0c9e906ba4
Copy and paste into fit2 righ after: num_int_background = simps(background_errors, x[numfit_min:numfit_max]) int_area = num_int_area-num_int_background ca line 180
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
plt.figure(figsize=(20, 10))
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plt.plot(x, y, "b-", label="Original data")
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plt.plot(x, comps["gaussian"], "r--", label="Gaussian component")
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plt.fill_between(x, comps["gaussian"], facecolor="red", alpha=0.4)
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plt.plot(x, comps["background"], "g--", label="Line component")
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plt.fill_between(x, comps["background"], facecolor="green", alpha=0.4)
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plt.fill_between(
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x[numfit_min:numfit_max],
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y[numfit_min:numfit_max],
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facecolor="yellow",
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alpha=0.4,
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label="Integrated area", )
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plt.plot(x, result.best_fit, "k-", label="Best fit")
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plt.title(
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"%s \n Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
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"background: slope = %9.4f, intercept = %9.4f \n"
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"Int. area = %9.4f +/- %9.4f \n"
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"fit area = %9.4f +/- %9.4f \n"
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"ratio((fit-int)/fit) = %9.4f"
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% (
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keys,
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result.params["g_cen"].value,
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result.params["g_width"].value,
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result.params["g_amp"].value,
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result.params["slope"].value,
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result.params["intercept"].value,
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int_area.n,
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int_area.s,
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result.params["g_amp"].value,
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result.params["g_amp"].stderr,
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(result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value
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)
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)
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plt.legend(loc="best")
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plt.show() |