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more | more-main/more_main.py | import argparse
from collections import defaultdict
import math
import time
from constants import (
COLOR_MEAN,
COLOR_STD,
DEPTH_MEAN,
DEPTH_STD,
GRASP_Q_GRASP_THRESHOLD,
GRASP_Q_PUSH_THRESHOLD,
GRIPPER_GRASP_WIDTH_PIXEL,
GRIPPER_PUSH_RADIUS_PIXEL,
IMAGE_PAD_WIDTH,
IS_REAL,
M... | 20,203 | 35.143113 | 155 | py |
more | more-main/environment_sim.py | import time
import glob
import os
import pybullet as pb
import pybullet_data
from pybullet_utils import bullet_client
import numpy as np
import cameras
from constants import PIXEL_SIZE, WORKSPACE_LIMITS
class Environment:
def __init__(self, gui=True, time_step=1 / 240):
"""Creates environment with PyBulle... | 32,129 | 37.387097 | 117 | py |
more | more-main/lifelong_trainer.py | import numpy as np
import utils
import torch
from models import PushNet, reinforcement_net
from dataset import LifelongDataset
import argparse
import time
import datetime
import cv2
from torchvision.transforms import ToPILImage
import os
from constants import (
GRIPPER_GRASP_INNER_DISTANCE,
GRIPPER_GRASP_INNER_... | 17,323 | 38.825287 | 112 | py |
more | more-main/evaluate.py | import os
import numpy as np
import argparse
import glob
parser = argparse.ArgumentParser()
# parser.add_argument('--file_reward', action='store', type=str)
# parser.add_argument('--file_action', action='store', type=str)
parser.add_argument('--log', action='store', type=str)
parser.add_argument('--num', default=1, ty... | 3,456 | 41.158537 | 94 | py |
more | more-main/constants.py | import numpy as np
import math
IS_REAL = False
WORKSPACE_LIMITS = np.asarray([[0.276, 0.724], [-0.224, 0.224], [-0.0001, 0.4]])
# image
REAL_PIXEL_SIZE = 0.002
REAL_IMAGE_SIZE = 224
PIXEL_SIZE = 0.002
IMAGE_SIZE = 224
IMAGE_OBJ_CROP_SIZE = 60 # this is related to the IMAGE_SIZE and PIXEL_SIZE
IMAGE_PAD_SIZE = math... | 6,251 | 28.07907 | 95 | py |
more | more-main/collect_image_data.py | import time
import datetime
import os
import glob
import pybullet as p
import numpy as np
import cv2
import utils
from environment_sim import Environment
from constants import (
DEPTH_MIN,
GRIPPER_PUSH_RADIUS_PIXEL,
GRIPPER_PUSH_RADIUS_SAFE_PIXEL,
IMAGE_SIZE,
WORKSPACE_LIMITS,
REAL_COLOR_SPACE,... | 12,818 | 38.564815 | 101 | py |
more | more-main/ppn_main.py | import argparse
from collections import defaultdict
import math
import time
from constants import (
COLOR_MEAN,
COLOR_STD,
DEPTH_MEAN,
DEPTH_STD,
GRASP_Q_GRASP_THRESHOLD,
GRIPPER_GRASP_WIDTH_PIXEL,
GRIPPER_PUSH_RADIUS_PIXEL,
IMAGE_PAD_WIDTH,
IS_REAL,
NUM_ROTATION,
PIXEL_SIZE,... | 20,013 | 36.270019 | 155 | py |
more | more-main/train_maskrcnn.py | import torch
import torchvision
from dataset import SegmentationDataset
import log_utils
import torch_utils
import datetime
import argparse
import time
import os
from vision.coco_utils import get_coco_api_from_dataset
from vision.coco_eval import CocoEvaluator
import vision.transforms as T
import math
from torchvision.... | 12,144 | 36.254601 | 168 | py |
more | more-main/collect_train_grasp_data.py | import numpy as np
import time
import cv2
import utils
import datetime
import os
import glob
import argparse
from threading import Thread
import pybullet as p
import torch
from trainer import Trainer
from constants import (
TARGET_LOWER,
TARGET_UPPER,
DEPTH_MIN,
PUSH_DISTANCE,
NUM_ROTATION,
GRA... | 67,276 | 46.646601 | 179 | py |
more | more-main/utils.py | import math
import numpy as np
import pybullet as p
import cv2
def get_heightmap(points, colors, bounds, pixel_size):
"""Get top-down (z-axis) orthographic heightmap image from 3D pointcloud.
