|
|
|
|
|
r'''############################################################################
|
|
################################################################################
|
|
#
|
|
#
|
|
# Tegridy Plots Python Module (TPLOTS)
|
|
# Version 1.0
|
|
#
|
|
# Project Los Angeles
|
|
#
|
|
# Tegridy Code 2024
|
|
#
|
|
# https://github.com/asigalov61/tegridy-tools
|
|
#
|
|
#
|
|
################################################################################
|
|
#
|
|
# Copyright 2024 Project Los Angeles / Tegridy Code
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
################################################################################
|
|
################################################################################
|
|
#
|
|
# Critical dependencies
|
|
#
|
|
# !pip install numpy
|
|
# !pip install scipy
|
|
# !pip install matplotlib
|
|
# !pip install networkx
|
|
# !pip3 install scikit-learn
|
|
#
|
|
################################################################################
|
|
#
|
|
# Future critical dependencies
|
|
#
|
|
# !pip install umap-learn
|
|
# !pip install alphashape
|
|
#
|
|
################################################################################
|
|
'''
|
|
|
|
|
|
|
|
|
|
|
|
import os
|
|
from collections import Counter
|
|
from itertools import groupby
|
|
|
|
import numpy as np
|
|
|
|
import networkx as nx
|
|
|
|
from sklearn.manifold import TSNE
|
|
from sklearn import metrics
|
|
from sklearn.preprocessing import MinMaxScaler
|
|
from sklearn.decomposition import PCA
|
|
|
|
from scipy.ndimage import zoom
|
|
from scipy.spatial import distance_matrix
|
|
from scipy.sparse.csgraph import minimum_spanning_tree
|
|
from scipy.stats import zscore
|
|
|
|
import matplotlib.pyplot as plt
|
|
from PIL import Image
|
|
|
|
|
|
|
|
|
|
|
|
ALL_CHORDS_FILTERED = [[0], [0, 3], [0, 3, 5], [0, 3, 5, 8], [0, 3, 5, 9], [0, 3, 5, 10], [0, 3, 7],
|
|
[0, 3, 7, 10], [0, 3, 8], [0, 3, 9], [0, 3, 10], [0, 4], [0, 4, 6],
|
|
[0, 4, 6, 9], [0, 4, 6, 10], [0, 4, 7], [0, 4, 7, 10], [0, 4, 8], [0, 4, 9],
|
|
[0, 4, 10], [0, 5], [0, 5, 8], [0, 5, 9], [0, 5, 10], [0, 6], [0, 6, 9],
|
|
[0, 6, 10], [0, 7], [0, 7, 10], [0, 8], [0, 9], [0, 10], [1], [1, 4],
|
|
[1, 4, 6], [1, 4, 6, 9], [1, 4, 6, 10], [1, 4, 6, 11], [1, 4, 7],
|
|
[1, 4, 7, 10], [1, 4, 7, 11], [1, 4, 8], [1, 4, 8, 11], [1, 4, 9], [1, 4, 10],
|
|
[1, 4, 11], [1, 5], [1, 5, 8], [1, 5, 8, 11], [1, 5, 9], [1, 5, 10],
|
|
[1, 5, 11], [1, 6], [1, 6, 9], [1, 6, 10], [1, 6, 11], [1, 7], [1, 7, 10],
|
|
[1, 7, 11], [1, 8], [1, 8, 11], [1, 9], [1, 10], [1, 11], [2], [2, 5],
|
|
[2, 5, 8], [2, 5, 8, 11], [2, 5, 9], [2, 5, 10], [2, 5, 11], [2, 6], [2, 6, 9],
|
|
[2, 6, 10], [2, 6, 11], [2, 7], [2, 7, 10], [2, 7, 11], [2, 8], [2, 8, 11],
|
|
[2, 9], [2, 10], [2, 11], [3], [3, 5], [3, 5, 8], [3, 5, 8, 11], [3, 5, 9],
|
|
[3, 5, 10], [3, 5, 11], [3, 7], [3, 7, 10], [3, 7, 11], [3, 8], [3, 8, 11],
|
|
[3, 9], [3, 10], [3, 11], [4], [4, 6], [4, 6, 9], [4, 6, 10], [4, 6, 11],
|
|
[4, 7], [4, 7, 10], [4, 7, 11], [4, 8], [4, 8, 11], [4, 9], [4, 10], [4, 11],
