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src/gz openwrt_core https://mirrors.tuna.tsinghua.edu.cn/openwrt/releases/19.07.0/targets/ramips/mt7620/packages
src/gz openwrt_kmods     https://mirrors.tuna.tsinghua.edu.cn/openwrt/releases/19.07.0/targets/ramips/mt7620/kmods/4.14.162-1-18384755d38fc43c447d83d4a3e07054
src/gz openwrt_base      https://mirrors.tuna.tsinghua.edu.cn/openwrt/releases/19.07.0/packages/mipsel_24kc/base
src/gz openwrt_luci      https://mirrors.tuna.tsinghua.edu.cn/openwrt/releases/19.07.0/packages/mipsel_24kc/luci
src/gz openwrt_packages  https://mirrors.tuna.tsinghua.edu.cn/openwrt/releases/19.07.0/packages/mipsel_24kc/packages
src/gz openwrt_routing   https://mirrors.tuna.tsinghua.edu.cn/openwrt/releases/19.07.0/packages/mipsel_24kc/routing
src/gz openwrt_telephony https://mirrors.tuna.tsinghua.edu.cn/openwrt/releases/19.07.0/packages/mipsel_24kc/telephony

steermomo

hs.hotkey.bind({"cmd", "alt", "ctrl"}, "Y", function()
    local win = hs.window.focusedWindow()
    local f = win:frame()
  
    f.x = f.x - 10
    f.y = f.y - 10
    win:setFrame(f)
  end)
  
  hs.hotkey.bind({"cmd", "alt", "ctrl"}, "K", function()

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# https://www.kaggle.com/cdeotte/xgb-fraud-with-magic-0-9600
# Encoding Functions
# Below are 5 encoding functions. 
# (1) encode_FE does frequency encoding where it combines train and test first and then encodes. 
# (2) encode_LE is a label encoded for categorical features 
# (3) encode_AG makes aggregated features such as aggregated mean and std 
# (4) encode_CB combines two columns 
# (5) encode_AG2 makes aggregated features where it counts how many unique values of one feature is within a group.
# For more explanation about feature engineering, see the discussion here

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from skimage import color
import numpy as np

def pseudo_color(arr: np.ndarray, hue: int):
    """生成伪彩图
    
    :param arr: 灰度图像
    :type arr: np.ndarray
    :param hue: hsv空间的hue值, 用于指定颜色, 0~360
    :type hue: int

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import numpy as np
import keras

class DataGenerator(keras.utils.Sequence):
    'Generates data for Keras'
    def __init__(self, list_IDs, labels, batch_size=32, dim=(32,32,32), n_channels=1,
                 n_classes=10, shuffle=True):
        'Initialization'
        self.dim = dim
        self.batch_size = batch_size

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def hist_match(source, template):
    """
    Adjust the pixel values of a grayscale image such that its histogram
    matches that of a target image

    Arguments:
    -----------
        source: np.ndarray
            Image to transform; the histogram is computed over the flattened
            array

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import sys
def pretty_print(header,col, data, file=sys.stdout):
    row_format ="{:<40}" + "{:<15}" * (len(header)) # format
    print(row_format.format("", *header), file=file) # print header
    for team, row in zip(col, data):  # print data part
        print(row_format.format(team, *row),file=file)
    print('-' * (40 + 15 * len(header)), file=file) # print end line

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import subprocess, re

# Nvidia-smi GPU memory parsing.
# Tested on nvidia-smi 370.23

def run_command(cmd):
    """Run command, return output as string."""
    output = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True).communicate()[0]
    return output.decode("ascii")

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  from keras.utils.data_utils import OrderedEnqueuer  
  val_generator = DataGenerator(target=target, dataset_type='test', batch_size=12,
                                dim=img_dim, shuffle=False)
  steps_per_epoch = len(val_generator)
  val_enqueuer = OrderedEnqueuer(
                      val_generator,
                      use_multiprocessing=use_multiprocessing)
  val_enqueuer.start(workers=workers,
                                 max_queue_size=max_queue_size)
  val_enqueuer_gen = val_enqueuer.get()  # 无限循环 ref:https://github.com/keras-team/keras/blob/master/keras/engine/training_generator.py#L180

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fuser -v /dev/nvidia* |awk '{for(i=1;i<=NF;i++)print "kill -9 " $i;}' | sh