xpart - pypi Package Compare versions

Comparing version 0.23.8 to 0.23.9

PKG-INFO

 Metadata-Version: 2.4
 Name: xpart
-Version: 0.23.8
+Version: 0.23.9
 Summary: Generation of Particle Ensembles
@@ -5,0 +5,0 @@ Home-page: https://xsuite.readthedocs.io/

pyproject.toml

@@ -8,5 +8,5 @@ [build-system]
 
-[pytest]
+[tool.pytest]
 markers = [
     "context_dependent: marks test as one that depends on the execution context",
 ]

tests/test_build_particles_normalized.py

@@ -168,3 +168,3 @@ # copyright ############################### #
     # Ensure there is a Marker between at_element and match_at_s (to test behave_likes_drift)
-    line.insert_element(element=xt.Marker(), name='test_marker', at_s=s_start + 0.3*(match_at_s-s_start))
+    line.insert('test_marker', obj=xt.Marker(), at=s_start + 0.3*(match_at_s-s_start))
 
@@ -188,3 +188,3 @@ line.build_tracker(_context=test_context)
     line.discard_tracker()
-    line.insert_element(element=xt.Marker(), name='match_at_s', at_s=match_at_s)
+    line.insert('match_at_s', obj=xt.Marker(), at=match_at_s)
     line.build_tracker(_context=test_context)
@@ -191,0 +191,0 @@ particles.move(_context=test_context)

tests/test_pencil_with_absolute_cut.py

@@ -86,3 +86,5 @@ # copyright ############################### #
 
-        tw_at_s = line.twiss(at_s=at_s)
+        line.cut_at_s([at_s])
+        tw = line.twiss()
+        tw_at_s = tw.rows[np.searchsorted(tw.s, at_s)]
         norm_coords = tw_at_s.get_normalized_coordinates(
@@ -89,0 +91,0 @@ particles, nemitt_x=nemitt_x, nemitt_y=nemitt_y,

xpart.egg-info/PKG-INFO

 Metadata-Version: 2.4
 Name: xpart
-Version: 0.23.8
+Version: 0.23.9
 Summary: Generation of Particle Ensembles
@@ -5,0 +5,0 @@ Home-page: https://xsuite.readthedocs.io/

xpart/_version.py

@@ -1,1 +0,1 @@
-__version__ = '0.23.8'
+__version__ = '0.23.9'

xpart/build_particles.py

@@ -41,23 +41,23 @@ # copyright ############################### #
 def build_particles(_context=None, _buffer=None, _offset=None, _capacity=None,
-                      mode=None,
-                      particle_ref=None,
-                      num_particles=None,
-                      x=None, px=None, y=None, py=None,
-                      zeta=None, delta=None, pzeta=None, ptau=None,
-                      x_norm=None, px_norm=None, y_norm=None, py_norm=None,
-                      zeta_norm=None, pzeta_norm=None,
-                      tracker=None,
-                      line=None,
-                      at_element=None,
-                      match_at_s=None,
-                      particle_on_co=None,
-                      R_matrix=None,
-                      W_matrix=None,
-                      method=None,
-                      nemitt_x=None, nemitt_y=None,nemitt_zeta = None,
-                      scale_with_transverse_norm_emitt=None,
-                      weight=None,
-                      s_tol=1e-6,
-                      include_collective=False,
-                      **kwargs, # They are passed to the twiss
+                    mode=None,
+                    particle_ref=None,
+                    num_particles=None,
+                    x=None, px=None, y=None, py=None,
+                    zeta=None, delta=None, pzeta=None, ptau=None,
+                    x_norm=None, px_norm=None, y_norm=None, py_norm=None,
+                    zeta_norm=None, pzeta_norm=None,
+                    tracker=None,
+                    line=None,
+                    at_element=None,
+                    match_at_s=None,
+                    particle_on_co=None,
+                    R_matrix=None,
+                    W_matrix=None,
+                    method=None,
+                    nemitt_x=None, nemitt_y=None,nemitt_zeta = None,
+                    scale_with_transverse_norm_emitt=None,
+                    weight=None,
+                    s_tol=1e-6,
+                    include_collective=False,
+                    **kwargs, # They are passed to the twiss
                     ):
@@ -201,11 +201,15 @@
             # Match at a position where there is no marker and backtrack to the previous marker
-            expected_at_element = np.where(np.array(s_elements)<=match_at_s)[0][-1]
-            assert at_element == expected_at_element or (
-                    at_element < expected_at_element and
-                        all([xt._is_aperture(line.element_dict[nn], line)
-                            or xt._behaves_like_drift(line.element_dict[nn], line)
-                    for nn in line._element_names_unique[at_element:expected_at_element]])), (
-                "`match_at_s` can only be placed in the drifts downstream of the "
-                "specified `at_element`. No active element can be present in between."
+            expected_at_element = np.searchsorted(s_elements, match_at_s)
+            names_between = line._element_names_unique[at_element:expected_at_element]
+            only_passives_between = all(
+                xt._is_aperture(line[nn], line) or xt._behaves_like_drift(line[nn], line)
+                for nn in names_between
+            )
+
+            if at_element != expected_at_element and (at_element >= expected_at_element or not only_passives_between):
+                raise ValueError(
+                    "`match_at_s` can only be placed in the drifts downstream of the "
+                    "specified `at_element`. No active element can be present in between."
                 )
+
             (tracker_rmat, _
@@ -215,4 +219,3 @@ ) = xt.twiss._build_auxiliary_tracker_with_extra_markers(
                     marker_prefix='xpart_rmat_')
-            at_element_line_rmat = tracker_rmat.line._element_names_unique.index(
-                                                                    'xpart_rmat_0')
+            at_element_line_rmat = tracker_rmat.line._element_names_unique.index('xpart_rmat_0')
             line_rmat = tracker_rmat.line
@@ -219,0 +222,0 @@ else:

