Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
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try:
return max(x)
except Exception:
return None
def _min(self, x):
try:
return min(x)
except Exception:
return None
# Interpolate f(x) if given lists Y = f(X)
def _interpolate(self, X, Y, x):
if len(X) == len(Y):
_len = len(X)
if x <= X[0]:
return Y[0]
elif x >= X[_len - 1]:
return Y[_len - 1]
else:
for i in range(_len - 1):
if x <= X[i + 1]:
return Y[i] + (Y[i + 1] - Y[i]) / (X[i + 1] - X[i]) * (x - X[i])
else:
logging.error("Both lists must have the same length. Exiting.")
sys.exit()
################
# test program #
################
def main():
import settings as s
fn = Functions()
for x in range(0, 251):
print(
"%.2f %.0f"
% (
x / 100.0,
s.MAX_CHARGE_CURRENT
* fn._interpolate(
s.CELL_CHARGE_LIMITING_VOLTAGE,
s.CELL_CHARGE_LIMITED_CURRENT,
x / 100.0,
),
)
)
if __name__ == "__main__":
main()
# <FILESEP>
import os
import cv2
import debug
import torch
import numpy as np
from src.crowd_counting import CrowdCounter
from src import network
from src.RawLoader import ImageDataLoader, basic_config
from src import utils
import argparse
from src.sampler import basic_config as sampler_config
from src.sampler import mode_func as sampler_func
import torchvision.transforms as transforms
from src.datasets import datasets, CreateDataLoader
import src.density_gen as dgen
from src.timer import Timer
import itertools
import time
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
#test data and model file path
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str)
parser.add_argument('--model_name', type=str)
parser.add_argument('--gpus', type=str, help='gpu_id')
parser.add_argument('--dataset', type=str)
parser.add_argument('--prefix', type=str)
parser.add_argument('--preload', dest='is_preload', action='store_true')
parser.add_argument('--no-preload', dest='is_preload', action='store_false')
parser.set_defaults(is_preload=True)
parser.add_argument('--wait', dest='is_wait', action='store_true')
parser.add_argument('--no-wait', dest='is_wait', action='store_false')
parser.set_defaults(is_wait=True)
parser.add_argument('--save', dest='save_output', action='store_true')
parser.add_argument('--no-save', dest='save_output', action='store_false')
parser.set_defaults(save_output=False)
# crop adap