diff --git a/docs/tutorials/circuits_advanced/06_building_pulse_schedules.ipynb b/docs/tutorials/circuits_advanced/06_building_pulse_schedules.ipynb
index 20ba9aa7d2e6..c567d76de14b 100644
--- a/docs/tutorials/circuits_advanced/06_building_pulse_schedules.ipynb
+++ b/docs/tutorials/circuits_advanced/06_building_pulse_schedules.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:49.766612Z",
@@ -31,7 +31,7 @@
        "ScheduleBlock(, name=\"my_example\", transform=AlignLeft())"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:50.235098Z",
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:50.243572Z",
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:50.460776Z",
@@ -185,7 +185,7 @@
     "\n",
     "### Pulses\n",
     "\n",
-    "A `Pulse` specifies an arbitrary pulse _envelope_. The modulation frequency and phase of the output waveform are controlled by the `set_frequency` and `shift_phase` instructions, which we will cover next.\n",
+    "A `Pulse` specifies an arbitrary pulse _envelope_. The modulation frequency and phase of the output waveform are tied not to the pulse object, but rather to the `Channel` in which the pulse is played. The `Channel`'s frequency and phase are controlled by the `set_frequency` and `shift_phase` instructions, which we will cover next.\n",
     "\n",
     "The image below may provide some intuition for why they are specified separately. Think of the pulses which describe their envelopes as input to an arbitrary waveform generator (AWG), a common lab instrument -- this is depicted in the left image. Notice the limited sample rate discritizes the signal. The signal produced by the AWG may be mixed with a continuous sine wave generator. The frequency of its output is controlled by instructions to the sine wave generator; see the middle image. Finally, the signal sent to the qubit is demonstrated by the right side of the image below.\n",
     "\n",
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:50.468209Z",
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:50.474488Z",
@@ -245,12 +245,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1300x165 with 1 Axes>"
+       "<Figure size 936x118.8 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:51.432148Z",
@@ -284,12 +284,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBcAAADeCAYAAABmFOheAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABOV0lEQVR4nO3dd1xT1/8/8FfAEMJeIooKioM6AHFPcCFqVdSqdbS4ap1trbNDq/bTOuooVb/aqnXV1bqLq1oB98RBq6KoKAoKKhtknt8f/JISk0AgDNHX8/Ggj3LOPfe+780N5r5zhkQIIUBEREREREREVEwG5R0AEREREREREVVsTC4QERERERERkV6YXCAiIiIiIiIivTC5QERERERERER6YXKBiIiIiIiIiPTC5AIRERERERER6YXJBSIiIiIiIiLSC5MLRERERERERKQXJheIiIiIiIiISC9FTi5IJBJIJBJYWVkhISFB4zYLFiyARCLBnDlz9AyvYrpy5QokEgkcHR011ufm5sLKygoSiQRjxozRuM2JEycgkUjQsGFDZdmGDRuU11/bz4YNG4oUqxACu3btwqBBg+Ds7AwTExPI5XI4OzvDz88Pa9asQVJSUpH2WZEMHz5c5foZGBjA0tISzs7O6NWrFxYtWoSnT5+Wd5glQiKRwNnZubzDKJJnz55h3bp1GDNmDDw8PFCpUqVC7/MHDx5g+fLl8PX1hYODA6RSKezs7ODr64v9+/cXeLyjR4+iZ8+eqFy5MqRSKWxtbeHj44M9e/YUK/5///0XAwYMQOXKlSGXy9G4cWP8+OOPyM3NLdb+tKmIry0RERERvVkqFbdhYmIili5dinnz5pVkPG8Ed3d3WFhYIDo6Gvfu3UPt2rVV6sPCwpCYmAgAOHXqlMZ9nDx5EgDQvn17tToXFxe0a9dOY7s6deroHGd0dDT69euH8+fPQyKRwN3dHc2aNYOhoSEePXqEQ4cOYd++fZg5cyZOnz4NV1dXnfdd0bRt21Z57VJTUxETE4O///4bgYGBmDVrFubNm4fp06dDIpGUc6SaBQcHo2PHjvD39y9ygul1durUKYwePbpIbYYOHYrTp09DJpOhVatWcHBwwL1793DkyBEcOXIEkydPxtKlS9Xa/fjjj5g8eTIkEglat26NGjVqICoqCseOHcPRo0fx5Zdf4rvvvtM5jrNnz6Jz585IT09HixYt4OzsjBMnTmDy5Mk4c+YMduzYUar304YNGzBixAh88803b22il4iIiIjKTrGSCxKJBDKZDAEBAZg8eTKsra1LOq4KzcDAAG3atMHhw4dx6tQpteSCInHg7u6O69ev4/nz57C1tdW4jabkQrt27fR+gExKSoKXlxciIiLQo0cPLF++XC3OlJQUrFu3Dv/73//w7NkzvY73uhs9ejSGDx+uUpaeno61a9di5syZmDlzJhITE/H999+XT4Al4ObNm5BKpeUdRpFUqVIF48ePR7NmzdC8eXP89NNPWLNmTYFtqlevjuXLl8Pf3x/m5ubK8gMHDsDPzw/Lli2Dr68vfHx8lHVxcXGYOXMmpFIpjh49Ci8vL2XdiRMn4OPjg/nz52PUqFFq7xNNsrKyMHToUKSnp2Pp0qWYPHkygLz3lI+PD/744w/06NFD7Z4jIiIiIqqoijXngoGBAcaMGYOkpCQsXry4pGN6IyiSApp6Jpw6dQpSqRSfffYZhBA4ffq0Sn1ubi7Onj2rsp+SNn36dERERKBbt27Yv3+/xgcmMzMzfPrppwgLC9PpgepNI5fLMWnSJBw4cACGhoaYP38+rl27Vt5hFZurqytcXFzKO4wiad26NVauXIkRI0agUaNGMDAo/E/W9u3bMXHiRJXEAgD07NkTI0eOBABs27ZNpe78+fPIyMhAp06dVBILANChQwd069YNQghcunRJp7j37NmD+/fvw93dXZlYAPLeUytWrAAALFmyRKd9ERERERFVBMWe0HHmzJmQy+VYvnw5nj9/rlObmJgYLFq0CF5eXnB0dISRkREcHBzQr18/XLx4UWMbZ2dnZdfhlStXolGjRpDL5ahVqxYWLVoEIQQAIDQ0FL169YKNjQ3MzMzQp08fPHjwQOM+hRDYtm0bOnXqBGtraxgbG+Odd97BnDlzkJaWVoyroU6RFFD0QMjv5MmT8PT0RNeuXTVuc+3aNSQlJaFmzZqoWbNmicST37Nnz5TzN/z0008wNDQscHsHBwdUq1ZNpSwiIgJz5sxB69at4eDgACMjI1SvXh0ffvghbt++rbaPyMhISCQSeHt7azzGnDlzNI6lV3yj3KBBA5iZmcHS0hL16tXDhx9+iAsXLhTpvIvL29sbgwcPBgAsX75cpS7//fmq4OBgSCQStW+nFfM8BAcH48iRI+jYsaNyDg7FPCYnT57ExIkT4ebmBmtra8jlcri6umLmzJlqc50MHz4cHTt2BABs3LhRZQ6J/N3hCxqXf/DgQXTt2lX5fqhfv77GYwGqr1VYWBh69+4Na2trmJqawsvLC2fOnNF8IV8D7u7uAPKGBOUnk8l0av9qDyNtDhw4AAB477331Oo8PT1Ru3Zt/PPPP4iMjNRpfwDw4sULTJw4EdWqVYOxsTEaNGiAgIAA5d/A/Ly9vTFixAgAwNy5c/Wal4WIiIiISBfFTi5UrVoVY8eORXJyMn744Qed2uzbtw8zZszA06dP4ebmhr59+6JatWrYs2cP2rZti7/++ktr28mTJ2PatGlwcnJCly5d8Pz5c8yYMQNz5szB6dOn0b59e0RHR6Nr166oWrUq9u/frxzvnF9ubi6GDh2KIUOG4OLFi/Dw8ECPHj2QmpqKuXPnomPHjmptFA/GRRkf3aJFC8hkMoSHh6sMKbh37x6io6PRrl07ODo6wsnJSa13Q0FDIkpCUFAQMjIy4OnpiXr16hVrH2vXrsW8efOQmpqK5s2bo3fv3rCwsMDmzZvRvHlzXL9+Xe84k5OT0bJlSyxcuBApKSno2rUrfHx8YG1tje3bt+PgwYN6H0NX77//PoC8a1dStm7diu7duyM1NRXdu3dH8+bNlffYtGnTsG7dOsjlcnTu3BmdO3dGUlISFi5ciHbt2iElJUW5n3bt2qFbt24A8ubj8Pf3V/54eHgUGsf8+fPRs2dPBAcHo2nTpvDz80NaWhoWLlyIli1bap3Q8tKlS2jVqhUiIyPRrVs31K1bFydOnEDnzp3xzz//qG3v7e1d7g+39+7dA5CXMMuvRYsWsLKywvHjxxESEqJSd+LECRw5cgR169bV+T2p6OHi6empsV5Rruv7JD4+Hu3atcPKlSshhECfPn3g6OiIqVOn4pNPPlHb3tfXF23btgWQl1DJf08UZV4WIiIiIiKdiSICIAwNDYUQQjx58kSYmJgIU1NTERsbq9xm/vz5AoD45ptvVNpev35d/PPPP2r7PHz4sDAyMhIuLi4iNzdXpc7JyUkAENWqVRMRERHK8ps3bwqZTCZMTEyEs7OzWLVqlbIuIyNDdOrUSQAQv/76q8r+Fi1aJAAIb29vERMTo9Jm1KhRAoCYMWOGSpv79+8LAKKol6tdu3YCgNi7d6+ybOPGjQKA2LNnjxBCiCFDhggjIyORlpam3GbAgAECgFi9erXK/tavXy8ACH9//yLF8aqvvvpKABCjR48u9j7Onj0r7t27p1b+66+/CgCiY8eOKuWKa+jl5aVxf998840AINavX6+2r969e4ucnByV7WNjY0VYWFix41fw9/dXO64mjx49Ut4DGRkZynLF/alJUFCQxtdLcUwAYvv27RrbHjx4UCQkJKiUvXz5UowZM0YAEHPnztXpWPkBEE5OTiplFy5cEAYGBsLMzEycO3dO5ViK+7B///4qbRSvFQAREBCgUvfZZ58JAOKDDz5QO76Xl5dO17ogH3/8cbH3ER8fLypXriwAiF27dqnV79q1S8hkMiGRSETbtm3FoEGDRNu2bYVEIhHt2rXTeL9rY21tLQCIa9euaaxXXKeffvpJp/2NHTtWABC+vr4iNTVVWX7+/HlhZmam8bVV/L149e8wEREREVFpKHbPBSBvsrVx48YhNTUVCxcuLHT7xo0bqyytqNCtWzcMGDAAd+/e1fiNJwDMmzdPZby4q6srevTogbS0NFSvXh1jx45V1hkZGeHTTz8FAJVvIbOzs7Fo0SKYmppi+/btKt9eGhkZYfny5XBwcMAvv/yislScVCpF/fr1Ub9+/ULPMT9NQyMU/6/4VrFt27bIzMzE+fPnldsoejJo+5b01a7vih9tQw5epRjGYmdnp7H+hx9+wPDhw1V+1q5dq7JNq1atUKtWLbW2I0aMQNu2bREcHKxcEaO44uLiAACdOnVSG2tfuXJlNGrUSK/9F0X+axUfH18i++zZsycGDRqksa579+6wtLRUKZPJZPjxxx9RqVIl7Nu3r0RiWLFiBXJzczFp0iS0bNlS5VgrVqyAXC7Hnj17EBUVpda2bdu2at+af/311wDyvu1/Vc2aNVG/fn218yorY8eORVxcHFq1aoW+ffuq1ffr1w+HDh2Cra0tTp8+jR07duD06dMwNzeHj4+P1qVlNVH0LDExMdFYb2pqCiCvd05hUlNTsXHjRhgYGGDFihUq+2zRogUmTJigc1xERERERKWl2EtRKsyYMQOrV6/GqlWrMG3aNFSpUqXA7TMyMnD48GFcuHABcXFxyMzMBJC3PCMA3LlzB40bN1Zrl39mdwXFJIMF1cXExCjLQkND8ezZM3Tt2lVjnHK5HE2bNsWBAwdw584dZTLB0dERt27dKvC8NGnfvj3mz5+vMuzh1KlTqFevHipXrgzgvyTDqVOn4O3tjbt37yImJga2trZ45513NO5X21KUJbVU5JEjR/D333+rlb+6JGBKSgr+/PNPXL16FS9evEBWVhaAvGsuhMDdu3e1dgvXRdOmTQHkJTuqVKmCnj17qk3SV1ZEvnHtJbV8YO/evQusf/z4Mf7880/cunULSUlJyoSXkZER7ty5UyIxKJJdQ4cOVauzt7eHj48P9u3bh9OnTyuHhihoet/Z2trCxsZG5X2nsGnTphKJuTgWLlyIHTt2wMbGBlu2bNH4Gi5ZsgTTp0+Hn58f5syZg9q1a+PevXuYPXs2Zs+ejfPnzyMwMLDMY798+bJyOUtNE3IOHjxYp+QuEREREVFp0ju5ULlyZUyYMAGLFi3CggULsGzZMq3bKiZ/K2gSM23f5Gn61tDMzKzQuoyMDGWZ4rhHjx4t9AHx2bNnRe6p8Ko2bdrAwMAAoaGhSE9PR0pKCm7duqWcsR7I681hYWGhTEAoHvbatWunNUZ9l6JUTEqnbXnJY8eOKf9/+/btyskM8zt+/Djef/99Ze8CTXT5VrYgnTt3xuTJk/Hjjz9i8ODBqFSpknIizJEjR5bpChb5r1VJLb1a0GSdS5cuxcyZM5UJm9KimNhQ20SPivLHjx+r1VWvXl1jG3Nzc7x48aJE4isJv/32G7744guYmpriwIEDGu+b4OBgTJ06FZ6envjjjz+UPWUaN26MnTt3olmzZjhw4AAOHTqE7t27F3pMMzMzxMfHa50gNjU1FQB0SpYpXiMnJyeN9dpeOyIiIiKisqTXsAiFadOmwczMDKtXr9b4jSWQ983vwIEDERkZibFjx+Lq1avKb2OFEPjiiy+U22kMtIAl6HRZng6A8pvfOnXqqExwpulH11nhC2JpaQk3NzdkZWXh3LlzygRC/l4HBgYGaNWqFc6ePYucnJxSn8wR+G/G/CtXrhSrfUpKCgYOHIhnz55h9uzZuHHjBlJTU5WvpSIZoe211CT/MJT8li5dips3b2LhwoXo2LEj/vnnH3z33XdwdXXFrl27ihV/cSiuVd26dSGVSnVqo+2cFIyNjTWWnzt3DlOmTIGJiQk2bNiAyMhIvHz5EkIICCFQtWrVogWvh4KScLq+78pTYGAgRowYAalUit27d6NVq1Yat9u8eTMAoG/fvmrnZWhoiH79+gHQPNxDE0Xi6NGjRxrrFeXaEgZERERERBWN3j0XgLzx6JMmTcL8+fMxf/58tWULAeDWrVu4desWmjVrhlWrVqnVK2ZxL02Kb1pdXV3LbMb69u3b4+rVqzh16pRyWb9XhzQoVsq4fv16ofMtlISOHTtCJpMhNDQUd+7cQd26dYvU/uTJk3j+/Dnee+89zJ07V61e02tpZGQEACqrHOSnaUy/Qv369TF9+nRMnz4dL1++xIoVKzBt2jSMGzcO/fv3L1LsxbVjxw4AUC75qJD/vBS9ZRQKOqeC7NmzBwDw3Xffwd/fX6UuPT0dT548KdZ+NalWrRru37+PBw8eoEGDBmr1it4+RZlv4HUREhKCAQMGQAiBrVu3ahzGoaB42Nc2H4SiXNf5Ntzd3XHt2jWEhoaiR48eavWhoaEAADc3t0L3pUgmaVtaV1s5EREREVFZKrGvHqdMmQJzc3P88ssvGrtQKz6Ua+pKHR8fj6NHj5ZUKFo1b94clpaWCAkJKbNu24okwalTp3Dy5ElUqVJF7WFeMe/C7t27cfv2bZiamuo1V0Fh7OzsMHz4cAghMGnSJOTk5BSpfUGvZUREhPLB6dVjVqpUCffv30d2drZKXVZWltryf9oYGxtj6tSpqFq1KuLi4hAbG1uk2IsjODgY27dvh0QiwaRJk1TqFA9+t2/fVmtX3Hu6oOv7xx9/aOwRokhyvHptC6O4P7dt26ZWFxcXhyNHjkAikSjv0YoiNDQUvXv3RkZGBtauXVtoEkoxueulS5c01l+8eBGA7kMQevbsCQDYuXOnWt2VK1dw7949NGrUSKf9NW3aFHK5HJcvX9aYuNu+fbvGdsW9J4iIiIiIiqPEkgu2trb45JNPkJGRgXXr1qnV16lTBwYGBjh+/LjKZHQvX77E2LFjy+RhXyaTYfr06UhOTka/fv00flB//Pixsot0/jJXV9diTZioeHg7c+YMrly5ovEhrWXLljA0NMTKlSsB5K3EUKlSiXQq0WrhwoVwcXHBkSNH0Lt3b43XIjMzU+PDVr169QDkJUPyz7mQkJCAUaNGaZwnwMjICK1bt8aLFy+U5wnkPfhMmTIF9+/fV2uzd+9enDt3Tq388uXLePr0KczMzGBlZaUs37BhQ5FWzSiMopdEz549kZOTg1mzZqmtUOHl5QUAmD9/vkqSZtu2bRof2HWhuL7r1q1TuZY3btzAjBkzNLZR9BYKDw8v0rEmTJgAAwMD/PTTTyqvdWZmJiZNmoT09HT069cPNWrUKOppqPnwww/h6uqq7JlRWsLDw+Hr64ukpCQEBARg+PDhhbbx8/MDAGzZskVt0sZ9+/Zh69atMDAwUFtlQts59e3bF7Vq1cK1a9dU5qFJTU1Vru4wZcoUnc7HzMwMH3zwAXJycpSvicKlS5ewYsUKje2Ke08QERERERVHiT7BTpkyBcuXL0dSUpJanb29PUaNGoU1a9bA3d0dnTp1glwux8mTJ5GTk4Phw4eXyVCFmTNn4tatW9i8eTPeeecdNGnSBLVq1UJmZibCw8Nx48YNuLm54YMPPlC2ycrKKvYHdAcHB9SpUwcREREA1IdEAHkPD+7u7spv/EtzSISCpaUlTpw4gX79+uHgwYM4dOgQ3N3dlUmg6OhohIWFITExEdbW1ipdu5s1a4auXbvi6NGjqFevnvJhPjg4GHZ2dujTp4/GpRJnz56Nbt264bPPPsOOHTvg4OCAy5cvIy0tDf7+/ti4caPK9sHBwQgICICjoyOaNGkCCwsLREdH4+TJk8jNzcXcuXOV384C/81xoOucCPmtXbsWwcHBAIC0tDQ8efJEGZtMJsOiRYswdepUtXYTJkzA6tWrsXPnTjRo0ABubm64c+cO/vnnH3z66acFTnCqzYgRI7BkyRL8+eefqF+/Ppo3b44XL14gJCQEfn5+uHDhglpXeGdnZ7i5ueHSpUto0aIFGjZsCENDQ/Tu3bvAVSlatGiBb7/9Fl999RVat24Nb29v2NnZ4fTp04iKikLdunVVkkH6ePjwIcLDw4u8RGn+eRIUSahvv/0Wq1evBgB4enri//7v/5TbKCYarVy5Mi5fvqwxueDq6oqZM2cqf/fz88OAAQPwxx9/oFevXmjWrBlq1aqF+/fvK5Mu3333ndokr9rOSSqV4rfffkOXLl3w+eefY8eOHXBycsLJkycRExOD9957T23IS0Hmz5+PkJAQHDx4EC4uLujQoQPi4+Nx/PhxfPzxxxpfo1atWsHe3h47d+6Et7c3ateuDQMDA4wcORJt2rTR+dhERERERDoRRQRAGBoaaq2fPXu2ACAAiG+++UalLjs7WyxZskQ0aNBAGBsbiypVqoihQ4eKyMhI8c033wgAYv369SptnJychLYwtbURQoj79+8LAMLLy0tj23379omePXsKe3t7IZVKhb29vWjatKmYPn26uHz5ssZ9FeNyCSGEGDFihLL9hQsXNG4zadIk5TZ///23xm3Wr18vAAh/f/9ixaFJbm6u2Llzp3jvvfdEjRo1hLGxsTA2NhY1atQQvXr1EqtWrRIJCQlq7dLS0sRXX30l6tatK2QymahRo4YYO3asePbsmfD39xcARFBQkFq7wMBA0bx5cyGTyYSNjY0YOHCguH//vsbX8sqVK2LKlCmiefPmwt7eXshkMuHk5CR69eoljh07prbvTz75RAAQmzZt0vn8FbEqfiQSiTA3NxdOTk6iZ8+eYtGiReLp06cF7uPmzZvi3XffFebm5sLU1FR06NBBHD9+XAQFBWl8vQq6PgpRUVFiyJAhwtHRURgbG4t33nlHLFiwQGRnZ2t9T9y5c0f4+fkJW1tbYWBgoPYeBCCcnJw0Hi8wMFB07txZWFpaCiMjI1GnTh0xffp08eLFC7VtC3rfCaH9Pevl5VVgO23yvz6afl59jyuOX5Q2QuS9F9atWyc6dOggrKysRKVKlYSdnZ3o0aOHOHTokMbYCjunf/75R/Tv31/Y2toKY2Nj0bBhQ7F06VKRk5NTpGsghBDPnj0T48aNEw4ODkImkwlXV1exePFikZubq/W1vXjxoujatauwtLQUEomkWNefiIiIiEgXEiGKMKU/0WvMzc0NmZmZ+Pfff2FoaFje4RAREREREb01SndgP1EZefbsGf755x9s2bKFiQUiIiIiIqIyxp4LRERERERERKQXnXou5ObmIjo6Gubm5pBIJKUdExEREREREREVgxACycnJqFatGgwMSmyByELplFyIjo4ukaXoiIiIiIiIiKj0RUVFoXr16mV2PJ2SC+bm5gCAm7fClf9PRERERERERK+X5ORkvONav8yf3XVKLiiGQpibm8PCwqJUAyIiIiIiIiIi/ZT1lAZlNwCDiIiIiIiIiN5ITC4QERERERERkV6YXCAiIiIiIiIivTC5QERERERERER6YXKBiIiIiIiIiPTC5AIRERERERER6YXJBSIiIiIiIiLSC5MLRERERERERKQXJheIiIiIiIiISC9MLhARERERERGRXphcICIiIiIiIiK9MLlARERERERERHphcoGIiIiIiIiI9MLkAhERERERERHphckFIiIiIiIiItILkwtEREREREREpBcmF4iIiIiIiIhIL0wuEBEREREREZFemFwgIiIiIiIiIr0wuUBEREREREREemFygYiIiIiIiIj0wuQCEREREREREemFyQUiIiIiIiIi0guTC0RERERERESkFyYXiIiIiIiIiEgvTC4QERERERERkV6YXCAiIiIiIiIivVQq7wCIiIiodKW8BCA016VlSJCdq7nOWAoYVdLcUFoJkElLJj4iIiKq+JhcICIiquByBZCYKtFa/yTeALlaEgjFZSoXsDTRkngwFDCTl+zxiIiI6PXG5AIREVEFJwQQ/bxsRzqmpkuQmq45oWEmFzCTl3A2g4iIiF5rTC4QERFVAOmZwINYw/IOQyepLyW49UhzrFJDAZeqTDwQERG9aZhcICIiqgCEAHJyyjsK3RQUq4H20RtERERUgTG5QERE9JpISJUg5sWbvZBTVrYEN6O098CoXz2HCQgiIqIKiMkFIiKi14QQKPGJF19Hb8M5EhERvW2YXCAiIipDqS/zln/U5GUmv7J/nqT9GlibCVSqGNNOEBERvXWYXCAiIipDKekSPEt6s4c+6CM2Qfu1MZfnMLlARET0muKnGyIiIiIiIiLSC3suEBERlbDnyRKtQxzSM8s4mDdIbKIBDLV8LWJjlgu5rGzjISIiov8wuUBERFTCUl9KkJzG+RNKWkHX1MxYArlMlGE0RERElB+HRRARERERERGRXthzgYiIqBgSUiXI0bKkYmZ22cZCQMpLCbK1vB5yIwETDpkgIiIqVUwuEBERFUNcogSZWRz68LpISJEA0Px6VLbKhQmHTBAREZUqDosgIiIiIiIiIr2w5wIREZEWgl92vxlEwa+lhB1QiIiI9MbkAhERkRY3HhqWdwhUAuISDRCXqLnO0lSgup2WyRqIiIhIZxwWQURERERERER6YXKBiIiIiIiIiPTCYRFERPTWysnNW1KS3l4ZWcDzZM33gKEBYGXKiTeIiIh0weQCERG9tbJzgCcv2InvbfYyU4InLzQnF4yNBJMLREREOuInKiIiIiIiIiLSC5MLRERERERERKQXDosgIqI3WupL4GmC5lx6Lnu8UwEysiW490TzvSORALWqcAlLIiIiBSYXiIjojZaTK0F6BidtpKITudB670jY95OIiEgF/2kkIiIiIiIiIr2w5wIREVV4Obl5P9rqiEqcADKztVdLDfOGThAREb0tmFwgIqIK70WyBLFa5lUgKg1CAHceG2qtr+uYAyN+yiIiorcIP4kRERERERERkV6YXCAiIiIiIiIivbDDHhERVQhJaRKkZ2iuS8vk4HZ6vTxLksBQy21pZSYgk5ZtPERERKWNyQUiIqoQktMlSEhhEoEqhvhk7Z1DTY1zmFwgIqI3DodFEBEREREREZFe2HOBiIheGwUtGylE2cVBVJpyhQQ5uZpvaIkEMGAHHSIiqoCYXCAiotfGg1gDpGfwyYrebFFx2juOOtjkwtacmTQiIqp4OCyCiIiIiIiIiPTC5AIRERERERER6YXDIoiIqExFxhogV8vcCi+zOCSC3m7PkyRITNX8PrAwEbCz4JAJIiJ6PTG5QEREZSo9Q6I1uUD0tsvKliArW3OdsRETC0RE9PrisAgiIiIiIiIi0gt7LhARUYnKyQWinmnPXWtZgY+ICpGSLkFkrOYhE3KpQBVrvrmIiKj8MLlAREQlSgggNZ1zJxCVtIKGTEAo/0NERFQuOCyCiIiIiIiIiPTCngtERFRkaRlAVJy2/DR7LRCVtbQMCcIfaX5PVjIEXKpyFlUiIipdTC4QEVGRCQFk5zCJQPS6KPg9yeESRERU+phcICIijTKygJeZmh9WMrLKOBgiKrZcIUFiqvZkoLmJgAFzhUREpCcmF4iISKO0DAmin3NqHqKKLjcXeFTACi6uNXI4momIiPTG5AIR0VssV+R1p9ZWR0RvvtwCpmMwkAASJh6IiEgHTC4QEb3FYhMkeJ7E3glEb7Pbjw211tV2yIFcVobBEBFRhcXkAhHRG66gHgjaei0QEQF5fz+0/Q2RgL0aiIjoP0wuEBG94aLiDJCSzicAIiq6yKfaezU42uXCypQZSiIiysPkAhHRGyD1pfZeCDlc3p6ISkFGJpCiZVSVtBIgk5ZtPEREVL6YXCAiegNEvzBAZhZ7JxBR2XmWZIBnSZrr7CxzUcWKvRqIiN4mTC4QEVUAQgCRsdonXszKZmKBiF4fiakSpGVo/rtkKhOwZ+KBiOiNw+QCEdFrIjsHSEjV/GFcCCDtJRMIRFQxZGVLkJWtuS4nBzDQkis1NACszZh4ICKqiJhcICIqQ5nZQKqWJEF2DhCbwGUhiejNlpElwdN4zX8HKxkWnFhg4oGI6PXF5AIRUQlLfQmt3YEzsiRI1NI7gYjobZedI0H0c+1/I7MLmKHWxlzAkPlZIqJyw+QCEZEWWdna13dPSJUgXUsCITObcyAQEZWGgnp3JacLGGj502tjLiCTav6DXskQTEoQEZUAJheI6I2WK4C0l9rrE1INoK2TbUq6BLlcxpGIqELQlvAFtA9HAwCZkdC6bKaskoCJTPO/EgYGgImsSCESEb3RmFwgKmFCj+GgxW5aQMOC9llQrEJobytEwW2zC3ggz8mRaN1vTg6Qo6UyJyevu6zGuty84QbaFPe6SiSAoWExGxMRUYWQnSNBdo7mulRI8CJFe9uC/uUpKPFgJBWQaGksNYTWOkOJgERLLwsJCu6BoW0STQBae3wA2mPJf9ziVZZ4s0JjJaLSxeQCUQnT5x+2YjetUP+Y6jMZFyfyIiIiIiJ6HXGEGRERERERERHphckFIiIiIiIiItILkwtEREREREREpBcmF4iIiIiIiIhIL0wuEBEREREREZFemFwgIiIiIiIiIr0wuUBEREREREREemFygYiIiIiIiIj0wuQCEREREREREemlUnkHQERE9LY4cuQIZkyfhqVLl6FT584qdQMHvIfbt29jzZq1aN6ihUqdbzcf2Fepgk2bNgMAunf3RUx0tMZjnL9wETKZrMA4UlJSsG3bVgQdP46HDx8iIyMDdnZ2aNzYDe/26oUOHTrocZZERET0NmJygYiIqIw0adIEAHDlyhWV5EJKSgoiIiJQqVIlXL16VSW58OTJEzx58gTdfH1V9lW/vis+/PBDtWNIpdICY3j48CHGjxuLmJgYdOzUCe++2wsmJiZ48vQJTp08iU8mTcT//vcd3u3VS59TJSIiorcMkwtERERlxN7eHo6OjrhyJVSl/Pq1axBCoEvXrmp1it8ViYn8++r57rtFOn52djY+n/wZnj9/jrXrflXb59ix43DmzBnk5uYUab9EREREnHOBiIioDDVp0gS3bt3Cy5cvlWVXr16Fi4sL2rVth+thYcjNzf2v7spVSCQSeHg00bS7Ijl69C9ERERgzJiP1RILCm3atEG7du2VvycmJmLpksV4r38/tG7VEm3btMaE8eMQHh6u0m7fvn3wcHfD48ePVcovXrwID3c3XLx4UVn24MEDTPl8Mjp36ogWzZvBp2sXzJg+HcnJyXqfIxEREZUP9lwgIiIqQx5NPBEYGIiwsDA0b94cAHD16hW4u3vA3cMDKcnJiIiIQL169f5/3VXUqlULVlZWKvvJzs5GfHy8SpmxsTHkcrnWY4eEhAAAevTsqXO8jx49QlBQELp29UE1R0e8eP4cO3f+gVGjRmL37j2wt7fXeV8AkJWVhfHjxiIzMwvvDx4MO1s7xMbG4sSJE0hOToa5uXmR9kdERESvByYXiIiIypCix8DVK1fQvHlzZGdnIywsDL1690aNGjVga2uLK1dCUa9ePaSmpiIi4g76+Pmp7efs2TPo6O2lUvbx2LEYN2681mNH3r8Pc3NzVKlSRaU8PS0NLzMylL9LpVKYmZkBAOrWrYt9+/+EgcF/nR17vvsu+vr1wd49ezDm44+LdP53797F48eP8cPixeja1UcldiIiIqq4mFwgIiIqQ7Vr14aVlZVyLoXbt28jPT0d7u4eAAB3dw9cvXoVgwa9j2vXriEnJ0fjEIbGjRtjwsRJKmXVq1cv8NipqakwMTFRK1++Yjm2btmi/L19+w5YvmIFAMDIyEhZnpOTg+TkZJiYmMDZ2Rk3b97U7aTzMf//SYszZ86gXbv2Bfa0ICIiooqDyQUiIqIyJJFI4O7ujsuhocjNzcXVK1dgY2ODmjVrAgDcPdyxfft2AHnDJQCgSRNPtf1YWVmjVatWRTq2iYkpEhIS1MoHDRyEDh3yekF89eUXKnW5ubnYsmULfv99B6IfP0ZOzn+TPVpaWhbp+ADgWL06PvjgQ2zevAmHDh5Ekyae8PL2Rs+ePTkkgoiIqALjhI5ERERlzKNJE6QkJ+POnTu4evWqstcCkNdzISY6Gk+fPsXVK1dQubJ9oT0SdFWrljOSk5Px9OlTlXInZ2e0atUKrVq1gpFMplK3bu1aLFn8A5p6NsV3332P/1u1Gqt//gUuLi7IFUK5nUSi+ZiaVp6YMnUq/ti5E6NGjUZGxkssWrgA/fv1xdOnT/Q/SSIiIioXTC4QERGVMUVPhCtXruDq1SvwaOKhrGvQoAGMjIxw6dIlhIWFqdTpq/3/751w8OABndscPXYUzZs3x5y5c+HbvTvatGmDVq1aqa3sYGFhAQBq5THRMRr3W7duPXw0Zgx+Xb8Bv67fgNjYWPzxxx9FOR0iIiJ6jTC5QEREVMYaNmwImUyGgwcPIDY2VqXngpGREVzfeQc7dmxHeno6mpTAEpQKPj4+qF3bBWt++QXXr1/TvFG+3ggAYGhg8GoR/vrrL8TGxqqUVa9eAwAQevmysiwnJwe7du1U2S4lJQXZ2dkqZXXr1oWBgQEyMzOLcjpERET0GuGcC0RERGVMKpWiYcOGCA0NhZGRERo0aKBS7+Hujk2bNgEAmniWXHJBKpVi2bJlGDduLEYMH45OnTvDs4kn5HI5YmNjERwSjJiYGLRr317Zpn2HDvjl558xe9YsuHu4I+JOBA4ePKA2VKNOnTpwc3PDTz8FIDEpEZYWljh85LDKHA0AcOHCBSyY/z26+vjAyckJOdk5CAwMhIGBAbp06VJi50pERERli8kFIiKicuDRpAlCQ0Pxzv8fBqFS59EEmzZtgqmpKerVq1+ix3VydsaO3//Atq1bcfz43zh96hSysrJga2uLRo0bY+zHY9HB678lLkeP/ggv09Nx6NAh/PXXEbi6voPlK1YgICBAbd/fz1+Ab7+dh/W//gpzc3P49e2L5s1bYOzHY5Tb1K9XD23atMWJkBDExsbC2NgY9erVw8r/+z+4ubmX6LkSERFR2ZEI8WpnR3VJSUmwtLTEo8fRyjGVRERERERERPR6SUpKQnXHakhMTCzT53fOuUBEREREREREemFygYiIiIiIiIj0wuQCEREREREREemFyQUiIiIiIiIi0guTC0RERERERESkFyYXiIiIiIiIiEgvTC4QERERERERkV6YXCAiIiIiIiIivTC5QERERERERER6YXKBiIiIiIiIiPTC5AIRERERERER6aWSLhsJIQAAycnJpRoMERERERERERWf4rld8RxfVnRKLiiCe8e1fqkGQ0RERERERET6e/78OSwtLcvseBKhQzojNzcX0dHRMDc3h0QiKYu4SkRSUhJq1KiBqKgoWFhYlHc4RGWK9z+9zXj/09uO7wF6m/H+p7ddYmIiatasifj4eFhZWZXZcXXquWBgYIDq1auXdiylxsLCgn9Y6K3F+5/eZrz/6W3H9wC9zXj/09vOwKBsp1jkhI5EREREREREpBcmF4iIiIiIiIhIL290ckEmk+Gbb76BTCYr71CIyhzvf3qb8f6ntx3fA/Q24/1Pb7vyeg/oNKEjEREREREREZE2b3TPBSIiIiIiIiIqfUwuEBEREREREZFemFwgIiIiIiIiIr28kcmF9PR0zJ49G/Xq1YOxsTGqVauGkSNH4vHjx+UdGpFe0tLSsHfvXowaNQr169eHsbExTE1N4e7ujnnz5iElJUWtjUQiKfSnU6dO5XA2RMXj7e1d4P18+PDhQvfRpUsX5faPHj0qg6iJSs7FixcxcOBAVKtWDVKpFFZWVmjfvj3Wr1+PV6fSCg8Px7JlyzB48GC4uLgo7/vIyMjyCZ5IB5cvX8aCBQvQr18/VK9eXXnfFmbDhg1o0aIFzMzMYGNjgx49euDMmTNat8/IyMDChQvh6ekJMzMzyGQy1KpVCx999BHu3btXkqdEpLOi3v/79++Hv78/GjduDDs7O0ilUtjb26NHjx4IDAzU+bjffvut8li//fZbsWJ/4yZ0fPnyJTp27Ihz586hatWqaN++PSIjI3HhwgVUrlwZ586dQ+3atcs7TKJiWbt2LT766CMAwDvvvINGjRohKSkJZ86cQXJyMlxdXRESEgJ7e3tlm+HDh2vd34EDB/Ds2TPMnj0bc+fOLe3wiUqEt7c3QkJC0L9/f5iZmanVT5kyBY0bN9bafsOGDRgxYgQkEgmEEIiKikL16tVLM2SiErNr1y4MGjQIOTk58PT0RJ06dRAXF4eTJ08iOzsbQ4YMwZYtW5Tbf/bZZwgICFDbz/379+Hs7FyGkRPpzs/PD/v27VMrL+ixRXGvy+Vy+Pj44OXLl/j7778hhMDOnTvh5+ensn3+ZwYrKyu0adMGxsbGCA0NRWRkJMzNzREUFISmTZuW9OkRFaio9/97772H3bt3o2HDhqhZsybMzc0RGRmJ8+fPAwC++OILfP/99wUeMzw8HO7u7sjMzIQQAps3b8awYcOKHrx4w3z11VcCgGjdurVITk5Wli9ZskQAEF5eXuUXHJGeNmzYIMaMGSNu3LihUh4dHS2aNGkiAIjBgwfrtK/4+Hghk8kEAHH79u3SCJeoVHh5eQkA4v79+0VuGxsbK2xsbISPj49wcnISAERUVFTJB0lUCrKysoS9vb0AILZs2aJSd+PGDWFjYyMAiOPHjyvL165dK2bMmCF27twpIiMjRf369Yv9/iEqKwsWLBCzZs0S+/fvFzExMcrPK9ocPXpUABC2trYqn2nOnDkjjIyMhJWVlYiPj1dpExAQIACI5s2bi4SEBGV5dna2mDhxogAgOnToUOLnRlSYot7/oaGh4tmzZ2rl586dE2ZmZkIikYjr169rbZ+bmys6dOggqlSpIvr06SMAiM2bNxcr9jcquZCRkSEsLS0FABEaGqpW7+bmJgCIS5culUN0RKXrzJkzAoCQyWQiIyOj0O1/+eUXAUC0atWqDKIjKjn6JBeGDBkijI2NRUREBJMLVOGEhYUJAKJ+/foa6z/55BMBQCxcuFDrPphcoIqosIer7t27CwBi2bJlanWK98XixYtVyvv37y8AiG3btqm1efHihQAg5HK53rET6auw+78go0aNEgBEQECA1m0UzwS//fab8Pf31yu58EbNuXD69GkkJibCxcUFTZo0Uat/7733AAB//vlnWYdGVOrc3d0B5I0ffP78eaHbK8ZSffDBB6UaF9Hr4vDhw9i6dSu++uoruLi4lHc4REUmk8l02s7W1raUIyF6faSnp+P48eMA/vusn5+2z/+6vJ/4XqKKTiqVAgCMjIw01j958gTTp09H586dMXToUL2P90YlF65duwYA8PT01FivKL9+/XqZxURUVhQTD0mlUtjY2BS47cOHD3Hy5ElIpVIMGjSoLMIjKnHr1q3D+PHjMXHiRPz00094+PCh1m1TU1Mxbtw4uLq6Yvr06WUYJVHJqV27NlxcXBAeHo6tW7eq1N28eRO//fYbrK2t0bdv33KKkKjshYeHIyMjA5UrV9Y4f462z/8+Pj4AgKVLlyIxMVFZnpOTg9mzZwMARo0aVVphE5W6sLAw7NixA1KpFF27dtW4zSeffIL09HSsWrWqRI5ZqUT28ppQfLDUNjGXovzBgwdlFhNRWVFM2OXr61toNn7Lli0QQqB79+7MylOF9b///U/l96lTp2LWrFmYNWuW2razZ89GZGQkgoODtWbviV53hoaG2LhxI959910MHToUS5YsQd26dREbG4uTJ0+iQYMG2LBhQ6EJZqI3SWGf/01NTWFlZYX4+HgkJyfD3NwcADBs2DAcPnwY27dvh7OzM9q2bQtjY2NcvnwZT58+xbRp0zT+e0L0uvrzzz+xa9cuZGVl4eHDhzhz5gykUinWrFmjscdmYGAg/vjjD8ydOxd169YtkRjeqOSCYhk+ExMTjfWmpqYAgOTk5DKLiagsHDx4EOvWrYNUKsW3335b6PYcEkEVWYcOHTB69Gi0adMGVatWRVRUFHbu3In//e9/mD17NiwsLPDpp58qtw8NDUVAQAD8/f3h5eVVjpET6a9t27YICQlB3759ERoaitDQUAB5XV67du3KFbHorVPY538g7xkgISFBJblgaGiI3377DTVr1sSiRYtw4MAB5faenp7o3LkzDA0NSzd4ohJ07do1bNy4Ufm7XC5HQECAxs/7KSkpGD9+POrVq4cZM2aUWAxv1LAIorfRrVu3MGzYMAgh8MMPPyjnXtAmNDQUN27cgJWVFXr16lVGURKVnHnz5mHYsGGoXbs25HI56tWrhy+//BJ79+4FAMyZMwfp6ekA8rq3jh49GlZWVli8eHE5Rk1UMrZt24YWLVqgRo0aOH/+PFJSUnD79m0MHz4cS5YsQadOnZCRkVHeYRK99uLj49G5c2esWLECAQEBePToEV68eIG9e/ciLi4OPXr0wI4dO8o7TCKdff311xBCID09HWFhYRgxYgTGjBmDPn36IDMzU2XbL7/8ElFRUVi1apXO8/no4o1KLijWO09LS9NYn5qaCgDKjCVRRff48WP4+voiPj4en3/+ucq3tdooei0MGDCgRP+YEJU3Hx8fNGvWDAkJCcq1nX/88UdcuXIFixYtgp2dXTlHSKSfO3fuwN/fH3Z2dggMDESLFi1gamqKunXr4ueff8a7776L0NBQ/Prrr+UdKlGZKezzP6D5GWDy5MkICQnBd999h08++QSOjo6wtrZGnz59sHv3bgghMGXKFGRlZZXuCRCVMGNjYzRq1AgrV67EpEmTEBgYiOXLlyvrL1y4gJUrV+KDDz5Ap06dSvTYb1RyoWbNmgCAR48eaaxXlDs5OZVZTESl5cWLF/Dx8cGDBw8wYsQInb6VzcnJwfbt2wHkjTUketMoxgzGxMQAyBt/KJFIsHHjRnh7e6v8PHnyBEBeos3b2xuHDx8ut7iJdLF9+3ZkZWXB19dX+UCV38CBAwEAJ06cKOvQiMpNYZ//U1NTkZCQAGtra2VyIScnB9u2bQOgeYWJZs2aoVatWnj8+LFywmyiikgxJGLfvn3KsoMHDyI3NxdhYWFqn40Un4W+++47eHt7Y8GCBUU63hs154KiO7hi/OGrFOVubm5lFhNRaUhJSUH37t1x48YN9OvXD2vWrIFEIim03d9//42YmBg4OTmhffv2ZRApUdmKj48H8N8cOwAghCjwYevcuXMAgOHDh5dqbET6Ujw8WVpaaqxXlCveB0Rvg/r160MmkyEuLg6PHz+Go6OjSr2mz/+xsbHKbuJ8P9GbTNFrMy4uTq3u6tWrWtvdunULt27dgrOzc5GO90b1XGjbti0sLS1x9+5djRdr586dAMBx5lShZWRkoE+fPrhw4QK6deuGbdu26TzhkGJIxLBhw3RKRhBVJHFxcTh58iSA/5YeCw4OhhBC44+iF1tUVBSEEEwu0GvPwcEBAHDp0iWN9RcvXgSAIn8YJKrI5HK5smv3H3/8oVav6fO/jY2NcuUgTe+npKQkhIeHA2CPZ6rYQkJCAEBltYg5c+Zo/Wzk7+8PANi8eTOEENiwYUORjvdGJReMjIwwceJEAMCECROU46uAvDVsr1+/Di8vLzRt2rS8QiTSS05ODgYPHozjx4+jffv22L17t87L6qWlpWHPnj0AuEoEVVxnzpzB3r17kZOTo1IeGRmJvn37IjU1Fb1799a6JBlRRdanTx8AecMeXl2T/Ny5c1i2bBkAzd28id5kn3/+OYC8JYrv3LmjLD979ix+/vlnWFlZYdSoUcpymUwGX19fZVvFUDoAePnyJcaPH4+0tDS0bdsWVatWLaOzICq6uLg4rFmzRuOcI0ePHsX06dMBACNGjCiTeN6oYRFA3iyZx44dw5kzZ1C3bl20b98eDx48wPnz51G5cmVOckQV2ooVK5QJAjs7O4wfP17jdosXL1abvG7v3r1ISUlB8+bNUb9+/VKPlag03L59GyNGjICDgwM8PT1hZWWFBw8e4PLly3j58iUaNmyINWvWlHeYRKXC09MTU6dOxeLFizF+/HisXLkSDRo0QHR0NM6ePYvc3FyMGTMGXbp0UbYJDQ1V+bfiwYMHAIC+ffsqJ/UdPXo0Ro8eXbYnQ1SAAwcOqCytrRjC0KpVK2XZrFmz0LNnTwBAly5d8OmnnyIgIAAeHh7o2rUrMjMzcfToUQghsH79elhZWakcY+nSpTh//jyuXr2K+vXro3Xr1pDL5bh48SKio6NhY2OD1atXl/7JEr2iKPd/amoqxowZg88++wxNmzZF9erVkZqaitu3b+PWrVsA8iYv7d+/f5nE/sYlF4yNjREUFIT58+dj69at2Lt3L2xsbDB8+HB8++23/DaLKrT84/4USQZN5syZo5ZcyD8kgqiiatmyJcaNG4fz58/j4sWLiI+Ph6mpKTw8PDBgwACMGzcOcrm8vMMkKjU//PAD2rRpg9WrV+Py5csIDw+Hubk5vLy88NFHH2Hw4MEq2yclJSlXT8kv//BRxTe4RK+LuLg4jfdt/rJXx5D/+OOP8PDwwIoVK3D06FEYGRmhS5cumDVrFtq0aaO2LxcXF1y7dg0LFy7EoUOHcOLECQghUKNGDUyYMAEzZ87kcwOVi6Lc//b29li0aBGCg4Px77//4tKlS8jNzUXVqlXx/vvv4+OPP4a3t3dZhQ6JEEKU2dGIiIiIiIiI6I3zRs25QERERERERERlj8kFIiIiIiIiItILkwtEREREREREpBcmF4iIiIiIiIhIL0wuEBEREREREZFemFwgIiIiIiIiIr0wuUBEREREREREemFygYiIiIiIiIj0wuQCEREVWVBQEPr37w9HR0cYGRnB2toa9evXx4ABA7BixQokJiaWd4hUDMHBwZBIJBg+fHi5xuHt7Q2JRILIyMhyjaO4Ro4cCVNTU8TGxurcZs6cOZBIJNiwYUORjuXn54cqVaogJSWliFESERGVLCYXiIioSObNm4dOnTph9+7dsLS0xLvvvgsfHx/I5XLs3r0bkyZNws2bN8ssnuHDh0MikSA4OLjMjkn6kUgkcHZ2Lu8wSkVYWBg2btyICRMmwN7eXu/9OTs7QyKRaK2fPXs2YmNjsWjRIr2PRUREpI9K5R0AERFVHJcvX8acOXMglUrx+++/w8/PT6X+yZMn+O2332BlZVUu8dGbYdOmTUhLS4Ojo2N5h1JkX3/9NQwNDTF16tQyOZ6npye6deuGJUuW4NNPP4WtrW2ZHJeIiOhV7LlAREQ62717N4QQGDhwoFpiAQAcHBwwdepUuLq6ln1w9MaoWbMmXF1dIZVKyzuUIomKikJgYCC6detWIr0WdDVs2DCkpaVh48aNZXZMIiKiVzG5QEREOouLiwMAVK5cWaftMzIyYGdnBxMTEyQkJGjc5syZM5BIJPDy8lKWCSGwZcsWtGvXDlWqVIGxsTFq1KiBLl26YOXKlcrtJBKJ8oGqY8eOkEgkyp9Xx+sfPnwYPXv2ROXKlSGTyVC7dm18/vnneP78uVpM+YdaHDt2DB06dIC5uTns7e3x0UcfKeeUiI2NxccffwxHR0cYGxujRYsWxRqekZWVhdWrV6Ndu3awsrKCXC5HnTp1MGLECFy+fBkAsHPnTkgkEgwZMkTrfsaMGQOJRIL169erlKempmLhwoVo1qwZLCwsYGpqCldXV0yYMAG3b9/WOc6iXENNNmzYoOzi/+DBA5XXy9vbW7mdtjkXFMMpsrOz8e2336JOnTqQy+V45513VM75+PHj6NixIywsLGBtbY0PP/xQa4zZ2dlYtWoVWrduDQsLC8jlcnh4eODHH39Edna2ztcGAH799Vfk5uZi8ODBWrfZv38/WrduDRMTE9ja2qJ///4aXwPF/BcPHjxQnrvi59UhJX5+fpDL5VizZk2R4iUiIipJHBZBREQ6q1GjBgBg165d+OKLLwr9dlYmk8Hf3x9Lly7Fli1bMGHCBLVtFA9EY8aMUZZNnz4dixcvhkwmQ4cOHWBnZ4cnT57g+vXriIiIUO7H398fp06dwt27d9GtWzc4ODgo92FmZqb8/5kzZ2LhwoUwMjJC8+bNUbVqVVy7dg3Lli3D/v37cfr0aVSpUkUttj179mDlypVo3bo1fH19ce7cOaxduxZ37tzBzp070bp1a+Tk5KB9+/aIjIzE+fPn4evri4sXL6Jx48Y6XdPU1FT06NEDJ06cgKmpqTLBEBkZiS1btsDS0hJNmzZFnz594ODggN27d+P58+dq3d9TUlKwbds2WFhYYNCgQcrymJgYdO3aFf/++y+sra3h7e0NmUyGe/fuYfXq1ahbty7q1atXaJzFvYb51alTB/7+/ti4cSNMTU3x3nvvKeuK0ttl4MCBygSCi4sLQkJCMHLkSACAubk5Bg8ejFatWqFbt244e/YsNm/ejPv37+PEiRMq8xekp6ejZ8+eCAoKgo2NDVq1agVjY2OcP38ekydPRlBQEPbs2QMDA92+iwkMDAQAlURJfqtXr8a4ceMgkUjQvn17VK1aFefOnUOLFi3Qq1cvlW0dHBzg7++PnTt3IjU1Ff7+/so6Ozs7lW3NzMzQrFkznDx5Evfu3UPt2rV1ipeIiKhECSIiIh3dvXtXyOVyAUCYm5sLf39/sWbNGhEaGiqys7M1tgkPDxcSiUS4u7ur1SUmJgoTExNhbW0t0tPThRBCpKenC5lMJszNzcW9e/dUts/KyhInTpxQKfP39xcARFBQkMbj//777wKAaNSokbhz546yPDc3V8yePVsAEIMGDdK4TwMDAxEYGKgsT0pKEo0aNRIARIMGDcSwYcNEZmamsv7rr78WAMSHH36oMRZNRo0aJQCIDh06iNjYWJW6J0+eiHPnzil///LLLwUAsWzZMrX9rFmzRgAQ48aNUynv3LmzACAGDhwokpOTVeru378vrl27pvw9KChIABD+/v4q2xXnGhYEgHByctJa7+XlJQCI+/fvq7VTxJH/Wh0/flwAEFWrVhW2trYqr1liYqJo2LChACCOHz+usr/x48crY09ISFCWJyUliR49eggAYtWqVTqdU3JysjA0NBTVqlXTWB8ZGSmMjY2FVCoVhw8fVpZnZmaKoUOHKs9t/fr1Ku2cnJyELh/XpkyZIgCIX3/9Vad4iYiIShqTC0REVCTHjh0TNWrUUD4MKX6srKzEuHHjRHR0tFqbTp06CQDiwoULKuWrVq0SAMQnn3yiLHv69KkAIDw8PHSKp7Dkgru7uwAgwsLC1Opyc3OFh4eHMDQ0FHFxcWr7HDZsmFqbgIAAAUBYWFiIFy9eqNQlJCQIiURS4INzfo8fPxaGhoZCJpOJyMjIQrePjIwUBgYGokGDBmp1LVu2FABEaGiosuz8+fMCgLC3txdJSUmF7l9bcqE417Ag+iYXjh07ptamSZMmhb5m33zzjbLs6dOnQiqViho1aoi0tDS1NjExMcLIyEi4ubnpdE6Ka92xY0eN9YokjKbE07Nnz4SJiYleyQVFcin/e4mIiKgscc4FIiIqks6dOyMiIgK7d+/G2LFj4enpiUqVKiEhIQGrVq2Ch4cHwsPDVdqMHTsWANTGhGsaEmFvb4/q1avj6tWrmDlzJu7du1fsWGNjY3Ht2jXUrVsXjRo1UquXSCRo27YtcnJylHMb5Ofj46NWpuhy3qxZM1hbW6vUWVpawsbGBjExMTrFFxwcjJycHPj6+sLJyanQ7Z2cnODr64sbN27gzJkzyvKwsDCcP38ezZo1Q5MmTZTlx44dAwAMHjwY5ubmOsX0Kn2vYUmTSqUahx0oXpeCXrP8r0twcDCysrLg6+sLuVyu1sbBwQF169ZFWFgY0tPTC40rNjYWANTuCYWTJ08CAN5//321OltbW41xF4WNjQ2A/+ZFISIiKmtMLhARUZEZGRmhb9++WLVqFS5fvoy4uDisWrUK1tbWiI2NxcSJE1W29/Pzg4ODA7Zt24aUlBQAQGhoKEJDQ9G6dWs0bNhQZfuNGzeicuXKWLhwIVxcXODs7Ax/f38cOnSoSHEqJgS8c+eOyoR4+X8UE0Q+e/ZMrb2mpRAVczloWybRzMwMmZmZOsUXFRUFAHBxcdFpe0Bzokbx/x999JHe+3+VvtewpDk4OMDQ0FCtvKDXRVGXkZGhLFOc15o1a7Se17///gshBF68eFFoXIpJPrUlcaKjowFAaxLp1Ukai8rCwgIAtE6cSkREVNo4oSMREenNysoKY8eORbVq1dCnTx8EBQUhLS0NJiYmAPK+bR45ciS+//57bN++HaNHj8batWsBqD8QA0CnTp0QERGBwMBAHD58GMHBwdi0aRM2bdqE/v37Y+fOnTrFlZubCyDvgbRbt24Fbqvpoa+gifx0neSvpPXo0QM1atTA77//joCAABgZGeG3336DmZlZgasUFJe+17CkFXbddX1dFOfl4eEBd3f3AreVyWSF7s/S0hIAkJycrNPxS5oiuWFlZVUuxyciImJygYiISkynTp0AADk5OUhISFAmF4C8oQ8LFizAmjVrMGTIEGzdulVtZYP8LCwsMGTIEOXSi+fOncOAAQOwa9cuHDx4ED169Cg0nurVqwPIm11/w4YNep5dyVOsvnH37l2d2xgaGuKjjz7C7NmzsWXLFlhYWCA+Ph6jR49W+9a8OPt/1et+DYtLcV7t2rXD8uXL9d6fYuUUbb0cqlativDwcDx48AANGjRQq1csOVlc8fHxAHRfJpaIiKikcVgEERHpTAhRYH1ERASAvGETry6Xp5gv4MKFC/j666+RmJiIoUOHqiQgCtKqVSt88MEHAIB//vlHWW5kZAQAyM7OVmtTvXp1uLq64saNG7h9+7ZOxylL3t7eMDQ0xJEjR5RDGHQxevRoVKpUCWvWrNE6JAIAunTpAgAqw1GKqjSuoVQq1fh6laWOHTvC0NAQgYGByMrK0nt/DRs2RKVKldTmG1Fo3749AOD3339Xq3vx4gX++usvje0Kur/zu3nzJoC8nhhERETlgckFIiLS2axZszBt2jSN34Q/fvwYH3/8MQCgd+/eyoei/BTzBSxbtgyA5gfihw8fYsOGDUhLS1Mpf/nyJYKCggD89408AFSrVg0AtD7UzZo1C7m5uejfvz+uXr2qVv/8+XO1iSbLSrVq1fDhhx/i5cuX8Pf3x/Pnz1XqY2Njcf78ebV2VatWRe/evXHlyhWEhITAzc0NLVq0UNuuRYsW6NixI2JjYzFmzBikpqaq1EdGRiIsLKzQOEv6GlarVg1Pnz4t1/kBHB0dMXLkSERGRmLw4MF4+vSp2jYRERHYtWuXTvszNTVFkyZNEBMTg8ePH6vVjxgxAjKZDFu2bFFOtAkAWVlZmDx5stpro1DY/a1w4cIFAICXl5dO8RIREZU0DosgIiKdpaSkICAgAIsXL0a9evXQoEEDGBsb49GjRzh//jyysrJQp04d/PjjjxrbK+YLiIqKUlvZQOHFixcYMWIEJkyYgGbNmqF69epITU3FmTNnEBcXh2bNmqFfv37K7Xv16oV58+Zh6tSpOHr0qLLHxMKFC2Fra4shQ4bg33//xffff4+mTZvCw8MDLi4uEELg7t27uH79OszMzDQmOspCQEAAwsPDERQUBCcnJ3To0AEWFhZ48OABQkNDMW7cOLRs2VKt3dixY7F7924AqqttvGrz5s3o3Lkztm3bhiNHjqBdu3aQyWS4e/curl69iiVLlqBx48YFxljS17B3795Yvnw5PD090aZNGxgbG6N+/fqYNm2aTu1LSkBAACIjI7Fr1y4cPnwYHh4eqFmzJlJTU3Hjxg1ERESgT58+6N+/v07769mzJy5evIjg4GAMHTpUpa5WrVpYsmQJJk6ciG7duqFDhw5wcHDAuXPnEB8fj6FDh2LLli1q++zduzdCQkLQuXNndOzYEaamprCzs8OCBQuU26SkpODSpUtwdXVVroxBRERU5sp3JUwiIqpI4uLixObNm8WwYcNE48aNha2trahUqZKwsbERbdu2FYsWLRIpKSkF7mPYsGECgPj555811iclJYklS5aIHj16CGdnZ2FsbCxsbW1Fs2bNxLJly0Rqaqpamy1btghPT08hl8sFAAFA3L9/X2WbkJAQMWDAAFGtWjUhlUqFra2tcHNzExMnThQhISEq2/r7+wsAIigoSO1YQUFBAoDw9/fXGL+Tk5Mo6j+vGRkZIiAgQLRo0UKYmZkJuVwuXFxcxIgRI8Tly5c1tklPTxdSqVTI5XIRHx9f4P6TkpLEvHnzhJubm5DL5cLMzEy4urqKiRMnijt37uh8bkW5hgVJSUkREydOFDVq1BCVKlUSAISXl5ey3svLS+NrCEA4OTlp3GdxX7Ps7GyxceNG0alTJ2FjYyOkUqmoVq2aaN26tZg7d64IDw/X+bwePnwoDA0NRY8ePbRus2fPHtGyZUshl8uFtbW16NOnj7h586b45ptvBACxfv16le2zsrLE119/LVxcXIRUKtV4DTZt2iQAiCVLlugcKxERUUmTCFHIAFoiIqISkpaWBkdHR2RnZyM6Olrrsn1UuG3btmHIkCHw9/d/oyZarOj69u2LwMBAREVFwcHBoUyO2a1bN5w6dQoPHz6Era1tmRyTiIjoVZxzgYiIyszKlSuRkJAAf39/Jhb0kJWVhYULFwIAJkyYUM7RUH7ffvstcnNzsXjx4jI5XmhoKP766y9MmTKFiQUiIipX7LlARESl6vnz55gxYwaePn2KgwcPwsTEBDdv3lQuBUi6279/P/bu3YsLFy7g33//hZ+fH/bs2VPeYdErRo4ciR07duD+/fvKJSpLi5+fH86ePYu7d+/CzMysVI9FRERUECYXiIioVEVGRqJWrVowMjJC48aNsXjxYnh7e5d3WBXSnDlzMHfuXFhbW6N79+5Yvnw5bGxsyjssIiIiIiYXiIiIiIiIiEg/nHOBiIiIiIiIiPTC5AIRERERERER6YXJBSIiIiIiIiLSC5MLRERERERERKQXJheIiIiIiIiISC9MLhARERERERGRXphcICIiIiIiIiK9MLlARERERERERHphcoGIiIiIiIiI9PL/ALo9gxuJV82KAAAAAElFTkSuQmCC",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1300x165 with 1 Axes>"
+       "<Figure size 936x118.8 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -310,12 +310,12 @@
    "source": [
     "#### Pulse library functions\n",
     "\n",
-    "Our own pulse library has sampling methods to build a `Waveform` from common functions."
+    "It is possible to convert `SymbolicPulse` objects into `Waveform`s via the `get_waveform()` method. Using our own pulse library you can build `Waveform` from common functions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:51.575812Z",
@@ -328,18 +328,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1300x165 with 1 Axes>"
+       "<Figure size 936x118.8 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gaus = library.gaussian(duration=num_samples, amp=amp, sigma=sigma, name=\"Lib Gaus\")\n",
+    "gaus = library.Gaussian(duration=num_samples, amp=amp, sigma=sigma, name=\"Lib Gaus\")\n",
+    "gaus.get_waveform()\n",
     "gaus.draw()"
    ]
   },
@@ -529,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {
     "execution": {
      "iopub.execute_input": "2023-08-25T18:25:51.956307Z",
@@ -541,12 +542,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 1300x330 with 1 Axes>"
+       "<Figure size 936x237.6 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +555,7 @@
    "source": [
     "with pulse.build(backend, name='Left align example') as program:\n",
     "    with pulse.align_left():\n",
-    "        gaussian_pulse = library.gaussian(100, 0.5, 20)\n",
+    "        gaussian_pulse = library.Gaussian(100, 0.5, 20)\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(0))\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(1))\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(1))\n",
@@ -600,7 +601,7 @@
    "source": [
     "with pulse.build(backend, name='Right align example') as program:\n",
     "    with pulse.align_right():\n",
-    "        gaussian_pulse = library.gaussian(100, 0.5, 20)\n",
+    "        gaussian_pulse = library.Gaussian(100, 0.5, 20)\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(0))\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(1))\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(1))\n",
@@ -644,7 +645,7 @@
    ],
    "source": [
     "with pulse.build(backend, name='example') as program:\n",
-    "    gaussian_pulse = library.gaussian(100, 0.5, 20)\n",
+    "    gaussian_pulse = library.Gaussian(100, 0.5, 20)\n",
     "    with pulse.align_equispaced(2*gaussian_pulse.duration):\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(0))\n",
     "    pulse.play(gaussian_pulse, pulse.drive_channel(1))\n",
@@ -690,7 +691,7 @@
    "source": [
     "with pulse.build(backend, name='example') as program:\n",
     "    with pulse.align_sequential():\n",
-    "        gaussian_pulse = library.gaussian(100, 0.5, 20)\n",
+    "        gaussian_pulse = library.Gaussian(100, 0.5, 20)\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(0))\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(1))\n",
     "        pulse.play(gaussian_pulse, pulse.drive_channel(1))\n",
@@ -808,7 +809,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.17"
+   "version": "3.9.12"
   },
   "vscode": {
    "interpreter": {
diff --git a/qiskit/pulse/library/discrete.py b/qiskit/pulse/library/discrete.py
index 046944471270..15e79fd14458 100644
--- a/qiskit/pulse/library/discrete.py
+++ b/qiskit/pulse/library/discrete.py
@@ -28,14 +28,14 @@
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including constant() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including constant() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Constant(...).get_waveform(). "
-    " Note that complex value support for the `amp` parameter is pending deprecation"
-    " in the SymbolicPulse library. It is therefore recommended to use two float values"
+    " Note that complex value support for the `amp` parameter is deprecated"
+    " in the SymbolicPulse library. Use two float values"
     " for (`amp`, `angle`) instead of complex `amp`",
-    pending=True,
+    pending=False,
 )
 def constant(duration: int, amp: complex, name: Optional[str] = None) -> Waveform:
     r"""Generates constant-sampled :class:`~qiskit.pulse.library.Waveform`.
@@ -58,11 +58,11 @@ def constant(duration: int, amp: complex, name: Optional[str] = None) -> Wavefor
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including zero() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including zero() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Constant(amp=0,...).get_waveform().",
-    pending=True,
+    pending=False,
 )
 def zero(duration: int, name: Optional[str] = None) -> Waveform:
     """Generates zero-sampled :class:`~qiskit.pulse.library.Waveform`.
@@ -84,13 +84,13 @@ def zero(duration: int, name: Optional[str] = None) -> Waveform:
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including square() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including square() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Square(...).get_waveform()."
     " Note that pulse.Square() does not support complex values for `amp`,"
     " and that the phase is defined differently. See documentation.",
-    pending=True,
+    pending=False,
 )
 def square(
     duration: int, amp: complex, freq: float = None, phase: float = 0, name: Optional[str] = None
@@ -125,15 +125,15 @@ def square(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including sawtooth() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including sawtooth() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Sawtooth(...).get_waveform()."
     " Note that pulse.Sawtooth() does not support complex values for `amp`."
     " Instead, use two float values for (`amp`, `angle`)."
     " Also note that the phase is defined differently, such that 2*pi phase"
     " shifts by a full cycle.",
-    pending=True,
+    pending=False,
 )
 def sawtooth(
     duration: int, amp: complex, freq: float = None, phase: float = 0, name: Optional[str] = None
@@ -182,13 +182,13 @@ def sawtooth(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including triangle() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including triangle() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Triangle(...).get_waveform()."
     " Note that pulse.Triangle() does not support complex values for `amp`."
     " Instead, use two float values for (`amp`, `angle`).",
-    pending=True,
+    pending=False,
 )
 def triangle(
     duration: int, amp: complex, freq: float = None, phase: float = 0, name: Optional[str] = None
@@ -237,13 +237,13 @@ def triangle(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including cos() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including cos() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Cos(...).get_waveform()."
     " Note that pulse.Cos() does not support complex values for `amp`."
     " Instead, use two float values for (`amp`, `angle`).",
-    pending=True,
+    pending=False,
 )
 def cos(
     duration: int, amp: complex, freq: float = None, phase: float = 0, name: Optional[str] = None
@@ -275,13 +275,13 @@ def cos(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including sin() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including sin() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Sin(...).get_waveform()."
     " Note that pulse.Sin() does not support complex values for `amp`."
     " Instead, use two float values for (`amp`, `angle`).",
-    pending=True,
+    pending=False,
 )
 def sin(
     duration: int, amp: complex, freq: float = None, phase: float = 0, name: Optional[str] = None
@@ -313,14 +313,14 @@ def sin(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including gaussian() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including gaussian() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Gaussian(...).get_waveform()."
-    " Note that complex value support for the `amp` parameter is pending deprecation"
+    " Note that complex value support for the `amp` parameter is deprecated"
     " in the SymbolicPulse library. It is therefore recommended to use two float values"
     " for (`amp`, `angle`) instead of complex `amp`",
-    pending=True,
+    pending=False,
 )
 def gaussian(
     duration: int, amp: complex, sigma: float, name: Optional[str] = None, zero_ends: bool = True
@@ -367,13 +367,13 @@ def gaussian(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including gaussian_deriv() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including gaussian_deriv() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.GaussianDeriv(...).get_waveform()."
     " Note that pulse.GaussianDeriv() does not support complex values for `amp`."
     " Instead, use two float values for (`amp`, `angle`).",
-    pending=True,
+    pending=False,
 )
 def gaussian_deriv(
     duration: int, amp: complex, sigma: float, name: Optional[str] = None
@@ -404,13 +404,13 @@ def gaussian_deriv(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including sech() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including sech() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Sech(...).get_waveform()."
     " Note that pulse.Sech() does not support complex values for `amp`."
     " Instead, use two float values for (`amp`, `angle`).",
-    pending=True,
+    pending=False,
 )
 def sech(
     duration: int, amp: complex, sigma: float, name: str = None, zero_ends: bool = True
@@ -455,13 +455,13 @@ def sech(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including sech_deriv() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including sech_deriv() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.SechDeriv(...).get_waveform()."
     " Note that pulse.SechDeriv() does not support complex values for `amp`."
     " Instead, use two float values for (`amp`, `angle`).",
-    pending=True,
+    pending=False,
 )
 def sech_deriv(duration: int, amp: complex, sigma: float, name: str = None) -> Waveform:
     r"""Generates unnormalized sech derivative :class:`~qiskit.pulse.library.Waveform`.
@@ -489,14 +489,14 @@ def sech_deriv(duration: int, amp: complex, sigma: float, name: str = None) -> W
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including gaussian_square() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including gaussian_square() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.GaussianSquare(...).get_waveform()."
-    " Note that complex value support for the `amp` parameter is pending deprecation"
+    " Note that complex value support for the `amp` parameter is deprecated"
     " in the SymbolicPulse library. It is therefore recommended to use two float values"
     " for (`amp`, `angle`) instead of complex `amp`",
-    pending=True,
+    pending=False,
 )
 def gaussian_square(
     duration: int,
@@ -565,14 +565,14 @@ def gaussian_square(
 
