From 37ac6e18127bd5a201e9d3657889701d8fe24809 Mon Sep 17 00:00:00 2001 From: smartalecH Date: Wed, 8 Jul 2020 21:30:42 -0400 Subject: [PATCH] fix gradients --- python/adjoint/objective.py | 5 +- python/adjoint/optimization_problem.py | 69 ++++++----- .../01-Introduction.ipynb | 14 +-- python/meep.i | 10 +- python/simulation.py | 6 +- src/meepgeom.cpp | 110 ++++++++++-------- src/meepgeom.hpp | 6 +- 7 files changed, 114 insertions(+), 106 deletions(-) diff --git a/python/adjoint/objective.py b/python/adjoint/objective.py index c22f84f56..3bcd1232d 100644 --- a/python/adjoint/objective.py +++ b/python/adjoint/objective.py @@ -76,8 +76,9 @@ def place_adjoint_source(self,dJ,dt): dV = 1/self.sim.resolution * 1/self.sim.resolution else: dV = 1/self.sim.resolution * 1/self.sim.resolution * 1/self.sim.resolution - da_dE = 0.5*(dV * self.cscale) - scale = da_dE * dJ * 1j * 2 * np.pi * self.frequencies / np.array([self.time_src.fourier_transform(f) for f in self.frequencies]) # final scale factor + da_dE = 0.5 * self.cscale + iomega = ((1.0 - np.exp(-1j * (2 * np.pi * self.frequencies) * dt)) * (1.0 / dt)) + scale = da_dE * dV * dJ * iomega / np.array([self.time_src.fourier_transform(f) for f in self.frequencies]) # final scale factor if self.frequencies.size == 1: # Single frequency simulations. We need to drive it with a time profile. src = self.time_src diff --git a/python/adjoint/optimization_problem.py b/python/adjoint/optimization_problem.py index 2c578197c..4da9e42ef 100644 --- a/python/adjoint/optimization_problem.py +++ b/python/adjoint/optimization_problem.py @@ -5,6 +5,7 @@ from collections import namedtuple Grid = namedtuple('Grid', ['x', 'y', 'z', 'w']) +YeeDims = namedtuple('YeeDims', ['Ex','Ey','Ez']) class DesignRegion(object): def __init__(self,design_parameters,volume=None, size=None, center=mp.Vector3()): @@ -16,9 +17,11 @@ def __init__(self,design_parameters,volume=None, size=None, center=mp.Vector3()) def update_design_parameters(self,design_parameters): self.design_parameters.update_parameters(design_parameters) def get_gradient(self,fields_a,fields_f,frequencies,geom_list,f): - # sanitize the input - if (fields_a.ndim < 5) or (fields_f.ndim < 5): - raise ValueError("Fields arrays must have 5 dimensions (x,y,z,frequency,component)") + for c in range(3): + fields_a[c] = fields_a[c].flatten(order='C') + fields_f[c] = fields_f[c].flatten(order='C') + fields_a = np.concatenate(fields_a) + fields_f = np.concatenate(fields_f) num_freqs = np.array(frequencies).size grad = np.zeros((self.num_design_params*num_freqs,)) # preallocate @@ -176,17 +179,20 @@ def prepare_forward_run(self): self.forward_monitors.append(m.register_monitors(self.frequencies)) # register design region - self.design_region_monitors = [self.sim.add_dft_fields([mp.Ex,mp.Ey,mp.Ez],self.frequencies,where=dr.volume,yee_grid=False) for dr in self.design_regions] + self.design_region_monitors = [self.sim.add_dft_fields([mp.Ex,mp.Ey,mp.Ez],self.frequencies,where=dr.volume,yee_grid=True) for dr in self.design_regions] # store design region voxel parameters - self.design_grids = [Grid(*self.sim.get_array_metadata(dft_cell=drm)) for drm in self.design_region_monitors] + self.design_grids = [] + for drm in self.design_region_monitors: + s = [self.sim.get_array_slice_dimensions(c,vol=drm.where) for c in [mp.Ex,mp.Ey,mp.Ez]] + self.design_grids += [YeeDims(*s)] def forward_run(self): # set up monitors self.prepare_forward_run() # Forward run - self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.forward_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, self.minimum_run_time)) + self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.forward_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, False, self.minimum_run_time)) # record objective quantities from user specified monitors self.results_list = [] @@ -199,11 +205,11 @@ def forward_run(self): self.f0 = self.f0[0] # Store forward fields for each set of design variables in array (x,y,z,field_components,frequencies) - self.d_E = [np.zeros((len(dg.x),len(dg.y),len(dg.z),self.nf,3),dtype=np.complex128) for dg in self.design_grids] + self.d_E = [[np.zeros((c[0],c[1],c[2],self.nf),dtype=np.complex128) for c in dg] for dg in self.design_grids] for nb, dgm in enumerate(self.design_region_monitors): - for f in range(self.nf): - for ic, c in enumerate([mp.Ex,mp.Ey,mp.Ez]): - self.d_E[nb][:,:,:,f,ic] = atleast_3d(self.sim.get_dft_array(dgm,c,f)) + for ic, c in enumerate([mp.Ex,mp.Ey,mp.Ez]): + for f in range(self.nf): + self.d_E[nb][ic][:,:,:,f] = atleast_3d(self.sim.get_dft_array(dgm,c,f)) # store objective function evaluation in memory self.f_bank.append(self.f0) @@ -227,17 +233,17 @@ def adjoint_run(self,objective_idx=0): # register design flux # TODO use yee grid directly - self.design_region_monitors = [self.sim.add_dft_fields([mp.Ex,mp.Ey,mp.Ez],self.frequencies,where=dr.volume,yee_grid=False) for dr in self.design_regions] + self.design_region_monitors = [self.sim.add_dft_fields([mp.Ex,mp.Ey,mp.Ez],self.frequencies,where=dr.volume,yee_grid=True) for dr in self.design_regions] # Adjoint run - self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.design_region_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, self.minimum_run_time)) + self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.design_region_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, True, self.minimum_run_time)) # Store adjoint fields