From f24e0195e1b70cad2eb61cbde6f211ac9a9c56e0 Mon Sep 17 00:00:00 2001 From: ebqu Date: Sat, 21 Dec 2024 14:46:46 +0000 Subject: [PATCH] hardware experiment plots --- .gitignore | 3 +- .../shots_n9_c7_rho0.0.pkl | Bin 0 -> 480162 bytes .../shots_n9_c7_rho0.4.pkl | Bin 0 -> 480162 bytes mvsp/preprocessing/gauss.py | 39 +++ mvsp/utils/discrete_density.py | 181 ++++++++++ mvsp/utils/shot_distribution.py | 102 ++++++ ...5-0.5_sigma-0.22-0.18_rho-0.0_hardware.pdf | Bin 0 -> 336090 bytes ...5-0.5_sigma-0.22-0.18_rho-0.4_hardware.pdf | Bin 0 -> 338257 bytes requirements.txt | 3 +- scripts/hardware_experiment_2d_gaussian.py | 216 ++++++++++++ .../hardware_experiment_2d_gaussian_plot.py | 320 ++++++++++++++++++ 11 files changed, 862 insertions(+), 2 deletions(-) create mode 100644 data/hardware_experiment/shots_n9_c7_rho0.0.pkl create mode 100644 data/hardware_experiment/shots_n9_c7_rho0.4.pkl create mode 100644 mvsp/preprocessing/gauss.py create mode 100644 mvsp/utils/discrete_density.py create mode 100644 mvsp/utils/shot_distribution.py create mode 100644 plots/2D_gauss_fourier_n9_c7_mu-0.5-0.5_sigma-0.22-0.18_rho-0.0_hardware.pdf create mode 100644 plots/2D_gauss_fourier_n9_c7_mu-0.5-0.5_sigma-0.22-0.18_rho-0.4_hardware.pdf create mode 100644 scripts/hardware_experiment_2d_gaussian.py create mode 100644 scripts/hardware_experiment_2d_gaussian_plot.py diff --git a/.gitignore b/.gitignore index 8833b29..3d342e6 100644 --- a/.gitignore +++ b/.gitignore @@ -14,4 +14,5 @@ __pypackages__/ .mvsp/ *.venv/ pyenv.cfg -docs/source/api_reference/*.rst \ No newline at end of file +docs/source/api_reference/*.rst +data/hardware_experiment/density_data/ \ No newline at end of file diff --git a/data/hardware_experiment/shots_n9_c7_rho0.0.pkl b/data/hardware_experiment/shots_n9_c7_rho0.0.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7b34fa3c43fbb35315faf7227c035b4c6f953be8 GIT binary patch literal 480162 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multivariate_normal(mean=mean, cov=cov) + return rv.pdf + + +def gaussian_function_old( + vec, mean, cov_inv +): # note, takes inverse covariance in input + factor = np.sqrt(np.linalg.det(cov_inv)) / (2 * np.pi) + value = factor * np.exp( + -0.5 * (vec - mean).transpose().conjugate() @ cov_inv @ (vec - mean) + ) + return value[0, 0] + + +def coefficient_function_old(vec, mean, cov): # note, takes covariance in input + factor = 2 ** (-2) + value = factor * np.exp( + -np.pi * 1j * mean.transpose().conjugate() @ vec + - 0.5 * np.pi**2 * vec.transpose().conjugate() @ cov @ vec + ) + return value[0, 0] + + +def gauss_coefficient_function(mean, cov): # note, takes covariance in input + factor = 2 ** (-2) + + def fun(vec): + return factor * np.exp( + -np.pi * 1j * mean.conjugate() @ vec + - 0.5 * np.pi**2 * vec.conjugate() @ cov @ vec + ) + + fun = np.vectorize(fun, signature="(a)->()") + return fun diff --git a/mvsp/utils/discrete_density.py b/mvsp/utils/discrete_density.py new file mode 100644 index 0000000..566756c --- /dev/null +++ b/mvsp/utils/discrete_density.py @@ -0,0 +1,181 @@ +"""Implementation of the discrete density module.""" + +import numpy as np +from numpy.typing import NDArray +from scipy.stats import multivariate_normal + + +def gaussian_function(mean=None, cov=None): + rv = multivariate_normal(mean=mean, cov=cov) + return rv.pdf + + +class DiscreteDensity: + def __init__( + self, + discrete_positive_function: NDArray, + coordinates: list[NDArray] | None = None, + dx: list[float] | None = None, + domain: list[tuple[float, float]] | None = None, + normalize: bool = True, + positivize: str | bool = False, + ): + if dx is not None: + assert len(dx) == len(discrete_positive_function.shape) + if domain is None: + raise Exception("If dx is defined you also need to define domain.") + elif coordinates is not None: + assert len(coordinates) == len(discrete_positive_function.shape) + else: + raise Exception("Either coordinates or dx must be defined.") + + self.coordinates = coordinates + self.dx = dx + self.domain = domain + self.normalize = normalize + self.positivize = positivize + if positivize is True: + self.positivize = "absolute" + if self.positivize == "absolute": + self.unnormalized_density = np.abs(discrete_positive_function) + elif self.positivize == "absolute square": + self.unnormalized_density = np.abs(discrete_positive_function) ** 2 + else: + self.unnormalized_density = discrete_positive_function + + self.normalization = self._get_normalization_constant() + if normalize: + self.data = self.unnormalized_density / self.normalization + else: + self.data = self.unnormalized_density + self.dim = len(self.unnormalized_density.shape) + + def _get_normalization_constant(self): + integrator = self.unnormalized_density + for i in range(len(self.unnormalized_density.shape) - 1, -1, -1): + if self.dx is not None: + integrator = np.trapz(integrator, dx=self.dx[i], axis=i) + else: + integrator = np.trapz(integrator, x=self.coordinates[i], axis=i) + return integrator + + def get_marginal(self, axes: tuple, normalize: bool | None = None): + marginal_discrete_density = self.data.copy() + + if normalize is None: + normalize = self.normalize + + for i in range(len(axes) - 1, -1, -1): + axis = axes[i] + if self.dx is not None: + marginal_discrete_density = np.trapz( + marginal_discrete_density, dx=self.dx[axis], axis=axis + ) + else: + marginal_discrete_density = np.trapz( + marginal_discrete_density, x=self.coordinates[axis], axis=axis + ) + + new_axes = list(set(range(len(self.data.shape))) - set(axes)) + if self.dx is None: + new_dx = None + new_domain = None + else: + new_dx = [self.dx[axis] for axis in new_axes] + new_domain = [self.domain[axis] for axis in new_axes] + if self.coordinates is None: + new_coordinates = None + else: + new_coordinates = [self.coordinates[axis] for axis in new_axes] + return DiscreteDensity( + marginal_discrete_density, + coordinates=new_coordinates, + dx=new_dx, + domain=new_domain, + normalize=normalize, + ) + + def integrate_discrete_function(self, discrete_function): + assert discrete_function.shape == self.data.shape + integrator = discrete_function * self.data + for i in range(len(discrete_function.shape) - 1, -1, -1): + if self.dx is not None: + integrator = np.trapz(integrator, dx=self.dx[i], axis=i) + else: + integrator = np.trapz(integrator, x=self.coordinates[i], axis=i) + + return integrator + + def _single_axis_meshgrid(self, axis): + assert ( + len(self.data.shape) < 53 + ), "Dimension of density must be smaller than 53." + + from string import ascii_lowercase, ascii_uppercase + + alphabet = list(ascii_lowercase) + list(ascii_uppercase) + + other_axes = list(set(range(len(self.data.shape))) - set([axis])) + + einsum_input_string = ( + "".join([alphabet[i] for i in other_axes]) + f",{alphabet[axis]}" + ) + einsum_output_string = "".join( + [alphabet[i] for i in range(len(self.data.shape))] + ) + einsum_string = einsum_input_string + "->" + einsum_output_string + + if self.dx is not None: + array_list = [np.ones(np.array(self.data.shape)[other_axes])] + [ + np.linspace( + self.domain[axis][0], + self.domain[axis][1], + round((self.domain[axis][1] - self.domain[axis][0]) / self.dx[axis]) + + 1, + ) + ] + else: + array_list = [np.ones(np.array(self.data.shape)[other_axes])] + [ + self.coordinates[axis] + ] + + return np.einsum(einsum_string, *array_list) + + def mean_value_of_axis(self, axis): + integrator = self._single_axis_meshgrid(axis=axis) + + return self.integrate_discrete_function(integrator) + + def mean_value(self): + return [self.mean_value_of_axis(axis) for axis in range(len(self.data.shape))] + + def covariance_matrix(self): + mean = self.mean_value() + + X = [ + self._single_axis_meshgrid(axis=axis) + for axis in range(len(self.data.shape)) + ] + + integrator = [ + [(X[i] - mean[i]) * (X[j] - mean[j]) for j in range(len(mean))] + for i in range(len(mean)) + ] + + covariance_matrix = np.array( + [ + [ + self.integrate_discrete_function(integrator[i][j]) + for j in range(len(mean)) + ] + for i in range(len(mean)) + ] + ) + + return covariance_matrix + + def correlation_matrix(self): + covariance_matrix = self.covariance_matrix() + standard_deviations = np.sqrt(covariance_matrix.diagonal()) + std_outer = np.outer(standard_deviations, standard_deviations) + return covariance_matrix / std_outer diff --git a/mvsp/utils/shot_distribution.py b/mvsp/utils/shot_distribution.py new file mode 100644 index 0000000..2ddb11f --- /dev/null +++ b/mvsp/utils/shot_distribution.py @@ -0,0 +1,102 @@ +from itertools import product + +import numpy as np +from numpy.typing import NDArray +from scipy.ndimage import gaussian_filter +from scipy.stats import moment as scipy_moment + + +class ShotDistribution: + def __init__( + self, + shots: list[tuple] | NDArray, + outcome_dict: dict, + ): + self.shots = shots + self.outcome_dict = outcome_dict + self._outcomes = None + self._shot_density = None + self._outcome_density = None + + @property + def outcomes(self) -> list: + if self._outcomes is None: + self._outcomes = [self.outcome_dict[tuple(shot)] for shot in self.shots] + return self._outcomes + + @property + def shot_density(self): + if self._shot_density is None: + bit_tuples = list(product([0, 1], repeat=len(self.shots[0]))) + shots_tuples = list(map(tuple, self.shots)) + shot_density = {} + for tup in bit_tuples: + shot_density[tup] = shots_tuples.count(tup) / len(shots_tuples) + self._shot_density = shot_density + return self._shot_density + + @property + def outcome_density(self): + if self._outcome_density is None: + shot_density = self.shot_density + outcomes_ordered = list(self.outcome_dict.values()) + if not isinstance(outcomes_ordered[0], (int, float)): + print("Transform outcomes to tuples.") + outcomes_ordered = list(map(tuple, outcomes_ordered)) + self._outcome_density = dict(zip(outcomes_ordered, shot_density.values())) + return self._outcome_density + + def filtered_outcome_density(self, sigma=1.0, dimension=1, shape=None): + keys = list(self.outcome_density.keys()) + vals = list(self.outcome_density.values()) + if dimension == 1: + filtered_vals = gaussian_filter(vals, sigma=sigma) + res = {} + res["outcomes"] = keys + res["values"] = filtered_vals + else: + keys_reshape = np.array(keys).reshape(tuple(list(shape) + [dimension])) + vals_reshape = np.array(vals).reshape(shape) + filtered_vals_reshape = gaussian_filter(vals_reshape, sigma=sigma) + res = {} + res["outcomes"] = keys_reshape + res["values"] = filtered_vals_reshape + return res + + def statistical_moment(self, moment: