conda install opencv

height, width, _ = img.shape

cv2.circle(img, (190, 35), 15, (255, 255, 255), thickness=-1)

fname="hoge.csv"
if os.path.exists(fname):
a = np.loadtxt(fname, unpack=True, dtype="unicode", delimiter=',', skiprows=1)

mmdd=[]
data["date"] = a[1].astype(str)
tmp = np.char.replace(data["date"], "/", "-")
for (i, x) in enumerate(tmp):
mmdd.append(datetime.strptime(x, '%Y-%m-%d'))
# 時間要素があればここを書き換えれば良いと思う・・・

data["T"] = a[2].astype(np.float64)

plt.plot(mmdd, data1)

plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=7))
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%m-%d"))

from matplotlib.dates import DateFormatter

set_major_locator(mdates.DayLocator(bymonthday=(range(5, 28, 7))))
set_major_formatter(mdates.DateFormatter("%Y-%m-%d"))


set_major_formatter(DateFormatter('%m/%d\n%H:%M'))

from datetime import datetime

xmin='2022-03-28'
xmax='2022-06-01'
xmin = datetime.strptime(xmin, '%Y-%m-%d')
xmax = datetime.strptime(xmax, '%Y-%m-%d')
plt.xlim([xmin,xmax])

from matplotlib import pyplot as plt
plt.figure(figsize=(10,7))
plt.errorbar(x, y, yerr = [y_min, y_max], capsize=5, fmt='o', markersize=10, ecolor='black', markeredgecolor = "black", color='w')

import numpy as np

fname="hoge.txt"
a = np.loadtxt(fname, unpack=True, dtype=str)

y = a["x"].astype(np.float)

import numpy as np

fname="hoge.csv"
a = np.loadtxt(fname, unpack=True, dtype=str, dtype="unicode", delimiter=',', skiprows=1)

from scipy import interpolate # to interpolate

x_out = np.linspace(min(x_in), max(x_in), n_samples)
fit_curve = interpolate.interp1d(x_in, y_in)
y_out = fit_curve(x_out)

from matplotlib import font_manager
for i in font_manager.fontManager.ttflist:
　if ".ttc" in i.fname:
　　 print(i)

import matplotlib as mpl
mpl.rc('font', family='Hiragino Sans')

pylab.ylabel(r"あああああああ",fontsize=30, fontname='Hiragino Sans')

pylab.legend(loc='upper right', prop={"family" :'Hiragino Sans'})

# matplotlibの日本語
macは ~/.matplotlib/matplotlibrc に書いときゃOK

cd ~/.matplotlib
echo "font.family : Hiragino sans" >> matplotlibrc

b = a[np.char.find(a, a_key) != -1]

cat hoge.json | python -m json.tool

alias jq='python -m json.tool'

base_dir = "/home/hogehoge/public_html/hoge/%s/" % (d.strftime("%Y%m%d"))

plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))

plt.gca().yaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))

from scipy.signal import butter, sosfreqz
import numpy as np
import matplotlib.pyplot as plt

fs=16384
fs_nyq = fs*0.5
low = 100 / fs_nyq
high = 500 /fs_nyq

sos = butter(20, [low, high], btype="bandpass", output='sos')
w, h = sosfreqz(sos, worN=8000)
plt.plot((fs_nyq/np.pi)*w, np.abs(h))
plt.plot((fs_nyq/np.pi)*w, np.angle(h))
ax = plt.gca()
ax.set_xlabel("frequency [Hz]")
ax.set_xlim([0, 1000])
plt.show()

fs=16384
fs_nyq = fs*0.5
low = 100 / fs_nyq
high = 500 /fs_nyq

sos = butter(5, [low, high], btype="bandpass", output='sos')
w, h = sosfreqz(sos, worN=8000)
plt.plot((fs_nyq/np.pi)*w, np.abs(h))
plt.plot((fs_nyq/np.pi)*w, np.angle(h))
ax = plt.gca()
ax.set_xlabel("frequency [Hz]")
ax.set_xlim([0, 1000])
plt.show()

from scipy.signal import butter, sosfreqz, ellip
import numpy as np
import matplotlib.pyplot as plt
import math

import matplotlib.ticker as ptick
from matplotlib.ticker import ScalarFormatter, NullFormatter


import matplotlib as mpl

mpl.rcParams['xtick.labelsize'] = 20
mpl.rcParams['ytick.labelsize'] = 20
mpl.rcParams["axes.labelsize"] = 20
mpl.rcParams["axes.titlesize"] = 20
mpl.rcParams["legend.fontsize"] = 20

