routine to instantiate an impactx lattice based on a synergia lattice

CMakeLists.txt

@@ -1,7 +1,7 @@
 # Preamble ####################################################################
 #
 cmake_minimum_required(VERSION 3.24.0)
-project(ImpactX VERSION 26.04)
+project(ImpactX VERSION 26.03)
 
 include(${ImpactX_SOURCE_DIR}/cmake/ImpactXFunctions.cmake)
 
@@ -72,6 +72,7 @@ option(ImpactX_MPI           "Multi-node support (message-passing)"         ON)
 option(ImpactX_SIMD          "CPU SIMD Acceleration"                        OFF)
 option(ImpactX_OPENPMD       "openPMD I/O (HDF5, ADIOS)"                    ON)
 option(ImpactX_PYTHON        "Python bindings"                              OFF)
+option(ImpactX_SYNMADX       "Standalone MAD-X lattice parser (synmadx)"    OFF)
 
 set(ImpactX_PRECISION_VALUES SINGLE DOUBLE)
 set(ImpactX_PRECISION DOUBLE CACHE STRING "Floating point precision (SINGLE/DOUBLE)")
@@ -305,7 +306,7 @@ if(ImpactX_PYTHON)
             impactx_enable_IPO(pyImpactX)
         else()
             # conditionally defined target in pybind11
-            # https://github.com/pybind/pybind11/blob/v3.0.3/tools/pybind11Common.cmake#L436-L442
+            # https://github.com/pybind/pybind11/blob/v3.0.0/tools/pybind11Common.cmake#L420-L426
             target_link_libraries(pyImpactX PRIVATE pybind11::lto)
         endif()
     endif()
@@ -452,8 +453,7 @@ if(ImpactX_PYTHON)
         WORKING_DIRECTORY
             ${ImpactX_BINARY_DIR}
         DEPENDS
-            pyImpactX
-            pyImpactX_python_sources
+            pyImpactX $<$<BOOL:${ImpactX_SYNMADX}>:pysynmadx>
     )
 
     # this will also upgrade/downgrade dependencies, e.g., when the version of numpy changes
@@ -516,6 +516,9 @@ if(ImpactX_PYTHON)
         VERBATIM
     )
     add_dependencies(pyImpactX_python_sources pyImpactX)
+    add_dependencies(${ImpactX_CUSTOM_TARGET_PREFIX}pip_wheel
+        pyImpactX_python_sources
+    )
 endif()
 
 

