diff options
| -rw-r--r-- | fg21sim/utils/analyze.py | 32 |
1 files changed, 27 insertions, 5 deletions
diff --git a/fg21sim/utils/analyze.py b/fg21sim/utils/analyze.py index a052ad4..b31f7d1 100644 --- a/fg21sim/utils/analyze.py +++ b/fg21sim/utils/analyze.py @@ -88,7 +88,10 @@ def countdist_integrated(x, nbin, log=True, xmin=None, xmax=None): return counts, bins, binedges -def loglinfit(x, y, coef0=(1, 1), **kwargs): +def loglinfit(x, y, + xlim=(None, None), ylim=(None, None), + coef0=(1, 1), + **kwargs): """ Fit the data points with a log-linear model: y = a * x^b @@ -96,7 +99,10 @@ def loglinfit(x, y, coef0=(1, 1), **kwargs): ---------- x, y : list[float] The data points. - coef0 : two-float tuple/list, optional + xlim, ylim : float tuple/list of length 2, optional + The minimum/maximum limit of x/y for the fitting. + Default: (None, None), i.e., use all the data. + coef0 : float tuple/list of length 2, optional The initial values of the coefficients (a0, b0). Default: (1, 1) **kwargs : @@ -115,9 +121,23 @@ def loglinfit(x, y, coef0=(1, 1), **kwargs): def _f_poly1(x, a, b): return a + b * x - logx = np.log(x) - logy = np.log(y) - f_scale = np.mean(logy) + x = np.asarray(x) + y = np.asarray(y) + xmin, xmax = xlim + ymin, ymax = ylim + if xmin is None: + xmin = np.min(x) + if xmax is None: + xmax = np.max(x) + if ymin is None: + ymin = np.min(y) + if ymax is None: + ymax = np.max(y) + + mask = (x >= xmin) & (x <= xmax) & (y >= ymin) & (y <= ymax) + logx = np.log(x[mask]) + logy = np.log(y[mask]) + args = { "method": "trf", "loss": "soft_l1", @@ -125,8 +145,10 @@ def loglinfit(x, y, coef0=(1, 1), **kwargs): } args.update(kwargs) p, pcov = optimize.curve_fit(_f_poly1, logx, logy, p0=coef0, **args) + coef = (np.exp(p[0]), p[1]) perr = np.sqrt(np.diag(pcov)) err = (np.exp(perr[0]), perr[1]) fun = lambda x: np.exp(_f_poly1(np.log(x), *p)) + return coef, err, fun |