I am currently using matplotlib.pyplot to create graphs and would like to have the major gridlines solid and black and the minor ones either greyed or dashed.

In the grid properties, which=both/major/mine, and then color and linestyle are defined simply by linestyle. Is there a way to specify minor linestyle only?

The appropriate code I have so far is

plt.plot(current, counts, 'rd', markersize=8)
plt.yscale('log')
plt.grid(b=True, which='both', color='0.65', linestyle='-')

Actually, it is as simple as setting major and minor separately:

In [9]: plot([23, 456, 676, 89, 906, 34, 2345])
Out[9]: [<matplotlib.lines.Line2D at 0x6112f90>]

In [10]: yscale('log')

In [11]: grid(b=True, which='major', color='b', linestyle='-')

In [12]: grid(b=True, which='minor', color='r', linestyle='--')

The gotcha with minor grids is that you have to have minor tick marks turned on too. In the above code this is done by yscale('log'), but it can also be done with plt.minorticks_on().

A simple DIY way would be to make the grid yourself:

import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111)

ax.plot([1,2,3], [2,3,4], 'ro')

for xmaj in ax.xaxis.get_majorticklocs():
  ax.axvline(x=xmaj, ls='-')
for xmin in ax.xaxis.get_minorticklocs():
  ax.axvline(x=xmin, ls='--')

for ymaj in ax.yaxis.get_majorticklocs():
  ax.axhline(y=ymaj, ls='-')
for ymin in ax.yaxis.get_minorticklocs():
  ax.axhline(y=ymin, ls='--')
plt.show()

Is there a way to save a Matplotlib figure such that it can be re-opened and have typical interaction restored? (Like the .fig format in MATLAB?)

I find myself running the same scripts many times to generate these interactive figures. Or I’m sending my colleagues multiple static PNG files to show different aspects of a plot. I’d rather send the figure object and have them interact with it themselves.

This would be a great feature, but AFAIK it isn’t implemented in Matplotlib and likely would be difficult to implement yourself due to the way figures are stored.

I’d suggest either (a) separate processing the data from generating the figure (which saves data with a unique name) and write a figure generating script (loading a specified file of the saved data) and editing as you see fit or (b) save as PDF/SVG/PostScript format and edit in some fancy figure editor like Adobe Illustrator (or Inkscape).

EDIT post Fall 2012: As others pointed out below (though mentioning here as this is the accepted answer), Matplotlib since version 1.2 allowed you to pickle figures. As the release notes state, it is an experimental feature and does not support saving a figure in one matplotlib version and opening in another. It’s also generally unsecure to restore a pickle from an untrusted source.

For sharing/later editing plots (that require significant data processing first and may need to be tweaked months later say during peer review for a scientific publication), I still recommend the workflow of (1) have a data processing script that before generating a plot saves the processed data (that goes into your plot) into a file, and (2) have a separate plot generation script (that you adjust as necessary) to recreate the plot. This way for each plot you can quickly run a script and re-generate it (and quickly copy over your plot settings with new data). That said, pickling a figure could be convenient for short term/interactive/exploratory data analysis.

I just found out how to do this. The “experimental pickle support” mentioned by @pelson works quite well.

Try this:

# Plot something
import matplotlib.pyplot as plt
fig,ax = plt.subplots()
ax.plot([1,2,3],[10,-10,30])

After your interactive tweaking, save the figure object as a binary file:

import pickle
pickle.dump(fig, open('FigureObject.fig.pickle', 'wb')) # This is for Python 3 - py2 may need `file` instead of `open`

Later, open the figure and the tweaks should be saved and GUI interactivity should be present:

import pickle
figx = pickle.load(open('FigureObject.fig.pickle', 'rb'))

figx.show() # Show the figure, edit it, etc.!

You can even extract the data from the plots:

data = figx.axes[0].lines[0].get_data()

(It works for lines, pcolor & imshow – pcolormesh works with some tricks to reconstruct the flattened data.)

I got the excellent tip from Saving Matplotlib Figures Using Pickle.

