Tôi có một danh sách raws các mảng mà tôi muốn vẽ trong máy tính xách tay Ipython. Đây là mã tôi đang cố gắng để làm việc:
fig, axes = subplots(len(raws),1, sharex=True, tight_layout=True, figsize=(12, 6), dpi=72) for r in range(len(raws)): axes[r].plot(raws)Tôi đã bị mất hàng giờ nếu không phải là ngày cố gắng tìm ra cách lập chỉ mục danh sách raws, sao cho tôi có thể vẽ từng mảng MXN trên trục riêng của nó trong đó n là số điểm thời gian, tức là, trục x và m là Số lượng hàm chuỗi thời gian được lấy mẫu tại mỗi điểm.
Khi tôi viết mã:
for r in range(len(raws)): axes[r].plot(raws[r])Tôi nhận được một giá trịerror: Đặt một phần tử mảng với một chuỗi.
Cho thông tin của bạn:
len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))]Sơ đồ các điểm dữ liệu x và y, với màu đỏ ..
- Để hiển thị hình, sử dụng phương thức show () ..
- Làm thế nào để bạn truy cập một mảng bên trong một mảng trong Python?x and y, using numpy.
- Một mảng trong Python được sử dụng để lưu trữ nhiều giá trị hoặc mục hoặc các phần tử cùng loại trong một biến duy nhất. Chúng ta có thể truy cập các phần tử của một mảng bằng toán tử chỉ mục []. Tất cả những gì bạn cần làm để truy cập một yếu tố cụ thể là gọi cho mảng bạn đã tạo.title() method.
- Để vẽ một mảng trong Python, chúng ta có thể thực hiện các bước sau -x and y data points, with red color.
- Đặt kích thước hình và điều chỉnh phần đệm giữa và xung quanh các ô con.show() method.
Tạo hai mảng, x và y, sử dụng numpy.
Đặt tiêu đề của đường cong bằng phương thức Tiêu đề ().Vẽ các điểm dữ liệu X và Y, với màu đỏ.
Để hiển thị hình, sử dụng phương thức show ().
- Thí dụ
- import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([5, 4, 1, 4, 5]) y = np.sort(x) plt.title("Line graph") plt.plot(x, y, color="red") plt.show()
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Cải thiện bài viết
Lưu bài viết
Cải thiện bài viết
Lưu bài viết
Đọcpyplot(), which is used to plot two-dimensional data.
Các chức năng khác nhau được sử dụng được giải thích dưới đây:
- np.arange (bắt đầu, kết thúc): hàm này trả về các giá trị cách đều nhau từ khoảng [bắt đầu, kết thúc).This function returns equally spaced values from the interval [start, end).
- plt.title (): Nó được sử dụng để đưa ra một tiêu đề cho biểu đồ. Tiêu đề được truyền làm tham số cho hàm này.It is used to give a title to the graph. Title is passed as the parameter to this function.
- plt.xlabel (): Nó đặt tên nhãn tại trục x. Tên của trục x được truyền làm đối số cho hàm này.It sets the label name at X-axis. Name of X-axis is passed as argument to this function.
- plt.ylabel (): Nó đặt tên nhãn tại trục y. Tên của trục y được truyền như là đối số cho hàm này.It sets the label name at Y-axis. Name of Y-axis is passed as argument to this function.
- plt.plot (): Nó biểu thị các giá trị của các tham số được truyền cho nó cùng nhau.It plots the values of parameters passed to it together.
- plt.show (): Nó hiển thị tất cả các biểu đồ cho bảng điều khiển.It shows all the graph to the console.
