def trigger(self):
common.gamestate['x'] = self.data['x']
common.gamestate['y'] = self.data['y']
common.gamestate['z'] = self.data['z']
common.gamestate['level'] = self.data['level']
common.gamestate['player']['direction'] = self.data['direction']
common.load()
def execute(self, args, config, study):
# feature_cache = common.diskcache.DiskCache("cache_{}".format(common.dict_to_filename(config, ["images", ])))
data = common.RDict(config=config)
for image_set in self.logger.loop(
data["config"]["images"], entry_message="Processing {count} image sets", item_prefix="image set"
):
self.logger.log("Processing query image '{}'...".format(image_set["query_image"]))
query_image = common.load(config["readers"]["query"]).execute(image_set["query_image"], data=data)
query_coefficients = common.load(config["curvelets"]["transform"]).execute(query_image, data=data)
# data["images"][image_set_key]["query_features"] =\
query_features = common.load(config["features"]["extractor"]).execute(query_coefficients, data=data)
for source_image_filename, features in self.logger.sync_loop(
process_image,
*common.augment_list(common.glob_list(image_set["source_images"]), data),
entry_message="Processing {count} images...",
item_prefix="image"
):
self.logger.log("Processing image '{}'...".format(source_image_filename))
# if not feature_cache.contains(source_image_filename):
# image = common.load(data["config"]["readers"]["image"]).execute(source_image_filename, data=data)
# coefficients = common.load(data["config"]["curvelets"]["transform"]).execute(image, data=data)
##data["images"][image_set_key]["source_images"][source_image_filename]["image_features"] =\
# features = common.load(data["config"]["features"]["extractor"]).execute(coefficients, data=data)
# feature_cache.set(source_image_filename, features)
# else:
# features = feature_cache.get(source_image_filename)
data["distances"][image_set["query_image"]][source_image_filename] = common.load(
config["metric"]["metric"]
).execute(query_features, features, data=data)
correlations, mean_correlation = self.correlate_to_study(data["distances"], study)
# self.logger.log("Mean correlation: {}".format(mean_correlation))
return (correlations, mean_correlation)
def _load_frames(self):
self.frames = {
'normal': common.load("mouse.png", True, (int(config.WIDTH * 0.023), 0)),
'over': common.load("over.png", True, (int(config.WIDTH * 0.023), 0)),
'dragging': common.load("dragging.png", True, (int(config.WIDTH * 0.023), 0)),
'hide': common.load("hide.png", True, (int(config.WIDTH * 0.023), 0)),
}
class Test(unittest.TestCase):
path = 'data/country_admin_export_clean.xlsx - Admin_2013.csv'
thresh = 50
ts = umd.main(path, thresh, national=False)
raw = common.load(path)
ts_nat = umd.main(path, thresh, national=True)
raw_nat = common.load(path)
def test_loss(self):
"""Check that loss is calculated properly for a given threshold."""
acre = self.ts.query("region == 'Acre'")
df = self.raw[self.raw['name'] == 'Brazil_Acre']
result = list(acre.query('year == 2003')['loss'])
expected = list(df['loss_75_2003'] + df['loss_100_2003'])
self.assertEqual(result, expected)
def test_gain(self):
"""Check that gain field is properly generated."""
acre = self.ts.query("region == 'Acre'")
df = self.raw[self.raw['name'] == 'Brazil_Acre']
result = list(acre[acre.year == 2003]['gain'])
expected = list(df.gain0012 / 12.)