for each design set of design variables in array (x,y,z,field_components,frequencies) - self.a_E.append([np.zeros((len(dg.x),len(dg.y),len(dg.z),self.nf,3),dtype=np.complex128) for dg in self.design_grids]) + self.a_E.append([[np.zeros((c[0],c[1],c[2],self.nf),dtype=np.complex128) for c in dg] for dg in self.design_grids]) for nb, dgm in enumerate(self.design_region_monitors): - for f in range(self.nf): - for ic, c in enumerate([mp.Ex,mp.Ey,mp.Ez]): - self.a_E[objective_idx][nb][:,:,:,f,ic] = atleast_3d(self.sim.get_dft_array(dgm,c,f)) + for ic, c in enumerate([mp.Ex,mp.Ey,mp.Ez]): + for f in range(self.nf): + self.a_E[objective_idx][nb][ic][:,:,:,f] = atleast_3d(self.sim.get_dft_array(dgm,c,f)) # update optimizer's state self.current_state = "ADJ" @@ -309,7 +315,7 @@ def calculate_fd_gradient(self,num_gradients=1,db=1e-4,design_variables_idx=0,fi for m in self.objective_arguments: self.forward_monitors.append(m.register_monitors(self.frequencies)) - self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.forward_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, self.minimum_run_time)) + self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.forward_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, False, self.minimum_run_time)) # record final objective function value results_list = [] @@ -332,7 +338,7 @@ def calculate_fd_gradient(self,num_gradients=1,db=1e-4,design_variables_idx=0,fi self.forward_monitors.append(m.register_monitors(self.frequencies)) # add monitor used to track dft convergence - self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.forward_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, self.minimum_run_time)) + self.sim.run(until_after_sources=stop_when_dft_decayed(self.sim, self.forward_monitors, self.decay_dt, self.decay_fields, self.fcen_idx, self.decay_by, False, self.minimum_run_time)) # record final objective function value results_list = [] @@ -380,7 +386,7 @@ def plot2D(self,init_opt=False, **kwargs): self.sim.plot2D(**kwargs) -def stop_when_dft_decayed(simob, mon, dt, c, fcen_idx, decay_by, minimum_run_time=0): +def stop_when_dft_decayed(simob, mon, dt, c, fcen_idx, decay_by, yee_grid=False, minimum_run_time=0): '''Step function that monitors the relative change in DFT fields for a list of monitors. mon ............. a list of monitors @@ -389,18 +395,17 @@ def stop_when_dft_decayed(simob, mon, dt, c, fcen_idx, decay_by, minimum_run_tim ''' # get monitor dft output array dimensions - x = [] - y = [] - z = [] + dims = [] for m in mon: - xi,yi,zi,wi = simob.get_array_metadata(dft_cell=m) - x.append(len(xi)) - y.append(len(yi)) - z.append(len(zi)) + ci_dims = [] + for ci in c: + comp = ci if yee_grid else mp.Dielectric + ci_dims += [simob.get_array_slice_dimensions(comp,vol=m.where)] + dims.append(ci_dims) # Record data in closure so that we can persitently edit closure = { - 'previous_fields': [np.ones((x[mi],y[mi],z[mi],len(c)),dtype=np.complex128) for mi, m in enumerate(mon)], + 'previous_fields': [[np.ones(di,dtype=np.complex128) for di in d] for d in dims], 't0': 0 } @@ -413,17 +418,17 @@ def _stop(sim): # Pull all the relevant frequency and spatial dft points relative_change = [] - current_fields = [np.zeros((x[mi],y[mi],z[mi],len(c)), dtype=np.complex128) for mi in range(len(mon))] + current_fields = [[np.zeros(di,dtype=np.complex128) for di in d] for d in dims] for mi, m in enumerate(mon): for ic, cc in enumerate(c): if isinstance(m,mp.DftFlux): - current_fields[mi][:,:,:,ic] = atleast_3d(mp.get_fluxes(m)[fcen_idx]) + current_fields[mi][ic][:,:,:] = atleast_3d(mp.get_fluxes(m)[fcen_idx]) elif isinstance(m,mp.DftFields): - current_fields[mi][:,:,:,ic] = atleast_3d(sim.get_dft_array(m, cc, fcen_idx)) + current_fields[mi][ic][:,:,:] = atleast_3d(sim.get_dft_array(m, cc, fcen_idx)) else: raise TypeError("Monitor of type {} not supported".format(type(m))) - relative_change_raw = np.abs(previous_fields[mi] - current_fields[mi]) / np.abs(previous_fields[mi]) - relative_change.append(np.mean(relative_change_raw.flatten())) # average across space and frequency + relative_change_raw = np.abs(previous_fields[mi][ic] - current_fields[mi][ic]) / np.abs(previous_fields[mi][ic]) + relative_change.append(np.mean(relative_change_raw.flatten())) # average across space and frequency relative_change = np.mean(relative_change) # average across monitors closure['previous_fields'] = current_fields closure['t0'] = sim.round_time() diff --git a/python/examples/adjoint_optimization/01-Introduction.ipynb b/python/examples/adjoint_optimization/01-Introduction.ipynb index 7914e8586..e64c885db 100644 --- a/python/examples/adjoint_optimization/01-Introduction.ipynb +++ b/python/examples/adjoint_optimization/01-Introduction.ipynb @@ -237,7 +237,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -298,7 +298,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -307,7 +307,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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" ] @@ -374,7 +374,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -439,7 +439,7 @@ "outputs": [ { "data": { - "image/png": 