Weighting the data for a fit I have 5 data points with errors associated to them $y_i\pm dy_i$ and the corresponding $x_i$ values (which don't have uncertainties associated to them). I need to calculate the difference between the first of these points, $y_1$ and the rest, and fit a straight line to it (basically the plot will be $\Delta y$ vs $x$). For the other 4 points the error associated with them is just $d(\Delta y_i)=\sqrt{(dy_1)^2+(dy_i)^2}$ for $i$ from 2 to 5. About the first point itself, at that value of $x_1$, $\Delta y_1$ value should be zero. Also, given that this is the reference point, the error associated to that should be zero, too (right?). Now, when I want to make a least square fit, I need to weight the difference between the model and the data by $1/(d(\Delta y_i))$. However that is infinity in the case of the first point (which I guess it makes sense, as I am sure that the line should pass through that point). However I am not sure how to make it work numerically i.e. using a fitting program (Python for example). Should I just replace 0 by something like $10^{-15}$? Does anyone have any advice on how should I handle this infinity? Thank you!
 A: Here is a graphical Python fitter with an example of making the first data point's uncertainty to be tiny - that is, the value is very certain - effectively forcing the straight line fit to pass through that point. For comparison the example includes a straight line fit where this is not done.

import numpy, scipy, matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

# turn off "no covariance" warnings for simple example
import warnings
warnings.filterwarnings("ignore")

# note the single very small uncertainty for the first point
uncertainties = numpy.array([1.0E-10, 1.0, 1.0, 1.0, 1.0])


# make the first point far from the other Y values
xData = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5])
yData = numpy.array([25.0, 20.2, 30.3, 40.4, 50.5])


def func(x, a, b):
    return (a * x) + b  


# these are the same as the scipy defaults
initialParameters = numpy.array([1.0, 1.0])

# curve fit the test data, first without uncertainties to
# get us closer to initial starting parameters
ssqParameters, pcov = curve_fit(func, xData, yData, p0 = initialParameters)

# now that we have better starting parameters, use uncertainties
fittedParameters, pcov = curve_fit(func, xData, yData, p0 = ssqParameters, sigma=uncertainties, absolute_sigma=True)

modelPredictions = func(xData, *fittedParameters) 

absError = modelPredictions - yData

SE = numpy.square(absError) # squared errors
MSE = numpy.mean(SE) # mean squared errors
RMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSE
Rsquared = 1.0 - (numpy.var(absError) / numpy.var(yData))

print('Parameters:', fittedParameters)
print('RMSE:', RMSE)
print('R-squared:', Rsquared)

print()


##########################################################
# graphics output section
def ModelAndScatterPlot(graphWidth, graphHeight):
    f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)
    axes = f.add_subplot(111)

    # first the raw data as a scatter plot
    axes.plot(xData, yData,  'D')

    # create data for the fitted equation plot with uncertanties
    xModel = numpy.linspace(min(xData), max(xData))
    yModel = func(xModel, *fittedParameters)

    # here is the curve without using uncertanties
    yModelNoUnc = func(xModel, *ssqParameters)

    # now the models as line plots
    axes.plot(xModel, yModel, label = 'Force first point')
    axes.plot(xModel, yModelNoUnc, label = 'No Uncertainties')
    plt.legend()

    axes.set_xlabel('X Data') # X axis data label
    axes.set_ylabel('Y Data') # Y axis data label

    plt.show()
    plt.close('all') # clean up after using pyplot

graphWidth = 800
graphHeight = 600
ModelAndScatterPlot(graphWidth, graphHeight)