 
 @deprecate_func(
-    since="0.25.0",
-    additional_msg="The discrete pulses library, including drag() is pending deprecation."
+    since="0.46.0",
+    additional_msg="The discrete pulses library, including drag() is deprecated."
     " Instead, use the SymbolicPulse library to create the waveform with"
     " pulse.Drag(...).get_waveform()."
-    " Note that complex value support for the `amp` parameter is pending deprecation"
+    " Note that complex value support for the `amp` parameter is deprecated"
     " in the SymbolicPulse library. It is therefore recommended to use two float values"
     " for (`amp`, `angle`) instead of complex `amp`",
-    pending=True,
+    pending=False,
 )
 def drag(
     duration: int,
diff --git a/releasenotes/notes/deprecate-discrete-pulse-library-d2482407d7965972.yaml b/releasenotes/notes/deprecate-discrete-pulse-library-d2482407d7965972.yaml
new file mode 100644
index 000000000000..a1324ea03669
--- /dev/null
+++ b/releasenotes/notes/deprecate-discrete-pulse-library-d2482407d7965972.yaml
@@ -0,0 +1,25 @@
+---
+deprecations:
+  - |
+    The discrete pulse library is now deprecated and will be removed in a future release. This includes:
+
+    * :func:`~qiskit.pulse.library.constant`
+    * :func:`~qiskit.pulse.library.zero`
+    * :func:`~qiskit.pulse.library.square`
+    * :func:`~qiskit.pulse.library.sawtooth`
+    * :func:`~qiskit.pulse.library.triangle`
+    * :func:`~qiskit.pulse.library.cos`
+    * :func:`~qiskit.pulse.library.sin`
+    * :func:`~qiskit.pulse.library.gaussian`
+    * :func:`~qiskit.pulse.library.gaussian_deriv`
+    * :func:`~qiskit.pulse.library.sech`
+    * :func:`~qiskit.pulse.library.sech_deriv`
+    * :func:`~qiskit.pulse.library.gaussian_square`
+    * :func:`~qiskit.pulse.library.drag`
+
+    Instead, use the corresponding :class:`~qiskit.pulse.SymbolicPulse`, with :meth:`~.SymbolicPulse.get_waveform()`.
+    For example, instead of ``pulse.gaussian(100,0.5,10)`` use ``pulse.Gaussian(100,0.5,10).get_waveform()``.
+
+    Note that the phase of both ``Sawtooth`` and ``Square`` is defined such that a phase of :math:``2\\pi``
+    shifts by a full cycle, contrary to the discrete counterpart. Also note that complex amplitude support is
+    deprecated in the symbolic pulse library - use ``float`` ``amp`` and  ``angle`` instead.
diff --git a/test/python/compiler/test_assembler.py b/test/python/compiler/test_assembler.py
index 2641012857a6..56f7e90b7af8 100644
--- a/test/python/compiler/test_assembler.py
+++ b/test/python/compiler/test_assembler.py
@@ -27,7 +27,6 @@
 from qiskit.pulse import Schedule, Acquire, Play
 from qiskit.pulse.channels import MemorySlot, AcquireChannel, DriveChannel, MeasureChannel
 from qiskit.pulse.configuration import Kernel, Discriminator
-from qiskit.pulse.library import gaussian
 from qiskit.qobj import QasmQobj, PulseQobj
 from qiskit.qobj.utils import MeasLevel, MeasReturnType
 from qiskit.pulse.macros import measure
@@ -1201,7 +1200,7 @@ def test_pulse_name_conflicts_in_other_schedule(self):
         ch_d0 = pulse.DriveChannel(0)
         for amp in (0.1, 0.2):
             sched = Schedule()
-            sched += Play(gaussian(duration=100, amp=amp, sigma=30, name="my_pulse"), ch_d0)
+            sched += Play(pulse.Gaussian(duration=100, amp=amp, sigma=30, name="my_pulse"), ch_d0)
             sched += measure(qubits=[0], backend=backend) << 100
             schedules.append(sched)
 
