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def _write_metrics(metrics_mngr, tb_mngr, metrics, round_num):
'Atomic metrics writer which inlines logic from MetricsHook class.'
if (not isinstance(metrics, dict)):
raise TypeError('metrics should be type `dict`.')
if (not isinstance(round_num, int)):
raise TypeError('round_num should be type `int`.')
logging.info('Metrics at round {:d}:\n{!s}'.format(round_num, pprint.pformat(metrics)))
metrics_mngr.save_metrics(metrics, round_num)
tb_mngr.save_metrics(metrics, round_num) | 1,945,417,938,138,571,300 | Atomic metrics writer which inlines logic from MetricsHook class. | utils/training_loop.py | _write_metrics | houcharlie/federated | python | def _write_metrics(metrics_mngr, tb_mngr, metrics, round_num):
if (not isinstance(metrics, dict)):
raise TypeError('metrics should be type `dict`.')
if (not isinstance(round_num, int)):
raise TypeError('round_num should be type `int`.')
logging.info('Metrics at round {:d}:\n{!s}'.format(round_num, pprint.pformat(metrics)))
metrics_mngr.save_metrics(metrics, round_num)
tb_mngr.save_metrics(metrics, round_num) |
def _check_iterative_process_compatibility(iterative_process):
'Checks the compatibility of an iterative process with the training loop.'
error_message = 'The iterative_process argument must be of type`tff.templates.IterativeProcess`, and must have an attribute `get_model_weights`, which must be a `tff.Computation`. This computation must accept as input the state of `iterative_process`, and its output must be a nested structure of tensors matching the input shape of `validation_fn`.'
compatibility_error = IterativeProcessCompatibilityError(error_message)
if (not isinstance(iterative_process, tff.templates.IterativeProcess)):
raise compatibility_error
if (not hasattr(iterative_process, 'get_model_weights')):
raise compatibility_error
elif (not callable(iterative_process.get_model_weights)):
raise compatibility_error
get_model_weights_fn = iterative_process.get_model_weights
if (not isinstance(get_model_weights_fn, tff.Computation)):
raise compatibility_error
input_type = get_model_weights_fn.type_signature.parameter
server_state_type = iterative_process.state_type.member
server_state_type.is_assignable_from(input_type) | -6,501,721,324,460,617,000 | Checks the compatibility of an iterative process with the training loop. | utils/training_loop.py | _check_iterative_process_compatibility | houcharlie/federated | python | def _check_iterative_process_compatibility(iterative_process):
error_message = 'The iterative_process argument must be of type`tff.templates.IterativeProcess`, and must have an attribute `get_model_weights`, which must be a `tff.Computation`. This computation must accept as input the state of `iterative_process`, and its output must be a nested structure of tensors matching the input shape of `validation_fn`.'
compatibility_error = IterativeProcessCompatibilityError(error_message)
if (not isinstance(iterative_process, tff.templates.IterativeProcess)):
raise compatibility_error
if (not hasattr(iterative_process, 'get_model_weights')):
raise compatibility_error
elif (not callable(iterative_process.get_model_weights)):
raise compatibility_error
get_model_weights_fn = iterative_process.get_model_weights
if (not isinstance(get_model_weights_fn, tff.Computation)):
raise compatibility_error
input_type = get_model_weights_fn.type_signature.parameter
server_state_type = iterative_process.state_type.member
server_state_type.is_assignable_from(input_type) |
def run(iterative_process: tff.templates.IterativeProcess, client_datasets_fn: Callable[([int], List[tf.data.Dataset])], validation_fn: Callable[([Any, int], Dict[(str, float)])], total_rounds: int, experiment_name: str, test_fn: Optional[Callable[([Any], Dict[(str, float)])]]=None, root_output_dir: Optional[str]='/tmp/fed_opt', rounds_per_eval: Optional[int]=1, rounds_per_checkpoint: Optional[int]=50, rounds_per_profile: Optional[int]=0):
'Runs federated training for a given `tff.templates.IterativeProcess`.\n\n We assume that the iterative process has the following functional type\n signatures:\n\n * `initialize`: `( -> S@SERVER)` where `S` represents the server state.\n * `next`: `<S@SERVER, {B*}@CLIENTS> -> <S@SERVER, T@SERVER>` where `S`\n represents the server state, `{B*}` represents the client datasets,\n and `T` represents a python `Mapping` object.\n\n The iterative process must also have a callable attribute `get_model_weights`\n that takes as input the state of the iterative process, and returns a\n `tff.learning.ModelWeights` object.\n\n Args:\n iterative_process: A `tff.templates.IterativeProcess` instance to run.\n client_datasets_fn: Function accepting an integer argument (the round\n number) and returning a list of client datasets to use as federated data\n for that round.\n validation_fn: A callable accepting a `tff.learning.ModelWeights` and the\n current round number, and returning a dict of evaluation metrics. Used to\n compute validation metrics throughout the training process.\n total_rounds: The number of federated training rounds to perform.\n experiment_name: The name of the experiment being run. This will be appended\n to the `root_output_dir` for purposes of writing outputs.\n test_fn: An optional callable accepting a `tff.learning.ModelWeights` and\n returning a dict of test set metrics. Used to compute test metrics at the\n end of the training process.\n root_output_dir: The name of the root output directory for writing\n experiment outputs.\n rounds_per_eval: How often to compute validation metrics.\n rounds_per_checkpoint: How often to checkpoint the iterative process state.\n If you expect the job to restart frequently, this should be small. If no\n interruptions are expected, this can be made larger.\n rounds_per_profile: Experimental setting. If set to a value greater than 0,\n this dictates how often a TensorFlow profiler is run.\n\n Returns:\n The final `state` of the iterative process after training.\n '
_check_iterative_process_compatibility(iterative_process)
if (not callable(client_datasets_fn)):
raise TypeError('client_datasets_fn should be callable.')
if (not callable(validation_fn)):
raise TypeError('validation_fn should be callable.')
if ((test_fn is not None) and (not callable(test_fn))):
raise TypeError('test_fn should be callable.')
logging.info('Starting iterative_process training loop...')
initial_state = iterative_process.initialize()
(checkpoint_mngr, metrics_mngr, tb_mngr, profiler) = _setup_outputs(root_output_dir, experiment_name, rounds_per_profile)
logging.info('Asking checkpoint manager to load checkpoint.')
(state, round_num) = checkpoint_mngr.load_latest_checkpoint(initial_state)
if (state is None):
logging.info('Initializing experiment from scratch.')
state = initial_state
round_num = 0
else:
logging.info('Restarted from checkpoint round %d', round_num)
round_num += 1
metrics_mngr.clear_metrics(round_num)
current_model = iterative_process.get_model_weights(state)
loop_start_time = time.time()
loop_start_round = round_num
while (round_num < total_rounds):
data_prep_start_time = time.time()
federated_train_data = client_datasets_fn(round_num)
train_metrics = {'prepare_datasets_secs': (time.time() - data_prep_start_time)}
training_start_time = time.time()
prev_model = current_model
try:
with profiler(round_num):
(state, round_metrics) = iterative_process.next(state, federated_train_data)
except (tf.errors.FailedPreconditionError, tf.errors.NotFoundError, tf.errors.InternalError) as e:
logging.warning('Caught %s exception while running round %d:\n\t%s', type(e), round_num, e)
continue
current_model = iterative_process.get_model_weights(state)
train_metrics['training_secs'] = (time.time() - training_start_time)
train_metrics['model_delta_l2_norm'] = _compute_numpy_l2_difference(current_model, prev_model)
train_metrics['client_drift'] = state.client_drift
train_metrics.update(round_metrics)
loop_time = (time.time() - loop_start_time)
loop_rounds = ((round_num - loop_start_round) + 1)
logging.info('Round {:2d}, {:.2f}s per round in average.'.format(round_num, (loop_time / loop_rounds)))
if (((round_num % rounds_per_checkpoint) == 0) or (round_num == (total_rounds - 1))):
save_checkpoint_start_time = time.time()
checkpoint_mngr.save_checkpoint(state, round_num)
train_metrics['save_checkpoint_secs'] = (time.time() - save_checkpoint_start_time)
metrics = {'train': train_metrics}
if ((round_num % rounds_per_eval) == 0):
evaluate_start_time = time.time()
validation_metrics = validation_fn(current_model, round_num)
validation_metrics['evaluate_secs'] = (time.time() - evaluate_start_time)
metrics['eval'] = validation_metrics
_write_metrics(metrics_mngr, tb_mngr, metrics, round_num)
round_num += 1
metrics = {}
evaluate_start_time = time.time()
validation_metrics = validation_fn(current_model, round_num)
validation_metrics['evaluate_secs'] = (time.time() - evaluate_start_time)
metrics['eval'] = validation_metrics
if test_fn:
test_start_time = time.time()
test_metrics = test_fn(current_model)
test_metrics['evaluate_secs'] = (time.time() - test_start_time)
metrics['test'] = test_metrics
_write_metrics(metrics_mngr, tb_mngr, metrics, total_rounds)
return state | -629,683,971,590,818,000 | Runs federated training for a given `tff.templates.IterativeProcess`.
We assume that the iterative process has the following functional type
signatures:
* `initialize`: `( -> S@SERVER)` where `S` represents the server state.
* `next`: `<S@SERVER, {B*}@CLIENTS> -> <S@SERVER, T@SERVER>` where `S`
represents the server state, `{B*}` represents the client datasets,
and `T` represents a python `Mapping` object.
The iterative process must also have a callable attribute `get_model_weights`
that takes as input the state of the iterative process, and returns a
`tff.learning.ModelWeights` object.
Args:
iterative_process: A `tff.templates.IterativeProcess` instance to run.
client_datasets_fn: Function accepting an integer argument (the round
number) and returning a list of client datasets to use as federated data
for that round.
validation_fn: A callable accepting a `tff.learning.ModelWeights` and the
current round number, and returning a dict of evaluation metrics. Used to
compute validation metrics throughout the training process.
total_rounds: The number of federated training rounds to perform.
experiment_name: The name of the experiment being run. This will be appended
to the `root_output_dir` for purposes of writing outputs.
test_fn: An optional callable accepting a `tff.learning.ModelWeights` and
returning a dict of test set metrics. Used to compute test metrics at the
end of the training process.
root_output_dir: The name of the root output directory for writing
experiment outputs.
rounds_per_eval: How often to compute validation metrics.
rounds_per_checkpoint: How often to checkpoint the iterative process state.
If you expect the job to restart frequently, this should be small. If no
interruptions are expected, this can be made larger.
rounds_per_profile: Experimental setting. If set to a value greater than 0,
this dictates how often a TensorFlow profiler is run.
Returns:
The final `state` of the iterative process after training. | utils/training_loop.py | run | houcharlie/federated | python | def run(iterative_process: tff.templates.IterativeProcess, client_datasets_fn: Callable[([int], List[tf.data.Dataset])], validation_fn: Callable[([Any, int], Dict[(str, float)])], total_rounds: int, experiment_name: str, test_fn: Optional[Callable[([Any], Dict[(str, float)])]]=None, root_output_dir: Optional[str]='/tmp/fed_opt', rounds_per_eval: Optional[int]=1, rounds_per_checkpoint: Optional[int]=50, rounds_per_profile: Optional[int]=0):
'Runs federated training for a given `tff.templates.IterativeProcess`.\n\n We assume that the iterative process has the following functional type\n signatures:\n\n * `initialize`: `( -> S@SERVER)` where `S` represents the server state.\n * `next`: `<S@SERVER, {B*}@CLIENTS> -> <S@SERVER, T@SERVER>` where `S`\n represents the server state, `{B*}` represents the client datasets,\n and `T` represents a python `Mapping` object.\n\n The iterative process must also have a callable attribute `get_model_weights`\n that takes as input the state of the iterative process, and returns a\n `tff.learning.ModelWeights` object.\n\n Args:\n iterative_process: A `tff.templates.IterativeProcess` instance to run.\n client_datasets_fn: Function accepting an integer argument (the round\n number) and returning a list of client datasets to use as federated data\n for that round.\n validation_fn: A callable accepting a `tff.learning.ModelWeights` and the\n current round number, and returning a dict of evaluation metrics. Used to\n compute validation metrics throughout the training process.\n total_rounds: The number of federated training rounds to perform.\n experiment_name: The name of the experiment being run. This will be appended\n to the `root_output_dir` for purposes of writing outputs.\n test_fn: An optional callable accepting a `tff.learning.ModelWeights` and\n returning a dict of test set metrics. Used to compute test metrics at the\n end of the training process.\n root_output_dir: The name of the root output directory for writing\n experiment outputs.\n rounds_per_eval: How often to compute validation metrics.\n rounds_per_checkpoint: How often to checkpoint the iterative process state.\n If you expect the job to restart frequently, this should be small. If no\n interruptions are expected, this can be made larger.\n rounds_per_profile: Experimental setting. If set to a value greater than 0,\n this dictates how often a TensorFlow profiler is run.\n\n Returns:\n The final `state` of the iterative process after training.\n '
_check_iterative_process_compatibility(iterative_process)
if (not callable(client_datasets_fn)):
raise TypeError('client_datasets_fn should be callable.')
if (not callable(validation_fn)):
raise TypeError('validation_fn should be callable.')
if ((test_fn is not None) and (not callable(test_fn))):
raise TypeError('test_fn should be callable.')
logging.info('Starting iterative_process training loop...')
initial_state = iterative_process.initialize()
(checkpoint_mngr, metrics_mngr, tb_mngr, profiler) = _setup_outputs(root_output_dir, experiment_name, rounds_per_profile)
logging.info('Asking checkpoint manager to load checkpoint.')
(state, round_num) = checkpoint_mngr.load_latest_checkpoint(initial_state)
if (state is None):
logging.info('Initializing experiment from scratch.')
state = initial_state
round_num = 0
else:
logging.info('Restarted from checkpoint round %d', round_num)
round_num += 1
metrics_mngr.clear_metrics(round_num)
current_model = iterative_process.get_model_weights(state)
loop_start_time = time.time()
loop_start_round = round_num
while (round_num < total_rounds):
data_prep_start_time = time.time()
federated_train_data = client_datasets_fn(round_num)
train_metrics = {'prepare_datasets_secs': (time.time() - data_prep_start_time)}
training_start_time = time.time()
prev_model = current_model
try:
with profiler(round_num):
(state, round_metrics) = iterative_process.next(state, federated_train_data)
except (tf.errors.FailedPreconditionError, tf.errors.NotFoundError, tf.errors.InternalError) as e:
logging.warning('Caught %s exception while running round %d:\n\t%s', type(e), round_num, e)
continue
current_model = iterative_process.get_model_weights(state)
train_metrics['training_secs'] = (time.time() - training_start_time)
train_metrics['model_delta_l2_norm'] = _compute_numpy_l2_difference(current_model, prev_model)
train_metrics['client_drift'] = state.client_drift
train_metrics.update(round_metrics)
loop_time = (time.time() - loop_start_time)
loop_rounds = ((round_num - loop_start_round) + 1)
logging.info('Round {:2d}, {:.2f}s per round in average.'.format(round_num, (loop_time / loop_rounds)))
if (((round_num % rounds_per_checkpoint) == 0) or (round_num == (total_rounds - 1))):
save_checkpoint_start_time = time.time()
checkpoint_mngr.save_checkpoint(state, round_num)
train_metrics['save_checkpoint_secs'] = (time.time() - save_checkpoint_start_time)
metrics = {'train': train_metrics}
if ((round_num % rounds_per_eval) == 0):
evaluate_start_time = time.time()
validation_metrics = validation_fn(current_model, round_num)
validation_metrics['evaluate_secs'] = (time.time() - evaluate_start_time)
metrics['eval'] = validation_metrics
_write_metrics(metrics_mngr, tb_mngr, metrics, round_num)
round_num += 1
metrics = {}
evaluate_start_time = time.time()
validation_metrics = validation_fn(current_model, round_num)
validation_metrics['evaluate_secs'] = (time.time() - evaluate_start_time)
metrics['eval'] = validation_metrics
if test_fn:
test_start_time = time.time()
test_metrics = test_fn(current_model)
test_metrics['evaluate_secs'] = (time.time() - test_start_time)
metrics['test'] = test_metrics
_write_metrics(metrics_mngr, tb_mngr, metrics, total_rounds)
return state |
def setup_inp(inp):
'Convert list of strings into list of lists, with glves/goblins replaced by tuples'
grid = []
for (rowI, row) in enumerate(inp.split('\n')):
grid.append([x for x in row])
for (colI, col) in enumerate(row):
if (col in ['G', 'E']):
char_tup = (col, 200, False)
grid[rowI][colI] = char_tup
return grid | -1,784,923,850,771,478,300 | Convert list of strings into list of lists, with glves/goblins replaced by tuples | 2018/15/helpme.py | setup_inp | mark-inderhees/aoc | python | def setup_inp(inp):
grid = []
for (rowI, row) in enumerate(inp.split('\n')):
grid.append([x for x in row])
for (colI, col) in enumerate(row):
if (col in ['G', 'E']):
char_tup = (col, 200, False)
grid[rowI][colI] = char_tup
return grid |
def move_character(inp, from_row, from_col, to_row, to_col, char):
'Move character on grid, and increment the i value so we can tell we already moved it'
inp[from_row][from_col] = '.'
inp[to_row][to_col] = (char[0], char[1], True)
return inp | -2,405,267,525,196,605,400 | Move character on grid, and increment the i value so we can tell we already moved it | 2018/15/helpme.py | move_character | mark-inderhees/aoc | python | def move_character(inp, from_row, from_col, to_row, to_col, char):
inp[from_row][from_col] = '.'
inp[to_row][to_col] = (char[0], char[1], True)
return inp |
def attack(inp, row, col, enemy, damage=3):
'\n Attack weakest adjacent enemy, if one is there\n If multiple weakest enemies, attack in reading order\n Return the modified board, and a boolean indicating whether anyone died\n '
if (not adjacent_enemy(inp, row, col, enemy)):
return (inp, False)
enemies = {}
for coords in [((row - 1), col), ((row + 1), col), (row, (col - 1)), (row, (col + 1))]:
if (inp[coords[0]][coords[1]][0] == enemy):
enemies[coords] = inp[coords[0]][coords[1]][1]
min_hp = min(enemies.values())
enemies = [x for x in enemies if (enemies[x] == min_hp)]
enemies.sort()
coords = enemies[0]
enemy = inp[coords[0]][coords[1]]
enemy_pts = (enemy[1] - damage)
enemy_tup = (enemy[0], enemy_pts, enemy[2])
if (enemy_pts <= 0):
inp[coords[0]][coords[1]] = '.'
return (inp, True)
else:
inp[coords[0]][coords[1]] = enemy_tup
return (inp, False) | -7,804,793,766,275,739,000 | Attack weakest adjacent enemy, if one is there
If multiple weakest enemies, attack in reading order
Return the modified board, and a boolean indicating whether anyone died | 2018/15/helpme.py | attack | mark-inderhees/aoc | python | def attack(inp, row, col, enemy, damage=3):
'\n Attack weakest adjacent enemy, if one is there\n If multiple weakest enemies, attack in reading order\n Return the modified board, and a boolean indicating whether anyone died\n '
if (not adjacent_enemy(inp, row, col, enemy)):
return (inp, False)
enemies = {}
for coords in [((row - 1), col), ((row + 1), col), (row, (col - 1)), (row, (col + 1))]:
if (inp[coords[0]][coords[1]][0] == enemy):
enemies[coords] = inp[coords[0]][coords[1]][1]
min_hp = min(enemies.values())
enemies = [x for x in enemies if (enemies[x] == min_hp)]
enemies.sort()
coords = enemies[0]
enemy = inp[coords[0]][coords[1]]
enemy_pts = (enemy[1] - damage)
enemy_tup = (enemy[0], enemy_pts, enemy[2])
if (enemy_pts <= 0):
inp[coords[0]][coords[1]] = '.'
return (inp, True)
else:
inp[coords[0]][coords[1]] = enemy_tup
return (inp, False) |
def adjacent_enemy(inp, rowI, colI, enemy):
'Check for enemy in adjacent square'
if any(((x[0] == enemy) for x in [inp[(rowI + 1)][colI], inp[(rowI - 1)][colI], inp[rowI][(colI + 1)], inp[rowI][(colI - 1)]])):
return True
return False | 3,321,948,015,826,023,400 | Check for enemy in adjacent square | 2018/15/helpme.py | adjacent_enemy | mark-inderhees/aoc | python | def adjacent_enemy(inp, rowI, colI, enemy):
if any(((x[0] == enemy) for x in [inp[(rowI + 1)][colI], inp[(rowI - 1)][colI], inp[rowI][(colI + 1)], inp[rowI][(colI - 1)]])):
return True
return False |
def get_best_move(best_moves):
'\n Takes a list of tuples of\n (first_move, number_of_moves, tile_coordinates), which might look like -\n ((12, 22), 8, (17, 25))\n ((12, 22), 8, (18, 24))\n ((12, 22), 8, (19, 21))\n ((13, 21), 6, (19, 21))\n ((13, 23), 6, (17, 25))\n ((13, 23), 6, (18, 24))\n ((14, 22), 6, (17, 25))\n ((14, 22), 6, (18, 24))\n ((14, 22), 6, (19, 21))\n And filters/sorts them to satisfy all the conditions\n '
if (not best_moves):
return None
min_steps = min([x[1] for x in best_moves])
best_moves = [x for x in best_moves if (x[1] == min_steps)]
best_moves.sort(key=(lambda x: x[2]))
best_moves = [x for x in best_moves if (x[2] == best_moves[0][2])]
best_moves.sort(key=(lambda x: x[0]))
best_moves = [x for x in best_moves if (x[0] == best_moves[0][0])]
return best_moves[0][0] | -3,099,320,645,593,120,300 | Takes a list of tuples of
(first_move, number_of_moves, tile_coordinates), which might look like -
((12, 22), 8, (17, 25))
((12, 22), 8, (18, 24))
((12, 22), 8, (19, 21))
((13, 21), 6, (19, 21))
((13, 23), 6, (17, 25))
((13, 23), 6, (18, 24))
((14, 22), 6, (17, 25))
((14, 22), 6, (18, 24))
((14, 22), 6, (19, 21))
And filters/sorts them to satisfy all the conditions | 2018/15/helpme.py | get_best_move | mark-inderhees/aoc | python | def get_best_move(best_moves):
'\n Takes a list of tuples of\n (first_move, number_of_moves, tile_coordinates), which might look like -\n ((12, 22), 8, (17, 25))\n ((12, 22), 8, (18, 24))\n ((12, 22), 8, (19, 21))\n ((13, 21), 6, (19, 21))\n ((13, 23), 6, (17, 25))\n ((13, 23), 6, (18, 24))\n ((14, 22), 6, (17, 25))\n ((14, 22), 6, (18, 24))\n ((14, 22), 6, (19, 21))\n And filters/sorts them to satisfy all the conditions\n '
if (not best_moves):
return None
min_steps = min([x[1] for x in best_moves])
best_moves = [x for x in best_moves if (x[1] == min_steps)]
best_moves.sort(key=(lambda x: x[2]))
best_moves = [x for x in best_moves if (x[2] == best_moves[0][2])]
best_moves.sort(key=(lambda x: x[0]))
best_moves = [x for x in best_moves if (x[0] == best_moves[0][0])]
return best_moves[0][0] |
def bfs_move(inp, rowI, colI, hero, enemy):
'\n Perform a breadth first search for each adjacent tile\n Although not the most efficient, the approach is still fast and makes it\n easy to sort in such a way that satisfies all the conditions\n '
if adjacent_enemy(inp, rowI, colI, enemy):
return None
first_moves = [((rowI + 1), colI), ((rowI - 1), colI), (rowI, (colI - 1)), (rowI, (colI + 1))]
first_moves = [x for x in first_moves if (inp[x[0]][x[1]] == '.')]
best_moves = []
for move in first_moves:
(r, c) = move
if adjacent_enemy(inp, r, c, enemy):
best_moves.append((move, 1, move))
continue
seen_coordinates = {(rowI, colI), (r, c)}
stack = [((r + 1), c), ((r - 1), c), (r, (c - 1)), (r, (c + 1))]
stack = [x for x in stack if ((inp[x[0]][x[1]] == '.') and ((x[0], x[1]) not in seen_coordinates))]
i = 1
run = True
while run:
i += 1
new_stack = []
for tile in stack:
if (tile in seen_coordinates):
continue
seen_coordinates.add(tile)
(r, c) = tile
if adjacent_enemy(inp, r, c, enemy):
best_moves.append((move, i, (r, c)))
run = False
continue
new_tiles = [((r + 1), c), ((r - 1), c), (r, (c - 1)), (r, (c + 1))]
new_stack += [x for x in new_tiles if ((inp[x[0]][x[1]] == '.') and ((x[0], x[1]) not in seen_coordinates))]
stack = list(set(new_stack))
if (not stack):
run = False
return get_best_move(best_moves) | 7,900,044,209,021,287,000 | Perform a breadth first search for each adjacent tile
Although not the most efficient, the approach is still fast and makes it
easy to sort in such a way that satisfies all the conditions | 2018/15/helpme.py | bfs_move | mark-inderhees/aoc | python | def bfs_move(inp, rowI, colI, hero, enemy):
'\n Perform a breadth first search for each adjacent tile\n Although not the most efficient, the approach is still fast and makes it\n easy to sort in such a way that satisfies all the conditions\n '
if adjacent_enemy(inp, rowI, colI, enemy):
return None
first_moves = [((rowI + 1), colI), ((rowI - 1), colI), (rowI, (colI - 1)), (rowI, (colI + 1))]
first_moves = [x for x in first_moves if (inp[x[0]][x[1]] == '.')]
best_moves = []
for move in first_moves:
(r, c) = move
if adjacent_enemy(inp, r, c, enemy):
best_moves.append((move, 1, move))
continue
seen_coordinates = {(rowI, colI), (r, c)}
stack = [((r + 1), c), ((r - 1), c), (r, (c - 1)), (r, (c + 1))]
stack = [x for x in stack if ((inp[x[0]][x[1]] == '.') and ((x[0], x[1]) not in seen_coordinates))]
i = 1
run = True
while run:
i += 1
new_stack = []
for tile in stack:
if (tile in seen_coordinates):
continue
seen_coordinates.add(tile)
(r, c) = tile
if adjacent_enemy(inp, r, c, enemy):
best_moves.append((move, i, (r, c)))
run = False
continue
new_tiles = [((r + 1), c), ((r - 1), c), (r, (c - 1)), (r, (c + 1))]
new_stack += [x for x in new_tiles if ((inp[x[0]][x[1]] == '.') and ((x[0], x[1]) not in seen_coordinates))]
stack = list(set(new_stack))
if (not stack):
run = False
return get_best_move(best_moves) |
def reset_moved_bools(inp):
"Reset the third value in our character tuples, which tracks whether they've moved in a round"
for (rowI, row) in enumerate(inp):
for (colI, col) in enumerate(row):
if (col[0] in ['G', 'E']):
char_tup = (col[0], col[1], False)
inp[rowI][colI] = char_tup
return inp | -8,201,011,777,282,888,000 | Reset the third value in our character tuples, which tracks whether they've moved in a round | 2018/15/helpme.py | reset_moved_bools | mark-inderhees/aoc | python | def reset_moved_bools(inp):
for (rowI, row) in enumerate(inp):
for (colI, col) in enumerate(row):
if (col[0] in ['G', 'E']):
char_tup = (col[0], col[1], False)
inp[rowI][colI] = char_tup
return inp |
def equal_devision(length, div_num):
'\n # 概要\n length を div_num で分割する。\n 端数が出た場合は誤差拡散法を使って上手い具合に分散させる。\n '
base = (length / div_num)
ret_array = [base for x in range(div_num)]
diff = 0
for idx in range(div_num):
diff += math.modf(ret_array[idx])[0]
if (diff >= 1.0):
diff -= 1.0
ret_array[idx] = int((math.floor(ret_array[idx]) + 1))
else:
ret_array[idx] = int(math.floor(ret_array[idx]))
diff = (length - sum(ret_array))
if (diff != 0):
ret_array[(- 1)] += diff
if (length != sum(ret_array)):
raise ValueError('the output of equal_division() is abnormal.')
return ret_array | -5,392,802,471,796,530,000 | # 概要
length を div_num で分割する。
端数が出た場合は誤差拡散法を使って上手い具合に分散させる。 | ty_lib/test_pattern_generator2.py | equal_devision | colour-science/sample_code | python | def equal_devision(length, div_num):
'\n # 概要\n length を div_num で分割する。\n 端数が出た場合は誤差拡散法を使って上手い具合に分散させる。\n '
base = (length / div_num)
ret_array = [base for x in range(div_num)]
diff = 0
for idx in range(div_num):
diff += math.modf(ret_array[idx])[0]
if (diff >= 1.0):
diff -= 1.0
ret_array[idx] = int((math.floor(ret_array[idx]) + 1))
else:
ret_array[idx] = int(math.floor(ret_array[idx]))
diff = (length - sum(ret_array))
if (diff != 0):
ret_array[(- 1)] += diff
if (length != sum(ret_array)):
raise ValueError('the output of equal_division() is abnormal.')
return ret_array |
def do_matrix(img, mtx):
'\n img に対して mtx を適用する。\n '
base_shape = img.shape
(r, g, b) = (img[(..., 0)], img[(..., 1)], img[(..., 2)])
ro = (((r * mtx[0][0]) + (g * mtx[0][1])) + (b * mtx[0][2]))
go = (((r * mtx[1][0]) + (g * mtx[1][1])) + (b * mtx[1][2]))
bo = (((r * mtx[2][0]) + (g * mtx[2][1])) + (b * mtx[2][2]))
out_img = np.dstack((ro, go, bo)).reshape(base_shape)
return out_img | -4,858,280,892,068,223,000 | img に対して mtx を適用する。 | ty_lib/test_pattern_generator2.py | do_matrix | colour-science/sample_code | python | def do_matrix(img, mtx):
'\n \n '
base_shape = img.shape
(r, g, b) = (img[(..., 0)], img[(..., 1)], img[(..., 2)])
ro = (((r * mtx[0][0]) + (g * mtx[0][1])) + (b * mtx[0][2]))
go = (((r * mtx[1][0]) + (g * mtx[1][1])) + (b * mtx[1][2]))
bo = (((r * mtx[2][0]) + (g * mtx[2][1])) + (b * mtx[2][2]))
out_img = np.dstack((ro, go, bo)).reshape(base_shape)
return out_img |
def _get_cmfs_xy():
'\n xy色度図のプロットのための馬蹄形の外枠のxy値を求める。\n\n Returns\n -------\n array_like\n xy coordinate for chromaticity diagram\n\n '
cmf = CMFS.get(CMFS_NAME)
d65_white = D65_WHITE
cmf_xy = XYZ_to_xy(cmf.values, d65_white)
return cmf_xy | 1,856,355,623,035,113,500 | xy色度図のプロットのための馬蹄形の外枠のxy値を求める。
Returns
-------
array_like
xy coordinate for chromaticity diagram | ty_lib/test_pattern_generator2.py | _get_cmfs_xy | colour-science/sample_code | python | def _get_cmfs_xy():
'\n xy色度図のプロットのための馬蹄形の外枠のxy値を求める。\n\n Returns\n -------\n array_like\n xy coordinate for chromaticity diagram\n\n '
cmf = CMFS.get(CMFS_NAME)
d65_white = D65_WHITE
cmf_xy = XYZ_to_xy(cmf.values, d65_white)
return cmf_xy |
def get_primaries(name='ITU-R BT.2020'):
'\n prmary color の座標を求める\n\n\n Parameters\n ----------\n name : str\n a name of the color space.\n\n Returns\n -------\n array_like\n prmaries. [[rx, ry], [gx, gy], [bx, by], [rx, ry]]\n\n '
primaries = RGB_COLOURSPACES[name].primaries
primaries = np.append(primaries, [primaries[0, :]], axis=0)
rgb = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
return (primaries, rgb) | -1,104,957,472,951,224,800 | prmary color の座標を求める
Parameters
----------
name : str
a name of the color space.