As of Matplotlib 1.2, we now have experimental pickle support. Give that a go and see if it works well for your case. If you have any issues, please let us know on the Matplotlib mailing list or by opening an issue on github.com/matplotlib/matplotlib.

Why not just send the Python script? MATLAB’s .fig files require the recipient to have MATLAB to display them, so that’s about equivalent to sending a Python script that requires Matplotlib to display.

Alternatively (disclaimer: I haven’t tried this yet), you could try pickling the figure:

import pickle
output = open('interactive figure.pickle', 'wb')
pickle.dump(gcf(), output)
output.close()

Good question. Here is the doc text from pylab.save:

pylab no longer provides a save function, though the old pylab function is still available as matplotlib.mlab.save (you can still refer to it in pylab as “mlab.save”). However, for plain text files, we recommend numpy.savetxt. For saving numpy arrays, we recommend numpy.save, and its analog numpy.load, which are available in pylab as np.save and np.load.

I figured out a relatively simple way (yet slightly unconventional) to save my matplotlib figures. It works like this:

import libscript

import matplotlib.pyplot as plt
import numpy as np

t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2*np.pi*t)

#<plot>
plt.plot(t, s)
plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('About as simple as it gets, folks')
plt.grid(True)
plt.show()
#</plot>

save_plot(fileName='plot_01.py',obj=sys.argv[0],sel='plot',ctx=libscript.get_ctx(ctx_global=globals(),ctx_local=locals()))

with function save_plot defined like this (simple version to understand the logic):

def save_plot(fileName='',obj=None,sel='',ctx={}):
    """
    Save of matplolib plot to a stand alone python script containing all the data and configuration instructions to regenerate the interactive matplotlib figure.

    Parameters
    ----------
    fileName : [string] Path of the python script file to be created.
    obj : [object] Function or python object containing the lines of code to create and configure the plot to be saved.
    sel : [string] Name of the tag enclosing the lines of code to create and configure the plot to be saved.
    ctx : [dict] Dictionary containing the execution context. Values for variables not defined in the lines of code for the plot will be fetched from the context.

    Returns
    -------
    Return ``'done'`` once the plot has been saved to a python script file. This file contains all the input data and configuration to re-create the original interactive matplotlib figure.
    """
    import os
    import libscript

    N_indent=4

    src=libscript.get_src(obj=obj,sel=sel)
    src=libscript.prepend_ctx(src=src,ctx=ctx,debug=False)
    src='\n'.join([' '*N_indent+line for line in src.split('\n')])

    if(os.path.isfile(fileName)): os.remove(fileName)
    with open(fileName,'w') as f:
        f.write('import sys\n')
        f.write('sys.dont_write_bytecode=True\n')
        f.write('def main():\n')
        f.write(src+'\n')

        f.write('if(__name__=="__main__"):\n')
        f.write(' '*N_indent+'main()\n')

return 'done'

or defining function save_plot like this (better version using zip compression to produce lighter figure files):

def save_plot(fileName='',obj=None,sel='',ctx={}):

    import os
    import json
    import zlib
    import base64
    import libscript

    N_indent=4
    level=9#0 to 9, default: 6
    src=libscript.get_src(obj=obj,sel=sel)
    obj=libscript.load_obj(src=src,ctx=ctx,debug=False)
    bin=base64.b64encode(zlib.compress(json.dumps(obj),level))

    if(os.path.isfile(fileName)): os.remove(fileName)
    with open(fileName,'w') as f:
        f.write('import sys\n')
        f.write('sys.dont_write_bytecode=True\n')
        f.write('def main():\n')
        f.write(' '*N_indent+'import base64\n')
        f.write(' '*N_indent+'import zlib\n')
        f.write(' '*N_indent+'import json\n')
        f.write(' '*N_indent+'import libscript\n')
        f.write(' '*N_indent+'bin="'+str(bin)+'"\n')
        f.write(' '*N_indent+'obj=json.loads(zlib.decompress(base64.b64decode(bin)))\n')
        f.write(' '*N_indent+'libscript.exec_obj(obj=obj,tempfile=False)\n')

        f.write('if(__name__=="__main__"):\n')
        f.write(' '*N_indent+'main()\n')

return 'done'

This makes use a module libscript of my own, which mostly relies on modules inspect and ast. I can try to share it on Github if interest is expressed (it would first require some cleanup and me to get started with Github).