self.assertEqual(result, expected)
def process_image(source_image_filename, data):
import common
import common.diskcache
cache_config = data["config"].get("cache", {})
if cache_config.get("reader_enabled", False):
reader_cache = common.diskcache.ReaderDiskCache.from_config(data["config"])
else:
reader_cache = common.diskcache.NullCache()
if cache_config.get("feature_enabled", False):
feature_cache = common.diskcache.FeatureDiskCache.from_config(data["config"])
else:
feature_cache = common.diskcache.NullCache()
if feature_cache.contains(source_image_filename):
features = feature_cache.get(source_image_filename)
else:
if reader_cache.contains(source_image_filename):
image = reader_cache.get(source_image_filename)
else:
image = common.load(data["config"]["readers"]["image"]).execute(source_image_filename, data=data)
reader_cache.set(source_image_filename, image)
coefficients = common.load(data["config"]["curvelets"]["transform"]).execute(image, data=data)
features = common.load(data["config"]["features"]["extractor"]).execute(coefficients, data=data)
feature_cache.set(source_image_filename, features)
return source_image_filename, features
def crear_barra_de_botones(self):
anterior = common.load("anterior.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
siguiente = common.load("siguiente.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
crear = common.load("crear.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
if self.editor.nivel > 1:
item = ItemBoton(anterior, self.editor.imagen_bloque.convert_alpha(),
self.editor.retroceder, config.WIDTH - config.BLOCK_SIZE * 2, 0)
self.editor.sprites.add(item)
self.items_creados.append(item)
if self.editor.es_ultimo_nivel():
item = ItemBoton(crear, self.editor.imagen_bloque.convert_alpha(),
self.editor.avanzar_y_crear_ese_nivel, config.WIDTH - config.BLOCK_SIZE, 0)
else:
item = ItemBoton(siguiente, self.editor.imagen_bloque.convert_alpha(),
self.editor.avanzar, config.WIDTH - config.BLOCK_SIZE, 0)
self.editor.sprites.add(item)
self.items_creados.append(item)
alternar = common.load("alternar.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
item = ItemBoton(alternar, self.editor.imagen_bloque.convert_alpha(), self.editor.alternar, 0, 0)
self.editor.sprites.add(item)
self.items_creados.append(item)
probar = common.load("probar.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
item = ItemBoton(probar, self.editor.imagen_bloque.convert_alpha(), self.editor.probar, config.BLOCK_SIZE, 0)
self.editor.sprites.add(item)
self.items_creados.append(item)
def agregar_boton_para_regresar_al_editor(self):
from editor import ItemBoton
regresar = common.load("regresar.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
imagen_bloque = common.load("bloque.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
item = ItemBoton(regresar, imagen_bloque.convert_alpha(), self.regresar_al_editor, 0, 0)
self.sprites.add(item)
def _load_frames(self):
self.frames = {
'normal': common.load("mouse.png", True),
'over': common.load("over.png", True),
'dragging': common.load("dragging.png", True),
'hide': common.load("hide.png", True),
}
def crear_barra_de_botones(self):
alternar = common.load("alternar.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
item = ItemBoton(alternar, self.editor.imagen_bloque.convert_alpha(), self.editor.alternar, 0, 0)
self.editor.sprites.add(item)
self.items_creados.append(item)
probar = common.load("probar.png", True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
item = ItemBoton(probar, self.editor.imagen_bloque.convert_alpha(), self.editor.probar, config.BLOCK_SIZE, 0)
self.editor.sprites.add(item)
self.items_creados.append(item)
def __init__(self, sprites):
self.sprites = sprites
self.nubes = []
imagenes = [common.load("nubes/1.png", True, (config.WIDTH * 0.05, 0)),
common.load("nubes/2.png", True, (config.WIDTH * 0.11, 0)),
common.load("nubes/3.png", True, (config.WIDTH * 0.14, 0))]
velocidades = [0.1, 0.2, 0.3, 0.4]
for x in range(8):
nube = Nube(random.choice(imagenes), random.choice(velocidades))
self.nubes.append(nube)
self.sprites.add(nube)
def execute(self, args, config, study):
if args.codebook is not None:
codebook = common.codebook.Codebook.load_from_path(args.codebook, size=config["codebook"]["codebook_size"])
else:
codebook = common.codebook.Codebook.load_from_config(config)
data = common.RDict(config=config)
data["codewords"] = codebook.codewords
for image_set in self.logger.loop(
data["config"]["images"],
entry_message="Processing {count} image sets",
item_prefix="image set"):
if image_set.get("skip_benchmark", False):
self.logger.log("Skipping distractor image set...")
