You down with plt (yeah you know me) [Issue 97]

examples/tomography_process.ipynb

@@ -130,13 +130,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'process_choi_ideal' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-cb314dff65b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplot_pauli_transfer_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoi2pauli_liouville\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_choi_ideal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Ideal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplot_pauli_transfer_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoi2pauli_liouville\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_choi_est\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Estimate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'process_choi_ideal' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12c740240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from forest.benchmarking.superoperator_tools import choi2pauli_liouville\n",
-    "from forest.benchmarking.tomography import plot_pauli_transfer_matrix\n",
+    "from forest.benchmarking.plotting.state_process import plot_pauli_transfer_matrix\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5))\n",
     "plot_pauli_transfer_matrix(choi2pauli_liouville(process_choi_ideal), ax1, title='Ideal')\n",

forest/benchmarking/plotting/hinton.py

@@ -1,11 +1,14 @@
-import matplotlib.pyplot as plt
 import numpy as np
+from typing import List
+import matplotlib.pyplot as plt
 from matplotlib import cm
+import matplotlib as mpl
 from matplotlib.colors import Normalize
 
 ANGLE_MAPPER = cm.ScalarMappable(norm=Normalize(vmin=-np.pi, vmax=np.pi))
 
 
+# Modified from the SciPy Cookbook.
 def hinton(matrix, max_weight=1.0, ax=None):
     """Draw Hinton diagram for visualizing a weight matrix."""
     ax = ax if ax is not None else plt.gca()
@@ -30,3 +33,96 @@ def hinton(matrix, max_weight=1.0, ax=None):
     ax.set_ylim((-max_weight / 2, matrix.shape[1] - max_weight / 2))
     ax.autoscale_view()
     ax.invert_yaxis()
+
+
+
+# From QuTiP which in turn modified the code from the SciPy Cookbook.
+def _blob(x, y, w, w_max, area, cmap=None):
+    """
+    Draws a square-shaped blob with the given area (< 1) at the given coordinates.
+    """
+    hs = np.sqrt(area) / 2
+    xcorners = np.array([x - hs, x + hs, x + hs, x - hs])
+    ycorners = np.array([y - hs, y - hs, y + hs, y + hs])
+
+    plt.fill(xcorners, ycorners, color=cmap(int((w + w_max) * 256 / (2 * w_max))))
+
+
+# Modified from QuTip (see https://bit.ly/2LrbayH ) which in turn modified the code from the
+# SciPy Cookbook.
+def hinton_real(matrix: np.ndarray,
+                max_weight: float = None,
+                xlabels: List[str] = None,
+                ylabels: List[str] = None,
+                title: str = None,
+                ax=None,
+                cmap=None,
+                label_top: bool = True):
+    '''
+    Draw Hinton diagram for visualizing a real valued weight matrix.
+
+    In the traditional Hinton diagram positive and negative values are represented by white and
+    black squares respectively. The size of each square represents the magnitude of each value.
+    The traditional Hinton diagram can be recovered by setting cmap = cm.Greys_r.
+
+    :param matrix: The matrix to be visualized.
+    :param max_weight: normalize size to this scalar.
+    :param xlabels: The labels for the operator basis.
+    :param ylabels: The labels for the operator basis.
+    :param title: The title for the plot.
+    :param ax: The matplotlib axes.
+    :param cmap: A matplotlib colormap to use when plotting.
+    :param label_top: If True, x-axis labels will be placed on top, otherwise they will appear
+    below the plot.
+    :return: A tuple of the matplotlib figure and axes instances used to produce the figure.
+    '''
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=(8, 6))
+    if cmap is None:
+        cmap = cm.RdBu
+
+    if title and fig:
+        ax.set_title(title,y=1.1)
+
+    ax.set_aspect('equal', 'box')
+    ax.set_frame_on(False)
+
+    height, width = matrix.shape
+    if max_weight is None:
+        max_weight = 1.25 * max(abs(np.diag(np.matrix(matrix))))
+        if max_weight <= 0.0:
+            max_weight = 1.0
+
+    ax.fill(np.array([0, width, width, 0]), np.array([0, 0, height, height]), color=cmap(128))
+    for x in range(width):
+        for y in range(height):
+            _x = x + 1
+            _y = y + 1
+            if np.real(matrix[x, y]) > 0.0:
+                _blob(_x - 0.5, height - _y + 0.5, abs(matrix[x, y]), max_weight,
+                      min(1, abs(matrix[x, y]) / max_weight), cmap=cmap)
+            else:
+                _blob(_x - 0.5, height - _y + 0.5, -abs(matrix[x, y]), max_weight,
+                      min(1, abs(matrix[x, y]) / max_weight), cmap=cmap)
+
+    # color axis
+    if max_weight is None:
+        norm = mpl.colors.Normalize(-np.abs(matrix).max(), np.abs(matrix).max())
+    else:
+        norm = mpl.colors.Normalize(-np.abs(max_weight), max_weight)
+    cax, kw = mpl.colorbar.make_axes(ax, shrink=0.75, pad=.1)
+    mpl.colorbar.ColorbarBase(cax, norm=norm, cmap=cmap)
+    # x axis
+    ax.xaxis.set_major_locator(plt.IndexLocator(1, 0.5))
+    if xlabels:
+        ax.set_xticklabels(xlabels)
+        if label_top:
+            ax.xaxis.tick_top()
+    ax.tick_params(axis='x', labelsize=14)
+    # y axis
+    ax.yaxis.set_major_locator(plt.IndexLocator(1, 0.5))
+    if ylabels:
+        ax.set_yticklabels(list(reversed(ylabels)))
+    ax.tick_params(axis='y', labelsize=14)
+
+    return fig, ax