The idea behind this save_plot function and libscript module is to fetch the python instructions that create the figure (using module inspect), analyze them (using module ast) to extract all variables, functions and modules import it relies on, extract these from the execution context and serialize them as python instructions (code for variables will be like t=[0.0,2.0,0.01] … and code for modules will be like import matplotlib.pyplot as plt …) prepended to the figure instructions. The resulting python instructions are saved as a python script whose execution will re-build the original matplotlib figure.

As you can imagine, this works well for most (if not all) matplotlib figures.

I can add a y label to the left y-axis using plt.ylabel, but how can I add it to the secondary y-axis?

table = sql.read_frame(query,connection)

table[0].plot(color=colors[0],ylim=(0,100))
table[1].plot(secondary_y=True,color=colors[1])
plt.ylabel('$')

The best way is to interact with the axes object directly

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 10, 0.1)
y1 = 0.05 * x**2
y2 = -1 *y1

fig, ax1 = plt.subplots()

ax2 = ax1.twinx()
ax1.plot(x, y1, 'g-')
ax2.plot(x, y2, 'b-')

ax1.set_xlabel('X data')
ax1.set_ylabel('Y1 data', color='g')
ax2.set_ylabel('Y2 data', color='b')

plt.show()

There is a straightforward solution without messing with matplotlib: just pandas.

Tweaking the original example:

table = sql.read_frame(query,connection)

ax = table[0].plot(color=colors[0],ylim=(0,100))
ax2 = table[1].plot(secondary_y=True,color=colors[1], ax=ax)

ax.set_ylabel('Left axes label')
ax2.set_ylabel('Right axes label')

Basically, when the secondary_y=True option is given (eventhough ax=ax is passed too) pandas.plot returns a different axes which we use to set the labels.

I know this was answered long ago, but I think this approach worths it.

I don’t have access to Python right now, but off the top of my head:

fig = plt.figure()

axes1 = fig.add_subplot(111)
# set props for left y-axis here

axes2 = axes1.twinx()   # mirror them
axes2.set_ylabel(...)

The code below produces gaps between the subplots. How do I remove the gaps between the subplots and make the image a tight grid?

import matplotlib.pyplot as plt

for i in range(16):
    i = i + 1
    ax1 = plt.subplot(4, 4, i)
    plt.axis('on')
    ax1.set_xticklabels([])
    ax1.set_yticklabels([])
    ax1.set_aspect('equal')
    plt.subplots_adjust(wspace=None, hspace=None)
plt.show()

You can use gridspec to control the spacing between axes. There’s more information here.

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

plt.figure(figsize = (4,4))
gs1 = gridspec.GridSpec(4, 4)
gs1.update(wspace=0.025, hspace=0.05) # set the spacing between axes. 

for i in range(16):
   # i = i + 1 # grid spec indexes from 0
    ax1 = plt.subplot(gs1[i])
    plt.axis('on')
    ax1.set_xticklabels([])
    ax1.set_yticklabels([])
    ax1.set_aspect('equal')

plt.show()

The problem is the use of aspect='equal', which prevents the subplots from stretching to an arbitrary aspect ratio and filling up all the empty space.