diff --git a/test/python/pulse/test_discrete_pulses.py b/test/python/pulse/test_discrete_pulses.py
index 53157d54c5f8..59cb2583f115 100644
--- a/test/python/pulse/test_discrete_pulses.py
+++ b/test/python/pulse/test_discrete_pulses.py
@@ -28,7 +28,8 @@ def test_constant(self):
         duration = 10
         times = np.arange(0, duration) + 0.5  # to match default midpoint sampling strategy
         constant_ref = continuous.constant(times, amp=amp)
-        constant_pulse = library.constant(duration, amp=amp)
+        with self.assertWarns(DeprecationWarning):
+            constant_pulse = library.constant(duration, amp=amp)
         self.assertIsInstance(constant_pulse, Waveform)
         np.testing.assert_array_almost_equal(constant_pulse.samples, constant_ref)
 
@@ -37,7 +38,8 @@ def test_zero(self):
         duration = 10
         times = np.arange(0, duration) + 0.5
         zero_ref = continuous.zero(times)
-        zero_pulse = library.zero(duration)
+        with self.assertWarns(DeprecationWarning):
+            zero_pulse = library.zero(duration)
         self.assertIsInstance(zero_pulse, Waveform)
         np.testing.assert_array_almost_equal(zero_pulse.samples, zero_ref)
 
