def plot(self): self.is_running = False pwd_path = os.getcwd() script_path = os.path.join(pwd_path, 'algo_galileo_2e235eb933c9_deg90\\An_test') folder_path = os.path.join(pwd_path, 'source\\Test_Source_Data') os.chdir(script_path) os.system('powershell -Command "./csv_output.sh {0}"'.format(folder_path)) time.sleep(30) 这段代码中,怎么把路径pwd_path改为os.getcwd()的上级目录

时间: 2024-02-13 14:02:49 浏览: 27
可以使用os.path.abspath(os.path.join(os.getcwd(), ".."))来获取当前工作目录的上级目录,然后将其赋值给pwd_path。所以,可以将第三行代码改为: ``` pwd_path = os.path.abspath(os.path.join(os.getcwd(), "..")) ``` 这样就能获取到当前工作目录的上级目录了。
相关问题

def plot(self): self.is_running = False pwd_path = os.path.abspath(os.path.join(os.getcwd(), "..")) script_path = os.path.join(pwd_path, 'callisto-test-tool\\algo_galileo_2e235eb933c9_deg90\\An_test') folder_path = os.path.join(pwd_path, 'callisto-test-tool\\source\\Test_Source_Data') os.chdir(script_path) os.system('powershell -Command "./csv_output.sh {0}"'.format(folder_path)) 怎么检测该段程序中powershell是否运行完毕

要检测该段程序中 `powershell` 是否运行完毕,可以使用 Python 的 `subprocess` 模块来代替 `os.system` 函数来运行 `powershell`,并使用 `communicate()` 方法来等待 `powershell` 进程结束。例如: ``` import subprocess def plot(self): self.is_running = False pwd_path = os.path.abspath(os.path.join(os.getcwd(), "..")) script_path = os.path.join(pwd_path, 'callisto-test-tool\\algo_galileo_2e235eb933c9_deg90\\An_test') folder_path = os.path.join(pwd_path, 'callisto-test-tool\\source\\Test_Source_Data') os.chdir(script_path) cmd = 'powershell -Command "./csv_output.sh {0}"'.format(folder_path) process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = process.communicate() # 等待进程结束 if process.returncode == 0: print('程序执行成功!') else: print('程序执行失败!') ``` 这样就可以在程序执行完成后得到 `powershell` 进程的返回值,并根据返回值来判断程序是否执行成功。如果返回值为 0,则表示程序执行成功,否则表示程序执行失败。

import tkinter as tk import pandas as pd import matplotlib.pyplot as plt from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import os class ExcelPlotter(tk.Frame): def init(self, master=None): super().init(master) self.master = master self.master.title("图方便") self.file_label = tk.Label(master=self, text="Excel File Path:") self.file_label.grid(row=0, column=0, sticky="w") self.file_entry = tk.Entry(master=self) self.file_entry.grid(row=0, column=1, columnspan=2, sticky="we") self.file_button = tk.Button(master=self, text="Open", command=self.open_file) self.file_button.grid(row=0, column=3, sticky="e") self.plot_button = tk.Button(master=self, text="Plot", command=self.plot_data) self.plot_button.grid(row=1, column=2, sticky="we") self.name_label = tk.Label(master=self, text="Out Image Name:") self.name_label.grid(row=2, column=0, sticky="w") self.name_entry = tk.Entry(master=self) self.name_entry.grid(row=2, column=1, columnspan=2, sticky="we") self.save_button = tk.Button(master=self, text="Save", command=self.save_image) self.save_button.grid(row=2, column=3, sticky="e") self.figure = plt.figure(figsize=(5, 4), dpi=150) self.canvas = FigureCanvasTkAgg(self.figure, master=self) self.canvas.get_tk_widget().grid(row=4, column=0, columnspan=4, sticky="we") self.pack() def open_file(self): file_path = tk.filedialog.askopenfilename(filetypes=[("Excel Files", "*.xls")]) self.file_entry.delete(0, tk.END) self.file_entry.insert(tk.END, file_path) def plot_data(self): file_path = self.file_entry.get() if os.path.exists(file_path): data = pd.read_excel(file_path) plt.plot(data['波长(nm)'], data['吸光度'], 'k') plt.xlim(300, 1000) plt.xlabel('Wavelength(nm)', fontsize=16) plt.ylabel('Abs.', fontsize=16) plt.gcf().subplots_adjust(left=0.13, top=0.91, bottom=0.16) plt.savefig('Last Fig', dpi=1000) plt.show() def save_image(self): if self.figure: file_path = tk.filedialog.asksaveasfilename(defaultextension=".png") if file_path: self.figure.savefig(file_path) root = tk.Tk() app = ExcelPlotter(master=root) app.mainloop()帮我增加一个删除当前图像的功能

