once more

Author: dweindl

doc/example/distributions.ipynb

@@ -32,34 +32,34 @@
     "import seaborn as sns\n",
     "\n",
     "from petab.v1.C import *\n",
-    "from petab.v1.distributions import *\n",
+    "from petab.v1.priors import Prior\n",
     "\n",
     "sns.set_style(None)\n",
     "\n",
     "\n",
-    "def plot(distr: Distribution, ax=None):\n",
+    "def plot(prior: Prior, ax=None):\n",
     "    \"\"\"Visualize a distribution.\"\"\"\n",
     "    if ax is None:\n",
     "        fig, ax = plt.subplots()\n",
     "\n",
-    "    sample = distr.sample(10000)\n",
+    "    sample = prior.sample(10000)\n",
     "\n",
     "    # pdf\n",
-    "    xmin = min(sample.min(), distr.lb_scaled if distr.bounds is not None else sample.min())\n",
-    "    xmax = max(sample.max(), distr.ub_scaled if distr.bounds is not None else sample.max())\n",
+    "    xmin = min(sample.min(), prior.lb_scaled if prior.bounds is not None else sample.min())\n",
+    "    xmax = max(sample.max(), prior.ub_scaled if prior.bounds is not None else sample.max())\n",
     "    x = np.linspace(xmin, xmax, 500)\n",
-    "    y = distr.pdf(x)\n",
+    "    y = prior.pdf(x)\n",
     "    ax.plot(x, y, color='red', label='pdf')\n",
     "\n",
     "    sns.histplot(sample, stat='density', ax=ax, label=\"sample\")\n",
     "\n",
     "    # bounds\n",
-    "    if distr.bounds is not None:\n",
-    "        for bound in (distr.lb_scaled, distr.ub_scaled):\n",
+    "    if prior.bounds is not None:\n",
+    "        for bound in (prior.lb_scaled, prior.ub_scaled):\n",
     "            if bound is not None and np.isfinite(bound):\n",
     "                ax.axvline(bound, color='black', linestyle='--', label='bound')\n",
     "\n",
-    "    ax.set_title(str(distr))\n",
+    "    ax.set_title(str(prior))\n",
     "    ax.set_xlabel('Parameter value on the parameter scale')\n",
     "    ax.grid(False)\n",
     "    handles, labels = ax.get_legend_handles_labels()\n",
@@ -81,11 +81,11 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "plot(Uniform(0, 1))\n",
-    "plot(Normal(0, 1))\n",
-    "plot(Laplace(0, 1))\n",
-    "plot(LogNormal(0, 1))\n",
-    "plot(LogLaplace(1, 0.5))"
+    "plot(Prior(UNIFORM, (0, 1)))\n",
+    "plot(Prior(NORMAL, (0, 1)))\n",
+    "plot(Prior(LAPLACE, (0, 1)))\n",
+    "plot(Prior(LOG_NORMAL, (0, 1)))\n",
+    "plot(Prior(LOG_LAPLACE, (1, 0.5)))"
    ],
    "id": "4f09e50a3db06d9f",
    "outputs": [],
@@ -101,10 +101,11 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "plot(Normal(10, 2, transformation=LIN))\n",
-    "plot(Normal(10, 2, transformation=LOG))\n",
+    "plot(Prior(NORMAL, (10, 2), transformation=LIN))\n",
+    "plot(Prior(NORMAL, (10, 2), transformation=LOG))\n",
+    "\n",
     "# Note that the log-normal distribution is different from a log-transformed normal distribution:\n",
-    "plot(LogNormal(10, 2, transformation=LIN))"
+    "plot(Prior(LOG_NORMAL, (10, 2), transformation=LIN))"
    ],
    "id": "f6192c226f179ef9",
    "outputs": [],
@@ -120,8 +121,8 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "plot(LogNormal(10, 2, transformation=LOG))\n",
-    "plot(ParameterScaleNormal(10, 2))"
+    "plot(Prior(LOG_NORMAL, (10, 2), transformation=LOG))\n",
+    "plot(Prior(PARAMETER_SCALE_NORMAL, (10, 2)))"
    ],
    "id": "34c95268e8921070",
    "outputs": [],
@@ -137,11 +138,11 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "plot(Uniform(0, 1, transformation=LOG10))\n",
-    "plot(ParameterScaleUniform(0, 1, transformation=LOG10))\n",
+    "plot(Prior(UNIFORM, (0.01, 2), transformation=LOG10))\n",
+    "plot(Prior(PARAMETER_SCALE_UNIFORM, (0.01, 2), transformation=LOG10))\n",
     "\n",
-    "plot(Uniform(0, 1, transformation=LIN))\n",
-    "plot(ParameterScaleUniform(0, 1, transformation=LIN))\n"
+    "plot(Prior(UNIFORM, (0.01, 2), transformation=LIN))\n",
+    "plot(Prior(PARAMETER_SCALE_UNIFORM, (0.01, 2), transformation=LIN))\n"
    ],
    "id": "5ca940bc24312fc6",
    "outputs": [],
@@ -157,8 +158,8 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "plot(Normal(0, 1, bounds=(-4, 4)))  # negligible clipping-bias at 4 sigma\n",
-    "plot(Uniform(0, 1, bounds=(0.1, 0.9)))  # significant clipping-bias"
+    "plot(Prior(NORMAL, (0, 1), bounds=(-4, 4)))  # negligible clipping-bias at 4 sigma\n",
+    "plot(Prior(UNIFORM, (0, 1), bounds=(0.1, 0.9)))  # significant clipping-bias"
    ],
    "id": "4ac42b1eed759bdd",
    "outputs": [],
@@ -174,8 +175,10 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "plot(Normal(10, 1, bounds=(6, 14), transformation=\"log10\"))\n",
-    "plot(ParameterScaleNormal(10, 1, bounds=(10**6, 10**14), transformation=\"log10\"))\n"
+    "plot(Prior(NORMAL, (10, 1), bounds=(6, 14), transformation=\"log10\"))\n",
+    "plot(Prior(PARAMETER_SCALE_NORMAL, (10, 1), bounds=(10**6, 10**14), transformation=\"log10\"))\n",
+    "plot(Prior(LAPLACE, (10, 2), bounds=(6, 14)))\n",
+    "\n"
    ],
    "id": "581e1ac431860419",
    "outputs": [],
@@ -185,7 +188,7 @@
    "metadata": {},
    "cell_type": "code",
    "source": "",
-   "id": "802a64be56a6c94f",
+   "id": "633733651bbc3ef0",
    "outputs": [],
    "execution_count": null
   }

petab/v1/C.py

@@ -173,7 +173,8 @@
 LOG10 = "log10"
 #: Supported observable transformations
 OBSERVABLE_TRANSFORMATIONS = [LIN, LOG, LOG10]
-
+#: Supported parameter transformations
+PARAMETER_SCALES = [LIN, LOG, LOG10]
 
 # NOISE MODELS