@@ -48,14 +50,16 @@ def test_square(self):
         duration = 10
         times = np.arange(0, duration) + 0.5
         square_ref = continuous.square(times, amp=amp, freq=freq)
-        square_pulse = library.square(duration, amp=amp, freq=freq)
+        with self.assertWarns(DeprecationWarning):
+            square_pulse = library.square(duration, amp=amp, freq=freq)
         self.assertIsInstance(square_pulse, Waveform)
         np.testing.assert_array_almost_equal(square_pulse.samples, square_ref)
 
         # test single cycle
         cycle_freq = 1.0 / duration
         square_cycle_ref = continuous.square(times, amp=amp, freq=cycle_freq)
-        square_cycle_pulse = library.square(duration, amp=amp)
+        with self.assertWarns(DeprecationWarning):
+            square_cycle_pulse = library.square(duration, amp=amp)
         np.testing.assert_array_almost_equal(square_cycle_pulse.samples, square_cycle_ref)
 
     def test_sawtooth(self):
@@ -65,14 +69,16 @@ def test_sawtooth(self):
         duration = 10
         times = np.arange(0, duration) + 0.5
         sawtooth_ref = continuous.sawtooth(times, amp=amp, freq=freq)
-        sawtooth_pulse = library.sawtooth(duration, amp=amp, freq=freq)
+        with self.assertWarns(DeprecationWarning):
+            sawtooth_pulse = library.sawtooth(duration, amp=amp, freq=freq)
         self.assertIsInstance(sawtooth_pulse, Waveform)
         np.testing.assert_array_equal(sawtooth_pulse.samples, sawtooth_ref)
 