else:
self.logger.log("Processing query image '{}'...".format(image_set["query_image"]))
query_image = common.load(config["readers"]["query"]).execute(image_set["query_image"], data=data)
query_coefficients = common.load(config["curvelets"]["transform"]).execute(query_image, data=data)
query_features = common.load(config["features"]["extractor"]).execute(query_coefficients, data=data)
query_signature = codebook.quantize(query_features,
use_stopwords=config["weights"]["use_stopwords"],
use_weights=config["weights"]["use_weights"],
)
#self.logger.log(query_signature)
for source_image_filename, signature in self.logger.sync_loop(
get_signature,
*common.augment_list(
common.glob_list(data["config"]["source_images"]),
data,
codebook,
),
entry_message="Processing {count} images...",
item_prefix="image"):
self.logger.log("Processing image '{}'...".format(source_image_filename))
#self.logger.log(signature)
data["distances"][image_set["query_image"]][source_image_filename] =\
common.load(config["metric"]["metric"]).execute(query_signature, signature, data=data)
self.logger.log("Calculating precisions for '{}'...".format(image_set["query_image"]))
a = data["precisions"][image_set["query_image"]] = self.get_precision_recall(image_set["query_image"], data["distances"][image_set["query_image"]], study)
self.logger.log("Precisions: {}".format(a))
#correlations, mean_correlation = self.correlate_to_study(data["distances"], study)
#precision_recall_stats, mean_stats = self.correlate_to_study(data["distances"], study)
#self.logger.log("Mean correlation: {}".format(mean_correlation))
return (data["precisions"], self.get_mean_average_precision(data["precisions"]))
def main(path, thresh, national):
'''Generate long-form datasets with year, thresh, gain, loss,
treecover (and associated percentages).'''
df = common.load(path)
if national:
df.drop('region', axis=1)
df = df.groupby(['country', 'iso']).sum().reset_index()
else:
pass
df = calc_extents(df, thresh)
df = wide_to_long(df, thresh)
df = calc_annual_gain(df)
df = set_2000_0(df, 'loss')
df = calc_perc(df, 'loss', thresh)
df = calc_perc(df, 'gain', thresh)
df = calc_perc(df, 'extent', thresh, denominator='land')
df['thresh'] = thresh
df = df.loc[:, OUTPUTFIELDS]
df['year'] = df['year'].astype(int)
df['id'] = df['id'].astype(int)
return df
def __init__(self, type, x, y):
Sprite.__init__(self)
if config.SHOW_PLACEHOLDERS:
self.image = common.load('placeholder.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
else:
self.image = common.load('hide.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
self.rect = self.image.get_rect()
self.rect.topleft = (x, y)
self.are_used = False
self.is_floor = False
self.rect.width = config.BLOCK_SIZE
self.rect.height = config.BLOCK_SIZE
self.type = type
def plotDeltas(i, t, range):
# plt.subplot(4, 2, i)
delta = common.load('tmp/' + t + '.p', [])
label = 'Predictions for next {} to {} minutes'.format(range[0], range[1])
ax = sns.distplot(delta, hist=False, label=label)
# f.set_title('Predictions for next {} to {} minutes'.format(range[0], range[1]))
ax.set_xlim([-1000, 1000])
def test_subtree_from_fragment(self):
structure = load("branchy_glycan")
fragment = Fragment(mass=1463.5284494, kind="1,5A0,3X", included_nodes=set([2, 3, 4, 5, 6, 7, 8, 9, 10]),
link_ids={}, name="0,3Xc4-1,5A4",
crossring_cleavages={2: ('1,5', 'A'), 7: ('0,3', 'X')})
subtree = glycan.fragment_to_substructure(fragment, structure)
self.assertAlmostEqual(subtree.mass(), fragment.mass)
def cargar_imagen_por_codigo(codigo):
referencias = {
'o': "player/ico.png",
'1': "pipes/1.png",
'2': "pipes/2.png",
'3': "pipes/3.png",
'4': "pipes/4.png",
'6': "pipes/6.png",
'7': "pipes/7.png",
'8': "pipes/8.png",
'9': "pipes/9.png",
'v': "pipes/v.png",
't': "pipes/t.png",
'n': "pipes/n.png",
'f': "pipes/f.png",
'r': "pipes/r.png",
'y': "pipes/y.png",
'z': "front_pipes/1.png",
'x': "front_pipes/2.png",
'c': "front_pipes/3.png",
'a': "front_pipes/4.png",
'q': "front_pipes/7.png",
'e': "front_pipes/9.png",
}
return common.load(referencias[codigo], True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
def crear_item(self, imagen, codigo, posicion):
imagen_para_el_item = common.load(imagen, True, (config.BLOCK_SIZE, config.BLOCK_SIZE))
dx = posicion % 14 + 1
dy = posicion / 14 + 6
item = Item(imagen_para_el_item, self.editor.imagen_bloque.convert_alpha(), codigo, dy, dx)