Args:
points: HxWx3 float array of 3D points in world coordinates.
colors: HxWx3 uint8 array of value... | 13,108 | 33.049351 | 117 | py |
more | more-main/dataset.py | from torch.utils.data.sampler import Sampler
import os
import math
import re
import numpy as np
import torch
import torch.utils.data
import cv2
import imutils
from torchvision.transforms import functional as TF
from PIL import Image
import random
from constants import (
IMAGE_OBJ_CROP_SIZE,
IMAGE_SIZE,
WORK... | 59,575 | 39.973865 | 139 | py |
more | more-main/old_utils.py | from collections import defaultdict, deque
import time
import datetime
import torch.distributed as dist
import torch
import torch.nn as nn
import torch.nn.functional as F
# Cross entropy loss for 2D outputs
class CrossEntropyLoss2d(nn.Module):
def __init__(self, weight=None, size_average=True):
super(Cros... | 9,084 | 31.216312 | 112 | py |
more | more-main/train_push_prediction.py | import torch
from torchvision import transforms as T
from push_net import PushPredictionNet
from dataset import PushPredictionMultiDataset, ClusterRandomSampler
import argparse
import time
import datetime
import os
import numpy as np
import cv2
from torch.utils.tensorboard import SummaryWriter
from constants import (
... | 44,879 | 38.402985 | 170 | py |
more | more-main/generate_hard_cases.py | """ Han """
import numpy as np
from numpy.core.fromnumeric import shape
from shapely.geometry import Point, Polygon, LineString, MultiLineString
import matplotlib.pyplot as plt
from signal import signal, SIGINT
from constants import WORKSPACE_LIMITS, PUSH_DISTANCE
def handler(signal_received, frame):
# Handle any... | 13,360 | 43.83557 | 146 | py |
more | more-main/torch_utils.py | import torch
import torch.distributed as dist
def warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor):
"""
https://github.com/pytorch/vision/blob/master/references/detection/utils.py
"""
def f(x):
if x >= warmup_iters:
return 1
alpha = float(x) / warmup_iters
... | 1,845 | 27.4 | 80 | py |
more | more-main/log_utils.py | from collections import defaultdict, deque
import datetime
import time
import logging
from termcolor import colored
import sys
import os
import torch
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
https://github.co... | 6,646 | 32.741117 | 129 | py |
more | more-main/range_detector.py | import cv2
import argparse
from operator import xor
def callback(value):
pass
def setup_trackbars(range_filter):
cv2.namedWindow("Trackbars", 0)
for i in ["MIN", "MAX"]:
v = 0 if i == "MIN" else 255
for j in range_filter:
if j == "H":
cv2.createTrackbar("%s_... | 2,729 | 25.504854 | 97 | py |
more | more-main/action_utils_mask.py | import cv2
import imutils
import math
import random
from constants import (
GRIPPER_PUSH_ADD_PIXEL,
colors_lower,
colors_upper,
IMAGE_PAD_SIZE,
IMAGE_SIZE,
IMAGE_PAD_WIDTH,
PUSH_DISTANCE,
GRIPPER_PUSH_RADIUS_PIXEL,
PIXEL_SIZE,
DEPTH_MIN,
IMAGE_SIZE,
CONSECUTIVE_DISTANCE_T... | 39,123 | 40.933548 | 127 | py |
more | more-main/models.py |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from vision.backbone_utils import resnet_fpn_net
from constants import NUM_ROTATION
class PushNet(nn.Module):
"""
The DQN Network.
