## a uniform data structure that can represent an ndarray with various size along a given axis

I can use the following code to generate three dimensional array.

I can use the following code to generate three dimensional array.

I can’t quite seem to figue out how to get my curve to be displayed smoothly instead of having so many sharp turns.

I am hoping to show a boltzmann probability distribution. With a nice smooth curve.

The objective is to find the leading and trailing valleys from a list of local maxima in a 1-D signal, as illustrated in the figure below

I have a NumPy array `vectors = np.random.randn(rows, cols)`

. I want to find differences between its rows according to some other array `diffs`

which is sparse and “2-hot”: containing a `1`

in its column corresponding to the first row of `vectors`

and a `-1`

corresponding to the second row. Perhaps an example shall make it clearer:

i am trying to calculate the correlation coefficient for a scatterplot with scipy, the thing is, i have a kind of complex dataset in an ndarray, and the basic syntax does not work for me…

I wonder if there is a direct way to import the contents of a CSV file into a record array, much in the way that R’s `read.table()`

, `read.delim()`

, and `read.csv()`

family imports data to R’s data frame?

Is it possible to read binary MATLAB .mat files in Python?

I have a set of data and I want to compare which line describes it best (polynomials of different orders, exponential or logarithmic).

Say I have an image of size 3841 x 7195 pixels. I would like to save the contents of the figure to disk, resulting in an image of the **exact size** I specify in pixels.

I have two arrays that have the shapes `N X T`

and `M X T`

. I’d like to compute the correlation coefficient across `T`

between every possible pair of rows `n`

and `m`

(from `N`

and `M`

, respectively).