# gitからデータを取得する。
%cd /content/
!rm -rf python_intermediate_2022
!git clone https://github.com/tendo-sms/python_intermediate_2022
%cd /content/python_intermediate_2022/05_GW_pandas

import pandas as pd

# CSVファイルの読み込み
df_data1 = pd.read_csv("sample_1.csv")
df_data2 = pd.read_csv("sample_2.csv")

# 先頭5行を表示する。
print(df_data1.head(5))
print(df_data2.head(5))

import inspect
import figsetting as fs

# figsettingの中身を表示
print(inspect.getsource(fs))

import figsetting as fs
from matplotlib import pyplot as plt

CONFIG = fs.CONFIG
plt.rcParams.update(CONFIG)
FONT_LABEL = fs.FONT_LABEL
FONT_TITLE = fs.FONT_TITLE

# 図の設定
fig, ax = plt.subplots(1,1, figsize = fs.FIGSIZE)

# 可視化パラメータ
X = df_data1["2theta"]
Y = df_data1["Intensity"]
LABEL_X = "2theta"
LABEL_Y = "Intensity"
GRAPH_TITLE = "data1"

# 可視化
ax.plot(X, Y, color = fs.LINE_COLOR)

# X軸ラベル設定
ax.set_xlabel(LABEL_X, **FONT_LABEL)

# Y軸ラベル設定
ax.set_ylabel(LABEL_Y, **FONT_LABEL)

# 軸の設定
ax.minorticks_on()
ax.tick_params(direction="in", which="both", length=5, labelsize=16)
ax.grid(which="major", axis="both", color="gray", linestyle="--", linewidth=0.6)
ax.set_xlim([25, 60])
ax.set_ylim([0, 4200])

# グラフタイトルの設定
ax.set_title(GRAPH_TITLE, **FONT_TITLE)

# 表示
plt.show()

# 図の設定
fig, ax = plt.subplots(1,1, figsize = fs.FIGSIZE)

# 可視化パラメータ
X1 = df_data1["2theta"]
Y1 = df_data1["Intensity"]
X2 = df_data2["2theta"]
Y2 = df_data2["Intensity"]
LABEL_X = "2theta"
LABEL_Y = "Intensity"
GRAPH_TITLE = "data1とdata2"
DISTANCE = 500

# 可視化
ax.plot(X, Y, color = fs.LINE_COLOR)
ax.plot(X, Y+DISTANCE, color = fs.LINE_COLOR2)

# X軸ラベル設定
ax.set_xlabel(LABEL_X, **FONT_LABEL)

# Y軸ラベル設定
ax.set_ylabel(LABEL_Y, **FONT_LABEL)

# 各線の右側にテキストを挿入
ax.text(60.3, 0*DISTANCE, "data1", **FONT_LABEL)
ax.text(60.3, 1*DISTANCE, "data2", **FONT_LABEL)

# 軸の設定
ax.minorticks_on()
ax.tick_params(direction="in", which="both", length=5, labelsize=16)
ax.grid(which="major", axis="both", color="gray", linestyle="--", linewidth=0.6)
ax.set_xlim([25, 60])
ax.set_ylim([0, 5200])

# グラフタイトルの設定
ax.set_title(GRAPH_TITLE, **FONT_TITLE)

# 表示
plt.show()

# 図の設定
fig, ax = plt.subplots(1,1, figsize = fs.FIGSIZE)

# 可視化パラメータ
X = df_data1["2theta"]
Y = df_data1["Intensity"]
LABEL_X = "2theta"
LABEL_Y = "Intensity"
GRAPH_TITLE = "data1"

# 可視化
ax.fill_between(X, Y, color="red", alpha=0.5)
ax.plot(X, Y, color = fs.LINE_COLOR)

# X軸ラベル設定
ax.set_xlabel(LABEL_X, **FONT_LABEL)

# Y軸ラベル設定
ax.set_ylabel(LABEL_Y, **FONT_LABEL)

# 軸の設定
ax.minorticks_on()
ax.tick_params(direction="in", which="both", length=5, labelsize=16)
ax.grid(which="major", axis="both", color="gray", linestyle="--", linewidth=0.6)
ax.set_xlim([27, 30])
ax.set_ylim([0, 4500])

# グラフタイトルの設定
ax.set_title(GRAPH_TITLE, **FONT_TITLE)

# 表示
plt.show()

import numpy as np
from scipy.stats import norm
from scipy.optimize import curve_fit

# ガウシアンフィッティング用の関数を準備する
def gauss(x, a, mu, sigma):
    return a*np.exp(-(x-mu)**2/(2*sigma**2))

# 対象範囲を絞り込む
mask = (df_data1["2theta"]>=27) & (df_data1["2theta"]<=30)
X = df_data1[mask]["2theta"]
Y = df_data1[mask]["Intensity"]

# ガウシアンフィッティングを実行する
param_ini = [4000, 28.4, 0.1]
popt, pcov = curve_fit(gauss, X, Y, p0=param_ini)
fitting = gauss(X, popt[0],popt[1],popt[2])

# 図の設定
fig, ax = plt.subplots(1,1, figsize = fs.FIGSIZE)

# 可視化パラメータ
LABEL_X = "2theta"
LABEL_Y = "Intensity"
GRAPH_TITLE = "data1 and fitting"

# 可視化
ax.fill_between(X, Y, color="red", alpha=0.5)
ax.plot(X, Y, color = fs.LINE_COLOR, label="data1")
ax.plot(X, fitting, color = "blue", linewidth=2, label="fitting")

# X軸ラベル設定
ax.set_xlabel(LABEL_X, **FONT_LABEL)

# Y軸ラベル設定
ax.set_ylabel(LABEL_Y, **FONT_LABEL)

# 軸の設定
ax.minorticks_on()
ax.tick_params(direction="in", which="both", length=5, labelsize=16)
ax.grid(which="major", axis="both", color="gray", linestyle="--", linewidth=0.6)
ax.set_xlim([27, 30])
ax.set_ylim([0, 4500])

# 凡例の設定
ax.legend(fontsize=18) 

# グラフタイトルの設定
ax.set_title(GRAPH_TITLE, **FONT_TITLE)

# 表示
plt.show()