Matplotlib provides RGB to HSV conversion function: matplotlib.colors.rgb_to_hsv(): matplotlib.colors.rgb_to_hsv(arr) convert rgb values in a numpy array to hsv values input and output arrays should have shape (M,N,3)
NumPy’s transpose() effectively reverses the shape of an array. If the array is one-dimensional, this means it has no effect. In NumPy, the arrays array([1, 2, 3]) and array([1, 2, 3]) are actually the same – they only differ in whitespace. What you probably want are the corresponding two-dimensional arrays, for which transpose() would work … Read more
The main difference between the two is the pivot_table expects your input data to already be a DataFrame; you pass a DataFrame to pivot_table and specify the index/columns/values by passing the column names as strings. With cross_tab, you don’t necessarily need to have a DataFrame going in, as you just pass array-like objects for index/columns/values. … Read more
The uniform distribution would probably do what you are asking. np.random.uniform(5,10) # A single value np.random.uniform(5,10,[2,3]) # A 2×3 array
I think i got it on my own, but nevertheless will share the solution: since getting only the diagonals of a matrix multiplication > Z = N.diag(X.dot(Y)) is equivalent to the individual sum of the scalar product of rows of X and columns of Y, the previous statement is equivalent to: > Z = (X … Read more
Yes, the same order is guaranteed if it is not modified. See the docs here. Edit: Regarding if changing the value (but not adding/removing a key) will affect the order, this is what the comments in the C-source says: /* CAUTION: PyDict_SetItem() must guarantee that it won’t resize the * dictionary if it’s merely replacing … Read more
If you don’t know the underlying distribution, then my first thought would be to use bootstrapping: https://en.wikipedia.org/wiki/Bootstrapping_(statistics) In pseudo-code, assuming x is a numpy array containing your data: import numpy as np N = 10000 mean_estimates = [] for _ in range(N): re_sample_idx = np.random.randint(0, len(x), x.shape) mean_estimates.append(np.mean(x[re_sample_idx])) mean_estimates is now a list of 10000 … Read more
1. Passing extra arguments through scipy.integrate.quad The quad docs say: If the user desires improved integration performance, then f may be a scipy.LowLevelCallable with one of the signatures: double func(double x) double func(double x, void *user_data) double func(int n, double *xx) double func(int n, double *xx, void *user_data) The user_data is the data contained in … Read more
Just a hint for everybody reading that: the functions above do not compute the divergence of a vector field. they sum the derivatives of a scalar field A: result = dA/dx + dA/dy in contrast to a vector field (with three dimensional example): result = sum dAi/dxi = dAx/dx + dAy/dy + dAz/dz Vote down … Read more
Just use adjustable=”box-forced” instead of adjustable=”box”. As @cronos mentions, you can pass it in using the subplot_kw kwarg (additional keyword arguments to subplots are passed on to the Figure not the Axes, thus the need for subplot_kw). Instead, I’m going to use setp, which basically just does for item in sequence: item.set(**kwargs). (All matplotlib artists … Read more