物理実験系研究者のJupyter cheatsheet

はじめに. ---最終更新 190801

• 物理実験系の研究をしています．
• Jupyterを実験・計算データ解析で使いはじめました．[190601~]
• （これまでは主にExcelとIgorを使っていました．）
• 一度使った分析手法(図示方法)を数ヶ月後とかにもう一度使うときがあります．
• 忘れていることがよくあります．
• 毎回コードを発掘するorググるのが面倒なのでcheat sheetにまとめます．
• もっとこう書けばスッキリする等，アドバイスがあれば教えてもらえると嬉しいです．
• 誰かの役に立てば幸いです．

For data analysis

Import module

###### Fundamentals ######
import numpy as np
import pandas as pd
import math
import cmath
import random
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

###### Figure setting ######
sns.set('talk', 'whitegrid', 'bright', font_scale=1.0,
        rc={"lines.linewidth": 2, 'grid.linestyle': '--', "xtick.major.size": 6, "ytick.major.size": 6})

###### Plotly ######
import plotly # only used in jupyter notebook
plotly.offline.init_notebook_mode(connected=False)

###### File operation ######
import glob
import re
import os
import time
import datetime

Figures

- Normal figure

fig = plt.figure(figsize=(6,4), dpi=80)
fig.subplots_adjust(left=0.2, wspace=0.6, bottom=0.15, hspace = 0.6)
fig.suptitle("")

ax1 = fig.add_subplot(1,1,1)
ax1.plot(df["xxx"], df["yyy"], "o-", color='black', linestyle='solid', linewidth=2)
ax1.axis('tight')
ax1.set_xlabel("x_label")
ax1.set_ylabel("$\mathrm{y_{label}}$")
ax1.set_ylim(1,1e5)
ax1.grid(True)

- Twin axis

ax2 = ax1.twinx()
ax2.plot(df["xxx"], df["yyy"], "o-", linewidth=2)
ax2.set_xlabel("x_label")
ax2.set_ylabel("y_label")
ax2.set_yscale('log')

- Plotly

-- Show fig

plotly.offline.iplot_mpl(fig)

- Heatmap

-- seaborn

ax = sns.heatmap(df)
ax.set(xlabel='x position', ylabel='y position')

--- Log color scale

from matplotlib.colors import LogNorm

log_norm = LogNorm(vmin=df.min().min(), vmax=df.max().max())
cbar_ticks = [math.pow(10, i) for i in range(math.floor(math.log10(df.min().min())), 1+math.ceil(math.log10(df.max().max())))]

ax = sns.heatmap(df,
                norm=log_norm,
                cbar_kws={"ticks": cbar_ticks}
                )
ax.set(xlabel='x position', ylabel='y position')

-- Plotly

fig = go.Figure(data=go.Heatmap(z=df.values.tolist()))

fig.update_layout(
    autosize=False,
    width=500,
    height=500,
    title = "Title",
    xaxis=dict(
        title="x_title",
        type='log',
        autorange=True,
        exponentformat='power'
    ),
    yaxis=dict(
        title="y_title"
        type='log',
        autorange=True,
        exponentformat='power'
    )
)

fig.update_yaxes(automargin=True)

fig.show()

3D plot

df = pd.read_csv("matrix_fill.csv", index_col=0)
x = np.array(df.columns, np.float32)
y = np.array(df.index, np.float32)
X,Y = np.meshgrid(x,y)
Z = df
fig = plt.figure()
ax = Axes3D(fig)

#ax.plot_surface(X, Y, Z, rstride=10, cstride=1, cmap='Reds', linewidth=5)
#ax.plot_surface(X, Y, Z, rstride=5, cstride=5)
ax.plot_surface(X, Y, Z, cmap='Reds')
#ax.plot_surface(X, Y, Z, rstride=5)
#ax.plot_surface(X, Y, Z, cstride=1, facecolors='Reds')
#ax.plot_surface(X, Y, Z)

ax.plot_wireframe(X,Y,Z, linewidth=1, color='b')

plt.show()

Data to images

for p in glob.iglob('*.dat'):
    name, ext = os.path.splitext(p)
    df = pd.read_table(p, header = 2)
    df.columns = ["xxx", "yyy"]
    plt.plot(df["xxx"], df["yyy"])
    plt.xlabel("x_label")
    plt.ylabel("y_label")
    plt.savefig("%s.png" % name, transparent=True, bbox_inches="tight")
    plt.close()

Import files

- Extract values

file_list = sorted([p for p in glob.glob('./*/*/**', recursive=True) if re.search('file.dat', p)])

for filename in file_list:
    f = open(filename, "r")
    data_list = f.read().split()
    try:
        a_list.append(data_list[0])
        b_list.append(data_list[1])
        c_list.append(data_list[2][3:])
        print("Success!")
        f.close()
    except:
        print("No such a index")
        a_list.append(np.nan)
        b_list.append(np.nan)
        c_list.append(np.nan)
        f.close()
#print("a:  ", a_list)
#print("b: ", b_list)
#print("c: ", c_list)

- Pandas

df = pd.DataFrame(data_list, dtype=np.float64)

df = pd.read_csv("./file.dat", sep='\t', header=None)

Constants

###### Constants ######
pi = math.pi
sqrt = math.sqrt
exp = cmath.exp
c0 = 2.99792458e8
q0 = 1.60217662e-19
E0 = 8.85418782e-12
m0 = 9.10938356e-31
h = 6.62607015e-34
hbar = 1.054571800e-34
n0 = 3.46

Others

Font check

import matplotlib.font_manager
matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')
import matplotlib as mpl
mpl.matplotlib_fname()