revert changes in docs and fixing issues

Author: JeanVanDyk

causalpy/experiments/interrupted_time_series.py

@@ -157,7 +157,7 @@ def data_postprocessing(self, model, data, idata, treatment_time, pre_y, pre_X):
             treatment_time,
         )
 
-    def plot_intervention_line(self, model, ax, idata, datapost, treatment_time):
+    def plot_intervention_line(self, ax, model, idata, datapost, treatment_time):
         """
         Plot a vertical line at the known treatment time on provided axes.
         """

docs/source/notebooks/its_covid.ipynb

@@ -53,7 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using the `InterventionTimeEstimator` PyMC model\n",
+    "### Using the `InterventionTimeEstimator` PyMC model\n",
     "\n",
     "The ``InterventionTimeEstimator`` (``ITE``) model is built to infer *when* an intervention likely occurred, based on the **type of effect** you expect it to have caused.\n",
     "\n",
@@ -66,7 +66,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 1 : Level Change\n",
+    "#### Example 1 : Level Change\n",
     "\n",
     "In this example, we apply the ``ITE`` model in its simplest form to a time series with a **level change**."
    ]
@@ -281,7 +281,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 2 : Trend Change\n",
+    "#### Example 2 : Trend Change\n",
     "\n",
     "In this example, we'll showcase how to use the ITE when the time series exhibits a **trend change**."
    ]
@@ -508,7 +508,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Example 3 : Impulse\n",
+    "#### Example 3 : Impulse\n",
     "\n",
     "In this final example, we'll demonstrate how to apply the ITE model to data that display an impulse-like effect."
    ]
@@ -734,7 +734,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Narrowing the Inference Window\n",
+    "#### Narrowing the Inference Window\n",
     "\n",
     "Instead of specifying a treatment_time, you can constrain the inference window by passing a time_range=(start, end) argument, where start and end correspond to the row indices or timestamps of your dataframe:\n",
     "\n",
@@ -889,7 +889,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Specifying the effect\n",
+    "#### Specifying the effect\n",
     "\n",
     "The effects also can be specified using a dictionary passed to the `treatment_type_effect` arguments :\n",
     "\n",
@@ -940,7 +940,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Interrupted Time Series (ITS) : Real Data Example"
+    "### Interrupted Time Series (ITS) : Real Data Example"
    ]
   },
   {