src/gui_qt/main/management.py

@@ -542,7 +542,9 @@ class UpperManagement(QtCore.QObject):
                     elif fit_limits[0] == 'in':
                         inside = np.where((_x >= fit_limits[1][0]) & (_x <= fit_limits[1][1]))
                     else:
-                        inside = np.where((_x < fit_limits[1][0]) | (_x > fit_limits[1][1]))
+                        x_lim, _ = self.graphs[self.current_graph].ranges
+                        inside_graph = (_x >= x_lim[0]) & (_x <= x_lim[1])
+                        inside = np.where(((_x < fit_limits[1][0]) | (_x > fit_limits[1][1])) & inside_graph)
 
                     try:
                         if isinstance(we, str):

src/nmreval/models/wideline.py

@@ -3,11 +3,42 @@ try:
     from scipy.integrate import simpson
 except ImportError:
     from scipy.integrate import simps as simpson
-from numpy import pi
 
 from ..math.orientations import zcw_spherical as crystallites
 
 
+__all__ = ['CSA', 'Pake', 'SecCentralLine']
+
+
+def _make_broadening(x: np.ndarray, sigma: float, mode: str):
+    dx = x[1] - x[0]
+    _x = np.arange(len(x)) * dx
+    _x -= 0.5 * _x[-1]
+    if mode == 'l':
+        apd = 2 * sigma / (4*_x**2 + sigma**2) / np.pi
+    else:
+        ln2 = np.log(2)
+        apd = np.exp(-4*ln2 * (_x/sigma)**2) * 2 * np.sqrt(ln2/np.pi) / sigma
+    return apd
+
+
+def _make_bins(x: np.ndarray) -> np.ndarray:
+    bins = 0.5 * (x[1:] + x[:-1])
+    return np.r_[0.5 * (-x[1] + 3 * x[0]), bins, 0.5 * (3 * x[-1] - x[-2])]
+
+
+def _make_x(x: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    _x = x
+    dx = x[1:] - x[:-1]
+    dx = np.min(dx)
+    width = x[-1] - x[0]
+    _x = np.arange(width/dx - 1) * dx + x[0]
+
+    bins = (_x[1:] + _x[:-1]) / 2
+    bins = np.r_[_x[0]-dx/2, bins, _x[-1] + dx/2]
+    return _x, bins
+
+
 class Pake:
     type = 'Spectrum'
     name = 'Pake'
@@ -17,38 +48,39 @@ class Pake:
     choices = [('Broadening', 'broad', {'Gaussian': 'g', 'Lorentzian': 'l'})]
 
     @staticmethod
-    def func(x, c, delta, eta, sigma, t_pulse, broad='g'):
+    def func(
+            x: np.ndarray,
+            c: float,
+            delta: float,
+            eta: float,
+            sigma: float,
+            t_pulse: float,
+            broad: str = 'g',
+    ) -> np.ndarray:
         a, b, _ = crystallites(100000)
-        bins = 0.5 * (x[1:] + x[:-1])
-        bins = np.r_[0.5*(3*x[0]-x[1]), bins, 0.5*(3*x[-1]-x[-2])]
-
         omega = delta * 0.5 * (3*np.cos(b)**2 - 1 - eta * np.sin(b)**2 * np.cos(2*a))
+        x_used, bins = _make_x(x)
 
         s_left = np.histogram(omega, bins=bins)[0]
         s_right = np.histogram(-omega, bins=bins)[0]
         s = s_left + s_right
 
         if sigma != 0:
-            _x = np.arange(len(x))*(x[1]-x[0])
-            _x -= 0.5*_x[-1]
-
-            if broad == 'l':
-                apd = 2 * sigma / (4 * _x**2 + sigma**2) / pi
-            else:
-                apd = np.exp(-4 * np.log(2) * (_x/sigma)**2) * 2 * np.sqrt(np.log(2) / pi) / sigma
-
+            apd = _make_broadening(x_used, sigma, broad)
             ret_val = np.convolve(s, apd, mode='same')
-
         else:
             ret_val = s
-            
-        omega_1 = pi/2/t_pulse
-        attn = omega_1 * np.sin(t_pulse*np.sqrt(omega_1**2+0.5*(2*pi*x)**2)) / \
-            np.sqrt(omega_1**2+(np.pi*x)**2)
+
+        omega_1 = np.pi/2/t_pulse
+        attn = omega_1 * np.sin(t_pulse*np.sqrt(omega_1**2 + 0.5*(2*np.pi*x_used)**2)) / np.sqrt(omega_1**2 + (np.pi*x_used)**2)
 
         ret_val *= attn
+        ret_val /= simpson(y=ret_val, x=x_used)
 
