import statsmodels.api as sm
a = sm.tsa.acf(ts, nlags=n

import statsmodels.api as sm
from gwpy.timeseries import TimeSeries

a = sm.tsa.acf(ts, nlags=int(T*fs))
a = TimeSeries(a, sample_rate=fs)
a.plot()

img_new = cv2.addWeighted(src1=img1, alpha=0.5, src2=img2, beta=0.5, gamma=0)

new_fig = np.uint16(img_old)

import cv2

img_orig = cv2.imread(fname, cv2.IMREAD_UNCHANGED)
img_gige = img_orig.copy()
height_gige, width_gige = img_orig.shape

# 余白を上下に追加する

img_gige2=cv2.copyMakeBorder(img_gige, margin_height, margin_height, margin_width, margin_width, cv2.BORDER_CONSTANT, value=[0, 0, 0])

hist, bins = np.histogram(input_data, nbin)
delta = (bins[1]-bins[0])/2
xr = np.linspace(np.min(bins)+delta,np.max(bins)-delta,len(bins)-1)
x_max = xr[np.argmax(hist)]

# print関数の引数endを指定する
print('文字列',end='')

# 先頭にキャリッジリターンをつけると、上書きできる
print('\r文字列',end='')

import numpy as np
b = np.around(a, 2)

import cv2
from IPython.display import Image
from IPython.display import display
from matplotlib import pyplot as plt

#
# ここで2枚の画像読み込み
#
# (お好きな画像を読み込んでください)

akaze = cv2.AKAZE_create()
kp1, des1 = akaze.detectAndCompute(img1, None)
kp2, des2 = akaze.detectAndCompute(img2, None)

#
# 内容のチェック
#
print('##### 特徴点の数 #####')
print(len(kp1))

print('##### 特徴量記述子 #####')
print(des1)

print('##### 特徴ベクトル #####')
print(des1.shape)

#
# マッチング
#
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)

#
# 特徴点間のハミング距離でソート
#
matches = sorted(matches, key=lambda x: x.distance)

#
# 2画像間のマッチング結果画像を作成
#
img1_2 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:40], None, flags=2)
plt.figure(figsize = (width*2/100, height/100), dpi=100)
plt.imshow(img1_2)
plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
plt.savefig("diff_features.png")

import matplotlib as mpl
mpl.rcParams['xtick.labelsize'] = 16
mpl.rcParams['ytick.labelsize'] = 16
mpl.rcParams["axes.labelsize"] = 20
mpl.rcParams["axes.titlesize"] = 20
mpl.rcParams["legend.fontsize"] = 20
mpl.rcParams['axes.linewidth'] = 2
mpl.rcParams['figure.facecolor'] = "white"
mpl.rcParams['mathtext.fontset'] = 'stix'
mpl.rcParams['font.family'] = 'STIXGeneral'

import numpy as np

n=100
x_diff = np.zeros(n)
y_diff = np.zeros(n)
for i in range(n):
x = kp1[matches[i].queryIdx].pt[0] - kp2[matches[i].trainIdx].pt[0]
if abs(x) > 20:
x_diff[i] = None
else:
x_diff[i] = x

x = kp1[matches[i].queryIdx].pt[1] - kp2[matches[i].trainIdx].pt[1]
if abs(x) > 20:
y_diff[i] = None
else:
y_diff[i] = x

fig = plt.figure(figsize=[12, 12])

x_diff = x_diff[~np.isnan(x_diff)]
y_diff = y_diff[~np.isnan(y_diff)]

plt.subplot(2, 1, 1)
plt.title("Comparison %s\nand %s" % (fname1, fname2))
plt.hist(x_diff, bins=20, label="horizontal difference\nmean=%.1f pixel\nstd=%.1f pixel" % (np.mean(x_diff), (np.std(x_diff))))
plt.legend(loc="upper right")
plt.xlabel("horizontal difference on features (x-value)")
plt.grid(linestyle='dotted', linewidth=1)

plt.subplot(2, 1, 2)
plt.hist(y_diff, bins=20, label="vertical difference\nmean=%.1f pixel\nstd=%.1f pixel" % (np.mean(y_diff), (np.std(y_diff))))
plt.legend(loc="upper right")
plt.xlabel("vertical difference on features (y-value)")
plt.grid(linestyle='dotted', linewidth=1)

fig.savefig("hist.png")

from matplotlib import pyplot as plt

plt.subplot(2, 1, 1)
plt.hist(x, bins=20, label="horizontal")
plt.legend(loc="upper right")
plt.xlabel("horizontal")
plt.grid(linestyle='dotted', linewidth=1)

plt.subplot(2, 1, 2)
plt.hist(y, bins=20, label="horizontal")
plt.legend(loc="upper right")
plt.xlabel("vertical")
plt.grid(linestyle='dotted', linewidth=1)

fig.savefig("hist.png")

x = x[~np.isnan(x)]

f = BytesIO()
plt.savefig(f, format="svg")

plt.gcf().set_size_inches(18, 8)
plt.tight_layout()

from datetime import datetime
date_beg = datetime(2023, 8, 20)
date_end = datetime(2023, 8, 28)
if date_beg < date_end:
　print("True")

AttributeError: module 'numpy' has no attribute 'int'.
`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use
eck the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

import cv2

photo = cv2.imread('hoge.png')
grays = cv2.cvtColor(photo, cv2.COLOR_RGB2GRAY)
rgb = cv2.cvtColor(grays, cv2.COLOR_GRAY2RGB)

import cv2
import numpy as np

size=(512,512)
black_img=np.zeros(size,np.uint8)

plt.grid(which='major',color='black',linestyle='-')
plt.grid(which='minor',color='black',linestyle='-')

l_2d = [[0, 1], [2, 3]]

print(sum(l_2d, []))
# [0, 1, 2, 3]

func="1+1"
a = eval(func)

func="x=5"
exec(func)

pip install git+https://github.com/acc_name/pkg_name.git

import numpy as np
a = np.loadtxt(fname, delimiter=",", unpack=True, comments="%")