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m5/1028m5.py

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+import matplotlib.pyplot as plt
+import numpy as np
+import csv
+import pandas as pd
+
+
+c_list = ["red", "green", "blue"] * 5
+h_list = ["ax/mg", "ay/mg", "az/mg", "gx_deg/s", "gy_deg/s", "gz_deg/s", "mx/mG", "my/mG", "mz/mG", "Yaw", "Pitch", "Roll", "rate/Hz"]
+# dic = dict(zip(h_list, c_list))
+for i in range(13):
+    d_list = []
+
+    with open('C:/Users/F1336489/Desktop/m5_data.csv', 'r') as csvfile:
+        # 创建一个阅读器：将f传给csv.reader
+        reader = csv.reader(csvfile)
+        # 使用csv的next函数，将reader传给next，将返回文件的下一行
+        header_row = next(reader)
+
+        # for index, column_header in enumerate(header_row):
+        #     print(index, column_header)
+
+
+        data = []
+            # 遍历reader的余下的所有行（next读取了第一行，reader每次读取后将返回下一行）
+        for row in reader:
+            # 将字符串转换成数字
+            high = float(row[i])
+            data.append(high)
+        #print(data)
+
+    # 绘制图形
+    fig = plt.figure(dpi=128, figsize=(10, 6))
+    # plt.subplot(431)
+    plt.plot(data, c=str(c_list[i]))
+    # 设置图形的格式
+    plt.title(str(h_list[i]), fontsize=24)
+    plt.xlabel('', fontsize=16)
+    plt.ylabel('', fontsize=16)
+    plt.tick_params(axis='both', which="major", labelsize=16)
+
+    plt.show()

m5/1029data3D.py

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+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+
+import matplotlib.pyplot as plt
+import numpy as np
+import csv
+import pandas as pd
+import pymysql
+from mpl_toolkits.mplot3d import Axes3D
+
+db = pymysql.connect(host="localhost", user="root",
+                     password="16888", db="csv_db", port=3306)
+
+df = pd.read_sql_query("select * from m5db;", db)
+yaw = pd.DataFrame(df, columns=['Yaw'])
+pitch = pd.DataFrame(df, columns=['Pitch'])
+roll = pd.DataFrame(df, columns=['Roll'])
+
+fig = plt.figure()
+ax = Axes3D(fig)
+ax.scatter(yaw, pitch, roll)
+ax.set_xlabel('pitch')
+ax.set_ylabel('yaw')
+ax.set_zlabel('roll')
+ax.view_init(elev=10, azim=235)
+plt.show()

m5/__init__.py

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+#!/usr/bin/python
+# -*- coding: UTF-8 -*-

m5/csv2db.py

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+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+
+
+import pymysql
+import pandas as pd
+
+# 参数设置,建立连接
+db = pymysql.connect(host="localhost", user="root",
+                     password="16888", db="localsql", port=3306)
+
+# 自动确认commit True
+db.autocommit(1)
+# 设置光标
+cursor = db.cursor()
+
+df = pd.read_csv('qe.csv', encoding='utf-8')
+
+
+# 一个根据pandas自动识别type来设定table的type
+def make_table_sql(df):
+    columns = df.columns.tolist()
+    types = df.ftypes
+    # 添加id 制动递增主键模式
+    make_table = []
+    char = ''
+    for item in columns:
+        if 'int' in types[item]:
+            char = item + ' INT'
+        elif 'float' in types[item]:
+            char = item + ' FLOAT'
+        elif 'object' in types[item]:
+            char = item + ' VARCHAR(255)'
+        elif 'datetime' in types[item]:
+            char = item + ' DATETIME'
+        make_table.append(char)
+    return ','.join(make_table)
+
+
+# csv 格式输入 mysql 中
+def csv2mysql(db_name, table_name, df):
+    # 创建database
+    cursor.execute('CREATE DATABASE IF NOT EXISTS {}'.format(db_name))
+    # 选择连接database
+
+    db.select_db(db_name)
+
+    # 创建table
+    cursor.execute('DROP TABLE IF EXISTS {}'.format(table_name))
+    cursor.execute('CREATE TABLE {}({})'.format(table_name, make_table_sql(df)))
+
+    values = df.values.tolist()
+    # 根据columns个数
+    s = ','.join(['%s' for _ in range(len(df.columns))])
+    # executemany批量操作 插入数据 批量操作比逐个操作速度快很多
+    cursor.executemany('INSERT INTO {} VALUES ({})'.format(table_name, s), values)
+
+
+csv2mysql('csv_db', 'm5db', df)
+# 光标关闭
+cursor.close()
+# 连接关闭
+db.close()
+
+
+

m5/mat1028.py

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+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.linspace(-1, 1, 50)
+y = 2*x + 1
+
+plt.figure()
+plt.plot(x, y)
+plt.show()
+