Normally, this would work:

import matplotlib.pyplot as plt

ax = [plt.subplot(2,2,i+1) for i in range(4)]

for a in ax:
    a.set_xticklabels([])
    a.set_yticklabels([])

plt.subplots_adjust(wspace=0, hspace=0)

The result is this:

However, with aspect='equal', as in the following code:

import matplotlib.pyplot as plt

ax = [plt.subplot(2,2,i+1) for i in range(4)]

for a in ax:
    a.set_xticklabels([])
    a.set_yticklabels([])
    a.set_aspect('equal')

plt.subplots_adjust(wspace=0, hspace=0)

This is what we get:

The difference in this second case is that you’ve forced the x- and y-axes to have the same number of units/pixel. Since the axes go from 0 to 1 by default (i.e., before you plot anything), using aspect='equal' forces each axis to be a square. Since the figure is not a square, pyplot adds in extra spacing between the axes horizontally.

To get around this problem, you can set your figure to have the correct aspect ratio. We’re going to use the object-oriented pyplot interface here, which I consider to be superior in general:

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8,8)) # Notice the equal aspect ratio
ax = [fig.add_subplot(2,2,i+1) for i in range(4)]

for a in ax:
    a.set_xticklabels([])
    a.set_yticklabels([])
    a.set_aspect('equal')

fig.subplots_adjust(wspace=0, hspace=0)

Here’s the result:

Without resorting gridspec entirely, the following might also be used to remove the gaps by setting wspace and hspace to zero:

import matplotlib.pyplot as plt

plt.clf()
f, axarr = plt.subplots(4, 4, gridspec_kw = {'wspace':0, 'hspace':0})

for i, ax in enumerate(f.axes):
    ax.grid('on', linestyle='--')
    ax.set_xticklabels([])
    ax.set_yticklabels([])

plt.show()
plt.close()

Resulting in:

Have you tried plt.tight_layout()?

with plt.tight_layout() without it:

Or: something like this (use add_axes)

left=[0.1,0.3,0.5,0.7]
width=[0.2,0.2, 0.2, 0.2]
rectLS=[]
for x in left:
   for y in left:
       rectLS.append([x, y, 0.2, 0.2])
axLS=[]
fig=plt.figure()
axLS.append(fig.add_axes(rectLS[0]))
for i in [1,2,3]:
     axLS.append(fig.add_axes(rectLS[i],sharey=axLS[-1]))    
axLS.append(fig.add_axes(rectLS[4]))
for i in [1,2,3]:
     axLS.append(fig.add_axes(rectLS[i+4],sharex=axLS[i],sharey=axLS[-1]))
axLS.append(fig.add_axes(rectLS[8]))
for i in [5,6,7]:
     axLS.append(fig.add_axes(rectLS[i+4],sharex=axLS[i],sharey=axLS[-1]))     
axLS.append(fig.add_axes(rectLS[12]))
for i in [9,10,11]:
     axLS.append(fig.add_axes(rectLS[i+4],sharex=axLS[i],sharey=axLS[-1]))

If you don’t need to share axes, then simply axLS=map(fig.add_axes, rectLS)

With recent matplotlib versions you might want to try Constrained Layout. This does not work with plt.subplot() however, so you need to use plt.subplots() instead:

fig, axs = plt.subplots(4, 4, constrained_layout=True)

Given a mean and a variance is there a simple function call which will plot a normal distribution?

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math

mu = 0
variance = 1
sigma = math.sqrt(variance)
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
plt.plot(x, stats.norm.pdf(x, mu, sigma))
plt.show()

I don’t think there is a function that does all that in a single call. However you can find the Gaussian probability density function in scipy.stats.

So the simplest way I could come up with is:

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

# Plot between -10 and 10 with .001 steps.
x_axis = np.arange(-10, 10, 0.001)
# Mean = 0, SD = 2.
plt.plot(x_axis, norm.pdf(x_axis,0,2))
plt.show()

Sources:

• http://www.johndcook.com/distributions_scipy.html
• http://docs.scipy.org/doc/scipy/reference/stats.html
• http://telliott99.blogspot.com/2010/02/plotting-normal-distribution-with.html

Use seaborn instead i am using distplot of seaborn with mean=5 std=3 of 1000 values

value = np.random.normal(loc=5,scale=3,size=1000)
sns.distplot(value)