"""
def __init__(self, pre_train=False):
super().__init__()
self.de... | 17,954 | 40.370968 | 110 | py |
more | more-main/push_net.py | import torch
import torch.nn as nn
from vision.backbone_utils import resent_backbone
from collections import OrderedDict
class PushPredictionNet(nn.Module):
def __init__(self):
super().__init__()
# single object state encoder
self.single_state_encoder = nn.Sequential(
OrderedD... | 4,492 | 33.829457 | 87 | py |
more | more-main/collect_push_data.py | import time
import datetime
import os
import glob
import pybullet as p
import numpy as np
import cv2
import utils
from environment import Environment
from constants import (
DEPTH_MIN,
PUSH_DISTANCE,
IMAGE_SIZE,
GRIPPER_PUSH_RADIUS_PIXEL,
GRIPPER_PUSH_RADIUS_SAFE_PIXEL,
)
class PushDataCollector:... | 17,799 | 38.821029 | 99 | py |
more | more-main/mcts_utils.py | from dataset import LifelongEvalDataset
import math
import random
import torch
from torchvision.transforms import functional as TF
import numpy as np
import cv2
import imutils
from models import reinforcement_net
from action_utils_mask import get_orientation, adjust_push_start_point
import utils
from constants import... | 42,442 | 42.48668 | 107 | py |
more | more-main/train_foreground.py | import torch
from models import reinforcement_net
from dataset import ForegroundDataset
import argparse
import time
import datetime
import os
from constants import PUSH_Q, GRASP_Q, NUM_ROTATION
from torch.utils.tensorboard import SummaryWriter
import log_utils
import torch_utils
def parse_args():
default_params ... | 24,462 | 35.241481 | 111 | py |
more | more-main/push_predictor.py | import copy
import torch
import gc
import numpy as np
import cv2
from torchvision.transforms import functional as TF
import math
from push_net import PushPredictionNet
from models import reinforcement_net
from train_maskrcnn import get_model_instance_segmentation
from dataset import PushPredictionMultiDatasetEvaluatio... | 29,449 | 43.961832 | 198 | py |
more | more-main/trainer.py | import os
import numpy as np
import math
import cv2
import torch
from torch.autograd import Variable
from models import reinforcement_net
from scipy import ndimage
from constants import (
COLOR_MEAN,
COLOR_STD,
DEPTH_MEAN,
DEPTH_STD,
DEPTH_MIN,
IMAGE_PAD_WIDTH,
NUM_ROTATION,
GRIPPER_GRAS... | 39,013 | 41.222944 | 100 | py |
more | more-main/mcts_main.py | """Test"""
import glob
import gc
import os
import time
import datetime
import pybullet as p
import cv2
import numpy as np
from graphviz import Digraph
import argparse
import random
import torch
import pandas as pd
from mcts_utils import MCTSHelper
from mcts.search import MonteCarloTreeSearch
from mcts.nodes import Pu... | 23,696 | 40.793651 | 183 | py |
more | more-main/collect_logs_mcts.py | import subprocess
import time
import glob
import logging
cases = glob.glob("test-cases/test/*") # glob.glob("test-cases/train/*")
cases = sorted(cases, reverse=False)
switches = [0] # [0,1,2,3,4]
logging.basicConfig(
filename="logs_grasp/collect.log",
filemode="w",
format="%(asctime)s - %(levelname)s ... | 1,456 | 23.283333 | 100 | py |
more | more-main/mcts_network/nodes.py | """Node for MCTS"""
import math
import numpy as np
from constants import (
MCTS_DISCOUNT,
)
class PushSearchNode:
"""MCTS search node for push prediction."""
def __init__(self, state=None, prev_move=None, parent=None):
self.state = state
self.prev_move = prev_move
self.parent = p... | 5,153 | 32.251613 | 141 | py |
more | more-main/mcts_network/push.py | """Class for MCTS."""
import math
from constants import (
MCTS_MAX_LEVEL,
GRASP_Q_PUSH_THRESHOLD,
)
from mcts_utils import _sampled_prediction_precise
import utils
class PushMove:
"""Represent a move from start to end pose"""
def __init__(self, pos0, pos1, q_value):
self.pos0 = pos0
... | 5,567 | 31.946746 | 93 | py |
more | more-main/mcts_network/__init__.py | 0 | 0 | 0 | py | |
more | more-main/mcts_network/search.py | from tqdm import tqdm
class MonteCarloTreeSearch(object):
def __init__(self, node):
self.root = node
self.root.pre_expand()
def best_action(self, simulations_number, early_stop_number, eval=False):
early_stop_sign = False
stop_level = 1
for itr in tqdm(range(simulation... | 1,585 | 37.682927 | 115 | py |
more | more-main/mcts/nodes.py | """Node for MCTS"""
import numpy as np
from constants import (
MCTS_DISCOUNT,
MCTS_TOP,
MCTS_UCT_RATIO,
)
class PushSearchNode:
"""MCTS search node for push prediction."""
def __init__(self, state, prev_move=None, parent=None):
self.state = state
self.prev_move = prev_move
... | 4,727 | 33.510949 | 96 | py |
more | more-main/mcts/push.py | """Class for MCTS."""
