Reset23/the-stack-v2-python · Datasets at Hugging Face

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def is_prime(p,n,l=0,r=None): r=len(p)-1 while r>=l: m=l+(r-l)//2 if p[m]==n:return'yes' elif p[m]>n:r=m-1 else:l=m+1 return'no'

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

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/morningproject/morningproject/wsgi.py

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sinha-sandeep/django-project

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""" WSGI config for morningproject project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "morningproject.settings") application = get_wsgi_application()

[ "sandeepsinha78148@gmail.com" ]

sandeepsinha78148@gmail.com

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/users/migrations/0002_auto_20180827_1332.py

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AteamVentures/ateam-chat

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2020-03-24T22:39:58.465030

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# -*- coding: utf-8 -*- # Generated by Django 1.11.5 on 2018-08-27 13:32 from __future__ import unicode_literals import django.core.validators from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('users', '0001_initial'), ] operations = [ migrations.AlterField( model_name='user', name='date_joined', field=models.DateTimeField(default=django.utils.timezone.now, verbose_name='가입일'), ), migrations.AlterField( model_name='user', name='display_name', field=models.CharField(max_length=20, verbose_name='닉네임'), ), migrations.AlterField( model_name='user', name='email', field=models.EmailField(db_index=True, max_length=190, verbose_name='이메일'), ), migrations.AlterField( model_name='user', name='name', field=models.CharField(max_length=50, verbose_name='이름'), ), migrations.AlterField( model_name='user', name='phone', field=models.CharField(db_index=True, max_length=30, validators=[django.core.validators.RegexValidator('^[0-9]+$', '숫자만 입력 가능합니다.', 'invalid')], verbose_name='연락처'), ), migrations.AlterField( model_name='user', name='username', field=models.CharField(help_text='필수입니다. 영문소문자, 숫자와 밑줄(_)만 입력가능합니다.', max_length=32, unique=True, validators=[django.core.validators.MinLengthValidator(3), django.core.validators.MaxLengthValidator(32), django.core.validators.RegexValidator('^(?!_)[a-zA-Z0-9_]+$', '영문소문자, 숫자와 밑줄(_)만 입력가능합니다.', 'invalid')], verbose_name='username'), ), ]

[ "libbom14@gmail.com" ]

libbom14@gmail.com

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/src/astrogun.py

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cyberaa/astrogun

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refs/heads/master

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#!/usr/bin/python from __future__ import absolute_import, division, print_function, unicode_literals """ Wavefront obj model loading. Material properties set in mtl file. Uses the import pi3d method to load *everything* """ import sys sys.path.append('../src') import pi3d import time import asteroids, bullets import numpy, numpy.linalg import util import math import RPi.GPIO as GPIO import os.path import pickle from settings import * import RTIMU import threading import pygame.mixer ###################################### #### IMUReader ###################################### class IMUReader(threading.Thread): def __init__(self, imu): threading.Thread.__init__(self) self.imu = imu self.data = (0, 0, 0) self.running = True; self.wait_s = imu.IMUGetPollInterval()*1.0/1000.0 def run(self): while(self.running): if self.imu.IMURead(): self.data = self.imu.getFusionData() time.sleep(self.wait_s) ###################################### #### GameLevel ###################################### # Operating modes MODE_READY = 0 MODE_READY_GO = 1 MODE_GO = 2 MODE_GO_OUT = 3 MODE_PLAY = 4 class GameLevel: def __init__(self, sprites): # Instantiate an Asteroid Generator self.gen = asteroids.AsteroidGenerator(ASTEROIDS, 0.1, None, shader_explosion, shader_uv_flat) self.bullet_gen = bullets.BulletGenerator() self.active_asteroids = {} self.asteroid_id = 0 self.active_bullets = [] self.hit_asteroids = [] self.azimuth = 0.0 self.incl = 0.0 self.self_hit = -1 self.sprites = sprites self.fixed_sprites = [] self.lives = INITIAL_LIVES self.scores = 0 self.scores_changed = True self.pause = False self.free_play = False self.fire_button_state = 1 self.frames = 0 self.mode = [MODE_READY, READY_TIME] self.ready_text = pi3d.String(font=FONT_BALLS, string = "READY?", x = -.3, y = 1, z = 3.9, sx=0.018, sy=0.018) self.ready_text.set_shader(shader_uv_flat) self.go_text = pi3d.String(font=FONT_BALLS, string = "GO!", x = -.2, y = 0.8, z = 3.9 + 5*.5, sx=0.018, sy=0.018) self.go_text.set_shader(shader_uv_flat) # Initial sprite location s = self.sprites['sight'] s.position(*SIGHT_POSITION) s.scale(*SIGHT_SCALE) self.fixed_sprites.append(s) s = sprites['radar_panel'] s.position(*RADAR_PANEL_POSITION) s.scale(*RADAR_PANEL_SCALE) self.fixed_sprites.append(s) self.radar_target = sprites['radar_target'] self.radar_target.scale(*TARGET_SCALE) self.life_full = sprites['life_full'] self.life_full.scale(*LIFE_BAR_SCALE) self.life_empty = sprites['life_empty'] self.life_empty.scale(*LIFE_BAR_SCALE) def create_bullet(self, now): b = self.bullet_gen.generate(self.azimuth, self.incl, now) self.active_bullets.append(b) SOUNDS['shot'].play() # For all asteroids, check if the bullet hits them I = b.get_direction() indx = 0 dest = None # Scan all the asteroids against incidence with the newly # created bullet. If more than one asteroid incides with # the bullet trajectory, pick the closest one for astid, ast in self.active_asteroids.items(): if (self.check_incidence(ast, I)): if dest is None: dest = (astid, ast) else: if (ast.distance2() < dest[1].distance2()): dest = (astid, ast) b.set_destination(dest) # Check wheter a bullet will hit an asteroid. # asteroid - An Asteroid class object # bullet - A unit vector designating the bullet direction # # The test is based on a line-sphere intersection test, as described # in http://en.wikipedia.org/wiki/Line%E2%80%93sphere_intersection # We are not interested in the full solution of the equation, only whether # the term under square root is non-negative. Also, the bullets always # originate at the origin (0,0,0) simplifying the equation further def check_incidence(self, asteroid, bullet): c = asteroid.get_position() r = asteroid.radius I = bullet sq = (I.dot(c))**2 - (I.dot(I)*(c.dot(c) - r**2)) return (sq >= 0) def play(self, keys): now = time.time() start_time = now imux = 0 imuy = 0 imuz = 0 while DISPLAY.loop_running(): now = time.time() self.frames += 1 # Self hit effect if self.self_hit > 0: DISPLAY.set_background(self.self_hit*1.0/10.0, 0, 0, 1) if self.self_hit < 10: self.self_hit += 1 else: self.self_hit = -1 DISPLAY.set_background(0.0,0,0,1.0) # (possibly) generate a new asteroid if not self.pause: ast = self.gen.generate_asteroid(now) if ast is not None: self.active_asteroids[self.asteroid_id] = ast self.asteroid_id += 1 # Draw all active asteroid for astid, ast in self.active_asteroids.items(): # Draw the asteroid itseld if not self.pause: ast.move(now) dist2_from_origin = ast.distance2() # Draw the target on the radar view dist_from_origin = (math.sqrt(dist2_from_origin)/INITIAL_DISTANCE)*TARGET_DIST_SCALE angle = math.radians(ast.azimuth + self.azimuth + 90) rtx = dist_from_origin*math.cos(angle) rty = dist_from_origin*math.sin(angle) self.radar_target.position(TARGET_CENTER_POSITION[0]+rtx, TARGET_CENTER_POSITION[1]+rty, TARGET_CENTER_POSITION[2]) self.radar_target.draw(camera = cam2d) if dist2_from_origin < SELF_IMPACT_RADIUS2: # Reached origin, destory it self.gen.return_asteroid(self.active_asteroids[astid]) del self.active_asteroids[astid] self.self_hit = 1 SOUNDS['self_hit'].play() if not self.free_play: self.lives -= 1 # Position, rotate and draw the asteroid ast.draw(camera = cam3d) # Delete all hit asteroids, whose time has passed for astid in range(len(self.hit_asteroids)): print (astid) print(self.hit_asteroids) ast = self.hit_asteroids[astid] if ast.hit_time > 8.0: self.gen.return_asteroid(self.hit_asteroids[astid]) del self.hit_asteroids[astid] # Draw all hit asteroids for ast in self.hit_asteroids: ast.move(now) if ast.hit_time > 8.0: self.hit_asteroids[0] ast.draw(camera = cam3d) # Draw all active bullets objindex = 0 for bull in self.active_bullets: if not self.pause: bull.move(now) dest = bull.get_destination() dist2_from_origin = bull.distance2() if (dest is not None) and (dest[0] in self.active_asteroids): ast_distance2 = dest[1].distance2() if dist2_from_origin > ast_distance2: # Bullet hit the asteroid del self.active_asteroids[dest[0]] dest[1].hit(now) self.hit_asteroids.append(dest[1]) del self.active_bullets[objindex] self.scores += 1 self.scores_changed = True SOUNDS['astro_hit'].play() elif dist2_from_origin > BULLET_DISTANCE2: # Reached final distance, destroy it del self.active_bullets[objindex] else: objindex += 1 bull.draw(camera = cam3d) # Draw Sprites for s in self.fixed_sprites: s.draw(camera = cam2d) # Draw lives for l in range(0, 5): if l+1 > self.lives: s = self.life_empty else: s = self.life_full s.position(LIFE_BAR_POSITION[0], LIFE_BAR_POSITION[1] + l*LIFE_BAR_STEP, LIFE_BAR_POSITION[2]) s.draw(camera = cam2d) # Draw scores if self.scores_changed: self.scores_str = pi3d.String(font=FONT_COMPUTER, string="%03d" % self.scores, x = SCORE_POSITION[0], y = SCORE_POSITION[1], z = SCORE_POSITION[2], sx=0.01, sy=0.01) self.scores_str.set_shader(shader_uv_flat) scores_changed = False self.scores_str.draw(camera = cam2d) # Draw READY-GO text if (self.mode[0] == MODE_READY): self.ready_text.draw(camera = cam2d) self.mode[1] -= 1 if (self.mode[1] == 0): self.mode = [MODE_READY_GO, 5] elif (self.mode[0] == MODE_READY_GO): self.ready_text.translateZ(.5) self.ready_text.set_custom_data(17, [self.mode[1]/5.0]) self.ready_text.draw(camera = cam2d) self.go_text.translateZ(-0.5) self.go_text.set_custom_data(17, [1.0 - self.mode[1]/5.0]) self.go_text.draw(camera = cam2d) self.mode[1] -= 1 if (self.mode[1] == 0): self.mode = [MODE_GO, GO_TIME] elif (self.mode[0] == MODE_GO): self.go_text.draw(camera = cam2d) self.mode[1] -= 1 if (self.mode[1] == 0): self.mode = [MODE_GO_OUT, 5] elif (self.mode[0] == MODE_GO_OUT): self.go_text.translateZ(.5) self.go_text.set_custom_data(17, [self.mode[1]/5.0]) self.go_text.draw(camera = cam2d) self.go_text.draw(camera = cam2d) self.mode[1] -= 1 if (self.mode[1] == 0): self.mode = [MODE_PLAY, 0] # Debugging #debug_str = "az: %f incl: %f" % (self.azimuth, self.incl) #debug_str_pi = pi3d.String(font=FONT_ARIAL, string=debug_str, # x = 0, y = 0, z = 5, sx=0.005, sy=0.005) #debug_str_pi.set_shader(shader_uv_flat) #debug_str_pi.draw(camera = cam2d) # Read the IMU angles imux, imuy, imuz = IMU.data self.incl = -math.degrees(imuy) self.azimuth = math.degrees(imuz) cam_rotate = True # TEMPORARY CODE k = keys.read() cam_rotate = False if k >-1: if k == ord('p'): # Toggle pause self.pause = not self.pause elif k == ord('f'): # Toggle free play mode self.free_play = not self.free_play elif k==ord(' '): self.create_bullet(now) elif (k == 27): break # Check if the trigger button is pressed fire_button = GPIO.input(BUTTON_FIRE_GPIO[0]) if (fire_button == 1 and self.fire_button_state == 0): self.create_bullet(now) pass self.fire_button_state = fire_button # Handle camera rotation if True: #cam_rotate: cam3d.reset() cam3d.rotateX(self.incl) cam3d.rotateY(-self.azimuth) # If no more lives left, terminate the game if self.lives == 0: break # Calculate average FPS end_time = time.time() self.FPS = (1.0*self.frames)/(1.0*(end_time - start_time)) ###################################### #### FullScreenImage ###################################### class FullScreenImage(object): def __init__(self, image_filename): # Create a sprite from the image file self.bg = pi3d.ImageSprite(image_filename, shader = shader_uv_flat, w = 1.6, h = 1) # Position the openinig screen graphics self.bg.position(0, 0, 4) self.bg.scale(3.7, 3.7, 1) def start(self): while DISPLAY.loop_running(): # Draw the background self.bg.draw(camera = cam2d) # Additional drawing self.draw(camera = cam2d) # Process input if not self.process_input(): break # Default additional draw - nothing def draw(self, camera): pass # Default input processing - always continue def process_input(self): return True ###################################### #### OpeningScreen ###################################### class OpeningScreen(FullScreenImage): def __init__(self): super(OpeningScreen, self).__init__(BITMAP_DIR + "opening.png") # Create a text string self.text = pi3d.String(font=FONT_COMPUTER, string = "Press the START Button to Begin", x = 0, y = .5, z = 3.9, sx=0.005, sy=0.005) self.text.set_shader(shader_uv_flat) self.text_ts_delta = 0.1 self.text_ts = 0 def draw(self, camera): # Set the transparency of the text self.text_ts += self.text_ts_delta self.text.set_custom_data(17, [abs(math.sin(self.text_ts))]) self.text.draw(camera = cam2d) def process_input(self): # Check if the START button was pressed b = GPIO.input(BUTTON_START_GPIO) if (b == 0): return False k = KEYS.read() if k >-1: return False; return True ###################################### #### EndingScreen ###################################### class EndingScreen(FullScreenImage): def __init__(self, image, sound = None, tmax = 8): super(EndingScreen, self).__init__(BITMAP_DIR + image) self.sound = sound self.t_end = time.time() + tmax def start(self): # Call the super to create the image super(EndingScreen, self).start() # If a sound is defined, play it if self.sound is not None: self.sound.play() def process_input(self): # Check if a designated number of seconds has passed since # the screen was created if time.time() > self.t_end: return False # Check if the START button was pressed b = GPIO.input(BUTTON_START_GPIO) if (b == 0): return False k = KEYS.read() if k >-1: return False; return True def load_sprites(): sprite_filenames = ['sight', 'radar_panel', 'radar_target', 'life_full', 'life_empty', 'trans'] sprites = {} sh = shader_uv_flat for fn in sprite_filenames: s = pi3d.ImageSprite('../media/bitmaps/' + fn + '.png', shader = sh, w = 1, h = 1) sprites[fn] = s return sprites def setup_io(): GPIO.setmode(GPIO.BCM) GPIO.setup(BUTTON_START_GPIO, GPIO.IN, GPIO.PUD_UP) GPIO.setup(BUTTON_FIRE_GPIO[0], GPIO.IN, GPIO.PUD_UP) GPIO.setup(BUTTON_FIRE_GPIO[1], GPIO.OUT) GPIO.output(BUTTON_FIRE_GPIO[1], 0) GPIO.setup(RUMBLE_FIRE_GPIO, GPIO.OUT) def load_asteroids(): # Check if a pre-loaded database exists db_filename = os.path.join(VAR_DIR, AST_DB_FILENAME) start = time.time() if os.path.exists(db_filename): # Load the database ast = pickle.load(file(db_filename, 'rb')) else: # Database does not exist. Load the models then save # the database ast = [] global_scale = 1.0 for mf in asteroids.models[0:5]: model_filename = mf[0] model_scale = mf[1] model_name = model_filename.split('.')[0] # Remove the .obj extention m = pi3d.Model(file_string='../media/models/' + model_filename, name=model_name) m.scale(model_scale*global_scale, model_scale*global_scale, model_scale*global_scale) ast.append(m) pickle.dump(ast, file(db_filename, 'wb')) # Set the shader for all models for a in ast: a.set_shader(shader_uv_flat) end = time.time() print("Loading time: %f\n" % (end-start)) return ast def init_imu(): s = RTIMU.Settings("RTIMU") imu = RTIMU.RTIMU(s) print("IMU Name: " + imu.IMUName()) if (not imu.IMUInit()): print("IMU Init Failed"); sys.exit(1) else: print("IMU Init Succeeded"); reader = IMUReader(imu) reader.start() return reader def init_sounds(): # Init the mixer pygame.mixer.init() # Load sounds sounds = { 'win': pygame.mixer.Sound(SOUNDS_DIR + '126000__xserra__campeones.wav'), 'shot': pygame.mixer.Sound(SOUNDS_DIR + '156895__halgrimm__a-shot.wav'), 'self_hit': pygame.mixer.Sound(SOUNDS_DIR + '218721__bareform__boom-bang.wav'), 'astro_hit': pygame.mixer.Sound(SOUNDS_DIR + '147584__cactus2003__far-off-boom.wav'), 'lose': pygame.mixer.Sound(SOUNDS_DIR + '178875__rocotilos__you-lose-evil.wav') } return sounds # Setup display and initialise pi3d DISPLAY = pi3d.Display.create(background=(0.0, 0, 0, 1)) DISPLAY.frames_per_second = 30 # Create Cameras ASPECT = DISPLAY.width / DISPLAY.height cam3d = pi3d.Camera((0,0,0), (0,0,-0.1), (1, 1000, 45, ASPECT), is_3d=True) cam2d = pi3d.Camera(is_3d=True) # Load shaders shader_uv_flat = pi3d.Shader('uv_flat') shader_mat_flat = pi3d.Shader('mat_flat') shader_explosion = pi3d.Shader("uv_flat_explode") # Load Fonts FONT_ARIAL = pi3d.Font("../media/fonts/FreeMonoBoldOblique.ttf", (221,0,170,255)) FONT_COMPUTER = pi3d.Font("../media/fonts/Computerfont.ttf", (0,0,255,255)) FONT_BALLS = pi3d.Font("../media/fonts/BallsoOnTheRampage.ttf", (50,70,120,255)) # Load Sprites SPRITES = load_sprites() # Load Asteroid models ASTEROIDS = load_asteroids() # Load sounds SOUNDS = init_sounds() # Setup I/O setup_io() # Initialize the IMU IMU = init_imu() # Fetch key presses KEYS = pi3d.Keyboard() EndingScreen('you_lost.png', SOUNDS['lose']).start() EndingScreen('new_high_scores.png').start() opening = OpeningScreen() opening.start() level = GameLevel(SPRITES) try: level.play(KEYS) KEYS.close() DISPLAY.destroy() IMU.running = False except: #mykeys.close() DISPLAY.destroy() IMU.running = False print(level.gen.asteroid_model_list) raise IMU.running = False

[ "avishorp@gmail.com" ]

avishorp@gmail.com

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rvsmegaraj1996/Megaraj

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2020-09-14T09:58:33

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alpha=int(input("tell us the no of floor: ")) beta=int(input("tell us the houses: ")) sum=0 for row1 in range(1,alpha+1): for row2 in range(1,beta+1): rent=int(input("bring the rent: ")) if row2==2 or row2==4: if rent>=8000: print("thanks for the payment");sum+=8000 print("balanced to be returned: ",(rent-8000)) else:print("need to pay") elif row2==1 or row2==3: if rent>=6000: print("thanks for the payment");sum+=6000 print("balanced to be returned: ",(rent-6000)) else:print("need to pay") print("total collection: ",sum)

[ "rvsmegaraj1996@gmail.com" ]

rvsmegaraj1996@gmail.com

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/pythonproject1/runserver.py

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uribracha/schoolproject1

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""" This script runs the pythonproject1 application using a development server. """ from os import environ from pythonproject1 import app if __name__ == '__main__': HOST = environ.get('SERVER_HOST', 'localhost') try: PORT = int(environ.get('SERVER_PORT', '5555')) except ValueError: PORT = 5555 app.run(HOST, PORT)

[ "uriluli@gmail.com" ]

uriluli@gmail.com

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063a95c05876944e45e204557c76f4140f028182

/Leetcode_Practice/DS-LinkedList.py

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Urvashi-91/Urvashi_Git_Repo

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class Node: def __init__(self, data: None, next: None): self.data = data self.next = next class SLinkedList: def __init__(self): self.head = None def insert_at_begining(self, data): node = Node(data, self.head) self.head = node def insert_at_end(self, data): if self.head == None: self.head = Node(data, None) return itr = self.head while itr.next: itr = itr.next itr.next = Node(data, None) def traverse(self): if self.head == None: return itr = self.head value = "" while itr: value += str(itr.data) + '--->' itr = itr.next print(value) def insert_list(self, data_list): self.head = None for data in data_list: self.insert_at_end(data) def get_length(self): count = 0 if self.head == None: return 0 itr = self.head while itr: count += 1 itr = itr.next return count def remove_at(self, index): if index < 0 or index >= self.get_length(): raise Exception("Invalid") if index == 0: self.head = self.head.next return count = 0 itr = self.head while index-1 != count: itr = itr.next count += 1 itr.next = itr.next.next def insert_at(self, data, index): if index < 0 or index >= self.get_length(): raise Exception("Invalid") if index == 0: self.insert_at_begining(data) return count = 0 itr = self.head while itr: if count == index-1: node = Node(data, itr.next) itr.next = node break itr = itr.next count += 1 if __name__ == '__main__': ll = SLinkedList() ll.insert_at_begining(3) ll.insert_at_begining(5) ll.insert_at_end(6) ll.insert_at_end(5) ll.insert_list(["1","2","3"]) ll.remove_at(2) ll.insert_at(6,1) ll.traverse()

[ "hanu@Urvashis-MacBook-Pro.local" ]

hanu@Urvashis-MacBook-Pro.local

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/dqe/apps.py

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[]

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Gavin188/WGT1

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2020-08-29T05:19:03.188790

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from django.apps import AppConfig class DqeConfig(AppConfig): name = 'dqe'

[ "gavin@foxconn.com" ]

gavin@foxconn.com

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/config/wsgi.py

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permissive

pavlovicr/bcs

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""" WSGI config for bcs project. This module contains the WSGI application used by Django's development server and any production WSGI deployments. It should expose a module-level variable named ``application``. Django's ``runserver`` and ``runfcgi`` commands discover this application via the ``WSGI_APPLICATION`` setting. Usually you will have the standard Django WSGI application here, but it also might make sense to replace the whole Django WSGI application with a custom one that later delegates to the Django one. For example, you could introduce WSGI middleware here, or combine a Django application with an application of another framework. """ import os import sys from django.core.wsgi import get_wsgi_application # This allows easy placement of apps within the interior # bcs directory. app_path = os.path.abspath(os.path.join( os.path.dirname(os.path.abspath(__file__)), os.pardir)) sys.path.append(os.path.join(app_path, 'bcs')) if os.environ.get('DJANGO_SETTINGS_MODULE') == 'config.settings.production': from raven.contrib.django.raven_compat.middleware.wsgi import Sentry # We defer to a DJANGO_SETTINGS_MODULE already in the environment. This breaks # if running multiple sites in the same mod_wsgi process. To fix this, use # mod_wsgi daemon mode with each site in its own daemon process, or use # os.environ["DJANGO_SETTINGS_MODULE"] = "config.settings.production" os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.production") # This application object is used by any WSGI server configured to use this # file. This includes Django's development server, if the WSGI_APPLICATION # setting points here. application = get_wsgi_application() if os.environ.get('DJANGO_SETTINGS_MODULE') == 'config.settings.production': application = Sentry(application) # Apply WSGI middleware here. # from helloworld.wsgi import HelloWorldApplication # application = HelloWorldApplication(application)

[ "rados.pavlovic@euroinvest.si" ]

rados.pavlovic@euroinvest.si

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/intercom_mattermost/asgi.py

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[]

no_license

LaBayVeTroi/intercom-mattermost

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refs/heads/master

2021-03-11T11:58:27.905597

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""" ASGI config for intercom_mattermost project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'intercom_mattermost.settings') application = get_asgi_application()

[ "phmtuan313@gmail.com" ]

phmtuan313@gmail.com

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/Python Library/EX_pyAesCrypt.py

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[]

no_license

dentiny/Python-applications

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refs/heads/master

2021-06-19T17:46:43.073939

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import pyAesCrypy; password="password"; buffer_size=64*1024; pyAesCrypt.encryptFile("file.txt", "file.txt.aes",password,buffer_size); pyAesCrypt.decryptFile("file.txt.aes","file.txt",password,buffer_size);

[ "noreply@github.com" ]

dentiny.noreply@github.com

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/test/unit/client/test_posts.py

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[]

no_license

culturemesh/culturemeshFFB

37e21975bff6e7e61dd961068f96cd17186d78da

53b4b61d541751d517d5e3a7a077358c4494d132

refs/heads/master

2022-03-01T23:38:27.329929

2019-10-28T07:29:58

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2019-10-28T07:15:39

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# # Tests client/posts.py # from nose.tools import assert_true, assert_equal import test.unit.client.client_test_prep from culturemesh.client import Client def test_get_post(): """ Tests we can get a single post. """ c = Client(mock=True) post = c.get_post(4) print(post) assert_equal(post['vid_link'], "https://www.lorempixel.com/1016/295") def test_get_post_replies(): """ Tests that we can get post replies as expected. """ c = Client(mock=True) posts1 = c.get_post_replies(1, count=5) posts2 = c.get_post_replies(2, count=5) print(posts1) posts3 = c.get_post_replies(1, count=1) posts4 = c.get_post_replies(1, count=3, max_id=1) assert_equal(len(posts1), 2) assert_equal(len(posts2), 0) assert_equal(len(posts3), 1) assert_equal(posts3[0]['id'], 2) assert_equal(len(posts4), 1) assert_equal(posts4[0]['id'], 1)

[ "alanf94@stanford.edu" ]

alanf94@stanford.edu

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/resgrid/nr/codebook1/para.py

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[]

no_license

liuyonggang1/liuyonggang1.github.io

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1b99f7576195d6169aa67b2b11136c8f0e13ab0c

refs/heads/master

2023-04-11T05:00:24.871853

2023-04-01T13:28:58

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from parabase import Parameter, ConfigBase, ParameterDisplay # import sys # sys._getframe().f_code.co_name = {File}_{Class}_{digit}_{function}_{digit} ############################################################################# # start add parameter ############################################################################# t = Parameter("codebookMode") t.range = [1, 2] t.spec = '38331: CodebookConfig->codebookType->type1->codebookMode' t = Parameter('pcsirs') t.desc = 'PCSI-RS' t.range = [4, 8, 12, 16, 24, 32] t.spec = '38.214 Table 5.2.2.2.1-2. The number of CSI-RS ports.' t = Parameter('n1n2') t.desc = 'N1,N2' t.range = [""] t.spec = '38331: CodebookConfig->codebookType->type1->subType->typeI-SinglePanel->nrOfAntennaPorts->moreThanTwo->n1-n2' t = ParameterDisplay('o1o2') t.desc = 'O1,O2' t.spec = '38.214 Table 5.2.2.2.1-2' t = Parameter('nlayers') t.desc = 'layers' t.range = [1,2,3,4,5,6,7,8] t.spec = 'number of layers' ############################################################################# # end add parameter ############################################################################# class Config(ConfigBase): ############################################################################# # start add consistency check ############################################################################# def pcsirs_change(self, event): m = self.mvalue if m == 4: new_range = ["2,1"] elif m == 8: new_range = ["2,2", "4,1"] elif m == 12: new_range = ["3,2", "6,1"] elif m == 16: new_range = ["4,2", "8,1"] elif m == 24: new_range = ["4,3", "6,2", "12,1"] elif m == 32: new_range = ["4,4", "8,2", "16,1"] self.set_range('n1n2', new_range) self.n1n2_change(None) if m == 4: new_range = range(1, 5) else: new_range = range(1, 9) self.set_range('nlayers', new_range) def n1n2_change(self, event): m = self.mvalue new_range = "4,1" if m.endswith(",1") else "4,4" self.set_range('o1o2', new_range)

[ "yonggang.liu@nokia-sbell.com" ]

yonggang.liu@nokia-sbell.com

a7a86059a9d0de0019b07e665dadab10f212e05d

ddcaa8f8c330ac79daf8893eb77252910b5fa369

/image_classification/setup.py

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[ "Apache-2.0" ]

permissive

waikato-datamining/tensorflow

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refs/heads/master

2023-04-06T20:20:40.954270

2022-09-30T00:27:36

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2023-03-26T20:18:05

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py

from setuptools import setup, find_namespace_packages def _read(f) -> bytes: """ Reads in the content of the file. :param f: the file to read :type f: str :return: the content :rtype: str """ return open(f, 'rb').read() setup( name="wai.tfimageclass", description="Image classification using tensorflow.", long_description=( _read('DESCRIPTION.rst') + b'\n' + _read('CHANGES.rst')).decode('utf-8'), url="https://github.com/waikato-datamining/tensorflow/tree/master/image_classification", classifiers=[ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: Apache Software License', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Programming Language :: Python :: 3', ], license='Apache 2.0 License', package_dir={ '': 'src' }, packages=find_namespace_packages(where='src'), namespace_packages=[ "wai", ], version="0.0.15", author='Peter Reutemann and TensorFlow Team', author_email='fracpete@waikato.ac.nz', install_requires=[ "argparse", "numpy", "pillow", "tensorflow_hub", "simple-confusion-matrix", "redis", "redis-docker-harness", ], entry_points={ "console_scripts": [ "tfic-retrain=wai.tfimageclass.train.retrain:sys_main", "tfic-stats=wai.tfimageclass.train.stats:sys_main", "tfic-export=wai.tfimageclass.train.export:sys_main", "tfic-labelimage=wai.tfimageclass.predict.label_image:sys_main", "tfic-label-redis=wai.tfimageclass.predict.label_redis:sys_main", "tfic-poll=wai.tfimageclass.predict.poll:sys_main", ] } )

[ "fracpete@gmail.com" ]

fracpete@gmail.com

a07cef6dbffcbf1eb0f07a581c979bed024b4f4a

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/Main/cnf_min.py

80885e4dfb7ee9e38adcd29fff085243e04a2cbd

[]

no_license

Shubhankar007/ECEN-699

f39aecda647bf61f6b151d41016e5c5f5d3bf52c

b738faf36a8fd891b9c7e98b95188d00bd6d2ef3

refs/heads/master

2021-01-10T15:45:06.091632

2016-05-06T21:41:17

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py

#!/usr/bin/python from pyeda.inter import * import sys import ast #function to split clause list def split_list(seq, size): newlist = [] splitsize = 1.0/size*len(seq) for i in range(size): newlist.append(seq[int(round(i*splitsize)):int(round((i+1)*splitsize))]) return newlist #Import CNF file and get clauses in an AST with open(sys.argv[1], 'r') as fin: f = parse_cnf(fin.read()) #f = ast2expr(parse_cnf(fin.read())) #Get CNF Clauses in a list l=len(f) f_clause_list =[] for i in range(1, l): f_clause_list.append(ast2expr(f[i])) f_clause_list_str =[] #Sort the Clauses # for current in range(len(f_clause_list)): # f_clause_list_str.append(str(f_clause_list[current])) # f_clause_list_str.sort() #Split the clauses into clusters num_of_clauses = int(l/50) f_clause_list_split = split_list(f_clause_list, (num_of_clauses + 1)) f_split = [] for current in range(len(f_clause_list_split)): f_split1 = 1 for current2 in range(len(f_clause_list_split[current])): f_split1 = And((f_clause_list_split[current][current2]),f_split1) f_split.append(f_split1) #APPLY DE MORGAN #Get NOT(F) f_bar_split = [] f_bar_2 = [] for current in range(len(f_split)): f_bar_2.append(((~f_split[current])).to_nnf()) f_bar_split.append(f_bar_2[current].to_dnf()) #print(f_bar) #Run Espresso on split NOT(F) f_bar_min_split = [] f_bar_min_split_2 = None for current in range(len(f_bar_split)): f_bar_min_split_2 = espresso_exprs(f_bar_split[current]) f_bar_min_split.append(expr(f_bar_min_split_2[0])) #print(f_bar_min_split) #Combine NOT(F) c = 0 for current in range(len(f_bar_min_split)): c = Or((f_bar_min_split[current]),c) #f_split.append(f_split1) f_bar_min = expr(c) #print(f_bar_min) #APPLY DE MORGAN #Get Minimized F f_n = (~f_bar_min).to_nnf() f_new = f_n.to_cnf() litmap, nvars, clauses = f_new.encode_cnf() f_nd = DimacsCNF(nvars, clauses) f_min = str(f_nd) #print(f_min) #Generate Output file inputfile = sys.argv[1] outputfile = inputfile.split(".")[0] + "_out.cnf" with open(outputfile, "w" ) as file_out: file_out.write(f_min)

[ "shubhankar.007@outlook.com" ]

shubhankar.007@outlook.com

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/utils/logger.py

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[]

no_license

dgl-prc/pfa_extraction

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refs/heads/master

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null

2023-03-24T23:18:02

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import sys import os class Logger(object): def __init__(self, filename='default.log', stream=sys.stdout): path = os.path.dirname(filename) if not os.path.exists(path): os.makedirs(path) self.terminal = stream self.log = open(filename, 'a') def write(self, message): self.terminal.write(message) self.log.write(message) def flush(self): pass

[ "2469039292@qq.com" ]

2469039292@qq.com

cf0077e02d0facb38182876a848e1d77a50cb7dc

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/mint_work/custom/website_support/models/__init__.py

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[]

no_license

merdhah/dubai_work

fc3a70dc0b1db6df19c825a3bf1eef2a373d79c0

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refs/heads/master

2022-01-07T11:22:07.628435

2018-10-17T13:37:24

null

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null

UTF-8

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# import website_support_ticket, res_partner, website_support_help import res_partner import project_case

[ "asghar0517@gmail.com" ]

asghar0517@gmail.com

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/users/migrations/0006_auto_20200115_1950.py

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[]

no_license

DimaSapsay/ma_book

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refs/heads/master

2020-12-15T09:52:22.669639

2020-02-24T22:38:03

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# Generated by Django 3.0.2 on 2020-01-15 17:50 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('users', '0005_auto_20200115_1948'), ] operations = [ migrations.AlterField( model_name='userprofile', name='user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), ]

[ "logotip123@yahoo.com" ]

logotip123@yahoo.com

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/performance/migrations/0009_auto_20171203_0959.py

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[]

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ahmadiga/personal-assistant

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refs/heads/master

2021-01-12T09:30:54.704880

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# -*- coding: utf-8 -*- # Generated by Django 1.10.4 on 2017-12-03 07:59 from __future__ import unicode_literals import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('performance', '0008_auto_20171203_0958'), ] operations = [ migrations.AlterField( model_name='performance', name='year', field=models.DateTimeField(blank=True, default=datetime.datetime(2017, 12, 3, 7, 59, 53, 307207, tzinfo=utc), null=True), ), ]

[ "m.bazadough@sit-mena.com" ]

m.bazadough@sit-mena.com

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/segmentation/test_coco.py

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[ "MIT" ]

permissive

daydreamer2023/VISTA-Net

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########################################################################### # Created by: Hang Zhang # Email: zhang.hang@rutgers.edu # Copyright (c) 2017 ########################################################################### import os import torch import torchvision.transforms as transform import scipy.io as sio import encoding.utils as utils import cv2 import numpy as np from tqdm import tqdm from torch.utils import data from encoding.nn import BatchNorm2d from encoding.datasets import get_dataset, test_batchify_fn from encoding.models import get_model, MultiEvalModule from option import Options import time def test(args): # output folder outdir = args.save_folder if not os.path.exists(outdir): os.makedirs(outdir) # data transforms input_transform = transform.Compose([ transform.ToTensor(), transform.Normalize([.485, .456, .406], [.229, .224, .225])]) # dataset testset = get_dataset(args.dataset, split=args.split, mode=args.mode, transform=input_transform) # dataloader loader_kwargs = {'num_workers': args.workers, 'pin_memory': True} \ if args.cuda else {} test_data = data.DataLoader(testset, batch_size=args.test_batch_size, drop_last=False, shuffle=False, collate_fn=test_batchify_fn, **loader_kwargs) # model if args.model_zoo is not None: model = get_model(args.model_zoo, pretrained=True) else: model = get_model(args.model, dataset=args.dataset, backbone=args.backbone, dilated=args.dilated, lateral=args.lateral, attentiongraph=args.attentiongraph, aux=args.aux, se_loss=args.se_loss, norm_layer=BatchNorm2d, base_size=args.base_size, crop_size=args.crop_size) # resuming checkpoint if args.resume is None or not os.path.isfile(args.resume): raise RuntimeError("=> no checkpoint found at '{}'".format(args.resume)) checkpoint = torch.load(args.resume) # strict=False, so that it is compatible with old pytorch saved models model.load_state_dict(checkpoint['state_dict']) print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) # print(model) scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25] if args.dataset == 'citys' else \ [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] if not args.ms: scales = [1.0] evaluator = MultiEvalModule(model, testset.num_class, scales=scales, flip=args.ms).cuda() evaluator.eval() metric = utils.SegmentationMetric(testset.num_class) tbar = tqdm(test_data) for i, (image) in enumerate(tbar): # colormap_dir = './output_seg_att' # if not os.path.isdir(colormap_dir): # os.mkdir(colormap_dir) # for img in image: # img = np.transpose(img.numpy(), (1, 2, 0)) # # print(img.shape) # cv2.imwrite(os.path.join(colormap_dir, str(i).zfill(4) + 'rgb.jpg'), np.uint8(img)) with torch.no_grad(): predicts = evaluator.parallel_forward(image) ###save_attention_map colormap_dir = './output_seg_att' if not os.path.isdir(colormap_dir): os.mkdir(colormap_dir) # # print(predicts[0].shape) predict = torch.argmax(torch.squeeze(predicts[0]),dim=0) cv2.imwrite(os.path.join(colormap_dir, str(i).zfill(4) + '.png'),predict.cpu().numpy()) if __name__ == "__main__": args = Options().parse() torch.manual_seed(args.seed) args.test_batch_size = torch.cuda.device_count() test(args)

[ "641807447@qq.com" ]

641807447@qq.com

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/Contests/Google/Codejam21/Round1C/B/B.py

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srinjoyray/Competitive

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refs/heads/main

2023-06-16T16:08:10.787883

2021-07-14T14:19:17

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py

t = int(input()) for test in range(1, t + 1): s = "Case #" + str(test) + ":" print(s, end=" ") y = int(input()) ys = str(y) a = [] ans = "" for i in ys: ans = ans + i tmp = ans for j in range(int(tmp) + 1, 10**26): tmp += str(j) a.append(int(tmp)) if len(tmp) > 20: break ys = str(int(ys[0]) + 1) + ys[1:] ans = "" for i in ys: ans = ans + i tmp = ans for j in range(int(tmp) + 1, 10**26): tmp += str(j) a.append(int(tmp)) if len(tmp) > 20: break m = 1; while m <= 10**18: tmp = str(m) for j in range(m + 1, 10**26): tmp += str(j) a.append(int(tmp)) if len(tmp) > 20: break m *= 10 a.sort() res = 0 for i in a: if i > y: res = i break print(res)

[ "ankurkayal1234@gmail.com" ]

ankurkayal1234@gmail.com

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/Artificial Intelligence/Assignments/Assignment 2/.ipynb_checkpoints/game-checkpoint.py

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masterashu/Semester-4

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refs/heads/main

2022-12-12T20:33:53.986287

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from abc import ABC, abstractmethod from minmax import * from typing import List class Player: def __init__(self, name='Player', mark=None, user: bool = False): self.name = name self.mark = mark self.user = user def __str__(self): return f'{self.name} - {self.mark}' class Game(ABC): def __init__(self, initial_state, all_moves, players: List[Player]): self.initial_state = initial_state self.all_moves = all_moves self.players = players self.state = None super(Game, self).__init__() @abstractmethod def make_move(self, move) -> bool: pass @abstractmethod def utility(self, state, player): pass @abstractmethod def explore_moves(self, state, player: Player): pass @staticmethod @abstractmethod def transition(state, move, player=None): pass @abstractmethod def game_end(self, state) -> bool: pass @abstractmethod def winner(self, state): pass @staticmethod @abstractmethod def get_user_move(player): pass @abstractmethod def heuristic(self, state): pass @abstractmethod def state_repr(self, state): pass @abstractmethod def reset(self): pass class GameSolvingAgent: def __init__(self, game: Game, current_player=None, algo: Algorithm = Algorithm.MinMax): self.game = game self.moves = [] self.current_player = current_player or game.players[0] self.algo = algo def predict_next_move(self, max_depth=5): move = search_minmax(self.game, self.algo, max_depth=max_depth) return move def make_agent_move(self, move): self.game.make_move(move) self.moves.append(move) # Duplicate Function to distinguish # user actions from agents without confusion def make_user_move(self, user_move): self.moves.append(user_move) if self.game.make_move(user_move) is False: raise AssertionError() class GamePlayingAgent: def __init__(self, game: Game, algo: Algorithm = Algorithm.MinMax, **kwargs): self.game = game self.algo = algo self.params = kwargs self.agent = GameSolvingAgent(game, algo=algo) @property def game_ended(self): return self.game.game_end(self.game.state) @property def game_state(self): return self.game.state_repr(self.game.state) def play(self): _move = self.agent.predict_next_move(**self.params) if _move: self.agent.make_agent_move(_move) else: print("No possible Moves") def request_input(self): player = self.game.players[1] # Verify The Player is User assert player.user _move = self.game.get_user_move(player) self.agent.make_user_move(_move) def user_move(self, pos): player = self.game.players[1] move = (player.mark, pos[0], pos[1]) try: self.agent.make_user_move(move) except AssertionError: return False return True def print_result(self): assert self.game_ended if self.game.winner(self.game.state) == "TIE": s = "It's a tie." else: s = self.game.winner(self.game.state) + ' wins'