Returns
-------
array_like
prmaries. [[rx, ry], [gx, gy], [bx, by], [rx, ry]] | ty_lib/test_pattern_generator2.py | get_primaries | colour-science/sample_code | python | def get_primaries(name='ITU-R BT.2020'):
'\n prmary color の座標を求める\n\n\n Parameters\n ----------\n name : str\n a name of the color space.\n\n Returns\n -------\n array_like\n prmaries. [[rx, ry], [gx, gy], [bx, by], [rx, ry]]\n\n '
primaries = RGB_COLOURSPACES[name].primaries
primaries = np.append(primaries, [primaries[0, :]], axis=0)
rgb = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
return (primaries, rgb) |
def xy_to_rgb(xy, name='ITU-R BT.2020', normalize='maximum', specific=None):
"\n xy値からRGB値を算出する。\n いい感じに正規化もしておく。\n\n Parameters\n ----------\n xy : array_like\n xy value.\n name : string\n color space name.\n normalize : string\n normalize method. You can select 'maximum', 'specific' or None.\n\n Returns\n -------\n array_like\n rgb value. the value is normalized.\n "
illuminant_XYZ = D65_WHITE
illuminant_RGB = D65_WHITE
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name)
if (normalize == 'specific'):
xyY = xy_to_xyY(xy)
xyY[(..., 2)] = specific
large_xyz = xyY_to_XYZ(xyY)
else:
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
'\n そのままだとビデオレベルが低かったりするので、\n 各ドット毎にRGB値を正規化＆最大化する。必要であれば。\n '
if (normalize == 'maximum'):
rgb = normalise_maximum(rgb, axis=(- 1))
else:
if (np.sum((rgb > 1.0)) > 0):
print('warning: over flow has occured at xy_to_rgb')
if (np.sum((rgb < 0.0)) > 0):
print('warning: under flow has occured at xy_to_rgb')
rgb[(rgb < 0)] = 0
rgb[(rgb > 1.0)] = 1.0
return rgb | -2,746,982,639,432,358,000 | xy値からRGB値を算出する。
いい感じに正規化もしておく。
Parameters
----------
xy : array_like
xy value.
name : string
color space name.
normalize : string
normalize method. You can select 'maximum', 'specific' or None.
Returns
-------
array_like
rgb value. the value is normalized. | ty_lib/test_pattern_generator2.py | xy_to_rgb | colour-science/sample_code | python | def xy_to_rgb(xy, name='ITU-R BT.2020', normalize='maximum', specific=None):
"\n xy値からRGB値を算出する。\n いい感じに正規化もしておく。\n\n Parameters\n ----------\n xy : array_like\n xy value.\n name : string\n color space name.\n normalize : string\n normalize method. You can select 'maximum', 'specific' or None.\n\n Returns\n -------\n array_like\n rgb value. the value is normalized.\n "
illuminant_XYZ = D65_WHITE
illuminant_RGB = D65_WHITE
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name)
if (normalize == 'specific'):
xyY = xy_to_xyY(xy)
xyY[(..., 2)] = specific
large_xyz = xyY_to_XYZ(xyY)
else:
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
'\n そのままだとビデオレベルが低かったりするので、\n 各ドット毎にRGB値を正規化＆最大化する。必要であれば。\n '
if (normalize == 'maximum'):
rgb = normalise_maximum(rgb, axis=(- 1))
else:
if (np.sum((rgb > 1.0)) > 0):
print('warning: over flow has occured at xy_to_rgb')
if (np.sum((rgb < 0.0)) > 0):
print('warning: under flow has occured at xy_to_rgb')
rgb[(rgb < 0)] = 0
rgb[(rgb > 1.0)] = 1.0
return rgb |
def get_white_point(name):
'\n white point を求める。CIE1931ベース。\n '
if (name != 'DCI-P3'):
illuminant = RGB_COLOURSPACES[name].illuminant
white_point = ILLUMINANTS[CMFS_NAME][illuminant]
else:
white_point = ILLUMINANTS[CMFS_NAME]['D65']
return white_point | 8,828,608,032,943,556,000 | white point を求める。CIE1931ベース。 | ty_lib/test_pattern_generator2.py | get_white_point | colour-science/sample_code | python | def get_white_point(name):
'\n \n '
if (name != 'DCI-P3'):
illuminant = RGB_COLOURSPACES[name].illuminant
white_point = ILLUMINANTS[CMFS_NAME][illuminant]
else:
white_point = ILLUMINANTS[CMFS_NAME]['D65']
return white_point |
def get_secondaries(name='ITU-R BT.2020'):
'\n secondary color の座標を求める\n\n Parameters\n ----------\n name : str\n a name of the color space.\n\n Returns\n -------\n array_like\n secondaries. the order is magenta, yellow, cyan.\n\n '
secondary_rgb = np.array([[1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0, 1.0]])
illuminant_XYZ = D65_WHITE
illuminant_RGB = D65_WHITE
chromatic_adaptation_transform = 'CAT02'
rgb_to_xyz_matrix = get_rgb_to_xyz_matrix(name)
large_xyz = RGB_to_XYZ(secondary_rgb, illuminant_RGB, illuminant_XYZ, rgb_to_xyz_matrix, chromatic_adaptation_transform)
xy = XYZ_to_xy(large_xyz, illuminant_XYZ)
return (xy, secondary_rgb.reshape((3, 3))) | 5,985,841,218,541,587,000 | secondary color の座標を求める
Parameters
----------
name : str
a name of the color space.
Returns
-------
array_like
secondaries. the order is magenta, yellow, cyan. | ty_lib/test_pattern_generator2.py | get_secondaries | colour-science/sample_code | python | def get_secondaries(name='ITU-R BT.2020'):
'\n secondary color の座標を求める\n\n Parameters\n ----------\n name : str\n a name of the color space.\n\n Returns\n -------\n array_like\n secondaries. the order is magenta, yellow, cyan.\n\n '
secondary_rgb = np.array([[1.0, 0.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0, 1.0]])
illuminant_XYZ = D65_WHITE
illuminant_RGB = D65_WHITE
chromatic_adaptation_transform = 'CAT02'
rgb_to_xyz_matrix = get_rgb_to_xyz_matrix(name)
large_xyz = RGB_to_XYZ(secondary_rgb, illuminant_RGB, illuminant_XYZ, rgb_to_xyz_matrix, chromatic_adaptation_transform)
xy = XYZ_to_xy(large_xyz, illuminant_XYZ)
return (xy, secondary_rgb.reshape((3, 3))) |
def get_chromaticity_image(samples=1024, antialiasing=True, bg_color=0.9, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0):
'\n xy色度図の馬蹄形の画像を生成する\n\n Returns\n -------\n ndarray\n rgb image.\n '
'\n 色域設定。sRGBだと狭くて少し変だったのでBT.2020に設定。\n 若干色が薄くなるのが難点。暇があれば改良したい。\n '
color_space = models.ACES_CG_COLOURSPACE
cmf_xy = _get_cmfs_xy()
"\n 馬蹄の内外の判別をするために三角形で領域分割する(ドロネー図を作成)。\n ドロネー図を作れば後は外積計算で領域の内外を判別できる（たぶん）。\n\n なお、作成したドロネー図は以下のコードでプロット可能。\n 1点補足しておくと、```plt.triplot``` の第三引数は、\n 第一、第二引数から三角形を作成するための **インデックス** のリスト\n になっている。[[0, 1, 2], [2, 4, 3], ...]的な。\n\n ```python\n plt.figure()\n plt.triplot(xy[:, 0], xy[:, 1], triangulation.simplices.copy(), '-o')\n plt.title('triplot of Delaunay triangulation')\n plt.show()\n ```\n "
triangulation = Delaunay(cmf_xy)
'\n ```triangulation.find_simplex()``` で xy がどのインデックスの領域か\n 調べることができる。戻り値が ```-1``` の場合は領域に含まれないため、\n 0以下のリストで領域判定の mask を作ることができる。\n '
(xx, yy) = np.meshgrid(np.linspace(xmin, xmax, samples), np.linspace(ymax, ymin, samples))
xy = np.dstack((xx, yy))
mask = (triangulation.find_simplex(xy) < 0).astype(np.float)
if antialiasing:
kernel = np.array([[0, 1, 0], [1, 2, 1], [0, 1, 0]]).astype(np.float)
kernel /= np.sum(kernel)
mask = convolve(mask, kernel)
mask = (1 - mask[:, :, np.newaxis])
illuminant_XYZ = D65_WHITE
illuminant_RGB = color_space.whitepoint
chromatic_adaptation_transform = 'XYZ Scaling'
large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix
xy[(xy == 0.0)] = 1.0
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
'\n そのままだとビデオレベルが低かったりするので、\n 各ドット毎にRGB値を正規化＆最大化する。\n '
rgb[(rgb == 0)] = 1.0
rgb = normalise_maximum(rgb, axis=(- 1))
mask_rgb = np.dstack((mask, mask, mask))
rgb *= mask_rgb
bg_rgb = np.ones_like(rgb)
bg_rgb *= ((1 - mask_rgb) * bg_color)
rgb += bg_rgb
rgb = (rgb ** (1 / 2.2))
return rgb | 9,152,031,279,301,552,000 | xy色度図の馬蹄形の画像を生成する
Returns
-------
ndarray
rgb image. | ty_lib/test_pattern_generator2.py | get_chromaticity_image | colour-science/sample_code | python | def get_chromaticity_image(samples=1024, antialiasing=True, bg_color=0.9, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0):
'\n xy色度図の馬蹄形の画像を生成する\n\n Returns\n -------\n ndarray\n rgb image.\n '
'\n 色域設定。sRGBだと狭くて少し変だったのでBT.2020に設定。\n 若干色が薄くなるのが難点。暇があれば改良したい。\n '
color_space = models.ACES_CG_COLOURSPACE
cmf_xy = _get_cmfs_xy()
"\n 馬蹄の内外の判別をするために三角形で領域分割する(ドロネー図を作成)。\n ドロネー図を作れば後は外積計算で領域の内外を判別できる（たぶん）。\n\n なお、作成したドロネー図は以下のコードでプロット可能。\n 1点補足しておくと、```plt.triplot``` の第三引数は、\n 第一、第二引数から三角形を作成するための **インデックス** のリスト\n になっている。[[0, 1, 2], [2, 4, 3], ...]的な。\n\n ```python\n plt.figure()\n plt.triplot(xy[:, 0], xy[:, 1], triangulation.simplices.copy(), '-o')\n plt.title('triplot of Delaunay triangulation')\n plt.show()\n ```\n "
triangulation = Delaunay(cmf_xy)
'\n ```triangulation.find_simplex()``` で xy がどのインデックスの領域か\n 調べることができる。戻り値が ```-1``` の場合は領域に含まれないため、\n 0以下のリストで領域判定の mask を作ることができる。\n '
(xx, yy) = np.meshgrid(np.linspace(xmin, xmax, samples), np.linspace(ymax, ymin, samples))
xy = np.dstack((xx, yy))
mask = (triangulation.find_simplex(xy) < 0).astype(np.float)
if antialiasing:
kernel = np.array([[0, 1, 0], [1, 2, 1], [0, 1, 0]]).astype(np.float)
kernel /= np.sum(kernel)
mask = convolve(mask, kernel)
mask = (1 - mask[:, :, np.newaxis])
illuminant_XYZ = D65_WHITE
illuminant_RGB = color_space.whitepoint
chromatic_adaptation_transform = 'XYZ Scaling'
large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix
xy[(xy == 0.0)] = 1.0
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
'\n そのままだとビデオレベルが低かったりするので、\n 各ドット毎にRGB値を正規化＆最大化する。\n '
rgb[(rgb == 0)] = 1.0
rgb = normalise_maximum(rgb, axis=(- 1))
mask_rgb = np.dstack((mask, mask, mask))
rgb *= mask_rgb
bg_rgb = np.ones_like(rgb)
bg_rgb *= ((1 - mask_rgb) * bg_color)
rgb += bg_rgb
rgb = (rgb ** (1 / 2.2))
return rgb |
def get_csf_color_image(width=640, height=480, lv1=np.uint16(((np.array([1.0, 1.0, 1.0]) * 1023) * 64)), lv2=np.uint16(((np.array([1.0, 1.0, 1.0]) * 512) * 64)), stripe_num=18):
'\n 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。\n 入力信号レベルは16bitに限定する。\n\n Parameters\n ----------\n width : numeric.\n width of the pattern image.\n height : numeric.\n height of the pattern image.\n lv1 : numeric\n video level 1. this value must be 10bit.\n lv2 : numeric\n video level 2. this value must be 10bit.\n stripe_num : numeric\n number of the stripe.\n\n Returns\n -------\n array_like\n a cms pattern image.\n '
width_list = equal_devision(width, stripe_num)
height_list = equal_devision(height, stripe_num)
h_pos_list = equal_devision((width // 2), stripe_num)
v_pos_list = equal_devision((height // 2), stripe_num)
lv1_16bit = lv1
lv2_16bit = lv2
img = np.zeros((height, width, 3), dtype=np.uint16)
width_temp = width
height_temp = height
h_pos_temp = 0
v_pos_temp = 0
for idx in range(stripe_num):
lv = (lv1_16bit if ((idx % 2) == 0) else lv2_16bit)
temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16)
temp_img[:, :] = lv
ed_pos_h = (h_pos_temp + width_temp)
ed_pos_v = (v_pos_temp + height_temp)
img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img
width_temp -= width_list[((stripe_num - 1) - idx)]
height_temp -= height_list[((stripe_num - 1) - idx)]
h_pos_temp += h_pos_list[idx]
v_pos_temp += v_pos_list[idx]
return img | -7,187,334,406,667,908,000 | 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。
入力信号レベルは16bitに限定する。
Parameters
----------
width : numeric.
width of the pattern image.
height : numeric.
height of the pattern image.
lv1 : numeric
video level 1. this value must be 10bit.
lv2 : numeric
video level 2. this value must be 10bit.
stripe_num : numeric
number of the stripe.
Returns
-------
array_like
a cms pattern image. | ty_lib/test_pattern_generator2.py | get_csf_color_image | colour-science/sample_code | python | def get_csf_color_image(width=640, height=480, lv1=np.uint16(((np.array([1.0, 1.0, 1.0]) * 1023) * 64)), lv2=np.uint16(((np.array([1.0, 1.0, 1.0]) * 512) * 64)), stripe_num=18):
'\n 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。\n 入力信号レベルは16bitに限定する。\n\n Parameters\n ----------\n width : numeric.\n width of the pattern image.\n height : numeric.\n height of the pattern image.\n lv1 : numeric\n video level 1. this value must be 10bit.\n lv2 : numeric\n video level 2. this value must be 10bit.\n stripe_num : numeric\n number of the stripe.\n\n Returns\n -------\n array_like\n a cms pattern image.\n '
width_list = equal_devision(width, stripe_num)
height_list = equal_devision(height, stripe_num)
h_pos_list = equal_devision((width // 2), stripe_num)
v_pos_list = equal_devision((height // 2), stripe_num)
lv1_16bit = lv1
lv2_16bit = lv2
img = np.zeros((height, width, 3), dtype=np.uint16)
width_temp = width
height_temp = height
h_pos_temp = 0
v_pos_temp = 0
for idx in range(stripe_num):
lv = (lv1_16bit if ((idx % 2) == 0) else lv2_16bit)
temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16)
temp_img[:, :] = lv
ed_pos_h = (h_pos_temp + width_temp)
ed_pos_v = (v_pos_temp + height_temp)
img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img
width_temp -= width_list[((stripe_num - 1) - idx)]
height_temp -= height_list[((stripe_num - 1) - idx)]
h_pos_temp += h_pos_list[idx]
v_pos_temp += v_pos_list[idx]
return img |
def plot_xyY_color_space(name='ITU-R BT.2020', samples=1024, antialiasing=True):
'\n SONY の HDR説明資料にあるような xyY の図を作る。\n\n Parameters\n ----------\n name : str\n name of the target color space.\n\n Returns\n -------\n None\n\n '
(primary_xy, _) = get_primaries(name=name)
triangulation = Delaunay(primary_xy)
(xx, yy) = np.meshgrid(np.linspace(0, 1, samples), np.linspace(1, 0, samples))
xy = np.dstack((xx, yy))
mask = (triangulation.find_simplex(xy) < 0).astype(np.float)
if antialiasing:
kernel = np.array([[0, 1, 0], [1, 2, 1], [0, 1, 0]]).astype(np.float)
kernel /= np.sum(kernel)
mask = convolve(mask, kernel)
mask = (1 - mask[:, :, np.newaxis])
illuminant_XYZ = D65_WHITE
illuminant_RGB = RGB_COLOURSPACES[name].whitepoint
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name)
rgb_to_large_xyz_matrix = get_rgb_to_xyz_matrix(name)
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
'\n そのままだとビデオレベルが低かったりするので、\n 各ドット毎にRGB値を正規化＆最大化する。\n '
rgb_org = normalise_maximum(rgb, axis=(- 1))
mask_rgb = np.dstack((mask, mask, mask))
rgb = (rgb_org * mask_rgb)
rgba = np.dstack((rgb, mask))
large_xyz2 = RGB_to_XYZ(rgb, illuminant_RGB, illuminant_XYZ, rgb_to_large_xyz_matrix, chromatic_adaptation_transform)
large_y = (large_xyz2[(..., 1)] * 1000)
large_y[(large_y < 1)] = 1.0
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(xy[(..., 0)], xy[(..., 1)], np.log10(large_y), rcount=64, ccount=64, facecolors=rgb_org)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('Y')
ax.set_zticks([0, 1, 2, 3])
ax.set_zticklabels([1, 10, 100, 1000])
cie1931_rgb = get_chromaticity_image(samples=samples, bg_color=0.0)
alpha = np.zeros_like(cie1931_rgb[(..., 0)])
rgb_sum = np.sum(cie1931_rgb, axis=(- 1))
alpha[(rgb_sum > 1e-05)] = 1
cie1931_rgb = np.dstack((cie1931_rgb[(..., 0)], cie1931_rgb[(..., 1)], cie1931_rgb[(..., 2)], alpha))
zz = np.zeros_like(xy[(..., 0)])
ax.plot_surface(xy[(..., 0)], xy[(..., 1)], zz, facecolors=cie1931_rgb)
plt.show() | 2,697,745,789,533,632,000 | SONY の HDR説明資料にあるような xyY の図を作る。
Parameters
----------
name : str
name of the target color space.
Returns
-------
None | ty_lib/test_pattern_generator2.py | plot_xyY_color_space | colour-science/sample_code | python | def plot_xyY_color_space(name='ITU-R BT.2020', samples=1024, antialiasing=True):
'\n SONY の HDR説明資料にあるような xyY の図を作る。\n\n Parameters\n ----------\n name : str\n name of the target color space.\n\n Returns\n -------\n None\n\n '
(primary_xy, _) = get_primaries(name=name)
triangulation = Delaunay(primary_xy)
(xx, yy) = np.meshgrid(np.linspace(0, 1, samples), np.linspace(1, 0, samples))
xy = np.dstack((xx, yy))
mask = (triangulation.find_simplex(xy) < 0).astype(np.float)
if antialiasing:
kernel = np.array([[0, 1, 0], [1, 2, 1], [0, 1, 0]]).astype(np.float)
kernel /= np.sum(kernel)
mask = convolve(mask, kernel)
mask = (1 - mask[:, :, np.newaxis])
illuminant_XYZ = D65_WHITE
illuminant_RGB = RGB_COLOURSPACES[name].whitepoint
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name)
rgb_to_large_xyz_matrix = get_rgb_to_xyz_matrix(name)
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
'\n そのままだとビデオレベルが低かったりするので、\n 各ドット毎にRGB値を正規化＆最大化する。\n '
rgb_org = normalise_maximum(rgb, axis=(- 1))
mask_rgb = np.dstack((mask, mask, mask))
rgb = (rgb_org * mask_rgb)
rgba = np.dstack((rgb, mask))
large_xyz2 = RGB_to_XYZ(rgb, illuminant_RGB, illuminant_XYZ, rgb_to_large_xyz_matrix, chromatic_adaptation_transform)
large_y = (large_xyz2[(..., 1)] * 1000)
large_y[(large_y < 1)] = 1.0
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(xy[(..., 0)], xy[(..., 1)], np.log10(large_y), rcount=64, ccount=64, facecolors=rgb_org)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('Y')
ax.set_zticks([0, 1, 2, 3])
ax.set_zticklabels([1, 10, 100, 1000])
cie1931_rgb = get_chromaticity_image(samples=samples, bg_color=0.0)
alpha = np.zeros_like(cie1931_rgb[(..., 0)])
rgb_sum = np.sum(cie1931_rgb, axis=(- 1))
alpha[(rgb_sum > 1e-05)] = 1
cie1931_rgb = np.dstack((cie1931_rgb[(..., 0)], cie1931_rgb[(..., 1)], cie1931_rgb[(..., 2)], alpha))
zz = np.zeros_like(xy[(..., 0)])
ax.plot_surface(xy[(..., 0)], xy[(..., 1)], zz, facecolors=cie1931_rgb)
plt.show() |
def get_3d_grid_cube_format(grid_num=4):
'\n # 概要\n (0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 0, 1), ...\n みたいな配列を返す。\n CUBE形式の3DLUTを作成する時に便利。\n '
base = np.linspace(0, 1, grid_num)
ones_x = np.ones((grid_num, grid_num, 1))
ones_y = np.ones((grid_num, 1, grid_num))
ones_z = np.ones((1, grid_num, grid_num))
r_3d = (base[np.newaxis, np.newaxis, :] * ones_x)
g_3d = (base[np.newaxis, :, np.newaxis] * ones_y)
b_3d = (base[:, np.newaxis, np.newaxis] * ones_z)
r_3d = r_3d.flatten()
g_3d = g_3d.flatten()
b_3d = b_3d.flatten()
return np.dstack((r_3d, g_3d, b_3d)) | 2,705,412,234,827,614,700 | # 概要
(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 0, 1), ...
みたいな配列を返す。
CUBE形式の3DLUTを作成する時に便利。 | ty_lib/test_pattern_generator2.py | get_3d_grid_cube_format | colour-science/sample_code | python | def get_3d_grid_cube_format(grid_num=4):
'\n # 概要\n (0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 0, 1), ...\n みたいな配列を返す。\n CUBE形式の3DLUTを作成する時に便利。\n '
base = np.linspace(0, 1, grid_num)
ones_x = np.ones((grid_num, grid_num, 1))
ones_y = np.ones((grid_num, 1, grid_num))
ones_z = np.ones((1, grid_num, grid_num))
r_3d = (base[np.newaxis, np.newaxis, :] * ones_x)
g_3d = (base[np.newaxis, :, np.newaxis] * ones_y)
b_3d = (base[:, np.newaxis, np.newaxis] * ones_z)
r_3d = r_3d.flatten()
g_3d = g_3d.flatten()
b_3d = b_3d.flatten()
return np.dstack((r_3d, g_3d, b_3d)) |
def gen_step_gradation(width=1024, height=128, step_num=17, bit_depth=10, color=(1.0, 1.0, 1.0), direction='h', debug=False):
"\n # 概要\n 階段状に変化するグラデーションパターンを作る。\n なお、引数の調整により正確に1階調ずつ変化するパターンも作成可能。\n\n # 注意事項\n 正確に1階調ずつ変化するグラデーションを作る場合は\n ```step_num = (2 ** bit_depth) + 1```\n となるようにパラメータを指定すること。具体例は以下のExample参照。\n\n # Example\n ```\n grad_8 = gen_step_gradation(width=grad_width, height=grad_height,\n step_num=257, bit_depth=8,\n color=(1.0, 1.0, 1.0), direction='h')\n\n grad_10 = gen_step_gradation(width=grad_width, height=grad_height,\n step_num=1025, bit_depth=10,\n color=(1.0, 1.0, 1.0), direction='h')\n ```\n "
max = (2 ** bit_depth)
if (direction == 'h'):
pass
else:
temp = height
height = width
width = temp
if ((max + 1) != step_num):
'\n 1階調ずつの増加では無いパターン。\n 末尾のデータが 256 や 1024 になるため -1 する。\n '
val_list = np.linspace(0, max, step_num)
val_list[(- 1)] -= 1
else:
'\n 正確に1階調ずつ変化するパターン。\n 末尾のデータが 256 や 1024 になるため除外する。\n '
val_list = np.linspace(0, max, step_num)[0:(- 1)]
step_num -= 1
diff = (val_list[1:] - val_list[0:(- 1)])
if (diff == 1).all():
pass
else:
raise ValueError('calculated value is invalid.')
step_length_list = equal_devision(width, step_num)
step_bar_list = []
for (step_idx, length) in enumerate(step_length_list):
step = [((np.ones(length) * color[c_idx]) * val_list[step_idx]) for c_idx in range(3)]
if (direction == 'h'):
step = np.dstack(step)
step_bar_list.append(step)
step_bar = np.hstack(step_bar_list)
else:
step = np.dstack(step).reshape((length, 1, 3))
step_bar_list.append(step)
step_bar = np.vstack(step_bar_list)
if (direction == 'h'):
img = (step_bar * np.ones((height, 1, 3)))
else:
img = (step_bar * np.ones((1, height, 3)))
if debug:
preview_image(img, 'rgb')
return img | -6,042,160,212,514,663,000 | # 概要
階段状に変化するグラデーションパターンを作る。
なお、引数の調整により正確に1階調ずつ変化するパターンも作成可能。
# 注意事項
正確に1階調ずつ変化するグラデーションを作る場合は
```step_num = (2 ** bit_depth) + 1```
となるようにパラメータを指定すること。具体例は以下のExample参照。
# Example
```
grad_8 = gen_step_gradation(width=grad_width, height=grad_height,
step_num=257, bit_depth=8,
color=(1.0, 1.0, 1.0), direction='h')
grad_10 = gen_step_gradation(width=grad_width, height=grad_height,
step_num=1025, bit_depth=10,
color=(1.0, 1.0, 1.0), direction='h')
``` | ty_lib/test_pattern_generator2.py | gen_step_gradation | colour-science/sample_code | python | def gen_step_gradation(width=1024, height=128, step_num=17, bit_depth=10, color=(1.0, 1.0, 1.0), direction='h', debug=False):
"\n # 概要\n 階段状に変化するグラデーションパターンを作る。\n なお、引数の調整により正確に1階調ずつ変化するパターンも作成可能。\n\n # 注意事項\n 正確に1階調ずつ変化するグラデーションを作る場合は\n ```step_num = (2 ** bit_depth) + 1```\n となるようにパラメータを指定すること。具体例は以下のExample参照。\n\n # Example\n ```\n grad_8 = gen_step_gradation(width=grad_width, height=grad_height,\n step_num=257, bit_depth=8,\n color=(1.0, 1.0, 1.0), direction='h')\n\n grad_10 = gen_step_gradation(width=grad_width, height=grad_height,\n step_num=1025, bit_depth=10,\n color=(1.0, 1.0, 1.0), direction='h')\n ```\n "
max = (2 ** bit_depth)
if (direction == 'h'):
pass
else:
temp = height
height = width
width = temp
if ((max + 1) != step_num):
'\n 1階調ずつの増加では無いパターン。\n 末尾のデータが 256 や 1024 になるため -1 する。\n '
val_list = np.linspace(0, max, step_num)
val_list[(- 1)] -= 1
else:
'\n 正確に1階調ずつ変化するパターン。\n 末尾のデータが 256 や 1024 になるため除外する。\n '
val_list = np.linspace(0, max, step_num)[0:(- 1)]
step_num -= 1
diff = (val_list[1:] - val_list[0:(- 1)])
if (diff == 1).all():
pass
else:
raise ValueError('calculated value is invalid.')
step_length_list = equal_devision(width, step_num)
step_bar_list = []
for (step_idx, length) in enumerate(step_length_list):
step = [((np.ones(length) * color[c_idx]) * val_list[step_idx]) for c_idx in range(3)]
if (direction == 'h'):
step = np.dstack(step)
step_bar_list.append(step)
step_bar = np.hstack(step_bar_list)
else:
step = np.dstack(step).reshape((length, 1, 3))
step_bar_list.append(step)
step_bar = np.vstack(step_bar_list)
if (direction == 'h'):
img = (step_bar * np.ones((height, 1, 3)))
else:
img = (step_bar * np.ones((1, height, 3)))
if debug:
preview_image(img, 'rgb')
return img |
def merge(img_a, img_b, pos=(0, 0)):
'\n img_a に img_b をマージする。\n img_a にデータを上書きする。\n\n pos = (horizontal_st, vertical_st)\n '
b_width = img_b.shape[1]
b_height = img_b.shape[0]
img_a[pos[1]:(b_height + pos[1]), pos[0]:(b_width + pos[0])] = img_b | 2,923,293,055,995,527,000 | img_a に img_b をマージする。
img_a にデータを上書きする。
pos = (horizontal_st, vertical_st) | ty_lib/test_pattern_generator2.py | merge | colour-science/sample_code | python | def merge(img_a, img_b, pos=(0, 0)):
'\n img_a に img_b をマージする。\n img_a にデータを上書きする。\n\n pos = (horizontal_st, vertical_st)\n '
b_width = img_b.shape[1]
b_height = img_b.shape[0]
img_a[pos[1]:(b_height + pos[1]), pos[0]:(b_width + pos[0])] = img_b |
def merge_with_alpha(bg_img, fg_img, tf_str=tf.SRGB, pos=(0, 0)):
'\n 合成する。\n\n Parameters\n ----------\n bg_img : array_like(float, 3-channel)\n image data.\n fg_img : array_like(float, 4-channel)\n image data\n tf : strings\n transfer function\n pos : list(int)\n (pos_h, pos_v)\n '
f_width = fg_img.shape[1]
f_height = fg_img.shape[0]
bg_merge_area = bg_img[pos[1]:(f_height + pos[1]), pos[0]:(f_width + pos[0])]
bg_linear = tf.eotf_to_luminance(bg_merge_area, tf_str)
fg_linear = tf.eotf_to_luminance(fg_img, tf_str)
alpha = (fg_linear[:, :, 3:] / tf.PEAK_LUMINANCE[tf_str])
out_linear = (((1 - alpha) * bg_linear) + fg_linear[:, :, :(- 1)])
out_merge_area = tf.oetf_from_luminance(out_linear, tf_str)
bg_img[pos[1]:(f_height + pos[1]), pos[0]:(f_width + pos[0])] = out_merge_area
return bg_img | -8,414,558,190,074,198,000 | 合成する。
Parameters
----------
bg_img : array_like(float, 3-channel)
image data.
fg_img : array_like(float, 4-channel)
image data
tf : strings
transfer function
pos : list(int)
(pos_h, pos_v) | ty_lib/test_pattern_generator2.py | merge_with_alpha | colour-science/sample_code | python | def merge_with_alpha(bg_img, fg_img, tf_str=tf.SRGB, pos=(0, 0)):
'\n 合成する。\n\n Parameters\n ----------\n bg_img : array_like(float, 3-channel)\n image data.\n fg_img : array_like(float, 4-channel)\n image data\n tf : strings\n transfer function\n pos : list(int)\n (pos_h, pos_v)\n '
f_width = fg_img.shape[1]
f_height = fg_img.shape[0]
bg_merge_area = bg_img[pos[1]:(f_height + pos[1]), pos[0]:(f_width + pos[0])]
bg_linear = tf.eotf_to_luminance(bg_merge_area, tf_str)
fg_linear = tf.eotf_to_luminance(fg_img, tf_str)
alpha = (fg_linear[:, :, 3:] / tf.PEAK_LUMINANCE[tf_str])
out_linear = (((1 - alpha) * bg_linear) + fg_linear[:, :, :(- 1)])
out_merge_area = tf.oetf_from_luminance(out_linear, tf_str)
bg_img[pos[1]:(f_height + pos[1]), pos[0]:(f_width + pos[0])] = out_merge_area
return bg_img |
def dot_pattern(dot_size=4, repeat=4, color=np.array([1.0, 1.0, 1.0])):
'\n dot pattern 作る。\n\n Parameters\n ----------\n dot_size : integer\n dot size.\n repeat : integer\n The number of high-low pairs.\n color : array_like\n color value.\n\n Returns\n -------\n array_like\n dot pattern image.\n\n '
pixel_num = ((dot_size * 2) * repeat)
even_logic = [(((np.arange(pixel_num) % (dot_size * 2)) - dot_size) < 0)]
even_logic = np.dstack((even_logic, even_logic, even_logic))
odd_logic = np.logical_not(even_logic)
color = color.reshape((1, 1, 3))
even_line = ((np.ones((1, pixel_num, 3)) * even_logic) * color)
odd_line = ((np.ones((1, pixel_num, 3)) * odd_logic) * color)
even_block = np.repeat(even_line, dot_size, axis=0)
odd_block = np.repeat(odd_line, dot_size, axis=0)
pair_block = np.vstack((even_block, odd_block))
img = np.vstack([pair_block for x in range(repeat)])
return img | -1,414,203,125,807,372,500 | dot pattern 作る。
Parameters
----------
dot_size : integer
dot size.
repeat : integer
The number of high-low pairs.
color : array_like
color value.