import math
from constants import (
MCTS_MAX_LEVEL,
GRASP_Q_PUSH_THRESHOLD,
)
class PushMove:
"""Represent a move from start to end pose"""
def __init__(self, pos0, pos1):
self.pos0 = pos0
self.pos1 = pos1
def __str__(self):
return f"{self.pos0[0]}... | 4,979 | 31.337662 | 93 | py |
more | more-main/mcts/__init__.py | 0 | 0 | 0 | py | |
more | more-main/mcts/search.py | from tqdm import tqdm
class MonteCarloTreeSearch(object):
def __init__(self, node):
self.root = node
def best_action(self, simulations_number, early_stop_number, eval=False):
early_stop_sign = False
stop_level = 1
for itr in tqdm(range(simulations_number)):
child_n... | 1,456 | 38.378378 | 114 | py |
more | more-main/vision/backbone_utils.py | from collections import OrderedDict
from torch import nn
from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
import torch.nn.functional as F
from torchvision.ops import misc as misc_nn_ops
from ._utils import IntermediateLayerGetter
from . import resnet
from constants import GRIP... | 13,140 | 38.821212 | 118 | py |
more | more-main/vision/_utils.py | from collections import OrderedDict
import torch
from torch import nn
from torch.jit.annotations import Dict
from torch.nn import functional as F
class IntermediateLayerGetter(nn.ModuleDict):
"""
Module wrapper that returns intermediate layers from a model
It has a strong assumption that the modules hav... | 2,641 | 37.289855 | 89 | py |
more | more-main/vision/resnet.py | import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
__all__ = [
"ResNet",
"resnet10",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
"resnext50_32x4d",
"resnext101_32x8d",
"wide_resnet50_2",
"wide_resnet101_2",
]
... | 14,901 | 34.229314 | 107 | py |
more | more-main/vision/__init__.py | from .resnet import *
| 22 | 10.5 | 21 | py |
more | more-main/vision/coco_utils.py | import copy
import os
from PIL import Image
import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
class FilterAndRemapCocoCategories(object):
def __init__(self, categories, remap=True):
self.categories = categories
sel... | 7,759 | 34.272727 | 83 | py |
more | more-main/vision/coco_eval.py | import json
import tempfile
import numpy as np
import copy
import time
import torch
import torch._six
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from collections import defaultdict
import old_utils as utils
class CocoEvaluator(object):
def ... | 12,012 | 33.421203 | 107 | py |
more | more-main/vision/transforms.py | import random
import torch
from torchvision.transforms import functional as F
def _flip_coco_person_keypoints(kps, width):
flip_inds = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
flipped_data = kps[:, flip_inds]
flipped_data[..., 0] = width - flipped_data[..., 0]
# Maintain COCO conven... | 1,358 | 28.543478 | 74 | py |
more | more-main/test-cases/blender_case.py | import bpy
content = ""
for a in bpy.context.selected_objects:
content += '' + a.name + " "
content += str(a.location[0])+' '+str(a.location[1])+' '+str(a.location[2]) + " "
content += str(a.rotation_euler[0])+' '+str(a.rotation_euler[1])+' '+str(a.rotation_euler[2])
content += "\n"
with open("/home/m... | 412 | 36.545455 | 97 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/plot_results.py | import random
import numpy as np
import time
import pickle
import matplotlib.pyplot as plt
import scipy.stats
def mean_confidence_interval(data, confidence=0.95):
"""
Compute the mean and confidence interval of the the input data array-like.