[ "masterashu@live.in" ]

masterashu@live.in

1a0c2038efd1f65eea978eb7cfed64dabe005087

0b6ed51cedd44df54e511a0bdeb28dcfb89d6c58

/age_func.py

91a82e5e1ecfd637e0aa1f61b3159319c1775946

[]

no_license

nansencenter/nextsim-age

b2180971058a3f64b049bc6479cff272cdb27dc9

51029ce58cf3ef073d540a5564649f79de978c7a

refs/heads/master

2020-03-31T21:40:09.354760

2019-06-18T12:03:03

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import numpy as np import matplotlib.pyplot as plt import regionmask import cartopy import cartopy.crs as ccrs import pyresample as pr import scipy.ndimage as ndimage from pyproj import Proj, transform def plot_pcolormesh(lons,lats,var,outname,vmin=0,vmax=1,cmap='jet',label='Variable'): # create the figure panel fig = plt.figure(figsize=(10,10), facecolor='w') # create the map using the cartopy NorthPoleStereo # +proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45" globe = cartopy.crs.Globe(semimajor_axis=6378273, semiminor_axis=6356889.44891) ax1 = plt.subplot(1,1,1, projection=ccrs.NorthPolarStereo(central_longitude=-45, true_scale_latitude=70, globe=globe)) ax1.set_extent([15, -180, 72, 62], crs=ccrs.PlateCarree()) # add coastlines, gridlines, make sure the projection is maximised inside the plot, and fill in the land with colour ax1.coastlines(resolution='110m', zorder=3) # zorder=3 makes sure that no other plots overlay the coastlines ax1.gridlines() ax1.add_feature(cartopy.feature.LAND, zorder=1,facecolor=cartopy.feature.COLORS['land_alt1']) # plot sea ice field pp = plt.pcolormesh(lons,lats,var,vmin=vmin,vmax=vmax, cmap=cmap, transform=ccrs.PlateCarree()) # add the colourbar to the bottom of the plot. # The first moves the bottom of the map up to 15% of total figure height, # the second makes the new axes for the colourbar, # the third makes the colourbar, and the final adds the label fig.subplots_adjust(bottom=0.15) cbar_ax = fig.add_axes([0.2, 0.1, 0.625, 0.033]) stp = (vmax-vmin)/10. cbar = plt.colorbar(pp, cax=cbar_ax, orientation='horizontal', ticks=np.arange(vmin,vmax+stp,stp)) cbar.set_label(label=label,size=14, family='serif') plt.savefig(outname,bbox_inches='tight') plt.close() def plot_contour(lons,lats,data,levels=[.15],colors=['purple'],lw=[1.],labels=['Variable'],outname='test.png'): # create the figure panel fig = plt.figure(figsize=(10,10), facecolor='w') # create the map using the cartopy NorthPoleStereo # +proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45" globe = cartopy.crs.Globe(semimajor_axis=6378273, semiminor_axis=6356889.44891) ax1 = plt.subplot(1,1,1, projection=ccrs.NorthPolarStereo(central_longitude=-45, true_scale_latitude=70, globe=globe)) ax1.set_extent([15, -180, 72, 62], crs=ccrs.PlateCarree()) # add coastlines, gridlines, make sure the projection is maximised inside the plot, and fill in the land with colour ax1.coastlines(resolution='110m', zorder=3) # zorder=3 makes sure that no other plots overlay the coastlines ax1.gridlines() ax1.add_feature(cartopy.feature.LAND, zorder=1,facecolor=cartopy.feature.COLORS['land_alt1']) # plot sea ice field for i in range(len(levels)): cs = plt.contour(lons,lats,data[i],levels=[levels[i]], colors=colors[i], linewidths=lw[i], transform=ccrs.PlateCarree()) cs.collections[0].set_label(labels[i]) ax1.legend(loc='upper right') plt.savefig(outname,bbox_inches='tight') plt.close() def plot_contour_bg(lons,lats,bg,data,levels=[.15],colors=['purple'],lw=[1.],ls=['-'],labels=['Variable'],bg_label='Snow_depth',outname='test.png',vmin=0,vmax=1,cmap='jet',cbar=True): # create the figure panel fig = plt.figure(figsize=(10,10), facecolor='w') # create the map using the cartopy NorthPoleStereo # +proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45" globe = cartopy.crs.Globe(semimajor_axis=6378273, semiminor_axis=6356889.44891) ax1 = plt.subplot(1,1,1, projection=ccrs.NorthPolarStereo(central_longitude=-45, true_scale_latitude=70, globe=globe)) ax1.set_extent([15, -180, 72, 62], crs=ccrs.PlateCarree()) # add coastlines, gridlines, make sure the projection is maximised inside the plot, and fill in the land with colour ax1.coastlines(resolution='110m', zorder=3) # zorder=3 makes sure that no other plots overlay the coastlines ax1.gridlines() ax1.add_feature(cartopy.feature.LAND, zorder=1,facecolor=cartopy.feature.COLORS['land_alt1']) #plot 'background' pp = plt.pcolormesh(lons,lats,bg,vmin=vmin,vmax=vmax, cmap=cmap, transform=ccrs.PlateCarree()) # add the colourbar to the bottom of the plot. # plot sea ice field handle_list = [] for i in range(len(levels)): cs = plt.contour(lons,lats,data[i],levels=[levels[i]], colors=colors[i], linewidths=lw[i], transform=ccrs.PlateCarree()) cs.collections[0].set_label(labels[i]) handle_list.append(cs.collections[0]) ax1.legend(loc='upper right') if cbar: fig.subplots_adjust(bottom=0.15) cbar_ax = fig.add_axes([0.2, 0.1, 0.625, 0.033]) cbar = plt.colorbar(pp, cax=cbar_ax, orientation='horizontal', ticks=np.arange(0,1.1,0.1)) cbar.set_label(label=bg_label,size=14, family='serif') else: import matplotlib.patches as mpatches red_patch = mpatches.Patch(color='darkred', label=bg_label) handle_list.append(red_patch) ax1.legend(handles=handle_list, loc='upper right') plt.savefig(outname,bbox_inches='tight') plt.close() def plot_quiver(x,y,u,v,outname,vmin=0,vmax=1,cmap='jet',label='Variable', scale=5): # create the figure panel fig = plt.figure(figsize=(10,10), facecolor='w') # create the map using the cartopy NorthPoleStereo # +proj=stere +a=6378273 +b=6356889.44891 +lat_0=90 +lat_ts=70 +lon_0=-45" globe = cartopy.crs.Globe(semimajor_axis=6378273, semiminor_axis=6356889.44891) ax1 = plt.subplot(1,1,1, projection=ccrs.NorthPolarStereo(central_longitude=-45, true_scale_latitude=70, globe=globe)) ax1.set_extent([15, -180, 72, 62], crs=ccrs.PlateCarree()) # add coastlines, gridlines, make sure the projection is maximised inside the plot, and fill in the land with colour ax1.coastlines(resolution='110m', zorder=3) # zorder=3 makes sure that no other plots overlay the coastlines #ax1.gridlines(crs=ccrs.PlateCarree(),xlocs=range(0,370,10),ylocs=range(60,90,5)) ax1.gridlines() ax1.add_feature(cartopy.feature.LAND,facecolor=cartopy.feature.COLORS['land_alt1']) # plot sea ice field speed = np.sqrt(u**2+v**2) pp = plt.pcolormesh(x,y,speed,vmin=vmin,vmax=vmax, cmap=cmap) #but our northings and eastings are in the projcted grid and not in lat, lon!!!! ax1.quiver(x, y, u, v, scale=scale) # add the colourbar to the bottom of the plot. # The first moves the bottom of the map up to 15% of total figure height, # the second makes the new axes for the colourbar, # the third makes the colourbar, and the final adds the label fig.subplots_adjust(bottom=0.15) cbar_ax = fig.add_axes([0.2, 0.1, 0.625, 0.033]) stp = (vmax-vmin)/10. cbar = plt.colorbar(pp, cax=cbar_ax, orientation='horizontal', ticks=np.arange(vmin,vmax+stp,stp)) cbar.set_label(label=label,size=14, family='serif') plt.savefig(outname,bbox_inches='tight') plt.close() def smooth_data(data,lon,lat,coarse_lon,coarse_lat): #smoothen the data for nicer contours with a lowpass filter #data = ndimage.gaussian_filter(data, 3) #sigma #regrid to equally spaced grid in latlon - otherwise there will be problems with cyclic point in contour plots orig_def = pr.geometry.SwathDefinition(lons=lon, lats=lat) targ_def = pr.geometry.SwathDefinition(lons=coarse_lon, lats=coarse_lat) #coarse_def = pr.geometry.SwathDefinition(lons=coarse_lon[::5,::5], lats=coarse_lat[::5,::5]) #data_coarse = pr.kd_tree.resample_nearest(orig_def, data, coarse_def, radius_of_influence=50000, fill_value=0) ##fill all nans with 0 >> closed contours #data_coarse = np.nan_to_num(data_coarse) data_smooth = pr.kd_tree.resample_gauss(orig_def, data, targ_def, radius_of_influence=500000, neighbours=10, sigmas=250000, fill_value=0) #data_smooth = pr.kd_tree.resample_nearest(coarse_def, data_coarse, targ_def, radius_of_influence=500000, fill_value=0) #wf = lambda r: 1 #data_smooth = pr.kd_tree.resample_custom(coarse_def, data_coarse, targ_def, radius_of_influence=100000, weight_funcs=wf) data_smooth = np.nan_to_num(data_smooth) #plot_pcolormesh(lon,lat,myi,'test.png',vmin=0,vmax=1,label='MYI fraction') #plot_pcolormesh(lon_g[::5],lat_g[::5],myi_coarse,'test1.png',vmin=0,vmax=1,label='MYI fraction') #plot_pcolormesh(lon_g,lat_g,myi_smooth,'test2.png',vmin=0,vmax=1,label='MYI fraction') #plot_contour(lon_g,lat_g,myi_smooth,'test3.png',levels=[.1], lw=[10], label='MYI extent') return(data_smooth) def regrid_data(data,inlon,inlat,outlon,outlat): #regrid to equally spaced grid in latlon - otherwise there will be problems with cyclic point in contour plots orig_def = pr.geometry.SwathDefinition(lons=inlon, lats=inlat) targ_def = pr.geometry.SwathDefinition(lons=outlon, lats=outlat) data = pr.kd_tree.resample_nearest(orig_def, data, targ_def, radius_of_influence=50000, fill_value=0) #fill all nans with 0 >> closed contours data = np.nan_to_num(data) return(data) def get_poly_mask(lons,lats): #create a geographical polygon for the Central Arctic (without the narrow band off the CAA) #https://regionmask.readthedocs.io/en/stable/_static/notebooks/create_own_regions.html #make two masks - one for W and one for E Arctic #regionmask does not handle well the circular polygons around the NP lon360 = np.where(lons<0,360+lons,lons) #print(lon360) #i,j coordinates of corner points can be found by exploring display in ncview #W Arctic poly1 = [] pt = [360,90];poly1.append(pt) pt = [360,lats[273,115]];poly1.append(pt) pt = [lon360[273,115],lats[273,115]];poly1.append(pt) pt = [lon360[260,128],lats[260,128]];poly1.append(pt) pt = [lon360[239,136],lats[239,136]];poly1.append(pt) pt = [lon360[228,145],lats[228,145]];poly1.append(pt) pt = [lon360[210,148],lats[210,148]];poly1.append(pt) pt = [lon360[194,147],lats[194,147]];poly1.append(pt) pt = [lon360[157,156],lats[157,156]];poly1.append(pt) pt = [lon360[113,174],lats[113,174]];poly1.append(pt) pt = [lon360[89,157],lats[89,157]];poly1.append(pt) pt = [lon360[29,123],lats[29,123]];poly1.append(pt) ##more radical (even further away from the CAA coast) #pt = [lon360[260,132],lats[260,132]];poly1.append(pt) #pt = [lon360[239,140],lats[239,140]];poly1.append(pt) #pt = [lon360[228,149],lats[228,149]];poly1.append(pt) #pt = [lon360[210,152],lats[210,152]];poly1.append(pt) #pt = [lon360[194,151],lats[194,151]];poly1.append(pt) #pt = [lon360[157,160],lats[157,160]];poly1.append(pt) #pt = [lon360[113,178],lats[113,178]];poly1.append(pt) #pt = [lon360[65,160],lats[65,160]];poly1.append(pt) #pt = [lon360[24,162],lats[29,162]];poly1.append(pt) pt = [lon360[3,194],lats[3,194]];poly1.append(pt) pt = [lon360[3,344],lats[3,344]];poly1.append(pt) pt = [180,65];poly1.append(pt) pt = [180,90];poly1.append(pt) pt = [270,90];poly1.append(pt) pt = [360,90];poly1.append(pt) #print(poly1) #E Arctic poly2 = [] pt = [0,90];poly2.append(pt) pt = [90,90];poly2.append(pt) pt = [180,90];poly2.append(pt) pt = [180,65];poly2.append(pt) pt = [lon360[135,386],lats[135,386]];poly2.append(pt) pt = [lon360[238,390],lats[238,390]];poly2.append(pt) pt = [lon360[310,344],lats[310,344]];poly2.append(pt) pt = [lon360[449,301],lats[449,301]];poly2.append(pt) pt = [lon360[350,122],lats[350,122]];poly2.append(pt) pt = [0,lats[273,115]];poly2.append(pt) pt = [0,90];poly2.append(pt) #print(poly2) numbers = [0, 1] names = ['Arctic_west', 'Arctic_east'] abbrevs = ['Aw', 'Ae'] Arctic_mask = regionmask.Regions_cls('Arctic_mask', numbers, names, abbrevs, [poly1, poly2]) ##Plot polygons in Mercator projection #ax=Arctic_mask.plot() #ax.set_extent([-180, 180, 45, 90], ccrs.PlateCarree()) #plt.show() #Make raster mask = Arctic_mask.mask(lons, lats, wrap_lon=True) #Merge mask mask = np.where(mask>=0,1,0) # pcolormesh does not handle NaNs, requires masked array age_mask = np.ma.masked_invalid(mask) ##Plot mask #outpath_plots = 'plots/run04/' #outname = outpath_plots+'age_mask_rest.png' #plot_pcolormesh(lons,lats,age_mask,outname,cmap='viridis',label='Central Arctic Mask=1') #exit() return(age_mask) def get_dra_mask(lons,lats): #get 'Data Release Area' mask for the Central Arctic, published by Rothrock et al, 2008 #this is the area for which submarine draft data is available (1979-2000) #and all Kwok papers use this area to show trend extended by IS and CS-2 data #regionmask does not handle well the circular polygons around the NP lon360 = np.where(lons<0,360+lons,lons) poly1 =[[360.,90.], [360.,87.], [345.,87.], [300.,86.58], [230.,80.], [219.,80.], [219.,70.], [205.,72.], [180.,74.], [180.,90.], [360.,90.]] poly2 =[[ 0.,86.], [ 0.,90.], [180.,90.], [180.,74.], [175.,75.50], [172.,78.50], [163.,80.50], [126.,78.50], [110.,84.33], [ 80.,84.42], [ 57.,85.17], [ 33.,83.83], [ 8.,84.08], [ 0.,86.]] numbers = [0, 1] names = ['Arctic_west', 'Arctic_east'] abbrevs = ['Aw', 'Ae'] Arctic_mask = regionmask.Regions_cls('DRA_mask', numbers, names, abbrevs, [poly1,poly2]) #Make raster mask = Arctic_mask.mask(lons, lats, wrap_lon=True) #Merge mask mask = np.where(mask>=0,1,0) # pcolormesh does not handle NaNs, requires masked array age_mask = np.ma.masked_invalid(mask) ##Plot mask #outpath_plots = 'plots/new/' #outname = outpath_plots+'age_mask_DRA.png' #plot_pcolormesh(lons,lats,age_mask,outname,cmap='viridis',label='Central Arctic Mask=1') #exit() return(age_mask) def read_sir(sirfile): #Matlab code #fid=fopen(filename,'r','ieee-be'); %ieee-be is big endian integer in python: <i2 #head=fread(fid,[256],'short'); % read header %usage:A = fread(fileID,sizeA,precision) %short: signed integers, 16bit, 2 byte (same as int16) data = np.fromfile(sirfile, dtype='<f') print(data) print(data.shape) print(data[0]) head = data[:256] #print(head) with open(sirfile,'rb') as fin: header = fin.read(256) print(header) hh = np.fromstring(header, dtype=np.int16) nhead = hh[40] #number of data blocks ipol = hh[44] #polarisation (valid options: 0=n/a,1=H,2=V) idatatype = hh[47] #head(48) = idatatype ! data type code 0,2=i*2,1=i*1,4=f print(nhead) print(ipol) print(idatatype) exit() return() def corr_pearson(x, y): """ Compute Pearson correlation. """ x_mean = np.mean(x, axis=0) x_stddev = np.std(x, axis=0) y_mean = np.mean(y, axis=0) y_stddev = np.std(y, axis=0) x1 = (x - x_mean)/x_stddev y1 = (y - y_mean)/y_stddev x1y1mult = x1 * y1 x1y1sum = np.sum(x1y1mult, axis=0) corr = x1y1sum/x.shape[0] return corr def corr_pearson_circ(x, y): """ Compute Pearson correlation for circular data (angles). """ #calculate means x_mean = circmean(x,axis=0) y_mean = circmean(y,axis=0) #mean angle difference diff = y_mean - x_mean diff = np.where(diff>180,diff-360,diff) diff = np.where(diff<-180,diff+360,diff) #calculate residuals resx = np.sin(np.radians(x)-np.radians(x_mean)) resy = np.sin(np.radians(y)-np.radians(y_mean)) #calculate Pearson correlation coefficient #this is original formula from Jammamaldaka, 2001 (Rozman et al, 2011 has an error in denominator - it summs before it multiplys) stev = np.sum(resx*resy,axis=0) imen = np.sqrt(np.sum(resx**2,axis=0)*np.sum(resy**2,axis=0)) corr = stev/imen return x_mean,y_mean,diff,corr def circmean(alpha,axis=None): #To convert from radians to degrees, multiply by (180o/(PI)) tod = 180/np.pi tor = np.pi/180 sa = np.mean(np.sin(alpha*tor),axis) ca = np.mean(np.cos(alpha*tor),axis) mean_angle = np.arctan2(sa,ca)*tod mean_angle = np.where(mean_angle<0,mean_angle+360,mean_angle) return mean_angle def plot_pdf(l1,l2,outname): fig = plt.figure(figsize=(8,8), facecolor='w') ax = plt.subplot(1,1,1) #plot a PDF bl = np.arange(0.,.41,.01) #n, bins, patches = plt.hist(slist, bl, normed=True, histtype='step', color='m', alpha=.8, label='neXtSIM', lw = 3) #n, bins, patches = plt.hist(slist_gauss, bl, normed=True, histtype='step', color='r', alpha=.8, label='neXtSIM', lw = 3) n, bins, patches = plt.hist(np.clip(l1, bl[0], bl[-1]), bl, normed=True, histtype='step', color='darkred', alpha=.8, label='neXtSIM', lw = 3) n, bins, patches = plt.hist(np.clip(l2, bl[0], bl[-1]), bl, normed=True, histtype='step', alpha=.8, label='OSI-SAF', lw = 3) plt.xlim(0,20) plt.xlabel('Speed (m/s)') plt.ylabel('Probability') plt.title('Probability distribution of mean 2-day speed \nfor January 2007-2015') #plt.text(60, .025, r'$\mu=100,\ \sigma=15$') #plt.axis([40, 160, 0, 0.03]) plt.legend(loc='upper right',prop={'size':16}) plt.grid(True) plt.savefig(outname) plt.close() #from pynextsim.projection_info import ProjectionInfo #mm = ProjectionInfo(f) ###def __init__(self, ###ecc = 0.081816153, ###a = 6378.273e3, ###lat_0 = 90., ###lon_0 = -45., ###lat_ts = 60., ###proj='stere', ##print(mm.proj,mm.lat_ts,mm.lat_0,mm.lon_0,mm.a,mm.ecc) #nextsim_proj = '+proj=%s +lat_ts=%f +lat_0=%f +lon_0=%f +a=%f +e=%f +units=m' %(mm.proj,mm.lat_ts,mm.lat_0,mm.lon_0,mm.a,0.081816153) #inProj = Proj(nextsim_proj,preserve_units=True) #outProj = Proj("+init=EPSG:4326") # WGS84 in degrees #lonc,latc=transform(inProj,outProj,xc,yc) #Hi @loniitkina, this class can't be initialised in this way. #To get the default nextsim projection: #proj=ProjectionInfo() #You can also get it from #nbi = NextsimBin(f) #proj = nbi.mesh_info.projection #and if you have an mppfile #proj==ProjectionInfo.init_from_mppfile(mppfile=...)

[ "geopolonica@gmail.com" ]

geopolonica@gmail.com

d9bf433949bfe44f549106417d3231148380ab7a

f0b3d4c9e6a5f8f4454adedf91db1b80c89401a7

/operatory.py

b74951f045fd8815a5b53e9c300d275522d85dee

[]

no_license

akotwicka/Learning_Python_Udemy

5b31656858e8d729cc0274b3b873f9d3852e67b9

c3d1c93d914ae1f2d4f497181ac41de39aeb0ce0

refs/heads/master

2020-06-24T18:28:42.294106

2019-08-06T10:45:34

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class Cake: bakery_offer = [] def __init__(self, name, kind, taste, additives, filling): self.name = name self.kind = kind self.taste = taste self.additives = additives.copy() self.filling = filling self.bakery_offer.append(self) def show_info(self): print("{}".format(self.name.upper())) print("Kind: {}".format(self.kind)) print("Taste: {}".format(self.taste)) if len(self.additives) > 0: print("Additives:") for a in self.additives: print("\t\t{}".format(a)) if len(self.filling) > 0: print("Filling: {}".format(self.filling)) print('-' * 20) def __str__(self): return "Kind: {}, Name: {}, Additives: {}".format(self.kind, self.name, self.additives) def __iadd__(self, other): if type(other) is str: self.additives.append(other) return self elif type(other) is list: self.additives.extend(other) return self else: raise Exception('Operation not possible') cake01 = Cake('Vanilla Cake', 'cake', 'vanilla', ['chocolade', 'nuts'], 'cream') print(cake01) cake01 += "almonds" print(cake01) cake01 += ['lemon', 'little meringues'] print(cake01) cake01 += 1 print(cake01)

[ "a_kotwicka@wp.pl" ]

a_kotwicka@wp.pl

60d29f8f859c00b316824ed6c3fc2e5ca0436598

f91c71f5dd3fdef91d7db2c8ebac03b0d4b1d22b

/Qt/g2.py

240716c1c65c1cd0f1e6d83417622622b4df2e6e

[]

no_license

vijayakumar75/py-programs

dd7ee9d6160e358b27498fc5e70376e146f485a2

a07056ddd400280cdf65b6cc57d0103d3581b54d

refs/heads/master

2020-05-04T18:45:21.811548

2016-08-29T11:56:42

null

0

null

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py

import logging import sys import traceback import logging.handlers class gi: def __init__(self,message): self.message=message def getMessage(self): return self.message def setMessage(self,message): self.message = message class StreamToLogger(object): def __init__(self, logger, log_level, std, handler): self.handler = handler self.logger = logger self.log_level = log_level self.linebuf = '' self.std = std self.gi = gi("") def write(self, buf): for line in buf.rstrip().splitlines(): self.gi.setMessage(line) self.logger.log(self.log_level, line.rstrip()) self.std.write(line+"\n") hand.flush() self.std.flush() def flush(self): self.std.flush() logging.basicConfig( level=logging.DEBUG, format='%(asctime)s:%(levelname)s:%(name)s:%(message)s', filename="history.log", filemode='a' ) hand = logging.handlers.TimedRotatingFileHandler("bot.log", when="S", interval=20) #my attempt at handling stdout_logger = logging.getLogger('STDOUT') sl = StreamToLogger(stdout_logger, logging.INFO, sys.__stdout__, hand) stderr_logger = logging.getLogger('STDERR') sl = StreamToLogger(stderr_logger, logging.ERROR, sys.__stderr__, hand) for i in range(2): sl.write("is this working")

[ "girishramnani95@gmail.com" ]

girishramnani95@gmail.com

acaed954e590d163f9df84e081c988bbabd00661

1a93478e72c6fb4528006d76d518cf3e1e4b676b

/medium_boardgame_bot.py

7ff3f910d8a8a4d2692901b11d2a50c40fe9e6cd

[]

no_license

iamohcy/medium_boardgame_bot

618db0d8426e70d00bea65524f572cf6bff4b909

b9209849c3809156a6b8d1120259e27cf1617b84

refs/heads/master

2022-04-10T19:28:15.182807

2020-04-01T10:54:08

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TOKEN = "953155266:AAF-g0tEk7qMCZwDxheNHQZD3oGMXn5w3G0" # TODO: # 1) don't let /begin work multiple times # 1) solve issue with empty entries, use original word instead # 2) solve issue with multiple ins # 3) solve issue with no past words # 1) Fixed issue with /begin working multiple times # 2) Fixed issue where original words could be re-used # 3) Fixed issue where multiple /in commands would screw things update # 4) Fixed issue where empty or multi-word entries were allowed # Medium Tele Bot v1.0 Beta is done! # 1) Added a "/left" command to see which players have yet to enter their words # 2) Added a "/points" command to see current point tallies # 3) Added an "/out" command to allow for people to leave the game # 4) Modified "/help" command to print more useful information # 5) Added reminder for new players to add the bot at @medium_boardgame_bot # 6) Game now stops when enough players have left # 7) **No longer need /enter command, can just type directly in to the bot chat** # 8) You can no kick idle players using the kick_idle command # 9) Various bug fixes and QOL improvements import telegram from telegram.ext import Updater, MessageHandler, CommandHandler, Filters # import requests from word_lib import getWords import logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') def kick_idle(update, context): chat_data = context.chat_data chat_id = update.message.chat_id chat_bot = context.bot if (update.message.chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return if ("gameStarted" not in context.chat_data): context.bot.send_message(chat_id=update.message.chat_id, text="Type /new to create a new game!", parse_mode=telegram.ParseMode.HTML) return if (context.chat_data["gameStarted"]): chat_id = update.message.chat_id context.bot.send_message(chat_id=update.message.chat_id, text="Kicking the following idle players...", parse_mode=telegram.ParseMode.HTML) for player in context.chat_data["playersArray"]: if (player["inGame"] == True) and (player["entry"] == None): kickPlayer(player["id"], update, context, True) if (context.chat_data["gameStarted"] == False): return checkForAllEntered(chat_data, chat_id, chat_bot) else: context.bot.send_message(chat_id=update.message.chat_id, text="Type /begin to begin the game first!", parse_mode=telegram.ParseMode.HTML) def kickPlayer(userId, update, context, forced): chat_data = context.chat_data chat_id = update.message.chat_id chat_bot = context.bot player = chat_data["playersDict"][userId] player["inGame"] = False player["entry"] = None if (not forced): context.bot.send_message(chat_id=update.message.chat_id, text="Psychic %s has left the game!" % player["name"], parse_mode=telegram.ParseMode.HTML) else: context.bot.send_message(chat_id=update.message.chat_id, text="Psychic %s has been booted from the game!" % player["name"], parse_mode=telegram.ParseMode.HTML) # Stop game if < 2 players numPlayersStillInGame = 0 for player in chat_data["playersArray"]: if (player["inGame"] == True): numPlayersStillInGame += 1 if numPlayersStillInGame < 2: context.bot.send_message(chat_id=update.message.chat_id, text="Not enough players to continue the game! Stopping game...", parse_mode=telegram.ParseMode.HTML) stop(update, context) return def deregister_user(update, context): chat_data = context.chat_data chat_id = update.message.chat_id chat_bot = context.bot if (chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return if ("gameStarted" not in context.chat_data): context.bot.send_message(chat_id=update.message.chat_id, text="Type /new to create a new game!", parse_mode=telegram.ParseMode.HTML) return userId = update.message.from_user.id kickPlayer(userId, update, context, False) checkForAllEntered(chat_data, chat_id, chat_bot) def register_user(update, context): if (update.message.chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return if ("gameStarted" not in context.chat_data): context.bot.send_message(chat_id=update.message.chat_id, text="Type /new to create a new game!", parse_mode=telegram.ParseMode.HTML) return userId = update.message.from_user.id name = update.message.from_user.first_name context.user_data["chat_data"] = context.chat_data context.user_data["chat_id"] = update.message.chat_id context.user_data["chat_bot"] = context.bot if userId not in context.chat_data["playersDict"]: player = {"id":userId, "name":name, "entry":None, "points": 0, "inGame": True, "isMainPlayer":False} # TEMP if name == "Wee Loong": player["name"] = "To Wee Or Not To Wee That Is The Question" context.chat_data["playersArray"].append(player) context.chat_data["playersDict"][userId] = player context.bot.send_message(chat_id=update.message.chat_id, text="Psychic %s has joined the game!" % player["name"], parse_mode=telegram.ParseMode.HTML) # Player has joined midway, send them the message if (context.chat_data["gameStarted"]): sendWordRequest(player, context.chat_data, context.bot) else: player = context.chat_data["playersDict"][userId] if (player["inGame"] == True): context.bot.send_message(chat_id=update.message.chat_id, text="Psychic %s is already in the game!" % player["name"], parse_mode=telegram.ParseMode.HTML) else: player["inGame"] = True player["entry"] = None context.bot.send_message(chat_id=update.message.chat_id, text="Psychic %s has re-joined the game!" % player["name"], parse_mode=telegram.ParseMode.HTML) sendWordRequest(player, context.chat_data, context.bot) # else: # context.bot.send_message(chat_id=update.message.chat_id, text="Game has not yet started!", parse_mode=telegram.ParseMode.HTML) def players_left(update, context): if (update.message.chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return if ("gameStarted" not in context.chat_data): context.bot.send_message(chat_id=update.message.chat_id, text="Type /new to create a new game!", parse_mode=telegram.ParseMode.HTML) return if (len(context.chat_data["playersArray"]) < 2): context.bot.send_message(chat_id=update.message.chat_id, text="Waiting for game to begin!", parse_mode=telegram.ParseMode.HTML) else: leftText = "Still waiting for: " for player in context.chat_data["playersArray"]: if player["inGame"] and (player["entry"] == None): leftText += "%s, " % player["name"] leftText = leftText[0:-2] context.bot.send_message(chat_id=update.message.chat_id, text=leftText, parse_mode=telegram.ParseMode.HTML) POINTS_ARRAY = [10,5,2] NON_MAIN_POINTS = 1 # points the non main players get for matching with main players NUM_ROUNDS = len(POINTS_ARRAY) def points(update, context): if (update.message.chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return if ("gameStarted" not in context.chat_data): context.bot.send_message(chat_id=update.message.chat_id, text="Type /new to create a new game!", parse_mode=telegram.ParseMode.HTML) return printScore(context.chat_data, update.message.chat_id, context.bot) def printScore(chat_data, chat_id, chat_bot): # print points pointsText = "Current points:\n" for player in chat_data["playersArray"]: if player["inGame"]: pointsText += "%s: %d points\n" % (player["name"], player["points"]) else: pointsText += "%s [out]: %d points\n" % (player["name"], player["points"]) chat_bot.send_message(chat_id=chat_id, text=pointsText, parse_mode=telegram.ParseMode.HTML) def sendWordRequest(player, chat_data, chat_bot): player["entry"] == None chat_bot.send_message(chat_id=player["id"], text="Current words are %s and %s!" % chat_data["words"], parse_mode=telegram.ParseMode.HTML) chat_bot.send_message(chat_id=player["id"], text="When you are ready, enter your Medium Word (just one) here!", parse_mode=telegram.ParseMode.HTML) def sendWordRequestToAll(chat_data, chat_id, chat_bot): for player in chat_data["playersArray"]: if player["inGame"]: sendWordRequest(player, chat_data, chat_bot) def handleNewRound(chat_data, chat_id, chat_bot): for player in chat_data["playersArray"]: player["entry"] = None if (chat_data["subRound"] == 0): (wordA, wordB) = getWords() chat_data["words"] = (wordA, wordB) chat_data["seenWords"] = [wordA.lower(), wordB.lower()] currentRound = chat_data["currentRound"] currentSubRound = chat_data["subRound"] if (currentRound > 0): printScore(chat_data, chat_id, chat_bot) # player1Index = currentRound % numPlayers # player2Index = (currentRound+1) % numPlayers # chat_data["nextPlayer1Index"] = player2Index; numPlayers = len(chat_data["playersArray"]) potentialPlayer1Index = chat_data["nextPlayer1Index"] # Initialize main player to false first for player in chat_data["playersArray"]: player["isMainPlayer"] = False mainPlayers = [] currentIndex = chat_data["nextPlayer1Index"] while len(mainPlayers) < 2: potentialPlayer = chat_data["playersArray"][currentIndex] if potentialPlayer["inGame"]: potentialPlayer["isMainPlayer"] = True mainPlayers.append(potentialPlayer) chat_data["nextPlayer1Index"] = currentIndex # Index of latest player currentIndex = (currentIndex + 1) % numPlayers chat_data["player1"] = mainPlayers[0] chat_data["player2"] = mainPlayers[1] startText = "Round %d - Attempt %d\n\n" % (currentRound+1, currentSubRound+1) startText += "Main players: %s and %s\n\n" % (chat_data["player1"]["name"], chat_data["player2"]["name"]) startText += "Let's get psychic! The two words are: %s and %s" % chat_data["words"] chat_bot.send_message(chat_id=chat_id, text=startText, parse_mode=telegram.ParseMode.HTML) sendWordRequestToAll(chat_data, chat_id, chat_bot) def begin(update, context): if (update.message.chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return if ("gameStarted" not in context.chat_data): context.bot.send_message(chat_id=update.message.chat_id, text="Type /new to create a new game!", parse_mode=telegram.ParseMode.HTML) return if (context.chat_data["gameStarted"]): context.bot.send_message(chat_id=update.message.chat_id, text="Game has already begun!", parse_mode=telegram.ParseMode.HTML) elif (len(context.chat_data["playersArray"]) < 2): context.bot.send_message(chat_id=update.message.chat_id, text="You need at least 2 players to begin a game!", parse_mode=telegram.ParseMode.HTML) else: context.chat_data["gameStarted"] = True context.chat_data["currentRound"] = 0 context.chat_data["subRound"] = 0 handleNewRound(context.chat_data, update.message.chat_id, context.bot) def new_game(update, context): if (update.message.chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return context.chat_data["gameStarted"] = False context.chat_data["playersArray"] = [] context.chat_data["playersDict"] = {} context.chat_data["chat_id"] = update.message.chat_id context.chat_data["currentRound"] = 0 context.chat_data["subRound"] = 0 context.chat_data["seenWords"] = [] context.chat_data["nextPlayer1Index"] = 0 userId = update.message.from_user.id context.bot.send_message(chat_id=update.message.chat_id, text="New game has begun! Type '/in' to join the game! When everyone has joined, type /begin to begin the first round.", parse_mode=telegram.ParseMode.HTML) context.bot.send_message(chat_id=update.message.chat_id, text="For completely new players, remember to add the bot by clicking @medium_boardgame_bot before joining the game!", parse_mode=telegram.ParseMode.HTML) def help(update, context): message = "Welcome to the Telegram Bot for the Medium Board Game!\n\n" message += "In the game Medium, players act as psychic mediums, harnessing their powerful extra-sensory abilities to access other players’ thoughts. Together in pairs, they mentally determine the Medium: the word that connects the words on their two cards, and then attempt to say the same word at the same time!\n\n" message += "For example, if the words are fruit and gravity a Medium Word might be apple. If both parties say the SAME Medium Word, they both get 10 points! Otherwise they fail and get a second attempt, except now the two new words to match are the words they've just given. If they match in the second attempt they get 5 points, and 2 if they match in the third and last attempt.\n\n" message += "Meanwhile, other players can try to snatch 1 point by matching either of the 2 main players\n\n" message += "To begin, add this bot at @medium_boardgame_bot and type /new to create a new game!\n\n" context.bot.send_message(chat_id=update.message.chat_id, text=message, parse_mode=telegram.ParseMode.HTML) def stop(update, context): if (update.message.chat_id > 0): context.bot.send_message(chat_id=update.message.chat_id, text="This command can only be sent in a group channel!", parse_mode=telegram.ParseMode.HTML) return if ("gameStarted" not in context.chat_data): context.bot.send_message(chat_id=update.message.chat_id, text="Type /new to create a new game!", parse_mode=telegram.ParseMode.HTML) return pointsText = "Game ended!\n-----------------------\nCurrent points:\n" currentMaxPoints = -1 winners = [] for player in context.chat_data["playersArray"]: name = player["name"] points = player["points"] if (points > currentMaxPoints): winners = [name] currentMaxPoints = points elif (points == currentMaxPoints): winners.append(name) pointsText += "%s: %d points\n" % (player["name"], player["points"]) pointsText += "\nWinner(s): " for name in winners: pointsText += name + ", " pointsText = pointsText[0:-2] context.bot.send_message(chat_id=update.message.chat_id, text=pointsText, parse_mode=telegram.ParseMode.HTML) # Reset data del context.chat_data["gameStarted"] del context.chat_data["playersArray"] del context.chat_data["playersDict"] del context.chat_data["currentRound"] del context.chat_data["subRound"] del context.chat_data["seenWords"] del context.chat_data["nextPlayer1Index"] def checkForAllEntered(chat_data, chat_id, chat_bot): allEntered = True enteredCount = 0 for player in chat_data["playersArray"]: if (player["inGame"] == True): if (player["entry"] == None): allEntered = False else: enteredCount += 1 if (allEntered): if enteredCount <= 1: return chat_bot.send_message(chat_id=chat_data["chat_id"], text="Everyone has entered their words!", parse_mode=telegram.ParseMode.HTML) currentEntry = None testPassed = True entryText = "Main Players:\n" for player in chat_data["playersArray"]: if player["inGame"] and player["isMainPlayer"]: entry = player["entry"] chat_data["seenWords"].append(entry.lower()) entryText += "Psychic %s entered - %s\n" % (player["name"], entry) # Check if anyone else matched if (len(chat_data["playersArray"]) > 2): found = False for player in chat_data["playersArray"]: if player["inGame"] and (not player["isMainPlayer"]): entry = player["entry"] if (chat_data["player1"]["inGame"] and entry.lower() == chat_data["player1"]["entry"].lower()) or (chat_data["player2"]["inGame"] and entry.lower() == chat_data["player2"]["entry"].lower()): # Give player one point for matching one of the main players player["points"] += NON_MAIN_POINTS if not found: found = True entryText += "\nOther Players:\n" entryText += "Psychic %s also entered - %s! (+%d points)\n" % (player["name"], entry, NON_MAIN_POINTS) chat_bot.send_message(chat_id=chat_id, text=entryText, parse_mode=telegram.ParseMode.HTML) # Main player has left the game! if not (chat_data["player1"]["inGame"] and chat_data["player2"]["inGame"]): chat_bot.send_message(chat_id=chat_id, text="One of the main players has temporarily left the game! Moving on to the next round...", parse_mode=telegram.ParseMode.HTML) chat_data["currentRound"] += 1 chat_data["subRound"] = 0 handleNewRound(chat_data, chat_id, chat_bot) return # Calculate if succeeded succeeded = chat_data["player1"]["entry"].lower() == chat_data["player2"]["entry"].lower() if succeeded: numPoints = POINTS_ARRAY[chat_data["subRound"]] chat_data["player1"]["points"] += numPoints chat_data["player2"]["points"] += numPoints chat_bot.send_message(chat_id=chat_id, text="Success! %s and %s get %d points each." % (chat_data["player1"]["name"], chat_data["player2"]["name"], numPoints), parse_mode=telegram.ParseMode.HTML) chat_data["currentRound"] += 1 chat_data["subRound"] = 0 handleNewRound(chat_data, chat_id, chat_bot) else: chat_data["subRound"] += 1 if (chat_data["subRound"] == NUM_ROUNDS): chat_data["currentRound"] += 1 chat_data["subRound"] = 0 chat_bot.send_message(chat_id=chat_id, text="Oops! Last attempt failed! Moving on to next round...", parse_mode=telegram.ParseMode.HTML) handleNewRound(chat_data, chat_id, chat_bot) else: chat_data["words"] = (chat_data["player1"]["entry"], chat_data["player2"]["entry"]) chat_bot.send_message(chat_id=chat_id, text="Attempt %d failed! Try again with these two new words - %s and %s" % (chat_data["subRound"], chat_data["words"][0], chat_data["words"][1]), parse_mode=telegram.ParseMode.HTML) for player in chat_data["playersArray"]: player["entry"] = None sendWordRequestToAll(chat_data, chat_id, chat_bot) def test(update, context): chat_id = update.effective_chat.id userId = update.message.from_user.id entry = update.message.text context.bot.send_message(chat_id=userId, text=entry, parse_mode=telegram.ParseMode.HTML) def enter(update, context): chat_id = update.effective_chat.id userId = update.message.from_user.id entry = update.message.text # Guarantees that this is private chat with player, rather than a group chat if (update.message.chat_id > 0): if ("chat_data" not in context.user_data): context.bot.send_message(chat_id=userId, text="Game has not yet started!", parse_mode=telegram.ParseMode.HTML) return chat_data = context.user_data["chat_data"] chat_bot = context.user_data["chat_bot"] chat_id = context.user_data["chat_id"] if ("gameStarted" in chat_data) and (chat_data["gameStarted"]): player = chat_data["playersDict"][userId] if player["inGame"]: if entry.strip() == "": context.bot.send_message(chat_id=userId, text="Empty entry detected, please try again!" % entry, parse_mode=telegram.ParseMode.HTML) elif len(entry.split()) > 1: context.bot.send_message(chat_id=userId, text="You can only send one word!" % entry, parse_mode=telegram.ParseMode.HTML) elif entry.lower() not in chat_data["seenWords"]: player["entry"] = entry context.bot.send_message(chat_id=userId, text="Received! - [%s]" % entry, parse_mode=telegram.ParseMode.HTML) else: context.bot.send_message(chat_id=userId, text="The word %s has been seen this round already!" % entry, parse_mode=telegram.ParseMode.HTML) checkForAllEntered(chat_data, chat_id, chat_bot) else: context.bot.send_message(chat_id=userId, text="You are currently not in the game! Type /in in the group chat to rejoin the game." % entry, parse_mode=telegram.ParseMode.HTML) else: context.bot.send_message(chat_id=userId, text="Game has not yet started!", parse_mode=telegram.ParseMode.HTML) def main(): updater = Updater(token=TOKEN, use_context=True) dispatcher = updater.dispatcher dispatcher.add_handler(CommandHandler('new',new_game)) dispatcher.add_handler(CommandHandler('in',register_user)) dispatcher.add_handler(CommandHandler('out',deregister_user)) dispatcher.add_handler(CommandHandler('begin',begin)) # dispatcher.add_handler(CommandHandler('enter',enter)) # dispatcher.add_handler(CommandHandler('e',enter)) dispatcher.add_handler(CommandHandler('help',help)) dispatcher.add_handler(CommandHandler('stop',stop)) dispatcher.add_handler(CommandHandler('points',points)) dispatcher.add_handler(CommandHandler('left',players_left)) dispatcher.add_handler(CommandHandler('kick_idle',kick_idle)) dispatcher.add_handler(MessageHandler(Filters.text, enter)) updater.start_polling() updater.idle() if __name__ == '__main__': main()

[ "iamohcy@gmail.com" ]

iamohcy@gmail.com

21127b91e1a1270f520744db39ed1654c850fbe7

f0117325b7a40779965b35ec6cefc8d12353d779

/python_exercises/py_part3_ex/lecture_ex/graph.py

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[]

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joshwestbury/Digital_Crafts

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66c06f198d110388781a30c0ecb7902d3a8daf5a

refs/heads/master

2021-07-24T05:37:02.370341

2017-11-04T14:36:39

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py

from matplotlib import pyplot def f(x): return 2 * x + 1 def g(x): return x + 1 for x in range(-3, 5): print("f({x})={y} \t g({x})={z}".format(x=x, y=f(x), z=g(x))) f_output = [] g_output =[] x_list = list(range(-3, 5)) for x in x_list: f_output.append(f(x)) g_output.append(g(x)) pyplot.plot(x_list, f_output, x_list, g_output) pyplot.show()

[ "joshwestbury@gmail.com" ]

joshwestbury@gmail.com

021499735f89ec69e09a67a4705a4f336386e591

c440f2cffdb41da5e940376c621a872f3eb93377

/maze_generator/mazegen

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[]

no_license

stephkno/CS_21_Python

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refs/heads/master

2023-04-30T02:39:19.868600

2023-04-22T01:56:21

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#!/usr/bin/python3 from lib import maze import argparse parser = argparse.ArgumentParser(description="Generate a random maze.") parser.add_argument('size', help="size of maze on one side", type=int) parser.add_argument('-r', help="render maze as ascii", action="store_true") args = parser.parse_args() maze = maze.Maze() if(args.size > 0): maze.generate(args.size) if(args.r): print(maze.getMazeRender()) else: print(maze.toString())

[ "Stephen@Janet.local" ]

Stephen@Janet.local

a3f8472743fefea8dc4b08dc4ae24ade165536c6

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/src/cookbook/settings/base.py

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[ "MIT" ]

permissive

triump0870/cookbook

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58aedc67d0bcf32a397953ae5389076edc26e1ae

refs/heads/master

2021-01-09T09:37:24.344609

2017-02-21T09:35:26

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""" Django settings for recipes project. For more information on this file, see https://docs.djangoproject.com/en/dev/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/dev/ref/settings/ """ from django.core.urlresolvers import reverse_lazy from os.path import dirname, join, exists import mongoengine # Build paths inside the project like this: join(BASE_DIR, "directory") BASE_DIR = dirname(dirname(dirname(__file__))) STATICFILES_DIRS = [join(BASE_DIR, 'static')] MEDIA_ROOT = join(BASE_DIR, 'media') MEDIA_URL = "/media/" # Use Django templates using the new Django 1.8 TEMPLATES settings TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ join(BASE_DIR, 'templates'), # insert more TEMPLATE_DIRS here ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ # Insert your TEMPLATE_CONTEXT_PROCESSORS here or use this # list if you haven't customized them: 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.contrib.messages.context_processors.messages', ], }, }, ] # Use 12factor inspired environment variables or from a file import environ env = environ.Env() # Ideally move env file should be outside the git repo # i.e. BASE_DIR.parent.parent env_file = join(dirname(__file__), 'local.env') if exists(env_file): environ.Env.read_env(str(env_file)) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/dev/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! # Raises ImproperlyConfigured exception if SECRET_KEY not in os.environ SECRET_KEY = env('SECRET_KEY') ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = ( 'django.contrib.auth', 'mongoengine.django.mongo_auth', 'django_admin_bootstrapped', 'django.contrib.admin', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'crispy_forms', 'easy_thumbnails', 'rest_framework', 'rest_framework_mongoengine', # 'profiles', 'accounts', 'recipes', 'apis', ) MIDDLEWARE_CLASSES = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) ROOT_URLCONF = 'cookbook.urls' WSGI_APPLICATION = 'cookbook.wsgi.application' # Database # https://docs.djangoproject.com/en/dev/ref/settings/#databases DATABASES = { # Raises ImproperlyConfigured exception if DATABASE_URL not in # os.environ 'default': { "ENGINE": 'django.db.backends.dummy' }, } # SESSION_ENGINE = 'mongoengine.django.sessions' # SESSION_SERIALIZER = 'mongoengine.django.sessions.BSONSerializer' _MONGODB_DATABASE_HOST = env("MONGODB_DATABASE_HOST") _MONGODB_NAME = env("MONGODB_NAME") mongoengine.connect(_MONGODB_NAME, host=_MONGODB_DATABASE_HOST) AUTHENTICATION_BACKENDS = ( 'mongoengine.django.auth.MongoEngineBackend', ) # Internationalization # https://docs.djangoproject.com/en/dev/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Asia/Kolkata' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/dev/howto/static-files/ STATIC_URL = '/static/' # Crispy Form Theme - Bootstrap 3 CRISPY_TEMPLATE_PACK = 'bootstrap3' # For Bootstrap 3, change error alert to 'danger' from django.contrib import messages MESSAGE_TAGS = { messages.ERROR: 'danger' } # Authentication Settings AUTH_USER_MODEL = 'mongo_auth.MongoUser' MONGOENGINE_USER_DOCUMENT = 'mongoengine.django.auth.User' LOGIN_REDIRECT_URL = reverse_lazy("profiles:show_self") LOGIN_URL = reverse_lazy("accounts:login") THUMBNAIL_EXTENSION = 'png' # Or any extn for your thumbnails

[ "b4you0870@gmail.com" ]

b4you0870@gmail.com

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/x64/Release/benchmark.py

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no_license

acraddoc91/PythonCorrelationsLibrary

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refs/heads/master

2021-09-10T12:42:06.513308

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from corrLib import g2ToFile import numpy as np import time import scipy mat_directory = "C:/Users/Ryd Berg/Google Drive/Rydberg Experiment/Matlab/CorrelationCalculations/" data_directory = "C:/Users/Ryd Berg/Downloads/" data_folder = data_directory+"g2_benchmark/" mat_file = mat_directory+"g2_n22_test_opencl" benchmark_mat = mat_directory+"g2_benchmark_gpu_cuda" bin_width = 82.3e-12*12 pulse_spacing = 100e-6 max_pulse_distance = 4 #half_tau_bins = np.array([200,500,1000,2000,4000]) #half_tau_bins = np.array([1,4,10,20,50,100,200,500,1000,2000,4000]) half_tau_bins = np.array([1,100,500,1000,2000,4000,8000,16000,32000,64000,128000,256000]) calc_bins = half_tau_bins * 2 + 1 time_taken = np.zeros(len(half_tau_bins)) for i in range(len(half_tau_bins)): max_time = half_tau_bins[i] * bin_width start_time = time.time() g2ToFile(data_folder,mat_file,max_time,bin_width,pulse_spacing,max_pulse_distance) time_taken[i] = time.time()-start_time scipy.io.savemat(benchmark_mat,{'num_bins':calc_bins,'time':time_taken,'device':'Threadripper 1950x','block_size':32})

[ "acraddoc@umd.edu" ]

acraddoc@umd.edu

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/treasury/urls.py

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[]

no_license

argon2008-aiti/lcidarkuman

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refs/heads/master

2021-01-13T03:09:32.388512

2019-05-19T14:16:48

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from django.conf.urls import url from django.contrib.auth import views from django.views.generic import TemplateView from views import * urlpatterns = [ url(r'^offering/all/$', get_all_offerings, name="offering-all"), url(r'^tithe/all/$', get_all_tithes, name="tithe-all"), url(r'^tithe/add/$', add_tithe, name="tithe-add"), url(r'^offering/add/$', add_offering, name="offering-add"), ]

[ "yunguta@gmail.com" ]

yunguta@gmail.com

76b81aa534882a53060e39b9e6a9a654146ec745

a9edafa96979580729aad2eacc3f70f1eacb3152

/src/budy/subscription.py

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[ "Apache-2.0" ]

permissive

gcandal/budy_api

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refs/heads/master

2021-01-18T01:11:46.190186

2016-08-09T14:02:29

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null

2016-07-11T12:00:32

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#!/usr/bin/python # -*- coding: utf-8 -*- # Hive Budy API # Copyright (c) 2008-2016 Hive Solutions Lda. # # This file is part of Hive Budy API. # # Hive Budy API is free software: you can redistribute it and/or modify # it under the terms of the Apache License as published by the Apache # Foundation, either version 2.0 of the License, or (at your option) any # later version. # # Hive Budy API is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # Apache License for more details. # # You should have received a copy of the Apache License along with # Hive Budy API. If not, see <http://www.apache.org/licenses/>. __author__ = "João Magalhães <joamag@hive.pt>" """ The author(s) of the module """ __version__ = "1.0.0" """ The version of the module """ __revision__ = "$LastChangedRevision$" """ The revision number of the module """ __date__ = "$LastChangedDate$" """ The last change date of the module """ __copyright__ = "Copyright (c) 2008-2016 Hive Solutions Lda." """ The copyright for the module """ __license__ = "Apache License, Version 2.0" """ The license for the module """ class SubscriptionApi(object): def create_subscription(self, payload): url = self.base_url + "subscriptions" contents = self.post(url, data_j = payload, auth = False) return contents