Returns
-------
array_like
dot pattern image. | ty_lib/test_pattern_generator2.py | dot_pattern | colour-science/sample_code | python | def dot_pattern(dot_size=4, repeat=4, color=np.array([1.0, 1.0, 1.0])):
'\n dot pattern 作る。\n\n Parameters\n ----------\n dot_size : integer\n dot size.\n repeat : integer\n The number of high-low pairs.\n color : array_like\n color value.\n\n Returns\n -------\n array_like\n dot pattern image.\n\n '
pixel_num = ((dot_size * 2) * repeat)
even_logic = [(((np.arange(pixel_num) % (dot_size * 2)) - dot_size) < 0)]
even_logic = np.dstack((even_logic, even_logic, even_logic))
odd_logic = np.logical_not(even_logic)
color = color.reshape((1, 1, 3))
even_line = ((np.ones((1, pixel_num, 3)) * even_logic) * color)
odd_line = ((np.ones((1, pixel_num, 3)) * odd_logic) * color)
even_block = np.repeat(even_line, dot_size, axis=0)
odd_block = np.repeat(odd_line, dot_size, axis=0)
pair_block = np.vstack((even_block, odd_block))
img = np.vstack([pair_block for x in range(repeat)])
return img |
def complex_dot_pattern(kind_num=3, whole_repeat=2, fg_color=np.array([1.0, 1.0, 1.0]), bg_color=np.array([0.15, 0.15, 0.15])):
'\n dot pattern 作る。\n\n Parameters\n ----------\n kind_num : integer\n 作成するドットサイズの種類。\n 例えば、kind_num=3 ならば、1dot, 2dot, 4dot のパターンを作成。\n whole_repeat : integer\n 異なる複数種類のドットパターンの組数。\n 例えば、kind_num=3, whole_repeat=2 ならば、\n 1dot, 2dot, 4dot のパターンを水平・垂直に2組作る。\n fg_color : array_like\n foreground color value.\n bg_color : array_like\n background color value.\n reduce : bool\n HDRテストパターンの3840x2160専用。縦横を半分にする。\n\n Returns\n -------\n array_like\n dot pattern image.\n\n '
max_dot_width = (2 ** kind_num)
img_list = []
for size_idx in range(kind_num)[::(- 1)]:
dot_size = (2 ** size_idx)
repeat = (max_dot_width // dot_size)
dot_img = dot_pattern(dot_size, repeat, fg_color)
img_list.append(dot_img)
img_list.append((np.ones_like(dot_img) * bg_color))
line_upper_img = np.hstack(img_list)
line_upper_img = np.hstack([line_upper_img for x in range(whole_repeat)])
line_lower_img = line_upper_img.copy()[:, ::(- 1), :]
h_unit_img = np.vstack((line_upper_img, line_lower_img))
img = np.vstack([h_unit_img for x in range((kind_num * whole_repeat))])
return img | 7,652,356,003,343,272,000 | dot pattern 作る。
Parameters
----------
kind_num : integer
作成するドットサイズの種類。
例えば、kind_num=3 ならば、1dot, 2dot, 4dot のパターンを作成。
whole_repeat : integer
異なる複数種類のドットパターンの組数。
例えば、kind_num=3, whole_repeat=2 ならば、
1dot, 2dot, 4dot のパターンを水平・垂直に2組作る。
fg_color : array_like
foreground color value.
bg_color : array_like
background color value.
reduce : bool
HDRテストパターンの3840x2160専用。縦横を半分にする。
Returns
-------
array_like
dot pattern image. | ty_lib/test_pattern_generator2.py | complex_dot_pattern | colour-science/sample_code | python | def complex_dot_pattern(kind_num=3, whole_repeat=2, fg_color=np.array([1.0, 1.0, 1.0]), bg_color=np.array([0.15, 0.15, 0.15])):
'\n dot pattern 作る。\n\n Parameters\n ----------\n kind_num : integer\n 作成するドットサイズの種類。\n 例えば、kind_num=3 ならば、1dot, 2dot, 4dot のパターンを作成。\n whole_repeat : integer\n 異なる複数種類のドットパターンの組数。\n 例えば、kind_num=3, whole_repeat=2 ならば、\n 1dot, 2dot, 4dot のパターンを水平・垂直に2組作る。\n fg_color : array_like\n foreground color value.\n bg_color : array_like\n background color value.\n reduce : bool\n HDRテストパターンの3840x2160専用。縦横を半分にする。\n\n Returns\n -------\n array_like\n dot pattern image.\n\n '
max_dot_width = (2 ** kind_num)
img_list = []
for size_idx in range(kind_num)[::(- 1)]:
dot_size = (2 ** size_idx)
repeat = (max_dot_width // dot_size)
dot_img = dot_pattern(dot_size, repeat, fg_color)
img_list.append(dot_img)
img_list.append((np.ones_like(dot_img) * bg_color))
line_upper_img = np.hstack(img_list)
line_upper_img = np.hstack([line_upper_img for x in range(whole_repeat)])
line_lower_img = line_upper_img.copy()[:, ::(- 1), :]
h_unit_img = np.vstack((line_upper_img, line_lower_img))
img = np.vstack([h_unit_img for x in range((kind_num * whole_repeat))])
return img |
def make_csf_color_image(width=640, height=640, lv1=np.array([940, 940, 940], dtype=np.uint16), lv2=np.array([1023, 1023, 1023], dtype=np.uint16), stripe_num=6):
'\n 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。\n 入力信号レベルは10bitに限定する。\n\n Parameters\n ----------\n width : numeric.\n width of the pattern image.\n height : numeric.\n height of the pattern image.\n lv1 : array_like\n video level 1. this value must be 10bit.\n lv2 : array_like\n video level 2. this value must be 10bit.\n stripe_num : numeric\n number of the stripe.\n\n Returns\n -------\n array_like\n a cms pattern image.\n '
width_list = equal_devision(width, stripe_num)
height_list = equal_devision(height, stripe_num)
h_pos_list = equal_devision((width // 2), stripe_num)
v_pos_list = equal_devision((height // 2), stripe_num)
img = np.zeros((height, width, 3), dtype=np.uint16)
width_temp = width
height_temp = height
h_pos_temp = 0
v_pos_temp = 0
for idx in range(stripe_num):
lv = (lv1 if ((idx % 2) == 0) else lv2)
temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16)
temp_img = (temp_img * lv.reshape((1, 1, 3)))
ed_pos_h = (h_pos_temp + width_temp)
ed_pos_v = (v_pos_temp + height_temp)
img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img
width_temp -= width_list[((stripe_num - 1) - idx)]
height_temp -= height_list[((stripe_num - 1) - idx)]
h_pos_temp += h_pos_list[idx]
v_pos_temp += v_pos_list[idx]
return img | 6,239,938,353,410,582,000 | 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。
入力信号レベルは10bitに限定する。
Parameters
----------
width : numeric.
width of the pattern image.
height : numeric.
height of the pattern image.
lv1 : array_like
video level 1. this value must be 10bit.
lv2 : array_like
video level 2. this value must be 10bit.
stripe_num : numeric
number of the stripe.
Returns
-------
array_like
a cms pattern image. | ty_lib/test_pattern_generator2.py | make_csf_color_image | colour-science/sample_code | python | def make_csf_color_image(width=640, height=640, lv1=np.array([940, 940, 940], dtype=np.uint16), lv2=np.array([1023, 1023, 1023], dtype=np.uint16), stripe_num=6):
'\n 長方形を複数個ズラして重ねることでCSFパターンっぽいのを作る。\n 入力信号レベルは10bitに限定する。\n\n Parameters\n ----------\n width : numeric.\n width of the pattern image.\n height : numeric.\n height of the pattern image.\n lv1 : array_like\n video level 1. this value must be 10bit.\n lv2 : array_like\n video level 2. this value must be 10bit.\n stripe_num : numeric\n number of the stripe.\n\n Returns\n -------\n array_like\n a cms pattern image.\n '
width_list = equal_devision(width, stripe_num)
height_list = equal_devision(height, stripe_num)
h_pos_list = equal_devision((width // 2), stripe_num)
v_pos_list = equal_devision((height // 2), stripe_num)
img = np.zeros((height, width, 3), dtype=np.uint16)
width_temp = width
height_temp = height
h_pos_temp = 0
v_pos_temp = 0
for idx in range(stripe_num):
lv = (lv1 if ((idx % 2) == 0) else lv2)
temp_img = np.ones((height_temp, width_temp, 3), dtype=np.uint16)
temp_img = (temp_img * lv.reshape((1, 1, 3)))
ed_pos_h = (h_pos_temp + width_temp)
ed_pos_v = (v_pos_temp + height_temp)
img[v_pos_temp:ed_pos_v, h_pos_temp:ed_pos_h] = temp_img
width_temp -= width_list[((stripe_num - 1) - idx)]
height_temp -= height_list[((stripe_num - 1) - idx)]
h_pos_temp += h_pos_list[idx]
v_pos_temp += v_pos_list[idx]
return img |
def make_tile_pattern(width=480, height=960, h_tile_num=4, v_tile_num=4, low_level=(940, 940, 940), high_level=(1023, 1023, 1023)):
'\n タイル状の縞々パターンを作る\n '
width_array = equal_devision(width, h_tile_num)
height_array = equal_devision(height, v_tile_num)
high_level = np.array(high_level, dtype=np.uint16)
low_level = np.array(low_level, dtype=np.uint16)
v_buf = []
for (v_idx, height) in enumerate(height_array):
h_buf = []
for (h_idx, width) in enumerate(width_array):
tile_judge = (((h_idx + v_idx) % 2) == 0)
h_temp = np.zeros((height, width, 3), dtype=np.uint16)
h_temp[:, :] = (high_level if tile_judge else low_level)
h_buf.append(h_temp)
v_buf.append(np.hstack(h_buf))
img = np.vstack(v_buf)
return img | -1,309,246,484,004,092,000 | タイル状の縞々パターンを作る | ty_lib/test_pattern_generator2.py | make_tile_pattern | colour-science/sample_code | python | def make_tile_pattern(width=480, height=960, h_tile_num=4, v_tile_num=4, low_level=(940, 940, 940), high_level=(1023, 1023, 1023)):
'\n \n '
width_array = equal_devision(width, h_tile_num)
height_array = equal_devision(height, v_tile_num)
high_level = np.array(high_level, dtype=np.uint16)
low_level = np.array(low_level, dtype=np.uint16)
v_buf = []
for (v_idx, height) in enumerate(height_array):
h_buf = []
for (h_idx, width) in enumerate(width_array):
tile_judge = (((h_idx + v_idx) % 2) == 0)
h_temp = np.zeros((height, width, 3), dtype=np.uint16)
h_temp[:, :] = (high_level if tile_judge else low_level)
h_buf.append(h_temp)
v_buf.append(np.hstack(h_buf))
img = np.vstack(v_buf)
return img |
def make_ycbcr_checker(height=480, v_tile_num=4):
'\n YCbCr係数誤りを確認するテストパターンを作る。\n 正直かなり汚い組み方です。雑に作ったパターンを悪魔合体させています。\n\n Parameters\n ----------\n height : numeric.\n height of the pattern image.\n v_tile_num : numeric\n number of the tile in the vertical direction.\n\n Note\n ----\n 横長のパターンになる。以下の式が成立する。\n\n ```\n h_tile_num = v_tile_num * 2\n width = height * 2\n ```\n\n Returns\n -------\n array_like\n ycbcr checker image\n '
cyan_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[0, 990, 990], high_level=[0, 1023, 1023])
magenta_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[990, 0, 312], high_level=[1023, 0, 312])
out_img = np.hstack([cyan_img, magenta_img])
return out_img | -2,052,380,776,987,882,000 | YCbCr係数誤りを確認するテストパターンを作る。
正直かなり汚い組み方です。雑に作ったパターンを悪魔合体させています。
Parameters
----------
height : numeric.
height of the pattern image.
v_tile_num : numeric
number of the tile in the vertical direction.
Note
----
横長のパターンになる。以下の式が成立する。
```
h_tile_num = v_tile_num * 2
width = height * 2
```
Returns
-------
array_like
ycbcr checker image | ty_lib/test_pattern_generator2.py | make_ycbcr_checker | colour-science/sample_code | python | def make_ycbcr_checker(height=480, v_tile_num=4):
'\n YCbCr係数誤りを確認するテストパターンを作る。\n 正直かなり汚い組み方です。雑に作ったパターンを悪魔合体させています。\n\n Parameters\n ----------\n height : numeric.\n height of the pattern image.\n v_tile_num : numeric\n number of the tile in the vertical direction.\n\n Note\n ----\n 横長のパターンになる。以下の式が成立する。\n\n ```\n h_tile_num = v_tile_num * 2\n width = height * 2\n ```\n\n Returns\n -------\n array_like\n ycbcr checker image\n '
cyan_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[0, 990, 990], high_level=[0, 1023, 1023])
magenta_img = make_tile_pattern(width=height, height=height, h_tile_num=v_tile_num, v_tile_num=v_tile_num, low_level=[990, 0, 312], high_level=[1023, 0, 312])
out_img = np.hstack([cyan_img, magenta_img])
return out_img |
def plot_color_checker_image(rgb, rgb2=None, size=(1920, 1080), block_size=(1 / 4.5), padding=0.01):
"\n ColorCheckerをプロットする\n\n Parameters\n ----------\n rgb : array_like\n RGB value of the ColorChecker.\n RGB's shape must be (24, 3).\n rgb2 : array_like\n It's a optional parameter.\n If You want to draw two different ColorCheckers,\n set the RGB value to this variable.\n size : tuple\n canvas size.\n block_size : float\n A each block's size.\n This value is ratio to height of the canvas.\n padding : float\n A padding to the block.\n\n Returns\n -------\n array_like\n A ColorChecker image.\n\n "
IMG_HEIGHT = size[1]
IMG_WIDTH = size[0]
COLOR_CHECKER_SIZE = block_size
COLOR_CHECKER_H_NUM = 6
COLOR_CHECKER_V_NUM = 4
COLOR_CHECKER_PADDING = 0.01
COLOR_CHECKER_H_NUM = 6
COLOR_CHECKER_V_NUM = 4
img_height = IMG_HEIGHT
img_width = IMG_WIDTH
patch_st_h = int(((IMG_WIDTH / 2.0) - ((((IMG_HEIGHT * COLOR_CHECKER_SIZE) * COLOR_CHECKER_H_NUM) / 2.0) + (((IMG_HEIGHT * COLOR_CHECKER_PADDING) * ((COLOR_CHECKER_H_NUM / 2.0) - 0.5)) / 2.0))))
patch_st_v = int(((IMG_HEIGHT / 2.0) - ((((IMG_HEIGHT * COLOR_CHECKER_SIZE) * COLOR_CHECKER_V_NUM) / 2.0) + (((IMG_HEIGHT * COLOR_CHECKER_PADDING) * ((COLOR_CHECKER_V_NUM / 2.0) - 0.5)) / 2.0))))
patch_width = int((img_height * COLOR_CHECKER_SIZE))
patch_height = patch_width
patch_space = int((img_height * COLOR_CHECKER_PADDING))
img_all_patch = np.zeros((img_height, img_width, 3), dtype=np.uint8)
for idx in range((COLOR_CHECKER_H_NUM * COLOR_CHECKER_V_NUM)):
v_idx = (idx // COLOR_CHECKER_H_NUM)
h_idx = (idx % COLOR_CHECKER_H_NUM)
patch = np.ones((patch_height, patch_width, 3))
patch[:, :] = rgb[idx]
st_h = (patch_st_h + ((patch_width + patch_space) * h_idx))
st_v = (patch_st_v + ((patch_height + patch_space) * v_idx))
img_all_patch[st_v:(st_v + patch_height), st_h:(st_h + patch_width)] = patch
pt2 = ((st_h + patch_width), st_v)
pt3 = (st_h, (st_v + patch_height))
pt4 = ((st_h + patch_width), (st_v + patch_height))
pts = np.array((pt2, pt3, pt4))
sub_color = (rgb[idx].tolist() if (rgb2 is None) else rgb2[idx].tolist())
cv2.fillPoly(img_all_patch, [pts], sub_color)
preview_image(img_all_patch)
return img_all_patch | 6,027,125,347,553,437,000 | ColorCheckerをプロットする
Parameters
----------
rgb : array_like
RGB value of the ColorChecker.
RGB's shape must be (24, 3).
rgb2 : array_like
It's a optional parameter.
If You want to draw two different ColorCheckers,
set the RGB value to this variable.
size : tuple
canvas size.
block_size : float
A each block's size.
This value is ratio to height of the canvas.
padding : float
A padding to the block.
Returns
-------
array_like
A ColorChecker image. | ty_lib/test_pattern_generator2.py | plot_color_checker_image | colour-science/sample_code | python | def plot_color_checker_image(rgb, rgb2=None, size=(1920, 1080), block_size=(1 / 4.5), padding=0.01):
"\n ColorCheckerをプロットする\n\n Parameters\n ----------\n rgb : array_like\n RGB value of the ColorChecker.\n RGB's shape must be (24, 3).\n rgb2 : array_like\n It's a optional parameter.\n If You want to draw two different ColorCheckers,\n set the RGB value to this variable.\n size : tuple\n canvas size.\n block_size : float\n A each block's size.\n This value is ratio to height of the canvas.\n padding : float\n A padding to the block.\n\n Returns\n -------\n array_like\n A ColorChecker image.\n\n "
IMG_HEIGHT = size[1]
IMG_WIDTH = size[0]
COLOR_CHECKER_SIZE = block_size
COLOR_CHECKER_H_NUM = 6
COLOR_CHECKER_V_NUM = 4
COLOR_CHECKER_PADDING = 0.01
COLOR_CHECKER_H_NUM = 6
COLOR_CHECKER_V_NUM = 4
img_height = IMG_HEIGHT
img_width = IMG_WIDTH
patch_st_h = int(((IMG_WIDTH / 2.0) - ((((IMG_HEIGHT * COLOR_CHECKER_SIZE) * COLOR_CHECKER_H_NUM) / 2.0) + (((IMG_HEIGHT * COLOR_CHECKER_PADDING) * ((COLOR_CHECKER_H_NUM / 2.0) - 0.5)) / 2.0))))
patch_st_v = int(((IMG_HEIGHT / 2.0) - ((((IMG_HEIGHT * COLOR_CHECKER_SIZE) * COLOR_CHECKER_V_NUM) / 2.0) + (((IMG_HEIGHT * COLOR_CHECKER_PADDING) * ((COLOR_CHECKER_V_NUM / 2.0) - 0.5)) / 2.0))))
patch_width = int((img_height * COLOR_CHECKER_SIZE))
patch_height = patch_width
patch_space = int((img_height * COLOR_CHECKER_PADDING))
img_all_patch = np.zeros((img_height, img_width, 3), dtype=np.uint8)
for idx in range((COLOR_CHECKER_H_NUM * COLOR_CHECKER_V_NUM)):
v_idx = (idx // COLOR_CHECKER_H_NUM)
h_idx = (idx % COLOR_CHECKER_H_NUM)
patch = np.ones((patch_height, patch_width, 3))
patch[:, :] = rgb[idx]
st_h = (patch_st_h + ((patch_width + patch_space) * h_idx))
st_v = (patch_st_v + ((patch_height + patch_space) * v_idx))
img_all_patch[st_v:(st_v + patch_height), st_h:(st_h + patch_width)] = patch
pt2 = ((st_h + patch_width), st_v)
pt3 = (st_h, (st_v + patch_height))
pt4 = ((st_h + patch_width), (st_v + patch_height))
pts = np.array((pt2, pt3, pt4))
sub_color = (rgb[idx].tolist() if (rgb2 is None) else rgb2[idx].tolist())
cv2.fillPoly(img_all_patch, [pts], sub_color)
preview_image(img_all_patch)
return img_all_patch |
def get_log10_x_scale(sample_num=8, ref_val=1.0, min_exposure=(- 1), max_exposure=6):
'\n Log10スケールのx軸データを作る。\n\n Examples\n --------\n >>> get_log2_x_scale(\n ... sample_num=8, ref_val=1.0, min_exposure=-1, max_exposure=6)\n array([ 1.0000e-01 1.0000e+00 1.0000e+01 1.0000e+02\n 1.0000e+03 1.0000e+04 1.0000e+05 1.0000e+06])\n '
x_min = np.log10((ref_val * (10 ** min_exposure)))
x_max = np.log10((ref_val * (10 ** max_exposure)))
x = np.linspace(x_min, x_max, sample_num)
return (10.0 ** x) | -7,178,071,663,818,056,000 | Log10スケールのx軸データを作る。
Examples
--------
>>> get_log2_x_scale(
... sample_num=8, ref_val=1.0, min_exposure=-1, max_exposure=6)
array([ 1.0000e-01 1.0000e+00 1.0000e+01 1.0000e+02
1.0000e+03 1.0000e+04 1.0000e+05 1.0000e+06]) | ty_lib/test_pattern_generator2.py | get_log10_x_scale | colour-science/sample_code | python | def get_log10_x_scale(sample_num=8, ref_val=1.0, min_exposure=(- 1), max_exposure=6):
'\n Log10スケールのx軸データを作る。\n\n Examples\n --------\n >>> get_log2_x_scale(\n ... sample_num=8, ref_val=1.0, min_exposure=-1, max_exposure=6)\n array([ 1.0000e-01 1.0000e+00 1.0000e+01 1.0000e+02\n 1.0000e+03 1.0000e+04 1.0000e+05 1.0000e+06])\n '
x_min = np.log10((ref_val * (10 ** min_exposure)))
x_max = np.log10((ref_val * (10 ** max_exposure)))
x = np.linspace(x_min, x_max, sample_num)
return (10.0 ** x) |
def get_log2_x_scale(sample_num=32, ref_val=1.0, min_exposure=(- 6.5), max_exposure=6.5):
'\n Log2スケールのx軸データを作る。\n\n Examples\n --------\n >>> get_log2_x_scale(sample_num=10, min_exposure=-4.0, max_exposure=4.0)\n array([[ 0.0625 0.11573434 0.214311 0.39685026 0.73486725\n 1.36079 2.5198421 4.66611616 8.64047791 16. ]])\n '
x_min = np.log2((ref_val * (2 ** min_exposure)))
x_max = np.log2((ref_val * (2 ** max_exposure)))
x = np.linspace(x_min, x_max, sample_num)
return (2.0 ** x) | -5,210,454,686,189,132,000 | Log2スケールのx軸データを作る。
Examples
--------
>>> get_log2_x_scale(sample_num=10, min_exposure=-4.0, max_exposure=4.0)
array([[ 0.0625 0.11573434 0.214311 0.39685026 0.73486725
1.36079 2.5198421 4.66611616 8.64047791 16. ]]) | ty_lib/test_pattern_generator2.py | get_log2_x_scale | colour-science/sample_code | python | def get_log2_x_scale(sample_num=32, ref_val=1.0, min_exposure=(- 6.5), max_exposure=6.5):
'\n Log2スケールのx軸データを作る。\n\n Examples\n --------\n >>> get_log2_x_scale(sample_num=10, min_exposure=-4.0, max_exposure=4.0)\n array([[ 0.0625 0.11573434 0.214311 0.39685026 0.73486725\n 1.36079 2.5198421 4.66611616 8.64047791 16. ]])\n '
x_min = np.log2((ref_val * (2 ** min_exposure)))
x_max = np.log2((ref_val * (2 ** max_exposure)))
x = np.linspace(x_min, x_max, sample_num)
return (2.0 ** x) |
def shaper_func_linear_to_log2(x, mid_gray=0.18, min_exposure=(- 6.5), max_exposure=6.5):
'\n ACESutil.Lin_to_Log2_param.ctl を参考に作成。\n https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Lin_to_Log2_param.ctl\n\n Parameters\n ----------\n x : array_like\n linear data.\n mid_gray : float\n 18% gray value on linear scale.\n min_exposure : float\n minimum value on log scale.\n max_exposure : float\n maximum value on log scale.\n\n Returns\n -------\n array_like\n log2 value that is transformed from linear x value.\n\n Examples\n --------\n >>> shaper_func_linear_to_log2(\n ... x=0.18, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)\n 0.5\n >>> shaper_func_linear_to_log2(\n ... x=np.array([0.00198873782209, 16.2917402385])\n ... mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)\n array([ 1.58232402e-13 1.00000000e+00])\n '
y = np.log2((x / mid_gray))
y_normalized = ((y - min_exposure) / (max_exposure - min_exposure))
y_normalized[(y_normalized < 0)] = 0
return y_normalized | -4,971,467,536,164,493,000 | ACESutil.Lin_to_Log2_param.ctl を参考に作成。
https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Lin_to_Log2_param.ctl
Parameters
----------
x : array_like
linear data.
mid_gray : float
18% gray value on linear scale.
min_exposure : float
minimum value on log scale.
max_exposure : float
maximum value on log scale.
Returns
-------
array_like
log2 value that is transformed from linear x value.
Examples
--------
>>> shaper_func_linear_to_log2(
... x=0.18, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)
0.5
>>> shaper_func_linear_to_log2(
... x=np.array([0.00198873782209, 16.2917402385])
... mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)
array([ 1.58232402e-13 1.00000000e+00]) | ty_lib/test_pattern_generator2.py | shaper_func_linear_to_log2 | colour-science/sample_code | python | def shaper_func_linear_to_log2(x, mid_gray=0.18, min_exposure=(- 6.5), max_exposure=6.5):
'\n ACESutil.Lin_to_Log2_param.ctl を参考に作成。\n https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Lin_to_Log2_param.ctl\n\n Parameters\n ----------\n x : array_like\n linear data.\n mid_gray : float\n 18% gray value on linear scale.\n min_exposure : float\n minimum value on log scale.\n max_exposure : float\n maximum value on log scale.\n\n Returns\n -------\n array_like\n log2 value that is transformed from linear x value.\n\n Examples\n --------\n >>> shaper_func_linear_to_log2(\n ... x=0.18, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)\n 0.5\n >>> shaper_func_linear_to_log2(\n ... x=np.array([0.00198873782209, 16.2917402385])\n ... mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)\n array([ 1.58232402e-13 1.00000000e+00])\n '
y = np.log2((x / mid_gray))
y_normalized = ((y - min_exposure) / (max_exposure - min_exposure))
y_normalized[(y_normalized < 0)] = 0
return y_normalized |
def shaper_func_log2_to_linear(x, mid_gray=0.18, min_exposure=(- 6.5), max_exposure=6.5):
'\n ACESutil.Log2_to_Lin_param.ctl を参考に作成。\n https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Log2_to_Lin_param.ctl\n\n Log2空間の補足は shaper_func_linear_to_log2() の説明を参照\n\n Examples\n --------\n >>> x = np.array([0.0, 1.0])\n >>> shaper_func_log2_to_linear(\n ... x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)\n array([0.00198873782209, 16.2917402385])\n '
x_re_scale = ((x * (max_exposure - min_exposure)) + min_exposure)
y = ((2.0 ** x_re_scale) * mid_gray)
return y | 5,381,534,480,915,387,000 | ACESutil.Log2_to_Lin_param.ctl を参考に作成。
https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Log2_to_Lin_param.ctl
Log2空間の補足は shaper_func_linear_to_log2() の説明を参照
Examples
--------
>>> x = np.array([0.0, 1.0])
>>> shaper_func_log2_to_linear(
... x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)
array([0.00198873782209, 16.2917402385]) | ty_lib/test_pattern_generator2.py | shaper_func_log2_to_linear | colour-science/sample_code | python | def shaper_func_log2_to_linear(x, mid_gray=0.18, min_exposure=(- 6.5), max_exposure=6.5):
'\n ACESutil.Log2_to_Lin_param.ctl を参考に作成。\n https://github.com/ampas/aces-dev/blob/master/transforms/ctl/utilities/ACESutil.Log2_to_Lin_param.ctl\n\n Log2空間の補足は shaper_func_linear_to_log2() の説明を参照\n\n Examples\n --------\n >>> x = np.array([0.0, 1.0])\n >>> shaper_func_log2_to_linear(\n ... x, mid_gray=0.18, min_exposure=-6.5, max_exposure=6.5)\n array([0.00198873782209, 16.2917402385])\n '
x_re_scale = ((x * (max_exposure - min_exposure)) + min_exposure)
y = ((2.0 ** x_re_scale) * mid_gray)
return y |
def draw_straight_line(img, pt1, pt2, color, thickness):
'\n 直線を引く。OpenCV だと 8bit しか対応してないっぽいので自作。\n\n Parameters\n ----------\n img : array_like\n image data.\n pt1 : list(pos_h, pos_v)\n start point.\n pt2 : list(pos_h, pos_v)\n end point.\n color : array_like\n color\n thickness : int\n thickness.\n\n Returns\n -------\n array_like\n image data with line.\n\n Notes\n -----\n thickness のパラメータは pt1 の点から右下方向に効きます。\n pt1 を中心として太さではない事に注意。\n\n Examples\n --------\n >>> pt1 = (0, 0)\n >>> pt2 = (1920, 0)\n >>> color = (940, 940, 940)\n >>> thickness = 4\n >>> draw_straight_line(img, pt1, pt2, color, thickness)\n '
if ((pt1[0] != pt2[0]) and (pt1[1] != pt2[1])):
raise ValueError('invalid pt1, pt2 parameters')
if (pt1[0] == pt2[0]):
thickness_direction = 'h'
else:
thickness_direction = 'v'
if (thickness_direction == 'h'):
for h_idx in range(thickness):
img[pt1[1]:pt2[1], (pt1[0] + h_idx), :] = color
elif (thickness_direction == 'v'):
for v_idx in range(thickness):
img[(pt1[1] + v_idx), pt1[0]:pt2[0], :] = color | -1,859,524,669,991,116,500 | 直線を引く。OpenCV だと 8bit しか対応してないっぽいので自作。
Parameters
----------
img : array_like
image data.
pt1 : list(pos_h, pos_v)
start point.
pt2 : list(pos_h, pos_v)
end point.
color : array_like
color
thickness : int
thickness.