self.editor.sprites.add(item)
self.items_creados.append(item)
def test_fragments_preserve(self):
structure = load("branchy_glycan")
dup = structure.clone()
self.assertEqual(structure, dup)
list(dup.fragments('ABY', 2))
self.assertEqual(structure, dup)
def __init__(self, type, x, y):
Sprite.__init__(self)
if config.SHOW_PLACEHOLDERS:
self.image = common.load('placeholder.png', True)
else:
self.image = common.load('hide.png', True)
self.rect = self.image.get_rect()
self.rect.topleft = (x, y)
self.are_used = False
self.is_floor = False
self.rect.width = 75
self.rect.height = 75
self.type = type
def __init__(self):
Sprite.__init__(self)
self.original_image = common.load('presents/gcoop.png', False)
self.image = self.original_image
self.alpha = 0
self.rect = self.image.get_rect()
self.rect.centerx = 1260 / 2
self.center = self.rect.center
self.y = 90
w, h = self.image.get_width(), self.image.get_height()
self.width = 0
self.height = 0
common.tweener.addTween(self,
width=w,
tweenTime=1700,
tweenType=pytweener.Easing.Elastic.easeInOut)
common.tweener.addTween(self,
height=h,
tweenTime=1800,
tweenType=pytweener.Easing.Elastic.easeInOut)
common.tweener.addTween(self,
alpha=255,
tweenTime=500,
tweenType=pytweener.Easing.Linear.easeNone)
self.update()
def plotDeltas(i, t, range):
# plt.subplot(4, 2, i)
delta = common.load('tmp/' + t + '.p', [])
label = 'Predictions for next {} to {} minutes'.format(range[0], range[1])
ax = sns.distplot(delta, hist=False, label=label)
# f.set_title('Predictions for next {} to {} minutes'.format(range[0], range[1]))
ax.set_xlim([-1000, 1000])
def reload(self):
log("Clearing alerts")
self.list.clear()
log("Done")
self.addpermanent()
cnt = load(self, "config/alerts.txt", Alert)
log("Loaded {:3d} alerts".format(cnt))
def __init__(self):
self.params = {}
load_config(self.params, "config/config.txt")
self.list = []
self.addpermanent()
cnt = load(self, "config/alerts.txt", Alert)
log("Loaded {:3d} alerts".format(cnt))
def __init__(self):
Sprite.__init__(self)
self.image = common.load('presents/presents.png', False, (config.WIDTH * 0.3, 0))
self.rect = self.image.get_rect()
self.rect.centerx = config.WIDTH / 2
self.rect.y = config.HEIGHT * 0.8
self.alpha = 0
self.update()
def __init__(self):
Sprite.__init__(self)
self.image = common.load('title.png', True, (config.WIDTH * 0.3, 0))
self.rect = self.image.get_rect()
self.rect.right = config.WIDTH * 0.9
self.y = config.HEIGHT
common.tweener.addTween(self, y=self.y * 0.05, tweenTime=1700,
tweenType=pytweener.Easing.Elastic.easeInOut)
def __init__(self):
Sprite.__init__(self)
self.image = common.load('continue.png', True, (config.WIDTH * 0.7, 0))
self.rect = self.image.get_rect()
self.y = config.HEIGHT + self.rect.h
self.rect.bottom = self.y
self.rect.centerx = config.WIDTH / 2
common.tweener.addTween(self, y=self.y * 0.8, tweenTime=1700)
def __init__(self):
Sprite.__init__(self)
self.image = common.load('presents/presents.png', False)
self.rect = self.image.get_rect()
self.rect.centerx = 1280 / 2
self.rect.y = 600
self.alpha = 0
self.update()
def __init__(self, world):
scene.Scene.__init__(self, world)
self.sprites = group.Group()
self.background = common.load("title_background.jpg", False)
self.title = title_sprite.TitleSprite()
self.sprites.add(self.title)
self.draw_background()
self.counter = 0
def _draw_background(self):
"Imprime y actualiza el fondo de pantalla para usar dirtyrectagles mas adelante."
self.background = common.load("background_menu.jpg", False, (config.WIDTH, config.HEIGHT))
#self.map.draw_over(self.background)
self.world.screen.blit(self.background, (0, 0))
# actualiza toda la pantalla.
pygame.display.flip()
def test_strip_derivatize(self):
glycan = load("common_glycan")
glycan.reducing_end = ReducedEnd()
mass = glycan.mass()
composition_transform.derivatize(glycan, 'methyl')
self.assertNotEqual(mass, glycan.mass())
composition_transform.strip_derivatization(glycan)
self.assertAlmostEqual(glycan.mass(), mass, 3)
def __init__(self):
Sprite.__init__(self)
self.image = common.load('continue.png', True)
self.rect = self.image.get_rect()
self.y = 720 + self.rect.h
self.rect.bottom = self.y
self.rect.centerx = 1280 / 2
common.tweener.addTween(self, y=710, tweenTime=1700)
def _draw_background_and_map(self):
"Imprime y actualiza el fondo de pantalla para usar dirtyrectagles mas adelante."