int, center: float | None = None): + return scipy_moment( + self.outcomes, + moment=moment, + center=center, + ) + + +def generate_outcome_dict(n_qubits, shots_postselect, dim=1): + if dim == 1: + bit_tuples = np.array(list(product([0, 1], repeat=n_qubits))) + n_all_outcome = 2**n_qubits + + x_min = 0 + x_max = 1 + outcomes = np.linspace(x_min, x_max, n_all_outcome) + + assert len(outcomes) == len(bit_tuples) + outcome_dict = dict(zip(map(tuple, bit_tuples), outcomes)) + elif dim == 2: + assert not n_qubits % 2, "n_qubits must be even for dim = 2." + + bit_tuples = np.array(list(product([0, 1], repeat=len(shots_postselect[0])))) + n_all_outcome = 2 ** (n_qubits // 2) + print(n_all_outcome) + + x_min = 0 + x_max = 1 + x_space = np.linspace(x_min, x_max, n_all_outcome) + xx, yy = np.meshgrid(x_space, x_space) + pos = np.stack((xx, yy), axis=-1).transpose((1, 0, 2)) + outcomes = pos.reshape((np.prod(pos.shape[:2]), 2)) + + assert len(outcomes) == len(bit_tuples) + outcome_dict = dict(zip(map(tuple, bit_tuples), outcomes)) + + return outcome_dict diff --git a/plots/2D_gauss_fourier_n9_c7_mu-0.5-0.5_sigma-0.22-0.18_rho-0.0_hardware.pdf b/plots/2D_gauss_fourier_n9_c7_mu-0.5-0.5_sigma-0.22-0.18_rho-0.0_hardware.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3e14ab1ad0924ac1d1d75fdb892105080b2f04a2 GIT binary patch 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b/scripts/hardware_experiment_2d_gaussian.py @@ -0,0 +1,216 @@ +"""Generate hardware experiment results from shot data.""" + +import os +import pickle + +import numpy as np +from sklearn.model_selection import GridSearchCV, LeaveOneOut +from sklearn.neighbors import KernelDensity + +# from mvsp.mvsp_nexus.fourier import FourierExpansion +from mvsp.preprocessing.fourier import FourierExpansion +from mvsp.preprocessing.gauss import gauss_coefficient_function, gaussian_function +from mvsp.utils.discrete_density import DiscreteDensity +from mvsp.utils.shot_distribution import ShotDistribution, generate_outcome_dict + + +def generate_data(rho: float): + # Set parameter + n = 9 + c = 7 + mu_x = 0.5 + mu_y = 0.5 + s_x = 0.22 + s_y = 0.18 + + # Import shots + read_path = f"../data/hardware_experiment/shots_n{n}_c{c}_rho{rho}.pkl" + with open(read_path, "rb") as file: + shots = pickle.load(file) + + n_post_select_qubits = 6 + n_state_register = 9 + + mask = np.all( + shots[:, :n_post_select_qubits] == np.zeros(n_post_select_qubits), axis=1 + ) + shots_postselect = shots[mask][:, n_post_select_qubits:] + shots_postselect_0 = shots_postselect[:, :n_state_register] + shots_postselect_1 = shots_postselect[:, n_state_register:] + + #### Ground truth + d_X = c - 1 + d_Y = c - 1 + n_grid_X = n + n_grid_Y = n + n_grid = np.array([n_grid_X, n_grid_Y]) + mean = np.array([mu_x, mu_y]) + cov = np.array([[s_x**2, rho * s_x * s_y], [rho * s_x * s_y, s_y**2]]) + gauss_fun = gaussian_function(mean=mean, cov=cov) + f_coeff = gauss_coefficient_function(mean=mean, cov=cov) + fourier = FourierExpansion( + func=gauss_fun, + coefficient_function=f_coeff, + degrees=[d_X, d_Y], + ) + # fourier._initialize_eval() + fourier._initialize_coeffs_dct() + + x = np.linspace(0.0, 1.0, 2 ** n_grid[0]) + y = np.linspace(0.0, 1.0, 2 ** n_grid[1]) + xx, yy = np.meshgrid(x, y) + pos = np.stack((xx, yy), axis=-1) + pos = pos.transpose((1, 0, 2)) # x varies along axis 0, i.e. axis 0 is the x-axis + + density_ground_truth_fourier = DiscreteDensity( + fourier(pos), coordinates=[x, y], positivize=True + ) + density_ground_truth_fourier_x = density_ground_truth_fourier.get_marginal(axes=[1]) + density_ground_truth_fourier_y = density_ground_truth_fourier.get_marginal(axes=[0]) + + # Full density + outcome_dict = generate_outcome_dict( + n_qubits=len(shots_postselect[0]), shots_postselect=shots_postselect, dim=2 + ) + shot_distribution_full = ShotDistribution( + shots_postselect, outcome_dict=outcome_dict + ) + outcomes = np.array(shot_distribution_full.outcomes) + x_vals = outcomes[:, 0] + y_vals = outcomes[:, 1] + + # Marginal densities + outcome_dict = generate_outcome_dict( + n_qubits=len(shots_postselect_0[0]), shots_postselect=shots_postselect_0, dim=1 + ) + + shot_distribution_x = ShotDistribution( + shots_postselect_0, outcome_dict=outcome_dict + ) + outcome_density_x = shot_distribution_x.outcome_density + + shot_distribution_y = ShotDistribution( + shots_postselect_1, outcome_dict=outcome_dict + ) + outcome_density_y = shot_distribution_y.outcome_density + + density_raw_x = DiscreteDensity( + np.array(list(outcome_density_x.values())), + coordinates=[np.array(list(outcome_density_x.keys()))], + normalize=False, + ) + density_raw_y = DiscreteDensity( + np.array(list(outcome_density_y.values())), + coordinates=[np.array(list(outcome_density_y.keys()))], + normalize=False, + ) + + #### KDE cross validation sklearn + bandwidths = 10 ** np.linspace(-3, 0, 50) + outcomes_x = np.array(shot_distribution_x.outcomes) + loo = LeaveOneOut() + loo.get_n_splits(outcomes_x) + grid_x = GridSearchCV( + KernelDensity(kernel="gaussian"), {"bandwidth": bandwidths}, cv=loo + ) + kdr_estimate_x = grid_x.fit(outcomes_x[:, None]) + outcomes_y = np.array(shot_distribution_y.outcomes) + loo = LeaveOneOut() + loo.get_n_splits(outcomes_y) + grid_y = GridSearchCV( + KernelDensity(kernel="gaussian"), {"bandwidth": bandwidths}, cv=loo + ) + kdr_estimate_y = grid_y.fit(outcomes_y[:, None]) + kdr_estimate_x = grid_x.best_estimator_ + kdr_estimate_y = grid_y.best_estimator_ + + x_d = np.linspace(0, 1, len(outcome_density_x.values())) + y_d = np.linspace(0, 1, len(outcome_density_y.values())) + probs_x = np.exp(kdr_estimate_x.score_samples(x_d[:, None])) + probs_y = np.exp(kdr_estimate_y.score_samples(y_d[:, None])) + + density_kde_x = DiscreteDensity(np.sqrt(probs_x), coordinates=[x_d]) + density_kde_y = DiscreteDensity(np.sqrt(probs_y), coordinates=[y_d]) + density_raw_kde_x = DiscreteDensity( + probs_x / np.sum(probs_x), + coordinates=[x_d], + normalize=False, + ) + density_raw_kde_y = DiscreteDensity( + probs_y / np.sum(probs_x), + coordinates=[y_d], + normalize=False, + ) + + #### KDE cross validation sklearn of 2D distribution + bandwidths = 10 ** np.linspace(-3, 0, 50) + outcomes_reduced = outcomes[:] + loo = LeaveOneOut() + loo.get_n_splits(outcomes_reduced) + grid_full = GridSearchCV( + KernelDensity(kernel="gaussian"), {"bandwidth": bandwidths}, cv=loo + ) + kdr_estimate_full = grid_full.fit(outcomes_reduced) + kdr_estimate_full = grid_full.best_estimator_ + + x_d = np.linspace(0, 1, len(outcome_density_x.values())) + y_d = np.linspace(0, 1, len(outcome_density_y.values())) + xx, yy = np.meshgrid(x_d, y_d) + pos = np.stack([xx, yy], axis=-1).reshape((-1, 2)) + probs_full = np.exp( + kdr_estimate_full.score_samples(pos).reshape((x_d.shape[0], y_d.shape[0])) + ) + probs_full = np.sqrt(probs_full) + probs_full = probs_full / np.sum(probs_full) + + x_d_coarse = np.linspace(0, 1, len(outcome_density_x.values()) // 4) + y_d_coarse = np.linspace(0, 1, len(outcome_density_y.values()) // 4) + xx, yy = np.meshgrid(x_d_coarse, y_d_coarse) + pos = np.stack([xx, yy], axis=-1).reshape((-1, 2)) + probs_full_coarse = np.exp( + kdr_estimate_full.score_samples(pos).reshape( + (x_d_coarse.shape[0], y_d_coarse.shape[0]) + ) + ) + probs_full_coarse = np.sqrt(probs_full_coarse) + probs_full_coarse = probs_full_coarse / np.sum(probs_full_coarse) + density_kde_full_coarse = DiscreteDensity( + probs_full_coarse.transpose(), coordinates=[x_d_coarse, y_d_coarse] + ) + + density_switcher = { + "density_kde_full_coarse": density_kde_full_coarse.data.transpose(), + "density_kde_x": density_kde_x.data, + "density_raw_x": density_raw_x.data, + "density_raw_kde_x": density_raw_kde_x.data, + "density_kde_y": density_kde_y.data, + "density_raw_y": density_raw_y.data, + "density_raw_kde_y": density_raw_kde_y.data, + "density_ground_truth_fourier_x": density_ground_truth_fourier_x.data, + "density_ground_truth_fourier_y": density_ground_truth_fourier_y.data, + "density_x_coordinates": density_raw_x.coordinates[0], + "density_y_coordinates": density_raw_y.coordinates[0], + "density_full_coarse_coordinates": density_kde_full_coarse.coordinates, + "outcomes_x": x_vals, + "outcomes_y": y_vals, + } + + folder_path = "../data/hardware_experiment/density_data/" + os.makedirs(folder_path, exist_ok=True) + + print("Save data") + for key, value in density_switcher.items(): + save_path = os.path.join(folder_path, f"{key}_n{n}_c{c}_rho{rho}.pkl") + with open(save_path, "wb") as file: + pickle.dump(value, file) + + +# Hardware: N=9, c=7, rho=0.0 +print("Generate and save data for N=9, c=7, rho=0.0") +rho = 0.0 +generate_data(rho=rho) + +# Hardware: N=9, c=7, rho=0.4 +print("Generate and save data for N=9, c=7, rho=0.4") +rho = 0.4 +generate_data(rho=rho) diff --git a/scripts/hardware_experiment_2d_gaussian_plot.py b/scripts/hardware_experiment_2d_gaussian_plot.py new file mode 100644 index 0000000..4485a7b --- /dev/null +++ b/scripts/hardware_experiment_2d_gaussian_plot.py @@ -0,0 +1,320 @@ +"""Plot hardware experiment results.""" + +import os +import pickle + +import numpy as np +from matplotlib import pyplot as plt +from matplotlib.ticker import ScalarFormatter + +plt.style.use("../plots/paper.mplstyle") +color_cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"] + + +def plot_colorbar(plot, fig, ax): + cb_formatter = ScalarFormatter(useMathText=True) + cb_formatter.set_scientific("%.2e") + cb_formatter.set_powerlimits((-2, 2)) + cb = fig.colorbar( + plot, ax=ax, format=cb_formatter, location="right", shrink=1.0, pad=-0.06 + ) + cb.ax.yaxis.set_offset_position("left") + cb.ax.tick_params(labelsize="small") + cb.ax.yaxis.get_offset_text().set(size="small") + + +n = 9 +c = 7 +mu_x = 0.5 +mu_y = 0.5 +s_x = 0.22 +s_y = 0.18 + +density_switcher = { + "density_kde_full_coarse", + "density_kde_x", + "density_raw_x", + "density_raw_kde_x", + "density_kde_y", + "density_raw_y", + "density_raw_kde_y", + "density_ground_truth_fourier_x", + "density_ground_truth_fourier_y", + "density_x_coordinates", + "density_y_coordinates", + "density_full_coarse_coordinates", + "outcomes_x", + "outcomes_y", +} + +folder_path = "../data/hardware_experiment/density_data/" + +# Import data for uncorrelated Gaussian +rho = 0.0 + +density_dict = {} +print("Load data") +for key in density_switcher: + load_path = os.path.join(folder_path, f"{key}_n{n}_c{c}_rho{rho}.pkl") + with open(load_path, "rb") as file: + density_dict[key] = pickle.load(file) + + +# Plot uncorrelated Gaussian +fig = plt.figure(figsize=[6.72, 2.42], constrained_layout=True) +gs0 = fig.add_gridspec(2, 8) +gs1 = gs0[:, :2].subgridspec(2, 1) +ax_raw_2D = fig.add_subplot(gs1[0, 0]) +ax_kde_2D = fig.add_subplot(gs1[1, 0]) +ax_raw_marginals = fig.add_subplot(gs0[:, 2:5]) +ax_truth_marginals = fig.add_subplot(gs0[:, 5:8]) + + +cmesh = ax_raw_2D.hist2d( + density_dict["outcomes_x"], density_dict["outcomes_y"], bins=20, cmap="Reds" +) +plot_colorbar(cmesh[3], fig, ax_raw_2D) +ax_raw_2D.set_title("Raw data") +ax_raw_2D.set_xticks([0.0, 0.5, 1.0]) +ax_raw_2D.set_yticks([0.0, 0.5, 1.0]) +ax_raw_2D.set_ylabel("y") +ax_raw_2D.set_aspect("equal") + +cmesh = ax_kde_2D.pcolormesh( + *np.meshgrid(*density_dict["density_full_coarse_coordinates"]), + density_dict["density_kde_full_coarse"], + cmap="Reds", +) +plot_colorbar(cmesh, fig, ax_kde_2D) +ax_kde_2D.set_title(r"$\hat{\mathfrak{f}}_d(x,y)$") +ax_kde_2D.set_xticks([0.0, 0.5, 1.0]) +ax_kde_2D.set_yticks([0.0, 0.5, 1.0]) +ax_kde_2D.set_xlabel("x") +ax_kde_2D.set_ylabel("y") +ax_kde_2D.set_aspect("equal") + +ax_raw_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_raw_x"], + label=r"$w_x(x)$", + color=color_cycle[0], + alpha=0.35, + linestyle="solid", + linewidth=0.5, +) +ax_raw_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_raw_kde_x"], + label=r"$k_x(x)$", + color=color_cycle[0], + linestyle="solid", +) +ax_raw_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_raw_y"], + label=r"$w_y(y)$", + color=color_cycle[1], + alpha=0.35, + linestyle="solid", + linewidth=0.5, +) +ax_raw_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_raw_kde_y"], + label=r"$k_y(y)$", + color=color_cycle[1], + linestyle="solid", +) + +ax_raw_marginals.set_title("Marginal kernel density estimates") +ax_raw_marginals.set_xlabel("x, y") +ax_raw_marginals.set_ylabel(r"probability [1e-2]") +ax_raw_marginals.set_yticks([0.0 * 1e-2, 0.5 * 1e-2, 1.0 * 1e-2], [0.0, 0.5, 1.0]) +ax_raw_marginals.legend(loc=1) + +labelstr = r"$\hat{\mathfrak{f}}_{d,x}(x)$" + +ax_truth_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_kde_x"], + label=labelstr, + color=color_cycle[0], +) +labelstr = "\n".join((r"$\mathfrak{f}_{d,x}(x)$",)) +ax_truth_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_ground_truth_fourier_x"], + label=labelstr, + color=color_cycle[0], + linestyle="dashed", +) + + +labelstr = r"$\hat{\mathfrak{f}}_{d,y}(y)$" +ax_truth_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_kde_y"], + label=labelstr, + color=color_cycle[1], +) +labelstr = "\n".join((r"$\mathfrak{f}_{d,y}(y)$",)) +ax_truth_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_ground_truth_fourier_y"], + label=labelstr, + color=color_cycle[1], + linestyle="dashed", +) +ax_truth_marginals.set_title("Comparison to target") +ax_truth_marginals.set_xlabel("x, y") +ax_truth_marginals.set_ylabel("density") +ax_truth_marginals.legend(loc=1) + +title_string = "".join(("Uncorrelated 2D Gaussian",)) +fig.suptitle(title_string, fontsize=9) +fig.text(x=0.03, y=0.92, s="(a)") +fig.text(x=0.25, y=0.92, s="(b)") +fig.text(x=0.65, y=0.92, s="(c)") + +file_path = f"../plots/2D_gauss_fourier_n{n}_c{c}_mu-{mu_x}-{mu_y}_sigma-{s_x}-{s_y}_rho-{rho}_hardware.pdf" +fig.savefig( + file_path, transparent=False, bbox_inches="tight", pad_inches="layout", dpi=600 +) + + +# ====================================================================================== + +# Import data for correlated Gaussian +rho = 0.4 + +density_dict = {} +print("Load data") +for key in density_switcher: + load_path = os.path.join(folder_path, f"{key}_n{n}_c{c}_rho{rho}.pkl") + with