forest/benchmarking/plotting/state_process.py

@@ -0,0 +1,119 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.colors import LinearSegmentedColormap
+
+THREE_COLOR_MAP = ['#48737F', '#FFFFFF', '#D6619E']
+rigetti_3_color_cm = LinearSegmentedColormap.from_list("Rigetti", THREE_COLOR_MAP[::-1], N=100)
+
+
+def plot_pauli_rep_of_state(state_pl_basis, ax, labels, title):
+    """
+    Visualize a quantum state in the Pauli-Liouville basis.
+
+    :param numpy.ndarray state_pl_basis: The quantum state represented in the Pauli-Liouville basis.
+    :param ax: The matplotlib axes.
+    :param labels: The labels for the operator basis states.
+    :param title: The title for the plot.
+
+    Examples
+    --------
+    from forest.benchmarking.superoperator_tools import *
+    from forest.benchmarking.utils import n_qubit_pauli_basis
+    # zero state in the (Z) computational basis
+    rho_std_basis = np.array([[1, 0], [0, 0]])
+    # change to Pauli-Liouville basis
+    n_qubits = 1
+    pl_basis = n_qubit_pauli_basis(n_qubits)
+    c2p = computational2pauli_basis_matrix(2*n_qubits)
+    rho_pl_basis = np.real(c2p@vec(rho_std_basis))
+    # plot
+    fig, ax = plt.subplots(1)
+    plot_pauli_rep_of_state(rho_pl_basis, ax, pl_basis.labels, 'Zero state |0>')
+    """
+    if len(state_pl_basis.shape) == 1:
+        raise ValueError("You must pass in a (N by 1) or a (1 by N) numpy.ndarray")
+    if np.iscomplexobj(state_pl_basis):
+        raise ValueError("You must pass in a real vector")
+
+    im = ax.imshow(state_pl_basis, interpolation="nearest", cmap="RdBu", vmin=-1 / 2, vmax=1 / 2)
+    dim = len(labels)
+    # make the colorbar ticks look pretty
+    rows, cols = state_pl_basis.shape
+    if rows > cols:
+        cb = plt.colorbar(im, ax=ax, ticks=[-1 / 2, -1 / 4, 0, 1 / 4, 1 / 2])
+        ticklabs = cb.ax.get_yticklabels()
+        cb.ax.set_yticklabels(ticklabs, ha='right')
+        cb.ax.yaxis.set_tick_params(pad=35)
+        # axis labels etc
+        ax.set_xlabel("Coefficient")
+        ax.set_xticks([])
+        ax.set_yticks(range(dim))
+        ax.set_ylabel("Pauli Operator")
+        ax.set_yticklabels(labels)
+    else:
+        cb = plt.colorbar(im, ax=ax, ticks=[-1 / 2, -1 / 4, 0, 1 / 4, 1 / 2],
+                          orientation="horizontal",
+                          pad=0.22)
+        ax.set_ylabel("Coefficient")
+        ax.set_yticks([])
+        ax.set_xticks(range(dim))
+        ax.set_xlabel("Pauli Operator")
+        ax.set_xticklabels(labels)
+    ax.set_title(title)
+    ax.grid(False)
+
+
+def plot_pauli_bar_rep_of_state(state_pl_basis, ax, labels, title):
+    """
+    Visualize a quantum state in the Pauli-Liouville basis. The magnitude of the operator
+    coeffiences are represent by the height of a bar in the bargraph.
+
+    :param numpy.ndarray state_pl_basis: The quantum state represented in the Pauli-Liouville basis.
+    :param ax: The matplotlib axes.
+    :param labels: The labels for the operator basis states.
+    :param title: The title for the plot.
+    """
+    dim = len(labels)
+    im = ax.bar(np.arange(dim) - .4, np.real(state_pl_basis), width=.8)
+    ax.set_xticks(range(dim))
+    ax.set_xlabel("Pauli Operator")
+    ax.set_ylabel("Coefficient")
+    ax.set_title(title)
+    ax.set_xticklabels(labels, rotation=45)
+    ax.grid(False)
+
+
+def plot_pauli_transfer_matrix(ptransfermatrix, ax, labels=None, title='', fontsizes: int = 16):
+    """
+    Visualize a quantum process using the Pauli-Liouville representation (aka the Pauli Transfer
+    Matrix) of the process.
+
+    :param numpy.ndarray ptransfermatrix: The Pauli Transfer Matrix
+    :param ax: The matplotlib axes.
+    :param labels: The labels for the operator basis states.
+    :param title: The title for the plot
+    :param fontsizes: Font size for axis labels
+    :return: The modified axis object.
+    :rtype: AxesSubplot
+    """
+    im = ax.imshow(ptransfermatrix, interpolation="nearest", cmap="RdBu", vmin=-1, vmax=1)
+    if labels is None:
+        dim_squared = ptransfermatrix.shape[0]
+        num_qubits = np.int(np.log2(np.sqrt(dim_squared)))
+        labels = [''.join(x) for x in itertools.product('IXYZ', repeat=num_qubits)]
+    else:
+        dim_squared = len(labels)
+
+    cb = plt.colorbar(im, ax=ax, ticks=[-1, -3 / 4, -1 / 2, -1 / 4, 0, 1 / 4, 1 / 2, 3 / 4, 1])
+    ticklabs = cb.ax.get_yticklabels()
+    cb.ax.set_yticklabels(ticklabs, ha='right')
+    cb.ax.yaxis.set_tick_params(pad=35)
+    ax.set_xticks(range(dim_squared))
+    ax.set_xlabel("Input Pauli Operator", fontsize=fontsizes)
+    ax.set_yticks(range(dim_squared))
+    ax.set_ylabel("Output Pauli Operator", fontsize=fontsizes)
+    ax.set_title(title, fontsize= int(np.floor(1.2*fontsizes)))
+    ax.set_xticklabels(labels, rotation=45, fontsize=int(np.floor(0.7*fontsizes)))
+    ax.set_yticklabels(labels, fontsize=int(np.floor(0.7*fontsizes)))
+    ax.grid(False)
+    return ax