self.background = common.load("background.jpg", False)
self.map.draw_over(self.background)
self.world.screen.blit(self.background, (0, 0))
# actualiza toda la pantalla.
pygame.display.flip()
def __init__(self, world, actions):
pygame.sprite.Sprite.__init__(self)
self.image = common.load("cursor.png", True, (int(config.WIDTH * 0.8), int(config.HEIGHT * 0.09)))
self.rect = self.image.get_rect()
self.rect.centerx = config.WIDTH / 2
self.posicion_actual = 0
self.world = world
self.actions = actions
def __init__(self, codigo):
pygame.sprite.Sprite.__init__(self)
imagen = common.load("elemento_actual.png", True, (config.BLOCK_SIZE * 2, config.BLOCK_SIZE))
item = cargar_imagen_por_codigo(codigo)
imagen.blit(item, (config.BLOCK_SIZE, 0))
self.image = imagen
self.rect = self.image.get_rect()
self.rect.right = config.WIDTH
self.z = 100
def __init__(self, rect):
pygame.sprite.Sprite.__init__(self)
self.original_image = common.load("particle_box.png", True)
self.image = self.original_image.convert_alpha()
self.rect = self.image.get_rect()
self.rect.center = rect.center
self.ttl = 40
self.z = 300
self.particles = []
def __init__(self):
Sprite.__init__(self)
self.image = common.load('level_complete.png', True, (config.WIDTH * 0.5, 0))
self.rect = self.image.get_rect()
self.rect.centerx = config.WIDTH / 2
self.y = -self.rect.h * 2
self.rect.y = self.y
common.tweener.addTween(self, y=config.HEIGHT * 0.4, tweenTime=1700,
tweenType=pytweener.Easing.Elastic.easeInOut)
def test_leaves(self):
structure = load("common_glycan")
leaves = list(structure.leaves())
for node in leaves:
self.assertTrue(len(list(node.children())) == 0)
leaves = list(structure.leaves(bidirectional=True))
for node in leaves:
self.assertTrue(
len(list(node.children())) == 0 or node == structure.root)
def test_traversal(self):
structure = load("common_glycan")
structure[-
1].add_monosaccharide(named_structures.monosaccharides['NeuGc'])
structure.reindex(method='dfs')
ref = structure.clone()
self.assertEqual(structure[-1], ref[-1])
structure.reindex(method='bfs')
self.assertNotEqual(structure[-1], ref[-1])
def __init__(self):
Sprite.__init__(self)
self.image = common.load('level_complete.png', True)
self.rect = self.image.get_rect()
self.rect.centerx = 1280 / 2
self.y = -self.rect.h
common.tweener.addTween(self,
y=300,
tweenTime=1700,
tweenType=pytweener.Easing.Elastic.easeInOut)
def test_indexing(self):
structure = load("common_glycan")
ref = structure.clone()
for i, node in enumerate(structure.index):
self.assertEqual(node.id, ref[i].id)
structure.deindex()
for i, node in enumerate(structure.index):
self.assertNotEqual(node.id, ref[i].id)
structure.index = None
self.assertRaises(IndexError, lambda: structure[0])
def __init__(self):
Sprite.__init__(self)
self.image = common.load('title.png', True)
self.rect = self.image.get_rect()
self.rect.right = 1180
self.y = 780
common.tweener.addTween(self,
y=40,
tweenTime=1700,
tweenType=pytweener.Easing.Elastic.easeInOut)
def createMitsubaVolume():
density_path = r'D:\Dataset\round2\silk\silk_density.dat'
orientation_path = r'D:\Dataset\round2\silk\silk_orientation.dat'
density_path_mis = r'D:\Dataset\round2\silk\silk_density_mis_xy200-800.vol'
orientation_path_mis = r'D:\Dataset\round2\silk\silk_orientation_mis_xy200-800.vol'
print 'create density mitsuba volume ...'