|
|
[5], [5, 8], [5, 8, 11], [5, 9], [5, 10], [5, 11], [6], [6, 9], [6, 10],
|
|
[6, 11], [7], [7, 10], [7, 11], [8], [8, 11], [9], [10], [11]]
|
|
|
|
|
|
|
|
CHORDS_TYPES = ['WHITE', 'BLACK', 'UNKNOWN', 'MIXED WHITE', 'MIXED BLACK', 'MIXED GRAY']
|
|
|
|
|
|
|
|
WHITE_NOTES = [0, 2, 4, 5, 7, 9, 11]
|
|
|
|
|
|
|
|
BLACK_NOTES = [1, 3, 6, 8, 10]
|
|
|
|
|
|
|
|
|
|
|
|
def tones_chord_type(tones_chord,
|
|
return_chord_type_index=True,
|
|
):
|
|
|
|
"""
|
|
Returns tones chord type
|
|
"""
|
|
|
|
WN = WHITE_NOTES
|
|
BN = BLACK_NOTES
|
|
MX = WHITE_NOTES + BLACK_NOTES
|
|
|
|
|
|
CHORDS = ALL_CHORDS_FILTERED
|
|
|
|
tones_chord = sorted(tones_chord)
|
|
|
|
ctype = 'UNKNOWN'
|
|
|
|
if tones_chord in CHORDS:
|
|
|
|
if sorted(set(tones_chord) & set(WN)) == tones_chord:
|
|
ctype = 'WHITE'
|
|
|
|
elif sorted(set(tones_chord) & set(BN)) == tones_chord:
|
|
ctype = 'BLACK'
|
|
|
|
if len(tones_chord) > 1 and sorted(set(tones_chord) & set(MX)) == tones_chord:
|
|
|
|
if len(sorted(set(tones_chord) & set(WN))) == len(sorted(set(tones_chord) & set(BN))):
|
|
ctype = 'MIXED GRAY'
|
|
|
|
elif len(sorted(set(tones_chord) & set(WN))) > len(sorted(set(tones_chord) & set(BN))):
|
|
ctype = 'MIXED WHITE'
|
|
|
|
elif len(sorted(set(tones_chord) & set(WN))) < len(sorted(set(tones_chord) & set(BN))):
|
|
ctype = 'MIXED BLACK'
|
|
|
|
if return_chord_type_index:
|
|
return CHORDS_TYPES.index(ctype)
|
|
|
|
else:
|
|
return ctype
|
|
|
|
|
|
|
|
def tone_type(tone,
|
|
return_tone_type_index=True
|
|
):
|
|
|
|
"""
|
|
Returns tone type
|
|
"""
|
|
|
|
tone = tone % 12
|
|
|
|
if tone in BLACK_NOTES:
|
|
if return_tone_type_index:
|
|
return CHORDS_TYPES.index('BLACK')
|
|
else:
|
|
return "BLACK"
|
|
|
|
else:
|
|
if return_tone_type_index:
|
|
return CHORDS_TYPES.index('WHITE')
|
|
else:
|
|
return "WHITE"
|
|
|
|
|
|
|
|
def find_closest_points(points, return_points=True):
|
|
|
|
"""
|
|
Find closest 2D points
|
|
"""
|
|
|
|
coords = np.array(points)
|
|
|
|
num_points = coords.shape[0]
|
|
closest_matches = np.zeros(num_points, dtype=int)
|
|
distances = np.zeros((num_points, num_points))
|
|
|
|
for i in range(num_points):
|
|
for j in range(num_points):
|
|
if i != j:
|
|
distances[i, j] = np.linalg.norm(coords[i] - coords[j])
|
|
else:
|
|
distances[i, j] = np.inf
|
|
|
|
closest_matches = np.argmin(distances, axis=1)
|
|
|
|
if return_points:
|
|
points_matches = coords[closest_matches].tolist()
|
|
return points_matches
|
|
|
|
else:
|
|
return closest_matches.tolist()
|
|
|
|
|
|
|
|
def reduce_dimensionality_tsne(list_of_valies,
|
|
n_comp=2,
|
|
n_iter=5000,
|
|
verbose=True
|
|
):
|
|
|
|
"""
|
|
Reduces the dimensionality of the values using t-SNE.
|
|
"""
|
|
|
|
vals = np.array(list_of_valies)
|
|
|
|
tsne = TSNE(n_components=n_comp,
|
|
n_iter=n_iter,
|
|
verbose=verbose)
|
|
|
|
reduced_vals = tsne.fit_transform(vals)
|
|
|
|
return reduced_vals.tolist()
|
|
|
|
|
|
|
|
def compute_mst_edges(similarity_scores_list):
|
|
|
|
"""
|
|
Computes the Minimum Spanning Tree (MST) edges based on the similarity scores.