:param data: (array-like)
:param confidence: probability of ... | 11,270 | 53.449275 | 224 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/mcnf_do_test.py | import random
import numpy as np
import time
from instance_mcnf import generate_instance
from mcnf import *
from simulated_annealing import simulated_annealing_unsplittable_flows
from VNS_masri import VNS_masri
from ant_colony import ant_colony_optimiser
# Here you choose the setting of the instances and of the solve... | 5,855 | 47.8 | 155 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/simulated_annealing.py | import random
import numpy as np
import heapq as hp
import time
from k_shortest_path import k_shortest_path_algorithm, k_shortest_path_all_destination
def simulated_annealing_unsplittable_flows(graph, commodity_list, nb_iterations=10**5, nb_k_shortest_paths=10, verbose=0):
nb_nodes = len(graph)
nb_commoditie... | 9,393 | 38.974468 | 186 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/mcnf_continuous.py | import numpy as np
import random
import time
import heapq as hp
import gurobipy
from mcnf_heuristics import find_fitting_most_capacited_path, compute_all_shortest_path
def gurobi_overload_sum_solver(graph, commodity_list, use_graph=None, flow_upper_bound_graph=None, verbose=0, proof_constaint=False, return_model=Fal... | 8,387 | 43.617021 | 240 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/create_and_store_instances.py | import random
import numpy as np
import time
import pickle
from instance_mcnf import generate_instance
# Here you choose the setting of the instances
nb_repetitions = 100
nb_unique_exp = 10
# Size of the graph : controls the number of nodes and arcs
size_list = [10]*nb_unique_exp
# size_list = [5, 7, 8, 10, 12, 13,... | 3,307 | 43.702703 | 158 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/ant_colony.py | import heapq as hp
import random
import numpy as np
import time
def ant_colony_optimiser(graph, commodity_list, nb_iterations, verbose=0):
nb_nodes = len(graph)
nb_commodities = len(commodity_list)
nb_edges = sum(len(neighbor_dict) for neighbor_dict in graph)
# Setting hyper-parameters
evaporation... | 12,009 | 40.701389 | 157 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/launch_dataset_test.py | import random
import time
import pickle
from multiprocessing import Process, Manager
from instance_mcnf import generate_instance, mutate_instance
from mcnf import *
from VNS_masri import VNS_masri
from ant_colony import ant_colony_optimiser
from simulated_annealing import simulated_annealing_unsplittable_flows
def la... | 7,873 | 56.897059 | 184 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/mcnf.py | import heapq as hp
import random
import numpy as np
import time
import gurobipy
from mcnf_continuous import gurobi_congestion_solver, gurobi_overload_sum_solver
from mcnf_heuristics import find_fitting_most_capacited_path
def gurobi_unsplittable_flows(graph, commodity_list, verbose=0, time_limit=None):
# MILP mo... | 10,814 | 44.441176 | 255 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/mcnf_heuristics.py | import random
import numpy as np
import heapq as hp
def single_source_mcnf_preprocessing(reverse_graph, commodity_list):
commodity_path_list = [[] for c in commodity_list]
process_graph = [{neighbor : reverse_graph[node][neighbor] for neighbor in reverse_graph[node]} for node in range(len(reverse_graph))]
... | 9,909 | 32.255034 | 179 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/instance_mcnf.py | import random
import heapq as hp
import numpy as np
import time
def generate_instance(graph_type, graph_generator_inputs, demand_generator_inputs):
# this function generates an intances according to the asked caracteristics :
# - first a graph is generated : a grid graph or a random graph
# - then commodi... | 9,547 | 42.009009 | 185 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/k_shortest_path.py | import heapq as hp
import random
import numpy as np
import time
def k_shortest_path_all_destination(graph, origin, k):
nb_nodes = len(graph)
parent_list, distances = dijkstra(graph, origin)
shortest_path_list = [[] for node in range(nb_nodes)]
shortest_path_list[origin].append(([origin], 0))
for... | 5,728 | 41.437037 | 141 | py |
randomized_rounding_paper_code | randomized_rounding_paper_code-master/VNS_masri.py | import heapq as hp
import random
import numpy as np
import time
import matplotlib.pyplot as plt
from k_shortest_path import k_shortest_path_algorithm, k_shortest_path_all_destination
from simulated_annealing import compute_all_distances
def VNS_masri(graph, commodity_list, nb_iterations, amelioration=False, verbose=0... | 7,809 | 44.144509 | 174 | py |
RWP | RWP-main/utils.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models_imagenet
import nu... | 14,352 | 38.215847 | 173 | py |
RWP | RWP-main/train_rwp_parallel.py | import argparse
from torch.nn.modules.batchnorm import _BatchNorm
import os
import time
import numpy as np
import random
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
imp... | 16,489 | 34.310493 | 165 | py |
RWP | RWP-main/train_rwp_imagenet.py | import argparse
import os
import random
import shutil
import time
import warnings
import os
import numpy as np
import pickle
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from utils import *
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
imp... | 21,710 | 36.890052 | 118 | py |
RWP | RWP-main/models/resnet.py | """resnet in pytorch
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
Deep Residual Learning for Image Recognition
https://arxiv.org/abs/1512.03385v1
"""
import torch
import torch.nn as nn
class BasicBlock(nn.Module):
"""Basic Block for resnet 18 and resnet 34
"""
#BasicBlock and Bottl... | 5,620 | 32.064706 | 118 | py |
RWP | RWP-main/models/vgg.py | """
VGG model definition
ported from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
"""
import math
import torch.nn as nn
import torchvision.transforms as transforms
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
def make_layers(cfg, batch_norm=False):
layers = list()
in... | 2,502 | 25.913978 | 97 | py |
RWP | RWP-main/models/wide_resnet.py | """
WideResNet model definition
ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math
__all__ = ['WideResNet28x10', 'WideRes... | 5,426 | 38.904412 | 114 | py |
RWP | RWP-main/models/__init__.py | from .resnet import *
from .vgg import *
from .wide_resnet import * | 67 | 21.666667 | 26 | py |
F-SHARP | F-SHARP-main/measure_coflex.py | import numpy as np
import sys,os
from astropy.cosmology import Planck15
import pandas as pd
from astropy import units as u
from scipy import interpolate
from fastdist import fastdist
"""
Script: Measure the cosmic flexion and shear-flexion two-point correlation functions from a dataset.