[ "joamag@gmail.com" ]

joamag@gmail.com

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/quantization/WqAq/IAO/models/util_wqaq.py

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jth19961209/SSD-Pruning-and-quantization

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refs/heads/master

2023-03-17T03:30:48.020816

2021-01-28T14:31:27

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import torch import torch.nn as nn import torch.nn.functional as F from torch import distributed from torch.nn import init from torch.nn.parameter import Parameter from torch.autograd import Function #Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference # ********************* range_trackers(范围统计器，统计量化前范围) ********************* class RangeTracker(nn.Module): def __init__(self, q_level): super().__init__() self.q_level = q_level def update_range(self, min_val, max_val): raise NotImplementedError @torch.no_grad() def forward(self, input): if self.q_level == 'L': # A,min_max_shape=(1, 1, 1, 1),layer级 min_val = torch.min(input) max_val = torch.max(input) elif self.q_level == 'C': # W,min_max_shape=(N, 1, 1, 1),channel级 out,in,w,h min_val = torch.min(torch.min(torch.min(input, 3, keepdim=True)[0], 2, keepdim=True)[0], 1, keepdim=True)[0] max_val = torch.max(torch.max(torch.max(input, 3, keepdim=True)[0], 2, keepdim=True)[0], 1, keepdim=True)[0] self.update_range(min_val, max_val) class GlobalRangeTracker(RangeTracker): # W,min_max_shape=(N, 1, 1, 1),channel级,取本次和之前相比的min_max —— (N, C, W, H) def __init__(self, q_level, out_channels): super().__init__(q_level) self.register_buffer('min_val', torch.zeros(out_channels, 1, 1, 1)) self.register_buffer('max_val', torch.zeros(out_channels, 1, 1, 1)) self.register_buffer('first_w', torch.zeros(1)) def update_range(self, min_val, max_val): temp_minval = self.min_val temp_maxval = self.max_val if self.first_w == 0: self.first_w.add_(1) self.min_val.add_(min_val) self.max_val.add_(max_val) else: self.min_val.add_(-temp_minval).add_(torch.min(temp_minval, min_val)) self.max_val.add_(-temp_maxval).add_(torch.max(temp_maxval, max_val)) class AveragedRangeTracker(RangeTracker): # A,min_max_shape=(1, 1, 1, 1),layer级,取running_min_max —— (N, C, W, H) def __init__(self, q_level, momentum=0.1): super().__init__(q_level) self.momentum = momentum self.register_buffer('min_val', torch.zeros(1)) self.register_buffer('max_val', torch.zeros(1)) self.register_buffer('first_a', torch.zeros(1)) def update_range(self, min_val, max_val): if self.first_a == 0: self.first_a.add_(1) self.min_val.add_(min_val) self.max_val.add_(max_val) else: self.min_val.mul_(1 - self.momentum).add_(min_val * self.momentum) self.max_val.mul_(1 - self.momentum).add_(max_val * self.momentum) # ********************* quantizers（量化器，量化） ********************* class Round(Function): @staticmethod def forward(self, input): output = torch.round(input) return output @staticmethod def backward(self, grad_output): grad_input = grad_output.clone() return grad_input class Quantizer(nn.Module): def __init__(self, bits, range_tracker): super().__init__() self.bits = bits self.range_tracker = range_tracker self.register_buffer('scale', torch.zeros_like(self.range_tracker.min_val)) # 量化比例因子 self.register_buffer('zero_point',torch.zeros_like(self.range_tracker.min_val)) # 量化零点 def update_params(self): raise NotImplementedError # 量化 def quantize(self, input): output = input * self.scale self.zero_point return output def round(self, input): output = Round.apply(input) return output # 截断 def clamp(self, input): output = torch.clamp(input, self.min_val, self.max_val) return output # 反量化 def dequantize(self, input): output = (input + self.zero_point) / self.scale return output def forward(self, input): if self.bits == 32: output = input elif self.bits == 1: print('！Binary quantization is not supported ！') assert self.bits != 1 else: self.range_tracker(input) self.update_params() output = self.quantize(input) # 量化 output = self.round(output) output = self.clamp(output) # 截断 output = self.dequantize(output)# 反量化 return output class SignedQuantizer(Quantizer): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.register_buffer('min_val', torch.tensor(-(1 << (self.bits - 1)))) self.register_buffer('max_val', torch.tensor((1 << (self.bits - 1)) - 1)) class UnsignedQuantizer(Quantizer): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.register_buffer('min_val', torch.tensor(0)) self.register_buffer('max_val', torch.tensor((1 << self.bits) - 1)) # 对称量化 class SymmetricQuantizer(SignedQuantizer): def update_params(self): quantized_range = torch.min(torch.abs(self.min_val), torch.abs(self.max_val)) # 量化后范围 float_range = torch.max(torch.abs(self.range_tracker.min_val), torch.abs(self.range_tracker.max_val)) # 量化前范围 self.scale = quantized_range / float_range # 量化比例因子 self.zero_point = torch.zeros_like(self.scale) # 量化零点 # 非对称量化 class AsymmetricQuantizer(UnsignedQuantizer): def update_params(self): quantized_range = self.max_val - self.min_val # 量化后范围 float_range = self.range_tracker.max_val - self.range_tracker.min_val # 量化前范围 self.scale = quantized_range / float_range # 量化比例因子 self.zero_point = torch.round(self.range_tracker.min_val * self.scale) # 量化零点 # ********************* 量化卷积（同时量化A/W，并做卷积） ********************* class Conv2d_Q(nn.Conv2d): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, a_bits=8, w_bits=8, q_type=0, first_layer=0, ): super().__init__( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias ) # 实例化量化器（A-layer级，W-channel级） if q_type == 0:#对称量化量化零点为0 self.activation_quantizer = SymmetricQuantizer(bits=a_bits, range_tracker=AveragedRangeTracker(q_level='L')) self.weight_quantizer = SymmetricQuantizer(bits=w_bits, range_tracker=GlobalRangeTracker(q_level='C', out_channels=out_channels)) else: self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, range_tracker=AveragedRangeTracker(q_level='L')) self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, range_tracker=GlobalRangeTracker(q_level='C', out_channels=out_channels)) self.first_layer = first_layer def forward(self, input): # 量化A和W if not self.first_layer: input = self.activation_quantizer(input) q_input = input q_weight = self.weight_quantizer(self.weight) # 量化卷积 output = F.conv2d( input=q_input, weight=q_weight, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups ) return output def reshape_to_activation(input): return input.reshape(1, -1, 1, 1) def reshape_to_weight(input): return input.reshape(-1, 1, 1, 1) def reshape_to_bias(input): return input.reshape(-1) # ********************* bn融合_量化卷积（bn融合后，同时量化A/W，并做卷积） ********************* class BNFold_Conv2d_Q(Conv2d_Q): def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False, #BN_fold这一版默认没有bias eps=1e-5, momentum=0.01, # 考虑量化带来的抖动影响,对momentum进行调整(0.1 ——> 0.01),削弱batch统计参数占比，一定程度抑制抖动。经实验量化训练效果更好,acc提升1%左右 a_bits=8, w_bits=8, q_type=0, first_layer=0, ): super().__init__( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias ) self.eps = eps self.momentum = momentum self.gamma = Parameter(torch.Tensor(out_channels)) self.beta = Parameter(torch.Tensor(out_channels)) self.register_buffer('running_mean', torch.zeros(out_channels)) self.register_buffer('running_var', torch.ones(out_channels)) self.register_buffer('first_bn', torch.zeros(1)) init.uniform_(self.gamma) init.zeros_(self.beta) # 实例化量化器（A-layer级，W-channel级） if q_type == 0: self.activation_quantizer = SymmetricQuantizer(bits=a_bits, range_tracker=AveragedRangeTracker(q_level='L')) self.weight_quantizer = SymmetricQuantizer(bits=w_bits, range_tracker=GlobalRangeTracker(q_level='C', out_channels=out_channels)) else: self.activation_quantizer = AsymmetricQuantizer(bits=a_bits, range_tracker=AveragedRangeTracker(q_level='L')) self.weight_quantizer = AsymmetricQuantizer(bits=w_bits, range_tracker=GlobalRangeTracker(q_level='C', out_channels=out_channels)) self.first_layer = first_layer def forward(self, input): # 训练态 if self.training: # 先做普通卷积得到A，以取得BN参数 output = F.conv2d( input=input, weight=self.weight, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups ) # 更新BN统计参数（batch和running） dims = [dim for dim in range(4) if dim != 1] batch_mean = torch.mean(output, dim=dims) batch_var = torch.var(output, dim=dims) with torch.no_grad(): if self.first_bn == 0: self.first_bn.add_(1) self.running_mean.add_(batch_mean) self.running_var.add_(batch_var) else: self.running_mean.mul_(1 - self.momentum).add_(batch_mean * self.momentum) self.running_var.mul_(1 - self.momentum).add_(batch_var * self.momentum) # BN融合 if self.bias is not None: bias = reshape_to_bias(self.beta + (self.bias - batch_mean) * (self.gamma / torch.sqrt(batch_var + self.eps))) else: bias = reshape_to_bias(self.beta - batch_mean * (self.gamma / torch.sqrt(batch_var + self.eps)))# b融batch weight = self.weight * reshape_to_weight(self.gamma / torch.sqrt(self.running_var + self.eps)) # w融running # 测试态 else: #print(self.running_mean, self.running_var) # BN融合 if self.bias is not None: bias = reshape_to_bias(self.beta + (self.bias - self.running_mean) * (self.gamma / torch.sqrt(self.running_var + self.eps))) else: bias = reshape_to_bias(self.beta - self.running_mean * (self.gamma / torch.sqrt(self.running_var + self.eps))) # b融running weight = self.weight * reshape_to_weight(self.gamma / torch.sqrt(self.running_var + self.eps)) # w融running # 量化A和bn融合后的W if not self.first_layer: input = self.activation_quantizer(input) q_input = input q_weight = self.weight_quantizer(weight) # 量化卷积 if self.training: # 训练态 output = F.conv2d( input=q_input, weight=q_weight, bias=self.bias, # 注意，这里不加bias（self.bias为None） stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups ) # （这里将训练态下，卷积中w融合running参数的效果转为融合batch参数的效果）running ——> batch output *= reshape_to_activation(torch.sqrt(self.running_var + self.eps) / torch.sqrt(batch_var + self.eps)) output += reshape_to_activation(bias) else: # 测试态 output = F.conv2d( input=q_input, weight=q_weight, bias=bias, # 注意，这里加bias，做完整的conv+bn stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups ) return output #量化文件夹下的那种写法避免偏置不为0 D:\yanxue\project\量化 # 网上的策略：activations：range取决与input，为了估计range，只用了exponential moving averages(EMA)。同时在训练的开始阶段，range变化非常的快，此时完全关停activations的quantization，等到稳定之后再此开启。

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psdh/WhatsintheVector

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ii = [('MarrFDI.py', 1), ('AubePRP2.py', 1), ('ChalTPW2.py', 1), ('WilkJMC2.py', 1), ('MarrFDI2.py', 1), ('LyelCPG.py', 1), ('WestJIT2.py', 1), ('WheeJPT.py', 1), ('MereHHB3.py', 1), ('MereHHB.py', 1), ('MereHHB2.py', 1)]

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/lieu_de_travail/admin.py

d7f55e6c15c0911af4df9df924f66887d67e7a72

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permissive

ghassen3699/Site_web_Projet

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from django.contrib import admin from . import models admin.site.register(models.Province) admin.site.register(models.Region) admin.site.register(models.Commissariat_De_Police) admin.site.register(models.Lieu_De_Travail)

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prateekgupta3991/justforlearn

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/usr/share/pyshared/Bio/Align/Applications/_TCoffee.py

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/main.py

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JejeDurden/TSP-Solver

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refs/heads/master

2021-05-14T23:51:26.693841

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#!/usr/bin/python3 import sys import pandas as pd from parser import parse_args from algos import kmeans, nearest_neighbor, two_opt, three_opt def get_csv(): cities_graph = pd.read_csv("cities.csv", header=None) return cities_graph def formatting(l): result = [] for i in range(len(l)): result.append(l[i].name) return result def get_result(K, cities_graph): result = [] if K > 1: for i in range(0, K): l = [] for index, city in cities_graph.iterrows(): if city["cluster"] == i: l.append(city) result.append(formatting(two_opt(three_opt(nearest_neighbor(l))))) else: l = [] for index, city in cities_graph.iterrows(): l.append(city) result.append(formatting(two_opt(three_opt(nearest_neighbor(l))))) return result def file_write(result): f = open("kopt.txt","w") for i in range(len(result)): l = len(result[i]) for nb in result[i]: f.write(str(nb) + "") l -= 1 if l > 0: f.write(", ") f.write("\n") f.close() def main(arg): parse_args(arg) K = int(arg[0]) cities_graph = get_csv() cities_graph.columns = ['name', 'x', 'y'] if K > 1: cities_graph = kmeans(cities_graph, K) cities_graph["marked"] = False result = get_result(K, cities_graph) file_write(result) if __name__ == "__main__": main(sys.argv[1:])

[ "jdesmare@e6r1p4.vp.42.fr" ]

jdesmare@e6r1p4.vp.42.fr

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danielhr444/SSD_EpiSci

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import time import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data from model import SSD300, MultiBoxLoss from datasets import PascalVOCDataset from utils import * # Data parameters: change as needed data_folder = './' # folder with data files keep_difficult = True # use objects considered difficult to detect? # Model parameters # Not too many here since the SSD300 has a very specific structure n_classes = len(label_map) # number of different types of objects device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Learning parameters checkpoint = None # path to model checkpoint, None if none batch_size = 8 # batch size start_epoch = 0 # start at this epoch epochs = 200 # number of epochs to run without early-stopping epochs_since_improvement = 0 # number of epochs since there was an improvement in the validation metric best_loss = 100. # assume a high loss at first workers = 4 # number of workers for loading data in the DataLoader print_freq = 200 # print training or validation status every __ batches lr = 1e-3 # learning rate momentum = 0.9 # momentum weight_decay = 5e-4 # weight decay grad_clip = None # clip if gradients are exploding, which may happen at larger batch sizes (sometimes at 32) - you will recognize it by a sorting error in the MuliBox loss calculation cudnn.benchmark = True def main(): """ Training and validation. """ global epochs_since_improvement, start_epoch, label_map, best_loss, epoch, checkpoint # Initialize model or load checkpoint if checkpoint is None: model = SSD300(n_classes=n_classes) # Initialize the optimizer, with twice the default learning rate for biases, as in the original Caffe repo biases = list() not_biases = list() for param_name, param in model.named_parameters(): if param.requires_grad: if param_name.endswith('.bias'): biases.append(param) else: not_biases.append(param) optimizer = torch.optim.SGD(params=[{'params': biases, 'lr': 2 * lr}, {'params': not_biases}], lr=lr, momentum=momentum, weight_decay=weight_decay) else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 epochs_since_improvement = checkpoint['epochs_since_improvement'] best_loss = checkpoint['best_loss'] print('\nLoaded checkpoint from epoch %d. Best loss so far is %.3f.\n' % (start_epoch, best_loss)) model = checkpoint['model'] optimizer = checkpoint['optimizer'] # Move to default device model = model.to(device) criterion = MultiBoxLoss(priors_cxcy=model.priors_cxcy).to(device) # Custom dataloaders train_dataset = PascalVOCDataset(data_folder, split='train', keep_difficult=keep_difficult) val_dataset = PascalVOCDataset(data_folder, split='test', keep_difficult=keep_difficult) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=train_dataset.collate_fn, num_workers=workers, pin_memory=True) # note that we're passing the collate function here val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True, collate_fn=val_dataset.collate_fn, num_workers=workers, pin_memory=True) # Epochs for epoch in range(start_epoch, epochs): # Paper describes decaying the learning rate at the 80000th, 100000th, 120000th 'iteration', i.e. model update or batch # The paper uses a batch size of 32, which means there were about 517 iterations in an epoch # Therefore, to find the epochs to decay at, you could do, # if epoch in {80000 // 517, 100000 // 517, 120000 // 517}: # adjust_learning_rate(optimizer, 0.1) # In practice, I just decayed the learning rate when loss stopped improving for long periods, # and I would resume from the last best checkpoint with the new learning rate, # since there's no point in resuming at the most recent and significantly worse checkpoint. # So, when you're ready to decay the learning rate, just set checkpoint = 'BEST_checkpoint_ssd300.pth.tar' above # and have adjust_learning_rate(optimizer, 0.1) BEFORE this 'for' loop # One epoch's training train(train_loader=train_loader, model=model, criterion=criterion, optimizer=optimizer, epoch=epoch) # One epoch's validation val_loss = validate(val_loader=val_loader, model=model, criterion=criterion) # Did validation loss improve? is_best = val_loss < best_loss best_loss = min(val_loss, best_loss) if not is_best: epochs_since_improvement += 1 print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,)) else: epochs_since_improvement = 0 # Save checkpoint save_checkpoint(epoch, epochs_since_improvement, model, optimizer, val_loss, best_loss, is_best) def train(train_loader, model, criterion, optimizer, epoch): """ One epoch's training. :param train_loader: DataLoader for training data :param model: model :param criterion: MultiBox loss :param optimizer: optimizer :param epoch: epoch number """ model.train() # training mode enables dropout batch_time = AverageMeter() # forward prop. + back prop. time data_time = AverageMeter() # data loading time losses = AverageMeter() # loss start = time.time() # Batches for i, (images, boxes, labels, _) in enumerate(train_loader): data_time.update(time.time() - start) # Move to default device images = images.to(device) # (batch_size (N), 3, 300, 300) boxes = [b.to(device) for b in boxes] labels = [l.to(device) for l in labels] # Forward prop. predicted_locs, predicted_scores = model(images) # (N, 8732, 4), (N, 8732, n_classes) # Loss loss = criterion(predicted_locs, predicted_scores, boxes, labels) # scalar # Backward prop. optimizer.zero_grad() loss.backward() # Clip gradients, if necessary. Used to prevent gradient explosion if grad_clip is not None: clip_gradient(optimizer, grad_clip) # Update model optimizer.step() losses.update(loss.item(), images.size(0)) batch_time.update(time.time() - start) start = time.time() # Print status if i % print_freq == 0: print('Epoch: [{0}][{1}/{2}]\t' 'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Data Time {data_time.val:.3f} ({data_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch, i, len(train_loader), batch_time=batch_time, data_time=data_time, loss=losses)) del predicted_locs, predicted_scores, images, boxes, labels # free some memory since their histories may be stored def validate(val_loader, model, criterion): """ One epoch's validation. :param val_loader: DataLoader for validation data :param model: model :param criterion: MultiBox loss :return: average validation loss """ model.eval() # eval mode disables dropout batch_time = AverageMeter() losses = AverageMeter() start = time.time() # Prohibit gradient computation explicity because I had some problems with memory with torch.no_grad(): # Batches for i, (images, boxes, labels, difficulties) in enumerate(val_loader): # Move to default device images = images.to(device) # (N, 3, 300, 300) boxes = [b.to(device) for b in boxes] labels = [l.to(device) for l in labels] # Forward prop. predicted_locs, predicted_scores = model(images) # (N, 8732, 4), (N, 8732, n_classes) # Loss loss = criterion(predicted_locs, predicted_scores, boxes, labels) losses.update(loss.item(), images.size(0)) batch_time.update(time.time() - start) start = time.time() # Print status if i % print_freq == 0: print('[{0}/{1}]\t' 'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(i, len(val_loader), batch_time=batch_time, loss=losses)) print('\n * LOSS - {loss.avg:.3f}\n'.format(loss=losses)) return losses.avg if __name__ == '__main__': main()

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user@gmail.com

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[]

no_license

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import pandas as pd import numpy as np FILEPATH = r'G:\Public\National Accounts\WeeklyBestsellerImports\Temp\MassClub.xlsx' SAVEPATH = r'G:\Public\National Accounts\WeeklyBestsellerImports\~MassClub.xlsx' INITIAL_COLUMNS = ['\n\nAgency', '\nMaster \nChain Code', '\nMaster \nChain Name','\n\nChain Code', '\n\nChain Name', 'Saturday \nWeek Ending\nDate','\n\nEAN', '\n\nTitle', '\nMktg\nCode', '\nProg\nCode','\nBusiness\nChannel', '\nVendor\nCode', '\nVendor\nName','\nPOS \nUnits', '\nPOS \nConsumer $', 'Chain \nOn Hand\nUnits**'] DESIRED_COLUMNS = ['\n\nChain Code', '\nPOS \nUnits', 'Chain \nOn Hand\nUnits**'] CLUBS_CODES = ['BJ','CW','SA'] MASS_CODES = ['AQ','BR','DH','GA','GE','HB','JJ','KO','KZ','MJ','NA','RA','SE','SF','SY','TR','TX',"WC","WF",'WM'] def mass_club_conv(chain_code): if chain_code in CLUBS_CODES: return "Clubs" if chain_code in MASS_CODES: return "Mass" initial_df = pd.DataFrame(pd.read_excel(FILEPATH,skip_footer=3, index_col=6, converters={'\n\nChain Code': mass_club_conv})) initial_df.index.names = ['EAN'] initial_df.index = initial_df.index.map(str) middle_df = initial_df[DESIRED_COLUMNS] clubs_df = middle_df[middle_df['\n\nChain Code'] == 'Clubs'].drop('\n\nChain Code',axis=1).groupby('EAN').sum() clubs_df.columns = ['ClubsSold', 'ClubsOH'] mass_df = middle_df[middle_df['\n\nChain Code'] == 'Mass'].drop('\n\nChain Code',axis=1).groupby('EAN').sum() mass_df.columns = ['MassSold', 'MassOH'] output_df = pd.DataFrame() output_df['EAN'] = initial_df.index.values output_df = output_df.set_index('EAN') output_df = output_df[~output_df.index.duplicated(keep='first')] output_df = output_df.join(clubs_df,how='outer').join(mass_df,how='outer') output_df['ClubsYTD'] = np.NaN output_df['MassYTD'] = np.NaN writer = pd.ExcelWriter(SAVEPATH) output_df.to_excel(writer) writer.save()

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/sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_04_01/aio/operations/_express_route_cross_connection_peerings_operations.py

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# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class ExpressRouteCrossConnectionPeeringsOperations: """ExpressRouteCrossConnectionPeeringsOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.network.v2018_04_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config def list( self, resource_group_name: str, cross_connection_name: str, **kwargs ) -> AsyncIterable["models.ExpressRouteCrossConnectionPeeringList"]: """Gets all peerings in a specified ExpressRouteCrossConnection. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param cross_connection_name: The name of the ExpressRouteCrossConnection. :type cross_connection_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either ExpressRouteCrossConnectionPeeringList or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2018_04_01.models.ExpressRouteCrossConnectionPeeringList] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.ExpressRouteCrossConnectionPeeringList"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-04-01" accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'crossConnectionName': self._serialize.url("cross_connection_name", cross_connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('ExpressRouteCrossConnectionPeeringList', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCrossConnections/{crossConnectionName}/peerings'} # type: ignore async def _delete_initial( self, resource_group_name: str, cross_connection_name: str, peering_name: str, **kwargs ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-04-01" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'crossConnectionName': self._serialize.url("cross_connection_name", cross_connection_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCrossConnections/{crossConnectionName}/peerings/{peeringName}'} # type: ignore async def begin_delete( self, resource_group_name: str, cross_connection_name: str, peering_name: str, **kwargs ) -> AsyncLROPoller[None]: """Deletes the specified peering from the ExpressRouteCrossConnection. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param cross_connection_name: The name of the ExpressRouteCrossConnection. :type cross_connection_name: str :param peering_name: The name of the peering. :type peering_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[None] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, cross_connection_name=cross_connection_name, peering_name=peering_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCrossConnections/{crossConnectionName}/peerings/{peeringName}'} # type: ignore async def get( self, resource_group_name: str, cross_connection_name: str, peering_name: str, **kwargs ) -> "models.ExpressRouteCrossConnectionPeering": """Gets the specified peering for the ExpressRouteCrossConnection. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param cross_connection_name: The name of the ExpressRouteCrossConnection. :type cross_connection_name: str :param peering_name: The name of the peering. :type peering_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ExpressRouteCrossConnectionPeering, or the result of cls(response) :rtype: ~azure.mgmt.network.v2018_04_01.models.ExpressRouteCrossConnectionPeering :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.ExpressRouteCrossConnectionPeering"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-04-01" accept = "application/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'crossConnectionName': self._serialize.url("cross_connection_name", cross_connection_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('ExpressRouteCrossConnectionPeering', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCrossConnections/{crossConnectionName}/peerings/{peeringName}'} # type: ignore async def _create_or_update_initial( self, resource_group_name: str, cross_connection_name: str, peering_name: str, peering_parameters: "models.ExpressRouteCrossConnectionPeering", **kwargs ) -> "models.ExpressRouteCrossConnectionPeering": cls = kwargs.pop('cls', None) # type: ClsType["models.ExpressRouteCrossConnectionPeering"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2018-04-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._create_or_update_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'crossConnectionName': self._serialize.url("cross_connection_name", cross_connection_name, 'str'), 'peeringName': self._serialize.url("peering_name", peering_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(peering_parameters, 'ExpressRouteCrossConnectionPeering') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if response.status_code == 200: deserialized = self._deserialize('ExpressRouteCrossConnectionPeering', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('ExpressRouteCrossConnectionPeering', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCrossConnections/{crossConnectionName}/peerings/{peeringName}'} # type: ignore async def begin_create_or_update( self, resource_group_name: str, cross_connection_name: str, peering_name: str, peering_parameters: "models.ExpressRouteCrossConnectionPeering", **kwargs ) -> AsyncLROPoller["models.ExpressRouteCrossConnectionPeering"]: """Creates or updates a peering in the specified ExpressRouteCrossConnection. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param cross_connection_name: The name of the ExpressRouteCrossConnection. :type cross_connection_name: str :param peering_name: The name of the peering. :type peering_name: str :param peering_parameters: Parameters supplied to the create or update ExpressRouteCrossConnection peering operation. :type peering_parameters: ~azure.mgmt.network.v2018_04_01.models.ExpressRouteCrossConnectionPeering :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either ExpressRouteCrossConnectionPeering or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2018_04_01.models.ExpressRouteCrossConnectionPeering] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["models.ExpressRouteCrossConnectionPeering"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._create_or_update_initial( resource_group_name=resource_group_name, cross_connection_name=cross_connection_name, peering_name=peering_name, peering_parameters=peering_parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('ExpressRouteCrossConnectionPeering', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/expressRouteCrossConnections/{crossConnectionName}/peerings/{peeringName}'} # type: ignore

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#!/usr/bin/env python3 # Copyright (c) 2016 The Bitcoin Core developers # Copyright (c) 2017 The LISYNetwork Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test using named arguments for RPCs.""" from test_framework.test_framework import LISYNetworkTestFramework from test_framework.util import ( assert_equal, assert_raises_rpc_error, ) class NamedArgumentTest(LISYNetworkTestFramework): def set_test_params(self): self.num_nodes = 1 def run_test(self): node = self.nodes[0] h = node.help(command='getinfo') assert(h.startswith('getinfo\n')) assert_raises_jsonrpc(-8, 'Unknown named parameter', node.help, random='getinfo') h = node.getblockhash(height=0) node.getblock(blockhash=h) assert_equal(node.echo(), []) assert_equal(node.echo(arg0=0,arg9=9), [0] + [None]*8 + [9]) assert_equal(node.echo(arg1=1), [None, 1]) assert_equal(node.echo(arg9=None), [None]*10) assert_equal(node.echo(arg0=0,arg3=3,arg9=9), [0] + [None]*2 + [3] + [None]*5 + [9]) if __name__ == '__main__': NamedArgumentTest().main()

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import csv from django.forms import Form, forms class ImportCsv(Form): file = forms.FileField(required=True)

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mylee16/food-classifier-docker

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import os import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.callbacks import ReduceLROnPlateau, TensorBoard, EarlyStopping, ModelCheckpoint from tensorflow.keras.optimizers import Adam from polyaxon_client.tracking import get_outputs_path class FoodClassifier: def __init__(self, data_path): self.batchsize = 16 self.train_dir = os.path.join(data_path, 'train') self.test_dir = os.path.join(data_path, 'test') self.train_datagen = None self.test_datagen = None self.train_generator = None self.validation_generator = None self.test_generator = None self.model = None tf.random.set_seed(16) def load_data(self): # make sure preprocessing is same as preprocessing as the network # reduce mean, and divide by a value to do scaling """ Split the data into train, validation, and test""" self.train_datagen = ImageDataGenerator( rescale=1./ 255, shear_range=0.05, rotation_range=20, # randomly rotate images in the range (degrees, 0 to 180) zoom_range=[0.9, 1.1], # Randomly zoom image width_shift_range=0.1, # randomly shift images horizontally (fraction of total width) height_shift_range=0.1, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images brightness_range=[0.8, 1.2], fill_mode='reflect', validation_split=0.2) self.test_datagen = ImageDataGenerator(rescale=1. / 255) self.train_generator = self.train_datagen.flow_from_directory( self.train_dir, target_size=(224, 224), shuffle=True, batch_size=self.batchsize, class_mode='categorical', subset="training") self.validation_generator = self.train_datagen.flow_from_directory( self.train_dir, target_size=(224, 224), shuffle=True, batch_size=self.batchsize, class_mode='categorical', subset="validation") self.test_generator = self.test_datagen.flow_from_directory( self.test_dir, target_size=(224, 224), shuffle=False, batch_size=1, class_mode='categorical') def create_model(self, model_base='MobileNetV2', base_model_trainable=False, dense_activation=128): """Create the neural net with pretrained weights""" if model_base == 'MobileNetV2': model_base = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) elif model_base == 'EfficientNetB0': model_base = tf.keras.applications.EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) model_base.trainable = base_model_trainable x = model_base.output x = tf.keras.layers.GlobalAveragePooling2D()(x) x = tf.keras.layers.Dense(dense_activation, activation='relu')(x) x = tf.keras.layers.Dropout(0.25)(x) predictions = tf.keras.layers.Dense(12, activation='softmax')(x) self.model = tf.keras.Model(inputs=model_base.input, outputs=predictions) def train_model(self): """Training the model""" # callbacks learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', patience=1, verbose=1, factor=0.6, min_lr=0.00001) checkpointer = ModelCheckpoint('checkpoint.h5', monitor='val_loss', verbose=1, save_best_only=True) early_stopper = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto') optimizer = Adam(learning_rate=0.01) self.model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) history = self.model.fit(self.train_generator, epochs=20, shuffle=True, verbose=1, validation_data=self.validation_generator, callbacks=[learning_rate_reduction, checkpointer, early_stopper]) return history def evaluate_model(self): """Evaluating the model""" model_loss, model_accuracy = self.model.evaluate(self.test_generator) return model_loss, model_accuracy def save_model(self, directory): """Saving the model""" os.makedirs(directory) h5_directory = get_outputs_path() + '/tensorfood.h5' # json_directory = os.path.join(directory, 'tensorfood.json') # save .h5 self.model.save(h5_directory) # save .json # model_json = self.model.to_json() # with open(json_directory, "w") as json_file: # json_file.write(model_json)

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anaruzz/holbertonschool-machine_learning

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#!/usr/bin/env python3 """ A script that prints: the Name of the launch The date (in local time) The rocket name The name (with the locality) of the launchpad """ import requests if __name__ == "__main__": """ returns: the Name of the launch The date (in local time) The rocket name The name (with the locality) of the launchpad """ url = "https://api.spacexdata.com/v4/" req1 = requests.get(url + "launches/upcoming") data = req1.json() data.sort(key=lambda json: json['date_unix']) data = data[0] v_name = data["name"] v_localtime = data["date_local"] req2 = requests.get(url + "rockets/" + data["rocket"]) rock_data = req2.json() v_rock_name = rock_data['name'] req3 = requests.get(url + "launchpads/" + data["launchpad"]) launch_data = req3.json() v_launch_name = launch_data['name'] v_lauch_local = launch_data['locality'] print("{} ({}) {} - {} ({})".format(v_name, v_localtime, v_rock_name, v_launch_name, v_lauch_local))

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""" Method to get napari style in magicgui based windows ==================================================== Example how to embed magicgui widget in dialog to inherit style from main napari window. """ from typing import Callable from qtpy.QtWidgets import QDialog, QWidget, QVBoxLayout, QPushButton, QGridLayout, QLabel, QSpinBox from magicgui import magicgui import napari from napari.qt import get_stylesheet from napari.settings import get_settings # The magicgui widget shown by selecting the 'Show widget' button of MyWidget @magicgui def sample_add(a: int, b: int) -> int: return a + b def change_style(): sample_add.native.setStyleSheet(get_stylesheet(get_settings().appearance.theme)) get_settings().appearance.events.theme.connect(change_style) change_style() class MyDialog(QDialog): def __init__(self, parent=None): super().__init__(parent) self.first_input = QSpinBox() self.second_input = QSpinBox() self.btn = QPushButton('Add') layout = QGridLayout() layout.addWidget(QLabel("first input"), 0, 0) layout.addWidget(self.first_input, 0, 1) layout.addWidget(QLabel("second input"), 1, 0) layout.addWidget(self.second_input, 1, 1) layout.addWidget(self.btn, 2, 0, 1, 2) self.setLayout(layout) self.btn.clicked.connect(self.run) def run(self): print('run', self.first_input.value() + self.second_input.value()) self.close() class MyWidget(QWidget): def __init__(self): super().__init__() self.btn1 = QPushButton('Show dialog') self.btn1.clicked.connect(self.show_dialog) self.btn2 = QPushButton('Show widget') self.btn2.clicked.connect(self.show_widget) self.layout = QVBoxLayout() self.layout.addWidget(self.btn1) self.layout.addWidget(self.btn2) self.setLayout(self.layout) def show_dialog(self): dialog = MyDialog(self) dialog.exec_() def show_widget(self): sample_add.show() viewer = napari.Viewer() widget = MyWidget() viewer.window.add_dock_widget(widget, area='right') napari.run()

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''' Resized된 화면에서 원하는 부분의 ROI 잡으면, 원본영상에서 확대되어 출력. "i" key : 화면 정지, ROI 지정모드 마우스 좌클릭-드레그 : ROI 지정 ''' import cv2 import numpy as np import glob import re import pyzbar.pyzbar as pyzbar col, width, row, height = -1, -1, -1, -1 frame = None frame2 = None inputmode = False rectangle = False trackWindow = None roi_hist = None # 키보드 'i' 키를 누를때, 화면을 멈추고 마우스 클릭 모드 활성화 def onMouse(event, x, y, flags, param): global col, width, row, height, frame, frame2, inputmode, img global rectangle, roi_hist, trackWindow if inputmode: # 왼쪽 마우스 클릭시 rectangle 플레그 활성화, if event == cv2.EVENT_LBUTTONDOWN: rectangle = True # 마우스가 움직일때 이벤트를 발생시키기 위해 col, row = x, y # 왼쪽마우스 클릭시 좌표를 기억. print("왼쪽마우스 클릭 위치", x, y) # 마우스를 움직일 때 발생 이벤트 elif event == cv2.EVENT_MOUSEMOVE: if rectangle: # 멈춘 화면에서 진행. frame = frame2.copy() cv2.rectangle(frame, (col, row), (x, y), (0, 255, 0), 2) cv2.imshow('frame', frame) elif event == cv2.EVENT_LBUTTONUP: print("좌표", (col, row), (x, y)) inputmode = False rectangle = False cv2.rectangle(frame, (col, row), (x, y), (0, 255, 0), 2) height, width = abs(row - y), abs(col - x) trackWindow = (col, row, width, height) # 선택영역 확대 displayRate(img, col, row, x, y) # roi_hist = cv2.calcHist([roi], [0], None, [180], [0, 180]) # cv2.normalize(roi_hist, roi_hist, 0, 255 , cv2.NORM_MINMAX) return def decode(im): # Find barcodes and QR codes decodedObjects = pyzbar.decode(im) print(decodedObjects) # Print results for obj in decodedObjects: print('Type : ', obj.type) print('Data : ', obj.data, '\n') return decodedObjects # Display barcode and QR code location def display(im, decodedObjects): # Loop over all decoded objects for decodedObject in decodedObjects: points = decodedObject.polygon # If the points do not form a quad, find convex hull if len(points) > 4: hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32)) hull = list(map(tuple, np.squeeze(hull))) else: hull = points # Number of points in the convex hull n = len(hull) # Draw the convext hull for j in range(0, n): cv2.line(im, hull[j], hull[(j + 1) % n], (255, 0, 0), 3) # 각도 회전시켜 수평으로 맞춤. im = rotate_bound(im, -90) # Display results cv2.imshow("Results", im) cv2.waitKey(0) cv2.destroyWindow('Results') # 화면 파괴 ''' 함수) Resized된 ROI 구간을 원본영상에서 확대 화면 비율 원본 - 4024 : 3036 축소 비율 - 1260 : 960 (계산 x축 => 1260:4024 = 1:x) (계산 y축 => 960:3036 = 1:y) 화면 배율 x = 3.1936, y = 3.1625 param ori_img : 원본 영상 x1, y1 : Resized 된 영상속에서 ROI 지정. (시작지점) x2, y2 : Resized 된 영상속에서 ROI 지정. (끝지점) ''' def displayRate(ori_img, x1, y1, x2, y2): x1 = int(x1 * 3.1936) x2 = int(x2 * 3.1936) y1 = int(y1 * 3.1625) y2 = int(y2 * 3.1625) # ROI 영역 원본영상에서 확대 roi = ori_img[y1:y2, x1:x2] # 바코드가 선명해지도록 Contrast(대조) 적용 roi = img_Contrast(roi) roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) decodedObjects = decode(roi) display(roi, decodedObjects) # print(st_pt) # print(end_pt) ''' 함수) 이미지와 각도를 입력하면, 회전된 이미지를 리턴. ''' def rotate_bound(image, angle): # grab the dimensions of the image and then determine the # center (h, w) = image.shape[:2] (cX, cY) = (w // 2, h // 2) # grab the rotation matrix (applying the negative of the # angle to rotate clockwise), then grab the sine and cosine # (i.e., the rotation components of the matrix) M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) cos = np.abs(M[0, 0]) sin = np.abs(M[0, 1]) # compute the new bounding dimensions of the image nW = int((h * sin) + (w * cos)) nH = int((h * cos) + (w * sin)) # adjust the rotation matrix to take into account translation M[0, 2] += (nW / 2) - cX M[1, 2] += (nH / 2) - cY # perform the actual rotation and return the image return cv2.warpAffine(image, M, (nW, nH)) ''' 함수) 이미지를 더 선명하게 Contrast(대조) 기법을 적용시킴. param : 컬러 이미지 return : 대조된 이미지 ''' def img_Contrast(img): # -----Converting image to LAB Color model----------------------------------- lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) # -----Splitting the LAB image to different channels------------------------- l, a, b = cv2.split(lab) # -----Applying CLAHE to L-channel------------------------------------------- clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8)) cl = clahe.apply(l) # -----Merge the CLAHE enhanced L-channel with the a and b channel----------- limg = cv2.merge((cl, a, b)) # -----Converting image from LAB Color model to RGB model-------------------- final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR) return final def main(): global frame2, frame, inputmode, trackWindow, roi_hist, img # img = cv2.imread("./img/backside.bmp") # 연속 이미지 취득 (비디오 프레임 가정) - 정상제품 img = cv2.imread("./img/backside_no.bmp") # 연속 이미지 취득 (비디오 프레임 가정) - 불량품 # 해상도 지정. resolution = (1260, 960) # 기본 프레임. cv2.namedWindow('frame') cv2.setMouseCallback('frame', onMouse, param=(frame, frame2)) # 'frame' 이라는 화면에 마우스 콜백 함수가 뒤에서 실행 while True: #img = cv2.imread("./img/backside.bmp") # 연속 이미지 취득 (비디오 프레임 가정) - 정상제품 img = cv2.imread("./img/backside_no.bmp") # 연속 이미지 취득 (비디오 프레임 가정) - 불량품 frame = img.copy() frame = cv2.resize(frame, resolution) # print(frame.shape) print('continue') cv2.imshow('frame', frame) k = cv2.waitKey(1) if k == 27: # ESC 종료 break # i 키를 누를때 input Mode 활성화하고 화면을 멈춤. (바코드 리딩할 ROI 지정) if k == ord('i'): print('Select Area for Camshift and Enter a Key') inputmode = True frame2 = frame.copy() while inputmode: cv2.imshow('frame', frame) cv2.waitKey(0) if __name__ == "__main__": main()

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lyj911111@naver.com

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import unittest from af_pan_mixer import AfPanGenerator class AfPanTests(unittest.TestCase): def test_split_single(self): self.assertEquals([[1]], AfPanGenerator.group_mixer_to_Is_and_Os("1",1)) def test_split_two_inputs(self): self.assertEquals([[1,2]], AfPanGenerator.group_mixer_to_Is_and_Os("1:2",2)) def test_split_two_inputs_two_outputs(self): self.assertEquals([[1,2],[3,4]], AfPanGenerator.group_mixer_to_Is_and_Os("1:2:3:4",2)) def test_split_three_inputs(self): self.assertEquals([[1,2,3]], AfPanGenerator.group_mixer_to_Is_and_Os("1:2:3",3)) def test_split_three_inputs_two_outputs(self): self.assertEquals([[1,2,3],[4,5,6]], AfPanGenerator.group_mixer_to_Is_and_Os("1:2:3:4:5:6",3)) def test_mixerstring_to_scales_and_cmdline(self): class MockMixerString(): def get(self): return "0.1:1.1:2.1:3.1:4.1:5.1" class MS: def __init__(self, value): self.value = value def __repr__(self): return str(self.value) def set(self, value): self.value = value afPan = AfPanGenerator(None) afPan.channelScales=[[MS(0),MS(1),MS(2)],[MS(3),MS(4),MS(5)]] afPan.mixerString = MockMixerString() afPan.output_channels = 3 afPan.mixerstring_to_scales_and_cmdline() self.assertEquals("[[0.1, 1.1, 2.1], [3.1, 4.1, 5.1]]", str(afPan.channelScales)) if __name__ == '__main__': unittest.main()

[ "hvrauhal@gmail.com" ]

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""" Create plots of Cal intNonlin curves inlXML2TXT <input_xml_file> <output_root_file where: <input_xml_file> = input intNonlin GLAST Cal offline calibration file <output_root_file> = output ROOT file with plots """ __facility__ = "Offline" __abstract__ = "Create plots of Cal intNonlin curves" __author__ = "Z. Fewtrell" __date__ = "$Date: 2008/04/21 14:36:57 $" __version__ = "$Revision: 1.1 $, $Author: fewtrell $" __release__ = "$Name: $" __credits__ = "NRL code 7650" import getopt import sys import calCalibXML import ROOT import cgc_util import array import numarray import calConstant if __name__ == '__main__': # check commandline try: (opts,args) = getopt.getopt(sys.argv[1:], "") except getopt.GetoptError: log.error(__doc__) sys.exit(1) if len(args) != 2: # should just be the one input file. print __doc__ sys.exit(1) # retrieve commandline parms inName = args[0] outName = args[1] # open and read XML IntNonlin file xmlFile = calCalibXML.calIntNonlinCalibXML(inName) (lenData, dacData, adcData) = xmlFile.read() towers = xmlFile.getTowers() xmlFile.close() # create output file rootFile = ROOT.TFile(outName, "RECREATE") # create summary plots for each ADC range summaryHists = {} for rng in range(calConstant.NUM_RNG): plot_name = "inl_summary_" + calConstant.CRNG[rng] summaryHists[rng] = ROOT.TH2S(plot_name, plot_name, 4096,0,4096, 4096,0,4096) for twr in towers: print "inlPLot.py processing TEM# " + str(twr) for offline_lyr in range(8): # calCalibXML uses 'row' indexing, not layer online_row = calCalibXML.layerToRow(offline_lyr) for col in range(12): for offline_face in range(2): online_face = calConstant.offline_face_to_online[offline_face] for rng in range(4): nPts = lenData[rng][twr,online_row,online_face, col,0] # hack: some xml files have splines w/ single point of (0,0) since dtd does not allow for missing data if nPts <= 1: continue adcs = array.array('d',adcData[rng][twr,online_row,online_face, col]) dacs = array.array('d',dacData[rng][twr,online_row,online_face, col]) # plot spline method channel_str = "T%dL%dC%dF%dR%d"%(twr,offline_lyr,col,offline_face,rng) spline = ROOT.TSpline3(channel_str, dacs, adcs, nPts) c = ROOT.TCanvas(channel_str, channel_str,-1) spline.Draw("C") g = ROOT.TGraph(nPts, dacs, adcs) g.Fit("pol1","Q") g.Draw("*") # save plot to file c.Write() # write points to summary histogram summaryHists[rng].FillN(len(adcs), dacs, adcs, array.array('d',[1]*len(adcs))) for rng in range(calConstant.NUM_RNG): summaryHists[rng].Write() rootFile.Close()

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zhuyifan1993/road-anomaly-benchmark