# filter coefficient
# order : [b0, b1, b2, a0, a1, a2]
#
label=band pass 0.03-0.1 Hz"
freq_range = [0.02, 0.2]
coeff1 = [1, -1.999998669372317, +0.999999999999999, 1, -1.999946242188092, +0.999946281201766]
coeff2 = [1, -1.999999767511141, +1.000000000000011, 1, -1.999955898604725, +0.999955963180191]
coeff3 = [1, -1.999999999400776, +0.999999999999995, 1, -1.999961099937338, +0.999961120373777]
coeff4 = [1, -1.999999853828710, +0.999999999999960, 1, -1.999976229681027, +0.999976314187963]
coeff5 = [1, -1.999999996570461, +1.000000000000034, 1, -1.999980731688244, +0.999980744035228]
coeff6 = [1, -1.999999994545118, +0.999999999999935, 1, -1.999992076212929, +0.999992085647952]
coeff7 = [1, -1.999999873305222, +1.000000000000034, 1, -1.999992919697037, +0.999993013663749]
coeff8 = [1, -1.999999993706660, +1.000000000000031, 1, -1.999997892111085, +0.999997900596364]

coeff = [coeff1, coeff2, coeff3, coeff4,
coeff5, coeff6, coeff7, coeff8]
sos = np.array(coeff, dtype="float64")
print(coeff)

#
# filter response
#
w, h2 = sosfreqz(sos, worN=1000000) # worN decided the frequency resolution

#
# plot
#

plt.figure(figsize=[12, 6])
plt.plot((fs_nyq/np.pi)*w / factor, np.abs(h), label="hoge", color="magenta")
ax = plt.gca()
ax.set_xlabel("frequency [Hz]")
ax.set_ylabel("frequency response")
ax.set_xlim([1e-4, fs_nyq])
ax.set_title("with correction of down-sampling")
plt.xscale("log")
plt.yscale("log")
plt.legend(loc="lower right")
plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%.2f'))#y軸小数点以下3桁表示
plt.show()

from matplotlib import pyplot as plt
fig = plt.figure()
n = 8
n2 = int(n/2)
fig, axes = plt.subplots(n2,2, figsize=(30, 15), sharex=True)

k=0
for ch in data.keys():
　ii=n
　k = int(ii / n2)
　i = ii % n2

　axes[i, k].plot(data[ch], label=labels[ii], color="green")
　axes[i, k].legend(loc="upper right")

　#q_1p = np.quantile(data[ch], 0.01)
　#q_99p = np.quantile(data[ch], 0.99)
　q_1p, q_99p = np.percentile(data[ch], q=[1, 99])
　axes[i, k].axhline(y=q_1p, color="m", alpha=0.7, linestyle='--')
　axes[i, k].axhline(y=q_99p, color="m", alpha=0.7, linestyle='--')

axes[0, 0].set_title("time from %s" % time1)
axes[0, 1].set_title("channel = %s" % ch_out)

fig.savefig("./fig/hoge.png")

from matplotlib import pyplot as plt

fig, axes = plt.subplots(2,1, figsize=(10, 12), sharex=True)
ax1 = plt.subplot(2, 1, 1)
ax1.plot(data_freq, data_TF, color="skyblue", marker="o", linestyle="None", label="aaaaa")
ax1.set_xscale('log')
ax1.set_yscale('log')
ax1.set_ylabel('magnitude')
ax1.set_xlim(fmin, fmax)
ax1.set_title("title daup")
ax1.legend(loc="upper right")

ax2 = plt.subplot(2, 1, 2)
ax2.plot(data1, data2, label="coherence (original)")
ax2.set_xscale('log')
ax2.set_xlabel('frequency [Hz]')
ax2.set_ylabel('coherence')
ax2.set_xlim(fmin, fmax)
ax2.legend(loc="lower right")
plt.savefig(fname)
plt.show()

# ↑のリンクからお借りした

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-3, 3, 101)
y1 = np.sin(x * np.pi)
y2 = np.cos(x * 2 * np.pi) * 0.5

fig = plt.figure()
ax = fig.add_subplot(111)

# labelオプションで凡例に用いる曲線名を指定
ax.plot(x, y1, c="r", label="$\mathrm{sin}(x)$")
ax.plot(x, y2, c="b", label="$0.5 \mathrm{cos}(2x)$")
ax.grid(axis='both')

# 凡例の表示
ax.legend()

import matplotlib.pyplot as plt
import matplotlib.collections as mcol
from matplotlib.legend_handler import HandlerLineCollection, HandlerTuple
from matplotlib.lines import Line2D
import numpy as np

t1 = np.arange(0.0, 2.0, 0.1)
t2 = np.arange(0.0, 2.0, 0.01)

fig, ax = plt.subplots()

# note that plot returns a list of lines. The "l1, = plot" usage
# extracts the first element of the list into l1 using tuple
# unpacking. So l1 is a Line2D instance, not a sequence of lines
l1, = ax.plot(t2, np.exp(-t2))
l2, l3 = ax.plot(t2, np.sin(2 * np.pi * t2), '--o', t1, np.log(1 + t1), '.')
l4, = ax.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 's-.')