Returns
-------
array_like
image data with line.
Notes
-----
thickness のパラメータは pt1 の点から右下方向に効きます。
pt1 を中心として太さではない事に注意。
Examples
--------
>>> pt1 = (0, 0)
>>> pt2 = (1920, 0)
>>> color = (940, 940, 940)
>>> thickness = 4
>>> draw_straight_line(img, pt1, pt2, color, thickness) | ty_lib/test_pattern_generator2.py | draw_straight_line | colour-science/sample_code | python | def draw_straight_line(img, pt1, pt2, color, thickness):
'\n 直線を引く。OpenCV だと 8bit しか対応してないっぽいので自作。\n\n Parameters\n ----------\n img : array_like\n image data.\n pt1 : list(pos_h, pos_v)\n start point.\n pt2 : list(pos_h, pos_v)\n end point.\n color : array_like\n color\n thickness : int\n thickness.\n\n Returns\n -------\n array_like\n image data with line.\n\n Notes\n -----\n thickness のパラメータは pt1 の点から右下方向に効きます。\n pt1 を中心として太さではない事に注意。\n\n Examples\n --------\n >>> pt1 = (0, 0)\n >>> pt2 = (1920, 0)\n >>> color = (940, 940, 940)\n >>> thickness = 4\n >>> draw_straight_line(img, pt1, pt2, color, thickness)\n '
if ((pt1[0] != pt2[0]) and (pt1[1] != pt2[1])):
raise ValueError('invalid pt1, pt2 parameters')
if (pt1[0] == pt2[0]):
thickness_direction = 'h'
else:
thickness_direction = 'v'
if (thickness_direction == 'h'):
for h_idx in range(thickness):
img[pt1[1]:pt2[1], (pt1[0] + h_idx), :] = color
elif (thickness_direction == 'v'):
for v_idx in range(thickness):
img[(pt1[1] + v_idx), pt1[0]:pt2[0], :] = color |
def draw_outline(img, fg_color, outline_width):
'\n img に対して外枠線を引く\n\n Parameters\n ----------\n img : array_like\n image data.\n fg_color : array_like\n color\n outline_width : int\n thickness.\n\n Returns\n -------\n array_like\n image data with line.\n\n Examples\n --------\n >>> img = np.zeros((1080, 1920, 3))\n >>> color = (940, 940, 940)\n >>> thickness = 2\n >>> draw_outline(img, color, thickness)\n '
width = img.shape[1]
height = img.shape[0]
pt1 = (0, 0)
pt2 = (width, 0)
draw_straight_line(img, pt1, pt2, fg_color, outline_width)
pt1 = (0, 0)
pt2 = (0, height)
draw_straight_line(img, pt1, pt2, fg_color, outline_width)
pt1 = ((width - outline_width), 0)
pt2 = ((width - outline_width), height)
draw_straight_line(img, pt1, pt2, fg_color, outline_width)
pt1 = (0, (height - outline_width))
pt2 = (width, (height - outline_width))
draw_straight_line(img, pt1, pt2, fg_color, outline_width) | 6,796,354,785,387,014,000 | img に対して外枠線を引く
Parameters
----------
img : array_like
image data.
fg_color : array_like
color
outline_width : int
thickness.
Returns
-------
array_like
image data with line.
Examples
--------
>>> img = np.zeros((1080, 1920, 3))
>>> color = (940, 940, 940)
>>> thickness = 2
>>> draw_outline(img, color, thickness) | ty_lib/test_pattern_generator2.py | draw_outline | colour-science/sample_code | python | def draw_outline(img, fg_color, outline_width):
'\n img に対して外枠線を引く\n\n Parameters\n ----------\n img : array_like\n image data.\n fg_color : array_like\n color\n outline_width : int\n thickness.\n\n Returns\n -------\n array_like\n image data with line.\n\n Examples\n --------\n >>> img = np.zeros((1080, 1920, 3))\n >>> color = (940, 940, 940)\n >>> thickness = 2\n >>> draw_outline(img, color, thickness)\n '
width = img.shape[1]
height = img.shape[0]
pt1 = (0, 0)
pt2 = (width, 0)
draw_straight_line(img, pt1, pt2, fg_color, outline_width)
pt1 = (0, 0)
pt2 = (0, height)
draw_straight_line(img, pt1, pt2, fg_color, outline_width)
pt1 = ((width - outline_width), 0)
pt2 = ((width - outline_width), height)
draw_straight_line(img, pt1, pt2, fg_color, outline_width)
pt1 = (0, (height - outline_width))
pt2 = (width, (height - outline_width))
draw_straight_line(img, pt1, pt2, fg_color, outline_width) |
def convert_luminance_to_color_value(luminance, transfer_function):
'\n 輝度[cd/m2] から code value の RGB値に変換する。\n luminance の単位は [cd/m2]。無彩色である。\n\n Examples\n --------\n >>> convert_luminance_to_color_value(100, tf.GAMMA24)\n >>> [ 1.0 1.0 1.0 ]\n >>> convert_luminance_to_color_value(100, tf.ST2084)\n >>> [ 0.50807842 0.50807842 0.50807842 ]\n '
code_value = convert_luminance_to_code_value(luminance, transfer_function)
return np.array([code_value, code_value, code_value]) | -9,198,726,890,243,419,000 | 輝度[cd/m2] から code value の RGB値に変換する。
luminance の単位は [cd/m2]。無彩色である。
Examples
--------
>>> convert_luminance_to_color_value(100, tf.GAMMA24)
>>> [ 1.0 1.0 1.0 ]
>>> convert_luminance_to_color_value(100, tf.ST2084)
>>> [ 0.50807842 0.50807842 0.50807842 ] | ty_lib/test_pattern_generator2.py | convert_luminance_to_color_value | colour-science/sample_code | python | def convert_luminance_to_color_value(luminance, transfer_function):
'\n 輝度[cd/m2] から code value の RGB値に変換する。\n luminance の単位は [cd/m2]。無彩色である。\n\n Examples\n --------\n >>> convert_luminance_to_color_value(100, tf.GAMMA24)\n >>> [ 1.0 1.0 1.0 ]\n >>> convert_luminance_to_color_value(100, tf.ST2084)\n >>> [ 0.50807842 0.50807842 0.50807842 ]\n '
code_value = convert_luminance_to_code_value(luminance, transfer_function)
return np.array([code_value, code_value, code_value]) |
def convert_luminance_to_code_value(luminance, transfer_function):
'\n 輝度[cd/m2] から code value に変換する。\n luminance の単位は [cd/m2]\n '
return tf.oetf_from_luminance(luminance, transfer_function) | 5,017,816,043,957,604,000 | 輝度[cd/m2] から code value に変換する。
luminance の単位は [cd/m2] | ty_lib/test_pattern_generator2.py | convert_luminance_to_code_value | colour-science/sample_code | python | def convert_luminance_to_code_value(luminance, transfer_function):
'\n 輝度[cd/m2] から code value に変換する。\n luminance の単位は [cd/m2]\n '
return tf.oetf_from_luminance(luminance, transfer_function) |
def calc_rad_patch_idx2(outmost_num=5, current_num=3):
'\n 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの\n RGB値のリストを得る。\n https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png\n\n 得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で\n 得られる変換テーブルを使った変換が必要。\n 本関数はまさにその変換を行う。\n '
base = np.arange((outmost_num ** 2)).reshape((outmost_num, outmost_num))
t_idx = ((outmost_num - current_num) // 2)
trimmed = base[t_idx:(t_idx + current_num), t_idx:(t_idx + current_num)]
half_num = (current_num // 2)
conv_idx = []
for idx in range(half_num):
val = ((((current_num ** 2) // 2) + half_num) - (current_num * idx))
conv_idx.append(val)
for idx in range(current_num)[::(- 1)]:
conv_idx.append(idx)
for idx in range(1, (current_num - 1)):
conv_idx.append((idx * current_num))
for idx in range(current_num):
val = (((current_num ** 2) - current_num) + idx)
conv_idx.append(val)
for idx in range(1, half_num):
val = (((current_num ** 2) - 1) - (idx * current_num))
conv_idx.append(val)
conv_idx = trimmed.flatten()[conv_idx]
return conv_idx | -7,178,791,033,226,871,000 | 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの
RGB値のリストを得る。
https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png
得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で
得られる変換テーブルを使った変換が必要。
本関数はまさにその変換を行う。 | ty_lib/test_pattern_generator2.py | calc_rad_patch_idx2 | colour-science/sample_code | python | def calc_rad_patch_idx2(outmost_num=5, current_num=3):
'\n 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの\n RGB値のリストを得る。\n https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png\n\n 得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で\n 得られる変換テーブルを使った変換が必要。\n 本関数はまさにその変換を行う。\n '
base = np.arange((outmost_num ** 2)).reshape((outmost_num, outmost_num))
t_idx = ((outmost_num - current_num) // 2)
trimmed = base[t_idx:(t_idx + current_num), t_idx:(t_idx + current_num)]
half_num = (current_num // 2)
conv_idx = []
for idx in range(half_num):
val = ((((current_num ** 2) // 2) + half_num) - (current_num * idx))
conv_idx.append(val)
for idx in range(current_num)[::(- 1)]:
conv_idx.append(idx)
for idx in range(1, (current_num - 1)):
conv_idx.append((idx * current_num))
for idx in range(current_num):
val = (((current_num ** 2) - current_num) + idx)
conv_idx.append(val)
for idx in range(1, half_num):
val = (((current_num ** 2) - 1) - (idx * current_num))
conv_idx.append(val)
conv_idx = trimmed.flatten()[conv_idx]
return conv_idx |
def _calc_rgb_from_same_lstar_radial_data(lstar, temp_chroma, current_num, color_space):
'\n 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの\n RGB値のリストを得る。\n https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png\n\n 得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で\n 得られる変換テーブルを使った変換が必要。\n '
current_patch_num = (((current_num - 1) * 4) if (current_num > 1) else 1)
rad = np.linspace(0, (2 * np.pi), current_patch_num, endpoint=False)
ll = (np.ones(current_patch_num) * lstar)
aa = (np.cos(rad) * temp_chroma)
bb = (np.sin(rad) * temp_chroma)
lab = np.dstack((ll, aa, bb))
large_xyz = Lab_to_XYZ(lab)
rgb = XYZ_to_RGB(large_xyz, D65_WHITE, D65_WHITE, color_space.XYZ_to_RGB_matrix)
return np.clip(rgb, 0.0, 1.0) | 5,056,633,246,826,132,000 | 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの
RGB値のリストを得る。
https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png
得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で
得られる変換テーブルを使った変換が必要。 | ty_lib/test_pattern_generator2.py | _calc_rgb_from_same_lstar_radial_data | colour-science/sample_code | python | def _calc_rgb_from_same_lstar_radial_data(lstar, temp_chroma, current_num, color_space):
'\n 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの\n RGB値のリストを得る。\n https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png\n\n 得られたデータは並べ替えが済んでいないため、calc_rad_patch_idx2() で\n 得られる変換テーブルを使った変換が必要。\n '
current_patch_num = (((current_num - 1) * 4) if (current_num > 1) else 1)
rad = np.linspace(0, (2 * np.pi), current_patch_num, endpoint=False)
ll = (np.ones(current_patch_num) * lstar)
aa = (np.cos(rad) * temp_chroma)
bb = (np.sin(rad) * temp_chroma)
lab = np.dstack((ll, aa, bb))
large_xyz = Lab_to_XYZ(lab)
rgb = XYZ_to_RGB(large_xyz, D65_WHITE, D65_WHITE, color_space.XYZ_to_RGB_matrix)
return np.clip(rgb, 0.0, 1.0) |
def calc_same_lstar_radial_color_patch_data(lstar=58, chroma=32.5, outmost_num=9, color_space=BT709_COLOURSPACE, transfer_function=tf.GAMMA24):
'\n 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの\n RGB値のリストを得る。\n https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png\n\n 得られた RGB値のリストは最初のデータが画像左上の緑データ、\n 最後のデータが画像右下の紫データとなるよう既に**並べ替え**が行われている。\n\n よってパッチをプロットする場合はRGB値リストの先頭から順にデータを取り出し、\n 右下に向かって並べていけば良い。\n '
patch_num = (outmost_num ** 2)
transfer_function = tf.GAMMA24
rgb_list = np.ones((patch_num, 3))
current_num_list = range(1, (outmost_num + 1), 2)
chroma_list = np.linspace(0, chroma, len(current_num_list))
for (temp_chroma, current_num) in zip(chroma_list, current_num_list):
current_patch_num = (((current_num - 1) * 4) if (current_num > 1) else 1)
rgb = _calc_rgb_from_same_lstar_radial_data(lstar, temp_chroma, current_num, color_space)
rgb = np.reshape(rgb, (current_patch_num, 3))
rgb = tf.oetf(rgb, transfer_function)
conv_idx = calc_rad_patch_idx2(outmost_num=outmost_num, current_num=current_num)
for idx in range(current_patch_num):
rgb_list[conv_idx[idx]] = rgb[idx]
return rgb_list | -4,732,374,722,857,101,000 | 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの
RGB値のリストを得る。
https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png
得られた RGB値のリストは最初のデータが画像左上の緑データ、
最後のデータが画像右下の紫データとなるよう既に**並べ替え**が行われている。
よってパッチをプロットする場合はRGB値リストの先頭から順にデータを取り出し、
右下に向かって並べていけば良い。 | ty_lib/test_pattern_generator2.py | calc_same_lstar_radial_color_patch_data | colour-science/sample_code | python | def calc_same_lstar_radial_color_patch_data(lstar=58, chroma=32.5, outmost_num=9, color_space=BT709_COLOURSPACE, transfer_function=tf.GAMMA24):
'\n 以下のような、中心がGray、周りは CIELAB 空間の a*b*平面のカラーパッチの\n RGB値のリストを得る。\n https://user-images.githubusercontent.com/3609012/75444470-d3bc5600-59a6-11ea-962b-c315648782a9.png\n\n 得られた RGB値のリストは最初のデータが画像左上の緑データ、\n 最後のデータが画像右下の紫データとなるよう既に**並べ替え**が行われている。\n\n よってパッチをプロットする場合はRGB値リストの先頭から順にデータを取り出し、\n 右下に向かって並べていけば良い。\n '
patch_num = (outmost_num ** 2)
transfer_function = tf.GAMMA24
rgb_list = np.ones((patch_num, 3))
current_num_list = range(1, (outmost_num + 1), 2)
chroma_list = np.linspace(0, chroma, len(current_num_list))
for (temp_chroma, current_num) in zip(chroma_list, current_num_list):
current_patch_num = (((current_num - 1) * 4) if (current_num > 1) else 1)
rgb = _calc_rgb_from_same_lstar_radial_data(lstar, temp_chroma, current_num, color_space)
rgb = np.reshape(rgb, (current_patch_num, 3))
rgb = tf.oetf(rgb, transfer_function)
conv_idx = calc_rad_patch_idx2(outmost_num=outmost_num, current_num=current_num)
for idx in range(current_patch_num):
rgb_list[conv_idx[idx]] = rgb[idx]
return rgb_list |
def get_accelerated_x_1x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n\n Examples\n --------\n >>> x0 = np.linspace(0, 1, 8)\n >>> x1 = get_accelerated_x_1x(8)\n >>> print(x0)\n >>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ]\n >>> print(x1)\n >>> [ 0. 0.049 0.188 0.388 0.611 0.811 0.950 1. ]\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
x = ((np.sin(rad) + 1) / 2)
return x | -6,556,408,200,352,419,000 | 単調増加ではなく、加速度が 0→1→0 となるような x を作る
Parameters
----------
sample_num : int
the number of the sample.
Returns
-------
array_like
accelerated value list
Examples
--------
>>> x0 = np.linspace(0, 1, 8)
>>> x1 = get_accelerated_x_1x(8)
>>> print(x0)
>>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ]
>>> print(x1)
>>> [ 0. 0.049 0.188 0.388 0.611 0.811 0.950 1. ] | ty_lib/test_pattern_generator2.py | get_accelerated_x_1x | colour-science/sample_code | python | def get_accelerated_x_1x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n\n Examples\n --------\n >>> x0 = np.linspace(0, 1, 8)\n >>> x1 = get_accelerated_x_1x(8)\n >>> print(x0)\n >>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ]\n >>> print(x1)\n >>> [ 0. 0.049 0.188 0.388 0.611 0.811 0.950 1. ]\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
x = ((np.sin(rad) + 1) / 2)
return x |
def get_accelerated_x_2x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る。\n 加速度が `get_accelerated_x_1x` の2倍！！\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n\n Examples\n --------\n >>> x0 = np.linspace(0, 1, 8)\n >>> x2 = get_accelerated_x_2x(8)\n >>> print(x0)\n >>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ]\n >>> print(x2)\n >>> [ 0. 0.006 0.084 0.328 0.671 0.915 0.993 1. ]\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
rad = ((np.sin(rad) * 0.5) * np.pi)
x = ((np.sin(rad) + 1) / 2)
return x | 8,900,704,438,845,922,000 | 単調増加ではなく、加速度が 0→1→0 となるような x を作る。
加速度が `get_accelerated_x_1x` の2倍！！
Parameters
----------
sample_num : int
the number of the sample.
Returns
-------
array_like
accelerated value list
Examples
--------
>>> x0 = np.linspace(0, 1, 8)
>>> x2 = get_accelerated_x_2x(8)
>>> print(x0)
>>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ]
>>> print(x2)
>>> [ 0. 0.006 0.084 0.328 0.671 0.915 0.993 1. ] | ty_lib/test_pattern_generator2.py | get_accelerated_x_2x | colour-science/sample_code | python | def get_accelerated_x_2x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る。\n 加速度が `get_accelerated_x_1x` の2倍！！\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n\n Examples\n --------\n >>> x0 = np.linspace(0, 1, 8)\n >>> x2 = get_accelerated_x_2x(8)\n >>> print(x0)\n >>> [ 0. 0.142 0.285 0.428 0.571 0.714 0.857 1. ]\n >>> print(x2)\n >>> [ 0. 0.006 0.084 0.328 0.671 0.915 0.993 1. ]\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
rad = ((np.sin(rad) * 0.5) * np.pi)
x = ((np.sin(rad) + 1) / 2)
return x |
def get_accelerated_x_4x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る。\n 加速度が `get_accelerated_x_1x` の4倍！！\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
rad = ((np.sin(rad) * 0.5) * np.pi)
rad = ((np.sin(rad) * 0.5) * np.pi)
x = ((np.sin(rad) + 1) / 2)
return x | 1,836,093,452,671,727,900 | 単調増加ではなく、加速度が 0→1→0 となるような x を作る。
加速度が `get_accelerated_x_1x` の4倍！！
Parameters
----------
sample_num : int
the number of the sample.
Returns
-------
array_like
accelerated value list | ty_lib/test_pattern_generator2.py | get_accelerated_x_4x | colour-science/sample_code | python | def get_accelerated_x_4x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る。\n 加速度が `get_accelerated_x_1x` の4倍！！\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
rad = ((np.sin(rad) * 0.5) * np.pi)
rad = ((np.sin(rad) * 0.5) * np.pi)
x = ((np.sin(rad) + 1) / 2)
return x |
def get_accelerated_x_8x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る。\n 加速度が `get_accelerated_x_1x` の4倍！！\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
rad = ((np.sin(rad) * 0.5) * np.pi)
rad = ((np.sin(rad) * 0.5) * np.pi)
rad = ((np.sin(rad) * 0.5) * np.pi)
x = ((np.sin(rad) + 1) / 2)
return x | 4,805,954,959,950,164,000 | 単調増加ではなく、加速度が 0→1→0 となるような x を作る。
加速度が `get_accelerated_x_1x` の4倍！！
Parameters
----------
sample_num : int
the number of the sample.
Returns
-------
array_like
accelerated value list | ty_lib/test_pattern_generator2.py | get_accelerated_x_8x | colour-science/sample_code | python | def get_accelerated_x_8x(sample_num=64):
'\n 単調増加ではなく、加速度が 0→1→0 となるような x を作る。\n 加速度が `get_accelerated_x_1x` の4倍！！\n\n Parameters\n ----------\n sample_num : int\n the number of the sample.\n\n Returns\n -------\n array_like\n accelerated value list\n '
rad = np.linspace(((- 0.5) * np.pi), (0.5 * np.pi), sample_num)
rad = ((np.sin(rad) * 0.5) * np.pi)
rad = ((np.sin(rad) * 0.5) * np.pi)
rad = ((np.sin(rad) * 0.5) * np.pi)
x = ((np.sin(rad) + 1) / 2)
return x |
def generate_color_checker_rgb_value(color_space=BT709_COLOURSPACE, target_white=D65_WHITE):
'\n Generate the 24 RGB values of the color checker.\n\n Parameters\n ----------\n color_space : color space\n color space object in `colour` module.\n\n target_white : array_like\n the xy values of the white point of target color space.\n\n Returns\n -------\n array_like\n 24 RGB values. This is linear. OETF is not applied.\n\n Examples\n --------\n >>> generate_color_checker_rgb_value(\n ... color_space=colour.models.BT709_COLOURSPACE,\n ... target_white=[0.3127, 0.3290])\n >>> [[ 0.17289286 0.08205728 0.05714562]\n >>> [ 0.5680292 0.29250401 0.21951748]\n >>> [ 0.10435534 0.19656108 0.32958666]\n >>> [ 0.1008804 0.14839018 0.05327639]\n >>> [ 0.22303549 0.2169701 0.43166537]\n >>> [ 0.10715338 0.513512 0.41415978]\n >>> [ 0.74639182 0.20020473 0.03081343]\n >>> [ 0.05947812 0.10659045 0.39897686]\n >>> [ 0.5673215 0.08485376 0.11945382]\n >>> [ 0.11177253 0.04285397 0.14166202]\n >>> [ 0.34250836 0.5062777 0.0557734 ]\n >>> [ 0.79262553 0.35803886 0.025485 ]\n >>> [ 0.01864598 0.05139665 0.28886469]\n >>> [ 0.054392 0.29876719 0.07187681]\n >>> [ 0.45628547 0.03075684 0.04092033]\n >>> [ 0.85379178 0.56503558 0.01475575]\n >>> [ 0.53533883 0.09006355 0.3047824 ]\n >>> [-0.03662977 0.24753781 0.39824679]\n >>> [ 0.91177068 0.91497623 0.89427332]\n >>> [ 0.57973934 0.59203191 0.59370647]\n >>> [ 0.35495537 0.36538027 0.36772001]\n >>> [ 0.19009594 0.19180133 0.19316719]\n >>> [ 0.08524707 0.08890587 0.09255774]\n >>> [ 0.03038879 0.03118623 0.03279615]]\n '
colour_checker_param = COLOURCHECKERS.get('ColorChecker 2005')
(_name, data, whitepoint) = colour_checker_param
temp_xyY = []
for key in data.keys():
temp_xyY.append(data[key])
temp_xyY = np.array(temp_xyY)
large_xyz = xyY_to_XYZ(temp_xyY)
rgb_white_point = D65_WHITE
illuminant_XYZ = whitepoint
illuminant_RGB = rgb_white_point
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
return rgb | 6,887,916,821,628,261,000 | Generate the 24 RGB values of the color checker.
Parameters
----------
color_space : color space
color space object in `colour` module.
target_white : array_like
the xy values of the white point of target color space.
Returns
-------
array_like
24 RGB values. This is linear. OETF is not applied.
Examples
--------
>>> generate_color_checker_rgb_value(
... color_space=colour.models.BT709_COLOURSPACE,
... target_white=[0.3127, 0.3290])
>>> [[ 0.17289286 0.08205728 0.05714562]
>>> [ 0.5680292 0.29250401 0.21951748]
>>> [ 0.10435534 0.19656108 0.32958666]
>>> [ 0.1008804 0.14839018 0.05327639]
>>> [ 0.22303549 0.2169701 0.43166537]
>>> [ 0.10715338 0.513512 0.41415978]
>>> [ 0.74639182 0.20020473 0.03081343]
>>> [ 0.05947812 0.10659045 0.39897686]
>>> [ 0.5673215 0.08485376 0.11945382]
>>> [ 0.11177253 0.04285397 0.14166202]
>>> [ 0.34250836 0.5062777 0.0557734 ]
>>> [ 0.79262553 0.35803886 0.025485 ]
>>> [ 0.01864598 0.05139665 0.28886469]
>>> [ 0.054392 0.29876719 0.07187681]
>>> [ 0.45628547 0.03075684 0.04092033]
>>> [ 0.85379178 0.56503558 0.01475575]
>>> [ 0.53533883 0.09006355 0.3047824 ]
>>> [-0.03662977 0.24753781 0.39824679]
>>> [ 0.91177068 0.91497623 0.89427332]
>>> [ 0.57973934 0.59203191 0.59370647]
>>> [ 0.35495537 0.36538027 0.36772001]
>>> [ 0.19009594 0.19180133 0.19316719]
>>> [ 0.08524707 0.08890587 0.09255774]
>>> [ 0.03038879 0.03118623 0.03279615]] | ty_lib/test_pattern_generator2.py | generate_color_checker_rgb_value | colour-science/sample_code | python | def generate_color_checker_rgb_value(color_space=BT709_COLOURSPACE, target_white=D65_WHITE):
'\n Generate the 24 RGB values of the color checker.\n\n Parameters\n ----------\n color_space : color space\n color space object in `colour` module.\n\n target_white : array_like\n the xy values of the white point of target color space.\n\n Returns\n -------\n array_like\n 24 RGB values. This is linear. OETF is not applied.\n\n Examples\n --------\n >>> generate_color_checker_rgb_value(\n ... color_space=colour.models.BT709_COLOURSPACE,\n ... target_white=[0.3127, 0.3290])\n >>> [[ 0.17289286 0.08205728 0.05714562]\n >>> [ 0.5680292 0.29250401 0.21951748]\n >>> [ 0.10435534 0.19656108 0.32958666]\n >>> [ 0.1008804 0.14839018 0.05327639]\n >>> [ 0.22303549 0.2169701 0.43166537]\n >>> [ 0.10715338 0.513512 0.41415978]\n >>> [ 0.74639182 0.20020473 0.03081343]\n >>> [ 0.05947812 0.10659045 0.39897686]\n >>> [ 0.5673215 0.08485376 0.11945382]\n >>> [ 0.11177253 0.04285397 0.14166202]\n >>> [ 0.34250836 0.5062777 0.0557734 ]\n >>> [ 0.79262553 0.35803886 0.025485 ]\n >>> [ 0.01864598 0.05139665 0.28886469]\n >>> [ 0.054392 0.29876719 0.07187681]\n >>> [ 0.45628547 0.03075684 0.04092033]\n >>> [ 0.85379178 0.56503558 0.01475575]\n >>> [ 0.53533883 0.09006355 0.3047824 ]\n >>> [-0.03662977 0.24753781 0.39824679]\n >>> [ 0.91177068 0.91497623 0.89427332]\n >>> [ 0.57973934 0.59203191 0.59370647]\n >>> [ 0.35495537 0.36538027 0.36772001]\n >>> [ 0.19009594 0.19180133 0.19316719]\n >>> [ 0.08524707 0.08890587 0.09255774]\n >>> [ 0.03038879 0.03118623 0.03279615]]\n '
colour_checker_param = COLOURCHECKERS.get('ColorChecker 2005')
(_name, data, whitepoint) = colour_checker_param
temp_xyY = []
for key in data.keys():
temp_xyY.append(data[key])
temp_xyY = np.array(temp_xyY)
large_xyz = xyY_to_XYZ(temp_xyY)
rgb_white_point = D65_WHITE
illuminant_XYZ = whitepoint
illuminant_RGB = rgb_white_point
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = color_space.XYZ_to_RGB_matrix
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB, large_xyz_to_rgb_matrix, chromatic_adaptation_transform)
return rgb |
def make_color_checker_image(rgb, width=1920, padding_rate=0.01):
'\n 6x4 の カラーチェッカーの画像を作る。\n Height は Width から自動計算される。padding_rate で少し値が変わる。\n '
h_patch_num = 6
v_patch_num = 4
each_padding = int(((width * padding_rate) + 0.5))
h_padding_total = (each_padding * (h_patch_num + 1))
h_patch_width_total = (width - h_padding_total)
patch_height = (h_patch_width_total // h_patch_num)
height = ((patch_height * v_patch_num) + (each_padding * (v_patch_num + 1)))
patch_width_list = equal_devision(h_patch_width_total, h_patch_num)
img = np.zeros((height, width, 3))
for v_idx in range(v_patch_num):
h_pos_st = each_padding
v_pos_st = (each_padding + (v_idx * (patch_height + each_padding)))
for h_idx in range(h_patch_num):
rgb_idx = ((v_idx * h_patch_num) + h_idx)
pos = (h_pos_st, v_pos_st)
patch_img = (np.ones((patch_height, patch_width_list[h_idx], 3)) * rgb[rgb_idx])
merge(img, patch_img, pos)
h_pos_st += (patch_width_list[h_idx] + each_padding)
return img | 2,040,358,170,524,852,500 | 6x4 の カラーチェッカーの画像を作る。
Height は Width から自動計算される。padding_rate で少し値が変わる。 | ty_lib/test_pattern_generator2.py | make_color_checker_image | colour-science/sample_code | python | def make_color_checker_image(rgb, width=1920, padding_rate=0.01):
'\n 6x4 の カラーチェッカーの画像を作る。\n Height は Width から自動計算される。padding_rate で少し値が変わる。\n '
h_patch_num = 6
v_patch_num = 4
each_padding = int(((width * padding_rate) + 0.5))
h_padding_total = (each_padding * (h_patch_num + 1))
h_patch_width_total = (width - h_padding_total)
patch_height = (h_patch_width_total // h_patch_num)
height = ((patch_height * v_patch_num) + (each_padding * (v_patch_num + 1)))
patch_width_list = equal_devision(h_patch_width_total, h_patch_num)
img = np.zeros((height, width, 3))
for v_idx in range(v_patch_num):
h_pos_st = each_padding
v_pos_st = (each_padding + (v_idx * (patch_height + each_padding)))
for h_idx in range(h_patch_num):
rgb_idx = ((v_idx * h_patch_num) + h_idx)
pos = (h_pos_st, v_pos_st)
patch_img = (np.ones((patch_height, patch_width_list[h_idx], 3)) * rgb[rgb_idx])
merge(img, patch_img, pos)
h_pos_st += (patch_width_list[h_idx] + each_padding)
return img |
def calc_st_pos_for_centering(bg_size, fg_size):
'\n Calculate start postion for centering.\n\n Parameters\n ----------\n bg_size : touple(int)\n (width, height) of the background image.\n\n fg_size : touple(int)\n (width, height) of the foreground image.\n\n Returns\n -------\n touple (int)\n (st_pos_h, st_pos_v)\n\n Examples\n --------\n >>> calc_st_pos_for_centering(bg_size=(1920, 1080), fg_size=(640, 480))\n >>> (640, 300)\n '
bg_width = bg_size[0]
bg_height = bg_size[1]
fg_width = fg_size[0]
fg_height = fg_size[1]
st_pos_h = ((bg_width // 2) - (fg_width // 2))
st_pos_v = ((bg_height // 2) - (fg_height // 2))
return (st_pos_h, st_pos_v) | -4,828,180,079,277,697,000 | Calculate start postion for centering.
