docs(model-input): fix the tutorials after changing the interface

#45

docs/tutorials/brownian_motion.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.16.3
 #   kernelspec:
 #     display_name: .venv
 #     language: python
@@ -56,7 +56,7 @@
 # Call the model to generate 1000 steps.
 
 # %%
-df_1d = bm_1d(n_steps=1000)
+df_1d = bm_1d.generate_from(n_steps=1000)
 
 # %%
 px.line(df_1d, x="t", y="x_0")
@@ -79,7 +79,7 @@
 # We call the model to generate 1000 steps.
 
 # %%
-df_2d = bm_2d(n_steps=500)
+df_2d = bm_2d.generate_from(n_steps=500)
 
 # %%
 (

docs/tutorials/complex_harmonic_oscillator.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.16.3
 #   kernelspec:
 #     display_name: .venv
 #     language: python

docs/tutorials/harmonic_oscillator.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.16.3
 #   kernelspec:
 #     display_name: .venv
 #     language: python

docs/tutorials/harmonic_oscillator_chain.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.16.3
 #   kernelspec:
 #     display_name: .venv
 #     language: python
@@ -113,22 +113,22 @@
 #
 # This system can be solved in terms of _travelling waves_, obtained by discrete Fourier transform.
 #
-
+#
 # We can complexify the system
 # $$S_L[x_i] = S_L[x_i, \phi_j] \equiv S_L[X^\ast_i, X_j] = \int_{t_0}^{t_1}\mathbb{d} t \left\\{ \frac{1}{2}m \dot X^\ast_i \delta_{ij} \dot X_j - \frac{1}{2}m X^\ast_i A_{ij} X_j\right\\}\\,,$$
 # where $A_{ij} / \omega^2$ is equal to $(-2)$ if $i=j$, $1$ if $|i-j|=1$ or $|i-j|=N$, and $0$ otherwise;
 # $X_i \coloneqq x_i \mathbb{e}^{-\phi_i}$, $X^\ast_i \coloneqq x_i \mathbb{e}^{+\phi_i}$.
-
+#
 # $A_{ij}$ can be diagonalised by the inverse discrete Fourier transform
 # $$X_i = (F^{-1})_{ik} Y_k = \frac{1}{\sqrt{N}}\sum_k \mathbb{e}^{i \frac{2\mathbb{\pi}}{N} k\mathbb{i}} Y_k\\,.$$
-
+#
 # Calculating gives
 # $$S_L[X^\ast_i, X_j] = S_L[Y^\ast_i, Y_j] = \sum_{k=0}^{N-1} \int_{t_0}^{t_1}\mathbb{d} t \left\\{ \frac{1}{2}m \dot Y^\ast_k \dot Y_k - \frac{1}{2}m \omega^2\cdot4\sin^2\frac{2\mathbb{\pi}k}{N} Y^\ast_k Y_k\right\\}\\,.$$
 # We can arrive at an action for the Fourier modes
 # $$S_L[Y, Y^*] = \sum_{k=0}^{N-1} \int_{t_0}^{t_1}\mathbb{d} t \left\\{ \frac{1}{2}m \dot Y_k^* Y_k - \frac{1}{2}m \omega^2\cdot4\sin^2\frac{2\mathbb{\pi}k}{N} Y_k^* Y_k\right\\}\\,.$$
-
+#
 # The origional system can then be solved by $N$ complex oscillators.
-
+#
 # Since the original degrees of freedom are real, the initial conditions of the propagating waves need to satisfy
 # $Y_k = Y^*_{-k \mod N}$, see [Wikipedia](https://en.wikipedia.org/wiki/Discrete_Fourier_transform#DFT_of_real_and_purely_imaginary_signals).
 

docs/tutorials/pendulum.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.16.3
 #   kernelspec:
 #     display_name: .venv
 #     language: python
@@ -46,7 +46,9 @@
 # ## Data
 
 # %%
-df_pen = pen(n_periods=n_periods, n_samples_per_period=n_samples_per_period)
+df_pen = pen.generate_from(
+    n_periods=n_periods, n_samples_per_period=n_samples_per_period
+)
 df_pen.head()
 
 # %%