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pBJDXmXpRGj16NPPmzePSpUuMGjUKwKIrAJPJxNGjR3FycuLmzZvUrl27wLFUrFgx43cbG5uM9zY2NqSmpmbMy9pkUinFG2+8QWBgIKtWrSIiIuK2EQ1zG972scceo0OHDoSEhNCvXz++/vpri+OztbXVVUBauXbycgyvrwhj/9lbBDatzjuDffGq7Fhs+9f3AArB4MGD+fnnn9m9ezd9+vQBwNXVlQMHDuT4atasGQCffPIJzZs358cff+Spp5667YwboGnTpkRGRrJ7927ASCqpqal06dKFH374AYATJ05w9uxZmjZtmq+YlyxZAhhXE25ubri5uREVFUWtWrUAo97fEuHh4TRo0IAXXniBQYMGERYWRpcuXVi9ejXx8fHExcWxatWqXJOhppVHyakmPv3tJEGfbifiWhyzHvHj25Hti7XwhzJ2BWAt9vb2BAYGUrlyZWxtbS1a5/jx48ydO5ddu3bh6upK165dmT59OtOmTbttu0uWLOH5558nISEBR0dHfv31V5599ln+7//+D19fXypUqMC8efNuO7O2hIODA/7+/qSkpPDtt98C8PrrrzNixAimT59OUFCQRdtZunQpCxcuxM7ODk9PTyZOnEjVqlUZOXIkAQEBgHGF5O/vz6FDh/IVo6aVRWHnb/H68jCOXYphQGsv3hzQgmou+fv/LTQiUmpebdu2layOHDmSbVpxS0tLk9atW8uJEycsWj46OrqII8pbt27dZPfu3cW+3+I47pLwfchq8+bN1g7BKvRx3y4+KVXeDTki9cevk4B3fpGNhy8VW0zAHsmhTNVXAAV05MgR+vfvz+DBg2ncuLG1w9E0rQT6I/w641eEEXE9nmEBdZjQrzmVHKw/aq1OAAXUokULwsPDrR1GvoSGhlo7BE0rF2ISU5jxv2P88OdZ6lZ14sfRHejcqJq1w8pgtQSglHIAtgIVzXEsF5E3rRWPpmlaYdp07DKTVh3icnQio++tzyu9m+Job9k9wuJizSuAJKCHiMQqpeyA7Uqp/4nIH1aMSdM0rUBikoUXF+9n9YGLNPFw4YvhnfGvW8XaYeXIagnAfGMi1vzWzvwSa8WjaZpWECLCT2GRTNoWT6IpgRd7NubZ7o2wr1Cw1vYh4SHM3jebS3GX8HT2JLhNMEENLGuldyfKKIetQyllC+wFGgGfi8i4HJYZC4wF8PDwaLt48eLb5ru5udGoUaNiiLbwpKWlWdxctCwpjuM+deoUUVFRRbqP/IqNjcXFxcXaYRS78nTcNxNNzD+czIGradRzEca0dqK2a8G7We2O3c2iG4tIkX/6CNkpO4ZVHUZ7l/YWbycwMHCviLTLOt2qCSAjCKUqA6uA50Uk18bi7dq1kz179tw27ejRozRv3ryII8ybra0tvr6+iAi2trZ89tlndO7cOdflc3s04qxZsxg7dixOTk7Z5m3bto1nnnkGOzs7QkJCCA4OZvny5Rw4cICLFy/Sr1+/Qj2molAcj8IsCd+HrEJDQ2/rVV1elIfjFhEW7z7HuyFHSTGZeLV3UxqknqFHYGDBN55wi96rBxCZfCvbrJrONdn44EaLN6WUyjEBlIiewCJyC9gM9LV2LHfD0dGRAwcO8Ndff/Hee+8xYcKEu9rOrFmziI+Pz3HeDz/8wIQJEzhw4AC1atXKGI75wIEDrF+//q5j1zTt7py5Hsdjc/5kwsqD+NRyY8OLXRndpQE2BX0yXVoK7JoDn/pzKelmjotcirtUsH2YWS0BKKWqm8/8UUo5Ar2AY9aKp7BER0dTpco/N3w+/PBD2rdvT6tWrXjzTaORU1xcHEFBQbRu3RofHx+WLFnCp59+ysWLFwkMDCQwy9nD3LlzWbp0KW+88QbDhw/PGI45OTmZKVOmsGTJEvz8/DKGd9A0reikmYS528LpM2srhy5E8d4QX34c04F67gUcuVMETmyALzvD+lfBoyWejtVzXNTT2bNg+zKzZiugmsB8830AG2CpiKwr8Fa/y+HmSMsHIGAMJMfDDw9ln+/3GPgPh7jrsPTJ2+c9FXLHXSYkJODn50diYiKRkZFs2rQJgI0bN3Ly5El27dqFiDBw4EC2bt3K2bNn8fLyIiTE2HZUVBRubm7MnDmTzZs3U63a7e2ER48ezfbt2+nfvz8PPvhgxgia9vb2vPXWW+zZs4fPPvvszp+NpmkFcvySMXjbX+du0bN5DaY/4Iunm0PhbPzWWVg0DKp4w6OLoOn9BP+9nqk7ppKYlpixmIOtA8Ftggtll9ZsBRQG+Ftr/4UpvQoIYOfOnTz55JMcOnSIjRs3snHjRvz9jcOMjY3l5MmT+Pv7M3nyZMaNG0f//v31QGmaVsIlp5r4fPMpvgg9RSUHO/4zzJ/+rWpmG1U332Iuw/EQaDcKqtSDJ9dAnQ5QwR4go7VPUbUCKns9gfM6Y7d3ynu+s7tFZ/x56dSpE9euXePq1auICBMmTODpp5++bZmYmBj27dvH+vXrmTx5Mvfddx9Tpkwp0H41TSsaB87d4vXlf3HiciwP+HkxZUBLqjrbF2yjKQmw83PY/gmkJkGjnlC5LtTPfjIY1CCo0Ar8rMpeArCyY8eOkZaWhru7O3369Mmot3dxceHChQvY2dlx69Yt6taty+OPP07lypWZO3cuYAwhHRMTk60KKC/p62iaVrgSktP4eONxvv39bzwqOfDtyHb0aOZRsI2aTHBoBfw6FaLPQ7P+0Osto/C3Ap0ACkH6PQAwmoXNnz8fW1tbevfuzdGjR+nUqRMALi4ufP/99xw+fJgHH3wQGxsb7Ozs+PLLLwEYO3Ysffv2xcvLK+M5v3cSGBjIjBkz8PPzY8KECTzyyCNFc5CaVo7sOH2N8SsOcvZGPMM71GX8/c1wLYzB2xJvQcgrRnXP4K9yPOMvTiWiH4ClSmo/gPwqjvbwJZHuB1C+lMbjjk5M4b31R1m06xze7k7MGNqKjg3c87WNbMd9MwL2zoceb4CNDVw5BtWaGL8Xk9z6AegrAE3TNOCXI5eZvPogV2OSeLprA17s2aRgg7clRsG2j+GPL8GmAvg+CB4toUazwgu6gHQC0DStXLsWm8TUtYdZFxZJM09X5jzZjla1K9/19pQpDXbPhc3vQvwNo5l5j8lQyasQoy4cOgFomlYuiQhrDlxk2k+HiUtK45VeTXi6W8MCD94GJtjxGVRvDn3eAS+/Qom3KOgEoGlauXPxVgKTVx9i07Er+NetzAdDW9HYowD3py4fgd9nQf9ZiI0djNoALjWgoP0EiphOAJqmlRsmk/DjrrPM+N8x0kzClP4tGNHZG1ubuyyoY68YVT375kNFV7h82JjuWsDmosVEJwBN08qFv6/FMX5FGH/+fYN7G1XjvSG+1KmafeRdi6Slwo5PYdtMSE2AgLHQbRw4VYXToYUad1EqEaOBlgWrV69GKcWxY7mPZzdy5MiMUTxHjx7NkSNH8txmv379uHUr+1Cwmc2bN4+LFy/mP2ArymuobE0rbKlpJr7ecpq+s7ZyJDKaD4a2YuG/Au6+8AewsYWTvxjt+J/9A+5/3yj8S5lylwBCwkPovbw3rea3ovfy3oSEF2zoh3SLFi3i3nvvZdGiRRYtP3fuXFq0aJHnMuvXr6dy5bxbI5SmBJCamgrAjh07rByJVl4cuRjN4C928N7/jtGtSXV+fbkbD7evc3dj+JzbDfMHQnSkUbf/+HIYtgiqNS78wItJuUoAIeEhTN0xlci4SAQhMi6SqTumFjgJxMbGsn37dv773/+S+YllIsJzzz1H06ZN6dmzJ1euXMmY1717d9I7tS1atAhfX198fHwYN+6fh6J5e3tz7do1IiIiaN68OWPGjKFly5b07t2bhIQEli9fzp49exg+fDh+fn4kJCTcFtepU6fo2bMnrVu3pk2bNpw+fRoR4bXXXsPHxwdfX9+MIaRDQ0Pp1q0bgwYNokGDBowfP54ffviBgIAAfH19OX36NGBcxTzzzDO0a9eOJk2asG6dMYBrREQEXbp0oU2bNrRp0yajkA8NDaVLly4MHDiQ9u2NJxilPyUqMjKSrl274ufnh4+PD9u2bcvz83BxcWHSpEm0bt2ajh07cvny5QL93bSyKynVGMZh4GfbiYxK4PPH2vD1E23xqHQXI3feOgvL/wX/7QlXj8ONcGO6fQGHfy4JRKTUvNq2bStZHTlyJNu03PRa1kt85vlke/Va1svibeTk+++/l1GjRomISKdOnWTPnj0iIrJixQrp2bOnpKamyoULF8TNzU2WLVsm0dHR0q1bN9m9e7dcuHBB6tSpI1euXJGUlBQJDAyUVatWiYhIvXr15OrVq/L333+Lra2t7N+/X0REHnroIVm4cKGISMZ2chIQECArV64UEZGEhASJi4uT5cuXZ8R06dIlqVOnjly8eFE2b94sbm5ucvHiRUlMTBQvLy+ZMmWKiIjMmjVLgoODRURkxIgR0qdPH0lLS5MTJ05IrVq1MradkJAgIiInTpyQ9L/V5s2bxcnJScLDwyU6OlpERJydnUVE5KOPPpLp06eLiEhqaqpER0fn+XkAsnbtWhERee211+Ttt9/Odsz5+T4Ul82bN1s7BKuw1nHvibgh930cKvXGrZOXluyXG7FJd7chk0nk17dE3qou8raHyKZ3RBJj7rhaSfx7A3skhzK1XF0B5PYUnYI+XWfRokU8+uijADz66KMZ1UBbt25l2LBh2Nra4uXlRY8ePbKtu3v3brp370716tWpUKECw4cPZ+vWrdmWq1+/fsZ4Q23bts14JkBuYmJiuHDhAoMHDwbAwcEBJycntm/fnhGTh4cH3bp1Y/fu3QC0b9+emjVrUrFiRRo2bEjv3r0B8PX1vW1/Dz/8MDY2NjRu3JgGDRpw7NgxUlJSGDNmDL6+vjz00EO33d8ICAigfv362WJs37493333HVOnTuXgwYO4urrm+XnY29vTv39/iz8DrXyJT05l2k+HefCrHcQnpfLdU+2Z+bAfVfI7cmf68DhKQUwktBwMz++BwIlQsWw947hctQLydPYkMi4yx+l368aNG2zatImDBw+ilCItLQ2lFB9++GFBQs2mYsWKGb/b2tpmq+4p7H3Y2NhkvLexscmovwey1Z8qpfjkk0/w8PDgr7/+wmQy4eDwz6W2s3POl8pdu3Zl69athISEMHLkSF5++WXc3Nxyjc/Ozi5j37a2trfFpJVv209eY/zKMM7fTODJTvV4vW8zXCreRfF26lfYOAUe+MLowDXwP8YN3zKqXF0BBLcJxsH29jrAgj5dZ/ny5TzxxBOcOXOGiIgIzp07R/369dm2bRtdu3ZlyZIlpKWlERkZmeMInwEBAWzZsoVr166RlpbGokWL6Natm8X7z204aFdXV2rXrs3q1asBSEpKIj4+ni5dumTEdPXqVbZu3UpAQEC+jnnZsmWYTCZOnz5NeHg4TZs2JSoqipo1a2JjY8PChQtJS0u743bOnDmDh4cHY8aMYfTo0ezbt6/An4dWvkTFp/D68r94/L9/Ym9rw9KnO/HWIJ/8F/5XjsH3Q41XShwkmf+nynDhD+XsCqAonq6zaNGi225UAgwdOpRFixbxxRdfsGnTJlq0aEHdunUzhoVOp5SiZs2azJgxg8DAQESEoKAgBg0aZPH+02/KOjo6snPnThwdHTPmLVy4kKeffpopU6ZgZ2fHsmXLGDx4MDt37qR169Yopfjggw/w9PTMs/lqVnXr1iUgIIDo6Gi++uorHBwcePbZZxk6dCgLFiygb9++uZ71ZxYaGsqHH36InZ0dLi4uLFiwoMCfh1Z+/HzoEm+sOcSNuGT+r3tDgu9rjIPdXRTYGyYZA7bZu0Dv6Uab/goV77xeWZDTjYGS+iroTeCSIjo6Wnx8fCQ8PNzaoeTbiBEjZNmyZXe1bvpN4KJUEr8PJfGmYHEoquO+Ep0oz36/V+qNWyf3z9oqB8/fyv9GUhKNm7wiIls+EAl5VST2WqHEVxL/3uRyE7hcXQGUFIMGDcLX1zfHG6OaVt6FhIfkeJUuIqzcd4G31h0hITmN1/o0ZWzXBtjZ5qMmWwSOrIZf3oS+70GzIOj6WtEdTAmnE4AVrFmzptQ+EGbevHnWDkErw9L76iSmJQJk9NW5EeCeLWkAACAASURBVJfMr7trseXEVdrWq8L7Q1vRqEY+W+Sc3wsbJsC5P8HDBxxLX8/dwlYmEoCI3F3PPq1MkVL0dDstZ7P3zc4o/NMlpiXy/p8zMZ2dxLSBLXmiYz1s8jt424ZJsPMzcK5htOzxG17mb/BaotQnAAcHB65fv467u7tOAuWYiHD9+vXbmp9qpU9ufXJUhVtseLFr/sbvSYoB24pQwR5q+kGXV+HeF41ROzWgDCSA2rVrc/78ea5evWrtUCyWmJhYLguqoj5uBwcHateuXWTb14pebn11arrUtLzwN6XB/oWw6R249yXo9Cy0eqiQIy0bSn0CsLOzK3U3U0NDQ/H397d2GMWuvB63Zrkh3mP44tAMRCVnTMtXX53Tm2DDZLhyGOp0MF5arkp9AtA0rfRLTEnjP5tO8tUWZ1yrPYSr5y9EpVzNX1+djZNhx3+gcl14aB60eKDEP5HL2nQC0DTNqvZE3OD1FWGEX43jwba1mRzUk8pO4y1bOe462NiAYxVoGgTO1SHgabArf1Wsd0MnAE3TrCI2KZUPfz7Ggj/O4OXmyIJRAXRtUt2ylVOTYNc3sOVD8BtmPJClXidC0m4we83AQuvpX9bpBKBpWrHbcuIqE1ce5GJUAiM6efNan6Y4WzJ+jwgcXQu/TIGbEdC4N7R9Csi9DwGgk0AudALQNK3Y3IpP5u11R1mx7zwNqzuz7OlOtPPOR4esze/C1g+gRgt4fCU0ui9jVm59CGbvm60TQC50AtA0rVj872Akb6w5zM34ZJ4LbMRzPRpZNnhb1HmjaWeVetD6UXCrBf5PZOvIVVTP+yjLrJYAlFJ1gAWAByDANyIy21rxaJpWNK5EJzJlzWF+PnyJll6VmD+qPS29cn/uQ4akWPh9ttGyp3FPeOR7cG9ovHJQFM/7KOuseQWQCrwiIvuUUq7AXqXULyJy5E4rappW8okIy/ac4+11R0hMNTGubzPGdKlPhTsN3mZKgwM/wqa3IfYy+AyFnlPvuL/gNsG33QOAgj/vo6yzWgIQkUgg0vx7jFLqKFAL0AlA00q5czfi+WhPIoevhxHgXZUZQ31pUN3Cwdt2fmbc5K3d3jjrr2PZA4uK4nkfZZ0qCQNoKaW8ga2Aj4hEZ5k3FhgL4OHh0Xbx4sXFHl9hi42NxcWlbD1b1BL6uMs+kwi/nUll+clkQHi4aUUC61TA5g4dshzjz2OblkisayMqpMRS5eYBrla/p1R25CqJf+/AwMC9ItIu63SrJwCllAuwBXhHRFbmtWy7du1kz549xRNYEQoNDaV79+7WDqPY6eMu205diWHcioPsPXOTbk2qM7BmLEPv75H3SvE3YMv7sHuuccY/6ufiCbYIlcS/t1IqxwRg1VZASik7YAXww50Kf03TSqaUNBNfbznNp7+dwqmiLTMfbs1g/1ps2bIl95VSk2H3HKPwT4qBtiOh+8Rii1kzWLMVkAL+CxwVkZnWikPTtLt36EIUry0P42hkNEGtajJ1QEuqu1rwPN2wJbBhIjTsAb3fAY8WRR+slo01rwDuAZ4ADiqlDpinTRSR9VaMSdM0CySmpDHr15PM2RaOu7M9Xz/Rlj4t79Dc8uJ+iL0CTfoY7fkr14EG3YsjXC0X1mwFtB0ofXd4NK2c+zP8OuNXHuTva3E80q4OE4Oa4+Zol/sK0Rfht7fgr0VQo6UxfIOtnS78S4A7JgCl1EIReeJO0zRNK9tiElP44OfjLPzjDHWqOvLD6A7c06harsvbpCXC5veMzlySBve8CF1eLpUte8oqS64AWmZ+o5SyBdoWTTiappVEm49fYdLKg0RGJzLqnvq82qcJTvZ5Fx+Vbx2BgzOg5RDo+SZU8S6eYDWL5foXVEpNACYCjkqp9Lb5CkgGvimG2DRNs7Kbccm8ve4IK/dfoHENF1b8X2fa1K2S+wp/b4Prp6DdU9yo6g//twM8Wua+vGZVuSYAEXkPeE8p9Z6ITCjGmDRNszIRIeRgJG+uOUxUQgov9GjEv3s0omKFXAZvu37a6L17bB24NwL/x42qHl34l2h3rAISkQlKqVpAvczLi8jWogxM0zTruBydyOTVh/jlyGVa1Xbj+9EdaF6zUs4Lx9+ArR8aD2ep4AA93oBO/zZu8molniU3gWcAj2KM0ZNmniwYQzdomlZGiAhL95xjeshRklNNTOzXjFH33GHwtphI2DUH/IZD4CRw9Si+gLUCs+Qm8GCgqYgkFXUwmqZZx9nr8YxfGcaO09fpUL8q7w9thXc15+wLisDx9XB+tzFCp0dLePEgVKpZ3CFrhcCSBBAO2AE6AWhaGZNmEubtiOCjDcextVG8M9iHYe3rYmOTQ1PNyL9gwySI2AbVmkKXV6Giiy78SzFLEkA8cEAp9RuZkoCIvFBkUWmaVuROXI7h9eVhHDh3ix7NavDOYB9qujlmXzDuGvzyJhz4AZyqQr+PjLF7dD1/qWdJAlhrfmmaVgYkp5r4MvQ0n20+iUvFCsx+1I+Brb1QuXXQEpNR7dP5eejyCjhWLt6AtSJjSSug+UopR6CuiBwvhpg0TSsif527xbgVYRy7FMPA1l68OaAF7i5ZBm8zmeDgUqPQf2g+uNSAlw6BfQ73BLRSzZJWQAOAjwB7oL5Syg94S0QGFnVwmqYVjoTkND759QRzt4VTw9WBuU+2o2eLHFrsRPxujNIZeQC8/CH+OjhX04V/GWVJFdBUIAAIBRCRA0qpBkUYk6ZphWjn6etMWBlGxPV4hgXUZUK/ZlRyyFJ/H3cN1r0IR3+CSrVg8Dfg+xDY3OH5vVqpZkkCSBGRqCz1g6YiikfTtEISnZjCjP8d48c/z1LP3Ykfx3Sgc8Msg7eJGD127V2M3ryBk42OXPZO1glaK1aWJIDDSqnHAFulVGPgBWBH0YalaVpB/Hb0MpNWHeJKTCJjutTn5V5NcbTPNIxDWgrs+c5o2TNqA9g5wDPbwSaXoR60MsmSBPA8MAmjCegiYAPwdlEGpWmaISQ8hNn7ZnMp7hKezp4EtwkmqEFQrstfj01i2k9HWPvXRZp6uPLVE23xq5Op1Y4InNgAGyfD9ZNQvysk3AA7L134l0OWtAKKx0gAk4o+HE3T0oWEhzB1x1QS0xIBiIyLZOqOqQDZkoCIsPavi0z76QgxiSm81LMJ/9e9IfYVMtXhJ9yCpU/C31uMAduGLYYmffX4/OVYXsNBzxKRF5VSP2GM/XMb3QpI04rW7H2zMwr/dIlpiczeN/u2BBAZlcDkVYf47dgVWtepzAdDW9HU0/WflVKToYI9OLiBnRPc/wG0G6U7cml5XgEsNP/8qDgC0TTtdpfiLuU53WQSFu8+x3vrj5JiMjE5qDkeNY/w/PahRpWRkwfBTo0IOroJntlmtOd/bHFxHoJWwuX1PIC95p9bii8cTdPSeTp7EhkXmeP0iGtxjF8Zxh/hN+jUwJ0ZQ305FBXK1B3T/qkyir/E1NiL4NmQoLSU4g5fKwXyqgI6SA5VP+lEpFWRRKRpGgDBbYJvuwcA4GDrQCvHYfSdvRU7GxtmDPHlkfZ1UEoxZnMOVUY2Nsx2SCXIrVZxh6+VAnlVAfU3//y3+Wd6ldDj5JEYNE0rHOn1/OmtgNwdaqBu9mP5oRr0bF6d6Q/44OnmYCwcf+OOVUaallVeVUBnAJRSvUTEP9OscUqpfcD4og5O08q7oAZB9Kzbl883n+aLzadwc7TjP8Na0r9VTWPwtoRbsO1j2DUHz8bNiUy8lm0bns6eVohcKw0s6QeglFL3iMjv5jedAd0/XNOKwf6zNxm3IowTl2MZ7F+LN/q3oKqzPaSlwt7vIPQ947GMfsMJbtyZqftnZasyCm4TbMUj0EoySxLAv4BvlVJugAJuAqOKNCpNK+fik1P5eOMJvv39bzwrOfDtyHb0aGYevM2UBnN7GA9o8e4Cfd6Bmq0JAnCsnK+OY1r5ZklHsL1Aa3MCQESiijwqTSvHdpy6xviVBzl7I57HO9ZlXN9muDrYwY1wqFLf6LHr9zh0GwdN+93WkSuoQZAu8DWLWXIFgFIqCGgJOKQPCicibxVhXJpW7kQlpPDe+qMs3n0Ob3cnFo/tSMcG7hBzGTa+A/sXwrAl0KQ3dBhr7XC1MsCS5wF8BTgBgcBc4EFgVxHHpWnlysbDl5i8+hDXYpN4ulsDXurZBAeSjRu822ZCaiJ0eAZqt7N2qFoZYskVQGcRaaWUChORaUqpj4H/FXVgmlYeXItNYuraw6wLi6SZpytzR7SjVe3KxqBtc/vDhT3QNAh6vQXVGlk7XK2MsSQBpDcpiFdKeQHXgZpFF5KmlX0iwuoDF5j20xHik9J4pVcTnuneELtL+yGtlTFOT5eXoaKrMWKnphUBSxLAT0qpysCHwD6MTmBzijQqTSvDLt5KYNKqg2w+fhX/usbgbY3tb8DKf8HhldD/E2Owtmb6Zq5WtPJMAEopG+A3EbkFrFBKrQMcdEsgTcs/k0n4YddZ3v/fMdJMwpT+LRjRtiq22z+CP74EZQPdxkOrR6wdqlZO5JkARMSklPoc8De/T8J4MEyhUEp9izHkxBUR8Sms7WpaSRN+NZbxKw6yK+IG9zaqxntDfKlT1QkWPADhm6H1Y3DfG1DJy9qhauWIJVVAvymlhgIrRaSwxwCaB3wGLCjk7WpaiZCaZmJ9eDJrft1GxQo2fPBgKx5yO45yTAKcoMcb0PNN8PK/47Y0rbBZMqTD08AyIEkpFa2UilFKRRfGzkVkK3CjMLalaSXNkYvRPPDF7yw9kUL3ptXZ/GQNHj4ajPphKOwy30ar3VYX/prVqMI/qc9nAEp5A+tyqwJSSo0FxgJ4eHi0Xby49D/QIjY2FhcXF2uHUezKy3GnmIS1p1NYH56Csx2MqHeLR1PX4hW5kTRbRyK8H+FCrX6ITdl+Ild5+XtnVRKPOzAwcK+IZOtEkmsCUErZAo4iEmt+3xGwN8/eLyIxhRHYnRJAZu3atZM9e/YUxm6tKjQ0lO7du1s7jGJXHo577xlj8LZTV2IZ0qYWbwS1IGXeYGpc2wntRxvDNzi7WzvMYlEe/t45KYnHrZTKMQHkdQ/gfeAK8IH5/SLgEOCA0Rx0XGEHqWmlVVxSKh9tPM68HRF4VXIg5L6rtGzTDJzt+aPBE9R4+BOo3sTaYWrabfJKAPcB7TO9vyUiA5QxGNC2og1L00qPbSevMmHlQc7fTGBiq1j+FfcJtr/vBtNz0OcdEh09CYk5yewtz+lROrUSJa8EYCMiqZnejwMQEVFKFUoFl1JqEdAdqKaUOg+8KSL/LYxta1pRi4pPYXrIEZbtPU8n9zjWNP8J9xNrwcUDBn4Gfo8BsDt2N0t3LP3nWb1xkUzdMRVAJwHNqvJKAPZKKdf0un4R2QhgHhbaoTB2LiLDCmM7mlbcfj50iTfWHOJGXDLPdm/Iy6bvqLDvF+j6OtwTDBX/OUf66dZP2Z/Vm5bI7H2zdQLQrCqvBDAHWKKUekZEzgIopeoBX2KMCqpp5c6VmESmrj3MhoMXCK76J/2HBtKgbTOIHwf3PA9utbOtczPtZo7b0s/q1awtr2cCz1RKxQPblVLO5smxwAwR+bJYotO0EkJEWLHvAtM2L8S+8mqcm8WzLjWVhn+fokHbnuBUFaia47pVbKvkmAT0s3o1a8uzI5iIfCUidQFvwFtE6unCXytvzt+MZ8R3u/lkw0zs3X8k2S4eURBpV4GpieGEhIfkuf6AygNwsL291lQ/q1crCSx6uLuIxBRWu39NKy1MJmH+jgh6f7KVPRE3sKv1O8lZ/mPS6/Lz0t6lPVM7T6Wmc00UiprONZnaeaqu/9eszqJHQmpaeXP6aiyTlu3B98IS/u3pzaDHn6ff2oQcl7WkLl8/q1criSx5JGRF8yigeU7TtLIgJc3EN1tOc2zz93xk+yO17a4gdZ9EVXHC09mTyLjIbOvounyttLKkCminhdM0rVQ7dCGKl2cvICD0Mf5jOwvPau7wxCrUoP8AENwmWNfla2VKrlcASilPoBbgqJTyB5R5ViWMh8RrWpmQmJLGp7+d5Out4QRU/Z2J9eK5XKEuns6VCFYJpFfcpFfhzN43W/fo1cqEvKqA+gAjgdrAzEzTY4CJRRiTpt21kPCQfBXQe0+e5ejSt5B4Wzr6tOa46QBJacaFcU49dnVdvlaW5NUPYD4wXyk1VERWFGNMmnZXQsJDmLpjqkVDLsQmJPHropl0PvMVbdUtIps9zAj71STF6R67WvlhSSugdUqpxzD6AmQsLyJvFVVQmnY3Zu+bbdGQC/t2/obrxpd5QCI45+JDwtDF1GzQiUvzW+W4Xd1jVyurLEkAa4AoYC+F+DxgTStsuRXU6dNvxSXxVshRju8/xhyHRMK7/4cG3Z4AZdze0q18tPLGkgRQW0T6FnkkmlZAuRbgTjWIWPhvDoRHsjZpNM9070nV7mNwqGh/23LBbYJvq0IC3cpHK9ssaQa6QynlW+SRaFoB5dRMs6Ky5ekzp6lz6gcq2Duw5t+deLVP02yFPxj3CXSPXa08seQK4F5gpFLqb4wqIIXxWICcK0w1zUpub6YZSY004aXrl3GNaczyjl8ztE9PKtjmfc6TWyuf/LYu0rTSwJIEcH+RR6FphSSobi9aVQ7k/WWh/OvCG6x3n8iw55+ia/W7f4ZRfloXaVppkldHsEoiEo3R7l/TSraoC5h+m8blc6fofe01bJQNHfqtZEJAXWxs1J3Xz4OlrYs0rbTJ6wrgR6A/Rusf4Z+ewJjfNyjCuDTNMkmxsONTTL/PJjU1jVWp99O5QSXeGtKGWpUdC2UXd2pdpGmlVV4dwfqbf9YvvnA0LR8iw5AfHkLFXmK9qTNf2A5nzNDuzPWrhVIFO+vPTDcP1coqi4aDVkoNBLqa34aKyLqiC0nT7iDhJjhW4VBSDaKS6vNx0jPU9O3GgoEtqeZSsdB3p5uHamWVJcNBzwDaAz+YJwUrpTqLiB4PSCte107BL29gunKUjxrN56vt56nm8ipvP+5Dn5ZFdzauB4HTyipLrgD6AX4iYgJQSs0H9qMHhNOKS/wN2PIB7J5Dmk1FvrUZwtxtf/Nw+wZM6NccN0e7Ig9BDwKnlUWWPhGsMnDD/LtbEcWiadldOwlzeyJJ0eyp2p//u3A/jlU9+W50K+5pVM3a0WlaqWZJAngP2K+U2ozREqgrML5Io9LKNxFCDs1n9vEfuRR3ieo1q1Px+sMcvdidUffW55XeTXCy108z1bSCuuN/kYgsUkqFYtwHABgnIrr9m1Zobutl61CVrgmJrJFYEs3t96+QBFV/45V72/NcQAsrR6tpZUeu/eKVUs3MP9sANYHz5peXUspfKVWveELUyrL0XraRcZEIQmTidZbwT+GfQaWw9uxc6wSpaWVUXlcArwBjgI9zme+ulPpLRJ4o/LC08iKnXrbk0oZfd7zStMKVV0ewMeafgbkto5TaWBRBaeWAyQQX9+WrUNcdrzStcOU1FtCQvFYUkZUi0rvwQ9LKvIjtsGEiXDqEZ3M/IhOu3nEV3fFK0wpfXlVAA8w/awCdgU3m94HADmBlEcallUGO8ZGweDgcWweVamF64EtanY/lYtznKJuUjOUcbB0Y1GgQW89v1R2vNK0I5VUF9BRkVPO0EJFI8/uawLxiiU4r8SweJz8xmrZ7XwQbW+gxmRP1R/Da2pP8de4WrZqNJM7xJ64mXNaFvaYVI0saU9dJL/zNLgN1C2PnSqm+wGzAFpgrIjMKY7ta8bjjOPlpKXBiAzTvDw6VONYsmCa9nuKLPTF8/tUeXB3smP2oHwNb90OpF6x4JJpWPlmSAH5TSm0AFpnfPwr8WtAdK6Vsgc+BXhjNS3crpdaKyJGCblsrHnmOk59iAxsnw/VTMPo3qN2OXfYdeOW7kxy/HMMgPy+m9G+BexEM3qZpmmUs6Qj2nFJqMP+MBvq1iKwqhH0HAKdEJBxAKbUYGAToBFBK5D5OfiQsehTcG8NjS0mo7sfMkCPM3ZmIRyWY+2Q7erbwKOZoNU3LSolI/lZQqgvwqIj8u0A7VupBoK+IjDa/fwLoICLPZVluLDAWwMPDo+3ixYsLstsSITY2FheXu39EYXFYcm0JO+J2YMKEDTZ0du7MI9UeuW2ZKeencDPtZrZ1a6am8XnFIC569eHITcV3h5O4Ei/c4ykMb+mMk13hjdVfGpSGv3dR0MddcgQGBu4VkXZZp1v6PAB/YBjwMPA3xdgCSES+Ab4BaNeunXTv3r24dl1kQkNDKcnHMf2P6WyP257x3oSJ7XHbqVW7FpM7Ts6YPi58XPZx8m3sCb5nHB7eg/l2/TEW7T5LPXcnfhzuS/K5QyX6uItKSf97FxV93CVfXkNBNFFKvamUOgb8BziHccUQKCL/KYR9XwDqZHpf2zxNs7JlJ5bdebrJRFBMLINi47ExX0XaKBsGNR5MRVM3es3cwpLdZxnbtQE/B3elc0M9cqemlTR5XQEcA7YB/UXkFIBS6qVC3PduoLFSqj5Gwf8o8Fghbl+7Sybj0Q+5Tz+zAzZMJCTqOGuqV8Ok/pm/7Pgq5l2ARk5d+eaJdrSuU7mYotY0Lb/ySgBDMArlzUqpn4HF3P5g+AIRkVSl1HPABoxmoN+KyOHC2r5muaxt+RUKIfu9IRtlYwzhsO5lSIxidq0GJKZE37aMiWSq193E2kcnYl8h1wtMTdNKgLw6gq0GViulnDFa57wI1FBKfQmsEpECjwMkIuuB9QXdjnb3cmrLb4NNjgngoQYPgI0NPPI9VPLi0qKOOW4zLu2aLvw1rRS443+piMSJyI8iMgCjnn4/MK7II9OKRU5t+U2YcLR1NM74ARsRHomKYXIVf2OBao0wVXDEtULO9fp60DZNKx3y9VglEbmJ0SLnm6IJRytuubXlT0xLICzGAa6dAO8u8NC7ULMVAH9fi2P8ijAuX++Bk9cqRCVnrKcHbdO00kM/V6+c83T2JDIuMvt0KoCYYNhiaNIXlCI1zcS3v//NxxtPYG9rw/SgJ3Fx9+PT/Z/qQds0rRTSCaCcC24TnL0tv60DwW1ehqYPgq0dAEcjoxm3Ioyw81H0bO7B9Ad88HRzAOrSv2F/K0WvaVpB6ARQzgXV6QFV1jH70hYu2drgaV+Z4I4TMs7ik1LT+Hzzab7YfAo3Rzs+e8yfIN+aqFye2qVpWumhE0B5dnA5/PImQdHnCWrWH3q9Be4NM2bvO3uTccvDOHkllsH+tZjSvwVVnO2tGLCmaYVJJ4Dy7MwOcKoKg7+C+l0yJscnp/LxxhN8+/vfeFZy4LuR7QlsVsOKgWqaVhR0AihPbkbAr1OhwzNQtyP0ng4VHIy2/Wa/n7rG+JVhnLuRwOMd6zKubzNcHeysFrKmaUVHJ4DyIDEKtn0Mf3wJNhWgUS8jAdg7ZSwSlZDCuyFHWbLnHPWrObNkbEc6NHC3YtCaphU1nQDKugM/wsY3IP46+D0GPSZDJa/bFtl4+BKTVx/ielwyz3RryIs9G+NgZ2ulgDVNKy46AZRF6c94UArirkL1ZtDnHfDyu22xqzFJTP3pMCFhkTSvWYn/jmiPb203KwSsaZo16ARQ1lw+QsjPzzPbLolLKdFG56yuLxCUqfAXEVbtv8Bb644Qn5TGq72b8HS3htjZ6vF7NK080QmgrIi9ApvfJeT4Uqa6VyUxxWinHxkXydSd00ApghoEceFWApNWHST0+FXa1K3MBw+2olENVysHr2maNegEUBbsnQ8bJkFqArPrNyDRlPOD2m9cbsmM/x3DJPDmgBY82ckbWxvdoUvTyit9zV9aiUBaivG7g5vRjv/ZP7hkSspx8cjYSN5Ycxj/ulXY+FJXnrqnvi78Na2c01cApdG53bBhAjTpA11fg5YPGC9yH9yNVKO656G2tfUwDpqmAfoKoHS5eQaWj4L/9oRb56ByvWyLBLcJxsHW4bZpNtgzodPLPNyuji78NU3LoK8ASot9CyDkVVA20G0cdH4BKrpkW+y+On0Jcb7IlusLsKlwi8r2NRjf8WU9RLOmadnoBFCSpaVCagJUdAWPltByMNw3Bdxq5bj43jM3eH15GKevejG0zWe80b85lZ304G2apuVMJ4CS6tSvsGEyIdXrMNsm6p8Hrlw/QFCWBBCXlMqHG44zf2cEXm6OzB8VQLcm1a0Tt6ZppYZOACXNlaOwcTKc+pWQGnWZmnSaRJPR2icyLpKpO6YCZFTpbD1xlQkrD3IxKoEnO9bjtb7NcKmo/6yapt2ZLilKkgOLYM2zYO8Kvacz+8JaEuNvf2Zvepv+ez1783bIEZbvPU+D6s4sfboT7b2rWilwTdNKI50ArC0lERJuQqWaUL8rBIyFrq+DszuX5s/JcZXIuEv0/GQLN+KSebZ7Q164Tw/epmla/ukEYC0icGQ1/PImVK4LI34ybu7e/37GIrm16Tclu1HdpSLfjWyPTy09eJumaXdH9wOwAtfoE/BtH1g20mjh0+UVY+TOLHJq