Parameters
----------
bg_size : touple(int)
(width, height) of the background image.
fg_size : touple(int)
(width, height) of the foreground image.
Returns
-------
touple (int)
(st_pos_h, st_pos_v)
Examples
--------
>>> calc_st_pos_for_centering(bg_size=(1920, 1080), fg_size=(640, 480))
>>> (640, 300) | ty_lib/test_pattern_generator2.py | calc_st_pos_for_centering | colour-science/sample_code | python | def calc_st_pos_for_centering(bg_size, fg_size):
'\n Calculate start postion for centering.\n\n Parameters\n ----------\n bg_size : touple(int)\n (width, height) of the background image.\n\n fg_size : touple(int)\n (width, height) of the foreground image.\n\n Returns\n -------\n touple (int)\n (st_pos_h, st_pos_v)\n\n Examples\n --------\n >>> calc_st_pos_for_centering(bg_size=(1920, 1080), fg_size=(640, 480))\n >>> (640, 300)\n '
bg_width = bg_size[0]
bg_height = bg_size[1]
fg_width = fg_size[0]
fg_height = fg_size[1]
st_pos_h = ((bg_width // 2) - (fg_width // 2))
st_pos_v = ((bg_height // 2) - (fg_height // 2))
return (st_pos_h, st_pos_v) |
def get_size_from_image(img):
'\n `calc_st_pos_for_centering()` の引数計算が面倒だったので関数化。\n '
return (img.shape[1], img.shape[0]) | 2,285,655,585,279,918,300 | `calc_st_pos_for_centering()` の引数計算が面倒だったので関数化。 | ty_lib/test_pattern_generator2.py | get_size_from_image | colour-science/sample_code | python | def get_size_from_image(img):
'\n \n '
return (img.shape[1], img.shape[0]) |
def _Args(parser, deprecate_maintenance_policy=False, container_mount_enabled=False):
'Add flags shared by all release tracks.'
parser.display_info.AddFormat(instances_flags.DEFAULT_LIST_FORMAT)
metadata_utils.AddMetadataArgs(parser)
instances_flags.AddDiskArgs(parser, True, container_mount_enabled=container_mount_enabled)
instances_flags.AddCreateDiskArgs(parser, container_mount_enabled=container_mount_enabled)
instances_flags.AddCanIpForwardArgs(parser)
instances_flags.AddAddressArgs(parser, instances=True)
instances_flags.AddMachineTypeArgs(parser)
instances_flags.AddMaintenancePolicyArgs(parser, deprecate=deprecate_maintenance_policy)
instances_flags.AddNoRestartOnFailureArgs(parser)
instances_flags.AddPreemptibleVmArgs(parser)
instances_flags.AddServiceAccountAndScopeArgs(parser, False)
instances_flags.AddTagsArgs(parser)
instances_flags.AddCustomMachineTypeArgs(parser)
instances_flags.AddNetworkArgs(parser)
instances_flags.AddPrivateNetworkIpArgs(parser)
instances_flags.AddKonletArgs(parser)
instances_flags.AddPublicDnsArgs(parser, instance=True)
instances_flags.AddPublicPtrArgs(parser, instance=True)
instances_flags.AddImageArgs(parser)
labels_util.AddCreateLabelsFlags(parser)
parser.add_argument('--description', help='Specifies a textual description of the instances.')
instances_flags.INSTANCES_ARG.AddArgument(parser, operation_type='create')
CreateWithContainer.SOURCE_INSTANCE_TEMPLATE = instances_flags.MakeSourceInstanceTemplateArg()
CreateWithContainer.SOURCE_INSTANCE_TEMPLATE.AddArgument(parser)
parser.display_info.AddCacheUpdater(completers.InstancesCompleter) | -7,180,734,195,565,543,000 | Add flags shared by all release tracks. | lib/surface/compute/instances/create_with_container.py | _Args | bshaffer/google-cloud-sdk | python | def _Args(parser, deprecate_maintenance_policy=False, container_mount_enabled=False):
parser.display_info.AddFormat(instances_flags.DEFAULT_LIST_FORMAT)
metadata_utils.AddMetadataArgs(parser)
instances_flags.AddDiskArgs(parser, True, container_mount_enabled=container_mount_enabled)
instances_flags.AddCreateDiskArgs(parser, container_mount_enabled=container_mount_enabled)
instances_flags.AddCanIpForwardArgs(parser)
instances_flags.AddAddressArgs(parser, instances=True)
instances_flags.AddMachineTypeArgs(parser)
instances_flags.AddMaintenancePolicyArgs(parser, deprecate=deprecate_maintenance_policy)
instances_flags.AddNoRestartOnFailureArgs(parser)
instances_flags.AddPreemptibleVmArgs(parser)
instances_flags.AddServiceAccountAndScopeArgs(parser, False)
instances_flags.AddTagsArgs(parser)
instances_flags.AddCustomMachineTypeArgs(parser)
instances_flags.AddNetworkArgs(parser)
instances_flags.AddPrivateNetworkIpArgs(parser)
instances_flags.AddKonletArgs(parser)
instances_flags.AddPublicDnsArgs(parser, instance=True)
instances_flags.AddPublicPtrArgs(parser, instance=True)
instances_flags.AddImageArgs(parser)
labels_util.AddCreateLabelsFlags(parser)
parser.add_argument('--description', help='Specifies a textual description of the instances.')
instances_flags.INSTANCES_ARG.AddArgument(parser, operation_type='create')
CreateWithContainer.SOURCE_INSTANCE_TEMPLATE = instances_flags.MakeSourceInstanceTemplateArg()
CreateWithContainer.SOURCE_INSTANCE_TEMPLATE.AddArgument(parser)
parser.display_info.AddCacheUpdater(completers.InstancesCompleter) |
@staticmethod
def Args(parser):
'Register parser args.'
_Args(parser)
instances_flags.AddNetworkTierArgs(parser, instance=True)
instances_flags.AddMinCpuPlatformArgs(parser, base.ReleaseTrack.GA) | 469,272,078,714,869,000 | Register parser args. | lib/surface/compute/instances/create_with_container.py | Args | bshaffer/google-cloud-sdk | python | @staticmethod
def Args(parser):
_Args(parser)
instances_flags.AddNetworkTierArgs(parser, instance=True)
instances_flags.AddMinCpuPlatformArgs(parser, base.ReleaseTrack.GA) |
@staticmethod
def Args(parser):
'Register parser args.'
_Args(parser, container_mount_enabled=True)
instances_flags.AddNetworkTierArgs(parser, instance=True)
instances_flags.AddContainerMountDiskFlag(parser)
instances_flags.AddLocalSsdArgsWithSize(parser)
instances_flags.AddMinCpuPlatformArgs(parser, base.ReleaseTrack.BETA) | -3,262,381,641,413,971,000 | Register parser args. | lib/surface/compute/instances/create_with_container.py | Args | bshaffer/google-cloud-sdk | python | @staticmethod
def Args(parser):
_Args(parser, container_mount_enabled=True)
instances_flags.AddNetworkTierArgs(parser, instance=True)
instances_flags.AddContainerMountDiskFlag(parser)
instances_flags.AddLocalSsdArgsWithSize(parser)
instances_flags.AddMinCpuPlatformArgs(parser, base.ReleaseTrack.BETA) |
def deprecated_arg_names(arg_mapping: Mapping[(str, str)]):
'\n Decorator which marks a functions keyword arguments as deprecated. It will\n result in a warning being emitted when the deprecated keyword argument is\n used, and the function being called with the new argument.\n\n Parameters\n ----------\n arg_mapping\n Mapping from deprecated argument name to current argument name.\n '
def decorator(func):
@wraps(func)
def func_wrapper(*args, **kwargs):
warnings.simplefilter('always', DeprecationWarning)
for (old, new) in arg_mapping.items():
if (old in kwargs):
warnings.warn(f"Keyword argument '{old}' has been deprecated in favour of '{new}'. '{old}' will be removed in a future version.", category=DeprecationWarning, stacklevel=2)
val = kwargs.pop(old)
kwargs[new] = val
warnings.simplefilter('default', DeprecationWarning)
return func(*args, **kwargs)
return func_wrapper
return decorator | 7,597,204,064,448,840,000 | Decorator which marks a functions keyword arguments as deprecated. It will
result in a warning being emitted when the deprecated keyword argument is
used, and the function being called with the new argument.
Parameters
----------
arg_mapping
Mapping from deprecated argument name to current argument name. | scanpy/_utils.py | deprecated_arg_names | VolkerBergen/scanpy | python | def deprecated_arg_names(arg_mapping: Mapping[(str, str)]):
'\n Decorator which marks a functions keyword arguments as deprecated. It will\n result in a warning being emitted when the deprecated keyword argument is\n used, and the function being called with the new argument.\n\n Parameters\n ----------\n arg_mapping\n Mapping from deprecated argument name to current argument name.\n '
def decorator(func):
@wraps(func)
def func_wrapper(*args, **kwargs):
warnings.simplefilter('always', DeprecationWarning)
for (old, new) in arg_mapping.items():
if (old in kwargs):
warnings.warn(f"Keyword argument '{old}' has been deprecated in favour of '{new}'. '{old}' will be removed in a future version.", category=DeprecationWarning, stacklevel=2)
val = kwargs.pop(old)
kwargs[new] = val
warnings.simplefilter('default', DeprecationWarning)
return func(*args, **kwargs)
return func_wrapper
return decorator |
def _doc_params(**kwds):
' Docstrings should start with "" in the first line for proper formatting.\n '
def dec(obj):
obj.__orig_doc__ = obj.__doc__
obj.__doc__ = dedent(obj.__doc__).format_map(kwds)
return obj
return dec | 4,215,397,845,106,700,000 | Docstrings should start with "" in the first line for proper formatting. | scanpy/_utils.py | _doc_params | VolkerBergen/scanpy | python | def _doc_params(**kwds):
' \n '
def dec(obj):
obj.__orig_doc__ = obj.__doc__
obj.__doc__ = dedent(obj.__doc__).format_map(kwds)
return obj
return dec |
def _check_array_function_arguments(**kwargs):
'Checks for invalid arguments when an array is passed.\n\n Helper for functions that work on either AnnData objects or array-likes.\n '
invalid_args = [k for (k, v) in kwargs.items() if (v is not None)]
if (len(invalid_args) > 0):
raise TypeError(f'Arguments {invalid_args} are only valid if an AnnData object is passed.') | 5,586,756,840,675,026,000 | Checks for invalid arguments when an array is passed.
Helper for functions that work on either AnnData objects or array-likes. | scanpy/_utils.py | _check_array_function_arguments | VolkerBergen/scanpy | python | def _check_array_function_arguments(**kwargs):
'Checks for invalid arguments when an array is passed.\n\n Helper for functions that work on either AnnData objects or array-likes.\n '
invalid_args = [k for (k, v) in kwargs.items() if (v is not None)]
if (len(invalid_args) > 0):
raise TypeError(f'Arguments {invalid_args} are only valid if an AnnData object is passed.') |
def _check_use_raw(adata: AnnData, use_raw: Union[(None, bool)]) -> bool:
'\n Normalize checking `use_raw`.\n\n My intentention here is to also provide a single place to throw a deprecation warning from in future.\n '
if (use_raw is not None):
return use_raw
elif (adata.raw is not None):
return True
else:
return False | -2,252,908,537,909,248,500 | Normalize checking `use_raw`.
My intentention here is to also provide a single place to throw a deprecation warning from in future. | scanpy/_utils.py | _check_use_raw | VolkerBergen/scanpy | python | def _check_use_raw(adata: AnnData, use_raw: Union[(None, bool)]) -> bool:
'\n Normalize checking `use_raw`.\n\n My intentention here is to also provide a single place to throw a deprecation warning from in future.\n '
if (use_raw is not None):
return use_raw
elif (adata.raw is not None):
return True
else:
return False |
def get_igraph_from_adjacency(adjacency, directed=None):
'Get igraph graph from adjacency matrix.'
import igraph as ig
(sources, targets) = adjacency.nonzero()
weights = adjacency[(sources, targets)]
if isinstance(weights, np.matrix):
weights = weights.A1
g = ig.Graph(directed=directed)
g.add_vertices(adjacency.shape[0])
g.add_edges(list(zip(sources, targets)))
try:
g.es['weight'] = weights
except:
pass
if (g.vcount() != adjacency.shape[0]):
logg.warning(f'The constructed graph has only {g.vcount()} nodes. Your adjacency matrix contained redundant nodes.')
return g | -752,489,011,673,892,500 | Get igraph graph from adjacency matrix. | scanpy/_utils.py | get_igraph_from_adjacency | VolkerBergen/scanpy | python | def get_igraph_from_adjacency(adjacency, directed=None):
import igraph as ig
(sources, targets) = adjacency.nonzero()
weights = adjacency[(sources, targets)]
if isinstance(weights, np.matrix):
weights = weights.A1
g = ig.Graph(directed=directed)
g.add_vertices(adjacency.shape[0])
g.add_edges(list(zip(sources, targets)))
try:
g.es['weight'] = weights
except:
pass
if (g.vcount() != adjacency.shape[0]):
logg.warning(f'The constructed graph has only {g.vcount()} nodes. Your adjacency matrix contained redundant nodes.')
return g |
def compute_association_matrix_of_groups(adata: AnnData, prediction: str, reference: str, normalization: Literal[('prediction', 'reference')]='prediction', threshold: float=0.01, max_n_names: Optional[int]=2):
'Compute overlaps between groups.\n\n See ``identify_groups`` for identifying the groups.\n\n Parameters\n ----------\n adata\n prediction\n Field name of adata.obs.\n reference\n Field name of adata.obs.\n normalization\n Whether to normalize with respect to the predicted groups or the\n reference groups.\n threshold\n Do not consider associations whose overlap is below this fraction.\n max_n_names\n Control how many reference names you want to be associated with per\n predicted name. Set to `None`, if you want all.\n\n Returns\n -------\n asso_names\n List of associated reference names\n (`max_n_names` for each predicted name).\n asso_matrix\n Matrix where rows correspond to the predicted labels and columns to the\n reference labels, entries are proportional to degree of association.\n '
if (normalization not in {'prediction', 'reference'}):
raise ValueError('`normalization` needs to be either "prediction" or "reference".')
sanitize_anndata(adata)
cats = adata.obs[reference].cat.categories
for cat in cats:
if (cat in settings.categories_to_ignore):
logg.info(f'Ignoring category {cat!r} as it’s in `settings.categories_to_ignore`.')
asso_names = []
asso_matrix = []
for (ipred_group, pred_group) in enumerate(adata.obs[prediction].cat.categories):
if ('?' in pred_group):
pred_group = str(ipred_group)
mask_pred = (adata.obs[prediction].values == pred_group)
mask_pred_int = mask_pred.astype(np.int8)
asso_matrix += [[]]
for ref_group in adata.obs[reference].cat.categories:
mask_ref = (adata.obs[reference].values == ref_group).astype(np.int8)
mask_ref_or_pred = mask_ref.copy()
mask_ref_or_pred[mask_pred] = 1
if (normalization == 'prediction'):
ratio_contained = ((np.sum(mask_pred_int) - np.sum((mask_ref_or_pred - mask_ref))) / np.sum(mask_pred_int))
else:
ratio_contained = ((np.sum(mask_ref) - np.sum((mask_ref_or_pred - mask_pred_int))) / np.sum(mask_ref))
asso_matrix[(- 1)] += [ratio_contained]
name_list_pred = [(cats[i] if (cats[i] not in settings.categories_to_ignore) else '') for i in np.argsort(asso_matrix[(- 1)])[::(- 1)] if (asso_matrix[(- 1)][i] > threshold)]
asso_names += ['\n'.join(name_list_pred[:max_n_names])]
Result = namedtuple('compute_association_matrix_of_groups', ['asso_names', 'asso_matrix'])
return Result(asso_names=asso_names, asso_matrix=np.array(asso_matrix)) | -2,052,598,117,330,322,400 | Compute overlaps between groups.
See ``identify_groups`` for identifying the groups.
Parameters
----------
adata
prediction
Field name of adata.obs.
reference
Field name of adata.obs.
normalization
Whether to normalize with respect to the predicted groups or the
reference groups.
threshold
Do not consider associations whose overlap is below this fraction.
max_n_names
Control how many reference names you want to be associated with per
predicted name. Set to `None`, if you want all.
Returns
-------
asso_names
List of associated reference names
(`max_n_names` for each predicted name).
asso_matrix
Matrix where rows correspond to the predicted labels and columns to the
reference labels, entries are proportional to degree of association. | scanpy/_utils.py | compute_association_matrix_of_groups | VolkerBergen/scanpy | python | def compute_association_matrix_of_groups(adata: AnnData, prediction: str, reference: str, normalization: Literal[('prediction', 'reference')]='prediction', threshold: float=0.01, max_n_names: Optional[int]=2):
'Compute overlaps between groups.\n\n See ``identify_groups`` for identifying the groups.\n\n Parameters\n ----------\n adata\n prediction\n Field name of adata.obs.\n reference\n Field name of adata.obs.\n normalization\n Whether to normalize with respect to the predicted groups or the\n reference groups.\n threshold\n Do not consider associations whose overlap is below this fraction.\n max_n_names\n Control how many reference names you want to be associated with per\n predicted name. Set to `None`, if you want all.\n\n Returns\n -------\n asso_names\n List of associated reference names\n (`max_n_names` for each predicted name).\n asso_matrix\n Matrix where rows correspond to the predicted labels and columns to the\n reference labels, entries are proportional to degree of association.\n '
if (normalization not in {'prediction', 'reference'}):
raise ValueError('`normalization` needs to be either "prediction" or "reference".')
sanitize_anndata(adata)
cats = adata.obs[reference].cat.categories
for cat in cats:
if (cat in settings.categories_to_ignore):
logg.info(f'Ignoring category {cat!r} as it’s in `settings.categories_to_ignore`.')
asso_names = []
asso_matrix = []
for (ipred_group, pred_group) in enumerate(adata.obs[prediction].cat.categories):
if ('?' in pred_group):
pred_group = str(ipred_group)
mask_pred = (adata.obs[prediction].values == pred_group)
mask_pred_int = mask_pred.astype(np.int8)
asso_matrix += [[]]
for ref_group in adata.obs[reference].cat.categories:
mask_ref = (adata.obs[reference].values == ref_group).astype(np.int8)
mask_ref_or_pred = mask_ref.copy()
mask_ref_or_pred[mask_pred] = 1
if (normalization == 'prediction'):
ratio_contained = ((np.sum(mask_pred_int) - np.sum((mask_ref_or_pred - mask_ref))) / np.sum(mask_pred_int))
else:
ratio_contained = ((np.sum(mask_ref) - np.sum((mask_ref_or_pred - mask_pred_int))) / np.sum(mask_ref))
asso_matrix[(- 1)] += [ratio_contained]
name_list_pred = [(cats[i] if (cats[i] not in settings.categories_to_ignore) else ) for i in np.argsort(asso_matrix[(- 1)])[::(- 1)] if (asso_matrix[(- 1)][i] > threshold)]
asso_names += ['\n'.join(name_list_pred[:max_n_names])]
Result = namedtuple('compute_association_matrix_of_groups', ['asso_names', 'asso_matrix'])
return Result(asso_names=asso_names, asso_matrix=np.array(asso_matrix)) |
def identify_groups(ref_labels, pred_labels, return_overlaps=False):
'Which predicted label explains which reference label?\n\n A predicted label explains the reference label which maximizes the minimum\n of ``relative_overlaps_pred`` and ``relative_overlaps_ref``.\n\n Compare this with ``compute_association_matrix_of_groups``.\n\n Returns\n -------\n A dictionary of length ``len(np.unique(ref_labels))`` that stores for each\n reference label the predicted label that best explains it.\n\n If ``return_overlaps`` is ``True``, this will in addition return the overlap\n of the reference group with the predicted group; normalized with respect to\n the reference group size and the predicted group size, respectively.\n '
(ref_unique, ref_counts) = np.unique(ref_labels, return_counts=True)
ref_dict = dict(zip(ref_unique, ref_counts))
(pred_unique, pred_counts) = np.unique(pred_labels, return_counts=True)
pred_dict = dict(zip(pred_unique, pred_counts))
associated_predictions = {}
associated_overlaps = {}
for ref_label in ref_unique:
(sub_pred_unique, sub_pred_counts) = np.unique(pred_labels[(ref_label == ref_labels)], return_counts=True)
relative_overlaps_pred = [(sub_pred_counts[i] / pred_dict[n]) for (i, n) in enumerate(sub_pred_unique)]
relative_overlaps_ref = [(sub_pred_counts[i] / ref_dict[ref_label]) for (i, n) in enumerate(sub_pred_unique)]
relative_overlaps = np.c_[(relative_overlaps_pred, relative_overlaps_ref)]
relative_overlaps_min = np.min(relative_overlaps, axis=1)
pred_best_index = np.argsort(relative_overlaps_min)[::(- 1)]
associated_predictions[ref_label] = sub_pred_unique[pred_best_index]
associated_overlaps[ref_label] = relative_overlaps[pred_best_index]
if return_overlaps:
return (associated_predictions, associated_overlaps)
else:
return associated_predictions | 8,182,894,112,265,649,000 | Which predicted label explains which reference label?
A predicted label explains the reference label which maximizes the minimum
of ``relative_overlaps_pred`` and ``relative_overlaps_ref``.
Compare this with ``compute_association_matrix_of_groups``.
Returns
-------
A dictionary of length ``len(np.unique(ref_labels))`` that stores for each
reference label the predicted label that best explains it.
If ``return_overlaps`` is ``True``, this will in addition return the overlap
of the reference group with the predicted group; normalized with respect to
the reference group size and the predicted group size, respectively. | scanpy/_utils.py | identify_groups | VolkerBergen/scanpy | python | def identify_groups(ref_labels, pred_labels, return_overlaps=False):
'Which predicted label explains which reference label?\n\n A predicted label explains the reference label which maximizes the minimum\n of ``relative_overlaps_pred`` and ``relative_overlaps_ref``.\n\n Compare this with ``compute_association_matrix_of_groups``.\n\n Returns\n -------\n A dictionary of length ``len(np.unique(ref_labels))`` that stores for each\n reference label the predicted label that best explains it.\n\n If ``return_overlaps`` is ``True``, this will in addition return the overlap\n of the reference group with the predicted group; normalized with respect to\n the reference group size and the predicted group size, respectively.\n '
(ref_unique, ref_counts) = np.unique(ref_labels, return_counts=True)
ref_dict = dict(zip(ref_unique, ref_counts))
(pred_unique, pred_counts) = np.unique(pred_labels, return_counts=True)
pred_dict = dict(zip(pred_unique, pred_counts))
associated_predictions = {}
associated_overlaps = {}
for ref_label in ref_unique:
(sub_pred_unique, sub_pred_counts) = np.unique(pred_labels[(ref_label == ref_labels)], return_counts=True)
relative_overlaps_pred = [(sub_pred_counts[i] / pred_dict[n]) for (i, n) in enumerate(sub_pred_unique)]
relative_overlaps_ref = [(sub_pred_counts[i] / ref_dict[ref_label]) for (i, n) in enumerate(sub_pred_unique)]
relative_overlaps = np.c_[(relative_overlaps_pred, relative_overlaps_ref)]
relative_overlaps_min = np.min(relative_overlaps, axis=1)
pred_best_index = np.argsort(relative_overlaps_min)[::(- 1)]
associated_predictions[ref_label] = sub_pred_unique[pred_best_index]
associated_overlaps[ref_label] = relative_overlaps[pred_best_index]
if return_overlaps:
return (associated_predictions, associated_overlaps)
else:
return associated_predictions |
def sanitize_anndata(adata):
'Transform string annotations to categoricals.'
adata._sanitize() | 4,622,148,062,683,588,000 | Transform string annotations to categoricals. | scanpy/_utils.py | sanitize_anndata | VolkerBergen/scanpy | python | def sanitize_anndata(adata):
adata._sanitize() |
def moving_average(a: np.ndarray, n: int):
'Moving average over one-dimensional array.\n\n Parameters\n ----------\n a\n One-dimensional array.\n n\n Number of entries to average over. n=2 means averaging over the currrent\n the previous entry.\n\n Returns\n -------\n An array view storing the moving average.\n '
ret = np.cumsum(a, dtype=float)
ret[n:] = (ret[n:] - ret[:(- n)])
return (ret[(n - 1):] / n) | -7,559,639,221,853,936,000 | Moving average over one-dimensional array.
Parameters
----------
a
One-dimensional array.
n
Number of entries to average over. n=2 means averaging over the currrent
the previous entry.
Returns
-------
An array view storing the moving average. | scanpy/_utils.py | moving_average | VolkerBergen/scanpy | python | def moving_average(a: np.ndarray, n: int):
'Moving average over one-dimensional array.\n\n Parameters\n ----------\n a\n One-dimensional array.\n n\n Number of entries to average over. n=2 means averaging over the currrent\n the previous entry.\n\n Returns\n -------\n An array view storing the moving average.\n '
ret = np.cumsum(a, dtype=float)
ret[n:] = (ret[n:] - ret[:(- n)])
return (ret[(n - 1):] / n) |
def update_params(old_params: Mapping[(str, Any)], new_params: Mapping[(str, Any)], check=False) -> Dict[(str, Any)]:
' Update old_params with new_params.\n\n If check==False, this merely adds and overwrites the content of old_params.\n\n If check==True, this only allows updating of parameters that are already\n present in old_params.\n\n Parameters\n ----------\n old_params\n new_params\n check\n\n Returns\n -------\n updated_params\n '
updated_params = dict(old_params)
if new_params:
for (key, val) in new_params.items():
if ((key not in old_params) and check):
raise ValueError((((("'" + key) + "' is not a valid parameter key, ") + 'consider one of \n') + str(list(old_params.keys()))))
if (val is not None):
updated_params[key] = val
return updated_params | -392,503,575,934,761,200 | Update old_params with new_params.
If check==False, this merely adds and overwrites the content of old_params.
If check==True, this only allows updating of parameters that are already
present in old_params.
