From 53f1f7cc4900f7349fb4b691b76a32f985a11140 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 4 Nov 2025 21:09:03 +0000
Subject: [PATCH 1/4] Initial plan


From 85a3fe253607eda0558c055c1e8bc851d981a0e1 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 4 Nov 2025 21:12:45 +0000
Subject: [PATCH 2/4] Convert code-block to plot directives for Fourier
 examples that generate images

Co-authored-by: juanitorduz <22996444+juanitorduz@users.noreply.github.com>
---
 pymc_marketing/mmm/fourier.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/pymc_marketing/mmm/fourier.py b/pymc_marketing/mmm/fourier.py
index 6a6d6c44e..efd1a0d93 100644
--- a/pymc_marketing/mmm/fourier.py
+++ b/pymc_marketing/mmm/fourier.py
@@ -52,7 +52,9 @@
 --------
 Use yearly fourier seasonality for custom Marketing Mix Model.
 
-.. code-block:: python
+.. plot::
+    :context: close-figs
+    :nofigs:
 
     import pandas as pd
     import pymc as pm
@@ -70,7 +72,8 @@
 
 Plot the prior fourier seasonality trend.
 
-.. code-block:: python
+.. plot::
+    :context: close-figs
 
     import matplotlib.pyplot as plt
 
@@ -91,7 +94,9 @@
 
 Even make it hierarchical...
 
-.. code-block:: python
+.. plot::
+    :context: close-figs
+    :nofigs:
 
     from pymc_marketing.mmm import YearlyFourier
     from pymc_extras.prior import Prior
@@ -107,7 +112,8 @@
 
 All the plotting will still work! Just pass any coords.
 
-.. code-block:: python
+.. plot::
+    :context: close-figs
 
     import matplotlib.pyplot as plt
 

From 3ed3567234bf5d356797383d2aab3628087cf424 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 4 Nov 2025 21:53:11 +0000
Subject: [PATCH 3/4] Revert setup code blocks to code-block directive per
 review feedback

Co-authored-by: juanitorduz <22996444+juanitorduz@users.noreply.github.com>
---
 pymc_marketing/mmm/fourier.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/pymc_marketing/mmm/fourier.py b/pymc_marketing/mmm/fourier.py
index efd1a0d93..2fce5be74 100644
--- a/pymc_marketing/mmm/fourier.py
+++ b/pymc_marketing/mmm/fourier.py
@@ -52,9 +52,7 @@
 --------
 Use yearly fourier seasonality for custom Marketing Mix Model.
 
-.. plot::
-    :context: close-figs
-    :nofigs:
+.. code-block:: python
 
     import pandas as pd
     import pymc as pm
@@ -94,9 +92,7 @@
 
 Even make it hierarchical...
 
-.. plot::
-    :context: close-figs
-    :nofigs:
+.. code-block:: python
 
     from pymc_marketing.mmm import YearlyFourier
     from pymc_extras.prior import Prior

From 582e9c225a8a60509a07a81ffe9e99ea80ca436f Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Tue, 4 Nov 2025 22:23:54 +0000
Subject: [PATCH 4/4] Add setup code to plot directives to fix rendering issues

Co-authored-by: juanitorduz <22996444+juanitorduz@users.noreply.github.com>
---
 pymc_marketing/mmm/fourier.py | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/pymc_marketing/mmm/fourier.py b/pymc_marketing/mmm/fourier.py
index 2fce5be74..f7ac7496e 100644
--- a/pymc_marketing/mmm/fourier.py
+++ b/pymc_marketing/mmm/fourier.py
@@ -73,8 +73,21 @@
 .. plot::
     :context: close-figs
 
+    import pandas as pd
+    import pymc as pm
     import matplotlib.pyplot as plt
 
+    from pymc_marketing.mmm import YearlyFourier
+
+    yearly = YearlyFourier(n_order=3)
+
+    dates = pd.date_range("2023-01-01", periods=52, freq="W-MON")
+
+    dayofyear = dates.dayofyear.to_numpy()
+
+    with pm.Model() as model:
+        fourier_trend = yearly.apply(dayofyear)
+
     prior = yearly.sample_prior()
     curve = yearly.sample_curve(prior)
     yearly.plot_curve(curve)
@@ -113,6 +126,18 @@
 
     import matplotlib.pyplot as plt
 
+    from pymc_marketing.mmm import YearlyFourier
+    from pymc_extras.prior import Prior
+
+    # "fourier" is the default prefix!
+    prior = Prior(
+        "Laplace",
+        mu=Prior("Normal", dims="fourier"),
+        b=Prior("HalfNormal", sigma=0.1, dims="fourier"),
+        dims=("fourier", "hierarchy"),
+    )
+    yearly = YearlyFourier(n_order=3, prior=prior)
+
     coords = {"hierarchy": ["A", "B", "C"]}
     prior = yearly.sample_prior(coords=coords)
     curve = yearly.sample_curve(prior)