0y8mO3p5jmTNc/fowl/TtALRVwDF7fAq2u57DZxrwMD/gN9wsMm5+iaoQRDXY5OYtW82ydzATqrynN8L/Mt/aDEHrWlaWaQTQHFIioGo81CjOTTqxd/ew6k/7H3j7D8XJpOwYGcEH2xwQjGBcfc34/EO9bDR4/domlZIdAIoSqY02L8QNr1jDNj27z+hogtnvB+mfh6F/6krsYxfEcaeMzfp2qQ67w72oXYVp1yX1zRNuxs6ARSV05tgw2S4chjqdIQ+7+Za1ZMuJc3EN1vDmf3rSRztbfn4odYMaVNLj9+jaVqR0AmgKJz6Fb4fagzW9tB8aDEox5u8mR26EMXry8M4EhlNP19Ppg30obprxWIKWNO08kgngMISdx2uHDHG5W/QAwZ8Cq0eATuHPFdLTElj9m8n+WZrOFWd7fnq8Tb09alZTEFrmlaeWSUBKKUeAqYCzYEAEdljsdBBCwAADJ1JREFUjTgKRWoS7PoGtnwItnbw0mGj0G874o6r7o64wbjlYYRfi+OhtrWZHNQCNye7Ygha0zTNelcAh4AhwNdW2n/BicDRtfDLFLgZAY16Qe+373jGD5CQKkxZc4gFO89Qu4ojC/8VQJfGevA2TdOKl1USgIgcBUr3zc2L+2Dpk1C9OTy+Ahr1tGi10ONXmLw9gRtJZ3jqHm9e7d0UZz14m6ZpVqBExHo7VyoUeDWvKiCl1FhgLICHh0fbxYsXF1N02VVMvIpb1GGueHQHoOr1vdys4ofcoXUPQGzy/7d351FSVFccx78/BoSBkU1AloCAooCAoxIkkeSAC4oaEWMiGj0ao1FjEqMmhohHMRqXGBfiGo1LNC4nirgwOWFRxwVERRwGEFACIkQBwYww6LDM3PxRb7QzzipMV0/X/ZxTh6rq6lf3dQ/vdle9fs94bOk2Zn+4g665xtlDctmnQ7LG7yktLSUv76uT2GQ7r3eyZGK9R40a9ZaZDa26v9ESgKRZQNdqHppoZs+EYwqpIwGkGjp0qM2bF8Ptgq2lMHsyzLkNmjWHixZBbvt6PdXM+OfCtVz57CJKPtvO+SP3ZkjzDznysFGNHHTmKSwsZOTIkXGHkXZe72TJxHpLqjYBNNq1BzOr3zWRTFZRDkWPwgtXQ+k6GHQSHHFlvRv/9ZvKuPzpRcx4Zx2De7TjobMOYWD3thQWVjNpu3POpZlffK5NyQcw7SLofiCc/Aj0/Ga9nmZmPDFvDVcXvMO2HRVMGNOfs0f0oXmOj73nnMsccXUDHQfcBnQGCiQVmdlRccTyFRvegyXPwXcuho594JwXoOvgOn/IVWn1J5/xu6cW8uryDQzr05HrTxxM386ZdT3QOecgvl5AU4GpcZy7Rp99AoXXw7z7oHkuHDAe2naHbkPq9fTyCuNvc97nxunLyGkmrjlhEKcO6+WDtznnMlbWXwIqWFHA5PmTv5xU/aALv5hPF4Ad2+DNe+GlG6JROw86A0ZdBnld6n2O99Zt5tIpxbz9QQkj9+vMteMG0719biPUxjnndp2sTgAFKwqYNGcSZeVlQPWTqrN9C7x8I/Q4GEb/AfYcWO/yt+2o4O6X/s3tLyynTcscbj05n7H53Zv27xucc4mR1Qlg8vzJXzT+lcrKy5j85o0cu3gmHHsT5HaA82ZH0zE2QPGaEi59spilazdz3JBuTDp+fzrl+eBtzrmmI6sTwNota6vf//kGWPUcfOsC6NSvQY1/2fZybpn5Lve+soLOu7fkntMPZvT+1f3cwTnnMltWJ4CaJlXv2rw1/HJ2NElLA8xdsZEJU4p5f+NnnDKsJxPGDKBdrg/e5pxrmrK6Y3p1k6q3ymnJhd++skGN/+ay7UycupDx98ylwuDRsw/huhOHeOPvnGvSsvobQOWN3lp7AdXhhaXrmDh1Ees2lXH2iD5cPHpfWu+W1S+bcy4hsr4lO7bvsQ1q8Ct9smUbv39uMU8XfUi/Lnncef63ObBXh0aI0Dnn4pH1CaChzIznij9i0rOL2fT5di48vB8/G7U3LZsna+RO51z28wSQYu2n0eBts5as44BvtOOGcw6hf9e2cYflnHONwhMA0af+x99czbUFS9heUcHEYwZw1og+5PgwDs65LJb4BLBq4xYmTFnIays2MrxvR64/cQi9O7WJOyznnGt0iU0A5RXGA7NX8qcZy2jRrBnXjhvM+G/29MHbnHOJkcgEsGxtNHjbgtUlHN6/C9eMG0S3dj54m3MuWRKVALbtqODOwuXc8eJydm/Vgsnj8zn+AB+8zTmXTIlJAEWrS/jtk8UsW7eZsfndueK4gezhg7c55xIsEQngtuff45ZZ79Jl91bcd8ZQDh+wZ9whOedc7BKRAHrt0Zrxw3oxYUx/2rby8Xuccw4SkgDG5vdgbH7Dxvt3zrlsl9WjgTrnnKuZJwDnnEsoTwDOOZdQngCccy6hPAE451xCeQJwzrmE8gTgnHMJ5QnAOecSSmYWdwz1JuljYFXccewCnYANcQcRA693sni9M8deZta56s4mlQCyhaR5ZjY07jjSzeudLF7vzOeXgJxzLqE8ATjnXEJ5AojHPXEHEBOvd7J4vTOc3wNwzrmE8m8AzjmXUJ4AnHMuoTwBxETSDyQtllQhqUl0GdsZko6WtEzSckkT4o4nHSTdL2m9pEVxx5JOknpKelHSO+Fv/MK4Y0oHSa0kvSFpQaj3VXHHVBdPAPFZBJwIvBx3II1NUg5wBzAGGAicImlgvFGlxYPA0XEHEYMdwCVmNhAYDlyQkPd7K3CYmR0A5ANHSxoec0y18gQQEzNbYmbL4o4jTYYBy81shZltAx4HxsYcU6Mzs5eBT+KOI93M7CMzmx/WNwNLgKyfk9UipWGzRVgyupeNJwCXDj2A1Snba0hAg+BAUm/gQOD1eCNJD0k5koqA9cBMM8voeidiUvi4SJoFdK3moYlm9ky643EunSTlAVOAX5nZprjjSQczKwfyJbUHpkoaZGYZew/IE0AjMrMj4o4hQ/wH6Jmy/Y2wz2UpSS2IGv9HzOypuONJNzMrkfQi0T2gjE0AfgnIpcObQD9JfSTtBowHno05JtdIJAm4D1hiZjfHHU+6SOocPvkjKRc4Elgab1S18wQQE0njJK0BvgUUSJoed0yNxcx2AD8HphPdEPyHmS2ON6rGJ+kx4DVgP0lrJP0k7pjS5FDgdOAwSUVhOSbuoNKgG/CipGKiDz0zzWxazDHVyoeCcM65hPJvAM45l1CeAJxzLqE8ATjnXEJ5AnDOuYTyBOCccwnlCcDtEpLKU7r8FUnqLWmopD/X47lzwr+9JZ26E+deHEZivERSs/DYFzFIailpVjj2ZEnfCc8pCv22M5KkiyUtlbQw1O/m8EOrr1te78oRSuv7HtVS1mVf97kuft4N1O0SkkrNLG8nyxgJ/NrMjvu655bUBXgUmG1mV1Y5bjhwTeUvtCXdDbxqZn+v53lE9H+moiHx7QxJ5wEnAOPDr0t3Ay4G7qw6vIKknDAUQV1l9gammdmgXRDfTr/vLkZm5osvO70ApdXsG0nU0ABMAu4HCoEVwC+rPheYC3wKFAEXATnAjUQ/qikGzq3PuYG+wEZAlTEAXYDlKeWfSzRS50qi4QoAfpNyrqvCvt7AMuAhYDGwFzCa6Ade84EngLxw7PvAVWH/QqB/2J8HPBD2FQPfD/urLadKXVYDfWp73YGbgAXACOCKUIdFRHPTVn7IOzgcsyC8pouqeY/ahPfoDeBtYGzYfybwFPAv4D3gj2H/9UB5eD0fiftv0Jev8f827gB8yY4lpSEoAqaGfVUTwBygJdApNNAtwmOlVY8P2z8FLg/rLYF51TWGVRNA2FcC7FklhqrlPwicFNZHVzaYRJdGpwHfDQmgAhgejutENIdDm7D9W+CKsP4+8Iuw/jPgr2H9BuDWlPN2qK2clOPaAv+t43U34Icp2x1T1h8GvhfWi4HvhvWaEsC1wGlhvT3wbkgKZxIl7XZAK2AV0LOm196XprP4YHBuV/nczPLrOKbAzLYCWyWtJ2qg19Ry/GhgiKSTwnY7oB/Rp/ZdbXRY3g7beeFcHwCrzGxu2D+caFKb2dEVIXYj+hRfqXLgs7eIJvwBOIJo/CMAzOy/ko6ro5yvkHQUUTJpD5xqZnOIEu+UlMNGSboUaA10BBZLegVob9H8BBAlhjE1vAbHS/p12G4F9Arrz5vZpyGOd4i+Ca3+ahGuKfEE4NJpa8p6OXX//YnoE3WDxkmS1DeUvx4YUN+nAdeZ2V+qlNUb2FLluJlmdkoN5VTWsa761VUOZrZJUqmkPma2MrwO0yVNI0oYAGUWrvtLagXcCQw1s9WSJhE14vUlostT/zdRkaRDaPh755oA7wXkMslmYPeU7enA+ZU9XiTtK6lNbQVI6gzcDdxuZg3p4TAdOCuMYY+kHuGGclVzgUMl7ROOayNp3zrKnglckBJjhwaUcx1wV8ook6LmRr1y/4ZQj5MgGpoYKJE0Ijz+oxqePx34RTgHkg6so14A23emR5KLl2dxl0mKgXJJC4iuz08mugY/PzRKHxP1iKkqN8zC1IJoPtqHgQYNQ2xmMyQNAF4L7V8pcBrRp93U4z6WdCbwmKSWYfflRNfLa3INcEfoellOdIP5qXqWcxfRdfjXJW0Ncc3my0tVqbGVSLqX6AbwWqKbwZV+DNwvyYAZNcR5NXArUBy60a4E6uqRdU84fr6Z1ZRYXIbybqDOOZdQfgnIOecSyhOAc84llCcA55xLKE8AzjmXUJ4AnHMuoTwBOOdcQnkCcM65hPofsftQBOwQWGgAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -505,7 +505,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -558,7 +558,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.1" } }, "nbformat": 4, diff --git a/python/meep.i b/python/meep.i index 1822a2ce4..b759d47c5 100644 --- a/python/meep.i +++ b/python/meep.i @@ -793,13 +793,7 @@ void _get_gradient(PyObject *grad, PyObject *fields_a, PyObject *fields_f, PyObj if (!PyArray_Check(pao_freqs)) meep::abort("frequencies parameter must be numpy array."); if (!PyArray_ISCARRAY(pao_freqs)) meep::abort("Numpy fields array must be C-style contiguous."); meep::realnum* frequencies_c = (meep::realnum *)PyArray_DATA(pao_freqs); - - // get the proper dimensions of the fields array - if (PyArray_NDIM(pao_fields_a) !