density = load(density_path)
density = density[200:800, 200:800]
print 'density shape: ', density.shape
createMitsubaGridVolumeSimple(density, density_path_mis, 0.5)
print 'create orientation mitsuba volume ...'
orientation = load(orientation_path)
orientation = orientation[200:800, 200:800]
print 'orientation shape: ', orientation.shape
createMitsubaGridVolumeSimple(orientation, orientation_path_mis, 0.5)
print 'Done.'
def createMitsubaVolume():
density_path = r'D:\Dataset\round2\silk\silk_density.dat'
orientation_path = r'D:\Dataset\round2\silk\silk_orientation.dat'
density_path_mis = r'D:\Dataset\round2\silk\silk_density_mis_xy200-800.vol'
orientation_path_mis = r'D:\Dataset\round2\silk\silk_orientation_mis_xy200-800.vol'
print 'create density mitsuba volume ...'
density = load(density_path)
density = density[200:800, 200:800]
print 'density shape: ', density.shape
createMitsubaGridVolumeSimple(density, density_path_mis, 0.5)
print 'create orientation mitsuba volume ...'
orientation = load(orientation_path)
orientation = orientation[200:800, 200:800]
print 'orientation shape: ', orientation.shape
createMitsubaGridVolumeSimple(orientation, orientation_path_mis, 0.5)
print 'Done.'
def test_traversal_by_name(self):
structure = load("common_glycan")
structure[-
1].add_monosaccharide(named_structures.monosaccharides['NeuGc'])
structure.reindex(method='dfs')
ref = structure.clone()
self.assertEqual(structure, ref)
structure.reindex(method='depth_first_traversal')
self.assertEqual(structure, ref)
self.assertRaises(
AttributeError, lambda: structure.reindex(method='not_real_traversal'))
def _load_images(self):
"Carga las imagenes de los pipes para pintar."
self.images = {
'1': common.load('pipes/1.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
'2': common.load('pipes/2.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
'3': common.load('pipes/3.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
'4': common.load('pipes/4.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
'6': common.load('pipes/6.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
'7': common.load('pipes/7.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
'8': common.load('pipes/8.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
'9': common.load('pipes/9.png', True, (config.BLOCK_SIZE, config.BLOCK_SIZE)),
}
def main(ds_name="a_example"):
data = load(ds_name)
best_points_per_startup_day = np.zeros(data["D"], dtype=np.float32)
for lib in tqdm(data["libs"]):
points_per_startup_day = get_points_per_startup_day(lib, data["D"], data["S"])
best_points_per_startup_day = np.maximum(
points_per_startup_day, best_points_per_startup_day
)
print(ds_name, best_points_per_startup_day.sum())
def test_subtree_from(self):
structure = load("branchy_glycan")
child = structure.root.children().next()[1]
subtree = Glycan.subtree_from(structure, child)
temp = structure.clone()
temproot = temp.root.children().next()[1]
for link in temp.root.links.values():
link.break_link(refund=True)
temp.root = temproot
self.assertEqual(temp, subtree)
self.assertEqual(Glycan.subtree_from(structure, 1), temp)
def main():
dlg_string = os.environ['DLG_UID']
dlg_string = dlg_string[(dlg_string.rindex('_') + 1):len(dlg_string)]
dlg_uid = dlg_string.split('/')
Freq_Iteration = int(dlg_uid[1]) # derived from ID
Facet_Iteration = int(dlg_uid[2]) # derived from ID
vt = load(1)
phasecentre_array = [[+15, -45], [+15.2, -45], [+15, -44], [+14.8, -45],
[+15, -46]]
phasecentre = SkyCoord(ra=phasecentre_array[Facet_Iteration][0] * u.deg,
dec=phasecentre_array[Facet_Iteration][1] * u.deg,
frame='icrs',
equinox='J2000')
model = create_image_from_visibility(vt,
phasecentre=phasecentre,
cellsize=0.001,
npixel=256)
dirty, sumwt = invert_function(vt, model)
psf, sumwt = invert_function(vt, model, dopsf=True)
#show_image(dirty)
print("Max, min in dirty image = %.6f, %.6f, sumwt = %f" %
(dirty.data.max(), dirty.data.min(), sumwt))
print("Max, min in PSF = %.6f, %.6f, sumwt = %f" %
(psf.data.max(), psf.data.min(), sumwt))
export_image_to_fits(
dirty, '%s/imaging_dirty_%02d_%02d.fits' %
(results_dir, Freq_Iteration, Facet_Iteration))
export_image_to_fits(
psf, '%s/imaging_psf_%02d_%02d.fits' %