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py

import torch.nn as nn import torch import torchvision.models import sys # sys.path.append("..") # from image_dissimilarity.models.semantic_encoder import SemanticEncoder, ResNetSemanticEncoder # from image_dissimilarity.models.vgg_features import VGGFeatures, VGGSPADE # from image_dissimilarity.models.resnet_features import resnet # from image_dissimilarity.models.normalization import SPADE, FILM, GuideCorrelation, GuideNormalization # from .semantic_encoder import SemanticEncoder from .vgg_features import VGGFeatures, VGGSPADE from .normalization import SPADE class DissimNet(nn.Module): def __init__(self, architecture='vgg16', semantic=True, pretrained=True, correlation = True, prior = False, spade='', num_semantic_classes = 19): super(DissimNet, self).__init__() #get initialization parameters self.correlation = correlation self.spade = spade self.semantic = semantic # generate encoders if self.spade == 'encoder' or self.spade == 'both': self.vgg_encoder = VGGSPADE(pretrained=pretrained, label_nc=num_semantic_classes) else: self.vgg_encoder = VGGFeatures(architecture=architecture, pretrained=pretrained) if self.semantic: self.semantic_encoder = SemanticEncoder(architecture=architecture, in_channels=num_semantic_classes) # layers for decoder # all the 3x3 convolutions if correlation: self.conv1 = nn.Sequential(nn.Conv2d(513, 256, kernel_size=3, padding=1), nn.SELU()) self.conv12 = nn.Sequential(nn.Conv2d(513, 256, kernel_size=3, padding=1), nn.SELU()) self.conv3 = nn.Sequential(nn.Conv2d(385, 128, kernel_size=3, padding=1), nn.SELU()) self.conv5 = nn.Sequential(nn.Conv2d(193, 64, kernel_size=3, padding=1), nn.SELU()) else: self.conv1 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) self.conv12 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) self.conv3 = nn.Sequential(nn.Conv2d(384, 128, kernel_size=3, padding=1), nn.SELU()) self.conv5 = nn.Sequential(nn.Conv2d(192, 64, kernel_size=3, padding=1), nn.SELU()) if self.spade == 'decoder' or self.spade == 'both': self.conv2 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) self.conv13 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) self.conv4 = SPADEDecoderLayer(nc=128, label_nc=num_semantic_classes) self.conv6 = SPADEDecoderLayer(nc=64, label_nc=num_semantic_classes) else: self.conv2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) self.conv13 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) self.conv4 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.SELU()) self.conv6 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.SELU()) # all the tranposed convolutions self.tconv1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) self.tconv3 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) self.tconv2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2, padding=0) # all the other 1x1 convolutions if self.semantic: self.conv7 = nn.Conv2d(1280, 512, kernel_size=1, padding=0) self.conv8 = nn.Conv2d(640, 256, kernel_size=1, padding=0) self.conv9 = nn.Conv2d(320, 128, kernel_size=1, padding=0) self.conv10 = nn.Conv2d(160, 64, kernel_size=1, padding=0) self.conv11 = nn.Conv2d(64, 2, kernel_size=1, padding=0) else: self.conv7 = nn.Conv2d(1024, 512, kernel_size=1, padding=0) self.conv8 = nn.Conv2d(512, 256, kernel_size=1, padding=0) self.conv9 = nn.Conv2d(256, 128, kernel_size=1, padding=0) self.conv10 = nn.Conv2d(128, 64, kernel_size=1, padding=0) self.conv11 = nn.Conv2d(64, 2, kernel_size=1, padding=0) #self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) def forward(self, original_img, synthesis_img, semantic_img, softmax_out=False): # get all the image encodings if self.spade == 'encoder' or self.spade == 'both': encoding_og = self.vgg_encoder(original_img, semantic_img) encoding_syn = self.vgg_encoder(synthesis_img, semantic_img) else: encoding_og = self.vgg_encoder(original_img) encoding_syn = self.vgg_encoder(synthesis_img) if self.semantic: encoding_sem = self.semantic_encoder(semantic_img) # concatenate the output of each encoder layer1_cat = torch.cat((encoding_og[0], encoding_syn[0], encoding_sem[0]), dim=1) layer2_cat = torch.cat((encoding_og[1], encoding_syn[1], encoding_sem[1]), dim=1) layer3_cat = torch.cat((encoding_og[2], encoding_syn[2], encoding_sem[2]), dim=1) layer4_cat = torch.cat((encoding_og[3], encoding_syn[3], encoding_sem[3]), dim=1) else: layer1_cat = torch.cat((encoding_og[0], encoding_syn[0]), dim=1) layer2_cat = torch.cat((encoding_og[1], encoding_syn[1]), dim=1) layer3_cat = torch.cat((encoding_og[2], encoding_syn[2]), dim=1) layer4_cat = torch.cat((encoding_og[3], encoding_syn[3]), dim=1) # use 1x1 convolutions to reduce dimensions of concatenations layer4_cat = self.conv7(layer4_cat) layer3_cat = self.conv8(layer3_cat) layer2_cat = self.conv9(layer2_cat) layer1_cat = self.conv10(layer1_cat) if self.correlation: # get correlation for each layer (multiplication + 1x1 conv) corr1 = torch.sum(torch.mul(encoding_og[0], encoding_syn[0]), dim=1).unsqueeze(dim=1) corr2 = torch.sum(torch.mul(encoding_og[1], encoding_syn[1]), dim=1).unsqueeze(dim=1) corr3 = torch.sum(torch.mul(encoding_og[2], encoding_syn[2]), dim=1).unsqueeze(dim=1) corr4 = torch.sum(torch.mul(encoding_og[3], encoding_syn[3]), dim=1).unsqueeze(dim=1) # concatenate correlation layers layer4_cat = torch.cat((corr4, layer4_cat), dim = 1) layer3_cat = torch.cat((corr3, layer3_cat), dim = 1) layer2_cat = torch.cat((corr2, layer2_cat), dim = 1) layer1_cat = torch.cat((corr1, layer1_cat), dim = 1) # Run Decoder x = self.conv1(layer4_cat) if self.spade == 'decoder' or self.spade == 'both': x = self.conv2(x, semantic_img) else: x = self.conv2(x) x = self.tconv1(x) x = torch.cat((x, layer3_cat), dim=1) x = self.conv12(x) if self.spade == 'decoder' or self.spade == 'both': x = self.conv13(x, semantic_img) else: x = self.conv13(x) x = self.tconv3(x) x = torch.cat((x, layer2_cat), dim=1) x = self.conv3(x) if self.spade == 'decoder' or self.spade == 'both': x = self.conv4(x, semantic_img) else: x = self.conv4(x) x = self.tconv2(x) x = torch.cat((x, layer1_cat), dim=1) x = self.conv5(x) if self.spade == 'decoder' or self.spade == 'both': x = self.conv6(x, semantic_img) else: x = self.conv6(x) logits = self.conv11(x) return logits class DissimNetPrior(nn.Module): def __init__(self, architecture='vgg16', semantic=True, pretrained=True, correlation=True, prior=False, spade='', num_semantic_classes=19): super(DissimNetPrior, self).__init__() # get initialization parameters self.correlation = correlation self.spade = spade # self.semantic = False if spade else semantic self.semantic = semantic self.prior = prior # generate encoders if self.spade == 'encoder' or self.spade == 'both': self.vgg_encoder = VGGSPADE(pretrained=pretrained, label_nc=num_semantic_classes) else: self.vgg_encoder = VGGFeatures(architecture=architecture, pretrained=pretrained) if self.semantic: self.semantic_encoder = SemanticEncoder(architecture=architecture, in_channels=num_semantic_classes) self.prior_encoder = SemanticEncoder(architecture=architecture, in_channels=3, base_feature_size=64) # layers for decoder # all the 3x3 convolutions if correlation: self.conv1 = nn.Sequential(nn.Conv2d(513, 256, kernel_size=3, padding=1), nn.SELU()) self.conv12 = nn.Sequential(nn.Conv2d(513, 256, kernel_size=3, padding=1), nn.SELU()) self.conv3 = nn.Sequential(nn.Conv2d(385, 128, kernel_size=3, padding=1), nn.SELU()) self.conv5 = nn.Sequential(nn.Conv2d(193, 64, kernel_size=3, padding=1), nn.SELU()) else: self.conv1 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) self.conv12 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) self.conv3 = nn.Sequential(nn.Conv2d(384, 128, kernel_size=3, padding=1), nn.SELU()) self.conv5 = nn.Sequential(nn.Conv2d(192, 64, kernel_size=3, padding=1), nn.SELU()) if self.spade == 'decoder' or self.spade == 'both': self.conv2 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) self.conv13 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) self.conv4 = SPADEDecoderLayer(nc=128, label_nc=num_semantic_classes) self.conv6 = SPADEDecoderLayer(nc=64, label_nc=num_semantic_classes) else: self.conv2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) self.conv13 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) self.conv4 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.SELU()) self.conv6 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.SELU()) # all the tranposed convolutions self.tconv1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) self.tconv3 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) self.tconv2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2, padding=0) # all the other 1x1 convolutions if self.semantic: self.conv7 = nn.Conv2d(1280, 512, kernel_size=1, padding=0) self.conv8 = nn.Conv2d(640, 256, kernel_size=1, padding=0) self.conv9 = nn.Conv2d(320, 128, kernel_size=1, padding=0) self.conv10 = nn.Conv2d(160, 64, kernel_size=1, padding=0) self.conv11 = nn.Conv2d(64, 2, kernel_size=1, padding=0) else: self.conv7 = nn.Conv2d(1024, 512, kernel_size=1, padding=0) self.conv8 = nn.Conv2d(512, 256, kernel_size=1, padding=0) self.conv9 = nn.Conv2d(256, 128, kernel_size=1, padding=0) self.conv10 = nn.Conv2d(128, 64, kernel_size=1, padding=0) self.conv11 = nn.Conv2d(64, 2, kernel_size=1, padding=0) # self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) def forward(self, original_img, synthesis_img, semantic_img, entropy, mae, distance, softmax_out=False): # get all the image encodings prior_img = torch.cat((entropy, mae, distance), dim=1) if self.spade == 'encoder' or self.spade == 'both': encoding_og = self.vgg_encoder(original_img, semantic_img) encoding_syn = self.vgg_encoder(synthesis_img, semantic_img) else: encoding_og = self.vgg_encoder(original_img) encoding_syn = self.vgg_encoder(synthesis_img) if self.semantic: encoding_sem = self.semantic_encoder(semantic_img) # concatenate the output of each encoder layer1_cat = torch.cat((encoding_og[0], encoding_syn[0], encoding_sem[0]), dim=1) layer2_cat = torch.cat((encoding_og[1], encoding_syn[1], encoding_sem[1]), dim=1) layer3_cat = torch.cat((encoding_og[2], encoding_syn[2], encoding_sem[2]), dim=1) layer4_cat = torch.cat((encoding_og[3], encoding_syn[3], encoding_sem[3]), dim=1) else: layer1_cat = torch.cat((encoding_og[0], encoding_syn[0]), dim=1) layer2_cat = torch.cat((encoding_og[1], encoding_syn[1]), dim=1) layer3_cat = torch.cat((encoding_og[2], encoding_syn[2]), dim=1) layer4_cat = torch.cat((encoding_og[3], encoding_syn[3]), dim=1) # use 1x1 convolutions to reduce dimensions of concatenations layer4_cat = self.conv7(layer4_cat) layer3_cat = self.conv8(layer3_cat) layer2_cat = self.conv9(layer2_cat) layer1_cat = self.conv10(layer1_cat) if self.prior: encoding_pior = self.prior_encoder(prior_img) layer1_cat = torch.mul(layer1_cat, encoding_pior[0]) layer2_cat = torch.mul(layer2_cat, encoding_pior[1]) layer3_cat = torch.mul(layer3_cat, encoding_pior[2]) layer4_cat = torch.mul(layer4_cat, encoding_pior[3]) if self.correlation: # get correlation for each layer (multiplication + 1x1 conv) corr1 = torch.sum(torch.mul(encoding_og[0], encoding_syn[0]), dim=1).unsqueeze(dim=1) corr2 = torch.sum(torch.mul(encoding_og[1], encoding_syn[1]), dim=1).unsqueeze(dim=1) corr3 = torch.sum(torch.mul(encoding_og[2], encoding_syn[2]), dim=1).unsqueeze(dim=1) corr4 = torch.sum(torch.mul(encoding_og[3], encoding_syn[3]), dim=1).unsqueeze(dim=1) # concatenate correlation layers layer4_cat = torch.cat((corr4, layer4_cat), dim=1) layer3_cat = torch.cat((corr3, layer3_cat), dim=1) layer2_cat = torch.cat((corr2, layer2_cat), dim=1) layer1_cat = torch.cat((corr1, layer1_cat), dim=1) # Run Decoder x = self.conv1(layer4_cat) if self.spade == 'decoder' or self.spade == 'both': x = self.conv2(x, semantic_img) else: x = self.conv2(x) x = self.tconv1(x) x = torch.cat((x, layer3_cat), dim=1) x = self.conv12(x) if self.spade == 'decoder' or self.spade == 'both': x = self.conv13(x, semantic_img) else: x = self.conv13(x) x = self.tconv3(x) x = torch.cat((x, layer2_cat), dim=1) x = self.conv3(x) if self.spade == 'decoder' or self.spade == 'both': x = self.conv4(x, semantic_img) else: x = self.conv4(x) x = self.tconv2(x) x = torch.cat((x, layer1_cat), dim=1) x = self.conv5(x) if self.spade == 'decoder' or self.spade == 'both': x = self.conv6(x, semantic_img) else: x = self.conv6(x) logits = self.conv11(x) return logits # class ResNetDissimNet(nn.Module): # def __init__(self, architecture='resnet18', semantic=True, pretrained=True, correlation=True, spade='', # num_semantic_classes = 19): # super(ResNetDissimNet, self).__init__() # # # get initialization parameters # self.correlation = correlation # self.spade = spade # self.semantic = False if spade else semantic # # # generate encoders # if self.spade == 'encoder' or self.spade == 'both': # raise NotImplementedError() # #self.encoder = VGGSPADE() # else: # self.encoder = resnet(architecture=architecture, pretrained=pretrained) # # if self.semantic: # self.semantic_encoder = ResNetSemanticEncoder() # # # layers for decoder # # all the 3x3 convolutions # if correlation: # self.conv1 = nn.Sequential(nn.Conv2d(513, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv12 = nn.Sequential(nn.Conv2d(513, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv3 = nn.Sequential(nn.Conv2d(385, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv5 = nn.Sequential(nn.Conv2d(193, 64, kernel_size=3, padding=1), nn.SELU()) # # else: # self.conv1 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv12 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv3 = nn.Sequential(nn.Conv2d(384, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv5 = nn.Sequential(nn.Conv2d(192, 64, kernel_size=3, padding=1), nn.SELU()) # # if self.spade == 'decoder' or self.spade == 'both': # self.conv2 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv13 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv4 = SPADEDecoderLayer(nc=128, label_nc=num_semantic_classes) # self.conv6 = SPADEDecoderLayer(nc=64, label_nc=num_semantic_classes) # else: # self.conv2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv13 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv4 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv6 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.SELU()) # # # all the tranposed convolutions # self.tconv1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) # self.tconv5 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) # self.tconv2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2, padding=0) # self.tconv3 = nn.ConvTranspose2d(64, 64, kernel_size=2, stride=2, padding=0) # self.tconv4 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2, padding=0) # # # all the other 1x1 convolutions # if self.semantic: # self.conv7 = nn.Conv2d(1280, 512, kernel_size=1, padding=0) # self.conv8 = nn.Conv2d(640, 256, kernel_size=1, padding=0) # self.conv9 = nn.Conv2d(320, 128, kernel_size=1, padding=0) # self.conv10 = nn.Conv2d(160, 64, kernel_size=1, padding=0) # self.conv11 = nn.Conv2d(32, 2, kernel_size=1, padding=0) # else: # self.conv7 = nn.Conv2d(1024, 512, kernel_size=1, padding=0) # self.conv8 = nn.Conv2d(512, 256, kernel_size=1, padding=0) # self.conv9 = nn.Conv2d(256, 128, kernel_size=1, padding=0) # self.conv10 = nn.Conv2d(128, 64, kernel_size=1, padding=0) # self.conv11 = nn.Conv2d(32, 2, kernel_size=1, padding=0) # # # self._initialize_weights() # # def _initialize_weights(self): # for m in self.modules(): # if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # if m.bias is not None: # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.BatchNorm2d): # nn.init.constant_(m.weight, 1) # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.Linear): # nn.init.normal_(m.weight, 0, 0.01) # nn.init.constant_(m.bias, 0) # # def forward(self, original_img, synthesis_img, semantic_img, softmax_out=False): # # get all the image encodings # if self.spade == 'encoder' or self.spade == 'both': # self.encoding_og = self.encoder(original_img, semantic_img) # self.encoding_syn = self.encoder(synthesis_img, semantic_img) # else: # self.encoding_og = self.encoder(original_img) # self.encoding_syn = self.encoder(synthesis_img) # # if self.semantic: # self.encoding_sem = self.semantic_encoder(semantic_img) # # concatenate the output of each encoder # layer1_cat = torch.cat((self.encoding_og[0], self.encoding_syn[0], self.encoding_sem[0]), dim=1) # layer2_cat = torch.cat((self.encoding_og[1], self.encoding_syn[1], self.encoding_sem[1]), dim=1) # layer3_cat = torch.cat((self.encoding_og[2], self.encoding_syn[2], self.encoding_sem[2]), dim=1) # layer4_cat = torch.cat((self.encoding_og[3], self.encoding_syn[3], self.encoding_sem[3]), dim=1) # else: # layer1_cat = torch.cat((self.encoding_og[0], self.encoding_syn[0]), dim=1) # layer2_cat = torch.cat((self.encoding_og[1], self.encoding_syn[1]), dim=1) # layer3_cat = torch.cat((self.encoding_og[2], self.encoding_syn[2]), dim=1) # layer4_cat = torch.cat((self.encoding_og[3], self.encoding_syn[3]), dim=1) # # # use 1x1 convolutions to reduce dimensions of concatenations # layer4_cat = self.conv7(layer4_cat) # layer3_cat = self.conv8(layer3_cat) # layer2_cat = self.conv9(layer2_cat) # layer1_cat = self.conv10(layer1_cat) # # if self.correlation: # # get correlation for each layer (multiplication + 1x1 conv) # corr1 = torch.sum(torch.mul(self.encoding_og[0], self.encoding_syn[0]), dim=1).unsqueeze(dim=1) # corr2 = torch.sum(torch.mul(self.encoding_og[1], self.encoding_syn[1]), dim=1).unsqueeze(dim=1) # corr3 = torch.sum(torch.mul(self.encoding_og[2], self.encoding_syn[2]), dim=1).unsqueeze(dim=1) # corr4 = torch.sum(torch.mul(self.encoding_og[3], self.encoding_syn[3]), dim=1).unsqueeze(dim=1) # # # concatenate correlation layers # layer4_cat = torch.cat((corr4, layer4_cat), dim=1) # layer3_cat = torch.cat((corr3, layer3_cat), dim=1) # layer2_cat = torch.cat((corr2, layer2_cat), dim=1) # layer1_cat = torch.cat((corr1, layer1_cat), dim=1) # # # Run Decoder # x = self.conv1(layer4_cat) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv2(x, semantic_img) # else: # x = self.conv2(x) # x = self.tconv1(x) # # x = torch.cat((x, layer3_cat), dim=1) # x = self.conv12(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv13(x, semantic_img) # else: # x = self.conv13(x) # x = self.tconv5(x) # # x = torch.cat((x, layer2_cat), dim=1) # x = self.conv3(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv4(x, semantic_img) # else: # x = self.conv4(x) # x = self.tconv2(x) # # x = torch.cat((x, layer1_cat), dim=1) # x = self.conv5(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv6(x, semantic_img) # else: # x = self.conv6(x) # x = self.tconv3(x) # x = self.tconv4(x) # # x = self.conv11(x) # # self.final_prediction = x # # return self.final_prediction # # class GuidedDissimNet(nn.Module): # def __init__(self, architecture='vgg16', semantic=True, pretrained=True, correlation = True, spade=True, # num_semantic_classes = 19): # super(GuidedDissimNet, self).__init__() # # vgg_pretrained_features = torchvision.models.vgg16_bn(pretrained=pretrained).features # # # Encoder # self.norm_layer_1 = FILM(nc=64, guide_nc=64) # self.norm_layer_2 = FILM(nc=64, guide_nc=64) # self.norm_layer_3 = FILM(nc=128, guide_nc=128) # self.norm_layer_4 = FILM(nc=128, guide_nc=128) # self.norm_layer_5 = FILM(nc=256, guide_nc=256) # self.norm_layer_6 = FILM(nc=256, guide_nc=256) # self.norm_layer_7 = FILM(nc=256, guide_nc=256) # self.norm_layer_8 = FILM(nc=512, guide_nc=512) # self.norm_layer_9 = FILM(nc=512, guide_nc=512) # self.norm_layer_10 = FILM(nc=512, guide_nc=512) # self.norm_layer_11 = FILM(nc=64, guide_nc=64) # self.norm_layer_12 = FILM(nc=64, guide_nc=64) # self.norm_layer_13 = FILM(nc=128, guide_nc=128) # self.norm_layer_14 = FILM(nc=128, guide_nc=128) # self.norm_layer_15 = FILM(nc=256, guide_nc=256) # self.norm_layer_16 = FILM(nc=256, guide_nc=256) # self.norm_layer_17 = FILM(nc=256, guide_nc=256) # self.norm_layer_18 = FILM(nc=512, guide_nc=512) # self.norm_layer_19 = FILM(nc=512, guide_nc=512) # self.norm_layer_20 = FILM(nc=512, guide_nc=512) # # # TODO Reformat to make it more efficient/clean code # self.slice1 = nn.Sequential() # self.slice2 = nn.Sequential() # self.slice3 = nn.Sequential() # self.slice4 = nn.Sequential() # self.slice5 = nn.Sequential() # self.slice6 = nn.Sequential() # self.slice7 = nn.Sequential() # self.slice8 = nn.Sequential() # self.slice9 = nn.Sequential() # self.slice10 = nn.Sequential() # self.slice11 = nn.Sequential() # self.slice12 = nn.Sequential() # self.slice13 = nn.Sequential() # self.slice14 = nn.Sequential() # self.slice15 = nn.Sequential() # self.slice16 = nn.Sequential() # self.slice17 = nn.Sequential() # self.slice18 = nn.Sequential() # self.slice19 = nn.Sequential() # self.slice20 = nn.Sequential() # self.slice21 = nn.Sequential() # self.slice22 = nn.Sequential() # self.slice23 = nn.Sequential() # self.slice24 = nn.Sequential() # self.slice25 = nn.Sequential() # self.slice26 = nn.Sequential() # self.slice27 = nn.Sequential() # self.slice28 = nn.Sequential() # # for x in range(1): # self.slice1.add_module(str(x), vgg_pretrained_features[x]) # self.slice15.add_module(str(x), vgg_pretrained_features[x]) # for x in range(2, 4): # self.slice2.add_module(str(x), vgg_pretrained_features[x]) # self.slice16.add_module(str(x), vgg_pretrained_features[x]) # for x in range(5, 6): # self.slice3.add_module(str(x), vgg_pretrained_features[x]) # self.slice17.add_module(str(x), vgg_pretrained_features[x]) # for x in range(6, 8): # self.slice4.add_module(str(x), vgg_pretrained_features[x]) # self.slice18.add_module(str(x), vgg_pretrained_features[x]) # for x in range(9, 11): # self.slice5.add_module(str(x), vgg_pretrained_features[x]) # self.slice19.add_module(str(x), vgg_pretrained_features[x]) # for x in range(12, 13): # self.slice6.add_module(str(x), vgg_pretrained_features[x]) # self.slice20.add_module(str(x), vgg_pretrained_features[x]) # for x in range(13, 15): # self.slice7.add_module(str(x), vgg_pretrained_features[x]) # self.slice21.add_module(str(x), vgg_pretrained_features[x]) # for x in range(16, 18): # self.slice8.add_module(str(x), vgg_pretrained_features[x]) # self.slice22.add_module(str(x), vgg_pretrained_features[x]) # for x in range(19, 21): # self.slice9.add_module(str(x), vgg_pretrained_features[x]) # self.slice23.add_module(str(x), vgg_pretrained_features[x]) # for x in range(22, 23): # self.slice10.add_module(str(x), vgg_pretrained_features[x]) # self.slice24.add_module(str(x), vgg_pretrained_features[x]) # for x in range(23, 25): # self.slice11.add_module(str(x), vgg_pretrained_features[x]) # self.slice25.add_module(str(x), vgg_pretrained_features[x]) # for x in range(26, 28): # self.slice12.add_module(str(x), vgg_pretrained_features[x]) # self.slice26.add_module(str(x), vgg_pretrained_features[x]) # for x in range(29, 31): # self.slice13.add_module(str(x), vgg_pretrained_features[x]) # self.slice27.add_module(str(x), vgg_pretrained_features[x]) # for x in range(32, 33): # self.slice14.add_module(str(x), vgg_pretrained_features[x]) # self.slice28.add_module(str(x), vgg_pretrained_features[x]) # # # layers for decoder # # all the 3x3 convolutions # self.conv1 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv12 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv3 = nn.Sequential(nn.Conv2d(384, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv5 = nn.Sequential(nn.Conv2d(192, 64, kernel_size=3, padding=1), nn.SELU()) # # # spade decoder # self.conv2 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv13 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv4 = SPADEDecoderLayer(nc=128, label_nc=num_semantic_classes) # self.conv6 = SPADEDecoderLayer(nc=64, label_nc=num_semantic_classes) # # # all the tranposed convolutions # self.tconv1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) # self.tconv2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2, padding=0) # # # all the other 1x1 convolutions # self.conv7 = nn.Conv2d(1024, 512, kernel_size=1, padding=0) # self.conv8 = nn.Conv2d(512, 256, kernel_size=1, padding=0) # self.conv9 = nn.Conv2d(256, 128, kernel_size=1, padding=0) # self.conv10 = nn.Conv2d(128, 64, kernel_size=1, padding=0) # self.conv11 = nn.Conv2d(64, 2, kernel_size=1, padding=0) # # # self._initialize_weights() # # def _initialize_weights(self): # for m in self.modules(): # if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # if m.bias is not None: # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.BatchNorm2d): # nn.init.constant_(m.weight, 1) # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.Linear): # nn.init.normal_(m.weight, 0, 0.01) # nn.init.constant_(m.bias, 0) # # def forward(self, original_img, synthesis_img, semantic_img): # # get all the image encodings # og_1 = self.slice1(original_img) # syn_1 = self.slice15(synthesis_img) # # og_2 = self.norm_layer_1(og_1, syn_1) # syn_2 = self.norm_layer_11(og_1, syn_1) # # og_1 = self.slice2(og_2) # syn_1 = self.slice16(syn_2) # # layer1_og = self.slice3(self.norm_layer_2(og_1, syn_1)) # layer1_syn = self.slice17(self.norm_layer_12(og_1, syn_1)) # # og_1 = self.slice4(layer1_og) # syn_1 = self.slice18(layer1_syn) # # og_2 = self.norm_layer_3(og_1, syn_1) # syn_2 = self.norm_layer_13(og_1, syn_1) # # og_1 = self.slice5(og_2) # syn_1 = self.slice19(syn_2) # # layer2_og = self.slice6(self.norm_layer_4(og_1, syn_1)) # layer2_syn = self.slice20(self.norm_layer_14(og_1, syn_1)) # # og_1 = self.slice7(layer2_og) # syn_1 = self.slice21(layer2_syn) # # og_2 = self.norm_layer_5(og_1, syn_1) # syn_2 = self.norm_layer_15(og_1, syn_1) # # og_1 = self.slice8(og_2) # syn_1 = self.slice22(syn_2) # # og_2 = self.norm_layer_6(og_1, syn_1) # syn_2 = self.norm_layer_16(og_1, syn_1) # # og_1 = self.slice9(og_2) # syn_1 = self.slice23(syn_2) # # layer3_og = self.slice10(self.norm_layer_7(og_1, syn_1)) # layer3_syn = self.slice24(self.norm_layer_17(og_1, syn_1)) # # og_1 = self.slice11(layer3_og) # syn_1 = self.slice25(layer3_syn) # # og_2 = self.norm_layer_8(og_1, syn_1) # syn_2 = self.norm_layer_18(og_1, syn_1) # # og_1 = self.slice12(og_2) # syn_1 = self.slice26(syn_2) # # og_2 = self.norm_layer_9(og_1, syn_1) # syn_2 = self.norm_layer_19(og_1, syn_1) # # og_1 = self.slice13(og_2) # syn_1 = self.slice27(syn_2) # # layer4_og = self.slice14(self.norm_layer_10(og_1, syn_1)) # layer4_syn = self.slice28(self.norm_layer_20(og_1, syn_1)) # # # concatenate the output of each encoder # layer1_cat = torch.cat((layer1_og, layer1_syn), dim=1) # layer2_cat = torch.cat((layer2_og, layer2_syn), dim=1) # layer3_cat = torch.cat((layer3_og, layer3_syn), dim=1) # layer4_cat = torch.cat((layer4_og, layer4_syn), dim=1) # # # use 1x1 convolutions to reduce dimensions of concatenations # layer4_cat = self.conv7(layer4_cat) # layer3_cat = self.conv8(layer3_cat) # layer2_cat = self.conv9(layer2_cat) # layer1_cat = self.conv10(layer1_cat) # # # Run Decoder # x = self.conv1(layer4_cat) # x = self.conv2(x, semantic_img) # x = self.tconv1(x) # # x = torch.cat((x, layer3_cat), dim=1) # x = self.conv12(x) # x = self.conv13(x, semantic_img) # x = self.tconv1(x) # # x = torch.cat((x, layer2_cat), dim=1) # x = self.conv3(x) # x = self.conv4(x, semantic_img) # x = self.tconv2(x) # # x = torch.cat((x, layer1_cat), dim=1) # x = self.conv5(x) # x = self.conv6(x, semantic_img) # x = self.conv11(x) # # self.final_prediction = x # # return self.final_prediction # # class CorrelatedDissimNet(nn.Module): # def __init__(self, architecture='vgg16', semantic=True, pretrained=True, correlation=True, spade=True, # num_semantic_classes = 19): # super(CorrelatedDissimNet, self).__init__() # # self.spade = spade # # # layers for encoder # self.og_gel1 = GuideEncoderLayer(nc_in=3, nc_out=64) # self.syn_gel1 = GuideEncoderLayer(nc_in=3, nc_out=64) # # self.og_conv1 = nn.Conv2d(64, 64, kernel_size=3, padding=1) # self.syn_conv1 = nn.Conv2d(64, 64, kernel_size=3, padding=1) # self.og_gc1 = GuideCorrelation(nc=64, guide_nc=64) # self.og_gc2 = GuideCorrelation(nc=64, guide_nc=num_semantic_classes) # self.og_gn1 = GuideNormalization(nc=64) # self.og_relu1 = nn.ReLU(inplace=True) # self.syn_gc1 = GuideCorrelation(nc=64, guide_nc=64) # self.syn_gc2 = GuideCorrelation(nc=64, guide_nc=num_semantic_classes) # self.syn_gn1 = GuideNormalization(nc=64) # self.syn_relu1 = nn.ReLU(inplace=True) # self.og_max1 = nn.MaxPool2d(kernel_size=2, stride=2) # self.syn_max1 = nn.MaxPool2d(kernel_size=2, stride=2) # # self.og_gel2 = GuideEncoderLayer(nc_in=64, nc_out=128) # self.syn_gel2 = GuideEncoderLayer(nc_in=64, nc_out=128) # # self.og_conv2 = nn.Conv2d(128, 128, kernel_size=3, padding=1) # self.syn_conv2 = nn.Conv2d(128, 128, kernel_size=3, padding=1) # self.og_gc3 = GuideCorrelation(nc=128, guide_nc=128) # self.og_gc4 = GuideCorrelation(nc=128, guide_nc=num_semantic_classes) # self.og_gn2 = GuideNormalization(nc=128) # self.og_relu2 = nn.ReLU(inplace=True) # self.syn_gc3 = GuideCorrelation(nc=128, guide_nc=128) # self.syn_gc4 = GuideCorrelation(nc=128, guide_nc=num_semantic_classes) # self.syn_gn2 = GuideNormalization(nc=128) # self.syn_relu2 = nn.ReLU(inplace=True) # self.og_max2 = nn.MaxPool2d(kernel_size=2, stride=2) # self.syn_max2 = nn.MaxPool2d(kernel_size=2, stride=2) # # self.og_gel3 = GuideEncoderLayer(nc_in=128, nc_out=256) # self.syn_gel3 = GuideEncoderLayer(nc_in=128, nc_out=256) # self.og_gel4 = GuideEncoderLayer(nc_in=256, nc_out=256) # self.syn_gel4 = GuideEncoderLayer(nc_in=256, nc_out=256) # # self.og_conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1) # self.syn_conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1) # self.og_gc5 = GuideCorrelation(nc=256, guide_nc=256) # self.og_gc6 = GuideCorrelation(nc=256, guide_nc=num_semantic_classes) # self.og_gn3 = GuideNormalization(nc=256) # self.og_relu3 = nn.ReLU(inplace=True) # self.syn_gc5 = GuideCorrelation(nc=256, guide_nc=256) # self.syn_gc6 = GuideCorrelation(nc=256, guide_nc=num_semantic_classes) # self.syn_gn3 = GuideNormalization(nc=256) # self.syn_relu3 = nn.ReLU(inplace=True) # self.og_max3 = nn.MaxPool2d(kernel_size=2, stride=2) # self.syn_max3 = nn.MaxPool2d(kernel_size=2, stride=2) # # self.og_gel5 = GuideEncoderLayer(nc_in=256, nc_out=512) # self.syn_gel5 = GuideEncoderLayer(nc_in=256, nc_out=512) # self.og_gel6 = GuideEncoderLayer(nc_in=512, nc_out=512) # self.syn_gel6 = GuideEncoderLayer(nc_in=512, nc_out=512) # # self.og_conv4 = nn.Conv2d(512, 512, kernel_size=3, padding=1) # self.syn_conv4 = nn.Conv2d(512, 512, kernel_size=3, padding=1) # self.og_gc7 = GuideCorrelation(nc=512, guide_nc=512) # self.og_gc8 = GuideCorrelation(nc=512, guide_nc=num_semantic_classes) # self.og_gn4 = GuideNormalization(nc=512) # self.og_relu4 = nn.ReLU(inplace=True) # self.syn_gc7 = GuideCorrelation(nc=512, guide_nc=512) # self.syn_gc8 = GuideCorrelation(nc=512, guide_nc=num_semantic_classes) # self.syn_gn4 = GuideNormalization(nc=512) # self.syn_relu4 = nn.ReLU(inplace=True) # # # layers for decoder # # all the 3x3 convolutions # self.conv1 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv12 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv3 = nn.Sequential(nn.Conv2d(384, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv5 = nn.Sequential(nn.Conv2d(192, 64, kernel_size=3, padding=1), nn.SELU()) # # # spade decoder # if self.spade == 'decoder' or self.spade == 'both': # self.conv2 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv13 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv4 = SPADEDecoderLayer(nc=128, label_nc=num_semantic_classes) # self.conv6 = SPADEDecoderLayer(nc=64, label_nc=num_semantic_classes) # else: # self.conv2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv13 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv4 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv6 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.SELU()) # # # all the tranposed convolutions # self.tconv1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) # self.tconv3 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) # self.tconv2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2, padding=0) # # # all the other 1x1 convolutions # self.conv7 = nn.Conv2d(1024, 512, kernel_size=1, padding=0) # self.conv8 = nn.Conv2d(512, 256, kernel_size=1, padding=0) # self.conv9 = nn.Conv2d(256, 128, kernel_size=1, padding=0) # self.conv10 = nn.Conv2d(128, 64, kernel_size=1, padding=0) # self.conv11 = nn.Conv2d(64, 2, kernel_size=1, padding=0) # # # self._initialize_weights() # # def _initialize_weights(self): # for m in self.modules(): # if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # if m.bias is not None: # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.BatchNorm2d): # nn.init.constant_(m.weight, 1) # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.Linear): # nn.init.normal_(m.weight, 0, 0.01) # nn.init.constant_(m.bias, 0) # # def forward(self, original_img, synthesis_img, semantic_img): # # get all the image encodings # og = self.og_gel1(original_img) # syn = self.syn_gel1(synthesis_img) # # og = self.og_conv1(og) # syn = self.syn_conv1(syn) # # gamma1, beta1 = self.og_gc1(og, syn) # gamma2, beta2 = self.og_gc2(og, semantic_img) # # gamma3, beta3 = self.syn_gc1(syn, og) # gamma4, beta4 = self.syn_gc2(syn, semantic_img) # # layer1_og = self.og_relu1(self.og_gn1(og, gamma1, beta1, gamma2, beta2)) # layer1_syn = self.syn_relu1(self.syn_gn1(syn, gamma3, beta3, gamma4, beta4)) # # og = self.og_gel2(self.og_max1(layer1_og)) # syn = self.syn_gel2(self.syn_max1(layer1_syn)) # # og = self.og_conv2(og) # syn = self.syn_conv2(syn) # # gamma1, beta1 = self.og_gc3(og, syn) # gamma2, beta2 = self.og_gc4(og, semantic_img) # # gamma3, beta3 = self.syn_gc3(syn, og) # gamma4, beta4 = self.syn_gc4(syn, semantic_img) # # layer2_og = self.og_relu2(self.og_gn2(og, gamma1, beta1, gamma2, beta2)) # layer2_syn = self.syn_relu2(self.syn_gn2(syn, gamma3, beta3, gamma4, beta4)) # # og = self.og_gel3(self.og_max2(layer2_og)) # syn = self.syn_gel3(self.syn_max2(layer2_syn)) # og = self.og_gel4(og) # syn = self.syn_gel4(syn) # # og = self.og_conv3(og) # syn = self.syn_conv3(syn) # # gamma1, beta1 = self.og_gc5(og, syn) # gamma2, beta2 = self.og_gc6(og, semantic_img) # # gamma3, beta3 = self.syn_gc5(syn, og) # gamma4, beta4 = self.syn_gc6(syn, semantic_img) # # layer3_og = self.og_relu3(self.og_gn3(og, gamma1, beta1, gamma2, beta2)) # layer3_syn = self.syn_relu3(self.syn_gn3(syn, gamma3, beta3, gamma4, beta4)) # # og = self.og_gel5(self.og_max3(layer3_og)) # syn = self.syn_gel5(self.syn_max3(layer3_syn)) # og = self.og_gel6(og) # syn = self.syn_gel6(syn) # # og = self.og_conv4(og) # syn = self.syn_conv4(syn) # # gamma1, beta1 = self.og_gc7(og, syn) # gamma2, beta2 = self.og_gc8(og, semantic_img) # # gamma3, beta3 = self.syn_gc7(syn, og) # gamma4, beta4 = self.syn_gc8(syn, semantic_img) # # layer4_og = self.og_relu4(self.og_gn4(og, gamma1, beta1, gamma2, beta2)) # layer4_syn = self.syn_relu4(self.syn_gn4(syn, gamma3, beta3, gamma4, beta4)) # # # concatenate the output of each encoder # layer1_cat = torch.cat((layer1_og, layer1_syn), dim=1) # layer2_cat = torch.cat((layer2_og, layer2_syn), dim=1) # layer3_cat = torch.cat((layer3_og, layer3_syn), dim=1) # layer4_cat = torch.cat((layer4_og, layer4_syn), dim=1) # # # use 1x1 convolutions to reduce dimensions of concatenations # layer4_cat = self.conv7(layer4_cat) # layer3_cat = self.conv8(layer3_cat) # layer2_cat = self.conv9(layer2_cat) # layer1_cat = self.conv10(layer1_cat) # # # Run Decoder # x = self.conv1(layer4_cat) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv2(x, semantic_img) # else: # x = self.conv2(x) # x = self.tconv1(x) # # x = torch.cat((x, layer3_cat), dim=1) # x = self.conv12(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv13(x, semantic_img) # else: # x = self.conv13(x) # x = self.tconv3(x) # # x = torch.cat((x, layer2_cat), dim=1) # x = self.conv3(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv4(x, semantic_img) # else: # x = self.conv4(x) # x = self.tconv2(x) # # x = torch.cat((x, layer1_cat), dim=1) # x = self.conv5(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv6(x, semantic_img) # else: # x = self.conv6(x) # x = self.conv11(x) # # self.final_prediction = x # # return self.final_prediction # # class CorrelatedDissimNetGuide(nn.Module): # def __init__(self, architecture='vgg16', semantic=True, pretrained=True, correlation=True, spade='decoder', # num_semantic_classes=19): # super(CorrelatedDissimNetGuide, self).__init__() # # self.spade = spade # # # layers for encoder # self.og_gel1 = GuideEncoderLayer(nc_in=3, nc_out=64) # self.syn_gel1 = GuideEncoderLayer(nc_in=3, nc_out=64) # # self.og_conv1 = nn.Conv2d(64, 64, kernel_size=3, padding=1) # self.syn_conv1 = nn.Conv2d(64, 64, kernel_size=3, padding=1) # self.og_gc1 = SPADE(norm_nc=64, label_nc=64) # self.og_relu1 = nn.ReLU(inplace=True) # self.syn_gc1 = SPADE(norm_nc=64, label_nc=64) # self.syn_relu1 = nn.ReLU(inplace=True) # self.og_max1 = nn.MaxPool2d(kernel_size=2, stride=2) # self.syn_max1 = nn.MaxPool2d(kernel_size=2, stride=2) # # self.og_gel2 = GuideEncoderLayer(nc_in=64, nc_out=128) # self.syn_gel2 = GuideEncoderLayer(nc_in=64, nc_out=128) # # self.og_conv2 = nn.Conv2d(128, 128, kernel_size=3, padding=1) # self.syn_conv2 = nn.Conv2d(128, 128, kernel_size=3, padding=1) # self.og_gc2 = SPADE(norm_nc=128, label_nc=128) # self.og_relu2 = nn.ReLU(inplace=True) # self.syn_gc2 = SPADE(norm_nc=128, label_nc=128) # self.syn_relu2 = nn.ReLU(inplace=True) # self.og_max2 = nn.MaxPool2d(kernel_size=2, stride=2) # self.syn_max2 = nn.MaxPool2d(kernel_size=2, stride=2) # # self.og_gel3 = GuideEncoderLayer(nc_in=128, nc_out=256) # self.syn_gel3 = GuideEncoderLayer(nc_in=128, nc_out=256) # self.og_gel4 = GuideEncoderLayer(nc_in=256, nc_out=256) # self.syn_gel4 = GuideEncoderLayer(nc_in=256, nc_out=256) # # self.og_conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1) # self.syn_conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1) # self.og_gc3 = SPADE(norm_nc=256, label_nc=256) # self.og_relu3 = nn.ReLU(inplace=True) # self.syn_gc3 = SPADE(norm_nc=256, label_nc=256) # self.syn_relu3 = nn.ReLU(inplace=True) # self.og_max3 = nn.MaxPool2d(kernel_size=2, stride=2) # self.syn_max3 = nn.MaxPool2d(kernel_size=2, stride=2) # # self.og_gel5 = GuideEncoderLayer(nc_in=256, nc_out=512) # self.syn_gel5 = GuideEncoderLayer(nc_in=256, nc_out=512) # self.og_gel6 = GuideEncoderLayer(nc_in=512, nc_out=512) # self.syn_gel6 = GuideEncoderLayer(nc_in=512, nc_out=512) # # self.og_conv4 = nn.Conv2d(512, 512, kernel_size=3, padding=1) # self.syn_conv4 = nn.Conv2d(512, 512, kernel_size=3, padding=1) # self.og_gc4 = SPADE(norm_nc=512, label_nc=512) # self.og_relu4 = nn.ReLU(inplace=True) # self.syn_gc4 = SPADE(norm_nc=512, label_nc=512) # self.syn_relu4 = nn.ReLU(inplace=True) # # # layers for decoder # # all the 3x3 convolutions # self.conv1 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv12 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv3 = nn.Sequential(nn.Conv2d(384, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv5 = nn.Sequential(nn.Conv2d(192, 64, kernel_size=3, padding=1), nn.SELU()) # # # spade decoder # if self.spade == 'decoder' or self.spade == 'both': # self.conv2 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv13 = SPADEDecoderLayer(nc=256, label_nc=num_semantic_classes) # self.conv4 = SPADEDecoderLayer(nc=128, label_nc=num_semantic_classes) # self.conv6 = SPADEDecoderLayer(nc=64, label_nc=num_semantic_classes) # else: # self.conv2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv13 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.SELU()) # self.conv4 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.SELU()) # self.conv6 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.SELU()) # # # all the tranposed convolutions # self.tconv1 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) # self.tconv3 = nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2, padding=0) # self.tconv2 = nn.ConvTranspose2d(128, 128, kernel_size=2, stride=2, padding=0) # # # all the other 1x1 convolutions # self.conv7 = nn.Conv2d(1024, 512, kernel_size=1, padding=0) # self.conv8 = nn.Conv2d(512, 256, kernel_size=1, padding=0) # self.conv9 = nn.Conv2d(256, 128, kernel_size=1, padding=0) # self.conv10 = nn.Conv2d(128, 64, kernel_size=1, padding=0) # self.conv11 = nn.Conv2d(64, 2, kernel_size=1, padding=0) # # # self._initialize_weights() # # def _initialize_weights(self): # for m in self.modules(): # if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # if m.bias is not None: # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.BatchNorm2d): # nn.init.constant_(m.weight, 1) # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.Linear): # nn.init.normal_(m.weight, 0, 0.01) # nn.init.constant_(m.bias, 0) # # def forward(self, original_img, synthesis_img, semantic_img): # # get all the image encodings # og = self.og_gel1(original_img) # syn = self.syn_gel1(synthesis_img) # # og_1 = self.og_conv1(og) # syn_1 = self.syn_conv1(syn) # # og_2 = self.og_gc1(og_1, syn_1) # syn_2 = self.syn_gc1(syn_1, og_1) # # layer1_og = self.og_relu1(og_2) # layer1_syn = self.syn_relu1(syn_2) # # og = self.og_gel2(self.og_max1(layer1_og)) # syn = self.syn_gel2(self.syn_max1(layer1_syn)) # # og_1 = self.og_conv2(og) # syn_1 = self.syn_conv2(syn) # # og_2 = self.og_gc2(og_1, syn_1) # syn_2 = self.syn_gc2(syn_1, og_1) # # layer2_og = self.og_relu2(og_2) # layer2_syn = self.syn_relu2(syn_2) # # og = self.og_gel3(self.og_max2(layer2_og)) # syn = self.syn_gel3(self.syn_max2(layer2_syn)) # og = self.og_gel4(og) # syn = self.syn_gel4(syn) # # og_1 = self.og_conv3(og) # syn_1 = self.syn_conv3(syn) # # og_2 = self.og_gc3(og_1, syn_1) # syn_2 = self.syn_gc3(syn_1, og_1) # # layer3_og = self.og_relu3(og_2) # layer3_syn = self.syn_relu3(syn_2) # # og = self.og_gel5(self.og_max3(layer3_og)) # syn = self.syn_gel5(self.syn_max3(layer3_syn)) # og = self.og_gel6(og) # syn = self.syn_gel6(syn) # # og_1 = self.og_conv4(og) # syn_1 = self.syn_conv4(syn) # # og_2 = self.og_gc4(og_1, syn_1) # syn_2 = self.syn_gc4(syn_1, og_1) # # layer4_og = self.og_relu4(og_2) # layer4_syn = self.syn_relu4(syn_2) # # # concatenate the output of each encoder # layer1_cat = torch.cat((layer1_og, layer1_syn), dim=1) # layer2_cat = torch.cat((layer2_og, layer2_syn), dim=1) # layer3_cat = torch.cat((layer3_og, layer3_syn), dim=1) # layer4_cat = torch.cat((layer4_og, layer4_syn), dim=1) # # # use 1x1 convolutions to reduce dimensions of concatenations # layer4_cat = self.conv7(layer4_cat) # layer3_cat = self.conv8(layer3_cat) # layer2_cat = self.conv9(layer2_cat) # layer1_cat = self.conv10(layer1_cat) # # # Run Decoder # x = self.conv1(layer4_cat) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv2(x, semantic_img) # else: # x = self.conv2(x) # x = self.tconv1(x) # # x = torch.cat((x, layer3_cat), dim=1) # x = self.conv12(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv13(x, semantic_img) # else: # x = self.conv13(x) # x = self.tconv3(x) # # x = torch.cat((x, layer2_cat), dim=1) # x = self.conv3(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv4(x, semantic_img) # else: # x = self.conv4(x) # x = self.tconv2(x) # # x = torch.cat((x, layer1_cat), dim=1) # x = self.conv5(x) # if self.spade == 'decoder' or self.spade == 'both': # x = self.conv6(x, semantic_img) # else: # x = self.conv6(x) # x = self.conv11(x) # # self.final_prediction = x # # return self.final_prediction class SPADEDecoderLayer(nn.Module): def __init__(self, nc=256, label_nc=19): super(SPADEDecoderLayer, self).__init__() # create conv layers self.norm1 = SPADE(norm_nc=nc, label_nc=label_nc) self.selu1 = nn.SELU() self.conv = nn.Conv2d(nc, nc, kernel_size=3, padding=1) self.norm2 = SPADE(norm_nc=nc, label_nc=label_nc) self.selu2 = nn.SELU() def forward(self, x, seg): out = self.selu2(self.norm2(self.conv(self.selu1(self.norm1(x, seg))), seg)) return out # # class GuideEncoderLayer(nn.Module): # def __init__(self, nc_in=3, nc_out=64): # super(GuideEncoderLayer, self).__init__() # # # create conv layers # self.conv = nn.Conv2d(nc_in, nc_out, kernel_size=3, padding=1) # self.norm = nn.BatchNorm2d(nc_out, affine=False) # self.relu = nn.ReLU(inplace=True) # # def forward(self, x): # x = self.conv(x) # x = self.norm(x) # x = self.relu(x) # return x # # # if __name__ == "__main__": # from PIL import Image # import torchvision.models as models # import torchvision.transforms as transforms # # img = Image.open('../../sample_images/zm0002_100000.png') # diss_model = CorrelatedDissimNet() # img_transform = transforms.Compose([transforms.ToTensor()]) # img_tensor = img_transform(img) # outputs = diss_model(img_tensor.unsqueeze(0), img_tensor.unsqueeze(0), img_tensor.unsqueeze(0)) # print(img_tensor[0].data.shape) # print(outputs.data.shape)