xpart/longitudinal/single_rf_harmonic_matcher.py

@@ -52,3 +52,3 @@ # copyright ############################### #
                 # analytical_tau_max = np.sqrt((5*tau_lim**3*rms_bunch_length**2 - 3*tau_lim**5)/(15*tau_lim*rms_bunch_length**2 - 5*tau_lim**3))
-                func_to_solve = lambda new_tau_max: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_tau_max), -tau_lim, tau_lim))[0]
+                func_to_solve = lambda new_tau_max: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_tau_max[0]), -tau_lim, tau_lim))[0]
                 corrected_tau_max = scipy.optimize.fsolve(func_to_solve, x0=tau_max)[0]
@@ -66,3 +66,3 @@ tau_max = corrected_tau_max
             # correct bunch length (due to truncation at separatrix)
-            func_to_solve = lambda new_rms: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_rms), -tau_lim, tau_lim))[0]
+            func_to_solve = lambda new_rms: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_rms[0]), -tau_lim, tau_lim))[0]
             corrected_rms = scipy.optimize.fsolve(func_to_solve, x0=rms_bunch_length)[0]
@@ -81,4 +81,4 @@
             lambda_dist = lambda tau, beta: np.sqrt(beta) / self._Cq(q) * self._eq(-beta * tau**2, q)
-            
-            func_to_solve = lambda new_beta: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_beta), -tau_lim, tau_lim))[0]
+
+            func_to_solve = lambda new_beta: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_beta[0]), -tau_lim, tau_lim))[0]
             corrected_beta = scipy.optimize.fsolve(func_to_solve, x0=beta)[0]
@@ -88,3 +88,3 @@ _print(f"SingleRFHarmonicMatcher: q-Gaussian parameter beta = {corrected_beta:.3f} for q={q:.3f} to achieve target RMS bunch length ({rms_bunch_length:.3f}m).")
             self.tau_distr_y = lambda_dist(self.tau_distr_x, corrected_beta)
-            
+
         elif distribution == "binomial":
@@ -94,3 +94,3 @@ # Binomial distribution adds tail to parabolic, see (Joho, 1980) at https://indico.psi.ch/event/3484/attachments/5948/7502/TM-11-14.pdf
             tau_max = 1.0 # starting value, will be adjusted. Used as benchmarking value with RMS factor for parabola
-            lambda_dist = lambda tau, tau_max: (1 - (tau/tau_max)**2)**(m-0.5) 
+            lambda_dist = lambda tau, tau_max: (1 - (tau/tau_max)**2)**(m-0.5)
             binomial_2nd = lambda tau, tau_max: (1 - (tau/tau_max)**2)**(m-0.5)*tau**2
@@ -100,4 +100,4 @@ RMS_binomial = np.sqrt(integrate.quad(binomial_2nd, -1, 1, args=(tau_max))[0] / integrate.quad(lambda_dist, -1, 1, args=(tau_max))[0])
             _print(f"RMS factor for binimial is {factor_binomial:.3f}")
-            tau_max = factor_binomial*rms_bunch_length 
-            func_to_solve = lambda new_tau_max: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_tau_max), -tau_lim, tau_lim))[0]
+            tau_max = factor_binomial*rms_bunch_length
+            func_to_solve = lambda new_tau_max: (scipy.integrate.quad(lambda x: (x**2 - rms_bunch_length**2)*lambda_dist(x, new_tau_max[0]), -tau_lim, tau_lim))[0]
             corrected_tau_max = scipy.optimize.fsolve(func_to_solve, x0=tau_max)[0]
@@ -233,9 +233,11 @@ tau_max = corrected_tau_max
             return Cq
-    
-    
+
     def _eq(self, x, q):
-        """ 
+        """
         Q-exponential function
         Available at https://link.springer.com/article/10.1007/s00032-008-0087-y
         """
+        isscalar = np.isscalar(x)
+        if isscalar:
+            x = np.array([x])
         eq = np.zeros(len(x))
@@ -249,2 +251,4 @@ for i, xx in enumerate(x):
                 eq[i] = 0
+        if isscalar:
+            eq = eq[0]
         return eq

xpart/transverse_generators/pencil.py

@@ -156,6 +156,3 @@ # copyright ############################### #
     if twiss is None:
-        twiss = line.twiss(at_s=match_at_s,
-            at_elements=([at_element] if match_at_s is None else None),
-            **kwargs)
-
+        twiss = line.twiss(at_elements=([at_element] if match_at_s is None else None), at_s=match_at_s, **kwargs)
     if side=='+':
@@ -162,0 +159,0 @@ assert twiss[plane][0] < absolute_cut, 'The cut is on the wrong side'

Alert delta unavailable

Currently unable to show alert delta for PyPI packages.

Improved metrics

Total package byte prevSize

281300

0.02%

Lines of code

5833

0.1%