|
|
"""
|
|
|
|
num_tokens = len(similarity_scores_list[0])
|
|
|
|
graph = nx.Graph()
|
|
|
|
for i in range(num_tokens):
|
|
for j in range(i + 1, num_tokens):
|
|
weight = 1 - similarity_scores_list[i][j]
|
|
graph.add_edge(i, j, weight=weight)
|
|
|
|
mst = nx.minimum_spanning_tree(graph)
|
|
|
|
mst_edges = list(mst.edges(data=False))
|
|
|
|
return mst_edges
|
|
|
|
|
|
|
|
def square_binary_matrix(binary_matrix,
|
|
matrix_size=128,
|
|
interpolation_order=5,
|
|
return_square_matrix_points=False
|
|
):
|
|
|
|
"""
|
|
Reduces an arbitrary binary matrix to a square binary matrix
|
|
"""
|
|
|
|
zoom_factors = (matrix_size / len(binary_matrix), 1)
|
|
|
|
resized_matrix = zoom(binary_matrix, zoom_factors, order=interpolation_order)
|
|
|
|
resized_matrix = (resized_matrix > 0.5).astype(int)
|
|
|
|
final_matrix = np.zeros((matrix_size, matrix_size), dtype=int)
|
|
final_matrix[:, :resized_matrix.shape[1]] = resized_matrix
|
|
|
|
points = np.column_stack(np.where(final_matrix == 1)).tolist()
|
|
|
|
if return_square_matrix_points:
|
|
return points
|
|
|
|
else:
|
|
return resized_matrix
|
|
|
|
|
|
|
|
def square_matrix_points_colors(square_matrix_points):
|
|
|
|
"""
|
|
Returns colors for square matrix points
|
|
"""
|
|
|
|
cmap = generate_colors(12)
|
|
|
|
chords = []
|
|
chords_dict = set()
|
|
counts = []
|
|
|
|
for k, v in groupby(square_matrix_points, key=lambda x: x[0]):
|
|
pgroup = [vv[1] for vv in v]
|
|
chord = sorted(set(pgroup))
|
|
tchord = sorted(set([p % 12 for p in chord]))
|
|
chords_dict.add(tuple(tchord))
|
|
chords.append(tuple(tchord))
|
|
counts.append(len(pgroup))
|
|
|
|
chords_dict = sorted(chords_dict)
|
|
|
|
colors = []
|
|
|
|
for i, c in enumerate(chords):
|
|
colors.extend([cmap[round(sum(c) / len(c))]] * counts[i])
|
|
|
|
return colors
|
|
|
|
|
|
|
|
def hsv_to_rgb(h, s, v):
|
|
|
|
if s == 0.0:
|
|
return v, v, v
|
|
|
|
i = int(h*6.0)
|
|
f = (h*6.0) - i
|
|
p = v*(1.0 - s)
|
|
q = v*(1.0 - s*f)
|
|
t = v*(1.0 - s*(1.0-f))
|
|
i = i%6
|
|
|
|
return [(v, t, p), (q, v, p), (p, v, t), (p, q, v), (t, p, v), (v, p, q)][i]
|
|
|
|
|
|
|
|
def generate_colors(n):
|
|
return [hsv_to_rgb(i/n, 1, 1) for i in range(n)]
|
|
|
|
|
|
|
|
def add_arrays(a, b):
|
|
return [sum(pair) for pair in zip(a, b)]
|
|
|
|
|
|
|
|
def calculate_similarities(lists_of_values, metric='cosine'):
|
|
return metrics.pairwise_distances(lists_of_values, metric=metric).tolist()
|
|
|
|
|
|
|
|
def get_tokens_embeddings(x_transformer_model):
|
|
return x_transformer_model.net.token_emb.emb.weight.detach().cpu().tolist()
|
|
|
|
|
|
|
|
def minkowski_distance_matrix(X, p=3):
|
|
|
|
X = np.array(X)
|
|
|
|
n = X.shape[0]
|
|
dist_matrix = np.zeros((n, n))
|
|
|
|
for i in range(n):
|
|
for j in range(n):
|
|
dist_matrix[i, j] = np.sum(np.abs(X[i] - X[j])**p)**(1/p)
|
|
|
|
return dist_matrix.tolist()
|
|
|
|
|
|
|
|
def robust_normalize(values):
|
|
|
|
values = np.array(values)
|
|
q1 = np.percentile(values, 25)
|
|
q3 = np.percentile(values, 75)
|
|
iqr = q3 - q1
|
|
|
|
filtered_values = values[(values >= q1 - 1.5 * iqr) & (values <= q3 + 1.5 * iqr)]
|
|
|
|
min_val = np.min(filtered_values)
|
|
max_val = np.max(filtered_values)
|
|
normalized_values = (values - min_val) / (max_val - min_val)
|
|
|
|
normalized_values = np.clip(normalized_values, 0, 1)
|
|
|
|
return normalized_values.tolist()
|
|
|
|
|
|
|
|
def min_max_normalize(values):
|
|
|
|
scaler = MinMaxScaler()
|
|
|
|
return scaler.fit_transform(values).tolist()
|
|
|
|
|
|
|
|
def remove_points_outliers(points, z_score_threshold=3):
|
|
|
|
points = np.array(points)
|
|
|
|
z_scores = np.abs(zscore(points, axis=0))
|
|
|
|
return points[(z_scores < z_score_threshold).all(axis=1)].tolist()
|
|
|
|
|
|
|
|
def generate_labels(lists_of_values,
|
|
return_indices_labels=False
|
|
):
|
|
|
|
ordered_indices = list(range(len(lists_of_values)))
|
|
ordered_indices_labels = [str(i) for i in ordered_indices]
|
|
ordered_values_labels = [str(lists_of_values[i]) for i in ordered_indices]
|
|
|
|
if return_indices_labels:
|
|
return ordered_indices_labels
|
|
|
|
else:
|
|
return ordered_values_labels
|
|
|
|
|
|
|
|
def reduce_dimensionality_pca(list_of_values, n_components=2):
|
|
|
|
"""
|
|
Reduces the dimensionality of the values using PCA.