-        return c * ret_val / simpson(ret_val, x)
+        if x_used.size == x.size:
+            return c * ret_val
+        else:
+            return c * np.interp(x=x, xp=x_used, fp=ret_val)
 
 
 class CSA:
@@ -60,28 +92,29 @@ class CSA:
     choices = [('Broadening', 'broad', {'Gaussian': 'g', 'Lorentzian': 'l'})]
 
     @staticmethod
-    def func(x, c, delta, eta, w_iso, sigma, broad='g'):
+    def func(
+            x: np.ndarray,
+            c: float,
+            delta: float,
+            eta: float,
+            w_iso: float,
+            sigma: float,
+            broad: str = 'g',
+    ) -> np.ndarray:
         a, b, _ = crystallites(100000)
-        bins = 0.5 * (x[1:] + x[:-1])
-        bins = np.r_[0.5*(-x[1] + 3*x[0]), bins, 0.5*(3*x[-1] - x[-2])]
 
         omega = w_iso + delta * 0.5 * (3*np.cos(b)**2 - 1 - eta * np.sin(b)**2 * np.cos(2*a))
 
-        s_left = np.histogram(omega, bins=bins)[0]
-        s = s_left
+        s = np.histogram(omega, bins=_make_bins(x))[0]
 
         if sigma != 0:
-            _x = np.arange(len(x)) * (x[1] - x[0])
-            _x -= 0.5 * _x[-1]
-            if broad == 'l':
-                apd = 2 * sigma / (4*_x**2 + sigma**2) / pi
-            else:
-                apd = np.exp(-4 * np.log(2) * (_x / sigma) ** 2) * 2 * np.sqrt(np.log(2) / pi) / sigma
+            print(len(s))
+            apd = _make_broadening(x, sigma, broad)
             ret_val = np.convolve(s, apd, mode='same')
         else:
             ret_val = s
 
-        return c * ret_val / simpson(ret_val, x)
+        return c * ret_val / simpson(y=ret_val, x=x)
 
 
 class SecCentralLine:
@@ -94,10 +127,18 @@ class SecCentralLine:
                ('Broadening', 'broad', {'Gaussian': 'g', 'Lorentzian': 'l'})]
 
     @staticmethod
-    def func(x, c, cq, eta, f_iso, gb, f_l, spin=2.5, broad='g'):
+    def func(
+            x: np.ndarray,
+            c: float,
+            cq: float,
+            eta: float,
+            f_iso: float,
+            gb: float,
+            f_l: float,
+            spin: float = 2.5,
+            broad: str = 'g',
+    ) -> np.ndarray:
         a, b, _ = crystallites(200000)
-        bins = 0.5 * (x[1:] + x[:-1])
-        bins = np.r_[0.5*(-x[1] + 3*x[0]), bins, 0.5*(3*x[-1] - x[-2])]
 
         # coupling constant
         omega_q = 2 * np.pi * cq / (2*spin*(2*spin-1))
@@ -116,17 +157,12 @@ class SecCentralLine:
         orient += prefactor_c
 
         omega = 2*np.pi*f_iso + coupling * orient
-        s = np.histogram(omega / (2*np.pi), bins=bins)[0]
+        s = np.histogram(omega / (2*np.pi), bins=_make_bins(x))[0]
 
         if gb != 0:
-            _x = np.arange(len(x)) * (x[1]-x[0])
-            _x -= 0.5*_x[-1]
-            if broad == 'l':
-                apd = 2*gb / (4*_x**2 + gb**2) / np.pi
-            else:
-                apd = np.exp(-4*np.log(2) * (_x/gb)**2) * 2 * np.sqrt(np.log(2)/np.pi) / gb
+            apd = _make_broadening(x, gb, broad)
             ret_val = np.convolve(s, apd, mode='same')
         else:
             ret_val = s
 
-        return c * ret_val / simpson(ret_val, x)
+        return c * ret_val / simpson(y=ret_val, x=x)