好的,您可以在 `ExcelPlotter` 类中添加一个名为 `delete_image` 的方法,实现删除当前图像的功能。可以在该方法中调用 `self.figure.clf()` 清空当前图像,然后在界面上更新显示。下面是修改后的代码: ```python import tkinter as tk import pandas as pd import matplotlib.pyplot as plt from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import os class ExcelPlotter(tk.Frame): def __init__(self, master=None): super().__init__(master) self.master = master self.master.title("图方便") self.file_label = tk.Label(master=self, text="Excel File Path:") self.file_label.grid(row=0, column=0, sticky="w") self.file_entry = tk.Entry(master=self) self.file_entry.grid(row=0, column=1, columnspan=2, sticky="we") self.file_button = tk.Button(master=self, text="Open", command=self.open_file) self.file_button.grid(row=0, column=3, sticky="e") self.plot_button = tk.Button(master=self, text="Plot", command=self.plot_data) self.plot_button.grid(row=1, column=2, sticky="we") self.delete_button = tk.Button(master=self, text="Delete", command=self.delete_image) self.delete_button.grid(row=1, column=3, sticky="e") self.name_label = tk.Label(master=self, text="Out Image Name:") self.name_label.grid(row=2, column=0, sticky="w") self.name_entry = tk.Entry(master=self) self.name_entry.grid(row=2, column=1, columnspan=2, sticky="we") self.save_button = tk.Button(master=self, text="Save", command=self.save_image) self.save_button.grid(row=2, column=3, sticky="e") self.figure = plt.figure(figsize=(5, 4), dpi=150) self.canvas = FigureCanvasTkAgg(self.figure, master=self) self.canvas.get_tk_widget().grid(row=4, column=0, columnspan=4, sticky="we") self.pack() def open_file(self): file_path = tk.filedialog.askopenfilename(filetypes=[("Excel Files", "*.xls")]) self.file_entry.delete(0, tk.END) self.file_entry.insert(tk.END, file_path) def plot_data(self): file_path = self.file_entry.get() if os.path.exists(file_path): data = pd.read_excel(file_path) plt.plot(data['波长(nm)'], data['吸光度'], 'k') plt.xlim(300, 1000) plt.xlabel('Wavelength(nm)', fontsize=16) plt.ylabel('Abs.', fontsize=16) plt.gcf().subplots_adjust(left=0.13, top=0.91, bottom=0.16) plt.show() def delete_image(self): if self.figure: self.figure.clf() self.canvas.draw() def save_image(self): if self.figure: file_path = tk.filedialog.asksaveasfilename(defaultextension=".png") if file_path: self.figure.savefig(file_path) root = tk.Tk() app = ExcelPlotter(master=root) app.mainloop() ``` 在界面上添加了一个名为 `Delete` 的按钮,点击该按钮会调用 `delete_image` 方法。该方法会清空当前图像并在界面上更新显示。