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# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from enum import Enum from azure.core import CaseInsensitiveEnumMeta class CreateMode(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The mode to create a new server. """ DEFAULT = "Default" POINT_IN_TIME_RESTORE = "PointInTimeRestore" GEO_RESTORE = "GeoRestore" REPLICA = "Replica" class GeoRedundantBackup(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Enable Geo-redundant or not for server backup. """ ENABLED = "Enabled" DISABLED = "Disabled" class IdentityType(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The identity type. Set this to 'SystemAssigned' in order to automatically create and assign an Azure Active Directory principal for the resource. """ SYSTEM_ASSIGNED = "SystemAssigned" class InfrastructureEncryption(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Add a second layer of encryption for your data using new encryption algorithm which gives additional data protection. Value is optional but if passed in, must be 'Disabled' or 'Enabled'. """ #: Default value for single layer of encryption for data at rest. ENABLED = "Enabled" #: Additional (2nd) layer of encryption for data at rest. DISABLED = "Disabled" class MinimalTlsVersionEnum(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Enforce a minimal Tls version for the server. """ TLS1_0 = "TLS1_0" TLS1_1 = "TLS1_1" TLS1_2 = "TLS1_2" TLS_ENFORCEMENT_DISABLED = "TLSEnforcementDisabled" class OperationOrigin(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The intended executor of the operation. """ NOT_SPECIFIED = "NotSpecified" USER = "user" SYSTEM = "system" class PrivateEndpointProvisioningState(str, Enum, metaclass=CaseInsensitiveEnumMeta): """State of the private endpoint connection. """ APPROVING = "Approving" READY = "Ready" DROPPING = "Dropping" FAILED = "Failed" REJECTING = "Rejecting" class PrivateLinkServiceConnectionStateActionsRequire(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The actions required for private link service connection. """ NONE = "None" class PrivateLinkServiceConnectionStateStatus(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The private link service connection status. """ APPROVED = "Approved" PENDING = "Pending" REJECTED = "Rejected" DISCONNECTED = "Disconnected" class PublicNetworkAccessEnum(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Whether or not public network access is allowed for this server. Value is optional but if passed in, must be 'Enabled' or 'Disabled' """ ENABLED = "Enabled" DISABLED = "Disabled" class QueryPerformanceInsightResetDataResultState(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Indicates result of the operation. """ SUCCEEDED = "Succeeded" FAILED = "Failed" class SecurityAlertPolicyName(str, Enum, metaclass=CaseInsensitiveEnumMeta): DEFAULT = "Default" class ServerKeyType(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The key type like 'AzureKeyVault'. """ AZURE_KEY_VAULT = "AzureKeyVault" class ServerSecurityAlertPolicyState(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Specifies the state of the policy, whether it is enabled or disabled. """ ENABLED = "Enabled" DISABLED = "Disabled" class ServerState(str, Enum, metaclass=CaseInsensitiveEnumMeta): """A state of a server that is visible to user. """ READY = "Ready" DROPPING = "Dropping" DISABLED = "Disabled" INACCESSIBLE = "Inaccessible" class ServerVersion(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The version of a server. """ FIVE6 = "5.6" FIVE7 = "5.7" EIGHT0 = "8.0" class SkuTier(str, Enum, metaclass=CaseInsensitiveEnumMeta): """The tier of the particular SKU, e.g. Basic. """ BASIC = "Basic" GENERAL_PURPOSE = "GeneralPurpose" MEMORY_OPTIMIZED = "MemoryOptimized" class SslEnforcementEnum(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Enable ssl enforcement or not when connect to server. """ ENABLED = "Enabled" DISABLED = "Disabled" class StorageAutogrow(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Enable Storage Auto Grow. """ ENABLED = "Enabled" DISABLED = "Disabled" class VirtualNetworkRuleState(str, Enum, metaclass=CaseInsensitiveEnumMeta): """Virtual Network Rule State """ INITIALIZING = "Initializing" IN_PROGRESS = "InProgress" READY = "Ready" DELETING = "Deleting" UNKNOWN = "Unknown"

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import os import re import sys import tarfile import cv2 import numpy as np from six.moves import urllib import tensorflow as tf import face_input FLAGS = tf.app.flags.FLAGS # Basic model parameters. tf.app.flags.DEFINE_integer('batch_size', 64, """Number of images to process in a batch.""") tf.app.flags.DEFINE_boolean('use_fp16', False, """Train the model using fp16.""") # Global constants describing the face data set. IMAGE_SIZE = face_input.IMAGE_SIZE NUM_CLASSES = face_input.NUM_CLASSES # Constants describing the training process. MOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average. # NUM_EPOCHS_PER_DECAY = 2.0 # Epochs after which learning rate decays. DECAY_STEPS = 3000 # after how many batches LEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor. INITIAL_LEARNING_RATE = 0.1 # Initial learning rate. # If a model is trained with multiple GPUs, prefix all Op names with tower_name # to differentiate the operations. Note that this prefix is removed from the # names of the summaries when visualizing a model. TOWER_NAME = 'tower' DATA_URL = 'https://ibm.box.com/shared/static/y28pnufdhzkjj3oyc4yytxema4tfxexr.gz' def _activation_summary(x): """Helper to create summaries for activations. Creates a summary that provides a histogram of activations. Creates a summary that measures the sparsity of activations. Args: x: Tensor Returns: nothing """ # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training # session. This helps the clarity of presentation on tensorboard. tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name) tf.summary.histogram(tensor_name + '/activations', x) tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) def _variable_on_cpu(name, shape, initializer): """Helper to create a Variable stored on CPU memory. Args: name: name of the variable shape: list of ints initializer: initializer for Variable Returns: Variable Tensor """ with tf.device('/cpu:0'): dtype = tf.float16 if FLAGS.use_fp16 else tf.float32 var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype) return var def _variable_with_weight_decay(name, shape, stddev, wd): """Helper to create an initialized Variable with weight decay. Note that the Variable is initialized with a truncated normal distribution. A weight decay is added only if one is specified. Args: name: name of the variable shape: list of ints stddev: standard deviation of a truncated Gaussian wd: add L2Loss weight decay multiplied by this float. If None, weight decay is not added for this Variable. Returns: Variable Tensor """ dtype = tf.float16 if FLAGS.use_fp16 else tf.float32 var = _variable_on_cpu( name, shape, tf.truncated_normal_initializer(stddev=stddev, dtype=dtype)) if wd is not None: weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) return var def distorted_inputs(): """Construct distorted input for CIFAR training using the Reader ops. Returns: images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. labels: Labels. 1D tensor of [batch_size] size. Raises: ValueError: If no data_dir """ if not FLAGS.data_dir: raise ValueError('Please supply a data_dir') features,labels = face_input.read_face2() if FLAGS.use_fp16: features = tf.cast(images, tf.float16) labels = tf.cast(labels, tf.float16) return features, labels def inputs(eval_data): """Construct input for CIFAR evaluation using the Reader ops. Args: eval_data: bool, indicating if one should use the train or eval data set. Returns: images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size. labels: Labels. 1D tensor of [batch_size] size. Raises: ValueError: If no data_dir """ if not FLAGS.data_dir: raise ValueError('Please supply a data_dir') data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin') images, labels = cifar10_input.inputs(eval_data=eval_data, data_dir=data_dir, batch_size=FLAGS.batch_size) if FLAGS.use_fp16: images = tf.cast(images, tf.float16) labels = tf.cast(labels, tf.float16) return images, labels def inference_color(images): """Build the SETI model. Args: images: Images returned from distorted_inputs() or inputs(). Returns: Logits. """ # We instantiate all variables using tf.get_variable() instead of # tf.Variable() in order to share variables across multiple GPU training runs. # If we only ran this model on a single GPU, we could simplify this function # by replacing all instances of tf.get_variable() with tf.Variable(). # # conv1 with tf.variable_scope('conv1') as scope: kernel = _variable_with_weight_decay('weights', shape=[5, 5, 3, 64], stddev=5e-2, wd=0.0) conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0)) pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) _activation_summary(conv1) # pool1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') # norm1 norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') # conv2 with tf.variable_scope('conv2') as scope: kernel = _variable_with_weight_decay('weights', shape=[5, 5, 64, 64], stddev=5e-2, wd=0.0) conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME') biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name=scope.name) _activation_summary(conv2) # norm2 norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') # pool2 pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2') # local3 with tf.variable_scope('local3') as scope: # Move everything into depth so we can perform a single matrix multiply. reshape = tf.reshape(pool2, [FLAGS.batch_size, -1]) dim = reshape.get_shape()[1].value weights = _variable_with_weight_decay('weights', shape=[dim, 384], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1)) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) _activation_summary(local3) # local4 with tf.variable_scope('local4') as scope: weights = _variable_with_weight_decay('weights', shape=[384, 192], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1)) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name) _activation_summary(local4) # linear layer(WX + b), # We don't apply softmax here because # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits # and performs the softmax internally for efficiency. with tf.variable_scope('softmax_linear') as scope: weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES], stddev=1/192.0, wd=0.0) biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0)) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name) _activation_summary(softmax_linear) return softmax_linear def inference(images): """Build the SETI model. Args: images: Images returned from distorted_inputs() or inputs(). Returns: Logits. """ # We instantiate all variables using tf.get_variable() instead of # tf.Variable() in order to share variables across multiple GPU training runs. # If we only ran this model on a single GPU, we could simplify this function # by replacing all instances of tf.get_variable() with tf.Variable(). # ######## Layer 1 # conv1 - 5x5 convolution with tf.variable_scope('conv1') as scope: kernel = _variable_with_weight_decay('weights', shape=[5, 5, 1, 4], stddev=5e-2, wd=0.0) conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') biases = _variable_on_cpu('biases', [4], tf.constant_initializer(0.0)) pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) _activation_summary(conv1) # pool1 pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') # norm1 norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') print("layer 1:" + str(norm1.shape)) ######## Layer 2 # conv2 - 3x3 convolution with tf.variable_scope('conv2') as scope: kernel = _variable_with_weight_decay('weights', shape=[3, 3, 4, 16], stddev=5e-2, wd=0.0) conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME') biases = _variable_on_cpu('biases', [16], tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name=scope.name) _activation_summary(conv2) # norm2 norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') # pool2 pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2') print("layer 2:" + str(pool2.shape)) ######## Layer 3 # conv3 - 3x3 convolution with tf.variable_scope('conv3') as scope: kernel = _variable_with_weight_decay('weights', shape=[3, 3, 16, 32], stddev=5e-2, wd=0.0) conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME') biases = _variable_on_cpu('biases', [32], tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv, biases) conv3 = tf.nn.relu(pre_activation, name=scope.name) _activation_summary(conv3) # norm3 norm3 = tf.nn.lrn(conv3, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm3') # pool3 pool3 = tf.nn.max_pool(norm3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool3') print("layer 3:" + str(pool3.shape)) #Fully connected layer 1 - 600 units with tf.variable_scope('fully1') as scope: # Move everything into depth so we can perform a single matrix multiply. dim = np.prod(pool3.get_shape()[1:]).value reshape = tf.reshape(pool3, shape=[-1, dim]) # dim = reshape.get_shape()[1].value weights = _variable_with_weight_decay('weights', shape=[dim, 600], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [600], tf.constant_initializer(0.1)) fullyConn1 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) print ("Fully conected 1:" + str(fullyConn1.shape)) _activation_summary(fullyConn1) #Fully connected layer 2- 200 units with tf.variable_scope('fully2') as scope: weights = _variable_with_weight_decay('weights', shape=[600, 200], stddev=0.04, wd=0.004) biases = _variable_on_cpu('biases', [200], tf.constant_initializer(0.1)) fullyConn2 = tf.nn.relu(tf.matmul(fullyConn1, weights) + biases, name=scope.name) print ("Fully conected 2:" + str(fullyConn2.shape)) _activation_summary(fullyConn2) # We don't apply softmax here because # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits # and performs the softmax internally for efficiency. #Output softmax layer - 2 unit with tf.variable_scope('softmax_linear') as scope: weights = _variable_with_weight_decay('weights', [200, NUM_CLASSES], stddev=1/200.0, wd=0.0) biases = _variable_on_cpu('biases', [NUM_CLASSES], tf.constant_initializer(0.0)) softmax_linear = tf.add(tf.matmul(fullyConn2, weights), biases, name=scope.name) _activation_summary(softmax_linear) return softmax_linear def maybe_download_and_extract(): """Download and extract the tarball from Alex's website.""" dest_directory = FLAGS.data_dir if not os.path.exists(dest_directory): os.makedirs(dest_directory) filename = DATA_URL.split('/')[-1] filepath = os.path.join(dest_directory, filename) if not os.path.exists(filepath): def _progress(count, block_size, total_size): sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename, float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) print() statinfo = os.stat(filepath) print('Successfully downloaded', filename, statinfo.st_size, 'bytes.') extracted_dir_path = os.path.join(dest_directory, 'SETI-batches-bin') if not os.path.exists(extracted_dir_path): tarfile.open(filepath, 'r:gz').extractall(dest_directory) def loss(logits, labels): """Add L2Loss to all the trainable variables. Add summary for "Loss" and "Loss/avg". Args: logits: Logits from inference(). labels: Labels from distorted_inputs or inputs(). 1-D tensor of shape [batch_size] Returns: Loss tensor of type float. """ # Calculate the average cross entropy loss across the batch. labels = tf.cast(labels, tf.int64) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=logits, name='cross_entropy_per_example') cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy') tf.add_to_collection('losses', cross_entropy_mean) # The total loss is defined as the cross entropy loss plus all of the weight # decay terms (L2 loss). return tf.add_n(tf.get_collection('losses'), name='total_loss') def _add_loss_summaries(total_loss): """Add summaries for losses in CIFAR-10 model. Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. tf.summary.scalar(l.op.name + ' (raw)', l) tf.summary.scalar(l.op.name, loss_averages.average(l)) return loss_averages_op def train(total_loss, global_step): """Train CIFAR-10 model. Create an optimizer and apply to all trainable variables. Add moving average for all trainable variables. Args: total_loss: Total loss from loss(). global_step: Integer Variable counting the number of training steps processed. Returns: train_op: op for training. """ # Variables that affect learning rate. # num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size # decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY) decay_steps = DECAY_STEPS # Decay the learning rate exponentially based on the number of steps. lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE, global_step, decay_steps, LEARNING_RATE_DECAY_FACTOR, staircase=True) tf.summary.scalar('learning_rate', lr) # Generate moving averages of all losses and associated summaries. loss_averages_op = _add_loss_summaries(total_loss) # Compute gradients. with tf.control_dependencies([loss_averages_op]): opt = tf.train.GradientDescentOptimizer(lr) grads = opt.compute_gradients(total_loss) # Apply gradients. apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) # Add histograms for trainable variables. for var in tf.trainable_variables(): tf.summary.histogram(var.op.name, var) # Add histograms for gradients. for grad, var in grads: if grad is not None: tf.summary.histogram(var.op.name + '/gradients', grad) # Track the moving averages of all trainable variables. variable_averages = tf.train.ExponentialMovingAverage( MOVING_AVERAGE_DECAY, global_step) variables_averages_op = variable_averages.apply(tf.trainable_variables()) with tf.control_dependencies([apply_gradient_op, variables_averages_op]): train_op = tf.no_op(name='train') return train_op, lr """Download and extract the tarball from Alex's website.""" dest_directory = FLAGS.data_dir if not os.path.exists(dest_directory): os.makedirs(dest_directory) filename = DATA_URL.split('/')[-1] filepath = os.path.join(dest_directory, filename) if not os.path.exists(filepath): def _progress(count, block_size, total_size): sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename, float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) print() statinfo = os.stat(filepath) print('Successfully downloaded', filename, statinfo.st_size, 'bytes.') extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin') if not os.path.exists(extracted_dir_path): tarfile.open(filepath, 'r:gz').extractall(dest_directory)

[ "saeed@ca.ibm.com" ]

saeed@ca.ibm.com

a4d4e2ccc8d69afb1d39cc45e2fb449b5ea67b2e

d301c28ca761a6e59681e86cfca8d479dd9ea106

/pymdwizard/gui/metainfo.py

6758f4931be450ea8b2e2447e9992716c01d82c0

[ "CC-BY-4.0", "LicenseRef-scancode-warranty-disclaimer" ]

permissive

ehbaker/fort-pymdwizard

1f184c957d25a1508e6ad128ba600757a323af4e

77375030bdd7184279fee7976ed9f92d5c276ef5

refs/heads/master

2021-06-20T04:33:45.156187

2017-07-12T20:15:29

94,485,610

0

null

2017-06-15T23:09:08

2017-06-15T23:09:07

null

UTF-8

Python

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6,532

py

#!/usr/bin/env python # -*- coding: utf8 -*- """ License: Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/ PURPOSE ------------------------------------------------------------------------------ Provide a pyqt widget for a Identification Information <idinfo> section SCRIPT DEPENDENCIES ------------------------------------------------------------------------------ None U.S. GEOLOGICAL SURVEY DISCLAIMER ------------------------------------------------------------------------------ Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Geological Survey. Although this information product, for the most part, is in the public domain, it also contains copyrighted material as noted in the text. Permission to reproduce copyrighted items for other than personal use must be secured from the copyright owner. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty, expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. Although this program has been used by the U.S. Geological Survey (USGS), no warranty, expressed or implied, is made by the USGS or the U.S. Government as to the accuracy and functioning of the program and related program material nor shall the fact of distribution constitute any such warranty, and no responsibility is assumed by the USGS in connection therewith. ------------------------------------------------------------------------------ """ import sys from lxml import etree from PyQt5.QtGui import QPainter, QFont, QPalette, QBrush, QColor, QPixmap from PyQt5.QtWidgets import QMainWindow, QApplication from PyQt5.QtWidgets import QWidget, QLineEdit, QSizePolicy, QTableView from PyQt5.QtWidgets import QHBoxLayout, QVBoxLayout from PyQt5.QtWidgets import QStyleOptionHeader, QHeaderView, QStyle, QSpacerItem from PyQt5.QtCore import QAbstractItemModel, QModelIndex, QSize, QRect, QPoint, Qt from pymdwizard.core import utils from pymdwizard.core import xml_utils from pymdwizard.gui.wiz_widget import WizardWidget from pymdwizard.gui.ui_files import UI_metainfo from pymdwizard.gui.ContactInfo import ContactInfo from pymdwizard.gui.fgdc_date import FGDCDate class MetaInfo(WizardWidget): drag_label = "Metadata Information <metainfo>" acceptable_tags = ['metainfo', 'cntinfo', 'ptcontact'] ui_class = UI_metainfo.Ui_fgdc_metainfo def __init__(self, root_widget=None): super(self.__class__, self).__init__() self.root_widget = root_widget def build_ui(self): self.ui = self.ui_class() self.ui.setupUi(self) self.setup_dragdrop(self) self.contactinfo = ContactInfo(parent=self) self.metd = FGDCDate(parent=self, fgdc_name='fgdc_metd') self.ui.help_metd.layout().addWidget(self.metd) self.ui.fgdc_metc.layout().addWidget(self.contactinfo) def connect_events(self): self.ui.fgdc_metstdn.currentTextChanged.connect(self.update_metstdv) self.ui.fgdc_metstdv.currentIndexChanged.connect(self.update_metstdn) self.ui.button_use_dataset.clicked.connect(self.pull_datasetcontact) def update_metstdn(self): if self.ui.fgdc_metstdv.currentText() == 'FGDC-STD-001-1998': self.ui.fgdc_metstdn.setCurrentIndex(0) self.root_widget.switch_schema('fgdc') elif self.ui.fgdc_metstdv.currentText() == 'FGDC-STD-001.1-1999': self.ui.fgdc_metstdn.setCurrentIndex(1) self.root_widget.switch_schema('bdp') def update_metstdv(self): if 'biological' in self.ui.fgdc_metstdn.currentText().lower() or \ 'bdp' in self.ui.fgdc_metstdn.currentText().lower(): self.ui.fgdc_metstdv.setCurrentIndex(1) self.root_widget.switch_schema('bdp') else: self.ui.fgdc_metstdv.setCurrentIndex(0) self.root_widget.switch_schema('fgdc') def pull_datasetcontact(self): self.contactinfo._from_xml(self.root_widget.idinfo.ptcontac._to_xml()) def _to_xml(self): # add code here to translate the form into xml representation metainfo_node = xml_utils.xml_node('metainfo') metd = xml_utils.xml_node('metd', text=self.metd.get_date(), parent_node=metainfo_node) metc = xml_utils.xml_node('metc', parent_node=metainfo_node) cntinfo = self.contactinfo._to_xml() metc.append(cntinfo) metstdn = xml_utils.xml_node('metstdn', text=self.ui.fgdc_metstdn.currentText(), parent_node=metainfo_node) metstdv = xml_utils.xml_node('metstdv', text=self.ui.fgdc_metstdv.currentText(), parent_node=metainfo_node) return metainfo_node def _from_xml(self, xml_metainfo): if xml_metainfo.tag == 'metainfo': if xml_metainfo.xpath('metc/cntinfo'): self.contactinfo._from_xml(xml_metainfo.xpath('metc/cntinfo')[0]) if xml_metainfo.xpath('metstdn'): standard = xml_utils.get_text_content(xml_metainfo, 'metstdn') self.ui.fgdc_metstdn.setCurrentText(standard) # switch wizard content to reflect the standard in this record if "biological" in standard.lower() \ or 'bdp' in standard.lower(): self.root_widget.switch_schema('bdp') else: self.root_widget.switch_schema('fgdc') metstdv = xml_utils.get_text_content(xml_metainfo, 'metstdv') self.ui.fgdc_metstdv.setCurrentText(metstdv) metd = xml_utils.get_text_content(xml_metainfo, 'metd') self.metd.set_date(metd) elif xml_metainfo.tag in ['ptcontac', 'cntinfo']: if xml_metainfo.tag == 'ptcontac': xml_metainfo = xml_utils.search_xpath(xml_metainfo, 'cntinfo') self.contactinfo._from_xml(xml_metainfo) if __name__ == "__main__": utils.launch_widget(MetaInfo, "MetaInfo testing")

[ "talbertc@usgs.gov" ]

talbertc@usgs.gov

2326a0aec4923a378c606ceb9a289d21a37f7006

189517b63e0dc8c6e9ee2defd5e5fc98f43425c7

/Gaps_meteorology/Scripts/gapswindspeed.py

54246545e7cf39ad3ef45e00ca839de26960bac2

[]

no_license

Raquel-Araujo/gap_dynamics_BCI50ha

bd15ff86b3aefd697de48f2d4782f78b3d801a85

15a05f60eb16d9ef215d422dbc2895c09a7910a8

refs/heads/main

2023-04-06T15:18:26.165602

2021-12-16T16:52:18

416,399,054

0

null

UTF-8

Python

false

15,809

py

import numpy as np import pandas as pd import matplotlib.pyplot as plt import datetime as dt from matplotlib.cm import ScalarMappable import collections import statsmodels.api as sm import sys import statsmodels.formula.api as smf from pandas.plotting import register_matplotlib_converters register_matplotlib_converters() from scipy import stats from scipy.stats import shapiro ###This code is for relations of gap area and wind speed gaps = pd.read_csv('../Entrance/gaps_area_days_5years.csv') meteo = pd.read_csv('../Entrance/tabmeteorology.csv') ###################################################################################### ###################################################################################### ###Gaps #Convert date to datetime format gaps.loc[:,'date1'] = pd.to_datetime(gaps.date, format='%Y/%m/%d') #Convert days in integer format to datetime format. Map atributes to each value of the column gaps['dayscor'] = gaps['days'].map(dt.timedelta) ###Vector of image dates dates = gaps.date dates.index = dates.index + 1 datestart = pd.Series('2014-10-02') datesall = datestart.append(dates) #Convert dateall to datetime format imgdates = pd.to_datetime(datesall, format='%Y/%m/%d') print(imgdates) ###################################################################################### ###################################################################################### ###wind 15 minutes #Convert datetime15 to datetime format meteo.loc[:,'datetime1'] = pd.to_datetime(meteo.datetime15, format='%Y/%m/%d %H:%M:%S') #Select wind until Nov30, 2019 (there is data until Dec 4) #Last day opened because cut function has opened interval in the last date wind = meteo.loc[meteo.datetime1.between('2014-10-02 00:00:00','2019-11-28 00:00:00'),['datetime1','wsmx']] #Add periods wind['per'] = pd.cut(wind.datetime1, imgdates, labels=range(1,len(imgdates)),include_lowest=True) print(wind) #Rename wind15m = wind ###################################################################################### ###################################################################################### ###wind hour #Convert datetime15 to datetime format meteo.loc[:,'datetime1'] = pd.to_datetime(meteo.datetime15, format='%Y/%m/%d %H:%M:%S') #Select wind until Nov30, 2019 (there is data until Dec 4) #Last day opened because cut function has opened interval in the last date wind = meteo.loc[meteo.datetime1.between('2014-10-02 00:00:00','2019-11-28 00:00:00'),['datetime1','wsmx']] print(wind) #Group by each hour wind['datehour'] = wind['datetime1'].dt.to_period("H") wind = pd.pivot_table(data=wind, values='wsmx', index='datehour', aggfunc=np.max).reset_index() print(wind) datehourstr = wind.datehour.astype(str) wind['datehour1'] = pd.to_datetime(datehourstr, format='%Y/%m/%d %H:%M:%S') # print(wind) #Add periods wind['per'] = pd.cut(wind.datehour1, imgdates, labels=range(1,len(imgdates)), include_lowest=True) print(wind) #Rename windhour = wind ###################################################################################### ###################################################################################### ###wind day #Convert datetime15 to datetime format meteo.loc[:,'datetime1'] = pd.to_datetime(meteo.datetime15, format='%Y/%m/%d %H:%M:%S') #Select wind until Nov30, 2019 (there is data until Dec 4) #Last day opened because cut function has opened interval in the last date wind = meteo.loc[meteo.datetime1.between('2014-10-02 00:00:00','2019-11-28 00:00:00'),['datetime1','wsmx']] #Group by each day wind['date'] = wind['datetime1'].dt.date wind = pd.pivot_table(data=wind, values='wsmx', index='date', aggfunc=np.max).reset_index() wind.date = pd.to_datetime(wind.date, format='%Y/%m/%d') #Add periods wind['per'] = pd.cut(wind.date, imgdates, labels=range(1,len(imgdates)), include_lowest=True) # print(wind) #Rename windday = wind listwind = [wind15m, windhour, windday] listname = ['15m', 'hour', 'day'] listprefix = ['m_p', 'h_p', 'd_p'] i = 0 while i < len(listwind): ###################################################################################### ###Input tables #Filter zero wind windp = listwind[i].loc[listwind[i].wsmx>0] # print(windp) #Calculate percentiles 90.0 until 99.9 #Percentiles calculated on positive wind values perc = np.percentile(windp.wsmx, np.arange(90.,100., 0.1)) num = np.arange(90.,100., 0.1) # print(num) # print(perc) # print(len(num)) # print(len(perc)) #To not change the entrance name entrance = perc # print(entrance) ###################################################################################### ###Create input tables and run the metrics functions = ['count'] prefix = map(lambda x: listprefix[i]+'{:.1f}'.format(x),num) # prefix = ['all', 'p']+pref # print(prefix) # print(len(prefix)) # print(len(entrance)) coletor = [] j=0 while j < len(entrance): mask = listwind[i].wsmx > entrance[j] wind_filter = listwind[i].loc[mask] pivotwind = pd.pivot_table(data=wind_filter, values='wsmx', index='per', aggfunc=functions) pivotwind.columns = [prefix[j]+'_wsnumber'] coletor.append(pivotwind) j+=1 windmetrics = reduce(lambda df1,df2: df1.join(df2), coletor) # print(windmetrics) ###################################################################################### ###Add area information to the wind metrics table - standardized values #Adjust index to start from 1 gaps.index = np.arange(1, len(gaps) + 1) windmetrics_gap = windmetrics.join(gaps['area']) windmetrics_days = windmetrics.join(gaps[['area','days']]) # print(windmetrics_days) #Divide all columns by number of days #Exclude the last column days #Multiply per 30 to have per metrics per month windstand = (windmetrics_days.iloc[:,:-1].div(windmetrics_days.days, axis=0))*30 # print(windstand) #Drop long interval windmetrics_stand = windstand.drop(15) # print(windmetrics_stand) #Rename to join at the end windmetrics_stand.to_csv('../Exit_ws/wind'+listname[i]+'_stand_metrics.csv') i+=1 # print(windmetrics_stand) ###################################################################################### ###################################################################################### ###wind analysis - standardized values (per month) wind15m_stand = pd.read_csv('../Exit_ws/wind15m_stand_metrics.csv').set_index('per') windhour_stand = pd.read_csv('../Exit_ws/windhour_stand_metrics.csv').set_index('per') windday_stand = pd.read_csv('../Exit_ws/windday_stand_metrics.csv').set_index('per') # print(windday_stand) #Join absolute tables windstand = wind15m_stand.iloc[:,0:100].join(windhour_stand.iloc[:,0:100]).join(windday_stand).fillna(0) print(windstand) ###Linear fit metrics = windstand.columns.values # print(metrics) #Create an empty sumario sumario = pd.DataFrame(columns=['model', 'a', 'b', 'r2', 'r', 'pvalue', 'n>0']) # print(sumario) #While to do the fit and create the summary results table i=0 while i < len(metrics): x = np.log(windstand.loc[:,metrics[i]]+1) y = np.log(windstand.area) x1 = sm.add_constant(x) model = sm.OLS(y, x1) results = model.fit() name = metrics[i] modell = 'a+bx' a = results.params[0] b = results.params[1] r2 = results.rsquared r = stats.pearsonr(x, y) p = results.pvalues[1] n = len(x[x>0]) sumario.loc[metrics[i],:] = modell, a, b, r2, r[0], p, n i += 1 #Choose the highest r values sumariosort = sumario.sort_values(by='r', ascending=False) print(sumariosort) sumario_sel = sumariosort.iloc[1:7,:] sumario_sel.to_csv('../Exit_ws/summary_highest_r_windstand.csv') ##Residual x = np.log(windstand.loc[:,sumario_sel.index.values[0]]+1) y = np.log(windstand.area) x1 = sm.add_constant(x) model = sm.OLS(y, x1) results = model.fit() modell = 'a+bx' a = results.params[0] b = results.params[1] r2 = results.rsquared r = stats.pearsonr(x, y) p = results.pvalues[1] n = len(x[x>0]) # ypred = a + b*x ypred = results.predict(x1) # res = y - ypred res = results.resid print(a) plt.hist(res) plt.savefig('../Exit_ws/hist_residual.png', dpi=300, bbox_inches='tight') plt.close() plt.scatter(ypred,res) plt.axhline(y=0) plt.savefig('../Exit_ws/scatter_residual.png', dpi=300, bbox_inches='tight') plt.close() #Log scale: p = 0.100399978459 (normal) stat, p = shapiro(res) print(stat) print(p) ##################################################################################################### ##################################################################################################### ##################################################################################################### ##################################################################################################### #Data for graph of windspeed (mm.s-1) and percentiles listwind = [wind15m, windhour, windday] listwindname = ['wind15m', 'windhour', 'windday'] # #Number of 15 min in 1 hour = 4 # const15min = 4 # constday = 1./24 # print(constday) coletor = [] i = 0 while i < len(listwind): #Filter zero wind windp = listwind[i].loc[listwind[i].wsmx>0] #I will only need the 1-day value #Value of windspeed of the p99.3 ###The percentile of 99.3 is 11 m/s pd.Series(np.percentile(windp.wsmx, 99.3)/3.6).to_csv('../Exit_ws/wind_ms'+listwindname[i]+'_equal_p993.csv') #Calculate percentiles 90.0 until 99.9 #Percentiles calculated on positive wind values perc = np.percentile(windp.wsmx, np.arange(90.,100., 0.1)) # if i == 0: # perc = perc*const15min # elif i == 2: # perc = perc*constday coletor.append(perc) i+=1 print(coletor) num = np.arange(90.,100., 0.1) print(num) ##################################################################################################### ##################################################################################################### ##################################################################################################### ####Season data = to color scatter by season # print(windstand) # print(gaps1) gaps1 = gaps.drop(15) season = pd.read_csv('../Entrance/seasons.csv') #the file is in exit folder season1 = season.set_index('per') # print(season1) windstand = windstand.merge(season1, left_index=True, right_index=True) gaps1 = gaps1.merge(season1, left_index=True, right_index=True) # print(gaps1) ##################################################################################################### ##################################################################################################### ##################################################################################################### ##################################################################################################### ###Plot scatter plots, R2 and wind rates together (3 subplots) ##Parameters scatter constha = (1/500000.)*100. metrics = sumario_sel.index.values # x = windstand.loc[:,metrics[0]] # y = gaps1.areapercmonth xdry = np.log(windstand.loc[windstand.season==0,metrics[0]]+1) ydry = gaps1.loc[gaps1.season==0, 'areapercmonth'] xwet = np.log(windstand.loc[windstand.season==1,metrics[0]]+1) ywet = gaps1.loc[gaps1.season==1, 'areapercmonth'] # print(xdry) # print(ydry) a = np.array(sumario_sel.iloc[0, 1]) b = np.array(sumario_sel.iloc[0, 2]) r2 = np.array(sumario_sel.iloc[0, 3]) r = np.array(sumario_sel.iloc[0, 4]) p = np.array(sumario_sel.iloc[0, 5]) xplot = np.array([np.min(x), np.max(x)]) yplot = (a+b*xplot)*constha print(yplot) print(xplot) print(a) print(b) #Multiply the coeficients by constha to write the equation on plot #I checked this in excel aha = a*constha bha = b*constha print(aha) print(bha) plt.rc('font', family='Times New Roman', size=12) fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(12, 3)) plt.subplots_adjust(wspace=0.3) #wspace=0.25 # ax1.scatter(xwet, ywet,s=30, facecolors='none', edgecolors='royalblue', label='Wet season') # ax1.scatter(xdry, ydry,s=30, marker='^', facecolors='m', edgecolors='none', label='Dry season', alpha=0.5) ax1.scatter(xdry, ydry,s=60, marker='^', facecolors='none', edgecolors='k', label='Dry season', alpha=0.5) #marker='^', ax1.scatter(xwet, ywet,s=20, facecolors='dimgrey', edgecolors='none', label='Wet season', alpha=0.5) # ax1.scatter(xwet, ywet,s=30, facecolors='royalblue', edgecolors='none', label='Wet season', alpha=0.4) # ax1.plot(xplot,yplot, color='k', linestyle='--', linewidth=1) ax1.set_xlabel(r'Log frequency periods with 1-day max windspeed > 99.3$^{th}$ (mo$^{-1}$)', labelpad=10, fontsize=12) ax1.set_ylabel(r'Canopy disturbance rate (% mo$^{-1}$)', labelpad=10, fontsize=12) ax1.text(0.65,0.45,'p-value = %.2f' % float(p)) ax1.legend(loc='best', prop={'size': 10}) # ax1.text(0,1.3,'y = %.2f + %.2fx' % (float(aha),float(bha))) ax1.set_yscale('log') ax1.set_yticks([0.01, 0.05, 0.1, 0.5, 1.5]) ax1.set_yticklabels(['0.01','0.05','0.1','0.5','1.5']) # ax1.set_xlim(-0.1, 2.1) ax1.set_ylim(0.005, 3) ##Parameters R2 graph #100 is the number of percentiles listcount15m = np.arange(0,100,1) listcounthour = 100+listcount15m listcountday = 100+100+listcount15m listall = [listcount15m, listcounthour, listcountday] listcor = ['darkmagenta', 'royalblue', 'k'] listlabel = ['Frequency 15-min', 'Frequency 1-hour', 'Frequency 1-day'] namesxticks = ['90', '91', '92', '93', '94', '95', '96', '97', '98', '99'] num = np.arange(90.,100., 0.1) #Position of ticks xtickspos = np.arange(0,100,10) i = 0 while i < len (listall): #r values of metrics y = np.array(sumario.iloc[listall[i],4]) # print(y) #Entrances # x = np.arange(1,len(y)+1, 1) x = num # print(x) # ax2.scatter(x,y, color=listcor[i], s=2) ax2.plot(x,y, color=listcor[i], label=listlabel[i], linewidth=1, zorder=1) i += 1 ax2.scatter(99.3, 0.21,s=20, edgecolors='r', facecolors='none', zorder=2) ax2.legend(loc='upper left', prop={'size': 10}) ax2.set_ylabel('r', labelpad=10) ax2.set_xlabel('Wind speed percentile thresholds', labelpad=10) # ax2.xaxis.set_tick_params(axis=0, which='minor', bottom=True ) # ax2.set_xticks(xtickspos,minor=True) # ax2.set_xticks(np.arange(90.,100., 0.1),minor=True) # ax2.set_xlim([95.5,99.9]) ax1.set_title('(a)', loc='left') ax2.set_title('(b)', loc='left') ax3.set_title('(c)', loc='left') ax2.set_xticks(np.arange(90.,100., 1)) ax2.set_xticklabels(namesxticks, rotation=45) # ax2.set_ylim(-0.25, 0.25) #Proportion of ylimites, scale transformation # ymax = (0.67-(-0.1))/(0.9-(-0.1)) # ax2.axvline(x=99.4, ymin=-0.1, ymax=ymax, ls='--', color='gray', linewidth=0.5, zorder=1) # ymax3 = (11/12)-0.2 # ymax3 = 0.7 xmax3 = (99.3/99.9)-0.0875 ax3.plot(num, coletor[0]/3.6, color='darkmagenta', linewidth=1, label='Wind 15-min') ax3.plot(num, coletor[1]/3.6, color='royalblue', linewidth=1, label='Wind 1-hour') ax3.plot(num, coletor[2]/3.6, color='k', linewidth=1, label='Wind 1-day') ax3.set_ylabel('Wind speed (m s$^{-1}$)') ax3.set_xlabel('Wind speed percentile thresholds', labelpad=10) ax3.set_xticks([90, 91, 92, 93, 94, 95, 96, 97, 98, 99]) ax3.set_xticklabels(namesxticks, rotation=45) ax3.minorticks_on() ax3.legend(loc='upper left', prop={'size': 10}) ax3.axvline(x=99.3, ymin=0, ymax=0.785,ls='--', color='r', linewidth=0.5, zorder=2) ax3.axhline(y=11.01, xmin=0.0001, xmax=xmax3,ls='--', color='r', linewidth=0.5, zorder=2) # ax2.set_xticks(xtickspos) # ax2.set_xticklabels(namesxticks) plt.savefig('../Exit_ws/graphs_scatter_r_wind_stand2.png', dpi=300, bbox_inches='tight') plt.close()