(results_dir, Freq_Iteration, Facet_Iteration))
# Deconvolve using clean
comp, residual = deconvolve_cube(dirty,
psf,
niter=1000,
threshold=0.001,
fracthresh=0.01,
window_shape='quarter',
gain=0.7,
scales=[0, 3, 10, 30])
restored = restore_cube(comp, psf, residual)
export_image_to_fits(
restored, '%s/imaging_clean%02d_%02d.fits' %
(results_dir, Freq_Iteration, Facet_Iteration))
dump(2, restored)
def __init__(self, game, type, x, y, map):
Sprite.__init__(self)
self.type = type
self.image = common.load('front_pipes/%d.png' % (type), True)
self.rect = self.image.get_rect()
self.map = map
self.can_be_clicked = True
self.x = x - self.rect.w / 2
self.y = y - self.rect.h
self.are_in_a_placeholder = False
self.z = -10
self.game = game
def __init__(self, text_image, x, y):
Sprite.__init__(self)
self.image = common.load('balloon.png', True)
self.rect = self.image.get_rect()
self.rect.right = x + 70
self.rect.bottom = y
self.time_to_live = 150
self.image.blit(text_image, (5, 5))
# Evita que el cuadro de dialogo salga de la pantalla
if self.rect.left < 2:
self.rect.left = 2
elif self.rect.right > 638:
self.rect.right = 638
def __init__(self, world):
pygame.mixer.init()
pygame.mixer.music.load('data/presents/music.ogg')
#music = pygame.mixer.Sound('presents/music.ogg')
#music.play()
pygame.mixer.music.play()
scene.Scene.__init__(self, world)
self.sprites = group.Group()
self.background = common.load("presents/background.png", False)
self.gcoop = GcoopLogo()
self.presents = PresentsText()
self.sprites.add(self.gcoop)
self.draw_background()
self.counter = 0
def sync_folder(folder, rsync_file=f'{HOME}/.rsync/jobs.sh'):
"""
rsync the given folder
If the folder is already in sync just uploaded in the moment,
avoiding repetitions in the rsync_file
"""
S = common.load()
## Clean special characters
folder = folder.replace(' ', '\ ')
com = 'rsync -r --delete-before -a --relative %s' % (folder)
com += f' {S.user}@{S.domain}:{S.folder}'
os.system(com)
## Read all the currently syncing folders and files
lines = open(rsync_file, 'r').read().splitlines()
if com not in lines:
with open(rsync_file, 'a') as f:
f.write(com + '\n')
def main(args):
if len(args) < 2:
Log.w("Arguments Error")
return
path = args[0]
mfile = args[1]
Log.i("Training file: %s" % path)
Log.i("----------------")
sentences = c.readconllfile(path)
Log.i("Model file: %s" % mfile)
Log.i("----------------")
featdict, weights = c.load(mfile)
output, score = parse(sentences, weights)
for each in output:
for token in each:
print(token)
print()
Log.i("[DONE] accuracy: {:.2%}".format(score))
def sync_file(abs_file, rsync_file=f'{HOME}/.rsync/jobs.sh'):
"""
rsync the given (the abs path) file
If the file is already in sync just uploaded in the moment,
avoiding repetitions in the rsync_file
"""
S = common.load()
abs_file = abs_file.replace(' ', '\ ')
#com = 'rsync -r %s'%(abs_file)
com = 'rsync -r --delete-before -a --relative %s' % (abs_file)
#com += ' [email protected]:/media/BACKUP/%s'%(HOSTNAME)
com += f' {S.user}@{S.domain}:{S.folder}'
os.system(com)
## Read all the currently syncing folders and files
lines = open(rsync_file, 'r').read().splitlines()
if com not in lines:
with open(rsync_file, 'a') as f:
f.write(com + '\n')
def _load_frames(self):
sheet_walk = animation.Sheet(common.load("player/walk.png", True), 4)
sheet_stand = animation.Sheet(common.load("player/stand.png", True), 1)
sheet_wait = animation.Sheet(common.load("player/wait.png", True), 2)
sheet_working = animation.Sheet(common.load("player/working.png", True), 2)
sheet_ok = animation.Sheet(common.load("player/ok.png", True), 1)
sheet_stand_moving = animation.Sheet(common.load("player/stand_moving.png", True), 1)
sheet_walk_moving = animation.Sheet(common.load("player/walk_moving.png", True), 4)
self.animations = {
"walk": animation.Animation(sheet_walk, 6, [0, 1, 2, 3]),
"stand": animation.Animation(sheet_stand, 1, [0]),
"working": animation.Animation(sheet_working, 6, [0, 1]),
"ok": animation.Animation(sheet_ok, 1, [0]),
"wait": animation.Animation(sheet_wait, 10, [0, 1]),
"stand_moving": animation.Animation(sheet_stand_moving, 1, [0]),
"walk_moving": animation.Animation(sheet_walk_moving, 6, [0, 1, 2, 3]),
}
def testFit():
print 'load volume ...'
