plot the rectangles yourself? Yes, I
could do this; but, why not extend the Matplotlib histogram class to allow
for this option? If I better understood Matplotlib, I would try this myself.
Maybe it is possible to get this into the next planned release (1.5).

If this request is inappropriate for this list, then please accept my
apology and direct me to where I should send this request.

Best regards.

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Virgil_Stokes · April 24, 2015, 11:26am

I had the same problem some time ago and what I did is to use bar() to
plot the histogram, which can be done in one line:

hist, bin_edges = np.histogram(data)
plt.bar(bin_edges[:-1], hist)

Very elegant Christian

Perhaps this trick can be added in the documentation?

Well, I vote to add it. However, I did find the following nice example (after reading your email) that shows how the bar function might be used (http://matplotlib.org/examples/api/barchart_demo.html) for my problem. Had I seen this before, I probably would not have posted this request.:-[

I am willing to code Virgil's request if many will find this useful.

I have some Python (2.7.9) code that processes some rather large data sets
to determine the curvatures along 2D curves. One feature of these data that
I like to look at is the distribution of the curvatures. I use NumPy to to
determine histograms for each set, and save the histogram parameters
returned from numpy.histogram in a file.

I would like to use Matplotlib to plot histograms from the parameters
returned in NumPy and stored in a file --- why? Because the size of my data
sets does not allow for the use of the histogram plot function in Matplotlib
(1.4.3); i.e., it needs the data sets to calculate the histogram, before
doing the plot. I would like to have a histogram plot function in Matplotlib
that could bypass the actual calculation of the bin counts and edges from
the data, and use values of these found a priori. Of course, an obvious
question is -- Why not write code to plot the rectangles yourself? Yes, I
could do this; but, why not extend the Matplotlib histogram class to allow
for this option? If I better understood Matplotlib, I would try this myself.
Maybe it is possible to get this into the next planned release (1.5).

If this request is inappropriate for this list, then please accept my
apology and direct me to where I should send this request.

Best regards.

------------------------------------------------------------------------------
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Widest out-of-the-box monitoring support with 50+ applications
Performance metrics, stats and reports that give you Actionable Insights
Deep dive visibility with transaction tracing using APM Insight.
Application Monitor Software & Tools - ManageEngine Applications Manager
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Keep up the good work on Matplotlib and I look forward to vers. 1.5

···

On 24-Apr-2015 12:58, Christian Alis wrote:

On Fri, Apr 24, 2015 at 11:33 AM, Virgil Stokes <vs@...2650...> wrote:

Jerzy_Karczmarczuk · April 24, 2015, 11:31am

Le 24/04/2015 12:58, Christian Alis responds to the problem posed by Virgil Stokes

I had the same problem some time ago and what I did is to use bar() to
plot the histogram, which can be done in one line:

hst, bin_edges = np.histogram(data)
plt.bar(bin_edges[:-1], hst)

Perhaps this trick can be added in the documentation?

I am willing to code Virgil's request if many will find this useful.

Separating the computation of the histogram, and plotting it is obviously useful.
(I needed this in a linguistical simulation context, where plotting had no sense).

Actually hist is more or less this,
see _axes.py, line 5678, the Axes method hist just calls numpy.histogram. And then plots bars (or uses some other style).

So, although completing the documentation might be of general interest, I would NOT recommend adding some new version of hist.
This would be misleading. Hist is hist is hist(ogram). It computes the histogram (and eventually plots it). If it is already computed elsewhere,
naming the procedure which JUST plots some bars a "histogram" is methodologically very dubious.

Jerzy Karczmarczuk

_Antonino_Ingargiola · April 24, 2015, 6:04pm

Hi,

I think there is a good reason to add the functionality to plot pre-computed histograms to hist() or to a new function with similar API.

Sometimes histograms are heavy or we don’t want to recompute them to perform a series of plots.

In this case, I miss the ability to easily set the plot style. hist() allows to chose between bar, step and stepfilled. To change the style with pre-computed histograms, I need to write 3 different “plots” using bar(), plot() and fill_between() respectively. This is quite inconvenient and error prone, considering that these function have different API for the input data.

Maybe the plotting part of hist() should be splitted in a standalone function (plotbins() ?)

In this way, hist() can call this function to generate the plot, but also the user can call it when the histogram is pre-computed. The bonus is that we retain the same API for plot style.

My 2cents,

Antonio

···

On Fri, Apr 24, 2015 at 4:31 AM, Jerzy Karczmarczuk <jerzy.karczmarczuk@…3937…> wrote:

Le 24/04/2015 12:58, Christian Alis responds to the problem posed by

Virgil Stokes

I had the same problem some time ago and what I did is to use bar() to

plot the histogram, which can be done in one line:

hst, bin_edges = np.histogram(data)

plt.bar(bin_edges[:-1], hst)

Perhaps this trick can be added in the documentation?

I am willing to code Virgil’s request if many will find this useful.

Separating the computation of the histogram, and plotting it is

obviously useful.

(I needed this in a linguistical simulation context, where plotting had

no sense).

Actually hist is more or less this,

see _axes.py, line 5678, the Axes method hist just calls

numpy.histogram. And then plots bars (or uses some other style).

So, although completing the documentation might be of general interest,

I would NOT recommend adding some new version of hist.

This would be misleading. Hist is hist is hist(ogram). It computes the

histogram (and eventually plots it). If it is already computed elsewhere,

naming the procedure which JUST plots some bars a “histogram” is

methodologically very dubious.

Jerzy Karczmarczuk

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