Visualize fit

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

pyzebra/visualize peak fitter.py

@@ -0,0 +1,39 @@
+plt.figure(figsize=(20, 10))
+plt.plot(x, y, "b-", label="Original data")
+plt.plot(x, comps["gaussian"], "r--", label="Gaussian component")
+plt.fill_between(x, comps["gaussian"], facecolor="red", alpha=0.4)
+plt.plot(x, comps["background"], "g--", label="Line component")
+plt.fill_between(x, comps["background"], facecolor="green", alpha=0.4)
+
+plt.fill_between(
+    x[numfit_min:numfit_max],
+    y[numfit_min:numfit_max],
+    facecolor="yellow",
+    alpha=0.4,
+    label="Integrated area", )
+
+plt.plot(x, result.best_fit, "k-", label="Best fit")
+plt.title(
+    "%s \n Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
+    "background: slope = %9.4f, intercept = %9.4f \n"
+    "Int. area = %9.4f +/- %9.4f \n"
+    "fit area = %9.4f +/- %9.4f \n"
+    "ratio((fit-int)/fit)  = %9.4f"
+    % (
+        keys,
+        result.params["g_cen"].value,
+        result.params["g_width"].value,
+        result.params["g_amp"].value,
+        result.params["slope"].value,
+        result.params["intercept"].value,
+        int_area.n,
+        int_area.s,
+        result.params["g_amp"].value,
+        result.params["g_amp"].stderr,
+        (result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value
+
+    )
+)
+
+plt.legend(loc="best")
+plt.show()