is there a way to pass array-range as argument to function ? Something like :
> blah(ary,arg1=1:5)
def blah(ary,arg1): print ary[arg1]
4 Answers
Python accepts the 1:5 syntax only within square brackets. The interpreter converts it into a slice object. The __getitem__ method of the object then applies the slice.
Look at numpy/lib/index_tricks.py for some functions that take advantage of this. Actually they aren't functions, but rather classes that define their own __getitem__ methods. That file may give you ideas.
But if you aren't up to that, then possibilities include:
blah(arr, slice(1, 5))
blah(arr, np.r_[1:5])
nd_grid, mgrid, ogrid extend the 'slice' concept to accept an imaginary 'step' value:
mgrid[-1:1:5j]
# array([-1. , -0.5, 0. , 0.5, 1. ])
Just be aware that anything which expands on a slice before passing it to your blah function, won't know about the shape of the other argument. So np.r_[:-1] just returns [].
And None can be used in slice: e.g. slice(None,None,-1) is equivalent of [::-1].
1 Comment
sten
How do I check if the argument I received is slice() vs ordinary number ? I got it isinstance(x,slice)
You can use the slice function
>>> def blah(ary,arg1):
... print ary[arg1]
>>> blah(range(10), slice(1, 5))
[1, 2, 3, 4]
Comments
you can try like this:
def blah(ary, arg):
arg = map(int, arg.split(":"))
print ary[arg[0]:arg[1]]
blah([1,2,3,4,5,6],"2:5")
output:
[3, 4, 5]
Comments
Just saw it happen today and I thought it was curious, notice that they are passing the argument test_idx as a plain range
plot_decision_regions(X=X_combined_std, y=y_combined,
classifier=ppn, test_idx=range(105, 150))
So what does this do in the end?
Using it to slice a Numpy Array
ndarray[range(105, 150), :]
However when I test this by copying the ndarray value, instantiate it and try to slice it myself (basically makes a list instead) it won't allow me to pass that range on the slice it seems.
[[ 0.73088538 1.57698181], [ 0.17316034 0.1348488 ]]
which I extracted/copied from an ndarray when you click to set new value
erick = [[ 0.73088538 1.57698181], [ 0.17316034 0.1348488 ]]
-- SyntaxError. invalid syntax
had to put the commas to be accepted as a syntax and instantiates it as a list obj
erick = [[ 0.73088538, 1.57698181], [ 0.17316034, 0.1348488 ]]
erick[:]
(works, returning whole thing)
erick[range(0, 1), :]
-- TypeError. list indices must be integrers or slices, not tuple
(breaks, we tested that slicing works before so it has to do with the range)
Try again with a homebrew Numpy Array (spoiler, it works)
erickNpa = np.asarray(erick, dtype=np.float32)
erickNpa[range(0, 1), :]
Conclussion
You can pass range as arguments as seen on the first part, at some point the code wasn't executing but that had to do with the nature of what it was doing (using it to slice a list), however with the right scaffolding everything seems to work as proved when using a Numpy Array instead.
Sample Code
I will also put the function def in case the git gets taken down even tho I linked the line.
def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
# setup marker generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
# plot the decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
c=colors[idx],
marker=markers[idx],
label=cl,
edgecolor='black')
# highlight test examples
if test_idx:
# plot all examples
X_test, y_test = X[test_idx, :], y[test_idx]
plt.scatter(X_test[:, 0],
X_test[:, 1],
c='',
edgecolor='black',
alpha=1.0,
linewidth=1,
marker='o',
s=100,
label='test set')
# Training a perceptron model using the standardized training data:
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std, y=y_combined,
classifier=ppn, test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
#plt.savefig('images/03_01.png', dpi=300)
plt.show()
slice(1, 5)is what you're looking for.