I know this is a bit late (almost one year!) but I’ve been doing some research on the lognorm function in scipy.stats. A lot of folks seem confused about the input parameters, so I hope to help these people out. The example above is almost correct, but I found it strange to set the mean … Read more
You have two options: Linearize the system, and fit a line to the log of the data. Use a non-linear solver (e.g. scipy.optimize.curve_fit The first option is by far the fastest and most robust. However, it requires that you know the y-offset a-priori, otherwise it’s impossible to linearize the equation. (i.e. y = A * … Read more
OK, I think I found the answer. First the solution: cov_x*s_sq is simply the covariance of the parameters which is what you want. Taking sqrt of the diagonal elements will give you standard deviation (but be careful about covariances!). Residual variance = reduced chi square = s_sq = sum[(f(x)-y)^2]/(N-n), where N is number of data … Read more
These functions calculate moments of the probability density distribution (that’s why it takes only one parameter) and doesn’t care about the “functional form” of the values. These are meant for “random datasets” (think of them as measures like mean, standard deviation, variance): import numpy as np from scipy.stats import kurtosis, skew x = np.random.normal(0, 2, … Read more
As other folks have mentioned, for a really large file, you’re better off iterating. However, you do commonly want the entire thing in memory for various reasons. genfromtxt is much less efficient than loadtxt (though it handles missing data, whereas loadtxt is more “lean and mean”, which is why the two functions co-exist). If your … Read more
Your problem is that you need to install a Fortran compiler to build scipy. Also, if you already have a numpy that’s built with Fortran support disabled, you may have to replace it. Some of Apple’s pre-installed Python versions have such a numpy build pre-installed. The easiest way to get Fortran is with Homebrew. As … Read more
Computing : The value can be found using the mean (), the total sum of squares (), and the residual sum of squares (). Each is defined as: where is the function value at point . Taken from Wikipedia. From scipy.optimize.curve_fit(): You can get the parameters (popt) from curve_fit() with popt, pcov = curve_fit(f, xdata, … Read more
Here’s what I put together. I tried to closely emulate your screenshot. Given import numpy as np import scipy as sp import scipy.stats as stats import matplotlib.pyplot as plt %matplotlib inline # Raw Data heights = np.array([50,52,53,54,58,60,62,64,66,67,68,70,72,74,76,55,50,45,65]) weights = np.array([25,50,55,75,80,85,50,65,85,55,45,45,50,75,95,65,50,40,45]) Two detailed options to plot confidence intervals: def plot_ci_manual(t, s_err, n, x, x2, y2, ax=None): … Read more
The fastest and the most efficient way would be to use a native scipy function from linregress which calculates everything: slope : slope of the regression line intercept : intercept of the regression line r-value : correlation coefficient p-value : two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero … Read more