forest/benchmarking/tomography.py

@@ -4,9 +4,7 @@
 from operator import mul
 from typing import Callable, Tuple, List, Optional, Union, Sequence
 
-import matplotlib.pyplot as plt
 import numpy as np
-from matplotlib.colors import LinearSegmentedColormap
 from scipy.linalg import logm, pinv, eigh
 
 import forest.benchmarking.distance_measures as dm
@@ -801,33 +799,3 @@ def estimate_variance(results: List[ExperimentResult],
             sample_estimate.append(np.real(functional(target_state, rho)))
 
     return np.mean(sample_estimate), np.var(sample_estimate)
-
-
-THREE_COLOR_MAP = ['#48737F', '#FFFFFF', '#D6619E']
-rigetti_3_color_cm = LinearSegmentedColormap.from_list("Rigetti", THREE_COLOR_MAP[::-1], N=100)
-
-
-def plot_pauli_transfer_matrix(ptransfermatrix, ax, title = ''):
-    """
-    Visualize the Pauli Transfer Matrix of a process.
-    :param numpy.ndarray ptransfermatrix: The Pauli Transfer Matrix
-    :param ax: The matplotlib axes.
-    :param labels: The labels for the operator basis states.
-    :param title: The title for the plot
-    :return: The modified axis object.
-    :rtype: AxesSubplot
-    """
-    im = ax.imshow(np.real(ptransfermatrix), interpolation="nearest", cmap=rigetti_3_color_cm, vmin=-1,vmax=1)
-    dim_squared = ptransfermatrix.shape[0]
-    num_qubits = np.int(np.log2(np.sqrt(dim_squared)))
-    labels = [''.join(x) for x in itertools.product('IXYZ', repeat=num_qubits)]
-    plt.colorbar(im, ax=ax)
-    ax.set_xticks(range(dim_squared))
-    ax.set_xlabel("Input Pauli Operator", fontsize=20)
-    ax.set_yticks(range(dim_squared))
-    ax.set_ylabel("Output Pauli Operator", fontsize=20)
-    ax.set_title(title, fontsize=25)
-    ax.set_xticklabels(labels, rotation=45)
-    ax.set_yticklabels(labels)
-    ax.grid(False)
-    return ax