Here you have an example working on py2.6 and py3.2: from scipy.stats import norm import matplotlib.mlab as mlab import matplotlib.pyplot as plt # read data from a text file. One number per line arch = “test/Log(2)_ACRatio.txt” datos = [] for item in open(arch,’r’): item = item.strip() if item != ”: try: datos.append(float(item)) except ValueError: pass … Read more
You can pass curve_fit a multi-dimensional array for the independent variables, but then your func must accept the same thing. For example, calling this array X and unpacking it to x, y for clarity: import numpy as np from scipy.optimize import curve_fit def func(X, a, b, c): x,y = X return np.log(a) + b*np.log(x) + … Read more
I suggest you to start with simple polynomial fit, scipy.optimize.curve_fit tries to fit a function f that you must know to a set of points. This is a simple 3 degree polynomial fit using numpy.polyfit and poly1d, the first performs a least squares polynomial fit and the second calculates the new points: import numpy as … Read more
An easy solution, as you mentioned, is to simply connect the points with a straight line. Here’s the code to do so: public void onDraw(Canvas canvas) { Path path = new Path(); boolean first = true; for(Point point : points){ if(first){ first = false; path.moveTo(point.x, point.y); } else{ path.lineTo(point.x, point.y); } } canvas.drawPath(path, paint); } … Read more
You can use numpy.piecewise() to create the piecewise function and then use curve_fit(), Here is the code from scipy import optimize import matplotlib.pyplot as plt import numpy as np %matplotlib inline x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ,11, 12, 13, 14, 15], dtype=float) y = np.array([5, 7, 9, 11, … Read more
To get a third order polynomial in x (x^3), you can do lm(y ~ x + I(x^2) + I(x^3)) or lm(y ~ poly(x, 3, raw=TRUE)) You could fit a 10th order polynomial and get a near-perfect fit, but should you? EDIT: poly(x, 3) is probably a better choice (see @hadley below).
I like loess() a lot for smoothing: x <- 1:10 y <- c(2,4,6,8,7,12,14,16,18,20) lo <- loess(y~x) plot(x,y) lines(predict(lo), col=”red”, lwd=2) Venables and Ripley’s MASS book has an entire section on smoothing that also covers splines and polynomials — but loess() is just about everybody’s favourite.
If I understand your question correctly, then you probably want a density estimate along with the histogram: X <- c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)) hist(X, prob=TRUE) # prob=TRUE for probabilities not counts lines(density(X)) # add a density estimate with defaults lines(density(X, adjust=2), lty=”dotted”) # add another “smoother” density Edit a long while … Read more
arange generates lists (well, numpy arrays); type help(np.arange) for the details. You don’t need to call it on existing lists. >>> x = [1,2,3,4] >>> y = [3,5,7,9] >>> >>> m,b = np.polyfit(x, y, 1) >>> m 2.0000000000000009 >>> b 0.99999999999999833 I should add that I tend to use poly1d here rather than write out … Read more
A very late reply, but just in case someone needs a ready function for this: scipy.stats.linregress i.e. slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y) as in @Adam Marples’s answer.