|
|
"""
|
|
|
|
pca = PCA(n_components=n_components)
|
|
pca_data = pca.fit_transform(list_of_values)
|
|
|
|
return pca_data.tolist()
|
|
|
|
def reduce_dimensionality_simple(list_of_values,
|
|
return_means=True,
|
|
return_std_devs=True,
|
|
return_medians=False,
|
|
return_vars=False
|
|
):
|
|
|
|
'''
|
|
Reduces dimensionality of the values in a simple way
|
|
'''
|
|
|
|
array = np.array(list_of_values)
|
|
results = []
|
|
|
|
if return_means:
|
|
means = np.mean(array, axis=1)
|
|
results.append(means)
|
|
|
|
if return_std_devs:
|
|
std_devs = np.std(array, axis=1)
|
|
results.append(std_devs)
|
|
|
|
if return_medians:
|
|
medians = np.median(array, axis=1)
|
|
results.append(medians)
|
|
|
|
if return_vars:
|
|
vars = np.var(array, axis=1)
|
|
results.append(vars)
|
|
|
|
merged_results = np.column_stack(results)
|
|
|
|
return merged_results.tolist()
|
|
|
|
|
|
|
|
def reduce_dimensionality_2d_distance(list_of_values, p=5):
|
|
|
|
'''
|
|
Reduces the dimensionality of the values using 2d distance
|
|
'''
|
|
|
|
values = np.array(list_of_values)
|
|
|
|
dist_matrix = distance_matrix(values, values, p=p)
|
|
|
|
mst = minimum_spanning_tree(dist_matrix).toarray()
|
|
|
|
points = []
|
|
|
|
for i in range(len(values)):
|
|
for j in range(len(values)):
|
|
if mst[i, j] > 0:
|
|
points.append([i, j])
|
|
|
|
return points
|
|
|
|
|
|
|
|
def normalize_to_range(values, n):
|
|
|
|
min_val = min(values)
|
|
max_val = max(values)
|
|
|
|
range_val = max_val - min_val
|
|
|
|
normalized_values = [((value - min_val) / range_val * 2 * n) - n for value in values]
|
|
|
|
return normalized_values
|
|
|
|
|
|
|
|
def reduce_dimensionality_simple_pca(list_of_values, n_components=2):
|
|
|
|
'''
|
|
Reduces the dimensionality of the values using simple PCA
|
|
'''
|
|
|
|
reduced_values = []
|
|
|
|
for l in list_of_values:
|
|
|
|
norm_values = [round(v * len(l)) for v in normalize_to_range(l, (n_components+1) // 2)]
|
|
|
|
pca_values = Counter(norm_values).most_common()
|
|
pca_values = [vv[0] / len(l) for vv in pca_values]
|
|
pca_values = pca_values[:n_components]
|
|
pca_values = pca_values + [0] * (n_components - len(pca_values))
|
|
|
|
reduced_values.append(pca_values)
|
|
|
|
return reduced_values
|
|
|
|
|
|
|
|
def filter_and_replace_values(list_of_values,
|
|
threshold,
|
|
replace_value,
|
|
replace_above_threshold=False
|
|
):
|
|
|
|
array = np.array(list_of_values)
|
|
|
|
modified_array = np.copy(array)
|
|
|
|
if replace_above_threshold:
|
|
modified_array[modified_array > threshold] = replace_value
|
|
|
|
else:
|
|
modified_array[modified_array < threshold] = replace_value
|
|
|
|
return modified_array.tolist()
|
|
|
|
|
|
|
|
def find_shortest_constellation_path(points,
|
|
start_point_idx,
|
|
end_point_idx,
|
|
p=5,
|
|
return_path_length=False,
|
|
return_path_points=False,
|
|
):
|
|
|
|
"""
|
|
Finds the shortest path between two points of the points constellation
|
|
"""
|
|
|
|
points = np.array(points)
|
|
|
|
dist_matrix = distance_matrix(points, points, p=p)
|
|
|
|
mst = minimum_spanning_tree(dist_matrix).toarray()
|
|
|
|
G = nx.Graph()
|
|
|
|
for i in range(len(points)):
|
|
for j in range(len(points)):
|
|
if mst[i, j] > 0:
|
|
G.add_edge(i, j, weight=mst[i, j])
|
|
|
|
path = nx.shortest_path(G,
|
|
source=start_point_idx,
|
|
target=end_point_idx,
|
|
weight='weight'
|
|
)
|
|
|
|
path_length = nx.shortest_path_length(G,
|
|
source=start_point_idx,
|
|
target=end_point_idx,
|
|
weight='weight')
|
|
|
|
path_points = points[np.array(path)].tolist()
|
|
|
|
|
|
if return_path_points:
|
|
return path_points
|
|
|
|
if return_path_length:
|
|
return path_length
|
|
|
|
return path
|
|
|
|
|
|
|
|
|
|
|
|
def plot_ms_SONG(ms_song,
|
|
preview_length_in_notes=0,
|
|
block_lines_times_list = None,
|
|
plot_title='ms Song',
|
|
max_num_colors=129,
|
|
drums_color_num=128,
|
|
plot_size=(11,4),
|
|
note_height = 0.75,
|
|
show_grid_lines=False,
|
|
return_plt = False,