ax.legend((l2, l4), ('oscillatory', 'damped'), loc='upper right', shadow=True)
ax.set_xlabel('time')
ax.set_ylabel('volts')
ax.set_title('Damped oscillation')
plt.show()

plot = Plot(lho, llo, figsize=(12, 6), sharex=True, yscale='log')
ax1, ax2 = plot.axes
for ifo, ax in zip(('Hanford', 'Livingston'), (ax1, ax2)):
ax.legend(['X', 'Y', 'Z'])
ax.text(1.01, 0.5, ifo, ha='left', va='center', transform=ax.transAxes,
fontsize=18)
ax1.set_ylabel(r'$1-3$\,Hz motion [nm/s]', y=-0.1)
ax2.set_ylabel('')
ax1.set_title('Magnitude 7.1 earthquake impact on LIGO')
plot.show()

plot = Plot(data1, data2, data3)
ax = plot.gca()
ax.legend(['data1dayo', 'data2 dayo', 'data3 dayo''])
plot.show()

import numpy as np

n_a=len(a)
n_clst=10
n_seg=int(n_a/n_clst)

a_split = np.split(a, n_seg)

import numpy as np

n_a=len(a)
n_clst=10
n_seg=int(n_a/n_clst)

a_split = np.split(a, n_seg)

for i, x in enumerate(a_split):
　j = np.argmax(x)
　i_max = j + i*n_clst

ax = plt.gca()
ax.set_yticklabels([])

cython（サイソン）は、C言語によるPythonの拡張モジュールの作成の労力を軽減することを目的として開発されたプログラミング言語である。その言語仕様はほとんどPythonのものと同じ (上位互換) だが、Cの関数を直接呼び出したり、C言語の変数の型やクラスを宣言できるなどの拡張が行われている。Cythonの処理系ではソースファイルをCのコードに変換し、コンパイルすればPythonの拡張モジュールになるようにして出力する。

このようにCとPythonをシームレスに取り混ぜて扱うCythonの利点の一つは、既にあるPythonコードを、いくつかの静的な型 (static type) を宣言して律速なループをうまく書き直すだけで、コンパイル後のコードの実行速度がC言語並みに高速化されることである。複雑なC言語インターフェイスを使う必要はない。コーディングのしやすさと可読性はPythonと変わらない、つまりPythonic（英語版）なままである。数値計算/配列操作では、多くの場合実行速度がおおよそ100倍になる。PythonのJITコンパイラであるPsycoの場合はおおよそ4倍である。

import random
import numpy as np
import matplotlib.pyplot as plt

a = [random.random() for i in range(100)]

val1, base1 = np.histogram(a, bins=100, range=(0.4, 0.8))

def plot_binned_data(axes, binedges, data,
*args, **kwargs):
#The dataset values are the bin centres
x = (binedges[1:] + binedges[:-1]) / 2.0
#The weights are the y-values of the input binned data
weights = data
return axes.hist(x, bins=binedges, weights=weights,
*args, **kwargs)


import numpy
import matplotlib.pyplot as plt

#Create a dataset
dataset = numpy.random.normal(size=100)
#Bin the dataset
binedges = numpy.linspace(-5.0, 5.0, num=10)
y, binedges = numpy.histogram(dataset, binedges)

#Plot the dataset
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plot_binned_data(ax, binedges, y, histtype="step")
plt.show()

import matplotlib.pyplot as plt
import numpy as np

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

# もし複数枚プロットしたい時はここをいじる
# 2x2の場合は fig.add_subplot(2,2,1)として、最後の数字を1~4で変える(最初のプロットはi=0ではないことに注意)
ax = fig.add_subplot(1,1,1)

# ヒストグラムを取る時の範囲はrange指定する
# 0〜最大値*1.1とかにしておくのが吉
ax.hist(x, bins=50, range=[0, 10])
ax.set_title('hogehoge')
ax.set_xlabel('xlabel')
ax.set_ylabel('histogram')
#fig.show()
fig.savefig("hoge.png")

fig.suptitle("graphs")

fig.suptitle("graphs", y=0.90)

del hoge

from matplotlib import pyplot as plt
%matplotlib agg

from matplotlib import pyplot as plt
%matplotlib inline

import matplotlib as mpl
mpl.rcParams['figure.facecolor'] = "white"

[c_match for c_match in c_list if "hoge" in c_match]
[c_match for c_match in c_list if "hoge" == c_match]

conda install -c conda-forge jupyterlab

conda install -c conda-forge nodejs

import matplotlib.pyplot as plt
import numpy as np

val1, base1 = np.histogram(input1, bins=nbins2)
cumulative1 = np.cumsum(val1)/len(input1) # 規格化してるのはここ
weight1 = np.ones(len(input1))/float(len(input1))

fig = plt.figure(figsize=[12, 10])

plt.plot(base1[:-1], 1-cumulative1, c='blue', label="aaaaaa", linewidth = 3.0)

plt.grid()
plt.yscale('log')
plt.xlabel('xxxx')
plt.ylabel('histogram')
plt.title("title")
plt.legend(loc='upper right')
fig.show()
fig.savefig("hoge.png")