[ "araujo.raquelf@gmail.com" ]

araujo.raquelf@gmail.com

5146d05cbe70510bac2fff4baf6eedb080681893

a5a0161cf91378479d1512ebf3000002317789dc

/src/pymeica/py_meica_subject.py

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pappyhammer/bonn_assemblies

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510eef8c9b6f7ea8c86f74554c56f859d59eec28

refs/heads/master

2023-01-02T19:19:28.231485

2020-10-20T15:02:21

150,748,917

0

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from cicada.analysis.cicada_analysis_format_wrapper import CicadaAnalysisFormatWrapper import os import hdf5storage import numpy as np from sortedcontainers import SortedList, SortedDict import neo import quantities as pq from pymeica.utils.spike_trains import create_spike_train_neo_format, spike_trains_threshold_by_firing_rate import elephant.conversion as elephant_conv from pymeica.utils.mcad import MCADOutcome from pymeica.utils.file_utils import find_files from pymeica.utils.misc import get_unit_label import yaml import pandas as pd class SpikeStructure: def __init__(self, patient, spike_trains, microwire_labels, cluster_labels, cluster_indices, spike_nums=None, title=None): # , ordered_indices=None, ordered_spike_data=None): """ Args: patient: spike_nums: spike_trains: microwire_labels: cluster_labels: cluster_indices: indicate what is the index of the units among the clusters (indexing starts at 0) activity_threshold: title: ordered_indices: ordered_spike_data: """ self.patient = patient self.spike_trains = spike_trains # self.ordered_spike_data = ordered_spike_data # array of int, representing the channel number actually such as in 'times_pos_CSC2.mat' self.microwire_labels = np.array(microwire_labels) # array of int self.cluster_labels = np.array(cluster_labels) self.cluster_indices = np.array(cluster_indices) self.title = title # cells labels self.labels = self.get_labels() # self.ordered_indices = ordered_indices # self.ordered_labels = None # self.ordered_spike_trains = None # if self.ordered_indices is not None: # self.ordered_spike_trains = list() # for index in ordered_indices: # self.ordered_spike_trains.append(self.spike_trains[index]) # self.ordered_labels = [] # # y_ticks_labels_ordered = spike_nums_struct.labels[best_seq] # for old_cell_index in self.ordered_indices: # self.ordered_labels.append(self.labels[old_cell_index]) def get_labels(self): labels = [] # cluster_to_label = {1: "MU ", 2: "SU ", -1: "Artif ", 0: ""} cluster_to_label = {1: "MU", 2: "SU", -1: "Artif", 0: ""} # print(f"get_labels self.microwire_labels {self.microwire_labels}") for i, micro_wire in enumerate(self.microwire_labels): channel = self.patient.channel_info_by_microwire[micro_wire] unit_label = get_unit_label(cluster_label=cluster_to_label[self.cluster_labels[i]], cluster_index=self.cluster_indices[i], channel_index=micro_wire, region_label=channel) labels.append(unit_label) # labels.append(f"{cluster_to_label[self.cluster_labels[i]]}{micro_wire} " # f"{channel}") return labels # def set_order(self, ordered_indices): # if ordered_indices is None: # self.ordered_spike_trains = np.copy(self.spike_trains) # else: # self.ordered_spike_trains = [] # for index in ordered_indices: # self.ordered_spike_trains.append(self.spike_trains[index]) # self.ordered_indices = ordered_indices # self.ordered_labels = [] # for old_cell_index in self.ordered_indices: # self.ordered_labels.append(self.labels[old_cell_index]) class SleepStage: def __init__(self, number, start_time, stop_time, sleep_stage, conversion_datetime, conversion_timestamp): # timstamps are float, it's needed to multiple by 10^3 to get the real value, with represents microseconds self.start_time = start_time * 1000 self.stop_time = stop_time * 1000 # self.stop_time = stop_time * 1000 # duration is in microseconds self.duration = self.stop_time - self.start_time self.duration_sec = self.duration / 1000000 # string describing the stage (like "W", "3", "R") self.sleep_stage = sleep_stage self.conversion_datetime = conversion_datetime self.conversion_timestamp = conversion_timestamp * 1000 self.number = number # first key is a tuple of int representing first_bin and last_bin # value is an instance of MCADOutcome, bin_size is available in MCADOutcome self.mcad_outcomes = SortedDict() # TODO: See to build an array with int key to get the MCADOutcome from a bin index or timestamps def add_mcad_outcome(self, mcad_outcome, bins_tuple): """ Add an instance of MCADOutcome :param mcad_outcome: :param bins_tuple: :return: """ self.mcad_outcomes[bins_tuple] = mcad_outcome def __str__(self): result = "" result += f"num {self.number}, " result += f"stage {self.sleep_stage}, " # result += f"start_time {self.start_time}, " # result += f"stop_time {self.stop_time}, \n" # result += f"duration (usec) {self.duration}, " result += f"duration: {self.duration_sec:.1f} sec, {(self.duration / 1000000) / 60:.1f} min\n" if len(self.mcad_outcomes) == 0: result += f" No MCAD outcome\n" else: for bins_tuple, mcad_outcome in self.mcad_outcomes.items(): first_bin_index = bins_tuple[0] last_bin_index = bins_tuple[1] chunk_duration = (last_bin_index - first_bin_index + 1) * mcad_outcome.spike_trains_bin_size # passing it in sec chunk_duration /= 1000 result += f"{bins_tuple} {mcad_outcome.side} {mcad_outcome.n_cell_assemblies} cell " \ f"assemblies on {chunk_duration:.2f} sec segment.\n" if mcad_outcome.n_cell_assemblies > 0: # cell_assembly is an instance of CellAssembly for ca_index, cell_assembly in enumerate(mcad_outcome.cell_assemblies): result += f" CA n° {ca_index}: {cell_assembly.n_units} units, " \ f"{cell_assembly.n_repeats} repeats, " \ f"{cell_assembly.n_invariant_units} RU, " \ f"{cell_assembly.n_responsive_units} IU, " \ f"score: {cell_assembly.probability_score:.4f} \n" # result += f",\n conversion_datetime {self.conversion_datetime}, " # result += f"conversion_timestamp {self.conversion_timestamp}, " return result # """ compatible with cicada.analysis.cicada_analysis_format_wrapper.CicadaAnalysisFormatWrapper because it implements identifier decorator, as well as load_data and is_data_valid But we don't import cicada code so this package could also be independent. """ class PyMeicaSubject(CicadaAnalysisFormatWrapper): DATA_FORMAT = "PyMEICA" WRAPPER_ID = "PyMEICASubject" def __init__(self, data_ref, load_data=True, verbose=1): CicadaAnalysisFormatWrapper.__init__(self, data_ref=data_ref, data_format=self.DATA_FORMAT, load_data=load_data) # self._data_ref = data_ref # self.load_data_at_init = load_data # self._data_format = self.DATA_FORMAT # always load at the start self._identifier = os.path.basename(data_ref) self.verbose = verbose # variables initiated in self.load_data() # number of units, one Multi-unit count as one unit self.n_units = 0 # The variable 'spikes' stores the spike shape from all spikes # measured in this channel. # This variable contains a matrix with dimension N_spikes x 64. # Each row corresponds to a single spike and gives 64 voltage values # of this spike aligned to the maximum. self.spikes_by_microwire = dict() # The variable 'cluster_class' provides information about the timing of # each spike and the cluster that it corresponds to. # This variable contains a N_spikes x 2 matrix in which the first # column contains the cluster that the spike belongs to and the # second column saves the time of the spike. # replace by the code of the type of unit: SU, MU etc... 1 = MU 2 = SU -1 = Artif. self.spikes_time_by_microwire = dict() self.channel_info_by_microwire = None # list of SleepStage instances (in chronological order) self.sleep_stages = list() self.cluster_info = None self.n_microwires = 0 # self.available_micro_wires = 0 self.nb_sleep_stages = 0 # list of int, corresponding of the int representing the micro_wire such as in files 'times_CSC1' self.available_micro_wires = list() # key the stimulus number (int) and as value the string describing the # stimulus (like "Barack Obama"). Init in load_stimuli_name self.stimuli_name_dict = dict() # key is the label (str) representing a unit such as 'MU 7 25 LMH2' # (SU or MU, cluster_index, microwireçindex, Side&Channel), # value is a list of two int representing the prefered stimulus in the evening and in the morning. # if -1, means no answer at this moment # if a label (cell) is not in this dict, it means it is not a responsive units self.is_responsive_units_dict = dict() # same as for is_responsive_units_dict but for invariant units self.is_invariant_units_dict = dict() if self.load_data_at_init: self.load_data() def _load_responsive_and_invariant_units(self, df, invariant_units): """ :param df: panda dataframe to explore :param invariant_units: (bool) if True then it's invariant_units, else it is responsive units :return: """ if invariant_units: units_dict = self.is_invariant_units_dict = dict() else: units_dict = self.is_responsive_units_dict = dict() df_response = df.loc[(df['Patient'] == int(self.identifier[1:3]))] if len(df_response) == 0: return # print(f"invariant_units {invariant_units}") for index in df_response.index: channel = df.loc[df.index[index], 'Channel'] # removing one so it matches the actual indexing channel -= 1 cluster = df.loc[df.index[index], 'Cluster'] hemisphere = df.loc[df.index[index], 'Hemisphere'] region = df.loc[df.index[index], 'Region'] wire = df.loc[df.index[index], 'Wire'] preferred_stim_num_e = df.loc[df.index[index], 'preferred_stim_num_e'] preferred_stim_num_m = df.loc[df.index[index], 'preferred_stim_num_m'] # print(f"channel {channel}, cluster {cluster}, hemisphere {hemisphere}, region {region}, " # f"wire {wire}, preferred_stim_num_e {preferred_stim_num_e}, " # f"preferred_stim_num_m {preferred_stim_num_m} ") # print(f"self.cluster_info[micro_wire] {len(self.cluster_info)}") cluster_infos = self.cluster_info[channel][0] cluster_match_index = cluster_infos[cluster] # print(f"cluster_infos {cluster_infos}") cluster_to_label = {1: "MU", 2: "SU", -1: "Artif", 0: ""} unit_label = get_unit_label(cluster_label=cluster_to_label[cluster_match_index], cluster_index=cluster, channel_index=channel, region_label=f"{hemisphere}{region}{wire}") units_dict[unit_label] = (preferred_stim_num_e, preferred_stim_num_m) @staticmethod def is_data_valid(data_ref): """ Check if the data can be an input for this wrapper as data_ref Args: data_ref: file or directory Returns: a boolean """ if not os.path.isdir(data_ref): return False files_in_dir = [item for item in os.listdir(data_ref) if os.path.isfile(os.path.join(data_ref, item))] identifier = os.path.basename(data_ref) files_to_find = ["cluster_info.mat", f"{identifier}_sleepstages.mat"] for file_to_find in files_to_find: if file_to_find not in files_in_dir: return False return True # def get_sleep_stage_epoch(self, sleep_stage_name): # """ # Return the epoch of a given type of slepe stage. # :param sleep_stage_name: # :return: List of list of 2 int represent the timestamps in sec of the beginning and end of each epoch # """ # epochs = [] # # for sleep_stage in self.sleep_stages: # if sleep_stage.sleep_stage != sleep_stage_name: # continue # epochs.append(sleep_stage.start_time, sleep_stage.stop_time) def load_data(self): CicadaAnalysisFormatWrapper.load_data(self) if self.verbose: print(f"Loading data for PyMeicaSubject {self._identifier}") # number of units, one Multi-unit count as one unit self.n_units = 0 # Filter the items and only keep files (strip out directories) files_in_dir = [item for item in os.listdir(self._data_ref) if os.path.isfile(os.path.join(self._data_ref, item))] # The variable 'spikes' stores the spike shape from all spikes # measured in this channel. # This variable contains a matrix with dimension N_spikes x 64. # Each row corresponds to a single spike and gives 64 voltage values # of this spike aligned to the maximum. self.spikes_by_microwire = dict() # The variable 'cluster_class' provides information about the timing of # each spike and the cluster that it corresponds to. # This variable contains a N_spikes x 2 matrix in which the first # column contains the cluster that the spike belongs to and the # second column saves the time of the spike. original_spikes_cluster_by_microwire = dict() # replace by the code of the type of unit: SU, MU etc... 1 = MU 2 = SU -1 = Artif. spikes_cluster_by_microwire = dict() self.spikes_time_by_microwire = dict() cluster_correspondance_by_microwire = dict() cluster_info_file = hdf5storage.loadmat(os.path.join(self._data_ref, "cluster_info.mat")) label_info = cluster_info_file["label_info"] # contains either an empty list if no cluster, or a list containing a list containing the type of cluster # 1 = MU 2 = SU -1 = Artif. # 0 = Unassigned (is ignored) self.cluster_info = cluster_info_file['cluster_info'][0, :] # adding cluster == 0 in index so it can match index in cluster_class for i, cluster in enumerate(self.cluster_info): if len(cluster) == 0: self.cluster_info[i] = [[0]] else: new_list = [0] new_list.extend(self.cluster_info[i][0]) self.cluster_info[i] = [new_list] self.channel_info_by_microwire = cluster_info_file["cluster_info"][1, :] self.channel_info_by_microwire = [c[0] for c in self.channel_info_by_microwire] # print_mat_file_content(cluster_info_file) sleep_stages_file = hdf5storage.loadmat(os.path.join(self._data_ref, self._identifier + "_sleepstages.mat"), mat_dtype=True) conversion_datetime = sleep_stages_file["conversion_datetime"] conversion_timestamp = sleep_stages_file["conversion_timestamp"] # The variable 'sleepstages' is a N_sleepstages list size that contains 2 lists # with the first having 3 elements: # the starttime and stoptime of each sleep stage and the sleepstage label. sleep_stages_tmp = sleep_stages_file["sleepstages"][0, :] self.sleep_stages = [] total_duration = 0 for ss_index, sleep_stage_data in enumerate(sleep_stages_tmp): # sleep_stage_data = sleep_stage_data[0] # print(f"{ss_index} sleep_stage_data {sleep_stage_data}") # The start time of the first stage, might not be the same as the one of the first spike # recorded for this stage, as the data we have don't start at the beginning of a stage. ss = SleepStage(number=ss_index, start_time=sleep_stage_data[0][0][0], stop_time=sleep_stage_data[1][0][0], sleep_stage=sleep_stage_data[2][0], conversion_datetime=conversion_datetime[0], conversion_timestamp=conversion_timestamp[0][0]) # print(f"ss {ss}") total_duration += ss.duration self.sleep_stages.append(ss) self.nb_sleep_stages = len(self.sleep_stages) # TODO: See to build a vector that give for any timestamps in the whole recording to which # Sleepstage it belongs # print(f"sleepstages[0]: {sleepstages[1]}") # print(f"conversion_datetime {conversion_datetime}") # print(f"conversion_timestamp {conversion_timestamp[0][0]}") # print(f"conversion_timestamp int ? {isinstance(conversion_timestamp[0][0], int)}") if self.verbose: print(f"Data total duration (min): {(total_duration / 1000000) / 60}") # print_mat_file_content(sleep_stages_file) self.available_micro_wires = [] for file_in_dir in files_in_dir: if file_in_dir.endswith("yaml") and (not file_in_dir.startswith(".")) and ("stimuli_name" in file_in_dir): self.load_stimuli_name(stimuli_yaml_file=os.path.join(self._data_ref, file_in_dir)) continue # times_pos_CSC matched the full night recordings if file_in_dir.startswith("times_pos_CSC") or file_in_dir.startswith("times_CSC"): if file_in_dir.startswith("times_pos_CSC"): # -1 to start by 0, to respect other matrices order microwire_number = int(file_in_dir[13:-4]) - 1 else: microwire_number = int(file_in_dir[9:-4]) - 1 self.available_micro_wires.append(microwire_number) data_file = hdf5storage.loadmat(os.path.join(self._data_ref, file_in_dir)) # print(f"data_file {data_file}") self.spikes_by_microwire[microwire_number] = data_file['spikes'] cluster_class = data_file['cluster_class'] # .astype(int) # if value is 0, no cluster original_spikes_cluster_by_microwire = cluster_class[:, 0].astype(int) # spikes_cluster_by_microwire[microwire_number] = cluster_class[:, 0].astype(int) # changing the cluster reference by the cluster type, final values will be # 1 = MU 2 = SU -1 = Artif. # 0 = Unassigned (is ignored) # We want for each microwire to create as many lines of "units" as cluster go_for_debug_mode = False if go_for_debug_mode: print(f"microwire_number {microwire_number}") print(f"channel {self.channel_info_by_microwire[microwire_number]}") print(f"self.cluster_info[microwire_number] {self.cluster_info[microwire_number]}") print(f"np.unique(original_spikes_cluster_by_microwire) " f"{np.unique(original_spikes_cluster_by_microwire)}") print(f"original_spikes_cluster_by_microwire " f"{original_spikes_cluster_by_microwire}") print("") # for i, cluster_ref in enumerate(spikes_cluster_by_microwire[microwire_number]): # if cluster_ref > 0: # cluster_ref -= 1 # spikes_cluster_by_microwire[microwire_number][i] = \ # self.cluster_info[microwire_number][0][cluster_ref] # it's matlab indices, so we need to start with zero # not needed anymore because we add 0 # for i, cluster_ref in enumerate(original_spikes_cluster_by_microwire): # if cluster_ref > 0: # original_spikes_cluster_by_microwire[i] -= 1 # rounded to int # for each microwire, we add a dict with as many key as cluster, and for each key # we give as a value the spikes for this cluster self.spikes_time_by_microwire[microwire_number] = dict() # cluster_infos contains the list of clusters for this microwire. # original_spikes_cluster_by_microwire is same length as cluster_class[:, 1].astype(int), ie # nb spikes cluster_infos = self.cluster_info[microwire_number][0] for index_cluster, n_cluster in enumerate(cluster_infos): # keep the spikes of the corresponding cluster mask = np.zeros(len(cluster_class[:, 1]), dtype="bool") mask[np.where(original_spikes_cluster_by_microwire == index_cluster)[0]] = True # timstamps are float, it's needed to multiple by 10^3 to get the real value, # represented as microseconds self.spikes_time_by_microwire[microwire_number][index_cluster] = \ (cluster_class[mask, 1] * 1000) # .astype(int) # print(f"cluster_class[mask, 1] {cluster_class[mask, 1]}") # print(f"- cluster_class[mask, 1] {cluster_class[mask, 1][0] - int(cluster_class[mask, 1][0])}") self.n_units += 1 if microwire_number < 0: print(f"times_pos_CSC{microwire_number}") print(f"spikes shape 0: {self.spikes_by_microwire[microwire_number][0, :]}") # plt.plot(spikes_by_microwire[microwire_number][0, :]) # plt.show() print(f"spikes cluster: {spikes_cluster_by_microwire[microwire_number]}") # print(f"spikes time: {self.spikes_time_by_microwire[microwire_number].astype(int)}") # print_mat_file_content(data_file) print(f"\n \n") # 2nd round for responsive and invariant units for file_in_dir in files_in_dir: if file_in_dir.endswith("csv") and "responsive_units" in file_in_dir: # load responsive_units info responsive_units_file = os.path.join(self._data_ref, file_in_dir) responsive_units_df = pd.read_csv(responsive_units_file) self._load_responsive_and_invariant_units(df=responsive_units_df, invariant_units=False) elif file_in_dir.endswith("csv") and "invariant_units" in file_in_dir: # load invariant_units info invariant_units_file = os.path.join(self._data_ref, file_in_dir) invariant_units_df = pd.read_csv(invariant_units_file) self._load_responsive_and_invariant_units(df=invariant_units_df, invariant_units=True) self.n_microwires = len(self.spikes_by_microwire) self.available_micro_wires = np.array(self.available_micro_wires) def elapsed_time_from_falling_asleep(self, sleep_stage, from_first_stage_available=False): """ Looking at the time of the first sleep sleep_stage (it could start with Wake), return the number of time that separate it in seconds (could be negative if the stage is the wake one before sleep) :param sleep_stage: SleepStage instance :param from_first_stage_available: if True, the time is measured from the first stage recorded. :return: """ for sleep_stage_index in range(len(self.sleep_stages)): ss = self.sleep_stages[sleep_stage_index] if not from_first_stage_available: if ss.sleep_stage == "W": continue return (sleep_stage.start_time - ss.start_time) / 1000000 return -1 def load_stimuli_name(self, stimuli_yaml_file): """ Load the file containing as key the stimulus number (int) and as value the string describing the stimulus (like "Barack Obama") :param stimuli_yaml_file: :return: """ with open(stimuli_yaml_file, 'r') as stream: self.stimuli_name_dict = yaml.load(stream, Loader=yaml.Loader) def load_mcad_data(self, data_path, side_to_load=None, sleep_stage_indices_to_load=None, macd_comparison_key=MCADOutcome.BEST_SILHOUETTE, min_repeat=3, update_progress_bar_fct=None, time_started=None, total_increment=1): """ Explore all directories in data_path (recursively) and load the data issues from Malvache Cell Assemblies Detection code in yaml file. :param data_path: :param macd_comparison_key: indicate how to compare two outcomes for the same spike_trains section Choice among: MCADOutcome.BEST_SILHOUETTE & MCADOutcome.MAX_N_ASSEMBLIES :param min_repeat: minimum of times of cell assembly should repeat to be considered True. :param side_to_load: (str) if None, both side are loaded, otherwise should be 'L' or 'R' :param sleep_stage_indices_to_load: (list of int) if None, all stages are loaded, otherwise only the ones listed :param update_progress_bar_fct: for Cicada progress bar progress (optional), fct that take the initial time, and the increment at each step of the loading :return: """ if data_path is None: return mcad_files = find_files(dir_to_explore=data_path, keywords=["stage"], extensions=("yaml", "yml")) # for progress bar purpose n_files = len(mcad_files) n_mcad_outcomes = 0 increment_value = 0 increment_step_for_files = (total_increment * 0.9) / n_files # first key: sleep_stage index, 2nd key: tuple of int representing firt and last bin, # value is a list of dict representing the content of the yaml file mcad_by_sleep_stage = dict() for file_index, mcad_file in enumerate(mcad_files): mcad_file_basename = os.path.basename(mcad_file) # to avoid loading the file, we filter based on the file_name, see to change if the file_names should # be changed, so far contain subject_id, sleep_index, side, bin of the chunk, stage_name if self.identifier not in mcad_file_basename: continue if (side_to_load is not None) and (side_to_load not in mcad_file_basename): continue if sleep_stage_indices_to_load is not None: split_file_name = mcad_file_basename.split() if split_file_name[4] == "index": stage_index_from_file = int(mcad_file_basename.split()[5]) else: stage_index_from_file = int(mcad_file_basename.split()[3]) if stage_index_from_file not in sleep_stage_indices_to_load: continue with open(mcad_file, 'r') as stream: mcad_results_dict = yaml.load(stream, Loader=yaml.Loader) # first we check if it contains some of the field typical of mcad file if ("subject_id" not in mcad_results_dict) or ("sleep_stage_index" not in mcad_results_dict): continue # then we check that it matches the actual subject_id if mcad_results_dict["subject_id"] != self.identifier: continue sleep_stage_index = mcad_results_dict["sleep_stage_index"] first_bin_index = mcad_results_dict["first_bin_index"] last_bin_index = mcad_results_dict["last_bin_index"] bins_tuple = (first_bin_index, last_bin_index) side = mcad_results_dict["side"] if (side_to_load is not None) and (side != side_to_load): continue if sleep_stage_indices_to_load is not None: if sleep_stage_index not in sleep_stage_indices_to_load: continue if sleep_stage_index not in mcad_by_sleep_stage: mcad_by_sleep_stage[sleep_stage_index] = dict() if bins_tuple not in mcad_by_sleep_stage[sleep_stage_index]: mcad_by_sleep_stage[sleep_stage_index][bins_tuple] = [] mcad_by_sleep_stage[sleep_stage_index][bins_tuple].append(mcad_results_dict) n_mcad_outcomes += 1 if update_progress_bar_fct is not None: increment_value += increment_step_for_files if increment_value > 1: update_progress_bar_fct(time_started=time_started, increment_value=1) increment_value -= 1 # now we want to keep only one result for each chunk a given sleep_stage # and add it to the SleepStage instance increment_step_for_mcad_outcomes = (total_increment * 0.1) / n_mcad_outcomes for sleep_stage_index in mcad_by_sleep_stage.keys(): for bins_tuple, mcad_dicts in mcad_by_sleep_stage[sleep_stage_index].items(): best_mcad_outcome = None for mcad_dict in mcad_dicts: mcad_outcome = MCADOutcome(mcad_yaml_dict=mcad_dict, comparison_key=macd_comparison_key, subject=self) if update_progress_bar_fct is not None: increment_value += increment_step_for_mcad_outcomes if increment_value > 1: update_progress_bar_fct(time_started=time_started, increment_value=1) increment_value -= 1 if best_mcad_outcome is None: best_mcad_outcome = mcad_outcome else: best_mcad_outcome = best_mcad_outcome.best_mcad_outcome(mcad_outcome) if best_mcad_outcome.n_cell_assemblies == 0: # if no cell assembly we don't keep it continue # if one cell assembly we test that it repeats a minimum of time if best_mcad_outcome.n_cell_assemblies == 1: if np.max(best_mcad_outcome.n_repeats_in_each_cell_assembly()) < min_repeat: continue sleep_stage = self.sleep_stages[sleep_stage_index] sleep_stage.add_mcad_outcome(mcad_outcome=best_mcad_outcome, bins_tuple=best_mcad_outcome.bins_tuple) def build_spike_nums(self, sleep_stage_index, side_to_analyse, keeping_only_SU, remove_high_firing_cells, firing_rate_threshold, spike_trains_binsize): """ Build a spike_nums (bin version of spike_trains) from a sleep stage index and side. :param sleep_stage_index: (int) :param side_to_analyse: (str) 'L' or 'R' :param keeping_only_SU: (bool) :param remove_high_firing_cells: (bool) :param firing_rate_threshold: (int) in Hz :param spike_trains_binsize: (int) in ms :return: """ spike_struct = self.construct_spike_structure(sleep_stage_indices=[sleep_stage_index], channels_starting_by=[side_to_analyse], keeping_only_SU=keeping_only_SU) spike_trains = spike_struct.spike_trains cells_label = spike_struct.labels binsize = spike_trains_binsize * pq.ms # first we create a spike_trains in the neo format spike_trains, t_start, t_stop = create_spike_train_neo_format(spike_trains) duration_in_sec = (t_stop - t_start) / 1000 if remove_high_firing_cells: filtered_spike_trains, cells_below_threshold = \ spike_trains_threshold_by_firing_rate(spike_trains=spike_trains, firing_rate_threshold=firing_rate_threshold, duration_in_sec=duration_in_sec) backup_spike_trains = spike_trains spike_trains = filtered_spike_trains n_cells_total = len(cells_label) cells_label_removed = [(index, label) for index, label in enumerate(cells_label) if index not in cells_below_threshold] cells_label = [label for index, label in enumerate(cells_label) if index in cells_below_threshold] n_cells = len(cells_label) # print( # f"{n_cells_total - n_cells} cells had firing rate > {firing_rate_threshold} Hz and have been removed.") # if len(cells_label_removed): # for index, label in cells_label_removed: # print(f"{label}, {len(backup_spike_trains[index])}") n_cells = len(spike_trains) neo_spike_trains = [] for cell in np.arange(n_cells): spike_train = spike_trains[cell] # print(f"n_spikes: {cells_label[cell]}: {len(spike_train)}") neo_spike_train = neo.SpikeTrain(times=spike_train, units='ms', t_start=t_start, t_stop=t_stop) neo_spike_trains.append(neo_spike_train) spike_trains_binned = elephant_conv.BinnedSpikeTrain(neo_spike_trains, binsize=binsize) # transform the binned spike train into array use_z_score_binned_spike_trains = False if use_z_score_binned_spike_trains: data = spike_trains_binned.to_array() # print(f"data.type() {type(data)}") # z-score spike_nums = np.zeros(data.shape, dtype="int8") for cell, binned_spike_train in enumerate(data): mean_train = np.mean(binned_spike_train) print(f"mean_train {mean_train} {np.max(binned_spike_train)}") binned_spike_train = binned_spike_train - mean_train n_before = len(np.where(data[cell] > 0)[0]) n = len(np.where(binned_spike_train >= 0)[0]) print(f"{cell}: n_before {n_before} vs {n}") spike_nums[cell, binned_spike_train >= 0] = 1 else: spike_nums = spike_trains_binned.to_bool_array().astype("int8") # A list of lists for each spike train (i.e., rows of the binned matrix), # that in turn contains for each spike the index into the binned matrix where this spike enters. spike_bins_indices = spike_trains_binned.spike_indices return spike_trains, spike_nums, cells_label, spike_bins_indices def construct_spike_structure(self, sleep_stage_indices=None, selection_range_time=None, sleep_stage_selection=None, channels_starting_by=None, channels_without_number=None, channels_with_number=None, title=None, keeping_only_SU=False): """ Construct a spike structure (instance of SpikeStructure containing a spike trains with the labels corresponding to each spike train. There might be big gap between spike train in case two non contiguous time interval are included. :param selection_range_time: (tuple of float) represents two timestamps determining an epoch over which to select the spikes. If given, It is prioritized over sleep stages selection. :param sleep_stage_indices: (list of int) list of sleep_stage_indices :param sleep_stage_selection: (list of str) list of sleep stage according to their identifier. All sleep stages in this category will be added :param channels: list of str, if empty list, take them all, otherwise take the one starting with the same name (like "RAH" take RAH1, RAH2 etc...., if just "R" take all microwire on the right) :param channels_to_study: full name without numbers :param keeping_only_SU: if True, MU are also included :return: """ # TODO: See to add in spike structure an option to know when there are time gaps # print(f"construct_spike_structure start for {self.identifier}") # don't put non-assigned clusters only_SU_and_MU = True micro_wire_to_keep = [] if (channels_starting_by is None) and (channels_without_number is None) and (channels_with_number is None): micro_wire_to_keep = self.available_micro_wires else: if channels_starting_by is None: channels_starting_by = [] if channels_without_number is None: channels_without_number = [] if channels_with_number is None: channels_with_number = [] indices, channels = self.select_channels_starting_by(channels_starting_by) micro_wire_to_keep.extend(indices) indices, channels = self.select_channels_with_exact_same_name_without_number(channels_without_number) micro_wire_to_keep.extend(indices) micro_wire_to_keep.extend(self.select_channels_with_exact_same_name_with_number(channels_with_number)) # remove redondant microwire and sort them micro_wire_to_keep = np.unique(micro_wire_to_keep) # then we check if all the micro_wire data are available to_del = np.setdiff1d(micro_wire_to_keep, self.available_micro_wires) if len(to_del) > 0: for d in to_del: micro_wire_to_keep = micro_wire_to_keep[micro_wire_to_keep != d] channels_to_keep = [self.channel_info_by_microwire[micro_wire] for micro_wire in micro_wire_to_keep] sleep_stages_to_keep = [] if sleep_stage_indices is not None: for index in sleep_stage_indices: sleep_stages_to_keep.append(self.sleep_stages[index]) if sleep_stage_selection is not None: sleep_stages_to_keep.extend(self.selection_sleep_stage_by_stage(sleep_stage_selection)) if len(sleep_stages_to_keep) == 0: # In case no stage have been selected, then we put all stages in the order they were recorded sleep_stages_to_keep = self.sleep_stages # selecting spikes that happen during the time interval of selected sleep stages # in order to plot a raster plot, a start time and end time is needed # so for each stage selected, we should keep the timestamp of the first spike and the timestamp of the # last spike # first we count how many spike_trains (how many SU & MU), nb_units_spike_nums = 0 for mw_index, micro_wire in enumerate(micro_wire_to_keep): if only_SU_and_MU: nb_units_to_keep = 0 cluster_infos = self.cluster_info[micro_wire][0] for unit_cluster, spikes_time in self.spikes_time_by_microwire[micro_wire].items(): cluster = cluster_infos[unit_cluster] if (cluster < 1) or (cluster > 2): continue # 1 == MU, 2 == SU if keeping_only_SU: if cluster == 1: # not taking into consideraiton MU continue # looking if there are spiking at_least_a_spike = False if selection_range_time is not None: start_time = selection_range_time[0] stop_time = selection_range_time[1] spikes_time = np.copy(spikes_time) spikes_time = spikes_time[spikes_time >= start_time] spikes_time = spikes_time[spikes_time <= stop_time] if len(spikes_time) > 0: at_least_a_spike = True else: for ss in sleep_stages_to_keep: start_time = ss.start_time stop_time = ss.stop_time spikes_time = np.copy(spikes_time) spikes_time = spikes_time[spikes_time >= start_time] spikes_time = spikes_time[spikes_time <= stop_time] if len(spikes_time) > 0: at_least_a_spike = True break # counting it only if there is some spike during that interval if at_least_a_spike: nb_units_to_keep += 1 nb_units_spike_nums += nb_units_to_keep else: nb_units_spike_nums += len(self.spikes_time_by_microwire[micro_wire]) spike_trains = [np.zeros(0)] * nb_units_spike_nums # used to labels the ticks micro_wire_labels = [] cluster_labels = [] # cluster_indices indicate what is the index of the units among the clusters (indexing starts at 0) cluster_indices = [] if selection_range_time is not None: start_time = selection_range_time[0] stop_time = selection_range_time[1] time_epochs = [(start_time, stop_time)] else: time_epochs = [(ss.start_time, ss.stop_time) for ss in sleep_stages_to_keep] for time_epoch in time_epochs: start_time = time_epoch[0] stop_time = time_epoch[1] unit_index = 0 for mw_index, micro_wire in enumerate(micro_wire_to_keep): cluster_infos = self.cluster_info[micro_wire][0] for unit_cluster_index, spikes_time in self.spikes_time_by_microwire[micro_wire].items(): cluster = cluster_infos[unit_cluster_index] # not taking into consideration artifact or non clustered if (cluster < 1) or (cluster > 2): continue if keeping_only_SU: if cluster == 1: # not taking into consideraiton MU continue spikes_time = np.copy(spikes_time) spikes_time = spikes_time[spikes_time >= start_time] spikes_time = spikes_time[spikes_time <= stop_time] if len(spikes_time) == 0: # if no spikes we don't keep it continue if len(spike_trains[unit_index]) == 0: spike_trains[unit_index] = spikes_time else: spike_trains[unit_index] = np.concatenate((spike_trains[unit_index], spikes_time)) micro_wire_labels.append(micro_wire) cluster_labels.append(cluster) cluster_indices.append(unit_cluster_index) unit_index += 1 # 1 = MU 2 = SU -1 = Artif. # 0 = Unassigned (is ignored) spike_struct = SpikeStructure(patient=self, spike_trains=spike_trains, microwire_labels=micro_wire_labels, cluster_labels=cluster_labels, title=title, cluster_indices=cluster_indices) # print(f"End of construct_spike_structure for {self.patient_id}") return spike_struct def select_channels_starting_by(self, channels_starting_by): """ :param channels_starting_by: list of str, if empty list, return empty list, otherwise take the one starting with the same name (like "RAH" take RAH1, RAH2 etc...., if just "R" take all microwire on the right) :return: """ result_indices = [] result_channels = [] for channel in channels_starting_by: result_indices.extend([i for i, ch in enumerate(self.channel_info_by_microwire) if isinstance(ch, str) and ch.startswith(channel)]) result_channels.extend([ch for ch in self.channel_info_by_microwire if isinstance(ch, str) and ch.startswith(channel)]) return result_indices, result_channels def select_channels_with_exact_same_name_without_number(self, channels): """ Select channels without the precise index, for example select all "A" (all amygdala channels) :param channels: list of str: full name without numbers :return: """ result_indices = [] result_channels = [] for channel in channels: result_indices.extend([i for i, ch in enumerate(self.channel_info_by_microwire) if (ch.startswith(channel) and (len(ch) == (len(channel) + 1)))]) result_channels.extend([ch for ch in self.channel_info_by_microwire if (ch.startswith(channel) and (len(ch) == (len(channel) + 1)))]) return result_indices, result_channels def select_channels_with_exact_same_name_with_number(self, channels): """ Select channels with the precise index, for example select all "A1" (amygdala channel 1) :param channels: list of full name with numbers :return: """ result = [] result.extend([i for i, ch in enumerate(self.channel_info_by_microwire) if ch in channels]) return result def selection_sleep_stage_by_stage(self, sleep_stage_selection): """ :param sleep_stage_selection: list of str :return: """ return [ss for ss in self.sleep_stages if ss.sleep_stage in sleep_stage_selection] def get_indices_of_sleep_stage(self, sleep_stage_name): return [i for i, ss in enumerate(self.sleep_stages) if ss.sleep_stage == sleep_stage_name] def descriptive_stats(self): """ Print some descriptive stats about a patient :return: """ for channels_starting_by in [None, "L", "R"]: n_su = 0 n_mu = 0 micro_wire_to_keep = [] if channels_starting_by is None: micro_wire_to_keep = self.available_micro_wires print(f"n units: {len(micro_wire_to_keep)}") print(f"n invariant units: {len(self.is_invariant_units_dict)}") print(f"n responsive units: {len(self.is_responsive_units_dict)}") else: indices, channels = self.select_channels_starting_by(channels_starting_by) micro_wire_to_keep.extend(indices) # remove redondant microwire and sort them micro_wire_to_keep = np.unique(micro_wire_to_keep) # then we check if all the micro_wire data are available to_del = np.setdiff1d(micro_wire_to_keep, self.available_micro_wires) if len(to_del) > 0: for d in to_del: micro_wire_to_keep = micro_wire_to_keep[micro_wire_to_keep != d] # print(f"n units in {channels_starting_by}: {len(micro_wire_to_keep)}") invariant_keys = list(self.is_invariant_units_dict.keys()) responsive_keys = list(self.is_responsive_units_dict.keys()) print(f"n invariant units: {len([k for k in invariant_keys if channels_starting_by in k])}") print(f"n responsive units: {len([k for k in responsive_keys if channels_starting_by in k])}") mu_by_area_count = SortedDict() su_by_area_count = SortedDict() # A AH EC MH PH PHC # print(f"self.channel_info_by_microwire {self.channel_info_by_microwire}") # print(f"self.available_micro_wires {self.available_micro_wires}") for micro_wire in micro_wire_to_keep: cluster_infos = self.cluster_info[micro_wire][0] for unit_cluster, spikes_time in self.spikes_time_by_microwire[micro_wire].items(): cluster = cluster_infos[unit_cluster] if (cluster < 1) or (cluster > 2): continue if cluster == 1: # == MU n_mu += 1 counter_dict = mu_by_area_count else: n_su += 1 counter_dict = su_by_area_count channel_name = self.channel_info_by_microwire[micro_wire] # print(f'channel_name {channel_name}') unique_channels = ["EC", "AH", "MH", "PHC"] for channel in unique_channels: if channel in channel_name: counter_dict[channel] = counter_dict.get(channel, 0) + 1 if ("A" in channel_name) and ("AH" not in channel_name): counter_dict["A"] = counter_dict.get("A", 0) + 1 if ("PH" in channel_name) and ("PHC" not in channel_name): counter_dict["PH"] = counter_dict.get("PH", 0) + 1 if channels_starting_by is None: print(f"From both side: n_su {n_su}, n_mu {n_mu}") else: print(f"For side {channels_starting_by}: n_su {n_su}, n_mu {n_mu}, total {n_su+n_mu}") print(f"mu_by_area_count: {mu_by_area_count}") print(f"su_by_area_count: {su_by_area_count}") print("") if len(self.stimuli_name_dict) > 0: print(f"Stimuli content: {self.stimuli_name_dict}") print(" ") print("sleep stages: ") for sleep_stage in self.sleep_stages: print(sleep_stage) @property def identifier(self): return self._identifier

[ "julien.denis3@gmail.com" ]

julien.denis3@gmail.com

8757ce41ff686de72bf880c4579a31a36cf628ba

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/test/test_rest_api.py

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permissive

tanhimislam/haystack

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5cfdabda2c339b16a327e6ff10877ecea00c3038

refs/heads/master

2023-08-24T11:52:59.058072

2021-10-15T08:29:36

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import os from pathlib import Path import pytest from fastapi.testclient import TestClient from haystack import Label from rest_api.application import app FEEDBACK={ "id": "123", "query": "Who made the PDF specification?", "document": { "content": "A sample PDF file\n\nHistory and standardization\nFormat (PDF) Adobe Systems made the PDF specification available free of charge in 1993. In the early years PDF was popular mainly in desktop publishing workflows, and competed with a variety of formats such as DjVu, Envoy, Common Ground Digital Paper, Farallon Replica and even Adobe's own PostScript format. PDF was a proprietary format controlled by Adobe until it was released as an open standard on July 1, 2008, and published by the International Organization for Standardization as ISO 32000-1:2008, at which time control of the specification passed to an ISO Committee of volunteer industry experts. In 2008, Adobe published a Public Patent License to ISO 32000-1 granting royalty-free rights for all patents owned by Adobe that are necessary to make, use, sell, and distribute PDF-compliant implementations. PDF 1.7, the sixth edition of the PDF specification that became ISO 32000-1, includes some proprietary technologies defined only by Adobe, such as Adobe XML Forms Architecture (XFA) and JavaScript extension for Acrobat, which are referenced by ISO 32000-1 as normative and indispensable for the full implementation of the ISO 32000-1 specification. These proprietary technologies are not standardized and their specification is published only on Adobes website. Many of them are also not supported by popular third-party implementations of PDF. Column 1", "content_type": "text", "score": None, "id": "fc18c987a8312e72a47fb1524f230bb0", "meta": {} }, "answer": { "answer": "Adobe Systems", "type": "extractive", "context": "A sample PDF file\n\nHistory and standardization\nFormat (PDF) Adobe Systems made the PDF specification available free of charge in 1993. In the early ye", "offsets_in_context": [{"start": 60, "end": 73}], "offsets_in_document": [{"start": 60, "end": 73}], "document_id": "fc18c987a8312e72a47fb1524f230bb0" }, "is_correct_answer": True, "is_correct_document": True, "origin": "user-feedback", "pipeline_id": "some-123", } @pytest.mark.elasticsearch @pytest.fixture(scope="session") def client() -> TestClient: os.environ["PIPELINE_YAML_PATH"] = str((Path(__file__).parent / "samples"/"pipeline"/"test_pipeline.yaml").absolute()) os.environ["INDEXING_PIPELINE_NAME"] = "indexing_text_pipeline" client = TestClient(app) yield client # Clean up client.post(url="/documents/delete_by_filters", data='{"filters": {}}') @pytest.mark.elasticsearch @pytest.fixture(scope="session") def populated_client(client: TestClient) -> TestClient: client.post(url="/documents/delete_by_filters", data='{"filters": {}}') files_to_upload = [ {'files': (Path(__file__).parent / "samples"/"pdf"/"sample_pdf_1.pdf").open('rb')}, {'files': (Path(__file__).parent / "samples"/"pdf"/"sample_pdf_2.pdf").open('rb')} ] for index, fi in enumerate(files_to_upload): response = client.post(url="/file-upload", files=fi, data={"meta": f'{{"meta_key": "meta_value", "meta_index": "{index}"}}'}) assert 200 == response.status_code yield client client.post(url="/documents/delete_by_filters", data='{"filters": {}}') def test_get_documents(): os.environ["PIPELINE_YAML_PATH"] = str((Path(__file__).parent / "samples"/"pipeline"/"test_pipeline.yaml").absolute()) os.environ["INDEXING_PIPELINE_NAME"] = "indexing_text_pipeline" client = TestClient(app) # Clean up to make sure the docstore is empty client.post(url="/documents/delete_by_filters", data='{"filters": {}}') # Upload the files files_to_upload = [ {'files': (Path(__file__).parent / "samples"/"docs"/"doc_1.txt").open('rb')}, {'files': (Path(__file__).parent / "samples"/"docs"/"doc_2.txt").open('rb')} ] for index, fi in enumerate(files_to_upload): response = client.post(url="/file-upload", files=fi, data={"meta": f'{{"meta_key": "meta_value_get"}}'}) assert 200 == response.status_code # Get the documents response = client.post(url="/documents/get_by_filters", data='{"filters": {"meta_key": ["meta_value_get"]}}') assert 200 == response.status_code response_json = response.json() # Make sure the right docs are found assert len(response_json) == 2 names = [doc["meta"]["name"] for doc in response_json] assert "doc_1.txt" in names assert "doc_2.txt" in names meta_keys = [doc["meta"]["meta_key"] for doc in response_json] assert all("meta_value_get"==meta_key for meta_key in meta_keys) def test_delete_documents(): os.environ["PIPELINE_YAML_PATH"] = str((Path(__file__).parent / "samples"/"pipeline"/"test_pipeline.yaml").absolute()) os.environ["INDEXING_PIPELINE_NAME"] = "indexing_text_pipeline" client = TestClient(app) # Clean up to make sure the docstore is empty client.post(url="/documents/delete_by_filters", data='{"filters": {}}') # Upload the files files_to_upload = [ {'files': (Path(__file__).parent / "samples"/"docs"/"doc_1.txt").open('rb')}, {'files': (Path(__file__).parent / "samples"/"docs"/"doc_2.txt").open('rb')} ] for index, fi in enumerate(files_to_upload): response = client.post(url="/file-upload", files=fi, data={"meta": f'{{"meta_key": "meta_value_del", "meta_index": "{index}"}}'}) assert 200 == response.status_code # Make sure there are two docs response = client.post(url="/documents/get_by_filters", data='{"filters": {"meta_key": ["meta_value_del"]}}') assert 200 == response.status_code response_json = response.json() assert len(response_json) == 2 # Delete one doc response = client.post(url="/documents/delete_by_filters", data='{"filters": {"meta_index": ["0"]}}') assert 200 == response.status_code # Now there should be only one doc response = client.post(url="/documents/get_by_filters", data='{"filters": {"meta_key": ["meta_value_del"]}}') assert 200 == response.status_code response_json = response.json() assert len(response_json) == 1 # Make sure the right doc was deleted response = client.post(url="/documents/get_by_filters", data='{"filters": {"meta_index": ["0"]}}') assert 200 == response.status_code response_json = response.json() assert len(response_json) == 0 response = client.post(url="/documents/get_by_filters", data='{"filters": {"meta_index": ["1"]}}') assert 200 == response.status_code response_json = response.json() assert len(response_json) == 1 def test_file_upload(client: TestClient): file_to_upload = {'files': (Path(__file__).parent / "samples"/"pdf"/"sample_pdf_1.pdf").open('rb')} response = client.post(url="/file-upload", files=file_to_upload, data={"meta": '{"meta_key": "meta_value"}'}) assert 200 == response.status_code client.post(url="/documents/delete_by_filters", data='{"filters": {}}') def test_query_with_no_filter(populated_client: TestClient): query_with_no_filter_value = {"query": "Who made the PDF specification?"} response = populated_client.post(url="/query", json=query_with_no_filter_value) assert 200 == response.status_code response_json = response.json() assert response_json["answers"][0]["answer"] == "Adobe Systems" def test_query_with_one_filter(populated_client: TestClient): query_with_filter = {"query": "Who made the PDF specification?", "params": {"filters": {"meta_key": "meta_value"}}} response = populated_client.post(url="/query", json=query_with_filter) assert 200 == response.status_code response_json = response.json() assert response_json["answers"][0]["answer"] == "Adobe Systems" def test_query_with_filter_list(populated_client: TestClient): query_with_filter_list = { "query": "Who made the PDF specification?", "params": {"filters": {"meta_key": ["meta_value", "another_value"]}} } response = populated_client.post(url="/query", json=query_with_filter_list) assert 200 == response.status_code response_json = response.json() assert response_json["answers"][0]["answer"] == "Adobe Systems" def test_query_with_invalid_filter(populated_client: TestClient): query_with_invalid_filter = { "query": "Who made the PDF specification?", "params": {"filters": {"meta_key": "invalid_value"}} } response = populated_client.post(url="/query", json=query_with_invalid_filter) assert 200 == response.status_code response_json = response.json() assert len(response_json["answers"]) == 0 def test_write_feedback(populated_client: TestClient): response = populated_client.post(url="/feedback", json=FEEDBACK) assert 200 == response.status_code def test_get_feedback(client: TestClient): response = client.post(url="/feedback", json=FEEDBACK) resp = client.get(url="/feedback") labels = [Label.from_dict(i) for i in resp.json()] def test_export_feedback(populated_client: TestClient): response = populated_client.post(url="/feedback", json=FEEDBACK) assert 200 == response.status_code feedback_urls = [ "/export-feedback?full_document_context=true", "/export-feedback?full_document_context=false&context_size=50", "/export-feedback?full_document_context=false&context_size=50000", ] for url in feedback_urls: response = populated_client.get(url=url, json=FEEDBACK) response_json = response.json() context = response_json["data"][0]["paragraphs"][0]["context"] answer_start = response_json["data"][0]["paragraphs"][0]["qas"][0]["answers"][0]["answer_start"] answer = response_json["data"][0]["paragraphs"][0]["qas"][0]["answers"][0]["text"] assert context[answer_start:answer_start+len(answer)] == answer

[ "noreply@github.com" ]

tanhimislam.noreply@github.com

77ef79ca9fa00d359c60f5fc9b20d0f93f23c4b1

42a4a77e417e59479d8aacb55ca2bc84640b178e

/NewsChefAPI/NewsChefAPI/NewsModel/serializers.py

6e290fd413ea7e07589717d10ff074124102a106

[]

no_license

rahulShrestha89/Midversion-newsChef

182e7744c7433716a7cca3f7287d1e7daa7209be

a585cfdfd549dc769c776e2796ea2ef983b1d30e

refs/heads/master

2020-07-20T01:02:02.713270

2016-10-27T21:38:40

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from rest_framework import serializers from NewsModel.models import NewsModel class NewsModelSerializer(serializers.ModelSerializer): class Meta: model = NewsModel fields = ('id','created','firstName','lastName','email','phoneNumber') def create(self, validated_data): """ Create and return User. """ return NewsModel.objects.create(**validated_data) def update(self, instance, validated_data): """ Update and return User. """ instance.firstName = validated_data.get('firstName',instance.firstName) instance.lastName = validated_data.get('lastName',instance.lastName) instance.email = validated_data.get('email',instance.email) instance.phoneNumber = validated_data.get('phoneNumber',instance.phoneNumber) instance.save() return instance

[ "PrayushPokharel@Prayushs-MacBook-Pro.local" ]

PrayushPokharel@Prayushs-MacBook-Pro.local

0ec93526b33317ffa6a2f38a2e79cc249998bd4a

42474f0f92339992d9e8ded982e0458d01c028fd

/inputnumber_and_displaymenu.py

dc043deb83e5b17b4accccb6ec067d8239ddaa0d

[]

no_license

Frehoni/TestProjekt-1

14871f5a7d994cce87202d6b7f51bca75d276cd4

ff37a4e96e9234c6c73f5fed0e144e3770afe47c

refs/heads/master

2020-09-06T04:47:17.608913

2019-11-14T07:51:58

220,326,705

0

null

UTF-8

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py

def inputNumber(prompt): #Inputnumber pronts user to input a number # #Usage: num=inputNumber(promt) Displays promt and asks user for a number. #Repeats until user inputs a valid number. while True: try: num = float(input(prompt)) break except ValueError: pass return num def displayMenu(options): #DisplayMenu displays a menu of options, ask the user to choose an number #and returns the number of the menu item chosen. # #Usage: choice = displayMenu(options) # #Input options Menu options (cell array of strings) #Output choice Chosen option (integer) for i in range(len(options)): print("{:d}. {:s}".format(i+1,options[i])) # Get a valid menu choice choice = 0 while not(np.any(choice == np.arange(len(options))+1)): choice = inputNumber("Please choose a menu item: ") return choice

[ "frehoni@gmail.com" ]

frehoni@gmail.com

9c11636c982ae342d684df2b366e4ec72d64b10e

f41c999e9e367bf6d091caa58633c9a277d1d920

/loottable.py

1779035cfbe4b109b62620fade4b542d870f24a8

[]

no_license

tntrobber123/megamanrpg

9deff5831626e2797a82c7f256d8bb42099866a2

05605618173a15531c38a19ad25300ff10ed83fd

refs/heads/master

2023-08-20T07:20:42.719557

2021-10-21T18:14:26

403,707,738

0

null

UTF-8

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py

commonloot = [] commonloot.append("Small HP ball") commonloot.append("Small HP ball") commonloot.append("Small HP ball") commonloot.append("Large HP ball") commonloot.append("Small energy ball") commonloot.append("Small energy ball") commonloot.append("Small energy ball") commonloot.append("Large energy ball") commonloot.append("e-TANK") uncommonloot = [] uncommonloot.append("Large HP ball") uncommonloot.append("Large HP ball") uncommonloot.append("Large energy ball") uncommonloot.append("Large energy ball") uncommonloot.append("e-TANK") uncommonloot.append("e-TANK") uncommonloot.append("E-TANK") rareloot = [] rareloot.append("e-TANK") rareloot.append("E-TANK") rareloot.append("E-TANK") rareloot.append("m-TANK") rareloot.append("m-TANK") rareloot.append("M-TANK") rareloot.append("Free screw box") rareloot.append("1-UP") weaponloot = [] bossloot = []