Parameters
----------
old_params
new_params
check
Returns
-------
updated_params | scanpy/_utils.py | update_params | VolkerBergen/scanpy | python | def update_params(old_params: Mapping[(str, Any)], new_params: Mapping[(str, Any)], check=False) -> Dict[(str, Any)]:
' Update old_params with new_params.\n\n If check==False, this merely adds and overwrites the content of old_params.\n\n If check==True, this only allows updating of parameters that are already\n present in old_params.\n\n Parameters\n ----------\n old_params\n new_params\n check\n\n Returns\n -------\n updated_params\n '
updated_params = dict(old_params)
if new_params:
for (key, val) in new_params.items():
if ((key not in old_params) and check):
raise ValueError((((("'" + key) + "' is not a valid parameter key, ") + 'consider one of \n') + str(list(old_params.keys()))))
if (val is not None):
updated_params[key] = val
return updated_params |
def check_nonnegative_integers(X: Union[(np.ndarray, sparse.spmatrix)]):
'Checks values of X to ensure it is count data'
from numbers import Integral
data = (X if isinstance(X, np.ndarray) else X.data)
if np.signbit(data).any():
return False
elif issubclass(data.dtype.type, Integral):
return True
elif np.any((~ np.equal(np.mod(data, 1), 0))):
return False
else:
return True | 2,527,016,762,427,564,000 | Checks values of X to ensure it is count data | scanpy/_utils.py | check_nonnegative_integers | VolkerBergen/scanpy | python | def check_nonnegative_integers(X: Union[(np.ndarray, sparse.spmatrix)]):
from numbers import Integral
data = (X if isinstance(X, np.ndarray) else X.data)
if np.signbit(data).any():
return False
elif issubclass(data.dtype.type, Integral):
return True
elif np.any((~ np.equal(np.mod(data, 1), 0))):
return False
else:
return True |
def select_groups(adata, groups_order_subset='all', key='groups'):
'Get subset of groups in adata.obs[key].'
groups_order = adata.obs[key].cat.categories
if ((key + '_masks') in adata.uns):
groups_masks = adata.uns[(key + '_masks')]
else:
groups_masks = np.zeros((len(adata.obs[key].cat.categories), adata.obs[key].values.size), dtype=bool)
for (iname, name) in enumerate(adata.obs[key].cat.categories):
if (adata.obs[key].cat.categories[iname] in adata.obs[key].values):
mask = (adata.obs[key].cat.categories[iname] == adata.obs[key].values)
else:
mask = (str(iname) == adata.obs[key].values)
groups_masks[iname] = mask
groups_ids = list(range(len(groups_order)))
if (groups_order_subset != 'all'):
groups_ids = []
for name in groups_order_subset:
groups_ids.append(np.where((adata.obs[key].cat.categories.values == name))[0][0])
if (len(groups_ids) == 0):
groups_ids = np.where(np.in1d(np.arange(len(adata.obs[key].cat.categories)).astype(str), np.array(groups_order_subset)))[0]
if (len(groups_ids) == 0):
logg.debug(f'{np.array(groups_order_subset)} invalid! specify valid groups_order (or indices) from {adata.obs[key].cat.categories}')
from sys import exit
exit(0)
groups_masks = groups_masks[groups_ids]
groups_order_subset = adata.obs[key].cat.categories[groups_ids].values
else:
groups_order_subset = groups_order.values
return (groups_order_subset, groups_masks) | -3,030,545,903,539,323,000 | Get subset of groups in adata.obs[key]. | scanpy/_utils.py | select_groups | VolkerBergen/scanpy | python | def select_groups(adata, groups_order_subset='all', key='groups'):
groups_order = adata.obs[key].cat.categories
if ((key + '_masks') in adata.uns):
groups_masks = adata.uns[(key + '_masks')]
else:
groups_masks = np.zeros((len(adata.obs[key].cat.categories), adata.obs[key].values.size), dtype=bool)
for (iname, name) in enumerate(adata.obs[key].cat.categories):
if (adata.obs[key].cat.categories[iname] in adata.obs[key].values):
mask = (adata.obs[key].cat.categories[iname] == adata.obs[key].values)
else:
mask = (str(iname) == adata.obs[key].values)
groups_masks[iname] = mask
groups_ids = list(range(len(groups_order)))
if (groups_order_subset != 'all'):
groups_ids = []
for name in groups_order_subset:
groups_ids.append(np.where((adata.obs[key].cat.categories.values == name))[0][0])
if (len(groups_ids) == 0):
groups_ids = np.where(np.in1d(np.arange(len(adata.obs[key].cat.categories)).astype(str), np.array(groups_order_subset)))[0]
if (len(groups_ids) == 0):
logg.debug(f'{np.array(groups_order_subset)} invalid! specify valid groups_order (or indices) from {adata.obs[key].cat.categories}')
from sys import exit
exit(0)
groups_masks = groups_masks[groups_ids]
groups_order_subset = adata.obs[key].cat.categories[groups_ids].values
else:
groups_order_subset = groups_order.values
return (groups_order_subset, groups_masks) |
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
'Get full tracebacks when warning is raised by setting\n\n warnings.showwarning = warn_with_traceback\n\n See also\n --------\n http://stackoverflow.com/questions/22373927/get-traceback-of-warnings\n '
import traceback
traceback.print_stack()
log = (file if hasattr(file, 'write') else sys.stderr)
settings.write(warnings.formatwarning(message, category, filename, lineno, line)) | 3,395,872,396,392,298,500 | Get full tracebacks when warning is raised by setting
warnings.showwarning = warn_with_traceback
See also
--------
http://stackoverflow.com/questions/22373927/get-traceback-of-warnings | scanpy/_utils.py | warn_with_traceback | VolkerBergen/scanpy | python | def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
'Get full tracebacks when warning is raised by setting\n\n warnings.showwarning = warn_with_traceback\n\n See also\n --------\n http://stackoverflow.com/questions/22373927/get-traceback-of-warnings\n '
import traceback
traceback.print_stack()
log = (file if hasattr(file, 'write') else sys.stderr)
settings.write(warnings.formatwarning(message, category, filename, lineno, line)) |
def subsample(X: np.ndarray, subsample: int=1, seed: int=0) -> Tuple[(np.ndarray, np.ndarray)]:
' Subsample a fraction of 1/subsample samples from the rows of X.\n\n Parameters\n ----------\n X\n Data array.\n subsample\n 1/subsample is the fraction of data sampled, n = X.shape[0]/subsample.\n seed\n Seed for sampling.\n\n Returns\n -------\n Xsampled\n Subsampled X.\n rows\n Indices of rows that are stored in Xsampled.\n '
if ((subsample == 1) and (seed == 0)):
return (X, np.arange(X.shape[0], dtype=int))
if (seed == 0):
rows = np.arange(0, X.shape[0], subsample, dtype=int)
n = rows.size
Xsampled = np.array(X[rows])
else:
if (seed < 0):
raise ValueError(f'Invalid seed value < 0: {seed}')
n = int((X.shape[0] / subsample))
np.random.seed(seed)
(Xsampled, rows) = subsample_n(X, n=n)
logg.debug(f'... subsampled to {n} of {X.shape[0]} data points')
return (Xsampled, rows) | 7,856,776,734,611,695,000 | Subsample a fraction of 1/subsample samples from the rows of X.
Parameters
----------
X
Data array.
subsample
1/subsample is the fraction of data sampled, n = X.shape[0]/subsample.
seed
Seed for sampling.
Returns
-------
Xsampled
Subsampled X.
rows
Indices of rows that are stored in Xsampled. | scanpy/_utils.py | subsample | VolkerBergen/scanpy | python | def subsample(X: np.ndarray, subsample: int=1, seed: int=0) -> Tuple[(np.ndarray, np.ndarray)]:
' Subsample a fraction of 1/subsample samples from the rows of X.\n\n Parameters\n ----------\n X\n Data array.\n subsample\n 1/subsample is the fraction of data sampled, n = X.shape[0]/subsample.\n seed\n Seed for sampling.\n\n Returns\n -------\n Xsampled\n Subsampled X.\n rows\n Indices of rows that are stored in Xsampled.\n '
if ((subsample == 1) and (seed == 0)):
return (X, np.arange(X.shape[0], dtype=int))
if (seed == 0):
rows = np.arange(0, X.shape[0], subsample, dtype=int)
n = rows.size
Xsampled = np.array(X[rows])
else:
if (seed < 0):
raise ValueError(f'Invalid seed value < 0: {seed}')
n = int((X.shape[0] / subsample))
np.random.seed(seed)
(Xsampled, rows) = subsample_n(X, n=n)
logg.debug(f'... subsampled to {n} of {X.shape[0]} data points')
return (Xsampled, rows) |
def subsample_n(X: np.ndarray, n: int=0, seed: int=0) -> Tuple[(np.ndarray, np.ndarray)]:
'Subsample n samples from rows of array.\n\n Parameters\n ----------\n X\n Data array.\n n\n Sample size.\n seed\n Seed for sampling.\n\n Returns\n -------\n Xsampled\n Subsampled X.\n rows\n Indices of rows that are stored in Xsampled.\n '
if (n < 0):
raise ValueError('n must be greater 0')
np.random.seed(seed)
n = (X.shape[0] if ((n == 0) or (n > X.shape[0])) else n)
rows = np.random.choice(X.shape[0], size=n, replace=False)
Xsampled = X[rows]
return (Xsampled, rows) | -170,607,391,929,341,630 | Subsample n samples from rows of array.
Parameters
----------
X
Data array.
n
Sample size.
seed
Seed for sampling.
Returns
-------
Xsampled
Subsampled X.
rows
Indices of rows that are stored in Xsampled. | scanpy/_utils.py | subsample_n | VolkerBergen/scanpy | python | def subsample_n(X: np.ndarray, n: int=0, seed: int=0) -> Tuple[(np.ndarray, np.ndarray)]:
'Subsample n samples from rows of array.\n\n Parameters\n ----------\n X\n Data array.\n n\n Sample size.\n seed\n Seed for sampling.\n\n Returns\n -------\n Xsampled\n Subsampled X.\n rows\n Indices of rows that are stored in Xsampled.\n '
if (n < 0):
raise ValueError('n must be greater 0')
np.random.seed(seed)
n = (X.shape[0] if ((n == 0) or (n > X.shape[0])) else n)
rows = np.random.choice(X.shape[0], size=n, replace=False)
Xsampled = X[rows]
return (Xsampled, rows) |
def check_presence_download(filename: Path, backup_url):
'Check if file is present otherwise download.'
if (not filename.is_file()):
from .readwrite import _download
_download(backup_url, filename) | 5,616,465,864,957,180,000 | Check if file is present otherwise download. | scanpy/_utils.py | check_presence_download | VolkerBergen/scanpy | python | def check_presence_download(filename: Path, backup_url):
if (not filename.is_file()):
from .readwrite import _download
_download(backup_url, filename) |
def lazy_import(full_name):
'Imports a module in a way that it’s only executed on member access'
try:
return sys.modules[full_name]
except KeyError:
spec = importlib.util.find_spec(full_name)
module = importlib.util.module_from_spec(spec)
loader = importlib.util.LazyLoader(spec.loader)
loader.exec_module(module)
return module | 5,384,361,978,549,375,000 | Imports a module in a way that it’s only executed on member access | scanpy/_utils.py | lazy_import | VolkerBergen/scanpy | python | def lazy_import(full_name):
try:
return sys.modules[full_name]
except KeyError:
spec = importlib.util.find_spec(full_name)
module = importlib.util.module_from_spec(spec)
loader = importlib.util.LazyLoader(spec.loader)
loader.exec_module(module)
return module |
def _choose_graph(adata, obsp, neighbors_key):
'Choose connectivities from neighbbors or another obsp column'
if ((obsp is not None) and (neighbors_key is not None)):
raise ValueError("You can't specify both obsp, neighbors_key. Please select only one.")
if (obsp is not None):
return adata.obsp[obsp]
else:
neighbors = NeighborsView(adata, neighbors_key)
if ('connectivities' not in neighbors):
raise ValueError('You need to run `pp.neighbors` first to compute a neighborhood graph.')
return neighbors['connectivities'] | -3,498,210,811,662,967,300 | Choose connectivities from neighbbors or another obsp column | scanpy/_utils.py | _choose_graph | VolkerBergen/scanpy | python | def _choose_graph(adata, obsp, neighbors_key):
if ((obsp is not None) and (neighbors_key is not None)):
raise ValueError("You can't specify both obsp, neighbors_key. Please select only one.")
if (obsp is not None):
return adata.obsp[obsp]
else:
neighbors = NeighborsView(adata, neighbors_key)
if ('connectivities' not in neighbors):
raise ValueError('You need to run `pp.neighbors` first to compute a neighborhood graph.')
return neighbors['connectivities'] |
def user_display_name(user):
'\n Returns the preferred display name for the given user object: the result of\n user.get_full_name() if implemented and non-empty, or user.get_username() otherwise.\n '
try:
full_name = user.get_full_name().strip()
if full_name:
return full_name
except AttributeError:
pass
try:
return user.get_username()
except AttributeError:
return '' | 1,981,022,365,203,509,000 | Returns the preferred display name for the given user object: the result of
user.get_full_name() if implemented and non-empty, or user.get_username() otherwise. | wagtail_review/text.py | user_display_name | icanbwell/wagtail-review | python | def user_display_name(user):
'\n Returns the preferred display name for the given user object: the result of\n user.get_full_name() if implemented and non-empty, or user.get_username() otherwise.\n '
try:
full_name = user.get_full_name().strip()
if full_name:
return full_name
except AttributeError:
pass
try:
return user.get_username()
except AttributeError:
return |
def testDataProtectionOfficer(self):
'Test DataProtectionOfficer'
pass | -2,687,668,233,648,560,600 | Test DataProtectionOfficer | test/test_data_protection_officer.py | testDataProtectionOfficer | My-Data-My-Consent/python-sdk | python | def testDataProtectionOfficer(self):
pass |
def select_server(server_type, config):
'Select a server type using different possible strings.\n\n Right now this just returns `OptimizationServer`, but this\n function could be useful when there are multiple choices of\n server.\n\n Args:\n server_type (str): indicates server choice.\n config (dict): config parsed from YAML, passed so that\n parameters can be used to select a given server.\n '
return OptimizationServer | 2,689,991,968,026,703,000 | Select a server type using different possible strings.
Right now this just returns `OptimizationServer`, but this
function could be useful when there are multiple choices of
server.
Args:
server_type (str): indicates server choice.
config (dict): config parsed from YAML, passed so that
parameters can be used to select a given server. | core/server.py | select_server | simra/msrflute | python | def select_server(server_type, config):
'Select a server type using different possible strings.\n\n Right now this just returns `OptimizationServer`, but this\n function could be useful when there are multiple choices of\n server.\n\n Args:\n server_type (str): indicates server choice.\n config (dict): config parsed from YAML, passed so that\n parameters can be used to select a given server.\n '
return OptimizationServer |
def __init__(self, num_clients, model, optimizer, ss_scheduler, data_path, model_path, train_dataloader, val_dataloader, test_dataloader, config, config_server):
"Implement Server's orchestration and aggregation.\n\n This is the main Server class, that actually implements orchestration\n and aggregation, inheriting from `federated.Server`, which deals with\n communication only.\n\n The `train` method is central in FLUTE, as it defines good part of what\n happens during training.\n\n Args:\n num_clients (int): total available clients.\n model (torch.nn.Module): neural network model.\n optimizer (torch.optim.Optimizer): optimizer.\n ss_scheduler: scheduled sampling scheduler.\n data_path (str): points to where data is.\n model_path (str): points to where pretrained model is.\n train_dataloader (torch.utils.data.DataLoader): dataloader for training\n val_dataloader (torch.utils.data.DataLoader): dataloader for validation\n test_dataloader (torch.utils.data.DataLoader): dataloader for test, can be None\n config (dict): JSON style configuration parameters\n config_server: deprecated, kept for API compatibility only.\n "
super().__init__()
self.client_idx_list = list(range(num_clients))
self.config = config
server_config = config['server_config']
decoder_config = config.get('decoder_config', None)
self.max_iteration = server_config['max_iteration']
self.do_clustering = server_config.get('clustering', False)
self.num_clients_per_iteration = ([int(x) for x in server_config['num_clients_per_iteration'].split(',')] if isinstance(server_config['num_clients_per_iteration'], str) else [server_config['num_clients_per_iteration']])
self.val_freq = server_config['val_freq']
self.req_freq = server_config['rec_freq']
self.evaluation = Evaluation(config, model_path, self.process_testvalidate, val_dataloader, test_dataloader)
self.metrics = {'best_val_loss': float('inf'), 'best_val_acc': 0.0, 'best_test_loss': float('inf'), 'best_test_acc': 0.0}
self.model_backup_freq = server_config.get('model_backup_freq', 100)
self.worker_trainer_config = server_config.get('trainer_config', {})
self.aggregate_median = server_config['aggregate_median']
self.initial_lr_client = server_config.get('initial_lr_client', (- 1.0))
self.lr_decay_factor = server_config.get('lr_decay_factor', 1.0)
self.model_type = config['model_config']['model_type']
self.quant_thresh = config['client_config'].get('quant_thresh', None)
self.quant_bits = config['client_config'].get('quant_bits', 10)
self.list_of_train_data = config['client_config']['data_config']['train']['list_of_train_data']
self.data_path = data_path
if ('train' in server_config['data_config']):
max_grad_norm = server_config['data_config']['train'].get('max_grad_norm', None)
else:
max_grad_norm = None
self.worker_trainer = ModelUpdater(model=model, optimizer=optimizer, ss_scheduler=ss_scheduler, train_dataloader=(train_dataloader if (train_dataloader is not None) else val_dataloader), val_dataloader=val_dataloader, max_grad_norm=max_grad_norm, anneal_config=server_config['annealing_config'], model_type=self.model_type, decoder_config=decoder_config)
self.metrics['worker_trainer'] = self.worker_trainer
self.server_replay_iterations = None
self.server_trainer = None
if (train_dataloader is not None):
assert ('server_replay_config' in server_config), 'server_replay_config is not set'
assert ('optimizer_config' in server_config['server_replay_config']), 'server-side replay training optimizer is not set'
self.server_optimizer_config = server_config['server_replay_config']['optimizer_config']
self.server_trainer_config = server_config['server_replay_config'].get('trainer_config', {})
self.server_replay_iterations = server_config['server_replay_config']['server_iterations']
self.server_trainer = Trainer(model=model, optimizer=None, ss_scheduler=ss_scheduler, train_dataloader=train_dataloader, server_replay_config=server_config['server_replay_config'], val_dataloader=None, max_grad_norm=server_config['server_replay_config'].get('max_grad_norm', server_config['data_config']['train'].get('max_grad_norm', None)), anneal_config=server_config['server_replay_config'].get('annealing_config', None))
self.skip_model_update = False
self.train_loss = 0.0
self.model_path = model_path
self.best_model_criterion = server_config['best_model_criterion']
self.fall_back_to_best_model = server_config['fall_back_to_best_model']
self.last_model_path = os.path.join(self.model_path, 'latest_model.tar')
self.best_model_path = os.path.join(self.model_path, 'best_val_{}_model.tar'.format(self.best_model_criterion))
self.log_path = os.path.join(self.model_path, 'status_log.json')
self.cur_iter_no = 0
self.lr_weight = 1.0
self.losses = []
self.no_label_updates = 0
if server_config.get('resume_from_checkpoint', False):
self.load_saved_status()
self.decoder_config = decoder_config
self.spm_model = server_config['data_config']['test'].get('spm_model', None)
self.do_profiling = server_config.get('do_profiling', False)
self.clients_in_parallel = config['client_config'].get('clients_in_parallel', None)
StrategyClass = select_strategy(config['strategy'])
self.strategy = StrategyClass('server', self.config, self.model_path)
print_rank(f'Server successfully instantiated strategy {self.strategy}', loglevel=logging.DEBUG) | -5,509,703,606,496,693,000 | Implement Server's orchestration and aggregation.
This is the main Server class, that actually implements orchestration
and aggregation, inheriting from `federated.Server`, which deals with
communication only.
The `train` method is central in FLUTE, as it defines good part of what
happens during training.
Args:
num_clients (int): total available clients.
model (torch.nn.Module): neural network model.
optimizer (torch.optim.Optimizer): optimizer.
ss_scheduler: scheduled sampling scheduler.
data_path (str): points to where data is.
model_path (str): points to where pretrained model is.
train_dataloader (torch.utils.data.DataLoader): dataloader for training
val_dataloader (torch.utils.data.DataLoader): dataloader for validation
test_dataloader (torch.utils.data.DataLoader): dataloader for test, can be None
config (dict): JSON style configuration parameters
config_server: deprecated, kept for API compatibility only. | core/server.py | __init__ | simra/msrflute | python | def __init__(self, num_clients, model, optimizer, ss_scheduler, data_path, model_path, train_dataloader, val_dataloader, test_dataloader, config, config_server):
"Implement Server's orchestration and aggregation.\n\n This is the main Server class, that actually implements orchestration\n and aggregation, inheriting from `federated.Server`, which deals with\n communication only.\n\n The `train` method is central in FLUTE, as it defines good part of what\n happens during training.\n\n Args:\n num_clients (int): total available clients.\n model (torch.nn.Module): neural network model.\n optimizer (torch.optim.Optimizer): optimizer.\n ss_scheduler: scheduled sampling scheduler.\n data_path (str): points to where data is.\n model_path (str): points to where pretrained model is.\n train_dataloader (torch.utils.data.DataLoader): dataloader for training\n val_dataloader (torch.utils.data.DataLoader): dataloader for validation\n test_dataloader (torch.utils.data.DataLoader): dataloader for test, can be None\n config (dict): JSON style configuration parameters\n config_server: deprecated, kept for API compatibility only.\n "
super().__init__()
self.client_idx_list = list(range(num_clients))
self.config = config
server_config = config['server_config']
decoder_config = config.get('decoder_config', None)
self.max_iteration = server_config['max_iteration']
self.do_clustering = server_config.get('clustering', False)
self.num_clients_per_iteration = ([int(x) for x in server_config['num_clients_per_iteration'].split(',')] if isinstance(server_config['num_clients_per_iteration'], str) else [server_config['num_clients_per_iteration']])
self.val_freq = server_config['val_freq']
self.req_freq = server_config['rec_freq']
self.evaluation = Evaluation(config, model_path, self.process_testvalidate, val_dataloader, test_dataloader)
self.metrics = {'best_val_loss': float('inf'), 'best_val_acc': 0.0, 'best_test_loss': float('inf'), 'best_test_acc': 0.0}
self.model_backup_freq = server_config.get('model_backup_freq', 100)
self.worker_trainer_config = server_config.get('trainer_config', {})
self.aggregate_median = server_config['aggregate_median']
self.initial_lr_client = server_config.get('initial_lr_client', (- 1.0))
self.lr_decay_factor = server_config.get('lr_decay_factor', 1.0)
self.model_type = config['model_config']['model_type']
self.quant_thresh = config['client_config'].get('quant_thresh', None)
self.quant_bits = config['client_config'].get('quant_bits', 10)
self.list_of_train_data = config['client_config']['data_config']['train']['list_of_train_data']
self.data_path = data_path
if ('train' in server_config['data_config']):
max_grad_norm = server_config['data_config']['train'].get('max_grad_norm', None)
else:
max_grad_norm = None
self.worker_trainer = ModelUpdater(model=model, optimizer=optimizer, ss_scheduler=ss_scheduler, train_dataloader=(train_dataloader if (train_dataloader is not None) else val_dataloader), val_dataloader=val_dataloader, max_grad_norm=max_grad_norm, anneal_config=server_config['annealing_config'], model_type=self.model_type, decoder_config=decoder_config)
self.metrics['worker_trainer'] = self.worker_trainer
self.server_replay_iterations = None
self.server_trainer = None
if (train_dataloader is not None):
assert ('server_replay_config' in server_config), 'server_replay_config is not set'
assert ('optimizer_config' in server_config['server_replay_config']), 'server-side replay training optimizer is not set'
self.server_optimizer_config = server_config['server_replay_config']['optimizer_config']
self.server_trainer_config = server_config['server_replay_config'].get('trainer_config', {})
self.server_replay_iterations = server_config['server_replay_config']['server_iterations']
self.server_trainer = Trainer(model=model, optimizer=None, ss_scheduler=ss_scheduler, train_dataloader=train_dataloader, server_replay_config=server_config['server_replay_config'], val_dataloader=None, max_grad_norm=server_config['server_replay_config'].get('max_grad_norm', server_config['data_config']['train'].get('max_grad_norm', None)), anneal_config=server_config['server_replay_config'].get('annealing_config', None))
self.skip_model_update = False
self.train_loss = 0.0
self.model_path = model_path
self.best_model_criterion = server_config['best_model_criterion']
self.fall_back_to_best_model = server_config['fall_back_to_best_model']
self.last_model_path = os.path.join(self.model_path, 'latest_model.tar')
self.best_model_path = os.path.join(self.model_path, 'best_val_{}_model.tar'.format(self.best_model_criterion))
self.log_path = os.path.join(self.model_path, 'status_log.json')
self.cur_iter_no = 0
self.lr_weight = 1.0
self.losses = []
self.no_label_updates = 0
if server_config.get('resume_from_checkpoint', False):
self.load_saved_status()
self.decoder_config = decoder_config
self.spm_model = server_config['data_config']['test'].get('spm_model', None)
self.do_profiling = server_config.get('do_profiling', False)
self.clients_in_parallel = config['client_config'].get('clients_in_parallel', None)
StrategyClass = select_strategy(config['strategy'])
self.strategy = StrategyClass('server', self.config, self.model_path)
print_rank(f'Server successfully instantiated strategy {self.strategy}', loglevel=logging.DEBUG) |
def load_saved_status(self):
'Load checkpoint from disk'
if os.path.exists(self.last_model_path):
print_rank('Resuming from checkpoint model {}'.format(self.last_model_path))
self.worker_trainer.load(self.last_model_path, update_lr_scheduler=True, update_ss_scheduler=True)
if (self.server_trainer is not None):
self.server_trainer.model = self.worker_trainer.model
if os.path.exists(self.log_path):
with open(self.log_path, 'r') as logfp:
elems = json.load(logfp)
self.cur_iter_no = elems.get('i', 0)
self.metrics['best_val_loss'] = elems.get('best_val_loss', float('inf'))
self.metrics['best_val_acc'] = elems.get('best_val_acc', 0)
self.metrics['best_test_loss'] = elems.get('best_test_loss', float('inf'))
self.metrics['best_test_acc'] = elems.get('best_test_acc', 0)
self.lr_weight = elems.get('weight', 1.0)
self.no_label_updates = elems.get('num_label_updates', 0)
print_rank(f'Resuming from status_log: cur_iter: {self.cur_iter_no}') | -5,987,352,651,717,467,000 | Load checkpoint from disk | core/server.py | load_saved_status | simra/msrflute | python | def load_saved_status(self):
if os.path.exists(self.last_model_path):
print_rank('Resuming from checkpoint model {}'.format(self.last_model_path))
self.worker_trainer.load(self.last_model_path, update_lr_scheduler=True, update_ss_scheduler=True)
if (self.server_trainer is not None):
self.server_trainer.model = self.worker_trainer.model
if os.path.exists(self.log_path):
with open(self.log_path, 'r') as logfp:
elems = json.load(logfp)
self.cur_iter_no = elems.get('i', 0)
self.metrics['best_val_loss'] = elems.get('best_val_loss', float('inf'))
self.metrics['best_val_acc'] = elems.get('best_val_acc', 0)
self.metrics['best_test_loss'] = elems.get('best_test_loss', float('inf'))
self.metrics['best_test_acc'] = elems.get('best_test_acc', 0)
self.lr_weight = elems.get('weight', 1.0)
self.no_label_updates = elems.get('num_label_updates', 0)
print_rank(f'Resuming from status_log: cur_iter: {self.cur_iter_no}') |
def run(self):
'Trigger training.\n\n This is a simple wrapper to the `train` method.\n '
print_rank('server started')
self.train()
print_rank('server terminated') | 5,204,790,440,284,381,000 | Trigger training.
This is a simple wrapper to the `train` method. | core/server.py | run | simra/msrflute | python | def run(self):
'Trigger training.\n\n This is a simple wrapper to the `train` method.\n '
print_rank('server started')
self.train()
print_rank('server terminated') |
def train(self):
'Main method for training.'