=5) {meep::abort("Fields array must have 5 dimensions.");} - int fdims_c[4]; - for (int i = 0; i < PyArray_NDIM(pao_fields_a); ++i) { - fdims_c[i] = PyArray_DIMS(pao_fields_a)[i]; - } + int nf = PyArray_DIMS(pao_freqs)[0]; // prepare a geometry_tree //TODO eventually it would be nice to store the geom tree within the structure object so we don't have to recreate it here @@ -814,7 +808,7 @@ void _get_gradient(PyObject *grad, PyObject *fields_a, PyObject *fields_f, PyObj meep::fields* f_c = (meep::fields *)f_v; // calculate the gradient - meep_geom::material_grids_addgradient(grad_c,fields_a_c,fields_f_c,frequencies_c,fdims_c,scalegrad,*where_vol,geometry_tree,f_c); + meep_geom::material_grids_addgradient(grad_c,fields_a_c,fields_f_c,frequencies_c,nf,scalegrad,*where_vol,geometry_tree,f_c); destroy_geom_box_tree(geometry_tree); delete[] l; diff --git a/python/simulation.py b/python/simulation.py index 4ea7ebfba..59595134e 100644 --- a/python/simulation.py +++ b/python/simulation.py @@ -3167,9 +3167,9 @@ def get_array_slice_dimensions(self, component, vol=None, center=None, size=None else: v = self._volume_from_kwargs(vol, center, size) dim_sizes = np.zeros(3, dtype=np.uintp) - _,_ = mp._get_array_slice_dimensions(self.fields, v, dim_sizes, False, True, component) - dims = [s for s in dim_sizes if s != 0] - return dims + _,_ = mp._get_array_slice_dimensions(self.fields, v, dim_sizes, False, False, component) + dim_sizes[dim_sizes==0] = 1 + return dim_sizes def get_eigenmode_coefficients(self, flux, bands, eig_parity=mp.NO_PARITY, eig_vol=None, eig_resolution=0, eig_tolerance=1e-12, kpoint_func=None, direction=mp.AUTOMATIC): diff --git a/src/meepgeom.cpp b/src/meepgeom.cpp index 232b2060e..887159f12 100644 --- a/src/meepgeom.cpp +++ b/src/meepgeom.cpp @@ -2224,23 +2224,22 @@ meep::realnum get_material_gradient( std::complex *fields_a, // adjoint field vector at current point (3 elements) std::complex *fields_f, // forward field vector at current point (3 elements) meep::realnum freq, // frequency - material_data *md, // material - meep::realnum du + material_data *md, // material + meep::component field_dir, // field component we care about + meep::realnum du // step size ){ + const medium_struct *mm = &(md->medium); const medium_struct *m1 = &(md->medium_1); const medium_struct *m2 = &(md->medium_2); - + // trivial case if ((mm->E_susceptibilities.num_items == 0) && mm->D_conductivity_diag.x == 0 && mm->D_conductivity_diag.y == 0 && mm->D_conductivity_diag.z == 0) - { - return 2 * (m2->epsilon_diag.x - m1->epsilon_diag.x) * ( - (fields_a[0]*fields_f[0]).real() + (fields_a[1]*fields_f[1]).real() + (fields_a[2]*fields_f[2]).real()); - } + return 2 * (m2->epsilon_diag.x - m1->epsilon_diag.x) * (fields_a[0]*fields_f[0]).real(); std::complex dA_du_0[9] = {std::complex(0,0)}; epsilon_material_grid(md, u-du); @@ -2253,18 +2252,13 @@ meep::realnum get_material_gradient( std::complex dA_du[9] = {std::complex(0,0)}; for (int i=0; i<9; i++) dA_du[i] = (dA_du_1[i] - dA_du_0[i]) / (2*du); - /*Calculate the vector-matrix-vector product conj(v1) A v2.*/ - std::complex dummy[3] = {std::complex(0,0)}; - // first matrix-vector product - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - int idx = i * 3 + j; - dummy[i] += dA_du[idx] * fields_f[j]; - } - } - - // inner product - std::complex result = dummy[0] * fields_a[0] + dummy[1] * fields_a[1] + dummy[2] * fields_a[2]; + int dir_idx; + if (field_dir==meep::Ex) dir_idx = 0; + else if(field_dir==meep::Ey) dir_idx = 1; + else if(field_dir==meep::Ez) dir_idx = 2; + else meep::abort("Invalid adjoint field component"); + + std::complex result = fields_a[0] * dA_du[3*dir_idx + dir_idx] * fields_f[0]; return 2*result.real(); } @@ -2336,7 +2330,7 @@ void add_interpolate_weights(meep::realnum rx, meep::realnum ry, meep::realnum r #undef U } -void material_grids_addgradient_point(meep::realnum *v,std::complex *fields_a, std::complex *fields_f, +void material_grids_addgradient_point(meep::realnum *v,std::complex *fields_a, std::complex *fields_f, meep::component field_dir, vector3 p, meep::realnum scalegrad, meep::realnum freq, geom_box_tree geometry_tree) { geom_box_tree tp; @@ -2387,7 +2381,7 @@ void material_grids_addgradient_point(meep::realnum *v,std::complex *fie do { vector3 pb = to_geom_box_coords(p, &tp->objects[oi]); add_interpolate_weights(pb.x, pb.y, pb.z, - vcur, sz.x, sz.y, sz.z, 1, get_material_gradient(uval, fields_a, fields_f, freq, mg,1e-6)*scalegrad, + vcur, sz.x, sz.y, sz.z, 1, get_material_gradient(uval, fields_a, fields_f, freq, mg, field_dir, 1e-6)*scalegrad, ucur, kind, uval); if (kind == material_data::U_DEFAULT) break; tp = geom_tree_search_next(p, tp, &oi); @@ -2401,55 +2395,69 @@ void material_grids_addgradient_point(meep::realnum *v,std::complex *fie ucur = mg->design_parameters; uval = matgrid_val(p, tp, oi, mg); add_interpolate_weights(pb.x, pb.y, pb.z, - vcur, sz.x, sz.y, sz.z, 1, get_material_gradient(uval, fields_a, fields_f, freq, mg)*scalegrad, + vcur, sz.x, sz.y, sz.z, 1, get_material_gradient(uval, fields_a, fields_f, freq, mg, field_dir)*scalegrad, ucur, kind, uval); tp = geom_tree_search_next(p, tp, &oi); } } -void material_grids_addgradient(meep::realnum *v, std::complex *fields_a, std::complex *fields_f, meep::realnum *frequencies, int *fdims, +void