examples/parse_madx_lattice/analyze_diags.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+
+import matplotlib.pyplot as plt
+import numpy as np
+import openpmd_api as io
+import scipy
+
+# columns in rbc file
+# step s mean_x min_x max_x mean_y min_y max_y mean_t min_t max_t sigma_x sigma_y sigma_t mean_px min_px max_px mean_py min_py max_py mean_pt min_pt max_pt sigma_px sigma_py sigma_pt emittance_x emittance_y emittance_t alpha_x alpha_y alpha_t beta_x beta_y beta_t dispersion_x dispersion_px dispersion_y dispersion_py emittance_xn emittance_yn emittance_tn charge_C
+
+# attributes in the monitor file:
+# >> series.iterations[1].particles["beam"].attributes
+# ['alpha_t', 'alpha_x', 'alpha_y', 'beta_gamma_ref', 'beta_ref', 'beta_t', 'beta_x', 'beta_y', 'charge_C', 'charge_ref', 'dispersion_px', 'dispersion_py', 'dispersion_x', 'dispersion_y', 'emittance_t', 'emittance_tn', 'emittance_x', 'emittance_xn', 'emittance_y', 'emittance_yn', 'gamma_ref', 'mass_ref', 'max_pt', 'max_px', 'max_py', 'max_t', 'max_x', 'max_y', 'mean_pt', 'mean_px', 'mean_py', 'mean_t', 'mean_x', 'mean_y', 'min_pt', 'min_px', 'min_py', 'min_t', 'min_x', 'min_y', 'pt_max', 'pt_mean', 'pt_min', 'pt_ref', 'px_max', 'px_mean', 'px_min', 'px_ref', 'py_max', 'py_mean', 'py_min', 'py_ref', 'pz_ref', 's_ref', 'sig_pt', 'sig_px', 'sig_py', 'sig_t', 'sig_x', 'sig_y', 'sigma_pt', 'sigma_px', 'sigma_py', 'sigma_t', 'sigma_x', 'sigma_y', 't_max', 't_mean', 't_min', 't_ref', 'x_max', 'x_mean', 'x_min', 'x_ref', 'y_max', 'y_mean', 'y_min', 'y_ref', 'z_ref']
+
+
+series = io.Series("diags/openPMD/monitor.h5", io.Access.read_only)
+iterations = list(series.iterations)
+iter0 = iterations[0]
+iter1 = iterations[-1]
+
+s_by_iterations = np.array(
+    [
+        series.iterations[it].particles["beam"].get_attribute("s_ref")
+        for it in iterations
+    ]
+)
+s_ref = s_by_iterations
+beamattrs = {}
+for at in [
+    "sigma_x",
+    "sigma_y",
+    "sigma_t",
+    "sigma_px",
+    "sigma_py",
+    "sigma_pt",
+    "min_x",
+    "min_y",
+    "min_t",
+    "px_min",
+    "py_min",
+    "pt_min",
+    "max_x",
+    "max_y",
+    "max_t",
+    "px_max",
+    "py_max",
+    "pt_max",
+]:
+    beamattrs[at] = [
+        series.iterations[it].particles["beam"].get_attribute(at) for it in iterations
+    ]
+
+npart_by_iterations = np.array(
+    [
+        series.iterations[it].particles["beam"]["position"]["x"].shape[0]
+        for it in iterations
+    ]
+)
+
+plt.figure()
+plt.title("macroparticles")
+plt.plot(s_ref, npart_by_iterations)
+plt.xlabel("s [m]")
+plt.ylabel("macroparticles")
+
+plt.figure()
+plt.subplot(221)
+plt.plot(s_ref, beamattrs["min_x"], label="min x")
+plt.legend(loc="best")
+plt.subplot(222)
+plt.plot(s_ref, beamattrs["max_x"], label="max x")
+plt.legend(loc="best")
+plt.subplot(223)
+plt.plot(s_ref, beamattrs["min_t"], label="min t")
+plt.legend(loc="best")
+plt.subplot(224)
+plt.plot(s_ref, beamattrs["max_t"], label="max t")
+plt.legend(loc="best")
+
+rbc_0 = np.loadtxt("diags/reduced_beam_characteristics.0.0", skiprows=1, max_rows=1)
+
+rbc_1 = np.loadtxt("diags/reduced_beam_characteristics.0.0", skiprows=4)
+
+rbc = np.vstack((rbc_0, rbc_1))
+
+step_s = rbc[:, 0]
+s = rbc[:, 1]
+
+mean_x = rbc[:, 2]
+min_x = rbc[:, 3]
+max_x = rbc[:, 4]
+
+mean_y = rbc[:, 5]
+min_y = rbc[:, 6]
+max_y = rbc[:, 7]
+
+mean_t = rbc[:, 8]
+min_t = rbc[:, 9]
+max_t = rbc[:, 10]
+
+sigma_x = rbc[:, 11]
+sigma_y = rbc[:, 12]
+sigma_t = rbc[:, 13]
+
+mean_px = rbc[:, 14]
+min_px = rbc[:, 15]
+max_px = rbc[:, 16]
+
+mean_py = rbc[:, 17]
+min_py = rbc[:, 18]
+max_py = rbc[:, 19]
+
+mean_pt = rbc[:, 20]
+min_pt = rbc[:, 21]
+max_pt = rbc[:, 22]
+
+sigma_px = rbc[:, 23]
+sigma_py = rbc[:, 24]
+sigma_pt = rbc[:, 25]
+
+emittance_x = rbc[:, 26]
+emittance_y = rbc[:, 27]
+emittance_t = rbc[:, 28]
+
+alpha_x = rbc[:, 29]
+alpha_y = rbc[:, 30]
+alpha_t = rbc[:, 31]
+
+beta_x = rbc[:, 32]
+beta_y = rbc[:, 33]
+beta_t = rbc[:, 34]
+
+dispersion_x = rbc[:, 35]
+dispersion_px = rbc[:, 36]
+dispersion_y = rbc[:, 37]
+dispersion_py = rbc[:, 38]
+
+emittance_xn = rbc[:, 39]
+emittance_yn = rbc[:, 40]
+emittance_tn = rbc[:, 41]
+
+charge = rbc[:, 42] / scipy.constants.eV
+
+
+print("initial rbc stdx: ", sigma_x[0])
+print(
+    "initial monitor stdx: ",
+    series.iterations[iter0].particles["beam"].get_attribute("sigma_x"),
+)
+print()
+print("final rbc std: ", sigma_x[-1])
+print(
+    "final monitor stdx: ",
+    series.iterations[iter1].particles["beam"].get_attribute("sigma_x"),
+)
+
+print("sigma_x")
+print(sigma_x[:30])
+print()
+print("sigma_y")
+print(sigma_y[:30])
+print()
+print("sigma_t")
+print(sigma_t[:30])
+print()
+print("sigma_pt")
+print(sigma_pt[:30])
+
+
+plt.figure()
+plt.subplot(221)
+plt.suptitle("sigmas vs. slice")
+plt.plot(sigma_x, label="sigma_x")
+plt.legend(loc="best")
+plt.subplot(222)
+plt.plot(sigma_y, label="sigma_y")
+plt.legend(loc="best")
+plt.subplot(223)
+plt.plot(sigma_t, label="sigma_t")
+plt.legend(loc="best")
+plt.subplot(224)
+plt.plot(charge, label="charge")
+plt.legend(loc="best")
+
+plt.figure()
+plt.suptitle("sigmas vs. s")
+plt.subplot(221)
+plt.plot(s, sigma_x, label="sigma_x")
+plt.legend(loc="best")
+plt.subplot(222)
+plt.plot(s, sigma_y, label="sigma_y")
+plt.legend(loc="best")
+plt.subplot(223)
+plt.plot(s, sigma_t, label="sigma_t")
+plt.legend(loc="best")
+plt.subplot(224)
+plt.plot(s, sigma_pt, label="sigma_pt")
+plt.legend(loc="best")
+
+plt.figure()
+plt.title("charge")
+plt.plot(s, charge, label="charge")
+plt.legend(loc="best")
+
+plt.show()