         # test single cycle
         cycle_freq = 1.0 / duration
         sawtooth_cycle_ref = continuous.sawtooth(times, amp=amp, freq=cycle_freq)
-        sawtooth_cycle_pulse = library.sawtooth(duration, amp=amp)
+        with self.assertWarns(DeprecationWarning):
+            sawtooth_cycle_pulse = library.sawtooth(duration, amp=amp)
         np.testing.assert_array_almost_equal(sawtooth_cycle_pulse.samples, sawtooth_cycle_ref)
 
     def test_triangle(self):
@@ -82,14 +88,16 @@ def test_triangle(self):
         duration = 10
         times = np.arange(0, duration) + 0.5
         triangle_ref = continuous.triangle(times, amp=amp, freq=freq)
-        triangle_pulse = library.triangle(duration, amp=amp, freq=freq)
+        with self.assertWarns(DeprecationWarning):
+            triangle_pulse = library.triangle(duration, amp=amp, freq=freq)
         self.assertIsInstance(triangle_pulse, Waveform)
         np.testing.assert_array_almost_equal(triangle_pulse.samples, triangle_ref)
 
         # test single cycle
         cycle_freq = 1.0 / duration
         triangle_cycle_ref = continuous.triangle(times, amp=amp, freq=cycle_freq)
-        triangle_cycle_pulse = library.triangle(duration, amp=amp)
+        with self.assertWarns(DeprecationWarning):
+            triangle_cycle_pulse = library.triangle(duration, amp=amp)
         np.testing.assert_array_equal(triangle_cycle_pulse.samples, triangle_cycle_ref)
 
     def test_cos(self):
@@ -100,14 +108,16 @@ def test_cos(self):
         duration = 10
         times = np.arange(0, duration) + 0.5
         cos_ref = continuous.cos(times, amp=amp, freq=freq)
-        cos_pulse = library.cos(duration, amp=amp, freq=freq)
+        with self.assertWarns(DeprecationWarning):
+            cos_pulse = library.cos(duration, amp=amp, freq=freq)
         self.assertIsInstance(cos_pulse, Waveform)
         np.testing.assert_array_almost_equal(cos_pulse.samples, cos_ref)
 
         # test single cycle
         cycle_freq = 1 / duration
         cos_cycle_ref = continuous.cos(times, amp=amp, freq=cycle_freq)
-        cos_cycle_pulse = library.cos(duration, amp=amp)
+        with self.assertWarns(DeprecationWarning):
+            cos_cycle_pulse = library.cos(duration, amp=amp)
         np.testing.assert_array_almost_equal(cos_cycle_pulse.samples, cos_cycle_ref)
 
     def test_sin(self):
@@ -118,14 +128,16 @@ def test_sin(self):
         duration = 10
         times = np.arange(0, duration) + 0.5
         sin_ref = continuous.sin(times, amp=amp, freq=freq)
-        sin_pulse = library.sin(duration, amp=amp, freq=freq)
+        with self.assertWarns(DeprecationWarning):
+            sin_pulse = library.sin(duration, amp=amp, freq=freq)
         self.assertIsInstance(sin_pulse, Waveform)
         np.testing.assert_array_equal(sin_pulse.samples, sin_ref)
 
         # test single cycle
         cycle_freq = 1 / duration
         sin_cycle_ref = continuous.sin(times, amp=amp, freq=cycle_freq)
-        sin_cycle_pulse = library.sin(duration, amp=amp)
+        with self.assertWarns(DeprecationWarning):
+            sin_cycle_pulse = library.sin(duration, amp=amp)
         np.testing.assert_array_almost_equal(sin_cycle_pulse.samples, sin_cycle_ref)
 
     def test_gaussian(self):
@@ -138,7 +150,8 @@ def test_gaussian(self):
         gaussian_ref = continuous.gaussian(
             times, amp, center, sigma, zeroed_width=2 * (center + 1), rescale_amp=True
         )
-        gaussian_pulse = library.gaussian(duration, amp, sigma)
+        with self.assertWarns(DeprecationWarning):
+            gaussian_pulse = library.gaussian(duration, amp, sigma)
         self.assertIsInstance(gaussian_pulse, Waveform)
         np.testing.assert_array_almost_equal(gaussian_pulse.samples, gaussian_ref)
 
@@ -150,7 +163,8 @@ def test_gaussian_deriv(self):
         center = duration / 2
         times = np.arange(0, duration) + 0.5
         gaussian_deriv_ref = continuous.gaussian_deriv(times, amp, center, sigma)
-        gaussian_deriv_pulse = library.gaussian_deriv(duration, amp, sigma)
+        with self.assertWarns(DeprecationWarning):
+            gaussian_deriv_pulse = library.gaussian_deriv(duration, amp, sigma)
         self.assertIsInstance(gaussian_deriv_pulse, Waveform)
         np.testing.assert_array_almost_equal(gaussian_deriv_pulse.samples, gaussian_deriv_ref)
 
@@ -164,7 +178,8 @@ def test_sech(self):
         sech_ref = continuous.sech(
             times, amp, center, sigma, zeroed_width=2 * (center + 1), rescale_amp=True
         )
-        sech_pulse = library.sech(duration, amp, sigma)
+        with self.assertWarns(DeprecationWarning):
+            sech_pulse = library.sech(duration, amp, sigma)
         self.assertIsInstance(sech_pulse, Waveform)
         np.testing.assert_array_almost_equal(sech_pulse.samples, sech_ref)
 
@@ -176,7 +191,8 @@ def test_sech_deriv(self):
         center = duration / 2
         times = np.arange(0, duration) + 0.5
         sech_deriv_ref = continuous.sech_deriv(times, amp, center, sigma)
-        sech_deriv_pulse = library.sech_deriv(duration, amp, sigma)
+        with self.assertWarns(DeprecationWarning):
+            sech_deriv_pulse = library.sech_deriv(duration, amp, sigma)
         self.assertIsInstance(sech_deriv_pulse, Waveform)
         np.testing.assert_array_almost_equal(sech_deriv_pulse.samples, sech_deriv_ref)
 
@@ -191,7 +207,8 @@ def test_gaussian_square(self):
         center = duration / 2
         times = np.arange(0, duration) + 0.5
         gaussian_square_ref = continuous.gaussian_square(times, amp, center, width, sigma)
-        gaussian_square_pulse = library.gaussian_square(duration, amp, sigma, risefall)
+        with self.assertWarns(DeprecationWarning):
+            gaussian_square_pulse = library.gaussian_square(duration, amp, sigma, risefall)
         self.assertIsInstance(gaussian_square_pulse, Waveform)
         np.testing.assert_array_almost_equal(gaussian_square_pulse.samples, gaussian_square_ref)
 
@@ -201,13 +218,17 @@ def test_gaussian_square_args(self):
         sigma = 0.1
         duration = 10
         # risefall and width consistent: no error
-        library.gaussian_square(duration, amp, sigma, 2, width=6)
+        with self.assertWarns(DeprecationWarning):
+            library.gaussian_square(duration, amp, sigma, 2, width=6)
         # supply width instead: no error
-        library.gaussian_square(duration, amp, sigma, width=6)
+        with self.assertWarns(DeprecationWarning):
+            library.gaussian_square(duration, amp, sigma, width=6)
         with self.assertRaises(PulseError):
-            library.gaussian_square(duration, amp, sigma, width=2, risefall=2)
+            with self.assertWarns(DeprecationWarning):
+                library.gaussian_square(duration, amp, sigma, width=2, risefall=2)
         with self.assertRaises(PulseError):
-            library.gaussian_square(duration, amp, sigma)
+            with self.assertWarns(DeprecationWarning):
+                library.gaussian_square(duration, amp, sigma)
 
     def test_drag(self):
         """Test discrete sampled drag pulse."""
@@ -221,35 +242,7 @@ def test_drag(self):
         drag_ref = continuous.drag(
             times, amp, center, sigma, beta=beta, zeroed_width=2 * (center + 1), rescale_amp=True
         )
-        drag_pulse = library.drag(duration, amp, sigma, beta=beta)
+        with self.assertWarns(DeprecationWarning):
+            drag_pulse = library.drag(duration, amp, sigma, beta=beta)
         self.assertIsInstance(drag_pulse, Waveform)
         np.testing.assert_array_almost_equal(drag_pulse.samples, drag_ref)
-
-    def test_pending_deprecation_warnings(self):
-        """Test that pending deprecation warnings are raised when the discrete library is used."""
-        with self.assertWarns(PendingDeprecationWarning):
-            library.drag(duration=10, amp=0.5, sigma=0.1, beta=0.1)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.gaussian_square(duration=10, amp=0.5, sigma=0.1, risefall=2, width=6)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.gaussian(duration=10, amp=0.5, sigma=0.1)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.sin(duration=10, amp=0.5)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.cos(duration=10, amp=0.5)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.sawtooth(duration=10, amp=0.5)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.zero(duration=10)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.constant(duration=10, amp=0.5)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.triangle(duration=10, amp=0.5)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.gaussian_deriv(duration=10, amp=0.5, sigma=3)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.sech_deriv(duration=10, amp=0.5, sigma=3)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.sech(duration=10, amp=0.5, sigma=3)
-        with self.assertWarns(PendingDeprecationWarning):
-            library.square(duration=10, amp=0.5)
diff --git a/test/python/pulse/test_instruction_schedule_map.py b/test/python/pulse/test_instruction_schedule_map.py
index 414a1d6566a5..72058ba08303 100644
--- a/test/python/pulse/test_instruction_schedule_map.py
+++ b/test/python/pulse/test_instruction_schedule_map.py
@@ -350,11 +350,15 @@ def test_schedule_generator(self):
 
         def test_func(dur: int):
             sched = Schedule()
-            sched += Play(library.constant(int(dur), amp), DriveChannel(0))
+            with self.assertWarns(DeprecationWarning):
+                waveform = library.constant(int(dur), amp)
+            sched += Play(waveform, DriveChannel(0))
             return sched
 
         expected_sched = Schedule()
-        expected_sched += Play(library.constant(dur_val, amp), DriveChannel(0))
+        with self.assertWarns(DeprecationWarning):
+            cons_waveform = library.constant(dur_val, amp)
+        expected_sched += Play(cons_waveform, DriveChannel(0))
 
         inst_map = InstructionScheduleMap()
         inst_map.add("f", (0,), test_func)
@@ -371,11 +375,15 @@ def test_schedule_generator_supports_parameter_expressions(self):
         def test_func(dur: ParameterExpression, t_val: int):
             dur_bound = dur.bind({t_param: t_val})
             sched = Schedule()
-            sched += Play(library.constant(int(float(dur_bound)), amp), DriveChannel(0))
+            with self.assertWarns(DeprecationWarning):
+                waveform = library.constant(int(float(dur_bound)), amp)
+            sched += Play(waveform, DriveChannel(0))
             return sched
 
         expected_sched = Schedule()
-        expected_sched += Play(library.constant(10, amp), DriveChannel(0))
+        with self.assertWarns(DeprecationWarning):
+            cons_waveform = library.constant(10, amp)
+        expected_sched += Play(cons_waveform, DriveChannel(0))
 
