generate pdf and rename folder

notebooks/walker/Step_5_reproducibility/solution/walker.py

@@ -1,60 +0,0 @@
-import numpy as np
-
-
-class Walker:
-    """ The Walker knows how to walk at random on a context map. """
-
-    def __init__(self, sigma_i, sigma_j, context_map):
-        self.sigma_i = sigma_i
-        self.sigma_j = sigma_j
-        self.size = context_map.shape[0]
-        # Make sure that the context map is normalized
-        context_map /= context_map.sum()
-        self.context_map = context_map
-
-        # Pre-compute a 2D grid of coordinates for efficiency
-        self._grid_ii, self._grid_jj = np.mgrid[0:self.size, 0:self.size]
-
-    # --- Walker public interface
-
-    def sample_next_step(self, current_i, current_j, random_state=np.random):
-        """ Sample a new position for the walker. """
-
-        # Combine the next-step proposal with the context map to get a
-        # next-step probability map
-        next_step_map = self._next_step_proposal(current_i, current_j)
-        selection_map = self._compute_next_step_probability(next_step_map)
-
-        # Draw a new position from the next-step probability map
-        r = random_state.rand()
-        cumulative_map = np.cumsum(selection_map)
-        cumulative_map = cumulative_map.reshape(selection_map.shape)
-        i_next, j_next = np.argwhere(cumulative_map >= r)[0]
-
-        return i_next, j_next
-
-    # --- Walker non-public interface
-
-    def _next_step_proposal(self, current_i, current_j):
-        """ Create the 2D proposal map for the next step of the walker. """
-
-        # 2D Gaussian distribution , centered at current position,
-        # and with different standard deviations for i and j
-        grid_ii, grid_jj = self._grid_ii, self._grid_jj
-        sigma_i, sigma_j = self.sigma_i, self.sigma_j
-
-        rad = (
-            (((grid_ii - current_i) ** 2) / (sigma_i ** 2))
-            + (((grid_jj - current_j) ** 2) / (sigma_j ** 2))
-        )
-
-        p_next_step = np.exp(-(rad / 2.0)) / (2.0 * np.pi * sigma_i * sigma_j)
-        return p_next_step / p_next_step.sum()
-
-    def _compute_next_step_probability(self, next_step_map):
-        """ Compute the next step probability map from next step proposal and
-        context map. """
-        next_step_probability = next_step_map * self.context_map
-        next_step_probability /= next_step_probability.sum()
-        return next_step_probability
-