# HG changeset patch # User imgteam # Date 1626982447 0 # Node ID e08061d196ce23e72972fae71e979a4133355393 "planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/curve_fitting/ commit ef82d0882741042922349499cafa35d20d70ce70" diff -r 000000000000 -r e08061d196ce curve_fitting.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/curve_fitting.py Thu Jul 22 19:34:07 2021 +0000 @@ -0,0 +1,106 @@ +""" +Copyright 2021 Biomedical Computer Vision Group, Heidelberg University. +Author: Qi Gao (qi.gao@bioquant.uni-heidelberg.de) + +Distributed under the MIT license. +See file LICENSE for detail or copy at https://opensource.org/licenses/MIT + +""" + +import argparse + +import numpy as np +import pandas as pd +from scipy.optimize import least_squares + + +def compute_curve(x, par): + assert len(par) in [2, 3], 'The degree of curve must be 1 or 2!' + if len(par) == 3: + return par[0] * x + par[1] + par[2] * x ** 2 + elif len(par) == 2: + return par[0] * x + par[1] + + +def fitting_err(par, xx, seq, penalty): + assert penalty in ['abs', 'quadratic', 'student-t'], 'Unknown penalty function!' + curve = compute_curve(xx, par) + if penalty == 'abs': + err = np.sqrt(np.abs(curve - seq)) + elif penalty == 'quadratic': + err = (curve - seq) + elif penalty == 'student-t': + a = 1000 + b = 0.001 + err = np.sqrt(a * np.log(1 + (b * (curve - seq)) ** 2)) + return err + + +def curve_fitting(seq, degree=2, penalty='abs'): + assert len(seq) > 5, 'Data is too short for curve fitting!' + assert degree in [1, 2], 'The degree of curve must be 1 or 2!' + + par0 = [(seq[-1] - seq[0]) / len(seq), np.mean(seq[0:3])] + if degree == 2: + par0.append(-0.001) + + xx = np.array([i for i in range(len(seq))]) + 1 + x = np.array(par0, dtype='float64') + result = least_squares(fitting_err, x, args=(xx, seq, penalty)) + + return compute_curve(xx, result.x) + + +def curve_fitting_io(fn_in, fn_out, degree=2, penalty='abs', alpha=0.01): + # read all sheets + xl = pd.ExcelFile(fn_in) + nSpots = len(xl.sheet_names) + data_all = [] + for i in range(nSpots): + df = pd.read_excel(xl, xl.sheet_names[i]) + data_all.append(np.array(df)) + col_names = df.columns.tolist() + ncols_ori = len(col_names) + + # curve_fitting + diff = np.array([], dtype=('float64')) + for i in range(nSpots): + seq = data_all[i][:, -1] + seq_fit = seq.copy() + idx_valid = ~np.isnan(seq) + seq_fit[idx_valid] = curve_fitting(seq[idx_valid], degree=2, penalty='abs') + data_all[i] = np.concatenate((data_all[i], seq_fit.reshape(-1, 1)), axis=1) + if alpha > 0: + diff = np.concatenate((diff, seq_fit[idx_valid] - seq[idx_valid])) + + # add assistive curve + if alpha > 0: + sorted_diff = np.sort(diff) + fac = 1 - alpha / 2 + sig3 = sorted_diff[int(diff.size * fac)] + for i in range(nSpots): + seq_assist = data_all[i][:, -1] + sig3 + data_all[i] = np.concatenate((data_all[i], seq_assist.reshape(-1, 1)), axis=1) + + # write to file + with pd.ExcelWriter(fn_out) as writer: + for i in range(nSpots): + df = pd.DataFrame() + for c in range(ncols_ori): + df[col_names[c]] = data_all[i][:, c] + df['CURVE'] = data_all[i][:, ncols_ori] + if alpha > 0: + df['CURVE_A'] = data_all[i][:, ncols_ori + 1] + df.to_excel(writer, sheet_name=xl.sheet_names[i], index=False, float_format='%.2f') + writer.save() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Fit (1st- or 2nd-degree) polynomial curves to data points") + parser.add_argument("fn_in", help="File name of input data points (xlsx)") + parser.add_argument("fn_out", help="File name of output fitted curves (xlsx)") + parser.add_argument("degree", type=int, help="Degree of the polynomial function") + parser.add_argument("penalty", help="Optimization objective for fitting") + parser.add_argument("alpha", type=float, help="Significance level for generating assistive curves") + args = parser.parse_args() + curve_fitting_io(args.fn_in, args.fn_out, args.degree, args.penalty, args.alpha) diff -r 000000000000 -r e08061d196ce curve_fitting.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/curve_fitting.xml Thu Jul 22 19:34:07 2021 +0000 @@ -0,0 +1,49 @@ + + to data points using (1st- or 2nd-degree) polynomial function + + numpy + openpyxl + pandas + scipy + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + **What it does** + + This tool fits (1st- or 2nd-degree) polynomial curves to data points. Optional: Given a significance level, assistive curves will also be generated. + + diff -r 000000000000 -r e08061d196ce test-data/curves_fitted.xlsx Binary file test-data/curves_fitted.xlsx has changed diff -r 000000000000 -r e08061d196ce test-data/spots_linked.xlsx Binary file test-data/spots_linked.xlsx has changed