self.run_stats = {'secsPerClientRound': [], 'secsPerClient': [], 'secsPerClientTraining': [], 'secsPerClientSetup': [], 'secsPerClientFull': [], 'secsPerRoundHousekeeping': [], 'secsPerRoundTotal': [], 'mpiCosts': []}
run.log('Max iterations', self.max_iteration)
try:
(self.worker_trainer.model.cuda() if torch.cuda.is_available() else None)
eval_list = []
if (self.cur_iter_no == 0):
if self.config['server_config']['initial_rec']:
eval_list.append('test')
if self.config['server_config']['initial_val']:
eval_list.append('val')
run.log('LR for agg. opt.', get_lr(self.worker_trainer.optimizer))
print_rank('Running {} at itr={}'.format(eval_list, self.cur_iter_no))
self.metrics = self.evaluation.run(eval_list, self.metrics, metric_logger=run.log)
eval_list = []
print_rank('Saving Model Before Starting Training', loglevel=logging.INFO)
for token in ['best_val_loss', 'best_val_acc', 'best_test_acc', 'latest']:
self.worker_trainer.save(model_path=self.model_path, token=token, config=self.config['server_config'])
self.worker_trainer.model.train()
for i in range(self.cur_iter_no, self.max_iteration):
begin = time.time()
metrics_payload = {}
def log_metric(k, v):
metrics_payload[k] = v
print_rank('==== iteration {}'.format(i))
log_metric('Current iteration', i)
initial_lr = (self.initial_lr_client * self.lr_weight)
print_rank('Client learning rate {}'.format(initial_lr))
self.worker_trainer.model.zero_grad()
self.train_loss = []
server_data = (initial_lr, [p.data.to(torch.device('cpu')) for p in self.worker_trainer.model.parameters()])
if (len(self.num_clients_per_iteration) > 1):
num_clients_curr_iter = random.randint(self.num_clients_per_iteration[0], self.num_clients_per_iteration[1])
else:
num_clients_curr_iter = self.num_clients_per_iteration[0]
log_metric('Clients for round', num_clients_curr_iter)
if (self.quant_thresh is not None):
self.config['client_config']['quant_thresh'] *= self.config['client_config'].get('quant_anneal', 1.0)
self.quant_thresh = self.config['client_config']['quant_thresh']
log_metric('Quantization Thresh.', self.config['client_config']['quant_thresh'])
sampled_idx_clients = (random.sample(self.client_idx_list, num_clients_curr_iter) if (num_clients_curr_iter > 0) else self.client_idx_list)
sampled_clients = [Client(client_id, self.config, (self.config['client_config']['type'] == 'optimization'), None) for client_id in sampled_idx_clients]
clients_begin = time.time()
client_losses = []
client_mag_grads = []
client_mean_grads = []
client_var_grads = []
client_norm_grads = []
self.run_stats['secsPerClient'].append([])
self.run_stats['secsPerClientFull'].append([])
self.run_stats['secsPerClientTraining'].append([])
self.run_stats['secsPerClientSetup'].append([])
self.run_stats['mpiCosts'].append([])
apply_privacy_metrics = (self.config.get('privacy_metrics_config', None) and self.config['privacy_metrics_config']['apply_metrics'])
adaptive_leakage = (apply_privacy_metrics and self.config['privacy_metrics_config'].get('adaptive_leakage_threshold', None))
if apply_privacy_metrics:
privacy_metrics_stats = defaultdict(list)
profiler = None
if self.do_profiling:
profiler = cProfile.Profile()
profiler.enable()
self.worker_trainer.model.zero_grad()
for client_output in self.process_clients(sampled_clients, server_data, self.clients_in_parallel):
client_timestamp = client_output['ts']
client_stats = client_output['cs']
client_loss = client_output['tl']
client_mag_grad = client_output['mg']
client_mean_grad = client_output['ng']
client_var_grad = client_output['vg']
client_norm_grad = client_output['rg']
client_payload = client_output['pl']
if apply_privacy_metrics:
privacy_stats = client_output['ps']
for (metric, value) in privacy_stats.items():
privacy_metrics_stats[metric].append(value)
self.run_stats['mpiCosts'][(- 1)].append((time.time() - client_timestamp))
payload_processed = self.strategy.process_individual_payload(self.worker_trainer, client_payload)
if (not payload_processed):
print_rank('Dropping client', loglevel=logging.DEBUG)
num_clients_curr_iter -= 1
continue
self.train_loss.append(client_loss)
client_losses.append(client_loss)
client_mag_grads.append(client_mag_grad.item())
client_mean_grads.append(client_mean_grad.item())
client_var_grads.append(client_var_grad.item())
client_norm_grads.append(client_norm_grad.item())
client_end = time.time()
self.run_stats['secsPerClientFull'][(- 1)].append(client_stats['full cost'])
self.run_stats['secsPerClientTraining'][(- 1)].append(client_stats['training'])
self.run_stats['secsPerClientSetup'][(- 1)].append(client_stats['setup'])
self.run_stats['secsPerClient'][(- 1)].append((client_end - clients_begin))
if self.do_profiling:
profiler.disable()
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative').print_stats()
client_mag_grads = np.array(client_mag_grads)
client_mean_grads = np.array(client_mean_grads)
client_var_grads = np.array(client_var_grads)
client_norm_grads = np.array(client_norm_grads)
client_stats = (client_mag_grads, client_mean_grads, client_var_grads)
dump_norm_stats = self.config.get('dump_norm_stats', False)
if dump_norm_stats:
with open(os.path.join(self.model_path, 'norm_stats.txt'), 'a', encoding='utf-8') as outF:
outF.write('{}\n'.format(json.dumps(list(client_norm_grads))))
if apply_privacy_metrics:
for (metric, values) in privacy_metrics_stats.items():
if (metric == 'Dropped clients'):
log_metric(metric, sum(values))
else:
log_metric(metric, max(values))
if (type(adaptive_leakage) is float):
values = privacy_metrics_stats['Practical epsilon (Max leakage)']
new_threshold = list(sorted(values))[int((adaptive_leakage * len(values)))]
print_rank('Updating leakage threshold to {}'.format(new_threshold))
self.config['privacy_metrics_config']['max_allowed_leakage'] = new_threshold
end = time.time()
self.run_stats['secsPerClientRound'].append((end - begin))
begin = end
log_metric('Training loss', sum(self.train_loss))
self.losses = self.strategy.combine_payloads(worker_trainer=self.worker_trainer, curr_iter=i, num_clients_curr_iter=num_clients_curr_iter, client_stats=client_stats, logger=log_metric)
if (self.server_trainer is not None):
print_rank('Running replay iterations on server')
if ('updatable_names' in self.server_trainer_config):
set_component_wise_lr(self.worker_trainer.model, self.server_optimizer_config, self.server_trainer_config['updatable_names'])
self.server_trainer.prepare_iteration(self.worker_trainer.model)
self.server_trainer.train_desired_samples(self.server_replay_iterations)
self.worker_trainer.model.load_state_dict(self.server_trainer.model.state_dict())
torch.cuda.empty_cache()
print_rank('Run ss scheduler')
self.worker_trainer.run_ss_scheduler()
if (((i + 1) % self.val_freq) == 0):
eval_list.append('val')
if (((i + 1) % self.req_freq) == 0):
eval_list.append('test')
if (len(eval_list) > 0):
print_rank('Running {} at itr={}'.format(eval_list, (i + 1)))
self.metrics['worker_trainer'] = self.worker_trainer
self.metrics = self.evaluation.run(eval_list, self.metrics, metric_logger=run.log)
self.losses = self.evaluation.losses
eval_list = []
if ('val' in eval_list):
run.log('LR for agg. opt.', get_lr(self.worker_trainer.optimizer))
if (not (self.losses[0] < self.metrics['best_val_loss'])):
self.lr_weight *= self.lr_decay_factor
print_rank('LOG: Client weight of learning rate {}..'.format(self.lr_weight))
self.backup_models(i)
self.fall_back_to_prev_best_status()
update_json_log(self.log_path, {'i': (i + 1), 'best_val_loss': float(self.metrics['best_val_loss']), 'best_val_acc': float(self.metrics['best_val_acc']), 'best_test_loss': float(self.metrics['best_test_loss']), 'best_test_acc': float(self.metrics['best_test_acc']), 'weight': float(self.lr_weight), 'num_label_updates': int(self.no_label_updates)})
end = time.time()
self.run_stats['secsPerRoundHousekeeping'].append((end - begin))
self.run_stats['secsPerRoundTotal'].append((self.run_stats['secsPerClientRound'][(- 1)] + self.run_stats['secsPerRoundHousekeeping'][(- 1)]))
log_metric('secsPerRoundTotal', self.run_stats['secsPerRoundTotal'][(- 1)])
if self.do_profiling:
log_metric('secsPerClientRound', self.run_stats['secsPerClientRound'][(- 1)])
log_metric('secsPerRoundHousekeeping', self.run_stats['secsPerRoundHousekeeping'][(- 1)])
metrics_for_stats = ['secsPerClient', 'secsPerClientTraining', 'secsPerClientFull', 'secsPerClientSetup', 'mpiCosts']
for metric in metrics_for_stats:
log_metric(f'{metric}Mean', np.mean(self.run_stats[metric][(- 1)]))
log_metric(f'{metric}Median', np.median(self.run_stats[metric][(- 1)]))
log_metric(f'{metric}Max', max(self.run_stats[metric][(- 1)]))
for k in self.run_stats:
if (k in metrics_for_stats):
print_rank('{}: {}'.format(k, max(self.run_stats[k][(- 1)])), loglevel=logging.DEBUG)
else:
print_rank('{}: {}'.format(k, self.run_stats[k][(- 1)]), loglevel=logging.DEBUG)
for k in metrics_payload:
run.log(k, metrics_payload[k])
finally:
self.terminate_workers(terminate=(not self.do_clustering)) | 1,279,950,367,566,187,500 | Main method for training. | core/server.py | train | simra/msrflute | python | def train(self):
self.run_stats = {'secsPerClientRound': [], 'secsPerClient': [], 'secsPerClientTraining': [], 'secsPerClientSetup': [], 'secsPerClientFull': [], 'secsPerRoundHousekeeping': [], 'secsPerRoundTotal': [], 'mpiCosts': []}
run.log('Max iterations', self.max_iteration)
try:
(self.worker_trainer.model.cuda() if torch.cuda.is_available() else None)
eval_list = []
if (self.cur_iter_no == 0):
if self.config['server_config']['initial_rec']:
eval_list.append('test')
if self.config['server_config']['initial_val']:
eval_list.append('val')
run.log('LR for agg. opt.', get_lr(self.worker_trainer.optimizer))
print_rank('Running {} at itr={}'.format(eval_list, self.cur_iter_no))
self.metrics = self.evaluation.run(eval_list, self.metrics, metric_logger=run.log)
eval_list = []
print_rank('Saving Model Before Starting Training', loglevel=logging.INFO)
for token in ['best_val_loss', 'best_val_acc', 'best_test_acc', 'latest']:
self.worker_trainer.save(model_path=self.model_path, token=token, config=self.config['server_config'])
self.worker_trainer.model.train()
for i in range(self.cur_iter_no, self.max_iteration):
begin = time.time()
metrics_payload = {}
def log_metric(k, v):
metrics_payload[k] = v
print_rank('==== iteration {}'.format(i))
log_metric('Current iteration', i)
initial_lr = (self.initial_lr_client * self.lr_weight)
print_rank('Client learning rate {}'.format(initial_lr))
self.worker_trainer.model.zero_grad()
self.train_loss = []
server_data = (initial_lr, [p.data.to(torch.device('cpu')) for p in self.worker_trainer.model.parameters()])
if (len(self.num_clients_per_iteration) > 1):
num_clients_curr_iter = random.randint(self.num_clients_per_iteration[0], self.num_clients_per_iteration[1])
else:
num_clients_curr_iter = self.num_clients_per_iteration[0]
log_metric('Clients for round', num_clients_curr_iter)
if (self.quant_thresh is not None):
self.config['client_config']['quant_thresh'] *= self.config['client_config'].get('quant_anneal', 1.0)
self.quant_thresh = self.config['client_config']['quant_thresh']
log_metric('Quantization Thresh.', self.config['client_config']['quant_thresh'])
sampled_idx_clients = (random.sample(self.client_idx_list, num_clients_curr_iter) if (num_clients_curr_iter > 0) else self.client_idx_list)
sampled_clients = [Client(client_id, self.config, (self.config['client_config']['type'] == 'optimization'), None) for client_id in sampled_idx_clients]
clients_begin = time.time()
client_losses = []
client_mag_grads = []
client_mean_grads = []
client_var_grads = []
client_norm_grads = []
self.run_stats['secsPerClient'].append([])
self.run_stats['secsPerClientFull'].append([])
self.run_stats['secsPerClientTraining'].append([])
self.run_stats['secsPerClientSetup'].append([])
self.run_stats['mpiCosts'].append([])
apply_privacy_metrics = (self.config.get('privacy_metrics_config', None) and self.config['privacy_metrics_config']['apply_metrics'])
adaptive_leakage = (apply_privacy_metrics and self.config['privacy_metrics_config'].get('adaptive_leakage_threshold', None))
if apply_privacy_metrics:
privacy_metrics_stats = defaultdict(list)
profiler = None
if self.do_profiling:
profiler = cProfile.Profile()
profiler.enable()
self.worker_trainer.model.zero_grad()
for client_output in self.process_clients(sampled_clients, server_data, self.clients_in_parallel):
client_timestamp = client_output['ts']
client_stats = client_output['cs']
client_loss = client_output['tl']
client_mag_grad = client_output['mg']
client_mean_grad = client_output['ng']
client_var_grad = client_output['vg']
client_norm_grad = client_output['rg']
client_payload = client_output['pl']
if apply_privacy_metrics:
privacy_stats = client_output['ps']
for (metric, value) in privacy_stats.items():
privacy_metrics_stats[metric].append(value)
self.run_stats['mpiCosts'][(- 1)].append((time.time() - client_timestamp))
payload_processed = self.strategy.process_individual_payload(self.worker_trainer, client_payload)
if (not payload_processed):
print_rank('Dropping client', loglevel=logging.DEBUG)
num_clients_curr_iter -= 1
continue
self.train_loss.append(client_loss)
client_losses.append(client_loss)
client_mag_grads.append(client_mag_grad.item())
client_mean_grads.append(client_mean_grad.item())
client_var_grads.append(client_var_grad.item())
client_norm_grads.append(client_norm_grad.item())
client_end = time.time()
self.run_stats['secsPerClientFull'][(- 1)].append(client_stats['full cost'])
self.run_stats['secsPerClientTraining'][(- 1)].append(client_stats['training'])
self.run_stats['secsPerClientSetup'][(- 1)].append(client_stats['setup'])
self.run_stats['secsPerClient'][(- 1)].append((client_end - clients_begin))
if self.do_profiling:
profiler.disable()
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative').print_stats()
client_mag_grads = np.array(client_mag_grads)
client_mean_grads = np.array(client_mean_grads)
client_var_grads = np.array(client_var_grads)
client_norm_grads = np.array(client_norm_grads)
client_stats = (client_mag_grads, client_mean_grads, client_var_grads)
dump_norm_stats = self.config.get('dump_norm_stats', False)
if dump_norm_stats:
with open(os.path.join(self.model_path, 'norm_stats.txt'), 'a', encoding='utf-8') as outF:
outF.write('{}\n'.format(json.dumps(list(client_norm_grads))))
if apply_privacy_metrics:
for (metric, values) in privacy_metrics_stats.items():
if (metric == 'Dropped clients'):
log_metric(metric, sum(values))
else:
log_metric(metric, max(values))
if (type(adaptive_leakage) is float):
values = privacy_metrics_stats['Practical epsilon (Max leakage)']
new_threshold = list(sorted(values))[int((adaptive_leakage * len(values)))]
print_rank('Updating leakage threshold to {}'.format(new_threshold))
self.config['privacy_metrics_config']['max_allowed_leakage'] = new_threshold
end = time.time()
self.run_stats['secsPerClientRound'].append((end - begin))
begin = end
log_metric('Training loss', sum(self.train_loss))
self.losses = self.strategy.combine_payloads(worker_trainer=self.worker_trainer, curr_iter=i, num_clients_curr_iter=num_clients_curr_iter, client_stats=client_stats, logger=log_metric)
if (self.server_trainer is not None):
print_rank('Running replay iterations on server')
if ('updatable_names' in self.server_trainer_config):
set_component_wise_lr(self.worker_trainer.model, self.server_optimizer_config, self.server_trainer_config['updatable_names'])
self.server_trainer.prepare_iteration(self.worker_trainer.model)
self.server_trainer.train_desired_samples(self.server_replay_iterations)
self.worker_trainer.model.load_state_dict(self.server_trainer.model.state_dict())
torch.cuda.empty_cache()
print_rank('Run ss scheduler')
self.worker_trainer.run_ss_scheduler()
if (((i + 1) % self.val_freq) == 0):
eval_list.append('val')
if (((i + 1) % self.req_freq) == 0):
eval_list.append('test')
if (len(eval_list) > 0):
print_rank('Running {} at itr={}'.format(eval_list, (i + 1)))
self.metrics['worker_trainer'] = self.worker_trainer
self.metrics = self.evaluation.run(eval_list, self.metrics, metric_logger=run.log)
self.losses = self.evaluation.losses
eval_list = []
if ('val' in eval_list):
run.log('LR for agg. opt.', get_lr(self.worker_trainer.optimizer))
if (not (self.losses[0] < self.metrics['best_val_loss'])):
self.lr_weight *= self.lr_decay_factor
print_rank('LOG: Client weight of learning rate {}..'.format(self.lr_weight))
self.backup_models(i)
self.fall_back_to_prev_best_status()
update_json_log(self.log_path, {'i': (i + 1), 'best_val_loss': float(self.metrics['best_val_loss']), 'best_val_acc': float(self.metrics['best_val_acc']), 'best_test_loss': float(self.metrics['best_test_loss']), 'best_test_acc': float(self.metrics['best_test_acc']), 'weight': float(self.lr_weight), 'num_label_updates': int(self.no_label_updates)})
end = time.time()
self.run_stats['secsPerRoundHousekeeping'].append((end - begin))
self.run_stats['secsPerRoundTotal'].append((self.run_stats['secsPerClientRound'][(- 1)] + self.run_stats['secsPerRoundHousekeeping'][(- 1)]))
log_metric('secsPerRoundTotal', self.run_stats['secsPerRoundTotal'][(- 1)])
if self.do_profiling:
log_metric('secsPerClientRound', self.run_stats['secsPerClientRound'][(- 1)])
log_metric('secsPerRoundHousekeeping', self.run_stats['secsPerRoundHousekeeping'][(- 1)])
metrics_for_stats = ['secsPerClient', 'secsPerClientTraining', 'secsPerClientFull', 'secsPerClientSetup', 'mpiCosts']
for metric in metrics_for_stats:
log_metric(f'{metric}Mean', np.mean(self.run_stats[metric][(- 1)]))
log_metric(f'{metric}Median', np.median(self.run_stats[metric][(- 1)]))
log_metric(f'{metric}Max', max(self.run_stats[metric][(- 1)]))
for k in self.run_stats:
if (k in metrics_for_stats):
print_rank('{}: {}'.format(k, max(self.run_stats[k][(- 1)])), loglevel=logging.DEBUG)
else:
print_rank('{}: {}'.format(k, self.run_stats[k][(- 1)]), loglevel=logging.DEBUG)
for k in metrics_payload:
run.log(k, metrics_payload[k])
finally:
self.terminate_workers(terminate=(not self.do_clustering)) |
def backup_models(self, i):
'Save the current best models.\n\n Save CER model, the best loss model and the best WER model. This occurs\n at a specified period.\n\n Args:\n i: no. of iterations.\n '
self.worker_trainer.save(model_path=self.model_path, token='latest', config=self.config['server_config'])
if ((i % self.model_backup_freq) == 0):
self.worker_trainer.save(model_path=self.model_path, token='epoch{}'.format(i), config=self.config['server_config'])
for bodyname in ['best_val_acc', 'best_val_loss', 'best_test_acc']:
src_model_path = os.path.join(self.model_path, '{}_model.tar'.format(bodyname))
if os.path.exists(src_model_path):
dst_model_path = os.path.join(self.model_path, 'epoch{}_{}_model.tar'.format(i, bodyname))
shutil.copyfile(src_model_path, dst_model_path)
print_rank('Saved {}'.format(dst_model_path)) | 363,412,540,844,676,350 | Save the current best models.
Save CER model, the best loss model and the best WER model. This occurs
at a specified period.
Args:
i: no. of iterations. | core/server.py | backup_models | simra/msrflute | python | def backup_models(self, i):
'Save the current best models.\n\n Save CER model, the best loss model and the best WER model. This occurs\n at a specified period.\n\n Args:\n i: no. of iterations.\n '
self.worker_trainer.save(model_path=self.model_path, token='latest', config=self.config['server_config'])
if ((i % self.model_backup_freq) == 0):
self.worker_trainer.save(model_path=self.model_path, token='epoch{}'.format(i), config=self.config['server_config'])
for bodyname in ['best_val_acc', 'best_val_loss', 'best_test_acc']:
src_model_path = os.path.join(self.model_path, '{}_model.tar'.format(bodyname))
if os.path.exists(src_model_path):
dst_model_path = os.path.join(self.model_path, 'epoch{}_{}_model.tar'.format(i, bodyname))
shutil.copyfile(src_model_path, dst_model_path)
print_rank('Saved {}'.format(dst_model_path)) |
def fall_back_to_prev_best_status(self):
'Go back to the past best status and switch to the recent best model.'
if self.fall_back_to_best_model:
print_rank('falling back to model {}'.format(self.best_model_path))
tmp_lr = get_lr(self.worker_trainer.optimizer)
self.worker_trainer.load(self.best_model_path, update_lr_scheduler=False, update_ss_scheduler=False)
for g in self.worker_trainer.optimizer.param_groups:
g['lr'] = tmp_lr
if (self.server_trainer is not None):
self.server_trainer.model = self.worker_trainer.model | -3,130,464,585,995,101,700 | Go back to the past best status and switch to the recent best model. | core/server.py | fall_back_to_prev_best_status | simra/msrflute | python | def fall_back_to_prev_best_status(self):
if self.fall_back_to_best_model:
print_rank('falling back to model {}'.format(self.best_model_path))
tmp_lr = get_lr(self.worker_trainer.optimizer)
self.worker_trainer.load(self.best_model_path, update_lr_scheduler=False, update_ss_scheduler=False)
for g in self.worker_trainer.optimizer.param_groups:
g['lr'] = tmp_lr
if (self.server_trainer is not None):
self.server_trainer.model = self.worker_trainer.model |
def get_http_authentication(private_key: RsaKey, private_key_id: str) -> HTTPSignatureHeaderAuth:
'\n Get HTTP signature authentication for a request.\n '
key = private_key.exportKey()
return HTTPSignatureHeaderAuth(headers=['(request-target)', 'user-agent', 'host', 'date'], algorithm='rsa-sha256', key=key, key_id=private_key_id) | 6,171,740,104,618,513,000 | Get HTTP signature authentication for a request. | federation/protocols/activitypub/signing.py | get_http_authentication | jaywink/federation | python | def get_http_authentication(private_key: RsaKey, private_key_id: str) -> HTTPSignatureHeaderAuth:
'\n \n '
key = private_key.exportKey()
return HTTPSignatureHeaderAuth(headers=['(request-target)', 'user-agent', 'host', 'date'], algorithm='rsa-sha256', key=key, key_id=private_key_id) |
def verify_request_signature(request: RequestType, public_key: Union[(str, bytes)]):
'\n Verify HTTP signature in request against a public key.\n '
key = encode_if_text(public_key)
date_header = request.headers.get('Date')
if (not date_header):
raise ValueError('Rquest Date header is missing')
ts = parse_http_date(date_header)
dt = datetime.datetime.utcfromtimestamp(ts).replace(tzinfo=pytz.utc)
past_delta = datetime.timedelta(hours=24)
future_delta = datetime.timedelta(seconds=30)
now = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
if ((dt < (now - past_delta)) or (dt > (now + future_delta))):
raise ValueError('Request Date is too far in future or past')
HTTPSignatureHeaderAuth.verify(request, key_resolver=(lambda **kwargs: key)) | 3,867,310,073,539,097,000 | Verify HTTP signature in request against a public key. | federation/protocols/activitypub/signing.py | verify_request_signature | jaywink/federation | python | def verify_request_signature(request: RequestType, public_key: Union[(str, bytes)]):
'\n \n '
key = encode_if_text(public_key)
date_header = request.headers.get('Date')
if (not date_header):
raise ValueError('Rquest Date header is missing')
ts = parse_http_date(date_header)
dt = datetime.datetime.utcfromtimestamp(ts).replace(tzinfo=pytz.utc)
past_delta = datetime.timedelta(hours=24)
future_delta = datetime.timedelta(seconds=30)
now = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
if ((dt < (now - past_delta)) or (dt > (now + future_delta))):
raise ValueError('Request Date is too far in future or past')
HTTPSignatureHeaderAuth.verify(request, key_resolver=(lambda **kwargs: key)) |
def __init__(self, selection):
'\n Create a new :py:class:`Selection` from the given ``selection`` string.\n '
ptr = self.ffi.chfl_selection(selection.encode('utf8'))
super(Selection, self).__init__(ptr, is_const=False) | 8,582,937,328,836,864,000 | Create a new :py:class:`Selection` from the given ``selection`` string. | chemfiles/selection.py | __init__ | Luthaf/Chemharp-python | python | def __init__(self, selection):
'\n \n '
ptr = self.ffi.chfl_selection(selection.encode('utf8'))
super(Selection, self).__init__(ptr, is_const=False) |
@property
def size(self):
"\n Get the size of this :py:class:`Selection`.\n\n The size of a selection is the number of atoms we are selecting\n together. This value is 1 for the 'atom' context, 2 for the 'pair' and\n 'bond' context, 3 for the 'three' and 'angles' contextes and 4 for the\n 'four' and 'dihedral' contextes.\n "
size = c_uint64()
self.ffi.chfl_selection_size(self.ptr, size)
return size.value | -4,548,979,829,295,121,000 | Get the size of this :py:class:`Selection`.
The size of a selection is the number of atoms we are selecting
together. This value is 1 for the 'atom' context, 2 for the 'pair' and
'bond' context, 3 for the 'three' and 'angles' contextes and 4 for the
'four' and 'dihedral' contextes. | chemfiles/selection.py | size | Luthaf/Chemharp-python | python | @property
def size(self):
"\n Get the size of this :py:class:`Selection`.\n\n The size of a selection is the number of atoms we are selecting\n together. This value is 1 for the 'atom' context, 2 for the 'pair' and\n 'bond' context, 3 for the 'three' and 'angles' contextes and 4 for the\n 'four' and 'dihedral' contextes.\n "
size = c_uint64()
self.ffi.chfl_selection_size(self.ptr, size)
return size.value |
@property
def string(self):
'\n Get the selection string used to create this :py:class:`Selection`.\n '
return _call_with_growing_buffer((lambda buffer, size: self.ffi.chfl_selection_string(self.ptr, buffer, size)), initial=128) | -8,501,303,544,896,859,000 | Get the selection string used to create this :py:class:`Selection`. | chemfiles/selection.py | string | Luthaf/Chemharp-python | python | @property
def string(self):
'\n \n '
return _call_with_growing_buffer((lambda buffer, size: self.ffi.chfl_selection_string(self.ptr, buffer, size)), initial=128) |
def evaluate(self, frame):
'\n Evaluate a :py:class:`Selection` for a given :py:class:`Frame`, and\n return a list of matching atoms, either as a list of index or a list\n of tuples of indexes.\n '
matching = c_uint64()
self.ffi.chfl_selection_evaluate(self.mut_ptr, frame.ptr, matching)
matches = np.zeros(matching.value, chfl_match)
self.ffi.chfl_selection_matches(self.mut_ptr, matches, matching)
size = self.size
result = []
for match in matches:
assert (match[0] == size)
atoms = match[1]
if (size == 1):
result.append(atoms[0])
elif (size == 2):
result.append((atoms[0], atoms[1]))
elif (size == 3):
result.append((atoms[0], atoms[1], atoms[2]))
elif (size == 4):
result.append((atoms[0], atoms[1], atoms[2], atoms[3]))
return result | 2,673,721,999,087,359,000 | Evaluate a :py:class:`Selection` for a given :py:class:`Frame`, and
return a list of matching atoms, either as a list of index or a list
of tuples of indexes. | chemfiles/selection.py | evaluate | Luthaf/Chemharp-python | python | def evaluate(self, frame):
'\n Evaluate a :py:class:`Selection` for a given :py:class:`Frame`, and\n return a list of matching atoms, either as a list of index or a list\n of tuples of indexes.\n '
matching = c_uint64()
self.ffi.chfl_selection_evaluate(self.mut_ptr, frame.ptr, matching)
matches = np.zeros(matching.value, chfl_match)
self.ffi.chfl_selection_matches(self.mut_ptr, matches, matching)
size = self.size
result = []
for match in matches:
assert (match[0] == size)
atoms = match[1]
if (size == 1):
result.append(atoms[0])
elif (size == 2):
result.append((atoms[0], atoms[1]))
elif (size == 3):
result.append((atoms[0], atoms[1], atoms[2]))
elif (size == 4):
result.append((atoms[0], atoms[1], atoms[2], atoms[3]))
return result |
def _find_neighbor_and_lambda(neighbor_indices, neighbor_distances, core_distances, min_samples):
'\n Find the nearest mutual reachability neighbor of a point, and compute\n the associated lambda value for the point, given the mutual reachability\n distance to a nearest neighbor.\n\n Parameters\n ----------\n neighbor_indices : array (2 * min_samples, )\n An array of raw distance based nearest neighbor indices.\n\n neighbor_distances : array (2 * min_samples, )\n An array of raw distances to the nearest neighbors.\n\n core_distances : array (n_samples, )\n An array of core distances for all points\n\n min_samples : int\n The min_samples value used to generate core distances.\n\n Returns\n -------\n neighbor : int\n The index into the full raw data set of the nearest mutual reachability\n distance neighbor of the point.\n\n lambda_ : float\n The lambda value at which this point joins/merges with `neighbor`.\n '
neighbor_core_distances = core_distances[neighbor_indices]
point_core_distances = (neighbor_distances[min_samples] * np.ones(neighbor_indices.shape[0]))
mr_distances = np.vstack((neighbor_core_distances, point_core_distances, neighbor_distances)).max(axis=0)
nn_index = mr_distances.argmin()
nearest_neighbor = neighbor_indices[nn_index]
if (mr_distances[nn_index] > 0.0):
lambda_ = (1.0 / mr_distances[nn_index])
else:
lambda_ = np.finfo(np.double).max
return (nearest_neighbor, lambda_) | -6,260,141,226,484,943,000 | Find the nearest mutual reachability neighbor of a point, and compute
the associated lambda value for the point, given the mutual reachability
distance to a nearest neighbor.
Parameters
----------
neighbor_indices : array (2 * min_samples, )
An array of raw distance based nearest neighbor indices.
neighbor_distances : array (2 * min_samples, )
An array of raw distances to the nearest neighbors.
core_distances : array (n_samples, )
An array of core distances for all points
min_samples : int
The min_samples value used to generate core distances.
Returns
-------
neighbor : int
The index into the full raw data set of the nearest mutual reachability
distance neighbor of the point.
lambda_ : float
The lambda value at which this point joins/merges with `neighbor`. | hdbscan/prediction.py | _find_neighbor_and_lambda | CKrawczyk/hdbscan | python | def _find_neighbor_and_lambda(neighbor_indices, neighbor_distances, core_distances, min_samples):
'\n Find the nearest mutual reachability neighbor of a point, and compute\n the associated lambda value for the point, given the mutual reachability\n distance to a nearest neighbor.\n\n Parameters\n ----------\n neighbor_indices : array (2 * min_samples, )\n An array of raw distance based nearest neighbor indices.\n\n neighbor_distances : array (2 * min_samples, )\n An array of raw distances to the nearest neighbors.\n\n core_distances : array (n_samples, )\n An array of core distances for all points\n\n min_samples : int\n The min_samples value used to generate core distances.\n\n Returns\n -------\n neighbor : int\n The index into the full raw data set of the nearest mutual reachability\n distance neighbor of the point.\n\n lambda_ : float\n The lambda value at which this point joins/merges with `neighbor`.\n '
neighbor_core_distances = core_distances[neighbor_indices]
point_core_distances = (neighbor_distances[min_samples] * np.ones(neighbor_indices.shape[0]))
mr_distances = np.vstack((neighbor_core_distances, point_core_distances, neighbor_distances)).max(axis=0)
nn_index = mr_distances.argmin()
nearest_neighbor = neighbor_indices[nn_index]
if (mr_distances[nn_index] > 0.0):
lambda_ = (1.0 / mr_distances[nn_index])
else:
lambda_ = np.finfo(np.double).max
return (nearest_neighbor, lambda_) |
def _extend_condensed_tree(tree, neighbor_indices, neighbor_distances, core_distances, min_samples):
'\n Create a new condensed tree with an additional point added, allowing for\n computations as if this point had been part of the original tree. Note\n that this makes as little change to the tree as possible, with no\n re-optimizing/re-condensing so that the selected clusters remain\n effectively unchanged.\n\n Parameters\n ----------\n tree : structured array\n The raw format condensed tree to update.\n\n neighbor_indices : array (2 * min_samples, )\n An array of raw distance based nearest neighbor indices.\n\n neighbor_distances : array (2 * min_samples, )\n An array of raw distances to the nearest neighbors.\n\n core_distances : array (n_samples, )\n An array of core distances for all points\n\n min_samples : int\n The min_samples value used to generate core distances.\n\n Returns\n -------\n new_tree : structured array\n The original tree with an extra row providing the parent cluster\n and lambda information for a new point given index -1.\n '
tree_root = tree['parent'].min()
(nearest_neighbor, lambda_) = _find_neighbor_and_lambda(neighbor_indices, neighbor_distances, core_distances, min_samples)
neighbor_tree_row = get_tree_row_with_child(tree, nearest_neighbor)
potential_cluster = neighbor_tree_row['parent']
if (neighbor_tree_row['lambda_val'] <= lambda_):
new_tree_row = (potential_cluster, (- 1), 1, neighbor_tree_row['lambda_val'])
else:
while ((potential_cluster > tree_root) and (tree[(tree['child'] == potential_cluster)]['lambda_val'] >= lambda_)):
potential_cluster = tree['parent'][(tree['child'] == potential_cluster)][0]
new_tree_row = (potential_cluster, (- 1), 1, lambda_)
return np.append(tree, new_tree_row) | 2,089,607,605,473,284,400 | Create a new condensed tree with an additional point added, allowing for
computations as if this point had been part of the original tree. Note
that this makes as little change to the tree as possible, with no
re-optimizing/re-condensing so that the selected clusters remain
effectively unchanged.
Parameters
----------
tree : structured array
The raw format condensed tree to update.
neighbor_indices : array (2 * min_samples, )
An array of raw distance based nearest neighbor indices.
neighbor_distances : array (2 * min_samples, )
An array of raw distances to the nearest neighbors.
core_distances : array (n_samples, )
An array of core distances for all points
min_samples : int
The min_samples value used to generate core distances.