open(load_path, "rb") as file: + density_dict[key] = pickle.load(file) + + +# Plot correlated Gaussian +fig = plt.figure(figsize=[6.72, 2.42], constrained_layout=True) + + +gs0 = fig.add_gridspec(2, 8) +gs1 = gs0[:, :2].subgridspec(2, 1) +ax_raw_2D = fig.add_subplot(gs1[0, 0]) +ax_kde_2D = fig.add_subplot(gs1[1, 0]) +ax_raw_marginals = fig.add_subplot(gs0[:, 2:5]) +ax_truth_marginals = fig.add_subplot(gs0[:, 5:8]) + + +cmesh = ax_raw_2D.hist2d( + density_dict["outcomes_x"], density_dict["outcomes_y"], bins=20, cmap="Reds" +) +plot_colorbar(cmesh[3], fig, ax_raw_2D) +ax_raw_2D.set_title("Raw data") +ax_raw_2D.set_xticks([0.0, 0.5, 1.0]) +ax_raw_2D.set_yticks([0.0, 0.5, 1.0]) +ax_raw_2D.set_ylabel("y") +ax_raw_2D.set_aspect("equal") + +cmesh = ax_kde_2D.pcolormesh( + *np.meshgrid(*density_dict["density_full_coarse_coordinates"]), + density_dict["density_kde_full_coarse"], + cmap="Reds", +) +plot_colorbar(cmesh, fig, ax_kde_2D) +# fig.colorbar(cmesh, ax=ax_kde_2D) +ax_kde_2D.set_title(r"$\hat{\mathfrak{f}}_d(x,y)$") +ax_kde_2D.set_xticks([0.0, 0.5, 1.0]) +ax_kde_2D.set_yticks([0.0, 0.5, 1.0]) +ax_kde_2D.set_xlabel("x") +ax_kde_2D.set_ylabel("y") +ax_kde_2D.set_aspect("equal") + +ax_raw_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_raw_x"], + label=r"$w_x(x)$", + color=color_cycle[0], + alpha=0.35, + linestyle="solid", + linewidth=0.5, +) +ax_raw_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_raw_kde_x"], + label=r"$k_x(x)$", + color=color_cycle[0], + linestyle="solid", +) +ax_raw_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_raw_y"], + label=r"$w_y(y)$", + color=color_cycle[1], + alpha=0.35, + linestyle="solid", + linewidth=0.5, +) +ax_raw_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_raw_kde_y"], + label=r"$k_y(y)$", + color=color_cycle[1], + linestyle="solid", +) +ax_raw_marginals.set_title("Marginal kernel density estimates") +ax_raw_marginals.set_xlabel("x, y") +ax_raw_marginals.set_ylabel(r"probability [1e-2]") +ax_raw_marginals.set_yticks( + [0.0 * 1e-2, 0.3 * 1e-2, 0.6 * 1e-2, 0.9 * 1e-2, 1.2 * 1e-2], + [0.0, 0.3, 0.6, 0.9, 1.2], +) +ax_raw_marginals.legend(loc=1) + +labelstr = r"$\hat{\mathfrak{f}}_{d,x}(x)$" +ax_truth_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_kde_x"], + label=labelstr, + color=color_cycle[0], +) +labelstr = "\n".join((r"$\mathfrak{f}_{d,x}(x)$",)) +ax_truth_marginals.plot( + density_dict["density_x_coordinates"], + density_dict["density_ground_truth_fourier_x"], + label=labelstr, + color=color_cycle[0], + linestyle="dashed", +) + +labelstr = r"$\hat{\mathfrak{f}}_{d,y}(y)$" +ax_truth_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_kde_y"], + label=labelstr, + color=color_cycle[1], +) +labelstr = "\n".join((r"$\mathfrak{f}_{d,y}(y)$",)) +ax_truth_marginals.plot( + density_dict["density_y_coordinates"], + density_dict["density_ground_truth_fourier_y"], + label=labelstr, + color=color_cycle[1], + linestyle="dashed", +) +ax_truth_marginals.set_title("Comparison to target") +ax_truth_marginals.set_xlabel("x, y") +ax_truth_marginals.set_ylabel("density") +ax_truth_marginals.legend(loc=1) + +title_string = "".join(("Correlated 2D Gaussian",)) +fig.suptitle(title_string, fontsize=9) +fig.text(x=0.03, y=0.92, s="(d)") +fig.text(x=0.25, y=0.92, s="(e)") +fig.text(x=0.65, y=0.92, s="(f)") + +file_path = f"../plots/2D_gauss_fourier_n{n}_c{c}_mu-{mu_x}-{mu_y}_sigma-{s_x}-{s_y}_rho-{rho}_hardware.pdf" +fig.savefig( + file_path, transparent=False, bbox_inches="tight", pad_inches="layout", dpi=600 +)