You can install pip from the pypl database directly with just: pip install scipy or if you want a particular version pip install “scipy==1.00” you shouldnt ever need to install from a local drive 1 solved Meeting Error when I install scipy
>>> import numpy as np >>> x = np.arange(-1,1,0.1) >>> print(x) [ -1.00000000e+00 -9.00000000e-01 -8.00000000e-01 -7.00000000e-01 -6.00000000e-01 -5.00000000e-01 -4.00000000e-01 -3.00000000e-01 -2.00000000e-01 -1.00000000e-01 -2.22044605e-16 1.00000000e-01 2.00000000e-01 3.00000000e-01 4.00000000e-01 5.00000000e-01 6.00000000e-01 7.00000000e-01 8.00000000e-01 9.00000000e-01] >>> type(x[0]) <type ‘numpy.float64’> >>> print(x.tolist()) [-1.0, -0.9, -0.8, -0.7000000000000001, -0.6000000000000001, -0.5000000000000001, -0.40000000000000013, -0.30000000000000016, -0.20000000000000018, -0.1000000000000002, -2.220446049250313e-16, 0.09999999999999964, 0.19999999999999973, 0.2999999999999998, 0.3999999999999997, 0.49999999999999956, 0.5999999999999996, … Read more
You could try Steven Masfaraud’s block module simulator (BMS). As of August 2016 it has nonlinear block elements for Saturation, Coulomb, Dead Zone, and Hysteresis. Here’s a link to one of his examples. The other option on this Quora post is PyLinX. It looks like SciPySim may no longer be under development, from this link … Read more
It’s here: def preos(molecule, T, P, plotcubic=True, printresults=True): Tr = T / molecule.Tc # reduced temperature … preos(“methane”, 160, 10, “true”, “true”) You’re clearly passing “methane” into the preos function as a string, then trying to call .Tc on that string. The error is saying exactly that. This doesn’t have anything to do with IPython. … Read more
Why do you think this a loadmat object not having a dictionary? The error is at: def get_events(matlab_obj): … subjects = matlab_obj[“s”][0] … for subject_object in subjects: try: subject_hash = subject_object.my_hashedNumber[0][0] # AttributeError here matlab_obj[“s”] successfully accesses the loaded object as a dictionary. subjects is a numpy record array of shape (106,), and 58 fields. … Read more
I’m basing this answer on a very similar answer I wrote just a few hours ago. #from sklearn.feature_extraction.image import extract_patches # similar to numpy’s stride_tricks from numpy.lib.stride_tricks import as_strided data = np.array([[1, 1 , 0 , 0 , 0 , 0 , 1 , 0], [1, 1 , 1 , 0 , 0 , 1 … Read more
Update: Now the situation looks a bit different as you updated the question. Here’s a quick snippet thrown together using pandas and numpy (for the sake of simplicity we replace missing ratings with zero): import numpy as np importport pandas as pd from itertools import combinations df = pd.DataFrame(critics).T.fillna(0) distances = [] for critic1, critic2 … Read more
Some parts of your question are unclear. It might help to give the context of what you’re trying to achieve, rather than what are the specific steps you’re taking. 1) + 3) In a Normal distribution – fitting the distribution, and estimating the mean and standard deviation – are basically the same thing. The mean … Read more
You have a bunch of 1000 element arrays: In [8]: p.shape Out[8]: (1000,) In [9]: K.shape Out[9]: (1000,) In [10]: R.shape Out[10]: (1000,) In [11]: np.minimum.reduce([p, K, R]).shape Out[11]: (1000,) In [12]: Vl(p).shape Out[12]: (1000,) In [8]: p.shape Out[8]: (1000,) In [9]: K.shape Out[9]: (1000,) In [10]: R.shape Out[10]: (1000,) In [11]: np.minimum.reduce([p, K, R]).shape … Read more
if d<r: vx,vy,ax,ay=0,0,0,0 return [vx,ax,vy,ay] This isn’t a function so you can’t use return. If you want to add those values to a list you can make a new list: new_list = [vx,ax,vy,ay] You don’t need to use return outside of a function because those variables are already in scope. When you use variables inside … Read more