Yes! An easy way to do this will be using numpy.linspace Numpy Docs numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None) Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. The endpoint of the interval can optionally be excluded. Example: [In 1] np.linspace(start=0, stop=50, num=5) [Out 1] … Read more
You probably still have negative values inside the log, which gives nan with real numbers. a and y should represent probability between 0 to 1, So you need to check why do you have smaller/larger values there. Adding 1e-7 shows there is something fishy, because np.log(0) gives -inf, which I think is the value you … Read more
Thanks to John Vinyard for suggesting scipy. After some good research and testing, here is the solution to this question: Prerequisites: Install Numpy and SciPy Import the SciPy and Numpy Modules Make a copy of the 5 dimensional array including just the X and Y values. Create an instance of a cKDTree as such: YourTreeName … Read more
The answer in a nutshell is you should generally use structured arrays rather than recarrays because structured arrays are faster and the only advantage of recarrays is to allow you to write arr.x instead of arr[‘x’], which can be a convenient shortcut, but also error prone if your column names conflict with numpy methods/attributes. See … Read more
You could certainly wrap whatever FFT implementation that you wanted to test using Cython or other like-minded tools that allow you to access external libraries. GPU-based If you’re going to test FFT implementations, you might also take a look at GPU-based codes (if you have access to the proper hardware). There are several: reikna.fft, scikits.cuda. … Read more
NumPy supports boolean indexing a[f] This assumes that a and f are NumPy arrays rather than Python lists (as in the question). You can convert with f = np.array(f).
Reshape doesn’t change the data as mentioned here. Resize changes the data as can be seen here. Here are some examples: >>> numpy.random.rand(2,3) array([[ 0.6832785 , 0.23452056, 0.25131171], [ 0.81549186, 0.64789272, 0.48778127]]) >>> ar = numpy.random.rand(2,3) >>> ar.reshape(1,6) array([[ 0.43968751, 0.95057451, 0.54744355, 0.33887095, 0.95809916, 0.88722904]]) >>> ar array([[ 0.43968751, 0.95057451, 0.54744355], [ 0.33887095, 0.95809916, 0.88722904]]) … Read more
I’m not sure whether they exist in TensorFlow 1 but TensorFlow 2.0 supports RaggedTensors, which the documentation describes as “… the TensorFlow equivalent of nested variable-length lists.” I think it would be trivial to convert your data to RaggedTensors. It might even be as easy as: data_tensor = tf.ragged.constant(data) Example: >>> a = tf.ragged.constant([[1],[2,3]]) >>> … Read more
Here are some examples of a different (loop free) way to work with blocks: import numpy as np from numpy.lib.stride_tricks import as_strided as ast A= np.arange(36).reshape(6, 6) print A #[[ 0 1 2 3 4 5] # [ 6 7 8 9 10 11] # … # [30 31 32 33 34 35]] # 2×2 … Read more
Using scipy.optimize.leastsq: import numpy as np import matplotlib.pyplot as plt import scipy.optimize def sigmoid(p,x): x0,y0,c,k=p y = c / (1 + np.exp(-k*(x-x0))) + y0 return y def residuals(p,x,y): return y – sigmoid(p,x) def resize(arr,lower=0.0,upper=1.0): arr=arr.copy() if lower>upper: lower,upper=upper,lower arr -= arr.min() arr *= (upper-lower)/arr.max() arr += lower return arr # raw data x = np.array([821,576,473,377,326],dtype=”float”) … Read more