DOC: plot with SVG where needed

ComPWA · Dec 2, 2023 · f65cd69 · f65cd69
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Showing 7 changed files with 69 additions and 47 deletions.
diff --git a/docs/amplitude-analysis.ipynb b/docs/amplitude-analysis.ipynb
@@ -748,15 +748,15 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib import cm\n",
     "\n",
     "resonances = sorted(\n",
     "    reaction.get_intermediate_particles(),\n",
     "    key=lambda p: p.mass,\n",
     ")\n",
     "evenly_spaced_interval = np.linspace(0, 1, len(resonances))\n",
-    "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n",
+    "colors = [plt.cm.rainbow(x) for x in evenly_spaced_interval]\n",
     "fig, ax = plt.subplots(figsize=(9, 4))\n",
     "ax.hist(\n",
     "    np.real(data_frame[\"m_12\"]),\n",
@@ -1465,15 +1465,14 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from matplotlib import cm\n",
     "\n",
     "resonances = sorted(\n",
     "    reaction.get_intermediate_particles(),\n",
     "    key=lambda p: p.mass,\n",
     ")\n",
     "\n",
     "evenly_spaced_interval = np.linspace(0, 1, len(resonances))\n",
-    "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n",
+    "colors = [plt.cm.rainbow(x) for x in evenly_spaced_interval]\n",
     "\n",
     "\n",
     "def indicate_masses(ax):\n",
@@ -1917,7 +1916,7 @@
    },
    "outputs": [],
    "source": [
-    "fig, ax = plt.subplots(1, figsize=(8, 5))\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
     "bins = 150\n",
     "phsp_projection = np.real(phsp[\"m_12\"])\n",
     "ax.hist(\n",
@@ -1989,7 +1988,7 @@
     "    sub_dataset = helicity_transformer(sub_events)\n",
     "    masses.append(np.real(sub_dataset[\"m_12\"]))\n",
     "\n",
-    "fig, ax = plt.subplots(1, figsize=(8, 5))\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
     "plt.hist(masses, bins=100, stacked=True, alpha=0.6)\n",
     "ax.set_xlim(0.25, 2.5)\n",
     "ax.set_xlabel(R\"$m_{\\pi^0\\pi^0}$ [GeV]\")\n",

diff --git a/docs/amplitude-analysis/analytic-continuation.ipynb b/docs/amplitude-analysis/analytic-continuation.ipynb
@@ -256,8 +256,8 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import numpy as np\n",
-    "from matplotlib import cm\n",
     "\n",
     "phsp = helicity_transformer(phsp_momenta)\n",
     "intensities = np.array(intensity(phsp))\n",
@@ -267,7 +267,7 @@
     "    key=lambda p: p.mass,\n",
     ")\n",
     "evenly_spaced_interval = np.linspace(0, 1, len(resonances))\n",
-    "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n",
+    "colors = [plt.cm.rainbow(x) for x in evenly_spaced_interval]\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.set_xlabel(\"$m_{02}$ [GeV]\")\n",

diff --git a/docs/usage.ipynb b/docs/usage.ipynb
@@ -191,6 +191,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(5, 3))\n",
@@ -245,6 +246,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "%matplotlib widget\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.animation import PillowWriter\n",
@@ -702,19 +704,20 @@
      "source_hidden": true
     },
     "tags": [
+     "full-width",
      "hide-input"
     ]
    },
    "outputs": [],
    "source": [
-    "from matplotlib import cm\n",
+    "%config InlineBackend.figure_formats = ['png']\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(7, 4.3))\n",
     "fig.canvas.toolbar_visible = False\n",
     "fig.canvas.header_visible = False\n",
     "fig.canvas.footer_visible = False\n",
-    "ax1.hist2d(*cartesian_data.values(), bins=100, cmap=cm.coolwarm)\n",
-    "ax2.hist2d(polar_data[\"phi\"], polar_data[\"r\"], bins=100, cmap=cm.coolwarm)\n",
+    "ax1.hist2d(*cartesian_data.values(), bins=100, cmap=plt.cm.coolwarm)\n",
+    "ax2.hist2d(polar_data[\"phi\"], polar_data[\"r\"], bins=100, cmap=plt.cm.coolwarm)\n",
     "fig.suptitle(\"Hit-and-miss intensity distribution\")\n",
     "ax1.set_title(\"cartesian\")\n",
     "ax2.set_title(\"polar\")\n",
@@ -770,11 +773,11 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "%matplotlib widget\n",
     "import ipywidgets\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from matplotlib import cm\n",
     "\n",
     "size = 200\n",
     "X, Y = np.meshgrid(\n",
@@ -809,7 +812,7 @@
     "    Z = polar_function(polar_domain)\n",
     "    if color_mesh is not None:\n",
     "        color_mesh.remove()\n",
-    "    color_mesh = ax_interactive.pcolormesh(X, Y, Z, cmap=cm.coolwarm)"
+    "    color_mesh = ax_interactive.pcolormesh(X, Y, Z, cmap=plt.cm.coolwarm)"
    ]
   },
   {