You will get a normal distribution curve

Unutbu answer is correct. But because our mean can be more or less than zero I would still like to change this :

x = np.linspace(-3 * sigma, 3 * sigma, 100)

to this :

x = np.linspace(-3 * sigma + mean, 3 * sigma + mean, 100)

If you prefer to use a step by step approach you could consider a solution like follows

import numpy as np
import matplotlib.pyplot as plt

mean = 0; std = 1; variance = np.square(std)
x = np.arange(-5,5,.01)
f = np.exp(-np.square(x-mean)/2*variance)/(np.sqrt(2*np.pi*variance))

plt.plot(x,f)
plt.ylabel('gaussian distribution')
plt.show()

I have just come back to this and I had to install scipy as matplotlib.mlab gave me the error message MatplotlibDeprecationWarning: scipy.stats.norm.pdf when trying example above. So the sample is now:

%matplotlib inline
import math
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats


mu = 0
variance = 1
sigma = math.sqrt(variance)
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
plt.plot(x, scipy.stats.norm.pdf(x, mu, sigma))

plt.show()

I believe that is important to set the height, so created this function:

def my_gauss(x, sigma=1, h=1, mid=0):
    from math import exp, pow
    variance = pow(sdev, 2)
    return h * exp(-pow(x-mid, 2)/(2*variance))

Where sigma is the standard deviation, h is the height and mid is the mean.

Here is the result using different heights and deviations:

you can get cdf easily. so pdf via cdf

    import numpy as np
    import matplotlib.pyplot as plt
    import scipy.interpolate
    import scipy.stats

    def setGridLine(ax):
        #http://jonathansoma.com/lede/data-studio/matplotlib/adding-grid-lines-to-a-matplotlib-chart/
        ax.set_axisbelow(True)
        ax.minorticks_on()
        ax.grid(which='major', linestyle='-', linewidth=0.5, color='grey')
        ax.grid(which='minor', linestyle=':', linewidth=0.5, color='#a6a6a6')
        ax.tick_params(which='both', # Options for both major and minor ticks
                        top=False, # turn off top ticks
                        left=False, # turn off left ticks
                        right=False,  # turn off right ticks
                        bottom=False) # turn off bottom ticks

    data1 = np.random.normal(0,1,1000000)
    x=np.sort(data1)
    y=np.arange(x.shape[0])/(x.shape[0]+1)

    f2 = scipy.interpolate.interp1d(x, y,kind='linear')
    x2 = np.linspace(x[0],x[-1],1001)
    y2 = f2(x2)

    y2b = np.diff(y2)/np.diff(x2)
    x2b=(x2[1:]+x2[:-1])/2.

    f3 = scipy.interpolate.interp1d(x, y,kind='cubic')
    x3 = np.linspace(x[0],x[-1],1001)
    y3 = f3(x3)

    y3b = np.diff(y3)/np.diff(x3)
    x3b=(x3[1:]+x3[:-1])/2.

    bins=np.arange(-4,4,0.1)
    bins_centers=0.5*(bins[1:]+bins[:-1])
    cdf = scipy.stats.norm.cdf(bins_centers)
    pdf = scipy.stats.norm.pdf(bins_centers)

    plt.rcParams["font.size"] = 18
    fig, ax = plt.subplots(3,1,figsize=(10,16))
    ax[0].set_title("cdf")
    ax[0].plot(x,y,label="data")
    ax[0].plot(x2,y2,label="linear")
    ax[0].plot(x3,y3,label="cubic")
    ax[0].plot(bins_centers,cdf,label="ans")

    ax[1].set_title("pdf:linear")
    ax[1].plot(x2b,y2b,label="linear")
    ax[1].plot(bins_centers,pdf,label="ans")

    ax[2].set_title("pdf:cubic")
    ax[2].plot(x3b,y3b,label="cubic")
    ax[2].plot(bins_centers,pdf,label="ans")

    for idx in range(3):
        ax[idx].legend()
        setGridLine(ax[idx])

    plt.show()
    plt.clf()
    plt.close()