p-geon

import copy
import itertools
from typing import List, Callable
import numpy as np; np.random.seed(seed=42)
from sklearn.preprocessing import StandardScaler, minmax_scale
# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler
# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html#sklearn.preprocessing.minmax_scale

def check_result(y1, y2, eps=1e-11):
    print(f'[y1] max: {np.round(np.max(y1), 4)}, min: {np.round(np.min(y1), 4)}, mean: {np.round(np.mean(y1), 4)}, std: {np.round(np.std(y1), 4)}')

p-geon

print('tf.__version__',  tf.__version__)

z_1 = [_*2 for _ in range(10)]
z_2 = [_*2+1 for _ in range(10)]

z_1 = tf.convert_to_tensor(z_1, dtype=tf.float32)
z_2 = tf.convert_to_tensor(z_2, dtype=tf.float32)


@tf.function

p-geon

class AllVariablesNamePrinter:
    def __init__(self):
        self.a = 10
        self.b = {
            "ba": 1,
            "bb": 2,
        }
        self.c = 20

    def __call__(self):

p-geon

! pip install -q termcolor==1.1.0
from termcolor import colored
print(colored('hello', 'red'), colored('world', 'green'))
print(colored("hello red world", 'red'))

# ---

red = "\x1b[31m"
red_ = "\x1b[0m"
print(f"{red}red{red_}a")

p-geon

echo "[loading] ~/.zprofile"

# =========================================
## hook command: cd -> cd + ls
cdls ()
{
  \cd "$@" && ls
}

p-geon

# 色を使用
autoload -Uz colors
colors

# scp needs locate this
echo "[loading] ~/.zshrc"

# 補完
# autoload -Uz compinit
# compinit

p-geon

from skimage import io
import matplotlib.pyplot as plt

def imshow(img, dpi=150):
    if(len(img.shape)==2):#grayscale        
        plt.figure(dpi)
        plt.imshow(img)
        plt.gray()
        plt.show()
    else:

p-geon

def unet(x, ch, depth, cut_path=False):
    conv_kwargs = {'kernel_size': (3, 3), 'strides'=(1, 1), 'dilation_rate'=(1, 1),
                  'padding'='same', 'use_bias'=True, 'bias_initializer'='zeros', 'kernel_initializer'='he_normal'}
    for _ in range(2):
        x = tf.keras.layers.Convolution2D(ch, **conv_kwargs)(x)
        x = tf.keras.layers.BatchNormalization()(x)
        x = tf.keras.layers.Activation('relu')(x)
    
    if(depth != 0):
        path = x

p-geon

touch Makefile
touch Dockerfile
touch .gitignore
touch .dockerignore
touch requirements.txt

# gigignore
echo '.ipynb_checkoints' >> .gitignore
echo '__pycache__' >> .gitignore

p-geon

# for docker inside, "ls ./ && ls ../"
import os; print(os.listdir('./'), os.listdir('../'))

# =====
# images
# =====
'''read tif'''
!pip install tifffile==2020.9.3
import tifffile
tifffile.imread('filename.tif')