Ví dụ 1 :
Python3
import numpy as np
import matplotlib.pyplot as plt
____10for r in range(len(raws)): axes[r].plot(raws[r]) 1 for r in range(len(raws)): axes[r].plot(raws[r]) 2for r in range(len(raws)): axes[r].plot(raws[r]) 3for r in range(len(raws)): axes[r].plot(raws[r]) 4for r in range(len(raws)): axes[r].plot(raws[r]) 5for r in range(len(raws)): axes[r].plot(raws[r]) 6
for r in range(len(raws)): axes[r].plot(raws[r]) 7for r in range(len(raws)): axes[r].plot(raws[r]) 1 for r in range(len(raws)): axes[r].plot(raws[r]) 0__ len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 1
len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 2 len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 3for r in range(len(raws)): axes[r].plot(raws[r]) 6
len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 5 len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 6for r in range(len(raws)): axes[r].plot(raws[r]) 6
len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 8 len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 9for r in range(len(raws)): axes[r].plot(raws[r]) 6
import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([5, 4, 1, 4, 5]) y = np.sort(x) plt.title("Line graph") plt.plot(x, y, color="red") plt.show()1for r in range(len(raws)): axes[r].plot(raws[r]) 1import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([5, 4, 1, 4, 5]) y = np.sort(x) plt.title("Line graph") plt.plot(x, y, color="red") plt.show()3for r in range(len(raws)): axes[r].plot(raws[r]) 6
import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([5, 4, 1, 4, 5]) y = np.sort(x) plt.title("Line graph") plt.plot(x, y, color="red") plt.show()5
& nbsp; đầu ra: & nbsp;
Output :
Ví dụ 2:
Python3
import numpy as np
import matplotlib.pyplot as plt
____10for r in range(len(raws)): axes[r].plot(raws[r]) 1 for r in range(len(raws)): axes[r].plot(raws[r]) 2for r in range(len(raws)): axes[r].plot(raws[r]) 3for r in range(len(raws)): axes[r].plot(raws[r]) 4for r in range(len(raws)): axes[r].plot(raws[r]) 5for r in range(len(raws)): axes[r].plot(raws[r]) 6
for r in range(len(raws)): axes[r].plot(raws[r]) 7for r in range(len(raws)): axes[r].plot(raws[r]) 1 for r in range(len(raws)): axes[r].plot(raws[r]) 0__ len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 1
len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 2 len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 3for r in range(len(raws)): axes[r].plot(raws[r]) 6
len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 5 len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 6for r in range(len(raws)): axes[r].plot(raws[r]) 6
len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 8 len(raws) = 2 type(raws) = 'list' np.shape(raws[0][0]) = (306, 10001) raws = [(array([[ -4.13211217e-12, -4.13287303e-12, -4.01705259e-12, ..., 1.36386023e-12, 1.65182851e-12, 2.00368966e-12], [ 1.08914129e-12, 1.47828466e-12, 1.82257607e-12, ..., -2.70151520e-12, -2.48631967e-12, -2.28625548e-12], [ -7.80962369e-14, -1.27119591e-13, -1.73610315e-13, ..., -1.13219629e-13, -1.15031720e-13, -1.12106621e-13], ..., [ 2.52774254e-12, 2.32293195e-12, 2.02644002e-12, ..., 4.20064191e-12, 3.94858906e-12, 3.69495394e-12], [ -4.38122146e-12, -4.96229676e-12, -5.47782145e-12, ..., 3.93820033e-12, 4.18850823e-12, 4.34950629e-12], [ -1.07284424e-13, -9.23447993e-14, -7.89852400e-14, ..., 7.92079631e-14, 5.60172215e-14, 3.04448868e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ])), (array([[ -6.71363108e-12, -5.80501003e-12, -4.95944514e-12, ..., -3.25087343e-12, -2.68982494e-12, -2.13637448e-12], [ -5.04818633e-12, -4.65757005e-12, -4.16084140e-12, ..., -4.26120531e-13, 2.20744290e-13, 7.81245614e-13], [ 1.97329506e-13, 1.64543867e-13, 1.32679812e-13, ..., 2.11645494e-13, 1.94795729e-13, 1.75781773e-13], ..., [ 3.04245661e-12, 2.28376461e-12, 1.54118900e-12, ..., -1.14020908e-14, -8.04647589e-13, -1.52676489e-12], [ -1.83485962e-13, -5.22949893e-13, -8.60038852e-13, ..., 7.70312553e-12, 7.20825156e-12, 6.58362857e-12], [ -7.26357906e-14, -7.11700989e-14, -6.88759767e-14, ..., -1.04171843e-13, -1.03084861e-13, -9.68462427e-14]]), array([ 60. , 60.001, 60.002, ..., 69.998, 69.999, 70. ]))] 9for r in range(len(raws)): axes[r].plot(raws[r]) 6
import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([5, 4, 1, 4, 5]) y = np.sort(x) plt.title("Line graph") plt.plot(x, y, color="red") plt.show()1for r in range(len(raws)): axes[r].plot(raws[r]) 1import1for r in range(len(raws)): axes[r].plot(raws[r]) 6
import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([5, 4, 1, 4, 5]) y = np.sort(x) plt.title("Line graph") plt.plot(x, y, color="red") plt.show()5
& nbsp; đầu ra: & nbsp;
Output :