has_value = s[1:-1, :] > -999.99
has_left = s[:-2, :] > -999.99
has_right = s[2:, :] > -999.99
dsdx = np.where(has_right & has_value, (s[2:, :] - s[1:-1, :]) / di, -1000)
dsdx = np.where(has_left & has_value, (s[1:-1, :] - s[:-2, :]) / di, dsdx)
dsdx = np.where(has_left & has_right, (s[2:, :] - s[:-2, :]) / (2. * di), dsdx)
apply_along_axis() does this, however, the mapping function's return value is a list/array, and apply_along_axis() then changes the result's outer shape to match that, while I want the inner array to be reshaped
apply_along_axis() as well as for other functions, but nothing seems to stand out.
I have a python script that takes the text file as input(inputfile defined inside a python script) and does some calculation(myscript.py). Now i want to run the same python script(myscript.py) inside the shell script so i did like
python my_shell.sh and i defined the input file a inputfile='data.txt' inside the shell script. (in simple language i need to run a python script over a data file but data file should be defined inside a shell script) I tried a lot but i am getting error like input file
1 2 3
4 5 6
7 8 9
c1 = np.loadtxt(inputfile)[:,0]
NameError: name 'inputfile' is not defined.
my python programme is like this
import numpy as np
c1 = np.loadtxt(inputfile)[:,0]
c2 = np.loadtxt(inputfile)[:,1]
c3 = np.loadtxt(inputfile)[:,2]
def function(x):
c1,c2,c3 = x
d=(c1+c2+c3)
return dprint(function([c1,c2,c3]))
then i call this programme from shell script
!/bin/sh
inputfile='data.txt'
python myscript.py
Dear experts i have a problem, I have a file that contain both numerics,strings and separator like .....how to import it using numpy.loadtxt
while loading
import numpy as np
np.loadtxt('file',dtype=str)
i am getting error like Convert each value according to its column and store
ValueError: Wrong number of columns at line 3
data file
10.0 c1
80.0 c2
...............
10.0 mr
2.0 no
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def _random_choice(probabilities, identifier):
np.random.seed(identifier)
choice = np.random.choice(range(len(probabilities)), p=probabilities)
return choiceidentifier, will the proportion of choices match probabilities, and is there any noticeable correlation between some characteristic of the identifier and the choice?
identifier but I don't know who they are beforehand
Hi. I am writing Python logic in C++ for speed (with pybind11). I am receiving arguments, which are expected to be numpy array or convertible to such object.
In practice I am doing a check py::isinstance(arg, py::array). When it's not a numpy array, I would like to convert it. Thus, I would like to call np.array(a) but from C++.
First option is to import numpy.array, and call if from C++, but it will go back to Python to go immediately back to C++ (seems to be unwanted).
I have found the C++ function associated to this:
https://github.com/numpy/numpy/blob/d3eae8be4d783948a0d71363bc07558524e905e5/numpy/core/src/multiarray/multiarraymodule.c#L3979 _array_fromobject.
In practice, i guess I would like to call it, but it's not exposed in any header. Is there an alternative? Do you have any advice?
EDIT: I guess I want asarray and not just array.
masked == masked doesnt return true
1 reply
#!/usr/bin/python3
import numpy as np
data = np.array([29.714498, 31.593990, 35.978494, 71.621345, 73.060123,
79.839773, 91.201094, 93.538623, 95.967625, 111.286755,
152.131551, 158.890394, 162.079048, 183.674307, 200.263680,
204.271583, 204.919013, 205.322027, 213.583217, 234.791289,
242.200068, 292.154278, 301.285016, 302.870495, 331.163927,
368.068559, 391.237075, 431.096627, 1607.974257, 1900.303876
])
# Using numpy value = 94.1458735
print("Numpy Calculation = ", np.quantile(data, .25, interpolation='linear'))
# Manual calculation value = 92.95424075
# Calculate location manually Ly = (n + 1) X (y/100)
location = (data.size + 1)*(25/100)
print("Location = ", location)
# Location comes out to be 7.75 which means 7th element + .75 of (8th - 7th)
value = data[6] + ((data[7] - data[6]) * .75)
print("Manual Calculation = ", value)
See numpy/numpy#10736 for a long discussion about this, in particular this image from that thread:
The red line is a mode not supported by numpy, that might be the one you're asking for