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I'm new to Python and I'm eagerly migrating from MATLAB to IPython as my preferred language for data analysis at the lab.

In MATLAB, after a session of data crunching, I would do

>>> save('myresults.mat','x','y','z');

and save the results of the variables x, y and z in a file called 'myresults.mat'. Later on, I could simply say:

>>> load('myresults');

and MATLAB would load the .mat file AND assign the stored values of the variables to the current workspace.

I've recently learned that I can do similarly for IPython using numpy, namely:

import numpy as np
a = 2
b = np.sqrt(2)
np.savez('myresultsinpython',a,b)

and later do something like

npzfile = np.load('myresultsinpython')

However, what I get is an object from which I can access my variables by:

npzfile['arr_1']

etc., but I loose all info on the original names of the variables. I know I could save the file by doing

np.savez('myresultsinpython',a=a,b=b)

but this is not that useful, as I still have to do something like:

npzfile['a']

to access the variable's value.

How do I both load the file and have the variables created in my workspace and their values assigned according to the values stored in the file?

Something like np.load('myresults') and be able to do a+b and get 3.4142135623730949 (=2+sqrt(2)) in return.

Michael Currie
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user2734434
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  • Please see : http://stackoverflow.com/questions/12504951/is-it-possible-to-save-session-in-ipython-like-in-matlab – Raj Ranjhan Jan 30 '15 at 03:59

2 Answers2

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locals().update(npzfile)
a   # and/or b

In the Ipython session, locals() is a large dictionary with variables the you've defined, the input history lines, and various outputs. update adds the dictionary values of npzfile to that larger one.

By the way, you can also load and save MATLAB .mat files. Use scipy.io.loadmat and savemat. It handles v4 (Level 1.0), v6 and v7 to 7.2 files. But you have same issue - the result is a dictionary.

Octave has an expression form of the load command, that loads the data into a structure

S = load ("file", "options", "v1", "v2", ...)

In general numpy and ipython does not have the same sort of 'save a snapshot of the workspace' idea that MATLAB does. I mostly write scripts to generate (and load) the necessary data, and run them either as stand alone python sessions or run them from with in ipython. ipython does have a good history feature. You might also find the ipython notebook tool useful. I haven't used that.

Within a function, locals() is different from outside it. It is 'local' to each function. There is a globals() dictionary that might allow setting variables outside the function. But this style of programming is not encouraged.

The SO question that Anurag cited points to a save_ipython_variables package. https://pypi.python.org/pypi/save_ipython_variables/0.0.3 It saves variables with pickle, and loads them by doing something like 

exec '__builtins__[name] = pickle.load(...)

With use of exec and __builtins__ it is pushing some safe programming boundaries. So use with caution. But a small test does work in my environment. The same technique might work with npz files.

hpaulj
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  • Thanks for your help, that reallly answers my question. So, doing "locals().update(npzfile)" after "npzfile=np.load('myresults)" gives me the variables in the workspace. I've tried putting this two pieces together into a new function, which i can simply call by passing the filename as an argument and have the function dump the variables into the workspace. However, this does not seem to work. Am i missing something here? Thanks for all your help. – user2734434 Jan 30 '15 at 20:26
2

I was looking for the same thing again and again, getting tired of having to load dozens of arrays each time in the way:

data = np.load("file.npz")
var1 = data["my_var1"]
...

Since I let computations regularly run on the campus cluster and post-process the results (numpy arrays) on my laptop, I really appreciate the free Spyder IDE (which btw runs an IPython console interactively), which has a Matlab GUI-like variable explorer that lets you load a whole npz-archive (or selective variables if you so wish) or spydata archive with a mouse click. It greatly speeds up my workflow with numpy arrays.

PeterL
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