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Contents:
153 |
147 | import numpy as np
148 | import matplotlib.pyplot as plt
149 | from .splinelib import *
150 |
151 |
152 | [docs]def add_spline_to_plot(spline, include_control_polygon=True, include_knots=True):
153 | """
154 | Adds given spline to current matplotlib plot
155 |
156 | Args:
157 | spline (Spline): Spline to add
158 | include_control_polygon (bool) Optional. Whether to include control polygon in
159 | plot or not. Default is True.
160 | include_knots (bool) Optional. Whether to include knots in plot or not.
161 | Default is True
162 |
163 | Returns:
164 | None
165 | """
166 | xs, ps = spline.evaluate_all()
167 | plt.plot(xs, ps)
168 |
169 | if include_control_polygon:
170 | plt.plot(*spline.get_control_polygon())
171 |
172 | min_val = np.min(ps)
173 | dist = np.max(ps) - min_val
174 |
175 | steps = [i * (dist / 8) - dist for i in range(spline.get_degree() + 1)]
176 | similar_knots = 0
177 | previous_knot = None
178 |
179 |
180 | def double_equals(d1, d2, tol=1e-10):
181 | if d1 is None or d2 is None:
182 | return False
183 | else:
184 | return abs(d1 - d2) < tol
185 |
186 |
187 | if not spline.is_parametric() and include_knots:
188 | for knot in spline.get_knots():
189 | if double_equals(knot, previous_knot):
190 | similar_knots += 1
191 | else:
192 | similar_knots = 0
193 |
194 | plt.scatter(knot, steps[similar_knots], color="k", s=10)
195 |
196 | previous_knot = knot
197 |
147 | """
148 | The 'utils' module contains some useful helper functions, mostly used during the
149 | implementation of the other modules.
150 | """
151 |
152 | import numpy as np
153 | import tensorflow as tf
154 |
155 |
156 | [docs]def adapt_filter(filter):
157 | """
158 | Expands dimensions of a 1d vector to match the required tensor dimensions in a TF
159 | graph.
160 |
161 | Args:
162 | filter (np.ndarray): A 1D vector containing filter coefficients
163 |
164 | Returns:
165 | np.ndarray: A 3D vector with two empty dimensions as dim 2 and 3.
166 |
167 | """
168 | # Add empty dimensions for batch size and channel num
169 | return np.expand_dims(np.expand_dims(filter, -1), -1)
170 |
171 |
172 | [docs]def to_tf_mat(matrices):
173 | """
174 | Expands dimensions of 2D matrices to match the required tensor dimensions in a TF
175 | graph, and wrapping them as TF constants.
176 |
177 | Args:
178 | matrices (iterable): A list (or tuple) of 2D numpy arrays.
179 |
180 | Returns:
181 | iterable: A list of all the matrices converted to 3D TF tensors.
182 | """
183 | result = []
184 |
185 | for matrix in matrices:
186 | result.append(
187 | tf.constant(np.expand_dims(matrix, 0), dtype=tf.float32)
188 | )
189 |
190 | return result
191 | | 157 | t | ||
| 161 | |
162 | tfwavelets | 163 | |
| 167 | |
168 | tfwavelets | 169 | |
| 172 | |
173 | tfwavelets.dwtcoeffs | 174 | |
| 177 | |
178 | tfwavelets.nodes | 179 | |
| 182 | |
183 | tfwavelets.utils | 184 | |
| 187 | |
188 | tfwavelets.wrappers | 189 | |
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