|
|
timings_multiplier=1,
|
|
save_plt='',
|
|
save_only_plt_image=True,
|
|
save_transparent=False
|
|
):
|
|
|
|
'''ms SONG plot'''
|
|
|
|
notes = [s for s in ms_song if s[0] == 'note']
|
|
|
|
if (len(max(notes, key=len)) != 7) and (len(min(notes, key=len)) != 7):
|
|
print('The song notes do not have patches information')
|
|
print('Ploease add patches to the notes in the song')
|
|
|
|
else:
|
|
|
|
start_times = [(s[1] * timings_multiplier) / 1000 for s in notes]
|
|
durations = [(s[2] * timings_multiplier) / 1000 for s in notes]
|
|
pitches = [s[4] for s in notes]
|
|
patches = [s[6] for s in notes]
|
|
|
|
colors = generate_colors(max_num_colors)
|
|
colors[drums_color_num] = (1, 1, 1)
|
|
|
|
pbl = (notes[preview_length_in_notes][1] * timings_multiplier) / 1000
|
|
|
|
fig, ax = plt.subplots(figsize=plot_size)
|
|
|
|
for start, duration, pitch, patch in zip(start_times, durations, pitches, patches):
|
|
rect = plt.Rectangle((start, pitch), duration, note_height, facecolor=colors[patch])
|
|
ax.add_patch(rect)
|
|
|
|
ax.set_xlim([min(start_times), max(add_arrays(start_times, durations))])
|
|
ax.set_ylim([min(pitches)-1, max(pitches)+1])
|
|
|
|
ax.set_facecolor('black')
|
|
fig.patch.set_facecolor('white')
|
|
|
|
if preview_length_in_notes > 0:
|
|
ax.axvline(x=pbl, c='white')
|
|
|
|
if block_lines_times_list:
|
|
for bl in block_lines_times_list:
|
|
ax.axvline(x=bl, c='white')
|
|
|
|
if show_grid_lines:
|
|
ax.grid(color='white')
|
|
|
|
plt.xlabel('Time (s)', c='black')
|
|
plt.ylabel('MIDI Pitch', c='black')
|
|
|
|
plt.title(plot_title)
|
|
|
|
if save_plt != '':
|
|
if save_only_plt_image:
|
|
plt.axis('off')
|
|
plt.title('')
|
|
plt.savefig(save_plt,
|
|
transparent=save_transparent,
|
|
bbox_inches='tight',
|
|
pad_inches=0,
|
|
facecolor='black'
|
|
)
|
|
plt.close()
|
|
|
|
else:
|
|
plt.savefig(save_plt)
|
|
plt.close()
|
|
|
|
if return_plt:
|
|
return fig
|
|
|
|
plt.show()
|
|
plt.close()
|
|
|
|
|
|
|
|
def plot_square_matrix_points(list_of_points,
|
|
list_of_points_colors,
|
|
plot_size=(7, 7),
|
|
point_size = 10,
|
|
show_grid_lines=False,
|
|
plot_title = 'Square Matrix Points Plot',
|
|
return_plt=False,
|
|
save_plt='',
|
|
save_only_plt_image=True,
|
|
save_transparent=False
|
|
):
|
|
|
|
'''Square matrix points plot'''
|
|
|
|
fig, ax = plt.subplots(figsize=plot_size)
|
|
|
|
ax.set_facecolor('black')
|
|
|
|
if show_grid_lines:
|
|
ax.grid(color='white')
|
|
|
|
plt.xlabel('Time Step', c='black')
|
|
plt.ylabel('MIDI Pitch', c='black')
|
|
|
|
plt.title(plot_title)
|
|
|
|
plt.scatter([p[0] for p in list_of_points],
|
|
[p[1] for p in list_of_points],
|
|
c=list_of_points_colors,
|
|
s=point_size
|
|
)
|
|
|
|
if save_plt != '':
|
|
if save_only_plt_image:
|
|
plt.axis('off')
|
|
plt.title('')
|
|
plt.savefig(save_plt,
|
|
transparent=save_transparent,
|
|
bbox_inches='tight',
|
|
pad_inches=0,
|
|
facecolor='black'
|
|
)
|
|
plt.close()
|
|
|
|
else:
|
|
plt.savefig(save_plt)
|
|
plt.close()
|
|
|
|
if return_plt:
|
|
return fig
|
|
|
|
plt.show()
|
|
plt.close()
|
|
|
|
|
|
|
|
def plot_cosine_similarities(lists_of_values,
|
|
plot_size=(7, 7),
|
|
save_plot=''
|
|
):
|
|
|
|
"""
|
|
Cosine similarities plot
|
|
"""
|
|
|
|
cos_sim = metrics.pairwise_distances(lists_of_values, metric='cosine')
|
|
|
|
plt.figure(figsize=plot_size)
|
|
|
|
plt.imshow(cos_sim, cmap="inferno", interpolation="nearest")
|
|
|
|
im_ratio = cos_sim.shape[0] / cos_sim.shape[1]
|
|
|
|
plt.colorbar(fraction=0.046 * im_ratio, pad=0.04)
|
|
|
|
plt.xlabel("Index")
|
|
plt.ylabel("Index")
|
|
|
|
plt.tight_layout()
|
|
|
|
if save_plot != '':
|
|
plt.savefig(save_plot, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
plt.show()
|
|
plt.close()
|
|
|
|
|
|
|
|
def plot_points_with_mst_lines(points,
|
|
points_labels,
|
|
points_mst_edges,
|
|
plot_size=(20, 20),
|
|
labels_size=24,
|
|
save_plot=''
|
|
):
|
|
|
|
"""
|
|
Plots 2D points with labels and MST lines.