diff --git a/docs/usage/basics.ipynb b/docs/usage/basics.ipynb
@@ -225,6 +225,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "parameter_defaults = {\n",
     "    a: 0.15,\n",
     "    b: 0.05,\n",
@@ -367,9 +368,12 @@
    },
    "outputs": [],
    "source": [
-    "plt.scatter(x_values, y_values)\n",
-    "plt.gca().set_xlabel(\"$x$\")\n",
-    "plt.gca().set_ylabel(\"$f(x)$\");"
+    "%config InlineBackend.figure_formats = ['svg']\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.scatter(x_values, y_values)\n",
+    "ax.set_xlabel(\"$x$\")\n",
+    "ax.set_ylabel(\"$f(x)$\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -420,50 +424,51 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "x_domain = np.linspace(0, 5, num=200)\n",
     "y_values = function_1d({\"x\": x_domain})\n",
-    "fig = plt.figure(figsize=(8, 5))\n",
-    "plt.plot(x_domain, y_values)\n",
-    "plt.gca().set_xlabel(\"$x$\")\n",
-    "plt.gca().set_ylabel(\"$f(x)$\")\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "ax.plot(x_domain, y_values)\n",
+    "ax.set_xlabel(\"$x$\")\n",
+    "ax.set_ylabel(\"$f(x)$\")\n",
     "\n",
     "x_min = x_range[1]\n",
     "x_max = x_range[2]\n",
     "y_max = 0.21\n",
     "x_value = 1.5\n",
     "\n",
     "line_segment = [[0, 0], [0, y_max]]\n",
-    "plt.plot(*line_segment, color=\"black\")\n",
-    "plt.text(\n",
+    "ax.plot(*line_segment, color=\"black\")\n",
+    "ax.text(\n",
     "    -0.22,\n",
     "    y_max / 2 * 0.5,\n",
     "    \"uniform sample $(0, y_{max})$\",\n",
     "    rotation=\"vertical\",\n",
     ")\n",
-    "plt.axhline(y=y_max, linestyle=\"dotted\", color=\"black\")\n",
-    "plt.text(\n",
+    "ax.axhline(y=y_max, linestyle=\"dotted\", color=\"black\")\n",
+    "ax.text(\n",
     "    x_min + 0.1,\n",
     "    y_max - 0.01,\n",
     "    \"$y_{max}$\",\n",
     ")\n",
     "\n",
     "line_segment = [[x_min, x_max], [0, 0]]\n",
-    "plt.plot(*line_segment, color=\"black\")\n",
-    "plt.text(\n",
+    "ax.plot(*line_segment, color=\"black\")\n",
+    "ax.text(\n",
     "    (x_max - x_min) / 2 - 0.22,\n",
     "    0.005,\n",
     "    R\"uniform sample $(x_\\mathrm{min}, x_\\mathrm{max})$\",\n",
     ")\n",
-    "plt.scatter(x_value, function_1d({\"x\": x_value}))\n",
-    "plt.axvline(x=x_value, linestyle=\"dotted\")\n",
+    "ax.scatter(x_value, function_1d({\"x\": x_value}))\n",
+    "ax.axvline(x=x_value, linestyle=\"dotted\")\n",
     "\n",
     "\n",
-    "def draw_y_hit(x_random, y_random):\n",
+    "def draw_y_hit(ax, x_random, y_random):\n",
     "    y_value = function_1d({\"x\": x_random})\n",
     "    color = \"green\" if y_random < y_value else \"red\"\n",
     "    text = \"hit\" if y_random < y_value else \"miss\"\n",
-    "    plt.scatter(0, y_random, color=color)\n",
-    "    plt.arrow(\n",
+    "    ax.scatter(0, y_random, color=color)\n",
+    "    ax.arrow(\n",
     "        x=0,\n",
     "        y=y_random,\n",
     "        dx=x_random,\n",
@@ -473,11 +478,11 @@
     "        color=color,\n",
     "        linestyle=\"dotted\",\n",
     "    )\n",
-    "    plt.text(x_value + 0.05, y_random, text)\n",
+    "    ax.text(x_value + 0.05, y_random, text)\n",
     "\n",
     "\n",
-    "draw_y_hit(x_random=x_value, y_random=0.05)\n",
-    "draw_y_hit(x_random=x_value, y_random=0.17)"
+    "draw_y_hit(ax, x_random=x_value, y_random=0.05)\n",
+    "draw_y_hit(ax, x_random=x_value, y_random=0.17)"
    ]
   },
   {
@@ -564,6 +569,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "plt.hist(\n",
     "    domain[\"x\"],\n",
     "    bins=200,\n",