Author: Evan J. Arena
Desc... | 40,759 | 49.197044 | 399 | py |
F-SHARP | F-SHARP-main/coflex_twopoint.py | import numpy as np
import pandas as pd
from classy import Class
import pickle
import sys,os
import astropy
from astropy.cosmology import Planck15
from astropy import units as u
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=Tr... | 10,108 | 39.2749 | 142 | py |
F-SHARP | F-SHARP-main/coflex_power.py | import numpy as np
import pandas as pd
from classy import Class
import pickle
import sys,os
import astropy
from astropy.cosmology import FlatLambdaCDM
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)
from scipy import int... | 10,490 | 37.149091 | 134 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/SCLP.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 6,369 | 39.833333 | 128 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/__init__.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 576 | 37.466667 | 74 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/__init__.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 576 | 37.466667 | 74 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/doe_utils.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 3,396 | 39.927711 | 140 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/results_producer.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 3,197 | 40 | 148 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/doe.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 11,653 | 38.239057 | 155 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/data_generators/write_CPLEX_dat.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 2,761 | 33.962025 | 74 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/data_generators/reentrant.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 3,035 | 37.43038 | 140 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/data_generators/data_loader.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 1,329 | 40.5625 | 74 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/data_generators/MCQN.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 4,490 | 38.743363 | 147 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/data_generators/WorkloadPlacement.py | # Copyright 2021 IBM Corporation
#
# 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, ... | 7,323 | 29.139918 | 117 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/data_generators/__init__.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 576 | 37.466667 | 74 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/data_generators/simple_reentrant.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 2,576 | 39.904762 | 161 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/cplex_integration/run_cplex_experiments.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 1,558 | 40.026316 | 106 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/cplex_integration/benchSclp1.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 2,069 | 30.846154 | 100 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/cplex_integration/doopl_test.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 1,703 | 31.769231 | 76 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/doe/robust/robust_reformulation.py | import numpy as np
#this function should get original matrix H, degrees of perturbation (d), i.e. \tau_gal = d \tau and uncertainty budget
# and return two matricies - one eith coefficients related to u_j and one with coefficients related to \alpha and \beta
def do_server_robust_reformulation(H, degree, budget, separ... | 1,170 | 49.913043 | 119 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/SCLP_solver.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 9,534 | 51.679558 | 158 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/classification.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 6,700 | 44.89726 | 135 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/parametric_line_ex.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 3,071 | 35.141176 | 133 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/SCLP_x0_solver.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 2,981 | 47.885246 | 149 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/time_collision_resolver.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 19,195 | 42.826484 | 147 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/collision_info.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 2,784 | 22.803419 | 139 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/matrix_constructor.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 9,513 | 43.251163 | 133 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/SCLP_subproblem.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 3,540 | 43.2625 | 136 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/SCLP_base_sequence.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 5,615 | 37.731034 | 128 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/utils.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 833 | 35.26087 | 96 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/solution_state.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 3,443 | 44.92 | 107 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/bases_memory_manager.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 1,123 | 32.058824 | 75 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/get_new_dict.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 1,333 | 36.055556 | 78 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/SCLP_solution.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 15,467 | 47.3375 | 178 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/calc_controls.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 1,505 | 39.702703 | 96 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/prepare_subproblem_data.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 2,604 | 48.150943 | 150 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/SCLP_pivot.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 9,779 | 43.253394 | 125 | py |
SCLPsolver | SCLPsolver-master/SCLPsolver/subroutines/calc_objective.py | # Copyright 2020 IBM Corporation
#
# 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, ... | 1,560 | 38.025 | 103 | py |
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