|
|
"""
|
|
|
|
plt.figure(figsize=plot_size)
|
|
|
|
for i, label in enumerate(points_labels):
|
|
plt.scatter(points[i][0], points[i][1])
|
|
plt.annotate(label, (points[i][0], points[i][1]), fontsize=labels_size)
|
|
|
|
for edge in points_mst_edges:
|
|
i, j = edge
|
|
plt.plot([points[i][0], points[j][0]], [points[i][1], points[j][1]], 'k-', alpha=0.5)
|
|
|
|
plt.title('Points Map with MST Lines', fontsize=labels_size)
|
|
plt.xlabel('X-axis', fontsize=labels_size)
|
|
plt.ylabel('Y-axis', fontsize=labels_size)
|
|
|
|
if save_plot != '':
|
|
plt.savefig(save_plot, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
plt.show()
|
|
|
|
plt.close()
|
|
|
|
|
|
|
|
def plot_points_constellation(points,
|
|
points_labels,
|
|
p=5,
|
|
plot_size=(15, 15),
|
|
labels_size=12,
|
|
show_grid=False,
|
|
save_plot=''
|
|
):
|
|
|
|
"""
|
|
Plots 2D points constellation
|
|
"""
|
|
|
|
points = np.array(points)
|
|
|
|
dist_matrix = distance_matrix(points, points, p=p)
|
|
|
|
mst = minimum_spanning_tree(dist_matrix).toarray()
|
|
|
|
plt.figure(figsize=plot_size)
|
|
|
|
plt.scatter(points[:, 0], points[:, 1], color='blue')
|
|
|
|
for i, label in enumerate(points_labels):
|
|
plt.annotate(label, (points[i, 0], points[i, 1]),
|
|
textcoords="offset points",
|
|
xytext=(0, 10),
|
|
ha='center',
|
|
fontsize=labels_size
|
|
)
|
|
|
|
for i in range(len(points)):
|
|
for j in range(len(points)):
|
|
if mst[i, j] > 0:
|
|
plt.plot([points[i, 0], points[j, 0]], [points[i, 1], points[j, 1]], 'k--')
|
|
|
|
plt.xlabel('X-axis', fontsize=labels_size)
|
|
plt.ylabel('Y-axis', fontsize=labels_size)
|
|
plt.title('2D Coordinates with Minimum Spanning Tree', fontsize=labels_size)
|
|
|
|
plt.grid(show_grid)
|
|
|
|
if save_plot != '':
|
|
plt.savefig(save_plot, bbox_inches="tight")
|
|
plt.close()
|
|
|
|
plt.show()
|
|
|
|
plt.close()
|
|
|
|
|
|
|
|
def binary_matrix_to_images(matrix,
|
|
step,
|
|
overlap,
|
|
output_folder='./Dataset/',
|
|
output_img_prefix='image',
|
|
output_img_ext='.png',
|
|
save_to_array=False,
|
|
verbose=True
|
|
):
|
|
|
|
if not save_to_array:
|
|
|
|
if verbose:
|
|
print('=' * 70)
|
|
print('Checking output folder dir...')
|
|
|
|
os.makedirs(os.path.dirname(output_folder), exist_ok=True)
|
|
|
|
if verbose:
|
|
print('Done!')
|
|
|
|
if verbose:
|
|
print('=' * 70)
|
|
print('Writing images...')
|
|
|
|
matrix = np.array(matrix, dtype=np.uint8)
|
|
|
|
image_array = []
|
|
|
|
for i in range(0, max(1, matrix.shape[0]), overlap):
|
|
|
|
submatrix = matrix[i:i+step, :]
|
|
|
|
if submatrix.shape[0] < 128:
|
|
zeros_array = np.zeros((128-submatrix.shape[0], 128))
|
|
submatrix = np.vstack((submatrix, zeros_array))
|
|
|
|
img = Image.fromarray(submatrix * 255).convert('1')
|
|
|
|
if save_to_array:
|
|
image_array.append(np.array(img))
|
|
|
|
else:
|
|
img.save(output_folder + output_img_prefix + '_' + str(matrix.shape[1]) + '_' + str(i).zfill(7) + output_img_ext)
|
|
|
|
if verbose:
|
|
print('Done!')