@@ -636,6 +642,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "plt.hist(data[\"x\"], bins=200);"
    ]
   },
@@ -694,6 +701,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "plt.hist(data[\"x\"], bins=200, density=True)\n",
     "plt.hist(\n",
     "    domain[\"x\"],\n",
@@ -803,6 +811,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "plt.hist(data[\"x\"], bins=200, density=True)\n",
     "plt.hist(\n",
     "    domain[\"x\"],\n",
@@ -874,14 +883,16 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "fit_traceback = pd.read_csv(\"traceback-1D.csv\")\n",
     "fig, (ax1, ax2) = plt.subplots(\n",
-    "    2, figsize=(7, 9), sharex=True, gridspec_kw={\"height_ratios\": [1, 2]}\n",
+    "    nrows=2, figsize=(7, 9), sharex=True, gridspec_kw={\"height_ratios\": [1, 2]}\n",
     ")\n",
     "fit_traceback.plot(\"function_call\", \"estimator_value\", ax=ax1)\n",
     "fit_traceback.plot(\"function_call\", sorted(initial_parameters), ax=ax2)\n",
     "fig.tight_layout()\n",
-    "ax2.set_xlabel(\"function call\");"
+    "ax2.set_xlabel(\"function call\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -962,9 +973,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "y_range = (y, -sp.pi, +sp.pi)\n",
     "substituted_expr_2d = expression_2d.subs(parameter_defaults)\n",
-    "plot3d(substituted_expr_2d, x_range, y_range)"
+    "plot3d(substituted_expr_2d, x_range, y_range);"
    ]
   },
   {
@@ -1015,6 +1027,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
     "intensities = np.array(function_2d(domain_2d))\n",
     "kwargs = {\n",
@@ -1072,15 +1085,17 @@
    },
    "outputs": [],
    "source": [
-    "fig, axes = plt.subplots(1, 2, figsize=(9, 4), sharey=True, tight_layout=True)\n",
+    "%config InlineBackend.figure_formats = ['png']\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True, tight_layout=True)\n",
     "axes[0].hist2d(**data_2d, bins=50)\n",
     "axes[1].hist2d(**domain_2d, weights=function_2d(domain_2d), bins=50)\n",
     "axes[0].set_xlabel(\"$x$\")\n",
     "axes[0].set_ylim([-3, +3])\n",
     "axes[1].set_xlabel(\"$x$\")\n",
     "axes[0].set_ylabel(\"$y$\")\n",
     "axes[0].set_title(\"Data sample\")\n",
-    "axes[1].set_title(\"Function with optimized parameters\");"
+    "axes[1].set_title(\"Function with optimized parameters\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -1224,6 +1239,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "fig, axes = plt.subplots(1, 2, figsize=(9, 4), sharey=True, tight_layout=True)\n",
     "fig.suptitle(\"Final fit result\")\n",
     "axes[0].hist2d(**data_2d, bins=50)\n",
@@ -1233,7 +1249,8 @@
     "axes[1].set_xlabel(\"$x$\")\n",
     "axes[0].set_ylabel(\"$y$\")\n",
     "axes[0].set_title(\"Data sample\")\n",
-    "axes[1].set_title(\"Function with optimized parameters\");"
+    "axes[1].set_title(\"Function with optimized parameters\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -1257,6 +1274,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "minuit_traceback = pd.read_csv(\"traceback.csv\")\n",
     "scipy_traceback = pd.read_csv(\"traceback-scipy.csv\")\n",
     "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(\n",
@@ -1284,7 +1302,8 @@
     "    loc=\"upper right\"\n",
     ")\n",
     "fig.tight_layout()\n",
-    "ax2.set_xlabel(\"function call\");"
+    "ax2.set_xlabel(\"function call\")\n",
+    "plt.show()"
    ]
   }
  ],

diff --git a/docs/usage/binned-fit.ipynb b/docs/usage/binned-fit.ipynb
@@ -106,6 +106,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 5))\n",

diff --git a/docs/usage/chi-squared.ipynb b/docs/usage/chi-squared.ipynb
@@ -169,6 +169,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "\n",