material_grids_addgradient(meep::realnum *v, std::complex *fields_a, std::complex *fields_f, meep::realnum *frequencies, size_t nf, meep::realnum scalegrad, const meep::volume &where, geom_box_tree geometry_tree, meep::fields *f) { - int n1, n2, n3, n4; - meep::realnum s1, s2, s3, c1, c2, c3; + int n1, n2, n3; + meep::realnum s[3][3], cen[3][3], c1, c2, c3, s1, s2, s3; vector3 p; - n1 = fdims[0]; n2 = fdims[1]; n3 = fdims[2];n4 = fdims[3]; - // calculate cell dimensions - size_t dims[3]; meep::direction dirs[3]; meep::vec min_max_loc[2]; // extremal points in subgrid bool collapse = false, snap = false; - f->get_array_slice_dimensions(where, dims, dirs, collapse, snap, min_max_loc); - - // size indices - vector3 max_corner = vec_to_vector3(where.get_max_corner()); - vector3 min_corner = vec_to_vector3(where.get_min_corner()); - s1 = (vec_to_vector3(min_max_loc[1]).x-vec_to_vector3(min_max_loc[0]).x) / n1; - s2 = (vec_to_vector3(min_max_loc[1]).y-vec_to_vector3(min_max_loc[0]).y) / n2; - s3 = (vec_to_vector3(min_max_loc[1]).z-vec_to_vector3(min_max_loc[0]).z) / n3; - // starting point - c1 = n1 <= 1 ? 0 : vec_to_vector3(min_max_loc[0]).x; - c2 = n2 <= 1 ? 0 : vec_to_vector3(min_max_loc[0]).y; - c3 = n3 <= 1 ? 0 : vec_to_vector3(min_max_loc[0]).z; + meep::component field_dir[3] = {meep::Ex,meep::Ey,meep::Ez}; + size_t dims[9] = {1,1,1,1,1,1,1,1,1}; + for (int c = 0; c < 3; c++){ + + int rank = f->get_array_slice_dimensions(where, &dims[3*c], dirs, collapse, snap, min_max_loc, 0, field_dir[c]); + vector3 max_corner = vec_to_vector3(where.get_max_corner()); + meep::realnum max_c_array[3] = {max_corner.x,max_corner.y,max_corner.z}; + vector3 min_corner = vec_to_vector3(where.get_min_corner()); + meep::realnum min_c_array[3] = {min_corner.x,min_corner.y,min_corner.z}; + for (int ci = 0; ci < 3; ci++){ + + s[c][ci] = max_c_array[ci] - min_c_array[ci]; + cen[c][ci] = min_c_array[ci]; + if ((c == ci) && (s[c][ci] > 0)) { + s[c][ci] += 1/f->gv.a; + cen[c][ci] -= (1/f->gv.a)/2; + } + s[c][ci] = s[c][ci] == 0 ? 0 : s[c][ci] / (dims[3*c+ci]-1); // normalize the size of the domain + cen[c][ci] = dims[3*c+ci] <= 1 ? 0 : cen[c][ci]; + } + } - // Loop over x,y,z and frequency dimensions + // Loop over component, x, y, z, and frequency dimensions // TODO speed up with MPI (if needed) - for (int i1 = 0; i1 < n1; ++i1){ - for (int i2 = 0; i2 < n2; ++i2){ - for (int i3 = 0; i3 < n3; ++i3){ - for (int i4 = 0; i4 < n4; ++i4){ - int xyz_index = (((i1 * n2 + i2) * n3 + i3) * n4 + i4) * 3; - std::complex *fields_a_cur, *fields_f_cur; - fields_a_cur = &fields_a[xyz_index]; - fields_f_cur = &fields_f[xyz_index]; - p.x = i1 * s1 + c1; p.y = i2 * s2 + c2; p.z = i3 * s3 + c3; - material_grids_addgradient_point(v, fields_a_cur, fields_f_cur, p, scalegrad, frequencies[i4], geometry_tree); + size_t c_offset = 0; + for (int c = 0; c < 3; c++){ // component + n1 = dims[c*3]; n2 = dims[c*3+1]; n3 = dims[c*3+2]; + c1 = cen[c][0]; c2 = cen[c][1]; c3 = cen[c][2]; + s1 = s[c][0]; s2 = s[c][1]; s3 = s[c][2]; + + for (int i1 = 0; i1 < n1; ++i1){ // x + for (int i2 = 0; i2 < n2; ++i2){ // y + for (int i3 = 0; i3 < n3; ++i3){ // z + for (int i4 = 0; i4 < nf; ++i4){ // freq + int xyz_index = c_offset + (((i1 * n2 + i2) * n3 + i3) * nf + i4); + std::complex *fields_a_cur, *fields_f_cur; + fields_a_cur = &fields_a[xyz_index]; + fields_f_cur = &fields_f[xyz_index]; + p.x = i1 * s1 + c1; p.y = i2 * s2 + c2; p.z = i3 * s3 + c3; + material_grids_addgradient_point(v, fields_a_cur, fields_f_cur, field_dir[c], p, scalegrad, frequencies[i4], geometry_tree); + } } } } + c_offset += dims[c*3] * dims[c*3+1] * dims[c*3+2]; } } diff --git a/src/meepgeom.hpp b/src/meepgeom.hpp index 43f0e2761..53a5d52eb 100644 --- a/src/meepgeom.hpp +++ b/src/meepgeom.hpp @@ -208,15 +208,15 @@ meep::realnum matgrid_val(vector3 p, geom_box_tree tp, int oi, material_data *md meep::realnum material_grid_val(vector3 p, material_data *md); geom_box_tree calculate_tree(const meep::volume &v, geometric_object_list g); void get_material_tensor(const medium_struct *mm, meep::realnum freq, std::complex *tensor); -meep::realnum get_material_gradient(meep::realnum u, std::complex *fields_a, std::complex *fields_f, meep::realnum freq, material_data *md,meep::realnum du=1.0e-3); +meep::realnum get_material_gradient(meep::realnum u, std::complex *fields_a, std::complex *fields_f, meep::realnum freq, material_data *md, meep::component field_dir, meep::realnum du=1.0e-3); void add_interpolate_weights(meep::realnum rx, meep::realnum ry, meep::realnum rz, meep::realnum *data, int nx, int ny, int nz, int stride, double scaleby, const meep::realnum *udata, int ukind, double uval); -void material_grids_addgradient_point(meep::realnum *v, std::complex *fields_a, std::complex *fields_f, +void material_grids_addgradient_point(meep::realnum *v, std::complex *fields_a, std::complex *fields_f, meep::component field_dir, vector3 p, meep::realnum scalegrad, meep::realnum freq, geom_box_tree geometry_tree); -void material_grids_addgradient(meep::realnum *v, std::complex *fields_a, std::complex *fields_f, meep::realnum *frequencies, int *fdims, meep::realnum scalegrad, +void material_grids_addgradient(meep::realnum *v, std::complex *fields_a, std::complex *fields_f, meep::realnum *frequencies, size_t nf, meep::realnum scalegrad, const meep::volume &where, geom_box_tree geometry_tree, meep::fields *f); /***************************************************************/