|
|
print('=' * 70)
|
|
print('Saved', (matrix.shape[0] // min(step, overlap))+1, 'imges!')
|
|
print('=' * 70)
|
|
|
|
if save_to_array:
|
|
return np.array(image_array).tolist()
|
|
|
|
|
|
|
|
def images_to_binary_matrix(list_of_images):
|
|
|
|
image_array = np.array(list_of_images)
|
|
|
|
original_matrix = []
|
|
|
|
for img in image_array:
|
|
|
|
submatrix = np.array(img)
|
|
original_matrix.extend(submatrix.tolist())
|
|
|
|
return original_matrix
|
|
|
|
|
|
|
|
def square_image_matrix(image_matrix,
|
|
matrix_size=128,
|
|
num_pca_components=5,
|
|
filter_out_zero_rows=False,
|
|
return_square_matrix_points=False
|
|
):
|
|
|
|
"""
|
|
Reduces an arbitrary image matrix to a square image matrix
|
|
"""
|
|
|
|
matrix = np.array(image_matrix)
|
|
|
|
if filter_out_zero_rows:
|
|
matrix = matrix[~np.all(matrix == 0, axis=1)]
|
|
|
|
target_rows = matrix_size
|
|
|
|
rows_per_group = matrix.shape[0] // target_rows
|
|
|
|
compressed_matrix = np.zeros((target_rows, matrix.shape[1]), dtype=np.int32)
|
|
|
|
for i in range(target_rows):
|
|
start_row = i * rows_per_group
|
|
end_row = (i + 1) * rows_per_group
|
|
group = matrix[start_row:end_row, :]
|
|
|
|
pca = PCA(n_components=num_pca_components)
|
|
pca.fit(group)
|
|
|
|
principal_component = np.mean(pca.components_, axis=0)
|
|
contributions = np.dot(group, principal_component)
|
|
selected_row_index = np.argmax(contributions)
|
|
|
|
compressed_matrix[i, :] = group[selected_row_index, :]
|
|
|
|
if return_square_matrix_points:
|
|
filtered_matrix = compressed_matrix[~np.all(compressed_matrix == 0, axis=1)]
|
|
|
|
row_indexes, col_indexes = np.where(filtered_matrix != 0)
|
|
points = np.column_stack((row_indexes, filtered_matrix[row_indexes, col_indexes])).tolist()
|
|
|
|
return points
|
|
|
|
else:
|
|
return compressed_matrix.tolist()
|
|
|
|
|
|
|
|
def image_matrix_to_images(image_matrix,
|
|
step,
|
|
overlap,
|
|
num_img_channels=3,
|
|
output_folder='./Dataset/',
|
|
output_img_prefix='image',
|
|
output_img_ext='.png',
|
|
save_to_array=False,
|
|
verbose=True
|
|
):
|
|
|
|
if num_img_channels > 1:
|
|
n_mat_channels = 3
|
|
|
|
else:
|
|
n_mat_channels = 1
|
|
|
|
if not save_to_array:
|
|
|
|
if verbose:
|
|
print('=' * 70)
|
|
print('Checking output folder dir...')
|
|
|
|
os.makedirs(os.path.dirname(output_folder), exist_ok=True)
|
|
|
|
if verbose:
|
|
print('Done!')
|
|
|
|
if verbose:
|
|
print('=' * 70)
|
|
print('Writing images...')
|
|
|
|
matrix = np.array(image_matrix)
|
|
|
|
image_array = []
|
|
|
|
for i in range(0, max(1, matrix.shape[0]), overlap):
|
|
|
|
submatrix = matrix[i:i+step, :]
|
|
|
|
if submatrix.shape[0] < 128:
|
|
zeros_array = np.zeros((128-submatrix.shape[0], 128))
|
|
submatrix = np.vstack((submatrix, zeros_array))
|
|
|
|
if n_mat_channels == 3:
|
|
|
|
r = (submatrix // (256*256)) % 256
|
|
g = (submatrix // 256) % 256
|
|
b = submatrix % 256
|
|
|
|
rgb_image = np.stack((r, g, b), axis=-1).astype(np.uint8)
|
|
img = Image.fromarray(rgb_image, 'RGB')
|
|
|
|
else:
|
|
grayscale_image = submatrix.astype(np.uint8)
|
|
img = Image.fromarray(grayscale_image, 'L')
|
|
|
|
if save_to_array:
|
|
image_array.append(np.array(img))
|
|
|
|
else:
|
|
img.save(output_folder + output_img_prefix + '_' + str(matrix.shape[1]) + '_' + str(i).zfill(7) + output_img_ext)
|
|
|
|
if verbose:
|
|
print('Done!')
|
|
print('=' * 70)
|
|
print('Saved', (matrix.shape[0] // min(step, overlap))+1, 'imges!')