[ "tntrobber@gmail.com" ]

tntrobber@gmail.com

79577c7f146364cb36e810bab124664c1cc42b1a

f2242ee2b26df80c6290d94a388de7a98a7bbc35

/measuring_polyphony/settings.py

683665cd6311a40d1b794f9aa8e6081e01ef16ad

[]

no_license

misingnoglic/measuring_polyphony_django

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refs/heads/master

2021-01-21T07:03:30.376918

2017-06-07T17:21:39

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""" Django settings for measuring_polyphony project. Generated by 'django-admin startproject' using Django 1.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! import secrets SECRET_KEY = secrets.SECRET_KEY # SECURITY WARNING: don't run with debug turned on in production! DEBUG = secrets.debug ALLOWED_HOSTS = ['174.138.49.237', '45.55.149.115', '127.0.0.1', 'localhost'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django_extensions', 'viewer', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'measuring_polyphony.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'measuring_polyphony.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'polyphony', 'USER': 'polyphony', 'PASSWORD': secrets.DATABASE_PASSWORD, 'HOST': 'localhost', 'PORT': '', } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static/') MEDIA_ROOT = 'media/' MEDIA_URL = '/media/' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ # insert your TEMPLATE_DIRS here 'templates' ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ # Insert your TEMPLATE_CONTEXT_PROCESSORS here or use this # list if you haven't customized them: 'django.contrib.auth.context_processors.auth', 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.template.context_processors.tz', 'django.contrib.messages.context_processors.messages', ], }, }, ]

[ "aboudaie@brandeis.edu" ]

aboudaie@brandeis.edu

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/feed/migrations/0001_initial.py

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# -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2017-09-26 17:17 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='UserComment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('comment_date', models.DateTimeField(auto_now_add=True)), ('comment_body', models.TextField(max_length=500)), ('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='usercomment', to=settings.AUTH_USER_MODEL)), ], options={ 'ordering': ['-comment_date'], }, ), migrations.CreateModel( name='UserPost', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('post_date', models.DateTimeField(auto_now_add=True)), ('title', models.CharField(max_length=150)), ('post_body', models.TextField(max_length=1000)), ('image', models.ImageField(blank=True, upload_to='post_pics')), ('author', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='userpost', to=settings.AUTH_USER_MODEL)), ], options={ 'ordering': ['-post_date'], }, ), migrations.AddField( model_name='usercomment', name='post', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='comments', to='feed.UserPost'), ), ]

[ "garrettlove@Garretts-MacBook-Pro.local" ]

garrettlove@Garretts-MacBook-Pro.local

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/tests/test_attributes.py

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dynata/python-demandapi-client

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2020-09-23T23:48:10

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# encoding: utf-8 from __future__ import unicode_literals, print_function import json import unittest import responses from dynatademand.api import DemandAPIClient BASE_HOST = "http://test-url.example" class TestAttributeEndpoints(unittest.TestCase): def setUp(self): self.api = DemandAPIClient(client_id='test', username='testuser', password='testpass', base_host=BASE_HOST) self.api._access_token = 'Bearer testtoken' @responses.activate def test_get_attributes(self): with open('./tests/test_files/get_attributes.json', 'r') as attributes_file: attributes_json = json.load(attributes_file) responses.add(responses.GET, '{}/sample/v1/attributes/no/no'.format(BASE_HOST), json=attributes_json, status=200) self.api.get_attributes('no', 'no') self.assertEqual(len(responses.calls), 1) print('flaws') print(responses.calls[0].response.json()) self.assertEqual(responses.calls[0].response.json(), attributes_json)

[ "bradley@wogsland.org" ]

bradley@wogsland.org

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/components/Dropdown.py

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import dash_core_components as dcc import dash_html_components as html from components import DashComponent CDashComponent = DashComponent.CDashComponent class CDropdown(CDashComponent): def __init__(self, options = [], placeholder = 'Select', value = '', multi = False, style = {}, name = None, screenName = None): super().__init__(name, screenName) self.setDropdown(options, placeholder, value, multi, style) def getValue(self): return self.__value def update(self, value): self.__value = value def setDropdown(self, options, placeholder, value, multi, style): self.__options = options self.__value = value super().setDashRendering(html.Div([dcc.Dropdown( id=str(super().getID()), options=options, placeholder=placeholder, multi=multi, value=value, )], style=style))

[ "alexandriksasha@mail.ru" ]

alexandriksasha@mail.ru

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/img_cap_server/main.py

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[ "MIT" ]

permissive

petergerasimov/VoiceCV

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refs/heads/master

2020-04-29T04:40:00.697161

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import base64 import os from PIL import Image from flask import Flask, make_response, request from img_cap.main import greedy_search_inference app = Flask(__name__) @app.route('/sendImage' , methods=['POST']) def sendImage(): req = request.get_json(force=True) img = base64.b64decode(req['imgData']) image = Image.frombytes('RGB', (320, 240), img) image.save('image.jpg') res = greedy_search_inference('./image.jpg') return make_response(res , 200) if __name__ == '__main__': app.run(host='0.0.0.0', port=4000)

[ "velev.victor@yahoo.com" ]

velev.victor@yahoo.com

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/h20.py

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n=int(input()) for a in range(1,6,1): print(n*a,end=" ")

[ "noreply@github.com" ]

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/Westar2/wsgi.py

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""" WSGI config for Westar2 project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Westar2.settings') application = get_wsgi_application()

[ "user@Userui-MacBookPro.local" ]

user@Userui-MacBookPro.local

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/polls/migrations/0006_auto_20160216_0027.py

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[]

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jasleenkaur/myproject

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refs/heads/master

2021-01-10T10:36:48.690386

2016-02-18T12:30:41

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('polls', '0005_auto_20160214_0955'), ] operations = [ migrations.AlterField( model_name='question', name='pub_date', field=models.DateTimeField(verbose_name='date published'), ), ]

[ "jasleen.7956@gmail.com" ]

jasleen.7956@gmail.com

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/FileScript.py

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nicolasagudelo/Regresion-Lineal-Series-de-Tiempo

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import numpy as np f = open('data chikun.csv', 'r') #Cargamos los datos np.set_printoptions(suppress=True) data = [] for line in f.readlines(): line = line.strip() line = line.split(";") line = [float(i) for i in line] data.append(line) x = np.matrix(data) x = x.reshape(22,6) #Los acomodamos de la forma que deseamos np.savetxt('Chikungunya_6_Lag.csv',x,fmt='%.2i',delimiter=';') #Los guardamos en un csv

[ "nicoalbert95@hotmail.com" ]

nicoalbert95@hotmail.com

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asmaaltahe/erp-system

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2021-10-17T08:37:44

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import datetime from odoo import models, fields, api, exceptions,_ class Eyeclinic(models.Model): _name = 'education.eye' _description = 'Eye' _order = "id desc" patient_id = fields.Char(string='Student Number' ) name = fields.Char(string="full name" ) first = fields.Char(string="First Name" ) second = fields.Char(string="Second Name" ) third = fields.Char(string="Third Name" ) last = fields.Char(string="Last Name" ) gender=fields.Char(string="gender" ) brath_day = fields.Date(string='Date Of Birth' ) date = fields.Char(string='Date', default=lambda self: datetime.datetime.today().strftime('%Y-%m-%d'), readonly=True) phone = fields.Char(string="Phone" ) email = fields.Char(string="Email" ) nationality = fields.Char(string='Nationality' ) religion = fields.Char(string='Religion' ) program = fields.Char(string='Program' ) address = fields.Char(string="address") # The End Personal Information general = fields.Char(string="General Vision") withoutglss = fields.Char(string="With Out Glasses") withglasses = fields.Char(string='With Glasses') color = fields.Char(string='Color Vision') near = fields.Char(string='Near Vision') opthahmologist = fields.Char(string='Opthahmologist', readonly=True, default=lambda self: self.env.user.name ) assessment = fields.Text(string="Assessment") diagonis = fields.Text(string='Diagonis') is_assessment = fields.Boolean(string="Is assessment") def get_student_name(self): for re in self: re.name = str(re.first) + " " + str(re.second) + " " + str(re.third) + " " + str(re.last)

[ "mis.drive119@gmail.com" ]

mis.drive119@gmail.com

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2022-12-23T20:53:51.699570

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py

#!/usr/bin/env python3 # Unix/Linux 操作系统的 fork() 调用 #调用 1 次，返回 2 次（拷贝一份到子进程执行） import os print('Process(%s) start...' % os.getpid()) #Only works on Unix/linux/Mac ''' pid = os.fork() if pid == 0: print('I am child process (%s) and my parent is process (%s)' %(os.getpid(),os.getppid())) else: print('I am process (%s) and I created a child process (%s)' % (os.getpid(),pid)) ''' # 跨平台多进程支持 # 创建子进程时只需传递一个执行函数和函数的参数，创建一个 Process 实例，用 start() 方法启动 from multiprocessing import Process import os def run_proc(name): print('Run child process %s pid(%s)' % (name,os.getpid())) if __name__ == '__main__': print('Parent process pid(%s)' % os.getpid()) p = Process(target=run_proc,args=('test',)) print('Child process will start.') p.start() p.join() print('Child process end.') # 进程池 from multiprocessing import Pool import os, time, random def long_time_task(name): print('Run task %s pid(%s)' % (name,os.getpid())) start = time.time() time.sleep(random.random() * 3) end = time.time() print('Task %s run %.2f seconds.' % (name,(end - start))) if __name__ == '__main__': print('Parent process %s.' % os.getpid()) p = Pool(3) for i in range(4): p.apply_async(long_time_task,args=(i,)) print('Waiting for all subprocess done...') p.close() p.join() print('All subprocess done.') # 子进程， subprocess 控制子进程的输入输出 import subprocess print('$ nslookup www.python.org') r = subprocess.call(['nslookup','www.python.org']) print('Exit code',r) print('$nslookup') p = subprocess.Popen(['nslookup'], stdin=subprocess.PIPE,stdout=subprocess.PIPE, stderr=subprocess.PIPE) output, err = p.communicate(b'set q=mx\npython.org\nexit\n') print(output.decode('gbk')) print('Exit code:', p.returncode) # 进程间通信 # Queue , Pipes from multiprocessing import Queue, Process import os, time, random # 写数据进程执行的代码: def write(q): print('Process to write: %s' % os.getpid()) for value in ['A', 'B', 'C']: print('Put %s to queue...' % value) q.put(value) time.sleep(random.random()) # 读数据进程执行的代码: def read(q): print('Process to read: %s' % os.getpid()) while True: value = q.get(True) print('Get %s from queue.' % value) if __name__=='__main__': # 父进程创建Queue，并传给各个子进程： q = Queue() pw = Process(target=write, args=(q,)) pr = Process(target=read, args=(q,)) # 启动子进程pw，写入: pw.start() # 启动子进程pr，读取: pr.start() # 等待pw结束: pw.join() # pr进程里是死循环，无法等待其结束，只能强行终止: pr.terminate()

[ "2544391722@qq.com" ]

2544391722@qq.com

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/vehicles.py

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[]

no_license

scottherold/python_OOP

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refs/heads/master

2020-04-02T13:07:49.803669

2018-10-24T08:46:55

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UTF-8

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py

# file vehicles.py class Vehicle: def __init__(self, wheels, capacity, make, model): self.wheels = wheels self.capacity = capacity self.make = make self.model = model self.mileage = 0 def drive(self,miles): self.mileage += miles return self def reverse(self,miles): self.mileage -= miles return self class Bike(Vehicle): def vehicle_type(self): return "Bike" class Car(Vehicle): def set_wheels(self): self.wheels = 4 return self class Airplane(Vehicle): def fly(self, miles): self.mileage += miles return self v = Vehicle(4,8,"dodge","minivan") print(v.make) b = Bike(2,1,"Schwinn","Paramount") print(b.vehicle_type()) c = Car(8,5,"Toyota", "Matrix") c.set_wheels() print(c.wheels) a = Airplane(22,853,"Airbus","A380") a.fly(580) print(a.mileage)

[ "sherold@mail.usf.edu" ]

sherold@mail.usf.edu

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/Utils/Compute_FDR.py

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[ "LicenseRef-scancode-unknown-license-reference", "MIT" ]

permissive

joeyee/Histogram_Layer

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refs/heads/master

2023-03-28T17:00:37.421362

2021-04-05T18:01:59

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MIT

2021-08-21T12:19:12

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# -*- coding: utf-8 -*- """ Created on Wed Mar 4 09:04:58 2020 Function to compute FDR score per class @author: jpeeples """ import numpy as np def Compute_Fisher_Score(features,labels): #Get index of labels that correspond to each class Classes = np.unique(labels) #Get number of instances of each class for P_i Instances = np.zeros(len(Classes)) for i in range(0,len(Classes)): Instances[i] = sum(labels==Classes[i]) P_i = Instances/sum(Instances); #Compute global mean global_mean = np.mean(features,axis=0) #For each class compute intra and inter class variations scores = np.zeros(len(Classes)) log_scores = np.zeros(len(Classes)) for current_class in range(0,len(Classes)): data = features[labels==Classes[current_class],:] #Within-class scatter matrix S_w = P_i[i]*np.cov(data.T) #Between-class scatter matrix S_b = P_i[i]*(np.outer((np.mean(data,axis=0)-global_mean), (np.mean(data,axis=0)-global_mean).T)) #Compute the score, compute abs of score, only care about magnitude #compute log of scores if too large #Using pseudoinverse if singular matrix try: scores[current_class] = abs((np.matmul(np.linalg.inv(S_w),S_b)).trace()) except: scores[current_class] = abs((np.matmul(np.linalg.pinv(S_w),S_b)).trace()) log_scores[current_class] = np.log(scores[current_class]) return scores, log_scores

[ "jpeeples@ufl.edu" ]

jpeeples@ufl.edu

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/run_all_simu.py

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[ "MIT" ]

permissive

prise-3d/LSTM-noise-detection

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refs/heads/master

2022-05-31T06:35:42.923077

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import os import argparse command = "python display/display_thresholds_scene_file.py --params \"{0}\" --method {1} --model {2} --selected_zones {3} --scene {4} --thresholds {5} --seq_norm {6} --sequence {7} --save 1 --save_thresholds {8} --label_thresholds {9} --every {10}" parser = argparse.ArgumentParser(description="Compute simulation for each scenes") parser.add_argument('--folder', type=str, help="data folder with scenes files", required=True) parser.add_argument('--method', type=str, help="method name", required=True) parser.add_argument('--model', type=str, help="model path for simulation", required=True) parser.add_argument('--params', type=str, help="expected params for model", required=True) parser.add_argument('--thresholds', type=str, help="thresholds file", required=True) parser.add_argument('--selected_zones', type=str, help="selected zone file", required=True) parser.add_argument('--sequence', type=str, help="sequence size of RNN model", required=True) parser.add_argument('--seqnorm', type=str, help="normalization or not of sequence", required=True) parser.add_argument('--output', type=str, help="output prediction filename", required=True) parser.add_argument('--every', type=int, help="every images only", default=1) args = parser.parse_args() p_folder = args.folder p_method = args.method p_model = args.model p_params = args.params p_thresholds = args.thresholds p_selected_zones = args.selected_zones p_sequence = args.sequence p_seqnorm = args.seqnorm p_output = args.output p_every = args.every for scene in sorted(os.listdir(p_folder)): scene_path = os.path.join(p_folder, scene) str_command = command.format(p_params, p_method, p_model, p_selected_zones, scene_path, p_thresholds, p_seqnorm, p_sequence, p_output, scene, p_every) print("Run simulation for {0}".format(scene)) os.system(str_command)

[ "contact@jeromebuisine.fr" ]

contact@jeromebuisine.fr

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from rest_framework import serializers from favourites.models import Favourites class FavouritesSerializer(serializers.ModelSerializer): class Meta: model = Favourites fields = ['id', 'isFavourite','product','user']

[ "ymukeshyadavmrj@gmail.com" ]

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MarvvanPal/foundations-sample-website

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from color_check.website import app from color_check.controllers.get_color_code import get_color_code # test the function we've written to check on the colors themselves def test_get_color_code(): # this test should pass right now assert get_color_code("blue") == "#0000ff" # the following test will fail at the beginning, # uncomment when you think you are finished! # assert get_color_code("red") == "#ff0000" # our very first functional test # instead of checking if a function() does it's job alone, this will check # the entire response from the flask app, including the http status code. def test_index(): # create a version of our website that we can use for testing with app.test_client() as test_client: # mimic a browser: 'GET /', as if you visit the site response = test_client.get('/') # check that the HTTP response is a success assert response.status_code == 200 # Store the contents of the html response in a local variable. # This should be a string with the same content as the file index.html html_content = response.data.decode() assert "<html>" in html_content # check that there is a route at "/colors" which accepts a POST request def test_colors(): with app.test_client() as test_client: response = test_client.post('/color') assert response.status_code == 200

[ "adam.j.roe@gmail.com" ]

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kilogram = 1 pound = 20 print("Kilorgams \t Pounds \t | \t Pounds \t Kilorgams") while (kilogram in range(1, 200)) or (pound in range(20, 520)): pounds = kilogram * 2.2 kilograms = pound / 2.2 print(str(format(kilogram, "<4.0f")) + str(format(pounds, "20.1f")) + " \t |"+ str(format(pound, "20.0f")) + str(format(kilograms, "20.2f"))) kilogram+=2 pound+=5

[ "noreply@github.com" ]

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/dynamic_commands/smartOBD/main.py

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AvraSaslow/3308project

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""" .. module:: main :platform: Unix :synopsis: Asynchronous connection .. moduleauthor:: Will Walker Initialization and interface Simple command line interface, with choices for asynchronous data and a full data query """ import sys,os sys.path.insert( 0, os.path.realpath(os.path.dirname(__file__))) import obd import time import psycopg2 import datetime from psycopg2.extensions import AsIs from psycopg2 import sql import smartOBD from smartOBD import asynco from smartOBD import test_commands ##main function # # initialization and interface for smartOBD # Simple command line interface, with choices for asynchronous data and a full data query def main(): """ This function determines which functionality the user would like to use, and calls it """ print("Welcome to smartOBD") print("Choose your action:\n") print("(0) Async allows smartOBD to give you live data on your vehicle\n") print("(1) Full Read will store all the data from your car's computer\n") choose_action = input("Async(0) or Full Read(1): ") # * make database connection # host=198.23.146.166 password=Sweden77 ## asynchronous # @param choose_action The action if(choose_action == '0'): asynco.getAsync(60) # x = 0 # while x < 30: # data = [datetime.datetime.now()] # asynco.new_speed(x+3, data) # asynco.new_rpm(1000+x, data) # asynco.new_temp(150+x, data) # asynco.new_fuel(35+x, data, dbtable, dbconn, cur) # x += 1 ## full query elif(choose_action == '1'): test_commands.fullQuery() ## constructor if __name__ == "__main__": main()

[ "trco9595@colorado.edu" ]

trco9595@colorado.edu

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/ex10/ex10.py

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[]

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gmzyyzgh/python_practice

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refs/heads/master

2020-05-30T12:29:08.710513

2019-07-06T13:56:59

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py

tabby_cat = "\tI'm tabbed in." persian_cat = "I'm split\non a line." backslash_cat = "I'm \\ a \\cat." fat_cat = """ I'll do a list: \t* Cat food \t* Fishies \t* Catnip\n\t* Grass """ print(tabby_cat) print(persian_cat) print(backslash_cat) print(fat_cat)

[ "31633088@qq.com" ]

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/Recursion/kickstart1.py

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no_license

AnkitAvi11/100-Days-of-Code

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2023-04-02T01:55:11.614657

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if __name__ == '__main__' : t = int(input()) for _ in range(t) : n, b = map(int, input().split()) arr = list(map(int, input().split())) count = 0 i = 0 while b > 0 and i < n : if b - arr[i] < 0 : i+=1 else : b -= arr[i] count += 1 i+=1 print(count)

[ "kumar.ankit383@gmail.com" ]

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/envs/block_pushing/oracles/oriented_push_oracle.py

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2023-05-25T03:30:05.481404

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# coding=utf-8 # Copyright 2022 The Reach ML Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Oracle for pushing task which orients the block then pushes it.""" import envs.block_pushing.oracles.pushing_info as pushing_info_module import numpy as np from tf_agents.policies import py_policy from tf_agents.trajectories import policy_step from tf_agents.trajectories import time_step as ts from tf_agents.typing import types # Only used for debug visualization. import pybullet # pylint: disable=unused-import class OrientedPushOracle(py_policy.PyPolicy): """Oracle for pushing task which orients the block then pushes it.""" def __init__(self, env, action_noise_std=0.0): super(OrientedPushOracle, self).__init__( env.time_step_spec(), env.action_spec() ) self._env = env self._np_random_state = np.random.RandomState(0) self.phase = "move_to_pre_block" self._action_noise_std = action_noise_std def reset(self): self.phase = "move_to_pre_block" def get_theta_from_vector(self, vector): return np.arctan2(vector[1], vector[0]) def theta_to_rotation2d(self, theta): r = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) return r def rotate(self, theta, xy_dir_block_to_ee): rot_2d = self.theta_to_rotation2d(theta) return rot_2d @ xy_dir_block_to_ee def _get_action_info(self, time_step, block, target): xy_block = time_step.observation["%s_translation" % block][:2] theta_block = time_step.observation["%s_orientation" % block] xy_target = time_step.observation["%s_translation" % target][:2] xy_ee = time_step.observation["effector_target_translation"][:2] xy_block_to_target = xy_target - xy_block xy_dir_block_to_target = (xy_block_to_target) / np.linalg.norm( xy_block_to_target ) theta_to_target = self.get_theta_from_vector(xy_dir_block_to_target) theta_error = theta_to_target - theta_block # Block has 4-way symmetry. while theta_error > np.pi / 4: theta_error -= np.pi / 2.0 while theta_error < -np.pi / 4: theta_error += np.pi / 2.0 xy_pre_block = xy_block + -xy_dir_block_to_target * 0.05 xy_nexttoblock = xy_block + -xy_dir_block_to_target * 0.03 xy_touchingblock = xy_block + -xy_dir_block_to_target * 0.01 xy_delta_to_nexttoblock = xy_nexttoblock - xy_ee xy_delta_to_touchingblock = xy_touchingblock - xy_ee xy_block_to_ee = xy_ee - xy_block xy_dir_block_to_ee = xy_block_to_ee / np.linalg.norm(xy_block_to_ee) theta_threshold_to_orient = 0.2 theta_threshold_flat_enough = 0.03 return pushing_info_module.PushingInfo( xy_block=xy_block, xy_ee=xy_ee, xy_pre_block=xy_pre_block, xy_delta_to_nexttoblock=xy_delta_to_nexttoblock, xy_delta_to_touchingblock=xy_delta_to_touchingblock, xy_dir_block_to_ee=xy_dir_block_to_ee, theta_threshold_to_orient=theta_threshold_to_orient, theta_threshold_flat_enough=theta_threshold_flat_enough, theta_error=theta_error, ) def _get_move_to_preblock(self, xy_pre_block, xy_ee): max_step_velocity = 0.3 # Go 5 cm away from the block, on the line between the block and target. xy_delta_to_preblock = xy_pre_block - xy_ee diff = np.linalg.norm(xy_delta_to_preblock) if diff < 0.001: self.phase = "move_to_block" xy_delta = xy_delta_to_preblock return xy_delta, max_step_velocity def _get_move_to_block( self, xy_delta_to_nexttoblock, theta_threshold_to_orient, theta_error ): diff = np.linalg.norm(xy_delta_to_nexttoblock) if diff < 0.001: self.phase = "push_block" # If need to re-oorient, then re-orient. if theta_error > theta_threshold_to_orient: self.phase = "orient_block_left" if theta_error < -theta_threshold_to_orient: self.phase = "orient_block_right" # Otherwise, push into the block. xy_delta = xy_delta_to_nexttoblock return xy_delta def _get_push_block( self, theta_error, theta_threshold_to_orient, xy_delta_to_touchingblock ): # If need to reorient, go back to move_to_pre_block, move_to_block first. if theta_error > theta_threshold_to_orient: self.phase = "move_to_pre_block" if theta_error < -theta_threshold_to_orient: self.phase = "move_to_pre_block" xy_delta = xy_delta_to_touchingblock return xy_delta def _get_orient_block_left( self, xy_dir_block_to_ee, orient_circle_diameter, xy_block, xy_ee, theta_error, theta_threshold_flat_enough, ): xy_dir_block_to_ee = self.rotate(0.2, xy_dir_block_to_ee) xy_block_to_ee = xy_dir_block_to_ee * orient_circle_diameter xy_push_left_spot = xy_block + xy_block_to_ee xy_delta = xy_push_left_spot - xy_ee if theta_error < theta_threshold_flat_enough: self.phase = "move_to_pre_block" return xy_delta def _get_orient_block_right( self, xy_dir_block_to_ee, orient_circle_diameter, xy_block, xy_ee, theta_error, theta_threshold_flat_enough, ): xy_dir_block_to_ee = self.rotate(-0.2, xy_dir_block_to_ee) xy_block_to_ee = xy_dir_block_to_ee * orient_circle_diameter xy_push_left_spot = xy_block + xy_block_to_ee xy_delta = xy_push_left_spot - xy_ee if theta_error > -theta_threshold_flat_enough: self.phase = "move_to_pre_block" return xy_delta def _get_action_for_block_target(self, time_step, block="block", target="target"): # Specifying this as velocity makes it independent of control frequency. max_step_velocity = 0.35 info = self._get_action_info(time_step, block, target) if self.phase == "move_to_pre_block": xy_delta, max_step_velocity = self._get_move_to_preblock( info.xy_pre_block, info.xy_ee ) if self.phase == "move_to_block": xy_delta = self._get_move_to_block( info.xy_delta_to_nexttoblock, info.theta_threshold_to_orient, info.theta_error, ) if self.phase == "push_block": xy_delta = self._get_push_block( info.theta_error, info.theta_threshold_to_orient, info.xy_delta_to_touchingblock, ) orient_circle_diameter = 0.025 if self.phase == "orient_block_left" or self.phase == "orient_block_right": max_step_velocity = 0.15 if self.phase == "orient_block_left": xy_delta = self._get_orient_block_left( info.xy_dir_block_to_ee, orient_circle_diameter, info.xy_block, info.xy_ee, info.theta_error, info.theta_threshold_flat_enough, ) if self.phase == "orient_block_right": xy_delta = self._get_orient_block_right( info.xy_dir_block_to_ee, orient_circle_diameter, info.xy_block, info.xy_ee, info.theta_error, info.theta_threshold_flat_enough, ) if self._action_noise_std != 0.0: xy_delta += self._np_random_state.randn(2) * self._action_noise_std max_step_distance = max_step_velocity * (1 / self._env.get_control_frequency()) length = np.linalg.norm(xy_delta) if length > max_step_distance: xy_direction = xy_delta / length xy_delta = xy_direction * max_step_distance return xy_delta def _action(self, time_step, policy_state): if time_step.is_first(): self.reset() xy_delta = self._get_action_for_block_target( time_step, block="block", target="target" ) return policy_step.PolicyStep(action=np.asarray(xy_delta, dtype=np.float32)) class OrientedPushNormalizedOracle(py_policy.PyPolicy): """Oracle for pushing task which orients the block then pushes it.""" def __init__(self, env): super(OrientedPushNormalizedOracle, self).__init__( env.time_step_spec(), env.action_spec() ) self._oracle = OrientedPushOracle(env) self._env = env def reset(self): self._oracle.reset() def _action(self, time_step, policy_state): time_step = time_step._asdict() time_step["observation"] = self._env.calc_unnormalized_state( time_step["observation"] ) step = self._oracle._action( ts.TimeStep(**time_step), policy_state ) # pylint: disable=protected-access return policy_step.PolicyStep( action=self._env.calc_normalized_action(step.action) )

[ "n.m.mahi@gmail.com" ]

n.m.mahi@gmail.com

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[]

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pashutk/pisdup

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refs/heads/master

2022-12-13T13:54:09.600887

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# import pyudev from enum import Enum, auto import time from pyee import BaseEventEmitter from config import Config from display import Display from cli import Cli from scene import Manager as SceneManager from io_event import IOEvent from devices_waiting import DevicesWaiting from scenes import SceneId from dummy import Dummy from loading import Loading class MainAppState(Enum): INIT = auto() WAITING_FOR_DEVICES = auto() class App: def __init__(self): self.state = MainAppState.INIT self.display = Display() self.ee = BaseEventEmitter() self.cli = None if Config.ENABLE_CLI: self.cli = Cli() def init(self): if Config.ENABLE_CLI: self.cli.run() def handle_cli_command(self, input_str): command_up = "up" command_ok = "ok" command_down = "down" if input_str == command_ok or input_str == command_up or input_str == command_down: self.ee.emit(IOEvent.BUTTON) if (input_str == command_up): self.ee.emit(IOEvent.BUTTON_UP) if (input_str == command_down): self.ee.emit(IOEvent.BUTTON_DOWN) if (input_str == command_ok): self.ee.emit(IOEvent.BUTTON_OK) if (input_str == "isd"): self.ee.emit(IOEvent.INSERT_SD) if (input_str == "ihd"): self.ee.emit(IOEvent.INSERT_HDD) if (input_str == "esd"): self.ee.emit(IOEvent.EJECT_SD) if (input_str == "ehd"): self.ee.emit(IOEvent.EJECT_HDD) def run(self): self.init() sm = SceneManager() sm.register_scene(DevicesWaiting(self.display, self.ee)) # sm.go(SceneId.DEVICES_WAITING) sm.register_scene(Dummy(self.display)) # sm.go(SceneId.DUMMY) sm.register_scene(Loading(self.display)) sm.go(SceneId.LOADING) while (True): if Config.ENABLE_CLI: input_str = self.cli.read() if input_str == 'exit': sm.destroy() break self.handle_cli_command(input_str) time.sleep(0.01) # context = pyudev.Context() # monitor = pyudev.Monitor.from_netlink(context) # monitor.filter_by('block') # for device in iter(monitor.poll, None): # if 'ID_FS_TYPE' in device: # print('{0} partition {1}, {2}'.format( # device.action, device.get('ID_FS_LABEL'), device.device_node)) # # # monitor = pyudev.Monitor.from_netlink(context) # monitor.filter_by('block') # def log_event(action, device): # if 'ID_FS_TYPE' in device: # with open('filesystems.log', 'a+') as stream: # print('{0} - {1}'.format(action, # device.get('ID_FS_LABEL')), file=stream) # observer = pyudev.MonitorObserver(monitor, log_event) # observer.start()

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me@pashutk.ru

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2021-06-14T07:35:52

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#!python3 # coding: utf-8 import requests, random, os,sys import time def refreshCaptcha(): url = "http://c2020502194rsy.scd.wezhan.cn/Common/GenerateCommentCaptcha" myheaders = { "Cookie": "yibu_rt_language=zh-CN; ASP.NET_SessionId=zehh3mndik3o5oeynn5pe0nm; __RequestVerificationToken=WgJnwrfswdgo-I4j_F7a6LpoU9HeniDdG0Vbg2rOgwSRWaAXASvV67zRcgLb0WLjpVgPzY0fzPp5-GpyQJZlM7ry63iSujDsOIpsryBdl741; acw_tc=781bad0b16169177065446661e51423afadeceb60e8520165bc7351d1e8a11; SERVERID=9cce0917ca076d8ead327ae4668516bf|1616917784|1616917111", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36" } timestamp = format(random.random(), ".16f") captchaResp = requests.get(url, headers=myheaders, params={"Timestamp": timestamp}) # 获取验证码图片 filename = "{}.jpg".format("captcha") with open(filename, "wb") as file: file.write(captchaResp.content) # 将文件保存到data文件夹下 file_jpg = 'data/'+str(time.time()) + '.jpg' with open(file_jpg, "wb") as file: file.write(captchaResp.content) print("Captcha image is : {}".format(os.path.abspath(filename))) return timestamp def addComment(captchanum, comment, timestamp): url = "http://c2020502194rsy.scd.wezhan.cn/Comment/AddComment" comment_headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", "X-Requested-With": "XMLHttpRequest", "Cookie": "yibu_rt_language=zh-CN; ASP.NET_SessionId=zehh3mndik3o5oeynn5pe0nm;" " __RequestVerificationToken=WgJnwrfswdgo-I4j_F7a6LpoU9HeniDdG0Vbg2rOgwSRWaAXASvV67zRcgLb0WLjpVgPzY0fzPp5-GpyQJZlM7ry63iSujDsOIpsryBdl741; " "acw_tc=781bad0b16169239350794725e513f23f81a57f4a0c5f45e49db4694011377; SERVERID=9cce0917ca076d8ead327ae4668516bf|1616924822|1616924768", "Referer": "http://c2020502194rsy.scd.wezhan.cn/lyhd", "Origin": "http://c2020502194rsy.scd.wezhan.cn" } comment_payload = { "CommentText": comment, "Captcha": captchanum, "EntityId": 293515, "EntityType": 1, "Timestamp": timestamp, #这个值要跟请求验证码时候的Timestamp保持一致 "__RequestVerificationToken": "WDw2cS0TSvXskn8kehzGX_Ixp_J_1fr4Mmb7_ETkCFYlMK5mwCrRXcNwS4lcVgByupVYNJehEIthw_pIntPkwmV2RSuiR5uufTlAt5TxGoo1" } resp = requests.post(url, headers=comment_headers, data=comment_payload) # print("Got response : {}".format(resp.text)) print(resp.json()['IsSuccess']) capnum = sys.argv[1] comment = sys.argv[2] timestamp = sys.argv[3] addComment(capnum, comment, timestamp) # capnum = "a723" # comment = "测试评论" # timestamp = "0.3147280830624921" # addComment(capnum, comment, timestamp)

[ "2667861645@qq.com" ]

2667861645@qq.com

ae81d7bfbd4cb98a77b9a988fc6541b94449cf0b

dfb605df317c8ab653eb8e7363e69876e4081e23

/Contraseñas.py

ed1fedbb9057b226d9b43a1a97e9279338ce45e2

[]

no_license

davalerova/Contrasenas

72468a495b35bdcabbb4a2ea874f2639c9348b9e

725da2969f3fbd7fcb0f0e8ef8b05b15a2c0cf14

refs/heads/master

2020-04-22T23:57:40.210245

2019-02-23T05:14:17

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from typing import List letras: List[str]="a b c d e f g h i j k l m n o p q r s t u v w x y z".split() opcion=input("Ingrese 1 para cifrar y 2 para decifrar") mensaje=input("Ingrese el mensaje") clave=input("Ingrese la clave") criptograma="" auxClave=clave.__len__() cont=0 for i in mensaje: if opcion=="1": criptograma+=letras[(letras.index(i)+(letras.index(clave[cont %auxClave])))%26] cont+=1 elif opcion=="2": criptograma += letras[(letras.index(i)+26-(letras.index(clave[cont % auxClave]))) % 26] cont +=1 if opcion=="1": print("El mensaje ",mensaje, "cifrado con la clave ",clave, "es ",criptograma) elif opcion=="2": print("El criptograma ",mensaje, "descifrado con la clave ",clave, "significa ",criptograma)

[ "dvdovni@gmail.com" ]

dvdovni@gmail.com

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/src/versionwidget.py

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[]

no_license

eckamm/rut

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f0fe26c5760c9887f2d989e150348bf1b2fc0da9

refs/heads/master

2020-05-20T05:02:06.068214

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from common import * from fonts import Fonts class VersionWidget: def __init__(self): antialias = True self.render = make_text(Fonts.f15, "Version %s" % (VERSION,), antialias, THECOLORS["white"], TEXT_BACKGROUND) self.box = self.render.get_rect() self.box.bottomright = (SCREEN_WIDTH, SCREEN_HEIGHT) def draw(self, surface): surface.blit(self.render, self.box)

[ "eckamm@gmail.com" ]

eckamm@gmail.com

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/docs/conf.py

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[ "LicenseRef-scancode-warranty-disclaimer", "Apache-2.0" ]

permissive

suzuken/dynamic-dynamodb

eddc76e21e1a5dba284841c3eeb43ccd206918b6

e1089a9330c65a5b3030220deea05c030701f8c2

refs/heads/master

2021-01-12T19:59:52.732542

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# -*- coding: utf-8 -*- # # Dynamic DynamoDB documentation build configuration file, created by # sphinx-quickstart on Fri Nov 15 17:42:37 2013. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Dynamic DynamoDB' copyright = u'2013, Sebastian Dahlgren' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1.10' # The full version, including alpha/beta/rc tags. release = '1.10.7' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'DynamicDynamoDBdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'DynamicDynamoDB.tex', u'Dynamic DynamoDB Documentation', u'Sebastian Dahlgren', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'dynamicdynamodb', u'Dynamic DynamoDB Documentation', [u'Sebastian Dahlgren'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'DynamicDynamoDB', u'Dynamic DynamoDB Documentation', u'Sebastian Dahlgren', 'DynamicDynamoDB', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False

[ "sebastian.dahlgren@gmail.com" ]

sebastian.dahlgren@gmail.com

1cc79a4b56db7f1e0cd4f30f446c7ce9b1d785e3

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/libs/bin/chardetect

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no_license

darrenoon/ASXETOScrape

f1c3a759e1d633b472c76c7fe6697b1b0f6db838

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refs/heads/master

2020-04-09T21:39:37.069129

2018-12-15T01:41:33

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#!/home/darrenoon/envs/hello_world/bin/python3 # -*- coding: utf-8 -*- import re import sys from chardet.cli.chardetect import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "darrenoon@gmail.com" ]

darrenoon@gmail.com

e8c95fbedcbe1eb5059ecb60b7717de86483c562

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/BackEnd/Semana3/Dia3/7-operadores-de-identidad.py

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[]

no_license

jorgegarba/CodiGo8

4defe235a790ebc248f9278f18ca050fde6c195f

f22b2b405ad999e5b960ce5f52936cd4e472af35

refs/heads/master

2023-01-09T02:14:27.134870

2020-03-18T03:15:52

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2023-01-07T22:11:22

2019-09-30T13:54:52

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# Operadores de Identidad # is ->es # is not ->no es # sirve para ver si estan apuntando a la misma direccion de # memoria frutas = ["manzana","pera"] frutas3 = frutas print(frutas3 is frutas) print(frutas is not frutas3)

[ "ederiveroman@gmail.com" ]

ederiveroman@gmail.com

38ae25b8603fcc76935e07a811ba2144eb374b9d

c224e403165e0461d90a0a1ec22e8e8a050a376e

/Week3/asymmetric_friendships.py

451f09deb1df2f7345126f8ac57d94d356090fdf

[]

no_license

nasimulhasan/Data_Science

14eae70c1983f1a24ec9944d270dd904508fd7e9

d2f037ce331d9936256b0eb0bc21f962de0c7f4d

refs/heads/master

2020-03-18T08:44:02.514118

2017-08-27T10:14:52

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import MapReduce import sys """ Word Count Example in the Simple Python MapReduce Framework """ mr = MapReduce.MapReduce() # ============================= # Do not modify above this line def mapper(record): # key: document identifier # value: document contents #print record key = record[0] value = record[1] ## print "value" ## print value ## print "Key: " + key + " Value: " + value ## words = value.split() ## for w in words: mr.emit_intermediate(key, [key, value]) def reducer(key, list_of_values): # key: word # value: list of occurrence counts #print (key, list_of_values) ## print key ## print type(list_of_values) dct = {} #mr.emit((list_of_values)) a = [key] #print a #print type(a) ## tab = list_of_values ## print "Mapped list: " ## print tab ## print "=======================================================" #print "================" for i in range(len(list_of_values)): l1 = list_of_values[i] l3 = tuple(list(l1)) #print l3 ## mr.emit((l3)) list_of_values[i][1], list_of_values[i][0] = list_of_values[i][0], list_of_values[i][1] x = list_of_values if x[i] != l1: l2 = x[i] #l1 = tuple(l1) #print l2 mr.emit((x[i])) ## print "Swapped list: " ## l = list_of_values ## print x ## print "=======================================================" ## for i in list_of_values: ## print i ## t = [] ## #if tab != list_of_values: ## x = x + tab ## print "Joined list: " ## print x #print tab ## for i in t: ## print i ## for j in tab: ## for k in list_of_values: ## if j != k: ## tab += list_of_values ## print tab ## for i in list_of_values: ## print type(i) ## print i[0] #print tab ## for i in range(len(tab)): ## print i #dct[tab[i]] = tab[i + 1] #print dct # Do not modify below this line # ============================= ##if __name__ == '__main__': ## inputdata = open(sys.argv[1]) ## mr.execute(inputdata, mapper, reducer) import json friends = open("friends.json", "r") #friends = open("asymmetric_friendships.json", "r") mr.execute(friends, mapper, reducer)

[ "taufeeq525@gmail.com" ]

taufeeq525@gmail.com

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/scripts/external_libs/scapy-2.3.1/python2/scapy/layers/vxlan.py

78171762a1f43034c9f8b411e0d440efde6f6ca7

[ "Apache-2.0", "GPL-1.0-or-later", "GPL-2.0-or-later", "GPL-2.0-only" ]

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elados93/trex-core

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3a6d63af1ff468f94887a091e3a408a8449cf832

refs/heads/master

2020-07-03T13:11:03.017040

2019-11-25T09:20:04

182,962,731

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Apache-2.0

2019-05-06T09:05:56

2019-04-23T07:49:11

C

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#! /usr/bin/env python # RFC 7348 - Virtual eXtensible Local Area Network (VXLAN): # A Framework for Overlaying Virtualized Layer 2 Networks over Layer 3 Networks # http://tools.ietf.org/html/rfc7348 # https://www.ietf.org/id/draft-ietf-nvo3-vxlan-gpe-02.txt # # VXLAN Group Policy Option: # http://tools.ietf.org/html/draft-smith-vxlan-group-policy-00 from scapy.packet import Packet, bind_layers from scapy.layers.l2 import Ether from scapy.layers.inet import IP, UDP from scapy.layers.inet6 import IPv6 from scapy.fields import FlagsField, XByteField, ThreeBytesField, \ ConditionalField, ShortField, ByteEnumField, X3BytesField _GP_FLAGS = ["R", "R", "R", "A", "R", "R", "D", "R"] class VXLAN(Packet): name = "VXLAN" fields_desc = [ FlagsField("flags", 0x8, 8, ['OAM', 'R', 'NextProtocol', 'Instance', 'V1', 'V2', 'R', 'G']), XByteField("reserved0", 0), # ConditionalField( # ShortField("reserved0", 0), # lambda pkt: pkt.flags.NextProtocol, # ), # ConditionalField( # ByteEnumField('NextProtocol', 0, # {0: 'NotDefined', # 1: 'IPv4', # 2: 'IPv6', # 3: 'Ethernet', # 4: 'NSH'}), # lambda pkt: pkt.flags.NextProtocol, # ), # ConditionalField( # ThreeBytesField("reserved1", 0), # lambda pkt: (not pkt.flags.G) and (not pkt.flags.NextProtocol), # ), ConditionalField( FlagsField("gpflags", 0, 8, _GP_FLAGS), lambda pkt: pkt.flags & 1, ), #ConditionalField( ShortField("gpid", 0), #lambda pkt: pkt.flags & 1, #), X3BytesField("vni", 0), XByteField("reserved2", 0), ] # Use default linux implementation port overload_fields = { UDP: {'dport': 8472}, } def mysummary(self): if self.flags.G: return self.sprintf("VXLAN (vni=%VXLAN.vni% gpid=%VXLAN.gpid%)") else: return self.sprintf("VXLAN (vni=%VXLAN.vni%)") bind_layers(UDP, VXLAN, dport=4789) # RFC standard vxlan port bind_layers(UDP, VXLAN, dport=4790) # RFC standard vxlan-gpe port bind_layers(UDP, VXLAN, dport=6633) # New IANA assigned port for use with NSH bind_layers(UDP, VXLAN, dport=8472) # Linux implementation port bind_layers(UDP, VXLAN, sport=4789) bind_layers(UDP, VXLAN, sport=4790) bind_layers(UDP, VXLAN, sport=6633) bind_layers(UDP, VXLAN, sport=8472) # By default, set both ports to the RFC standard bind_layers(UDP, VXLAN, sport=4789, dport=4789) bind_layers(VXLAN, Ether) # bind_layers(VXLAN, IP, NextProtocol=1) # bind_layers(VXLAN, IPv6, NextProtocol=2) # bind_layers(VXLAN, Ether, flags=4, NextProtocol=0) # bind_layers(VXLAN, IP, flags=4, NextProtocol=1) # bind_layers(VXLAN, IPv6, flags=4, NextProtocol=2) # bind_layers(VXLAN, Ether, flags=4, NextProtocol=3)

[ "mamonney@cisco.com" ]

mamonney@cisco.com

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/configs/recognition/r2plus1d/r2plus1d_r34_8x8x1_750e_hmdb51_rgb_40percent_vidssl.py