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import akshare as ak import numpy as np import pandas as pd import random import matplotlib.pyplot as plt class StockTradingEnv: def __init__(self): self.df = ak.stock_zh_a_daily(symbol='sh000001', adjust="qfq").iloc[::-1] self.observation_space = self.df.shape[1] self.action_space = 3 self.reset() def reset(self): self.current_step = 0 self.total_profit = 0 self.done = False self.state = self.df.iloc[self.current_step].values return self.state def step(self, action): assert self.action_space.contains(action) if action == 0: # 买入 self.buy_stock() elif action == 1: # 卖出 self.sell_stock() else: # 保持不变 pass self.current_step += 1 if self.current_step >= len(self.df) - 1: self.done = True else: self.state = self.df.iloc[self.current_step].values reward = self.get_reward() self.total_profit += reward return self.state, reward, self.done, {} def buy_stock(self): pass def sell_stock(self): pass def get_reward(self): pass class QLearningAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.995 self.learning_rate = 0.1 self.discount_factor = 0.99 self.q_table = np.zeros((self.state_size, self.action_size)) def act(self, state): if np.random.rand() <= self.epsilon: return random.randrange(self.action_size) else: return np.argmax(self.q_table[state, :]) def learn(self, state, action, reward, next_state, done): target = reward + self.discount_factor * np.max(self.q_table[next_state, :]) self.q_table[state, action] = (1 - self.learning_rate) * self.q_table[state, action] + self.learning_rate * target if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay env = StockTradingEnv() agent = QLearningAgent(env.observation_space, env.action_space) for episode in range(1000): state = env.reset() done = False while not done: action = agent.act(state) next_state, reward, done, _ = env.step(action) agent.learn(state, action, reward, next_state, done) state = next_state if episode % 10 == 0: print("Episode: %d, Total Profit: %f" % (episode, env.total_profit)) agent.save_model("model-%d.h5" % episode) def plot_profit(env, title): plt.figure(figsize=(12, 6)) plt.plot(env.df.index, env.df.close, label="Price") plt.plot(env.df.index, env.profits, label="Profits") plt.legend() plt.title(title) plt.show() env = StockTradingEnv() agent = QLearningAgent(env.observation_space, env.action_space) agent.load_model("model-100.h5") state = env.reset() done = False while not done: action = agent.act(state) next_state, reward, done, _ = env.step(action) state = next_state plot_profit(env, "QLearning Trading Strategy")优化代码

def draw_stats(self, vals, vals1, vals2, vals3, vals4, vals5, vals6): self.ax1 = plt.subplot(self.gs[0, 0]) self.ax1.plot(vals) self.ax1.set_xlim(self.xlim) locs = self.ax1.get_xticks() locs[0] = self.xlim[0] locs[-1] = self.xlim[1] self.ax1.set_xticks(locs) self.ax1.use_sticky_edges = False self.ax1.set_title(f'Connected Clients Ratio') self.ax2 = plt.subplot(self.gs[1, 0]) self.ax2.plot(vals1) self.ax2.set_xlim(self.xlim) self.ax2.set_xticks(locs) self.ax2.yaxis.set_major_formatter(FuncFormatter(format_bps)) self.ax2.use_sticky_edges = False self.ax2.set_title('Total Bandwidth Usage') self.ax3 = plt.subplot(self.gs[2, 0]) self.ax3.plot(vals2) self.ax3.set_xlim(self.xlim) self.ax3.set_xticks(locs) self.ax3.use_sticky_edges = False self.ax3.set_title('Bandwidth Usage Ratio in Slices (Averaged)') self.ax4 = plt.subplot(self.gs[3, 0]) self.ax4.plot(vals3) self.ax4.set_xlim(self.xlim) self.ax4.set_xticks(locs) self.ax4.use_sticky_edges = False self.ax4.set_title('Client Count Ratio per Slice') self.ax5 = plt.subplot(self.gs[0, 1]) self.ax5.plot(vals4) self.ax5.set_xlim(self.xlim) self.ax5.set_xticks(locs) self.ax5.use_sticky_edges = False self.ax5.set_title('Coverage Ratio') self.ax6 = plt.subplot(self.gs[1, 1]) self.ax6.plot(vals5) self.ax6.set_xlim(self.xlim) self.ax6.set_xticks(locs) self.ax6.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax6.use_sticky_edges = False self.ax6.set_title('Block ratio') self.ax7 = plt.subplot(self.gs[2, 1]) self.ax7.plot(vals6) self.ax7.set_xlim(self.xlim) self.ax7.set_xticks(locs) self.ax7.yaxis.set_major_formatter(FormatStrFormatter('%.3f')) self.ax7.use_sticky_edges = False self.ax7.set_title('Handover ratio')修改为一张张输出图片