v_path = r''
v = load(v_path) # float32, [0, 65535]
print 'volume shape: ', v.shape
# first, denoise using ed, and eJ?!
print 'denoise using ed ...'
bg = [v < 0.4 * 65535]
v[bg] = 0.0
# need binarize?
print 'binarize ...'
fiber = np.logical_not(bg)
v[fiber] = 1.0
m = poly2ndfitVolume(v)
X, Y, Z = generatePoly2ndSurfaceSimple(m, v.shape[0], v.shape[1])
plotSurface(X, Y, Z)
def main(output_name, mode="points_div_opportunities", pick_rand_topn=1):
for ds_name in dsets:
if ds_name in output_name:
break
assert ds_name in output_name
rest_name = osp.basename(output_name)[len(ds_name) + 1:-len(".out")]
data = load(ds_name)
prev_output = load_output(output_name)
output = construct_empty_output(prev_output, data)
lib_id_to_output_id = dict(
zip([d["index"] for d in output], range(len(output))))
if mode == "points_div_opportunities":
book_score_func = books_by_points_div_opportunities
else:
assert mode == "opportunities"
book_score_func = books_by_opportunities
est_score = 0
# books_left_data = init_books_left_data(
# output, scores=data["S"], lib_data=data["libs"]
# )
while True:
books_left_data, nbr_empty_slots = get_books_left_data(
output, data["S"], data["libs"])
print(nbr_empty_slots, len(books_left_data))
if books_left_data is None or len(books_left_data) == 0:
break
sel_books = sorted(books_left_data,
key=book_score_func)[-pick_rand_topn:]
np.random.shuffle(sel_books)
sel_book = sel_books[0]
est_score += data["S"][sel_book["id"]]
output[lib_id_to_output_id[sel_book["put_lib_idx"]]]["ids"].append(
sel_book["id"])
print(est_score)
save(
output,
method_name="greedy_best_books_%s_pick_top_%d_from_%s" %
(mode, pick_rand_topn, rest_name),
ds_name=ds_name,
)
return est_score
def main(args):
# Set SIGCONF settings.
sigconf_settings()
# Load predictions.
data = load(args.data)
# Define labels.
labels = [
'Averaging',
r'\textsc{SubSVD-Bernoulli}',
]
# Define colors.
colors = [
'black',
'C3',
]
# Define line styles.
linestyles = [
'-',
# '--',
'-'
]
# Set plot positions.
positions = ['top', 'bottom']
# Set titles.
titles = ['Affordable Houses', 'Ban on Homophobia']
fig, axes = plt.subplots(nrows=2, figsize=(3.6, 2.8))
for vote, preds in data.items():
if vote not in DATE2VOTES['2020-02-09']:
continue
i = VOTE2IDX[vote]
plot(preds, axes[i], labels, colors, linestyles, titles[i],
positions[i])
fig.tight_layout()
plt.savefig(args.fig)
print(f'Figure saved to {args.fig}')
def main(args) -> None:
# Load Keras model
model = common.create_cnn_model()
# Load a subset of MNIST to simulate the local data partition
(x_train, y_train), (x_test, y_test) = common.load(args.num_clients)[args.partition]
# drop samples to form exact batches for dpsgd
# this is necessary since dpsgd is sensitive to uneven batches
# due to microbatching
if args.dpsgd and x_train.shape[0] % args.batch_size != 0:
drop_num = x_train.shape[0] % args.batch_size
x_train = x_train[:-drop_num]
y_train = y_train[:-drop_num]
# Start Flower client
client = MnistClient(model, x_train, y_train, x_test, y_test, args)
fl.client.start_numpy_client("[::]:8080", client=client)
if args.dpsgd:
print("Privacy Loss: ", PRIVACY_LOSS)