         inst_map = InstructionScheduleMap()
         inst_map.add("f", (0,), test_func)
diff --git a/test/python/pulse/test_pulse_lib.py b/test/python/pulse/test_pulse_lib.py
index 524861020d22..97c9cf332d2c 100644
--- a/test/python/pulse/test_pulse_lib.py
+++ b/test/python/pulse/test_pulse_lib.py
@@ -189,7 +189,8 @@ def test_gaussian_pulse(self):
         gauss = Gaussian(duration=25, sigma=4, amp=0.5, angle=np.pi / 2)
         sample_pulse = gauss.get_waveform()
         self.assertIsInstance(sample_pulse, Waveform)
-        pulse_lib_gauss = gaussian(duration=25, sigma=4, amp=0.5j, zero_ends=True).samples
+        with self.assertWarns(DeprecationWarning):
+            pulse_lib_gauss = gaussian(duration=25, sigma=4, amp=0.5j, zero_ends=True).samples
         np.testing.assert_almost_equal(sample_pulse.samples, pulse_lib_gauss)
 
     def test_gaussian_square_pulse(self):
@@ -197,18 +198,20 @@ def test_gaussian_square_pulse(self):
         gauss_sq = GaussianSquare(duration=125, sigma=4, amp=0.5, width=100, angle=np.pi / 2)
         sample_pulse = gauss_sq.get_waveform()
         self.assertIsInstance(sample_pulse, Waveform)
-        pulse_lib_gauss_sq = gaussian_square(
-            duration=125, sigma=4, amp=0.5j, width=100, zero_ends=True
-        ).samples
+        with self.assertWarns(DeprecationWarning):
+            pulse_lib_gauss_sq = gaussian_square(
+                duration=125, sigma=4, amp=0.5j, width=100, zero_ends=True
+            ).samples
         np.testing.assert_almost_equal(sample_pulse.samples, pulse_lib_gauss_sq)
         gauss_sq = GaussianSquare(
             duration=125, sigma=4, amp=0.5, risefall_sigma_ratio=3.125, angle=np.pi / 2
         )
         sample_pulse = gauss_sq.get_waveform()
         self.assertIsInstance(sample_pulse, Waveform)
-        pulse_lib_gauss_sq = gaussian_square(
-            duration=125, sigma=4, amp=0.5j, width=100, zero_ends=True
-        ).samples
+        with self.assertWarns(DeprecationWarning):
+            pulse_lib_gauss_sq = gaussian_square(
+                duration=125, sigma=4, amp=0.5j, width=100, zero_ends=True
+            ).samples
         np.testing.assert_almost_equal(sample_pulse.samples, pulse_lib_gauss_sq)
 
     def test_gauss_square_extremes(self):
@@ -374,7 +377,8 @@ def test_drag_pulse(self):
         drag = Drag(duration=25, sigma=4, amp=0.5, beta=1, angle=np.pi / 2)
         sample_pulse = drag.get_waveform()
         self.assertIsInstance(sample_pulse, Waveform)
-        pulse_lib_drag = pl_drag(duration=25, sigma=4, amp=0.5j, beta=1, zero_ends=True).samples
+        with self.assertWarns(DeprecationWarning):
+            pulse_lib_drag = pl_drag(duration=25, sigma=4, amp=0.5j, beta=1, zero_ends=True).samples
         np.testing.assert_almost_equal(sample_pulse.samples, pulse_lib_drag)
 
     def test_drag_validation(self):
@@ -426,7 +430,8 @@ def test_sin_pulse(self):
         phase = 0
 
         sin_pulse = Sin(duration=duration, amp=amp, freq=freq, phase=phase)
-        sin_waveform = sin(duration=duration, amp=amp, freq=freq, phase=phase)
+        with self.assertWarns(DeprecationWarning):
+            sin_waveform = sin(duration=duration, amp=amp, freq=freq, phase=phase)
 
         np.testing.assert_almost_equal(sin_pulse.get_waveform().samples, sin_waveform.samples)
 
@@ -440,7 +445,8 @@ def test_cos_pulse(self):
         freq = 0.1
         phase = 0
         cos_pulse = Cos(duration=duration, amp=amp, freq=freq, phase=phase)
-        cos_waveform = cos(duration=duration, amp=amp, freq=freq, phase=phase)
+        with self.assertWarns(DeprecationWarning):
+            cos_waveform = cos(duration=duration, amp=amp, freq=freq, phase=phase)
         np.testing.assert_almost_equal(cos_pulse.get_waveform().samples, cos_waveform.samples)
 
         shifted_sin_pulse = Sin(duration=duration, amp=amp, freq=freq, phase=phase + np.pi / 2)
@@ -457,7 +463,8 @@ def test_square_pulse(self):
         freq = 0.1
         phase = 0.3
         square_pulse = Square(duration=duration, amp=amp, freq=freq, phase=phase)
-        square_waveform = square(duration=duration, amp=amp, freq=freq, phase=phase / 2)
+        with self.assertWarns(DeprecationWarning):
+            square_waveform = square(duration=duration, amp=amp, freq=freq, phase=phase / 2)
 
         np.testing.assert_almost_equal(square_pulse.get_waveform().samples, square_waveform.samples)
 
@@ -471,7 +478,8 @@ def test_sawtooth_pulse(self):
         freq = 0.1
         phase = 0.5
         sawtooth_pulse = Sawtooth(duration=duration, amp=amp, freq=freq, phase=phase)
-        sawtooth_waveform = sawtooth(duration=duration, amp=amp, freq=freq, phase=phase / 2)
+        with self.assertWarns(DeprecationWarning):
+            sawtooth_waveform = sawtooth(duration=duration, amp=amp, freq=freq, phase=phase / 2)
         # Note that the phase definition in `Sawtooth` was changed compared to `sawtooth`
         np.testing.assert_almost_equal(
             sawtooth_pulse.get_waveform().samples, sawtooth_waveform.samples
@@ -491,7 +499,8 @@ def test_triangle_pulse(self):
         freq = 0.1
         phase = 0.5
         triangle_pulse = Triangle(duration=duration, amp=amp, freq=freq, phase=phase)
-        triangle_waveform = triangle(duration=duration, amp=amp, freq=freq, phase=phase)
+        with self.assertWarns(DeprecationWarning):
+            triangle_waveform = triangle(duration=duration, amp=amp, freq=freq, phase=phase)
         np.testing.assert_almost_equal(
             triangle_pulse.get_waveform().samples, triangle_waveform.samples
         )
@@ -509,7 +518,8 @@ def test_gaussian_deriv_pulse(self):
         amp = 0.5
         sigma = 100
         gaussian_deriv_pulse = GaussianDeriv(duration=duration, amp=amp, sigma=sigma)
-        gaussian_deriv_waveform = gaussian_deriv(duration=duration, amp=amp, sigma=sigma)
+        with self.assertWarns(DeprecationWarning):
+            gaussian_deriv_waveform = gaussian_deriv(duration=duration, amp=amp, sigma=sigma)
         np.testing.assert_almost_equal(
             gaussian_deriv_pulse.get_waveform().samples, gaussian_deriv_waveform.samples
         )
@@ -523,12 +533,14 @@ def test_sech_pulse(self):
         sigma = 10
         # Zero ends = True
         sech_pulse = Sech(duration=duration, amp=amp, sigma=sigma)
-        sech_waveform = sech(duration=duration, amp=amp, sigma=sigma)
+        with self.assertWarns(DeprecationWarning):
+            sech_waveform = sech(duration=duration, amp=amp, sigma=sigma)
         np.testing.assert_almost_equal(sech_pulse.get_waveform().samples, sech_waveform.samples)
 
         # Zero ends = False
         sech_pulse = Sech(duration=duration, amp=amp, sigma=sigma, zero_ends=False)
-        sech_waveform = sech(duration=duration, amp=amp, sigma=sigma, zero_ends=False)
+        with self.assertWarns(DeprecationWarning):
+            sech_waveform = sech(duration=duration, amp=amp, sigma=sigma, zero_ends=False)
         np.testing.assert_almost_equal(sech_pulse.get_waveform().samples, sech_waveform.samples)
 
         with self.assertRaises(PulseError):
@@ -540,7 +552,8 @@ def test_sech_deriv_pulse(self):
         amp = 0.5
         sigma = 10
         sech_deriv_pulse = SechDeriv(duration=duration, amp=amp, sigma=sigma)
-        sech_deriv_waveform = sech_deriv(duration=duration, amp=amp, sigma=sigma)
+        with self.assertWarns(DeprecationWarning):
+            sech_deriv_waveform = sech_deriv(duration=duration, amp=amp, sigma=sigma)
         np.testing.assert_almost_equal(
             sech_deriv_pulse.get_waveform().samples, sech_deriv_waveform.samples
         )
diff --git a/test/python/pulse/test_schedule.py b/test/python/pulse/test_schedule.py
index 654b4ffe39f9..006925c586ba 100644
--- a/test/python/pulse/test_schedule.py
+++ b/test/python/pulse/test_schedule.py
@@ -117,8 +117,9 @@ def test_fail_to_insert_instruction_into_occupied_timing(self):
 
     def test_can_create_valid_schedule(self):
         """Test valid schedule creation without error."""
-        gp0 = library.gaussian(duration=20, amp=0.7, sigma=3)
-        gp1 = library.gaussian(duration=20, amp=0.7, sigma=3)
+        with self.assertWarns(DeprecationWarning):
+            gp0 = library.gaussian(duration=20, amp=0.7, sigma=3)
+            gp1 = library.gaussian(duration=20, amp=0.7, sigma=3)
 
         sched = Schedule()
         sched = sched.append(Play(gp0, self.config.drive(0)))
@@ -147,8 +148,9 @@ def test_can_create_valid_schedule(self):
     def test_can_create_valid_schedule_with_syntax_sugar(self):
         """Test that in place operations on schedule are still immutable
         and return equivalent schedules."""
-        gp0 = library.gaussian(duration=20, amp=0.7, sigma=3)
-        gp1 = library.gaussian(duration=20, amp=0.5, sigma=3)
+        with self.assertWarns(DeprecationWarning):
+            gp0 = library.gaussian(duration=20, amp=0.7, sigma=3)
+            gp1 = library.gaussian(duration=20, amp=0.5, sigma=3)
 
         sched = Schedule()
         sched += Play(gp0, self.config.drive(0))
@@ -162,8 +164,9 @@ def test_can_create_valid_schedule_with_syntax_sugar(self):
 
     def test_immutability(self):
         """Test that operations are immutable."""
-        gp0 = library.gaussian(duration=100, amp=0.7, sigma=3)
-        gp1 = library.gaussian(duration=20, amp=0.5, sigma=3)
+        with self.assertWarns(DeprecationWarning):
+            gp0 = library.gaussian(duration=100, amp=0.7, sigma=3)
+            gp1 = library.gaussian(duration=20, amp=0.5, sigma=3)
 
         sched = Play(gp1, self.config.drive(0)) << 100
         # if schedule was mutable the next two sequences would overlap and an error
@@ -173,8 +176,9 @@ def test_immutability(self):
 
     def test_inplace(self):
         """Test that in place operations on schedule are still immutable."""
-        gp0 = library.gaussian(duration=100, amp=0.7, sigma=3)
-        gp1 = library.gaussian(duration=20, amp=0.5, sigma=3)
+        with self.assertWarns(DeprecationWarning):
+            gp0 = library.gaussian(duration=100, amp=0.7, sigma=3)
+            gp1 = library.gaussian(duration=20, amp=0.5, sigma=3)
 
         sched = Schedule()
         sched = sched + Play(gp1, self.config.drive(0))
@@ -300,7 +304,8 @@ def test_auto_naming(self, is_main_process_mock):
 
     def test_name_inherited(self):
         """Test that schedule keeps name if an instruction is added."""
-        gp0 = library.gaussian(duration=100, amp=0.7, sigma=3, name="pulse_name")
+        with self.assertWarns(DeprecationWarning):
+            gp0 = library.gaussian(duration=100, amp=0.7, sigma=3, name="pulse_name")
         snapshot = Snapshot("snapshot_label", "state")
 
         sched1 = Schedule(name="test_name")
diff --git a/test/python/visualization/pulse_v2/test_generators.py b/test/python/visualization/pulse_v2/test_generators.py
index 9c9c5110f8bc..35f1ad16c6dc 100644
--- a/test/python/visualization/pulse_v2/test_generators.py
+++ b/test/python/visualization/pulse_v2/test_generators.py
@@ -83,7 +83,7 @@ def test_consecutive_index_tiny_diff(self):
 
     def test_parse_waveform(self):
         """Test helper function that parse waveform with Waveform instance."""
-        test_pulse = pulse.library.gaussian(10, 0.1, 3)
+        test_pulse = pulse.library.Gaussian(10, 0.1, 3).get_waveform()
 
         inst = pulse.Play(test_pulse, pulse.DriveChannel(0))
         inst_data = create_instruction(inst, 0, 0, 10, 0.1)