import sys import threading import time from PyQt5.QtWidgets import * from PyQt5 import uic import pandas as pd import random # import pyqtgraph as pg import matplotlib.pyplot as plt from PyQt5.QtWidgets import QGroupBox from PyQt5 import QtWidgets from login_4 import Ui_CK from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas # df = pd.read_excel('shu.xlsx') class MyWindow(QWidget and QMainWindow,Ui_CK): def __init__(self): super().__init__() self.init_ui() groupbox = QGroupBox('Title',self) # self.plot = pg.PlotWidget(enableAutoRange=True) # self.ui.verticalLayout.addWidget(self.plot) # self.curve = self.plot.plot() #self.ui = uic.loadUi("./login_4.ui") def init_ui(self): print('1.1') try: self.ui = uic.loadUi("./login_4.ui") #print(threading.current_thread()) #print(self.ui.__dict__) # print(self.ui.label) # print(self.ui.label.text()) # 查看ui文件中有哪些控件 # 提取要操作的控件 self.user_name_qwidget = self.ui.lineEdit # 单位输入框 self.password_qwidget = self.ui.lineEdit_2 # 二级单位输入框 self.zhicheng_qwidget = self.ui.lineEdit_3 # 职称输入框 self.jiaoyuan_qwidget = self.ui.lineEdit_4 # 教员输入框 self.login_btn = self.ui.pushButton # 登录抽课按钮 self.textBrowser = self.ui.textBrowser # 授课对象显示区域 # 绑定信号与槽函数 self.textBrowser_2 = self.ui.textBrowser_2 # 文本显示区域课程名称 self.textBrowser_3 = self.ui.textBrowser_3 # 文本显示区域课次 self.textBrowser_4 = self.ui.textBrowser_4 # 文本显示区域教研室 self.login_btn.clicked.connect(self.login) self.login_btna = self.ui.pushButton_2 self.login_btna.clicked.connect(lambda: self.plot_q()) except Exception as e: print(e.__class__.__name__, e) def login(self): print('1.2') """登录按钮的槽函数""" #print(self.user_name_qwidget.text()) a = self.user_name_qwidget.text() e = sel 为什么会报错

下面的这段python代码,哪里有错误,修改一下:import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def __init__(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).__init__() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))

优化代码 def module_split(self, save_on=True): """ split module data :param save_on: :return: """ for ms in range(self.mod_num): m_sn = self.module_list[ms] module_path = os.path.join(self.result_path_down, m_sn) cols_obj = ChuNengPackMustCols(ms, self.mod_cell_num, self.mod_cell_num) # 传入当前的module序号(如0,1,2,3,4),电芯电压个数,温度NTC个数。 aim_cols = [i for i in cols_obj.total_cols if i in self.df.columns] print(m_sn, aim_cols) self.modules[m_sn] = rename_cols_normal(self.df.loc[:, aim_cols], ms, self.mod_cell_num) print("after change cols name:", ms, m_sn, self.modules[m_sn].columns.tolist()) self.modules[m_sn].dropna(axis=0, how='any', subset=['soc'], inplace=True) volt_col = [f'volt{i}' for i in range(self.mod_cell_num)] temp_col = [f'temp{i}' for i in range(self.mod_cell_num)] self.modules[m_sn].dropna(axis=0, how='any', subset=volt_col, inplace=True) self.modules[m_sn] = stat(self.modules[m_sn], volt_col, temp_col) self.modules[m_sn].reset_index(drop=True, inplace=True) print(self.modules[m_sn]['discharge_ah'].iloc[-1]) self.module_cap[m_sn] = [self.modules[m_sn]['discharge_ah'].iloc[-1], self.modules[m_sn]['charge_ah'].iloc[-1], self.modules[m_sn]['soh'].iloc[-1]] self.module_peaks[m_sn] = list(quick_report(self.modules[m_sn], module_path, f'quick_report_{m_sn[:8]}')) # check soc status mod_soc = self.modules[m_sn]['soc'] self.module_soc_sig[m_sn] = [np.nanmedian(mod_soc), np.max(mod_soc), np.min(mod_soc)] if save_on: single_variables_plot(mod_soc, module_path, f'{m_sn[:8]}_soc_distribution_box.png', 'box', 'SOC') single_variables_plot(mod_soc, module_path, f'{m_sn[:8]}_soc_distribution_violin.png', 'violin', 'SOC')

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