|
|
print('=' * 70)
|
|
|
|
if save_to_array:
|
|
return np.array(image_array).tolist()
|
|
|
|
|
|
|
|
def images_to_image_matrix(list_of_images,
|
|
num_img_channels=3
|
|
):
|
|
|
|
if num_img_channels > 1:
|
|
n_mat_channels = 3
|
|
|
|
else:
|
|
n_mat_channels = 1
|
|
|
|
image_array = np.array(list_of_images)
|
|
|
|
original_matrix = []
|
|
|
|
for img in image_array:
|
|
|
|
if num_img_channels == 3:
|
|
|
|
rgb_array = np.array(img)
|
|
|
|
matrix = (rgb_array[..., 0].astype(np.int64) * 256*256 +
|
|
rgb_array[..., 1].astype(np.int64) * 256 +
|
|
rgb_array[..., 2].astype(np.int64))
|
|
|
|
else:
|
|
matrix = np.array(img)
|
|
|
|
original_matrix.extend(matrix)
|
|
|
|
return original_matrix
|
|
|
|
|
|
|
|
|
|
|
|
'''
|
|
import umap
|
|
|
|
def reduce_dimensionality_umap(list_of_values,
|
|
n_comp=2,
|
|
n_neighbors=15,
|
|
):
|
|
|
|
"""
|
|
Reduces the dimensionality of the values using UMAP.
|
|
"""
|
|
|
|
vals = np.array(list_of_values)
|
|
|
|
umap_reducer = umap.UMAP(n_components=n_comp,
|
|
n_neighbors=n_neighbors,
|
|
n_epochs=5000,
|
|
verbose=True
|
|
)
|
|
|
|
reduced_vals = umap_reducer.fit_transform(vals)
|
|
|
|
return reduced_vals.tolist()
|
|
'''
|
|
|
|
|
|
|
|
'''
|
|
import alphashape
|
|
from shapely.geometry import Point
|
|
from matplotlib.tri import Triangulation, LinearTriInterpolator
|
|
from scipy.stats import zscore
|
|
|
|
#===============================================================================
|
|
|
|
coordinates = points
|
|
|
|
dist_matrix = minkowski_distance_matrix(coordinates, p=3) # You can change the value of p as needed
|
|
|
|
# Centering matrix
|
|
n = dist_matrix.shape[0]
|
|
H = np.eye(n) - np.ones((n, n)) / n
|
|
|
|
# Apply double centering
|
|
B = -0.5 * H @ dist_matrix**2 @ H
|
|
|
|
# Eigen decomposition
|
|
eigvals, eigvecs = np.linalg.eigh(B)
|
|
|
|
# Sort eigenvalues and eigenvectors
|
|
idx = np.argsort(eigvals)[::-1]
|
|
eigvals = eigvals[idx]
|
|
eigvecs = eigvecs[:, idx]
|
|
|
|
# Select the top 2 eigenvectors
|
|
X_transformed = eigvecs[:, :2] * np.sqrt(eigvals[:2])
|
|
|
|
#===============================================================================
|
|
|
|
src_points = X_transformed
|
|
src_values = np.array([[p[1]] for p in points]) #np.random.rand(X_transformed.shape[0])
|
|
|
|
#===============================================================================
|
|
|
|
# Normalize the points to the range [0, 1]
|
|
scaler = MinMaxScaler()
|
|
points_normalized = scaler.fit_transform(src_points)
|
|
|
|
values_normalized = custom_normalize(src_values)
|
|
|
|
# Remove outliers based on z-score
|
|
z_scores = np.abs(zscore(points_normalized, axis=0))
|
|
filtered_points = points_normalized[(z_scores < 3).all(axis=1)]
|
|
filtered_values = values_normalized[(z_scores < 3).all(axis=1)]
|
|
|
|
# Compute the concave hull (alpha shape)
|
|
alpha = 8 # Adjust alpha as needed
|
|
hull = alphashape.alphashape(filtered_points, alpha)
|
|
|
|
# Create a triangulation
|
|
tri = Triangulation(filtered_points[:, 0], filtered_points[:, 1])
|
|
|
|
# Interpolate the values on the triangulation
|
|
interpolator = LinearTriInterpolator(tri, filtered_values[:, 0])
|
|
xi, yi = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, 1, 100))
|
|
zi = interpolator(xi, yi)
|
|
|
|
# Mask out points outside the concave hull
|
|
mask = np.array([hull.contains(Point(x, y)) for x, y in zip(xi.flatten(), yi.flatten())])
|
|
zi = np.ma.array(zi, mask=~mask.reshape(zi.shape))
|
|
|
|
# Plot the filled contour based on the interpolated values
|
|
plt.contourf(xi, yi, zi, levels=50, cmap='viridis')
|
|
|
|
# Plot the original points
|
|
#plt.scatter(filtered_points[:, 0], filtered_points[:, 1], c=filtered_values, edgecolors='k')
|
|
|
|
plt.title('Filled Contour Plot with Original Values')
|
|
plt.xlabel('X-axis')
|
|
plt.ylabel('Y-axis')
|
|
plt.colorbar(label='Value')
|
|
plt.show()
|
|
'''
|
|
|
|
|
|
|
|
|
|
|
|
|