Returns
-------
new_tree : structured array
The original tree with an extra row providing the parent cluster
and lambda information for a new point given index -1. | hdbscan/prediction.py | _extend_condensed_tree | CKrawczyk/hdbscan | python | def _extend_condensed_tree(tree, neighbor_indices, neighbor_distances, core_distances, min_samples):
'\n Create a new condensed tree with an additional point added, allowing for\n computations as if this point had been part of the original tree. Note\n that this makes as little change to the tree as possible, with no\n re-optimizing/re-condensing so that the selected clusters remain\n effectively unchanged.\n\n Parameters\n ----------\n tree : structured array\n The raw format condensed tree to update.\n\n neighbor_indices : array (2 * min_samples, )\n An array of raw distance based nearest neighbor indices.\n\n neighbor_distances : array (2 * min_samples, )\n An array of raw distances to the nearest neighbors.\n\n core_distances : array (n_samples, )\n An array of core distances for all points\n\n min_samples : int\n The min_samples value used to generate core distances.\n\n Returns\n -------\n new_tree : structured array\n The original tree with an extra row providing the parent cluster\n and lambda information for a new point given index -1.\n '
tree_root = tree['parent'].min()
(nearest_neighbor, lambda_) = _find_neighbor_and_lambda(neighbor_indices, neighbor_distances, core_distances, min_samples)
neighbor_tree_row = get_tree_row_with_child(tree, nearest_neighbor)
potential_cluster = neighbor_tree_row['parent']
if (neighbor_tree_row['lambda_val'] <= lambda_):
new_tree_row = (potential_cluster, (- 1), 1, neighbor_tree_row['lambda_val'])
else:
while ((potential_cluster > tree_root) and (tree[(tree['child'] == potential_cluster)]['lambda_val'] >= lambda_)):
potential_cluster = tree['parent'][(tree['child'] == potential_cluster)][0]
new_tree_row = (potential_cluster, (- 1), 1, lambda_)
return np.append(tree, new_tree_row) |
def _find_cluster_and_probability(tree, cluster_tree, neighbor_indices, neighbor_distances, core_distances, cluster_map, max_lambdas, min_samples):
'\n Return the cluster label (of the original clustering) and membership\n probability of a new data point.\n\n Parameters\n ----------\n tree : CondensedTree\n The condensed tree associated with the clustering.\n\n cluster_tree : structured_array\n The raw form of the condensed tree with only cluster information (no\n data on individual points). This is significantly more compact.\n\n neighbor_indices : array (2 * min_samples, )\n An array of raw distance based nearest neighbor indices.\n\n neighbor_distances : array (2 * min_samples, )\n An array of raw distances to the nearest neighbors.\n\n core_distances : array (n_samples, )\n An array of core distances for all points\n\n cluster_map : dict\n A dictionary mapping cluster numbers in the condensed tree to labels\n in the final selected clustering.\n\n max_lambdas : dict\n A dictionary mapping cluster numbers in the condensed tree to the\n maximum lambda value seen in that cluster.\n\n min_samples : int\n The min_samples value used to generate core distances.\n '
raw_tree = tree._raw_tree
tree_root = cluster_tree['parent'].min()
(nearest_neighbor, lambda_) = _find_neighbor_and_lambda(neighbor_indices, neighbor_distances, core_distances, min_samples)
neighbor_tree_row = get_tree_row_with_child(raw_tree, nearest_neighbor)
potential_cluster = neighbor_tree_row['parent']
if (neighbor_tree_row['lambda_val'] > lambda_):
while ((potential_cluster > tree_root) and (cluster_tree['lambda_val'][(cluster_tree['child'] == potential_cluster)] >= lambda_)):
potential_cluster = cluster_tree['parent'][(cluster_tree['child'] == potential_cluster)][0]
if (potential_cluster in cluster_map):
cluster_label = cluster_map[potential_cluster]
else:
cluster_label = (- 1)
if (cluster_label >= 0):
max_lambda = max_lambdas[potential_cluster]
if (max_lambda > 0.0):
lambda_ = min(max_lambda, lambda_)
prob = (lambda_ / max_lambda)
else:
prob = 1.0
else:
prob = 0.0
return (cluster_label, prob) | -6,342,860,168,396,817,000 | Return the cluster label (of the original clustering) and membership
probability of a new data point.
Parameters
----------
tree : CondensedTree
The condensed tree associated with the clustering.
cluster_tree : structured_array
The raw form of the condensed tree with only cluster information (no
data on individual points). This is significantly more compact.
neighbor_indices : array (2 * min_samples, )
An array of raw distance based nearest neighbor indices.
neighbor_distances : array (2 * min_samples, )
An array of raw distances to the nearest neighbors.
core_distances : array (n_samples, )
An array of core distances for all points
cluster_map : dict
A dictionary mapping cluster numbers in the condensed tree to labels
in the final selected clustering.
max_lambdas : dict
A dictionary mapping cluster numbers in the condensed tree to the
maximum lambda value seen in that cluster.
min_samples : int
The min_samples value used to generate core distances. | hdbscan/prediction.py | _find_cluster_and_probability | CKrawczyk/hdbscan | python | def _find_cluster_and_probability(tree, cluster_tree, neighbor_indices, neighbor_distances, core_distances, cluster_map, max_lambdas, min_samples):
'\n Return the cluster label (of the original clustering) and membership\n probability of a new data point.\n\n Parameters\n ----------\n tree : CondensedTree\n The condensed tree associated with the clustering.\n\n cluster_tree : structured_array\n The raw form of the condensed tree with only cluster information (no\n data on individual points). This is significantly more compact.\n\n neighbor_indices : array (2 * min_samples, )\n An array of raw distance based nearest neighbor indices.\n\n neighbor_distances : array (2 * min_samples, )\n An array of raw distances to the nearest neighbors.\n\n core_distances : array (n_samples, )\n An array of core distances for all points\n\n cluster_map : dict\n A dictionary mapping cluster numbers in the condensed tree to labels\n in the final selected clustering.\n\n max_lambdas : dict\n A dictionary mapping cluster numbers in the condensed tree to the\n maximum lambda value seen in that cluster.\n\n min_samples : int\n The min_samples value used to generate core distances.\n '
raw_tree = tree._raw_tree
tree_root = cluster_tree['parent'].min()
(nearest_neighbor, lambda_) = _find_neighbor_and_lambda(neighbor_indices, neighbor_distances, core_distances, min_samples)
neighbor_tree_row = get_tree_row_with_child(raw_tree, nearest_neighbor)
potential_cluster = neighbor_tree_row['parent']
if (neighbor_tree_row['lambda_val'] > lambda_):
while ((potential_cluster > tree_root) and (cluster_tree['lambda_val'][(cluster_tree['child'] == potential_cluster)] >= lambda_)):
potential_cluster = cluster_tree['parent'][(cluster_tree['child'] == potential_cluster)][0]
if (potential_cluster in cluster_map):
cluster_label = cluster_map[potential_cluster]
else:
cluster_label = (- 1)
if (cluster_label >= 0):
max_lambda = max_lambdas[potential_cluster]
if (max_lambda > 0.0):
lambda_ = min(max_lambda, lambda_)
prob = (lambda_ / max_lambda)
else:
prob = 1.0
else:
prob = 0.0
return (cluster_label, prob) |
def approximate_predict(clusterer, points_to_predict):
"Predict the cluster label of new points. The returned labels\n will be those of the original clustering found by ``clusterer``,\n and therefore are not (necessarily) the cluster labels that would\n be found by clustering the original data combined with\n ``points_to_predict``, hence the 'approximate' label.\n\n If you simply wish to assign new points to an existing clustering\n in the 'best' way possible, this is the function to use. If you\n want to predict how ``points_to_predict`` would cluster with\n the original data under HDBSCAN the most efficient existing approach\n is to simply recluster with the new point(s) added to the original dataset.\n\n Parameters\n ----------\n clusterer : HDBSCAN\n A clustering object that has been fit to the data and\n either had ``prediction_data=True`` set, or called the\n ``generate_prediction_data`` method after the fact.\n\n points_to_predict : array, or array-like (n_samples, n_features)\n The new data points to predict cluster labels for. They should\n have the same dimensionality as the original dataset over which\n clusterer was fit.\n\n Returns\n -------\n labels : array (n_samples,)\n The predicted labels of the ``points_to_predict``\n\n probabilities : array (n_samples,)\n The soft cluster scores for each of the ``points_to_predict``\n\n See Also\n --------\n :py:func:`hdbscan.predict.membership_vector`\n :py:func:`hdbscan.predict.all_points_membership_vectors`\n\n "
if (clusterer.prediction_data_ is None):
raise ValueError('Clusterer does not have prediction data! Try fitting with prediction_data=True set, or run generate_prediction_data on the clusterer')
points_to_predict = np.asarray(points_to_predict)
if (points_to_predict.shape[1] != clusterer.prediction_data_.raw_data.shape[1]):
raise ValueError('New points dimension does not match fit data!')
if (clusterer.prediction_data_.cluster_tree.shape[0] == 0):
warn('Clusterer does not have any defined clusters, new data will be automatically predicted as noise.')
labels = ((- 1) * np.ones(points_to_predict.shape[0], dtype=np.int32))
probabilities = np.zeros(points_to_predict.shape[0], dtype=np.float32)
return (labels, probabilities)
labels = np.empty(points_to_predict.shape[0], dtype=np.int)
probabilities = np.empty(points_to_predict.shape[0], dtype=np.float64)
min_samples = (clusterer.min_samples or clusterer.min_cluster_size)
(neighbor_distances, neighbor_indices) = clusterer.prediction_data_.tree.query(points_to_predict, k=(2 * min_samples))
for i in range(points_to_predict.shape[0]):
(label, prob) = _find_cluster_and_probability(clusterer.condensed_tree_, clusterer.prediction_data_.cluster_tree, neighbor_indices[i], neighbor_distances[i], clusterer.prediction_data_.core_distances, clusterer.prediction_data_.cluster_map, clusterer.prediction_data_.max_lambdas, min_samples)
labels[i] = label
probabilities[i] = prob
return (labels, probabilities) | -9,133,352,897,079,680,000 | Predict the cluster label of new points. The returned labels
will be those of the original clustering found by ``clusterer``,
and therefore are not (necessarily) the cluster labels that would
be found by clustering the original data combined with
``points_to_predict``, hence the 'approximate' label.
If you simply wish to assign new points to an existing clustering
in the 'best' way possible, this is the function to use. If you
want to predict how ``points_to_predict`` would cluster with
the original data under HDBSCAN the most efficient existing approach
is to simply recluster with the new point(s) added to the original dataset.
Parameters
----------
clusterer : HDBSCAN
A clustering object that has been fit to the data and
either had ``prediction_data=True`` set, or called the
``generate_prediction_data`` method after the fact.
points_to_predict : array, or array-like (n_samples, n_features)
The new data points to predict cluster labels for. They should
have the same dimensionality as the original dataset over which
clusterer was fit.
Returns
-------
labels : array (n_samples,)
The predicted labels of the ``points_to_predict``
probabilities : array (n_samples,)
The soft cluster scores for each of the ``points_to_predict``
See Also
--------
:py:func:`hdbscan.predict.membership_vector`
:py:func:`hdbscan.predict.all_points_membership_vectors` | hdbscan/prediction.py | approximate_predict | CKrawczyk/hdbscan | python | def approximate_predict(clusterer, points_to_predict):
"Predict the cluster label of new points. The returned labels\n will be those of the original clustering found by ``clusterer``,\n and therefore are not (necessarily) the cluster labels that would\n be found by clustering the original data combined with\n ``points_to_predict``, hence the 'approximate' label.\n\n If you simply wish to assign new points to an existing clustering\n in the 'best' way possible, this is the function to use. If you\n want to predict how ``points_to_predict`` would cluster with\n the original data under HDBSCAN the most efficient existing approach\n is to simply recluster with the new point(s) added to the original dataset.\n\n Parameters\n ----------\n clusterer : HDBSCAN\n A clustering object that has been fit to the data and\n either had ``prediction_data=True`` set, or called the\n ``generate_prediction_data`` method after the fact.\n\n points_to_predict : array, or array-like (n_samples, n_features)\n The new data points to predict cluster labels for. They should\n have the same dimensionality as the original dataset over which\n clusterer was fit.\n\n Returns\n -------\n labels : array (n_samples,)\n The predicted labels of the ``points_to_predict``\n\n probabilities : array (n_samples,)\n The soft cluster scores for each of the ``points_to_predict``\n\n See Also\n --------\n :py:func:`hdbscan.predict.membership_vector`\n :py:func:`hdbscan.predict.all_points_membership_vectors`\n\n "
if (clusterer.prediction_data_ is None):
raise ValueError('Clusterer does not have prediction data! Try fitting with prediction_data=True set, or run generate_prediction_data on the clusterer')
points_to_predict = np.asarray(points_to_predict)
if (points_to_predict.shape[1] != clusterer.prediction_data_.raw_data.shape[1]):
raise ValueError('New points dimension does not match fit data!')
if (clusterer.prediction_data_.cluster_tree.shape[0] == 0):
warn('Clusterer does not have any defined clusters, new data will be automatically predicted as noise.')
labels = ((- 1) * np.ones(points_to_predict.shape[0], dtype=np.int32))
probabilities = np.zeros(points_to_predict.shape[0], dtype=np.float32)
return (labels, probabilities)
labels = np.empty(points_to_predict.shape[0], dtype=np.int)
probabilities = np.empty(points_to_predict.shape[0], dtype=np.float64)
min_samples = (clusterer.min_samples or clusterer.min_cluster_size)
(neighbor_distances, neighbor_indices) = clusterer.prediction_data_.tree.query(points_to_predict, k=(2 * min_samples))
for i in range(points_to_predict.shape[0]):
(label, prob) = _find_cluster_and_probability(clusterer.condensed_tree_, clusterer.prediction_data_.cluster_tree, neighbor_indices[i], neighbor_distances[i], clusterer.prediction_data_.core_distances, clusterer.prediction_data_.cluster_map, clusterer.prediction_data_.max_lambdas, min_samples)
labels[i] = label
probabilities[i] = prob
return (labels, probabilities) |
def membership_vector(clusterer, points_to_predict):
'Predict soft cluster membership. The result produces a vector\n for each point in ``points_to_predict`` that gives a probability that\n the given point is a member of a cluster for each of the selected clusters\n of the ``clusterer``.\n\n Parameters\n ----------\n clusterer : HDBSCAN\n A clustering object that has been fit to the data and\n either had ``prediction_data=True`` set, or called the\n ``generate_prediction_data`` method after the fact.\n\n points_to_predict : array, or array-like (n_samples, n_features)\n The new data points to predict cluster labels for. They should\n have the same dimensionality as the original dataset over which\n clusterer was fit.\n\n Returns\n -------\n membership_vectors : array (n_samples, n_clusters)\n The probability that point ``i`` is a member of cluster ``j`` is\n in ``membership_vectors[i, j]``.\n\n See Also\n --------\n :py:func:`hdbscan.predict.predict`\n :py:func:`hdbscan.predict.all_points_membership_vectors`\n'
clusters = np.array(sorted(list(clusterer.condensed_tree_._select_clusters()))).astype(np.intp)
result = np.empty((points_to_predict.shape[0], clusters.shape[0]), dtype=np.float64)
min_samples = (clusterer.min_samples or clusterer.min_cluster_size)
(neighbor_distances, neighbor_indices) = clusterer.prediction_data_.tree.query(points_to_predict, k=(2 * min_samples))
for i in range(points_to_predict.shape[0]):
(nearest_neighbor, lambda_) = _find_neighbor_and_lambda(neighbor_indices[i], neighbor_distances[i], clusterer.prediction_data_.core_distances, min_samples)
neighbor_tree_row = get_tree_row_with_child(clusterer.condensed_tree_._raw_tree, nearest_neighbor)
if (neighbor_tree_row['lambda_val'] <= lambda_):
lambda_ = neighbor_tree_row['lambda_val']
distance_vec = dist_membership_vector(points_to_predict[i], clusterer.prediction_data_.exemplars, clusterer.prediction_data_.dist_metric)
outlier_vec = outlier_membership_vector(nearest_neighbor, lambda_, clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
result[i] = ((distance_vec ** 0.5) * (outlier_vec ** 2.0))
result[i] /= result[i].sum()
result[i] *= prob_in_some_cluster(nearest_neighbor, lambda_, clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
return result | 1,341,451,579,952,617,700 | Predict soft cluster membership. The result produces a vector
for each point in ``points_to_predict`` that gives a probability that
the given point is a member of a cluster for each of the selected clusters
of the ``clusterer``.
Parameters
----------
clusterer : HDBSCAN
A clustering object that has been fit to the data and
either had ``prediction_data=True`` set, or called the
``generate_prediction_data`` method after the fact.
points_to_predict : array, or array-like (n_samples, n_features)
The new data points to predict cluster labels for. They should
have the same dimensionality as the original dataset over which
clusterer was fit.
Returns
-------
membership_vectors : array (n_samples, n_clusters)
The probability that point ``i`` is a member of cluster ``j`` is
in ``membership_vectors[i, j]``.
See Also
--------
:py:func:`hdbscan.predict.predict`
:py:func:`hdbscan.predict.all_points_membership_vectors` | hdbscan/prediction.py | membership_vector | CKrawczyk/hdbscan | python | def membership_vector(clusterer, points_to_predict):
'Predict soft cluster membership. The result produces a vector\n for each point in ``points_to_predict`` that gives a probability that\n the given point is a member of a cluster for each of the selected clusters\n of the ``clusterer``.\n\n Parameters\n ----------\n clusterer : HDBSCAN\n A clustering object that has been fit to the data and\n either had ``prediction_data=True`` set, or called the\n ``generate_prediction_data`` method after the fact.\n\n points_to_predict : array, or array-like (n_samples, n_features)\n The new data points to predict cluster labels for. They should\n have the same dimensionality as the original dataset over which\n clusterer was fit.\n\n Returns\n -------\n membership_vectors : array (n_samples, n_clusters)\n The probability that point ``i`` is a member of cluster ``j`` is\n in ``membership_vectors[i, j]``.\n\n See Also\n --------\n :py:func:`hdbscan.predict.predict`\n :py:func:`hdbscan.predict.all_points_membership_vectors`\n'
clusters = np.array(sorted(list(clusterer.condensed_tree_._select_clusters()))).astype(np.intp)
result = np.empty((points_to_predict.shape[0], clusters.shape[0]), dtype=np.float64)
min_samples = (clusterer.min_samples or clusterer.min_cluster_size)
(neighbor_distances, neighbor_indices) = clusterer.prediction_data_.tree.query(points_to_predict, k=(2 * min_samples))
for i in range(points_to_predict.shape[0]):
(nearest_neighbor, lambda_) = _find_neighbor_and_lambda(neighbor_indices[i], neighbor_distances[i], clusterer.prediction_data_.core_distances, min_samples)
neighbor_tree_row = get_tree_row_with_child(clusterer.condensed_tree_._raw_tree, nearest_neighbor)
if (neighbor_tree_row['lambda_val'] <= lambda_):
lambda_ = neighbor_tree_row['lambda_val']
distance_vec = dist_membership_vector(points_to_predict[i], clusterer.prediction_data_.exemplars, clusterer.prediction_data_.dist_metric)
outlier_vec = outlier_membership_vector(nearest_neighbor, lambda_, clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
result[i] = ((distance_vec ** 0.5) * (outlier_vec ** 2.0))
result[i] /= result[i].sum()
result[i] *= prob_in_some_cluster(nearest_neighbor, lambda_, clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
return result |
def all_points_membership_vectors(clusterer):
"Predict soft cluster membership vectors for all points in the\n original dataset the clusterer was trained on. This function is more\n efficient by making use of the fact that all points are already in the\n condensed tree, and processing in bulk.\n\n Parameters\n ----------\n clusterer : HDBSCAN\n A clustering object that has been fit to the data and\n either had ``prediction_data=True`` set, or called the\n ``generate_prediction_data`` method after the fact.\n This method does not work if the clusterer was trained\n with ``metric='precomputed'``.\n\n Returns\n -------\n membership_vectors : array (n_samples, n_clusters)\n The probability that point ``i`` of the original dataset is a member of\n cluster ``j`` is in ``membership_vectors[i, j]``.\n\n See Also\n --------\n :py:func:`hdbscan.predict.predict`\n :py:func:`hdbscan.predict.all_points_membership_vectors`\n "
clusters = np.array(sorted(list(clusterer.condensed_tree_._select_clusters()))).astype(np.intp)
all_points = clusterer.prediction_data_.raw_data
if (clusters.size == 0):
return np.zeros(all_points.shape[0])
distance_vecs = all_points_dist_membership_vector(all_points, clusterer.prediction_data_.exemplars, clusterer.prediction_data_.dist_metric)
outlier_vecs = all_points_outlier_membership_vector(clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
in_cluster_probs = all_points_prob_in_some_cluster(clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
result = (distance_vecs * outlier_vecs)
row_sums = result.sum(axis=1)
result = (result / row_sums[:, np.newaxis])
result *= in_cluster_probs[:, np.newaxis]
return result | 8,111,365,759,064,287,000 | Predict soft cluster membership vectors for all points in the
original dataset the clusterer was trained on. This function is more
efficient by making use of the fact that all points are already in the
condensed tree, and processing in bulk.
Parameters
----------
clusterer : HDBSCAN
A clustering object that has been fit to the data and
either had ``prediction_data=True`` set, or called the
``generate_prediction_data`` method after the fact.
This method does not work if the clusterer was trained
with ``metric='precomputed'``.
Returns
-------
membership_vectors : array (n_samples, n_clusters)
The probability that point ``i`` of the original dataset is a member of
cluster ``j`` is in ``membership_vectors[i, j]``.
See Also
--------
:py:func:`hdbscan.predict.predict`
:py:func:`hdbscan.predict.all_points_membership_vectors` | hdbscan/prediction.py | all_points_membership_vectors | CKrawczyk/hdbscan | python | def all_points_membership_vectors(clusterer):
"Predict soft cluster membership vectors for all points in the\n original dataset the clusterer was trained on. This function is more\n efficient by making use of the fact that all points are already in the\n condensed tree, and processing in bulk.\n\n Parameters\n ----------\n clusterer : HDBSCAN\n A clustering object that has been fit to the data and\n either had ``prediction_data=True`` set, or called the\n ``generate_prediction_data`` method after the fact.\n This method does not work if the clusterer was trained\n with ``metric='precomputed'``.\n\n Returns\n -------\n membership_vectors : array (n_samples, n_clusters)\n The probability that point ``i`` of the original dataset is a member of\n cluster ``j`` is in ``membership_vectors[i, j]``.\n\n See Also\n --------\n :py:func:`hdbscan.predict.predict`\n :py:func:`hdbscan.predict.all_points_membership_vectors`\n "
clusters = np.array(sorted(list(clusterer.condensed_tree_._select_clusters()))).astype(np.intp)
all_points = clusterer.prediction_data_.raw_data
if (clusters.size == 0):
return np.zeros(all_points.shape[0])
distance_vecs = all_points_dist_membership_vector(all_points, clusterer.prediction_data_.exemplars, clusterer.prediction_data_.dist_metric)
outlier_vecs = all_points_outlier_membership_vector(clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
in_cluster_probs = all_points_prob_in_some_cluster(clusters, clusterer.condensed_tree_._raw_tree, clusterer.prediction_data_.leaf_max_lambdas, clusterer.prediction_data_.cluster_tree)
result = (distance_vecs * outlier_vecs)
row_sums = result.sum(axis=1)
result = (result / row_sums[:, np.newaxis])
result *= in_cluster_probs[:, np.newaxis]
return result |
def index(request):
'\n View for the static index page\n '
return render(request, 'public/home.html', _get_context('Home')) | 5,071,290,183,332,569,000 | View for the static index page | sigmapiweb/apps/PubSite/views.py | index | Jacobvs/sigmapi-web | python | def index(request):
'\n \n '
return render(request, 'public/home.html', _get_context('Home')) |
def about(request):
'\n View for the static chapter history page.\n '
return render(request, 'public/about.html', _get_context('About')) | 3,343,995,519,148,531,000 | View for the static chapter history page. | sigmapiweb/apps/PubSite/views.py | about | Jacobvs/sigmapi-web | python | def about(request):
'\n \n '
return render(request, 'public/about.html', _get_context('About')) |
def activities(request):
'\n View for the static chapter service page.\n '
return render(request, 'public/activities.html', _get_context('Service & Activities')) | 5,797,297,894,108,019,000 | View for the static chapter service page. | sigmapiweb/apps/PubSite/views.py | activities | Jacobvs/sigmapi-web | python | def activities(request):
'\n \n '
return render(request, 'public/activities.html', _get_context('Service & Activities')) |
def rush(request):
'\n View for the static chapter service page.\n '
return render(request, 'public/rush.html', _get_context('Rush')) | 2,282,172,684,523,212,000 | View for the static chapter service page. | sigmapiweb/apps/PubSite/views.py | rush | Jacobvs/sigmapi-web | python | def rush(request):
'\n \n '
return render(request, 'public/rush.html', _get_context('Rush')) |
def campaign(request):
'\n View for the campaign service page.\n '
class NoRebuildAuthSession(requests.Session):
def rebuild_auth(self, prepared_request, response):
'\n No code here means requests will always preserve the Authorization\n header when redirected.\n Be careful not to leak your credentials to untrusted hosts!\n '
url = 'https://api.givebutter.com/v1/transactions/'
headers = {'Authorization': f'Bearer {settings.GIVEBUTTER_API_KEY}'}
response = None
session = NoRebuildAuthSession()
try:
r = session.get(url, headers=headers, timeout=0.75)
if (r.status_code == 200):
response = r.json()
else:
logger.error(f'ERROR in request: {r.status_code}')
except requests.exceptions.Timeout:
logger.warning('Connection to GiveButter API Timed out')
except requests.ConnectionError:
logger.warning('Connection to GiveButter API could not be resolved')
except requests.exceptions.RequestException:
logger.error('An unknown issue occurred while trying to retrieve GiveButter Donor List')
ctx = _get_context('Campaign')
if (response and ('data' in response)):
response = response['data']
logger.debug(f'GiveButter API Response: {response}')
successful_txs = [tx for tx in response if (tx['status'] == 'succeeded')]
sorted_txs = sorted(successful_txs, key=(lambda tx: tx['amount']), reverse=True)
transactions = [{'name': tx['giving_space']['name'], 'amount': tx['giving_space']['amount'], 'message': tx['giving_space']['message']} for tx in sorted_txs[:20]]
ctx['transactions'] = transactions
ctx['num_txs'] = len(successful_txs)
return render(request, 'public/campaign.html', ctx) | 8,055,564,660,194,777,000 | View for the campaign service page. | sigmapiweb/apps/PubSite/views.py | campaign | Jacobvs/sigmapi-web | python | def campaign(request):
'\n \n '
class NoRebuildAuthSession(requests.Session):
def rebuild_auth(self, prepared_request, response):
'\n No code here means requests will always preserve the Authorization\n header when redirected.\n Be careful not to leak your credentials to untrusted hosts!\n '
url = 'https://api.givebutter.com/v1/transactions/'
headers = {'Authorization': f'Bearer {settings.GIVEBUTTER_API_KEY}'}
response = None
session = NoRebuildAuthSession()
try:
r = session.get(url, headers=headers, timeout=0.75)
if (r.status_code == 200):
response = r.json()
else:
logger.error(f'ERROR in request: {r.status_code}')
except requests.exceptions.Timeout:
logger.warning('Connection to GiveButter API Timed out')
except requests.ConnectionError:
logger.warning('Connection to GiveButter API could not be resolved')
except requests.exceptions.RequestException:
logger.error('An unknown issue occurred while trying to retrieve GiveButter Donor List')
ctx = _get_context('Campaign')
if (response and ('data' in response)):
response = response['data']
logger.debug(f'GiveButter API Response: {response}')
successful_txs = [tx for tx in response if (tx['status'] == 'succeeded')]
sorted_txs = sorted(successful_txs, key=(lambda tx: tx['amount']), reverse=True)
transactions = [{'name': tx['giving_space']['name'], 'amount': tx['giving_space']['amount'], 'message': tx['giving_space']['message']} for tx in sorted_txs[:20]]
ctx['transactions'] = transactions
ctx['num_txs'] = len(successful_txs)
return render(request, 'public/campaign.html', ctx) |
def permission_denied(request):
'\n View for 403 (Permission Denied) error.\n '
return render(request, 'common/403.html', _get_context('Permission Denied')) | -3,855,063,181,969,753,600 | View for 403 (Permission Denied) error. | sigmapiweb/apps/PubSite/views.py | permission_denied | Jacobvs/sigmapi-web | python | def permission_denied(request):
'\n \n '
return render(request, 'common/403.html', _get_context('Permission Denied')) |
def rebuild_auth(self, prepared_request, response):
'\n No code here means requests will always preserve the Authorization\n header when redirected.\n Be careful not to leak your credentials to untrusted hosts!\n ' | -5,803,225,964,835,695,000 | No code here means requests will always preserve the Authorization
header when redirected.
Be careful not to leak your credentials to untrusted hosts! | sigmapiweb/apps/PubSite/views.py | rebuild_auth | Jacobvs/sigmapi-web | python | def rebuild_auth(self, prepared_request, response):
'\n No code here means requests will always preserve the Authorization\n header when redirected.\n Be careful not to leak your credentials to untrusted hosts!\n ' |
def normalize(self, text: str) -> str:
'Normalize text.\n \n Args:\n text (str): text to be normalized\n '
for (normalize_fn, repl) in self._normalize:
text = normalize_fn(text, repl)
return text | 6,344,072,354,142,538,000 | Normalize text.
Args:
text (str): text to be normalized | prenlp/data/normalizer.py | normalize | awesome-archive/prenlp | python | def normalize(self, text: str) -> str:
'Normalize text.\n \n Args:\n text (str): text to be normalized\n '
for (normalize_fn, repl) in self._normalize:
text = normalize_fn(text, repl)
return text |
def _init_normalize(self) -> None:
"Initialize normalize function.\n If 'repl' is None, normalization is not applied to the pattern corresponding to 'repl'.\n "
if (self.url_repl is not None):
self._normalize.append((self._url_normalize, self.url_repl))
if (self.tag_repl is not None):
self._normalize.append((self._tag_normalize, self.tag_repl))
if (self.emoji_repl is not None):
self._normalize.append((self._emoji_normalize, self.emoji_repl))
if (self.email_repl is not None):
self._normalize.append((self._email_normalize, self.email_repl))
if (self.tel_repl is not None):
self._normalize.append((self._tel_normalize, self.tel_repl)) | 9,143,328,064,171,123,000 | Initialize normalize function.
If 'repl' is None, normalization is not applied to the pattern corresponding to 'repl'. | prenlp/data/normalizer.py | _init_normalize | awesome-archive/prenlp | python | def _init_normalize(self) -> None:
"Initialize normalize function.\n If 'repl' is None, normalization is not applied to the pattern corresponding to 'repl'.\n "
if (self.url_repl is not None):
self._normalize.append((self._url_normalize, self.url_repl))
if (self.tag_repl is not None):
self._normalize.append((self._tag_normalize, self.tag_repl))
if (self.emoji_repl is not None):
self._normalize.append((self._emoji_normalize, self.emoji_repl))
if (self.email_repl is not None):
self._normalize.append((self._email_normalize, self.email_repl))
if (self.tel_repl is not None):
self._normalize.append((self._tel_normalize, self.tel_repl)) |
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