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vt-vl-lab/video-data-aug

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2023-09-01T02:36:40.034893

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# model settings model = dict( type='Recognizer3D', backbone=dict( type='ResNet2Plus1d', depth=34, pretrained=None, pretrained2d=False, norm_eval=False, conv_cfg=dict(type='Conv2plus1d'), norm_cfg=dict(type='SyncBN', requires_grad=True, eps=1e-3), act_cfg=dict(type='ReLU'), conv1_kernel=(3, 7, 7), conv1_stride_t=1, pool1_stride_t=1, inflate=(1, 1, 1, 1), spatial_strides=(1, 2, 2, 2), temporal_strides=(1, 2, 2, 2), zero_init_residual=False), cls_head=dict( type='I3DHead', num_classes=101, in_channels=512, spatial_type='avg', dropout_ratio=0.5, init_std=0.01)) # model training and testing settings train_cfg = None test_cfg = dict(average_clips=None) # dataset settings dataset_type = 'RawframeDataset' data_root = 'data/hmdb51/rawframes/' data_root_val = 'data/hmdb51/rawframes/' split = 1 # official train/test splits. valid numbers: 1, 2, 3 ann_file_train = f'data/hmdb51/videossl_splits/hmdb51_train_40_percent_labeled_split_{split}_rawframes.txt' ann_file_val = f'data/hmdb51/hmdb51_val_split_{split}_rawframes.txt' ann_file_test = f'data/hmdb51/hmdb51_val_split_{split}_rawframes.txt' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False) train_pipeline = [ dict(type='SampleFrames', clip_len=8, frame_interval=8, num_clips=1), dict(type='RawFrameDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='RandomResizedCrop'), dict(type='Resize', scale=(224, 224), keep_ratio=False), dict(type='Flip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='FormatShape', input_format='NCTHW'), dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), dict(type='ToTensor', keys=['imgs', 'label']) ] val_pipeline = [ dict( type='SampleFrames', clip_len=8, frame_interval=8, num_clips=1, test_mode=True), dict(type='RawFrameDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='CenterCrop', crop_size=224), dict(type='Flip', flip_ratio=0), dict(type='Normalize', **img_norm_cfg), dict(type='FormatShape', input_format='NCTHW'), dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), dict(type='ToTensor', keys=['imgs']) ] test_pipeline = [ dict( type='SampleFrames', clip_len=8, frame_interval=8, num_clips=10, test_mode=True), dict(type='RawFrameDecode'), dict(type='Resize', scale=(-1, 256)), dict(type='ThreeCrop', crop_size=256), dict(type='Flip', flip_ratio=0), dict(type='Normalize', **img_norm_cfg), dict(type='FormatShape', input_format='NCTHW'), dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), dict(type='ToTensor', keys=['imgs']) ] data = dict( videos_per_gpu=16, workers_per_gpu=4, train=dict( type=dataset_type, ann_file=ann_file_train, data_prefix=data_root, pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=ann_file_val, data_prefix=data_root_val, pipeline=val_pipeline, test_mode=True), test=dict( type=dataset_type, ann_file=ann_file_val, data_prefix=data_root_val, pipeline=test_pipeline, test_mode=True)) # optimizer optimizer = dict( type='SGD', lr=0.2, momentum=0.9, weight_decay=0.0001) # this lr is used for 8 gpus optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) # learning policy lr_config = dict(policy='CosineAnnealing', min_lr=0) total_epochs = 750 checkpoint_config = dict(interval=5) evaluation = dict( interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) log_config = dict( interval=20, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook'), ]) # runtime settings dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/r2plus1d_r34_8x8x1_750e_hmdb51_rgb_40percent_vidssl/' load_from = None resume_from = None workflow = [('train', 1)] find_unused_parameters = False

[ "zouyuliang123@gmail.com" ]

zouyuliang123@gmail.com

cc571ebd67196de901eb9f0656e5b556db4137d6

86cbaea221b5404dfdde68c89e2471ba07609bff

/anti_code/config/defaults.py

82aee2ce9df5304e3c728e9394f219dcc873dc3b

[]

no_license

tommyjiang/iccv-2021-anti-spoofing

d3481e086f452cac560da1d1a69e7e452d304be4

144748c381bdf32040f985f60b76a4fdc2965115

refs/heads/main

2023-06-04T12:43:52.076534

2021-06-24T06:41:02

378,965,229

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py

from yacs.config import CfgNode as CN # ----------------------------------------------------------------------------- # Convention about Training / Test specific parameters # ----------------------------------------------------------------------------- # Whenever an argument can be either used for training or for testing, the # corresponding name will be post-fixed by a _TRAIN for a training parameter, # or _TEST for a test-specific parameter. # For example, the number of images during training will be # IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be # IMAGES_PER_BATCH_TEST # ----------------------------------------------------------------------------- # Config definition # ----------------------------------------------------------------------------- _C = CN() _C.MODEL = CN() # Using cuda or cpu for training _C.MODEL.DEVICE = "cuda" # ID number of GPU _C.MODEL.DEVICE_ID = '0,1,2,3,4,5,6,7' #0,1,2,3,4,5,6,7 # Name of backbone _C.MODEL.NAME = 'resnet50' _C.MODEL.ARCH = 'b0' _C.MODEL.ENCODER = 'resnet50' # Last stride of backbone _C.MODEL.LAST_STRIDE = 1 # Path to pretrained model of backbone _C.MODEL.PRETRAIN_PATH = '' # Use ImageNet pretrained model to initialize backbone or use self trained model to initialize the whole model # Options: 'imagenet' or 'self' _C.MODEL.PRETRAIN_CHOICE = 'imagenet' # If train with BNNeck, options: 'bnneck' or 'no' _C.MODEL.NECK = 'bnneck' # If train loss include center loss, options: 'yes' or 'no'. Loss with center loss has different optimizer configuration _C.MODEL.IF_WITH_CENTER = 'no' # The loss type of metric loss # options:['triplet'](without center loss) or ['center','triplet_center'](with center loss) _C.MODEL.METRIC_LOSS_TYPE = 'triplet' # For example, if loss type is cross entropy loss + triplet loss + center loss # the setting should be: _C.MODEL.METRIC_LOSS_TYPE = 'triplet_center' and _C.MODEL.IF_WITH_CENTER = 'yes' # If train with label smooth, options: 'on', 'off' _C.MODEL.IF_LABELSMOOTH = 'on' # ----------------------------------------------------------------------------- # INPUT # ----------------------------------------------------------------------------- _C.INPUT = CN() # Size of the image during training _C.INPUT.SIZE_TRAIN = [333, 333] _C.INPUT.TARGET_TRAIN = [256,256] # Size of the image during test _C.INPUT.SIZE_TEST = [256, 256] # Random probability for image horizontal flip _C.INPUT.PROB = 0.5 # Random probability for random erasing _C.INPUT.RE_PROB = 0.5 # Values to be used for image normalization _C.INPUT.PIXEL_MEAN = [0.485, 0.456, 0.406] # Values to be used for image normalization _C.INPUT.PIXEL_STD = [0.229, 0.224, 0.225] # Value of padding size _C.INPUT.PADDING = 10 # ----------------------------------------------------------------------------- # Dataset # ----------------------------------------------------------------------------- _C.DATASETS = CN() # List of the dataset names for training, as present in paths_catalog.py _C.DATASETS.NAMES = ('market1501') # Root directory where datasets should be used (and downloaded if not found) _C.DATASETS.ROOT_DIR = ('./data') # ----------------------------------------------------------------------------- # DataLoader # ----------------------------------------------------------------------------- _C.DATALOADER = CN() # Number of data loading threads _C.DATALOADER.NUM_WORKERS = 8 # Sampler for data loading _C.DATALOADER.SAMPLER = 'softmax' # Number of instance for one batch _C.DATALOADER.NUM_INSTANCE = 16 _C.DATALOADER.TRANSFORMS = 'torch' ## or albu # ---------------------------------------------------------------------------- # # Solver # ---------------------------------------------------------------------------- # _C.SOLVER = CN() # Name of optimizer _C.SOLVER.OPTIMIZER_NAME = "Adam" # Number of max epoches _C.SOLVER.MAX_EPOCHS = 50 # Base learning rate _C.SOLVER.BASE_LR = 3e-4 # Factor of learning bias _C.SOLVER.BIAS_LR_FACTOR = 2 # Momentum _C.SOLVER.MOMENTUM = 0.9 # Margin of triplet loss _C.SOLVER.MARGIN = 0.3 # Margin of cluster ;pss _C.SOLVER.CLUSTER_MARGIN = 0.3 # Learning rate of SGD to learn the centers of center loss _C.SOLVER.CENTER_LR = 0.5 # Balanced weight of center loss # _C.SOLVER.CENTER_LOSS_WEIGHT = 0.0005 # # Settings of range loss # _C.SOLVER.RANGE_K = 2 # _C.SOLVER.RANGE_MARGIN = 0.3 # _C.SOLVER.RANGE_ALPHA = 0 # _C.SOLVER.RANGE_BETA = 1 # _C.SOLVER.RANGE_LOSS_WEIGHT = 1 # Settings of weight decay _C.SOLVER.WEIGHT_DECAY = 0.0005 _C.SOLVER.WEIGHT_DECAY_BIAS = 0. # decay rate of learning rate _C.SOLVER.GAMMA = 0.1 # decay step of learning rate _C.SOLVER.STEPS = (30, 55) # warm up factor _C.SOLVER.WARMUP_FACTOR = 1.0 / 3 # iterations of warm up _C.SOLVER.WARMUP_ITERS = 500 # method of warm up, option: 'constant','linear' _C.SOLVER.WARMUP_METHOD = "linear" # epoch number of saving checkpoints _C.SOLVER.CHECKPOINT_PERIOD = 50 # iteration of display training log _C.SOLVER.LOG_PERIOD = 100 # epoch number of validation _C.SOLVER.EVAL_PERIOD = 50 _C.SOLVER.PESUDO_UPDATE_PERIOD = 1 _C.SOLVER.PESUDO_SKIP = 5 # Number of images per batch # This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will # see 2 images per batch _C.SOLVER.IMS_PER_BATCH = 64 # This is global, so if we have 8 GPUs and IMS_PER_BATCH = 16, each GPU will # see 2 images per batch _C.TEST = CN() # Number of images per batch during test _C.TEST.IMS_PER_BATCH = 128 # If test with re-ranking, options: 'yes','no' _C.TEST.RE_RANKING = 'no' # Path to trained model _C.TEST.WEIGHT = "" # Which feature of BNNeck to be used for test, before or after BNNneck, options: 'before' or 'after' _C.TEST.NECK_FEAT = 'after' # Whether feature is nomalized before test, if yes, it is equivalent to cosine distance _C.TEST.FEAT_NORM = 'yes' # ---------------------------------------------------------------------------- # # Misc options # ---------------------------------------------------------------------------- # # Path to checkpoint and saved log of trained model _C.OUTPUT_DIR = ""

[ "tommy_jiang@foxmail.com" ]

tommy_jiang@foxmail.com

4060f4f822289e06e61ca70655568a106382745c

594680eb2d243ea0d5c10c1f8bd74cbc180f0165

/core_models/view.py

ccff4a7b2e6424d56c1cc8447b632cdd95e02a2b

[]

no_license

shalevy1/flare

d538ce20a221c0c59c251245ccdaa6ab16d59d99

18c8ff329e17e24c381d840cdd584abfa528db1e

refs/heads/master

2022-12-22T08:35:30.163090

2020-09-26T19:11:57

null

0

null

UTF-8

Python

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621

py

import peewee as pw from flare import BaseModel, Registry import json class JSONField(pw.TextField): def db_value(self, value): return json.dumps(value) def python_value(self, value): if value is not None: return json.loads(value) class FlrView(BaseModel): name = pw.CharField() definition = JSONField() view_type = pw.CharField(choices=[("list","List"),("form","Form")],default="list") menu_id = pw.ForeignKeyField(Registry["FlrMenu"], null=True, backref="views") model = pw.CharField() sequence = pw.IntegerField(default=1) FlrView.r()

[ "yayforme789@gmail.com" ]

yayforme789@gmail.com

6671c788b36f34bfed85317a2c93531493733fab

0567fcd808397a7024b5009cc290de1c414eff06

/src/633.sum-of-square-numbers.py

1809710572c9e6319075ad61abf8525f28204e92

[]

no_license

tientheshy/leetcode-solutions

d3897035a7fd453b9f47647e95f0f92a03bff4f3

218a8a97e3926788bb6320dda889bd379083570a

refs/heads/master

2023-08-23T17:06:52.538337

2021-10-03T01:47:50

null

0

null

UTF-8

Python

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py

# # @lc app=leetcode id=633 lang=python3 # # [633] Sum of Square Numbers # # @lc code=start # TAGS: Math, Two Pointers, Binary Search class Solution: # Time: O(logN). Space: O(logN) def judgeSquareSum(self, c: int) -> bool: squares = set(n*n for n in range(int(c**0.5) + 1)) for square in squares: if square <= c and (c - square) in squares: return True return False # @lc code=end

[ "trung.nang.hoang@gmail.com" ]

trung.nang.hoang@gmail.com

fe584bf4c09946e95cd5192c04c0686749124445

083ca3df7dba08779976d02d848315f85c45bf75

/StrongPasswordChecker.py

d78b7b329b3d78aed6ae00e785028b68a397ea7a

[]

no_license

jiangshen95/UbuntuLeetCode

6427ce4dc8d9f0f6e74475faced1bcaaa9fc9f94

fa02b469344cf7c82510249fba9aa59ae0cb4cc0

refs/heads/master

2021-05-07T02:04:47.215580

2020-06-11T02:33:35

110,397,909

0

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UTF-8

Python

false

1,620

py

class Solution: def strongPasswordChecker(self, s): """ :type s: str :rtype: int """ needLower, needUpper, needDigit = 1, 1, 1 lenCounts = [] count = 1 for i in range(len(s)): if 'a' <= s[i] <= 'z': needLower = 0 elif 'A' <= s[i] <= 'Z': needUpper = 0 elif '0' <= s[i] <= '9': needDigit = 0 if i > 0 and s[i] == s[i - 1]: count += 1 else: if count >= 3: lenCounts.append(count) count = 1 if count >= 3: lenCounts.append(count) if len(s) < 6: return max(6 - len(s), needLower + needUpper + needDigit) else: over = max(0, len(s) - 20) step = over for i in range(len(lenCounts)): if over > 0 and lenCounts[i] % 3 != 2: t = lenCounts[i] % 3 + 1 if over - t >= 0: over -= t lenCounts[i] -= t left = 0 for i in range(len(lenCounts)): if over > 0 and lenCounts[i] >= 3: t = lenCounts[i] - 2 lenCounts[i] -= over over -= t if lenCounts[i] >= 3: left += lenCounts[i] // 3 return step + max(left, needLower + needUpper + needDigit) if __name__ == '__main__': s = input() solution = Solution() print(solution.strongPasswordChecker(s))

[ "jiangshen95@163.com" ]

jiangshen95@163.com

ca2d4092b151262bd72862c203860802ce9addff

45197bf7c0921435035b0fd4b7c83057f75e50f1

/app/logger.py

1e08509f2233940fabf03c81154f90433f78a099

[]

no_license

fiefdx/CallingViewer

4307e9376afc1ace2bcbd05e918e05109498c14d

6c1d5ff6e098f69cbaab31f61dcdd81b1a81fc06

refs/heads/master

2020-07-03T10:20:55.385050

2018-06-23T04:19:12

67,335,981

34

0

null

UTF-8

Python

false

5,204

py

# -*- coding: utf-8 -*- ''' Created on 2014-05-07 @summary: A custom logging @author: YangHaitao ''' import os import logging import logging.handlers CWD = os.path.split(os.path.realpath(__file__))[0] LEVELS = {'NOSET': logging.NOTSET, 'DEBUG': logging.DEBUG, 'INFO': logging.INFO, 'WARNING': logging.WARNING, 'ERROR': logging.ERROR, 'CRITICAL': logging.CRITICAL} class ConsoleStreamHandler(logging.StreamHandler): # color names to indices color_map = { 'black': 0, 'red': 1, 'green': 2, 'yellow': 3, 'blue': 4, 'magenta': 5, 'cyan': 6, 'white': 7, } # levels to (background, foreground, bold/intense) level_map = { logging.DEBUG: (None, 'blue', False), logging.INFO: (None, 'white', False), logging.WARNING: (None, 'yellow', False), logging.ERROR: (None, 'red', False), logging.CRITICAL: ('red', 'white', True), } csi = '\x1b[' reset = '\x1b[0m' def colorize(self, message, record): """ Colorize a message for a logging event. This implementation uses the ``level_map`` class attribute to map the LogRecord's level to a colour/intensity setting, which is then applied to the whole message. :param message: The message to colorize. :param record: The ``LogRecord`` for the message. """ if record.levelno in self.level_map: bg, fg, bold = self.level_map[record.levelno] params = [] if bg in self.color_map: params.append(str(self.color_map[bg] + 40)) if fg in self.color_map: params.append(str(self.color_map[fg] + 30)) if bold: params.append('1') if params: message = ''.join((self.csi, ';'.join(params), 'm', message, self.reset)) return message def format(self, record): """ Formats a record for output. This implementation colorizes the message line, but leaves any traceback unolorized. """ message = logging.StreamHandler.format(self, record) parts = message.split('\n', 1) parts[0] = self.colorize(parts[0], record) message = '\n'.join(parts) return message def emit(self, record): try: message = self.format(record) stream = self.stream if unicode and isinstance(message, unicode): enc = getattr(stream, 'encoding', 'utf-8') message = message.encode(enc, 'replace') stream.write(message) stream.write(getattr(self, 'terminator', '\n')) self.flush() except (KeyboardInterrupt, SystemExit): raise except: self.handleError(record) def config_logging(logger_name = "", file_name = "main.log", log_level = "NOSET", dir_name = "logs", day_rotate = False, when = "D", interval = 1, max_size = 50, backup_count = 5, console = True): format_log_string = "%(asctime)s %(name)-12s %(levelname)-8s %(message)s" format_console_string = "%(name)-12s: %(levelname)-8s %(message)s" logs_dir = os.path.join(CWD, dir_name) file_dir = os.path.join(logs_dir, file_name) # init logs directory if os.path.exists(logs_dir) and os.path.isdir(logs_dir): pass else: os.makedirs(logs_dir) # clear all handlers logging.getLogger(logger_name).handlers = [] # init rotating handler if day_rotate == True: rotatingFileHandler = logging.handlers.TimedRotatingFileHandler(filename = file_dir, when = when, interval = interval, backupCount = backup_count) else: rotatingFileHandler = logging.handlers.RotatingFileHandler(filename = file_dir, maxBytes = 1024 * 1024 * max_size, backupCount = backup_count) formatter = logging.Formatter(format_log_string) rotatingFileHandler.setFormatter(formatter) logging.getLogger(logger_name).addHandler(rotatingFileHandler) # add a console handler if console == True: if os.name == 'nt': consoleHandler = logging.StreamHandler() else: consoleHandler = ConsoleStreamHandler() # set console log level consoleHandler.setLevel(LEVELS[log_level.upper()]) formatter = logging.Formatter(format_console_string) consoleHandler.setFormatter(formatter) logging.getLogger(logger_name).addHandler(consoleHandler) # set log level logger = logging.getLogger(logger_name) level = LEVELS[log_level.upper()] logger.setLevel(level)

[ "fiefdx@163.com" ]

fiefdx@163.com

964bd18c9fbcba2039e1c36cbe3a58d254079f78

2859786cee0205545cda3af367e031c54778176b

/webui.py

3b4f567d2e261f0ecd32e6333df8b4bad176af30

[]

no_license

indivisible/rpi_switch_control_webui

5e784fca630d662c7f2f8045f62c5d408daa57a2

68e99a4447549f111918898717dec4af21fcb4e5

refs/heads/master

2023-07-07T13:43:28.821328

2021-08-06T06:13:29

393,043,415

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py

#!/usr/bin/env python3 import logging import asyncio import threading import json from http.server import HTTPServer, SimpleHTTPRequestHandler from functools import partial from pathlib import Path import websockets from backend_manager import BackendManager class SocketConnection: def __init__(self, socket, backend: BackendManager): self.socket = socket self.backend = backend async def send(self, obj): return await self.socket.send(json.dumps(obj)) async def send_message(self, severity: str, message): return await self.send(self.message(severity, message)) def action(self, action: str, **rest): rest['action'] = action return rest def message(self, severity: str, message): return self.action( 'message', severity=severity, message=message) def error(self, msg): return self.message('error', message=msg) async def handle_message(self, raw: str): data = json.loads(raw) action = data.pop('action').replace('-', '_') try: handler = getattr(self, f'handle_action_{action}') except AttributeError: return self.error(f'Unkown action f{action}') try: reply = await handler(**data) return reply except Exception as e: logging.exception(f'Error handling {action}:') return self.error(f'Error handling {action}: {e}') async def serve(self): async for message in self.socket: try: reply = await self.handle_message(message) except Exception: logging.error(f'Error handling message {message}: ') reply = self.error('Invalid message') if reply is not None: await self.send(reply) async def handle_action_status(self): # return self.action('status', ok=(con is not None)) return self.action('status', ok=True) async def handle_action_run_script(self, text): if not text: return self.error('empty script') else: try: self.backend.start_script(text) return self.message('info', 'script started') except Exception as e: logging.exception('Error running script:') return self.error(f'error running script: {e!r}') async def handle_action_abort_script(self): self.backend.abort_script() return self.message('warning', 'Script aborted') async def handle_action_restart(self): Path('restart_app').touch() return self.error('Restarting app') async def handle_action_input(self, state): await self.backend.manual_input(state) class SocketServer: def __init__(self, backend): self.backend = backend backend.socket_send_message = self.send_message self.connections = [] async def send_message(self, severity: str, message): for con in self.connections: try: await con.send_message(severity, message) except Exception: logging.exception('error sending message to websocket') async def serve(self, websocket, path): connection = SocketConnection(websocket, self.backend) self.connections.append(connection) try: await connection.serve() finally: self.connections.remove(connection) async def start_websocket_server(backend): await backend.start() server = SocketServer(backend) await websockets.serve(server.serve, "0.0.0.0", 6789) logging.debug('started websocket server') def run_http_server(path): server_address = ('', 8000) handler = partial(SimpleHTTPRequestHandler, directory=path) httpd = HTTPServer(server_address, handler) logging.debug('starting HTTP server') httpd.serve_forever() def main(): import argparse parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('cmd', default=['/bin/cat'], nargs='*') args = parser.parse_args() logging.getLogger().setLevel(logging.DEBUG) logging.getLogger('websockets.protocol').setLevel(logging.WARNING) logging.getLogger('websockets.server').setLevel(logging.WARNING) cmd, *cmd_args = args.cmd backend = BackendManager(cmd, cmd_args) # http server for static files of the GUI httpd_thread = threading.Thread( target=run_http_server, # the directory where the served files are args=('html',), daemon=True) httpd_thread.start() # websocket for client controls loop = asyncio.get_event_loop() loop.run_until_complete(start_websocket_server(backend)) loop.run_forever() return 0 if __name__ == '__main__': import sys sys.exit(main())

[ "islandofcalmness@gmail.com" ]

islandofcalmness@gmail.com

f31e8bdef0f35d1da25ef414dec22eaf8212ec51

b27aa0989f06d46da7f650d1ae0048cc394dbbef

/FFA_action_pattern_analysis/4representation.py

6fcd9d71862c0dd15a06868edb0bf33f5391a18b

[]

no_license

sunshineDrizzle/Projects

fb97be79d46c39a0d07f07cfe717fb64148c99c4

e5eac7aca238a887a7fd1fa556333758fff1505d

refs/heads/master

2020-03-31T11:59:40.113282

2020-03-21T05:27:07

152,199,295

0

null

UTF-8

Python

false

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import numpy as np from scipy.spatial.distance import cdist from scipy.stats import sem from matplotlib import pyplot as plt def subgroup_mean_representation(mean_maps, maps, group_labels): labels_uniq = np.unique(group_labels) representations = np.zeros_like(labels_uniq, np.object) for i, label in enumerate(labels_uniq): mean_map = mean_maps[[i]] sub_maps = np.atleast_2d(maps[group_labels == label]) representations[i] = 1 - cdist(mean_map, sub_maps, 'correlation')[0] representation_means = [] representation_sems = [] for repre in representations: representation_means.append(np.mean(repre)) representation_sems.append(sem(repre)) x = np.arange(len(representations)) plt.bar(x, representation_means, yerr=representation_sems, color='white', edgecolor='black') plt.title('{}FFA_patterns'.format(hemi[0])) plt.ylabel('correlation') plt.xticks(x, labels_uniq) plt.tight_layout() plt.show() def leave_one_out_representation(FFA_patterns, group_labels, metric): labels_uniq = np.unique(group_labels) labels_num = len(labels_uniq) sub_FFA_patterns_list = [np.atleast_2d(FFA_patterns[group_labels == label]) for label in labels_uniq] X = np.zeros((labels_num, labels_num), np.object) for row in range(labels_num): sub_FFA_patterns_mean = np.atleast_2d(np.mean(sub_FFA_patterns_list[row], 0)) for col in range(labels_num): if row == col: sub_subjects = list(range(sub_FFA_patterns_list[row].shape[0])) dists = [] for subject in sub_subjects: sub_FFA_patterns_leave_out = np.atleast_2d(sub_FFA_patterns_list[row][subject]) sub_subjects_reserve = sub_subjects.copy() sub_subjects_reserve.remove(subject) sub_FFA_patterns_reserve = np.atleast_2d(sub_FFA_patterns_list[row][sub_subjects_reserve]) sub_FFA_patterns_reserve_mean = np.atleast_2d(np.mean(sub_FFA_patterns_reserve, 0)) dists.append(cdist(sub_FFA_patterns_reserve_mean, sub_FFA_patterns_leave_out, metric)[0][0]) X[row, col] = np.array(dists) else: X[row, col] = cdist(sub_FFA_patterns_mean, sub_FFA_patterns_list[col], metric)[0] fig, axes = plt.subplots(labels_num) axes[0].set_title('{}FFA_patterns'.format(hemi[0])) xlabels = 'mean{} and individual{}' for row in range(labels_num): print('row{0}col1 vs. row{0}col2'.format(row + 1), ttest_ind(X[row][0], X[row][1])) axes[row].violinplot(X[row], showmeans=True) axes[row].set_ylabel(metric) axes[row].set_xticks(np.arange(1, labels_num + 1)) axes[row].set_xticklabels([xlabels.format(labels_uniq[row], labels_uniq[col]) for col in range(labels_num)]) plt.tight_layout() plt.show() return X if __name__ == '__main__': import nibabel as nib from os.path import join as pjoin from commontool.io.io import CiftiReader hemi = 'lh' brain_structure = { 'lh': 'CIFTI_STRUCTURE_CORTEX_LEFT', 'rh': 'CIFTI_STRUCTURE_CORTEX_RIGHT' } project_dir = '/nfs/s2/userhome/chenxiayu/workingdir/study/FFA_clustering/' analysis_dir = pjoin(project_dir, 's2_25_zscore') cluster_num_dir = pjoin(analysis_dir, 'HAC_ward_euclidean/50clusters') acti_dir = pjoin(cluster_num_dir, 'activation') mean_map_file = pjoin(acti_dir, '{}_mean_maps.nii.gz'.format(hemi)) FFA_label_file = pjoin(project_dir, 'data/HCP_1080/face-avg_s2/label/{}FFA_25.label'.format(hemi[0])) map_file = pjoin(project_dir, 'data/HCP_1080/face-avg_s2/S1200.1080.FACE-AVG_level2_zstat_hp200_s2_MSMAll.dscalar.nii') group_labels_file = pjoin(cluster_num_dir, 'group_labels') mean_maps = nib.load(mean_map_file).get_data() FFA_vertices = nib.freesurfer.read_label(FFA_label_file) reader = CiftiReader(map_file) maps = reader.get_data(brain_structure[hemi], True) group_labels = np.array(open(group_labels_file).read().split(' '), dtype=np.uint16) subgroup_mean_representation(mean_maps[:, FFA_vertices], maps[:, FFA_vertices], group_labels)

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#! /usr/bin/env python # -*- coding: utf-8 -*- from itertools import cycle from matplotlib import cm as cm import matplotlib.pyplot as plt import numpy as np import theano from vae import GaussianVAE, BernoulliVAE from utils import load_data from gaussian_vae import M1_GVAE def test_vae( opt='adagrad', n_iters=1000, learning_rate=1e-4, n_mc_samples=1, scale_init=0.01, dim_h=100, dim_z=2, model='Gaussian'): ################## # load data ################## datasets = load_data('../../20150717-/mnist.pkl.gz') train_set_x, train_set_y = datasets xs = train_set_x[:10000] sgd_params = { 'learning_rate' : learning_rate, 'n_iters' : n_iters, 'size_minibatch': 100, 'calc_hist' : 'all', 'n_mod_hist' : 100, } adagrad_params = sgd_params all_params = { 'hyper_params': { 'rng_seed' : 1234, 'dim_z' : dim_z, 'dim_h_generate' : dim_h, 'dim_h_recognize' : dim_h, 'n_mc_samples' : n_mc_samples, 'scale_init' : scale_init } } if opt == 'adagrad': all_params.update({'adagrad_params': adagrad_params}) elif opt == 'sgd': all_params.update({'sgd_params': sgd_params}) if model == 'Gaussian': model = GaussianVAE(**all_params) # model = M1_GVAE(**all_params) elif model == 'Bernoulli': model = BernoulliVAE(**all_params) model.fit(xs) zs = model.encode(xs) xs_recon = model.decode(zs) err = np.sum(0.5 * (xs - xs_recon) ** 2) / xs.shape[0] print ('Error: %f' % err) return datasets, model def plot_weights(model): fig, axes = plt.subplots(nrows=10, ncols=10) fig.subplots_adjust(hspace=.001, wspace=.001) fig.set_size_inches(10, 10) w3 = model.model_params_['w2_'].get_value() nx = np.sqrt(w3.shape[1]).astype(int) ny = nx w3 = w3.reshape((w3.shape[0], ny, nx)) for i, ax in enumerate(axes.reshape(-1)): ax.imshow(w3[i], interpolation='none', cmap=cm.gray) def plot_manifold( model, z1s=np.arange(-0.8, 1.2, .2), z2s=np.arange(-0.8, 1.2, .2)): zs = np.array([[z1, z2] for z2 in z2s for z1 in z1s]).astype(theano.config.floatX) xs = model.decode(zs) nx = np.sqrt(xs.shape[1]).astype(int) ny = nx xs = xs.reshape((xs.shape[0], ny, nx)) fig, axes = plt.subplots(nrows=len(z1s), ncols=len(z2s)) fig.subplots_adjust(hspace=.001, wspace=.001) fig.set_size_inches(10, 10) for i, ax in enumerate(axes.reshape(-1)): ax.imshow(xs[i], interpolation='none', cmap=cm.gray) ax.set_xticklabels([]) ax.set_yticklabels([]) def plot_hiddens(model, xs, cs): zs = model.encode(xs) colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] markers = ['+', 'o', '^'] plt.figure(figsize=(7, 7)) for c, color, marker in zip(np.unique(cs), cycle(colors), cycle(markers)): ixs = np.where(cs == c)[0] plt.scatter(zs[ixs, 0], zs[ixs, 1], c=color, marker=marker, label=c) plt.legend(loc='best', scatterpoints=1, framealpha=1) if __name__ == '__main__': data, model = test_vae( n_iters=10000, learning_rate=0.01, n_mc_samples=1, scale_init=1., dim_h=500, dim_z=2, model='Gaussian', opt='adagrad' ) hist = np.vstack(model.hist) plt.plot(hist[:, 0], hist[:, 1]) test_vae.plot_manifold( model, z1s=np.arange(-8., 8., 1.), z2s=np.arange(8., -8., -1.)) plt.show() # End of Line.

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makoto.kawano@gmail.com

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import torchvision.models as models import torch.nn as nn import torch import torch.optim as optim import torchvision.transforms as transforms import numpy as np from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True from PIL import Image class Resnet50_pretrained: def __init__(self, num_classes): # self.device = device self.num_classes = num_classes self.model = models.resnet50(pretrained=True) self.fc_out = nn.Linear(2048, num_classes, bias=True) # freeze model params for features for param in self.model.parameters(): param.requires_grad = False self.model.fc = self.fc_out # def forward(self): # No forward needed imported model

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hello@sageelliott.com

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2434325680/Learnbgame

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2021-10-17T07:26:07

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# ##### BEGIN GPL LICENSE BLOCK ##### # # Procedural building generator # Copyright (C) 2019 Luka Simic # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 3 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see <https://www.gnu.org/licenses/>. # # ##### END GPL LICENSE BLOCK ##### from bpy.types import Panel, PropertyGroup from bpy.props import FloatProperty, BoolProperty, EnumProperty, IntProperty class PBGPropertyGroup(PropertyGroup): # TODO: docstring building_width = FloatProperty( name="Building width", default=25.0 ) building_depth = FloatProperty( name="Building depth", default=15.0 ) building_chamfer = FloatProperty( name="Chamfer size", default=1 ) building_wedge_depth = FloatProperty( name="Wedge depth", default=1.5 ) building_wedge_width = FloatProperty( name="Wedge width", default=8 ) floor_first_offset = FloatProperty( name="FIrst floor offset", default=0.7 ) floor_height = FloatProperty( name="Floor height", default=3 ) floor_count = IntProperty( name="Number of floors", default=2 ) floor_separator_include = BoolProperty( name="Separator between floors", default=True ) floor_separator_height = FloatProperty( name="Separator height", default=0.5 ) floor_separator_width = FloatProperty( name="Separator width", default=0.5 ) window_width = FloatProperty( name="Total window width", default=1.2 ) distance_window_window = FloatProperty( name="Distance between windows", default=2.5 ) generate_pillar = BoolProperty( name="Generate Pillar", default=True ) distance_window_pillar = FloatProperty( name="Distance Window to Pillar", default=0.8 ) pillar_width = FloatProperty( name="Pillar width", default=0.2 ) pillar_depth = FloatProperty( name="Pillar depth", default=0.15 ) pillar_chamfer = FloatProperty( name="Pillar Chamfer", default=0.05 ) pillar_offset_height = FloatProperty( name="Pillar Offset Height", default=0.7 ) pillar_offset_size = FloatProperty( name="Pillar Offset Size", default=0.05 ) pillar_include_floor_separator = BoolProperty( name="Include floor separator", default=True ) pillar_include_first_floor = BoolProperty( name="Include first floor", default=True ) wall_types = [ ("FLAT", "FLAT", "", 0), ("ROWS", "ROWS", "", 1) ] wall_type = EnumProperty( items=wall_types, default="ROWS" ) wall_mortar_size = FloatProperty( name="Mortar size", default=0.02 ) wall_section_size = FloatProperty( name="Brick section size", default=0.04 ) wall_row_count = IntProperty( name="Rows per floor", default=7 ) wall_offset_size = FloatProperty( name="Wall offset size", default=0.1 ) wall_offset_type = EnumProperty( items=wall_types, default="ROWS" ) wall_offset_mortar_size = FloatProperty( name="Offset Mortar size", default=0.03 ) wall_offset_section_size = FloatProperty( name="Offset Brick section size", default=0.06 ) wall_offset_row_count = IntProperty( name="Offset Rows per floor", default=3 ) window_height = FloatProperty( name="Window total height", default=1.0 ) window_offset = FloatProperty( name="Window offset", default=0.5 ) window_under_types = [ ("WALL", "WALL", "", 0), ("PILLARS", "PILLARS", "", 1), ("SIMPLE", "SIMPLE", "", 2), ("SINE", "SINE", "", 3), ("CYCLOID", "CYCLOID", "", 4) ] windows_under_type = EnumProperty( items=window_under_types, default="WALL" ) windows_under_width = FloatProperty( name="under window offset width", default=0.1 ) windows_under_height = FloatProperty( name="Under Window offset height", default=0.1 ) windows_under_depth = FloatProperty( name="under Window offset depth", default=0.05 ) windows_under_inset_depth = FloatProperty( name="under Window inset depth", default=0.1 ) windows_under_amplitude = FloatProperty( name="under Window amplitude", default=0.05 ) windows_under_period_count = IntProperty( name="under Window period count", default=8 ) windows_under_simple_width = FloatProperty( name="Under window simple width", default=0.04 ) windows_under_simple_depth = FloatProperty( name="Under window simple depth", default=0.03 ) windows_under_pillar_base_diameter = FloatProperty( name="Under window pillar base diameter", default=0.08 ) windows_under_pillar_base_height = FloatProperty( name="Under window pillar base height", default=0.04 ) windows_under_pillar_min_diameter = FloatProperty( name="Under window pillar min diameter", default=0.05 ) windows_under_pillar_max_diameter = FloatProperty( name="Under window pillar max diameter", default=0.08 ) door_size = FloatProperty( name="Door size", default=2.5 ) # end PBGPropertyGroup class PBGToolbarGeneralPanel(Panel): # TODO: docstring bl_label = "General Settings" bl_category = "PBG" bl_space_type = "VIEW_3D" bl_region_type = "TOOLS" bl_context = "objectmode" def draw(self, context): layout = self.layout properties = context.scene.PBGPropertyGroup col = layout.column(align=True) col.label(text="Overall Building Dimensions") col.prop(properties, "building_width") col.prop(properties, "building_depth") col.prop(properties, "building_chamfer") col.prop(properties, "building_wedge_depth") col.prop(properties, "building_wedge_width") col.label(text="Floor and separator layout") col.prop(properties, "floor_count") col.prop(properties, "floor_height") col.prop(properties, "floor_first_offset") col.prop(properties, "floor_separator_include") col.prop(properties, "floor_separator_width") col.prop(properties, "floor_separator_height") # end draw # end PBGToolbarPanel class PBGToolbarLayoutPanel(Panel): # TODO: docstring bl_label = "Layout Settings" bl_category = "PBG" bl_space_type = "VIEW_3D" bl_region_type = "TOOLS" bl_context = "objectmode" def draw(self, context): layout = self.layout properties = context.scene.PBGPropertyGroup col = layout.column(align=True) col.prop(properties, "distance_window_window") col.prop(properties, "distance_window_pillar") # end draw # end PBGLayoutPanel class PBGToolbarPillarPanel(Panel): # TODO: docstring bl_label = "Pillar Settings" bl_category = "PBG" bl_space_type = "VIEW_3D" bl_region_type = "TOOLS" bl_context = "objectmode" def draw(self, context): layout = self.layout properties = context.scene.PBGPropertyGroup col = layout.column(align=True) col.prop(properties, "generate_pillar") col.prop(properties, "pillar_width") col.prop(properties, "pillar_depth") col.prop(properties, "pillar_chamfer") col.prop(properties, "pillar_offset_height") col.prop(properties, "pillar_offset_size") col.prop(properties, "pillar_include_floor_separator") col.prop(properties, "pillar_include_first_floor") # end draw # end PBGPillarPanel class PBGToolbarWallPanel(Panel): # TODO: docstring bl_label = "Wall settings" bl_category = "PBG" bl_space_type = "VIEW_3D" bl_region_type = "TOOLS" bl_context = "objectmode" def draw(self, context): layout = self.layout properties = context.scene.PBGPropertyGroup col = layout.column(align=True) col.label(text="Wall settings") col.prop(properties, "wall_type") col.prop(properties, "wall_mortar_size") col.prop(properties, "wall_section_size") col.prop(properties, "wall_row_count") col.label(text="First floor offset settings") col.prop(properties, "wall_offset_size") col.prop(properties, "wall_offset_type") col.prop(properties, "wall_offset_mortar_size") col.prop(properties, "wall_offset_section_size") col.prop(properties, "wall_offset_row_count") # end draw # end PBGToolbarWallPanel class PBGToolbarWindowPanel(Panel): bl_label = "Window Settings" bl_category = "PBG" bl_space_type = "VIEW_3D" bl_region_type = "TOOLS" bl_context = "objectmode" def draw(self, context): layout = self.layout properties = context.scene.PBGPropertyGroup col = layout.column(align=True) col.label(text="Overall window dimensions") col.prop(properties, "window_width") col.prop(properties, "window_height") col.prop(properties, "window_offset") col.label(text="Under windows area") col.prop(properties, "windows_under_type") col.prop(properties, "windows_under_width") col.prop(properties, "windows_under_height") col.prop(properties, "windows_under_depth") col.prop(properties, "windows_under_inset_depth") col.label(text="Sine/Cycloid params") col.prop(properties, "windows_under_amplitude") col.prop(properties, "windows_under_period_count") col.label(text="Simple params") col.prop(properties, "windows_under_simple_width") col.prop(properties, "windows_under_simple_depth") col.label(text="Pillar params") col.prop(properties, "windows_under_pillar_base_diameter") col.prop(properties, "windows_under_pillar_base_height") col.prop(properties, "windows_under_pillar_min_diameter") col.prop(properties, "windows_under_pillar_max_diameter") # end draw # end PBGToolbarWindowPanel class PBGToolbarGeneratePanel(Panel): # TODO: docstring bl_label = "Generate" bl_category = "PBG" bl_space_type = "VIEW_3D" bl_region_type = "TOOLS" bl_context = "objectmode" def draw(self, context): layout = self.layout row = layout.row(align=True) row.operator("pbg.generate_building", text="Generate") # end draw # end PBGGeneratePanel

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############################################################################## # Institute for the Design of Advanced Energy Systems Process Systems # Engineering Framework (IDAES PSE Framework) Copyright (c) 2018-2019, by the # software owners: The Regents of the University of California, through # Lawrence Berkeley National Laboratory, National Technology & Engineering # Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia # University Research Corporation, et al. All rights reserved. # # Please see the files COPYRIGHT.txt and LICENSE.txt for full copyright and # license information, respectively. Both files are also available online # at the URL "https://github.com/IDAES/idaes-pse". ############################################################################## """ Tests for 0D heat exchanger models. Author: John Eslick """ import pytest from pyomo.environ import ConcreteModel, SolverFactory, value from idaes.core import FlowsheetBlock from idaes.unit_models import Heater, HeatExchanger from idaes.property_models import iapws95_ph from idaes.property_models.iapws95 import iapws95_available from idaes.core.util.model_statistics import degrees_of_freedom prop_available = iapws95_available() # ----------------------------------------------------------------------------- # See if ipopt is available and set up solver if SolverFactory('ipopt').available(): solver = SolverFactory('ipopt') solver.options = {'tol': 1e-6} else: solver = None @pytest.fixture() def build_heater(): m = ConcreteModel() m.fs = FlowsheetBlock(default={"dynamic": False}) m.fs.properties = iapws95_ph.Iapws95ParameterBlock() m.fs.heater = Heater(default={"property_package": m.fs.properties}) return m @pytest.fixture() def build_heat_exchanger(): m = ConcreteModel() m.fs = FlowsheetBlock(default={"dynamic": False}) m.fs.properties = iapws95_ph.Iapws95ParameterBlock() m.fs.heat_exchanger = HeatExchanger(default={ "side_1":{"property_package": m.fs.properties}, "side_2":{"property_package": m.fs.properties}}) return m def test_build_heat_exchanger(build_heat_exchanger): m = build_heat_exchanger assert hasattr(m.fs.heat_exchanger, "inlet_1") assert hasattr(m.fs.heat_exchanger, "outlet_1") assert hasattr(m.fs.heat_exchanger, "inlet_2") assert hasattr(m.fs.heat_exchanger, "outlet_2") m.fs.heat_exchanger.set_scaling_factor_energy(1e-3) assert(m.fs.heat_exchanger.side_1.scaling_factor_energy == 1e-3) assert(m.fs.heat_exchanger.side_2.scaling_factor_energy == 1e-3) @pytest.mark.skipif(not prop_available, reason="IAPWS not available") @pytest.mark.skipif(solver is None, reason="Solver not available") def test_initialize_heat_exchanger(build_heat_exchanger): m = build_heat_exchanger init_state1 = { "flow_mol":100, "pressure":101325, "enth_mol":4000} init_state2 = { "flow_mol":100, "pressure":101325, "enth_mol":3500} m.fs.heat_exchanger.area.fix(1000) m.fs.heat_exchanger.overall_heat_transfer_coefficient.fix(100) prop_in_1 = m.fs.heat_exchanger.side_1.properties_in[0] prop_out_1 = m.fs.heat_exchanger.side_1.properties_out[0] prop_in_2 = m.fs.heat_exchanger.side_2.properties_in[0] prop_out_2 = m.fs.heat_exchanger.side_2.properties_out[0] prop_in_1.flow_mol.fix(100) prop_in_1.pressure.fix(101325) prop_in_1.enth_mol.fix(4000) prop_in_2.flow_mol.fix(100) prop_in_2.pressure.fix(101325) prop_in_2.enth_mol.fix(3000) m.fs.heat_exchanger.heat_duty.value = 10000 m.fs.heat_exchanger.initialize(state_args_1=init_state1, state_args_2=init_state2, outlvl=5) solver.solve(m) assert degrees_of_freedom(m) == 0 print(value(m.fs.heat_exchanger.delta_temperature[0])) print(value(m.fs.heat_exchanger.side_1.heat[0])) print(value(m.fs.heat_exchanger.side_2.heat[0])) assert abs(value(prop_in_1.temperature) - 326.1667075078748) <= 1e-4 assert abs(value(prop_out_1.temperature) - 313.81921851031814) <= 1e-4 assert abs(value(prop_in_2.temperature) - 312.88896252921734) <= 1e-4 assert abs(value(prop_out_2.temperature) - 325.23704823703537) <= 1e-4 assert abs(value(prop_in_1.phase_frac["Liq"]) - 1) <= 1e-6 assert abs(value(prop_out_1.phase_frac["Liq"]) - 1) <= 1e-6 assert abs(value(prop_in_1.phase_frac["Vap"]) - 0) <= 1e-6 assert abs(value(prop_out_1.phase_frac["Vap"]) - 0) <= 1e-6 def test_build_heater(build_heater): m = build_heater assert hasattr(m.fs.heater, "inlet") assert hasattr(m.fs.heater, "outlet") assert len(m.fs.heater.inlet.vars) == 3 assert len(m.fs.heater.outlet.vars) == 3 for port in [m.fs.heater.inlet, m.fs.heater.outlet]: assert hasattr(port, "flow_mol") assert hasattr(port, "enth_mol") assert hasattr(port, "pressure") @pytest.mark.skipif(not prop_available, reason="IAPWS not available") @pytest.mark.skipif(solver is None, reason="Solver not available") def test_initialize_heater(build_heater): m = build_heater m.fs.heater.inlet.enth_mol.fix(4000) m.fs.heater.inlet.flow_mol.fix(100) m.fs.heater.inlet.pressure.fix(101325) m.fs.heater.heat_duty[0].fix(100*20000) m.fs.heater.initialize() prop_in = m.fs.heater.control_volume.properties_in[0] prop_out = m.fs.heater.control_volume.properties_out[0] assert abs(value(prop_in.temperature) - 326.1667075078748) <= 1e-4 assert abs(value(prop_out.temperature) - 373.12429584768876) <= 1e-4 assert abs(value(prop_in.phase_frac["Liq"]) - 1) <= 1e-6 assert abs(value(prop_out.phase_frac["Liq"]) - 0.5953218682380845) <= 1e-6 assert abs(value(prop_in.phase_frac["Vap"]) - 0) <= 1e-6 assert abs(value(prop_out.phase_frac["Vap"]) - 0.40467813176191547) <= 1e-6 @pytest.mark.skipif(not prop_available, reason="IAPWS not available") @pytest.mark.skipif(solver is None, reason="Solver not available") def test_heater_q1(build_heater): m = build_heater m.fs.heater.inlet.enth_mol.fix(4000) m.fs.heater.inlet.flow_mol.fix(100) m.fs.heater.inlet.pressure.fix(101325) m.fs.heater.heat_duty[0].fix(100*20000) m.fs.heater.initialize() assert degrees_of_freedom(m) == 0 solver.solve(m) prop_in = m.fs.heater.control_volume.properties_in[0] prop_out = m.fs.heater.control_volume.properties_out[0] assert abs(value(prop_in.temperature) - 326.1667075078748) <= 1e-4 assert abs(value(prop_out.temperature) - 373.12429584768876) <= 1e-4 assert abs(value(prop_in.phase_frac["Liq"]) - 1) <= 1e-6 assert abs(value(prop_out.phase_frac["Liq"]) - 0.5953218682380845) <= 1e-6 assert abs(value(prop_in.phase_frac["Vap"]) - 0) <= 1e-6 assert abs(value(prop_out.phase_frac["Vap"]) - 0.40467813176191547) <= 1e-6

[ "KSBeattie@lbl.gov" ]

KSBeattie@lbl.gov