scipy

问题 I'm new to python,I'm trying get a FFT value of a uploaded wav file and return the FFT of each frame in each line of a text file (using GCP) using scipy or librosa Frame rate i require is 30fps wave file will be of 48k sample rate so my questions are how do i divide the samples for the whole wav file into samples of each frame How do add empty samples to make the length of the frame samples power of 2 (as 48000/30 = 1600 add 448 empty samples to make it 2048) how do i normalize the resulting

问题 I have a dataframe like as shown below df = pd.DataFrame({'person_id': [11,11,11,11,11,11,11,11,12,12,12], 'time' :[0,0,0,1,2,3,4,4,0,0,1], 'value':[101,102,np.nan,120,143,153,160,170,96,97,99]}) What I would like to do is a) Get the summary statistics for each subject for each time point (ex: 0hr, 1hr, 2hr etc) b) Please note that NA rows shouldn't be counted as separate record/row during computing mean I was trying the below for i in df['subject_id'].unique() df[df['subject_id'].isin([i])]

问题 Im trying to execute scipy broyden1 function with extra parameters (called "data" in the example), here is the code: data = [radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis, ellipsoid_semiminor_axis, srange] target_xyz = broyden1(Pixlinexyx_2Bsolved, start_xyz, args=data) def Pixlinexyx_2Bsolved(target, *data): radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis, ellipsoid_semiminor_axis, srange = data print target print radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis,

问题 Im trying to execute scipy broyden1 function with extra parameters (called "data" in the example), here is the code: data = [radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis, ellipsoid_semiminor_axis, srange] target_xyz = broyden1(Pixlinexyx_2Bsolved, start_xyz, args=data) def Pixlinexyx_2Bsolved(target, *data): radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis, ellipsoid_semiminor_axis, srange = data print target print radar_wavelen, satpos, satvel, ellipsoid_semimajor_axis,

问题 In python, I have two functions f1(x) and f2(x) returning a number. I would like to calculate a definite integral after their multiplication, i.e., something like: scipy.integrate.quad(f1*f2, 0, 1) What is the best way to do it? Is it even possible in python? 回答1: I found out just a second ago, that I can use lambda :) scipy.integrate.quad(lambda x: f1(x)*f2(x), 0, 1) Anyway, I'm leaving it here. Maybe it will help somebody out. 回答2: When I had the same problem, I used this (based on the

问题 In python, I have two functions f1(x) and f2(x) returning a number. I would like to calculate a definite integral after their multiplication, i.e., something like: scipy.integrate.quad(f1*f2, 0, 1) What is the best way to do it? Is it even possible in python? 回答1: I found out just a second ago, that I can use lambda :) scipy.integrate.quad(lambda x: f1(x)*f2(x), 0, 1) Anyway, I'm leaving it here. Maybe it will help somebody out. 回答2: When I had the same problem, I used this (based on the

问题 In python, I have two functions f1(x) and f2(x) returning a number. I would like to calculate a definite integral after their multiplication, i.e., something like: scipy.integrate.quad(f1*f2, 0, 1) What is the best way to do it? Is it even possible in python? 回答1: I found out just a second ago, that I can use lambda :) scipy.integrate.quad(lambda x: f1(x)*f2(x), 0, 1) Anyway, I'm leaving it here. Maybe it will help somebody out. 回答2: When I had the same problem, I used this (based on the

问题 I was wondering how to calculate skewness and kurtosis correctly in pandas. Pandas gives some values for skew() and kurtosis() values but they seem much different from scipy.stats values. Which one to trust pandas or scipy.stats ? Here is my code: import numpy as np import scipy.stats as stats import pandas as pd np.random.seed(100) x = np.random.normal(size=(20)) kurtosis_scipy = stats.kurtosis(x) kurtosis_pandas = pd.DataFrame(x).kurtosis()[0] print(kurtosis_scipy, kurtosis_pandas) # -0

问题 I was wondering how to calculate skewness and kurtosis correctly in pandas. Pandas gives some values for skew() and kurtosis() values but they seem much different from scipy.stats values. Which one to trust pandas or scipy.stats ? Here is my code: import numpy as np import scipy.stats as stats import pandas as pd np.random.seed(100) x = np.random.normal(size=(20)) kurtosis_scipy = stats.kurtosis(x) kurtosis_pandas = pd.DataFrame(x).kurtosis()[0] print(kurtosis_scipy, kurtosis_pandas) # -0

问题 I have some which I am fitting to the gamma distribution using scipy.stats. I am able to extract the shape, loc, and scale params and they look reasonable with the data ranges I expect. My question is: is there a way to also get the errors in the parameters? something like the output of curve_fit. NOTE: I don't use curve fit directly because it is not working properly and most of the time is not able to compute the parameters of the gamma distribution. On the other hand, scipy.stats.gamma.fit