matplotlib-show for loop

最新推荐文章于 2023-02-10 21:27:35 发布
转载 最新推荐文章于 2023-02-10 21:27:35 发布 · 477 阅读 · 0

https://stackoverflow.com/questions/11874767/real-time-plotting-in-while-loop-with-matplotlib
matplotlib简单使用之循环对比图
weixin_43874447的博客
06-09 406
支持多列数据提取画图，在同一个图中展示。
Matplotlib系列(七)：动画
hustlei的专栏
01-23 1万+
Matplotlib系列目录 文章目录一、 简介二、 思维导图三、 Matplotlib动画及图形修改操作1. 手写代码更新图形实现动画2. animation模块动画2.1 Animation类简介2.2 FuncAnimation动画2.3 ArtistAnimation动画2.4 保存动画3. 常用图形更新函数参考文章 一、 简介 ‎matplotlib的animation模块可以实现高效的动画绘制，并能够保持到gif或者视频文件中。 ‎matplotlib中的图形，如线条、点、坐标系、柱形图等
matplotlib 三题练习
qq_37875323的博客
05-30 461
ex 1import matplotlib.pyplot as plt import math import numpy as np x = np.linspace(0, 5, 100) y1=np.sin((x-2))**2 y2=math.e**(-1*x**2) y = y1*y2 plt.figure() plt.plot(x, y, 'b') plt.xlabel('x') plt.y...
python matplotlib 循环不断绘制显示下张图
宇脩
02-10 6354
循环里添加绘制代码，实现不断刷新显示下一张图的效果。
python使用plt画图时，在for循环中生成多张图
黎木的博客
04-13 7006
for topic_id,topic in enumerate(lda.components_): topword = pd.DataFrame({"word":[tf_feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]], "componets":topic[topic.argsort()[:-n_top_words - 1:-1]]}) topword.s.
用matplotlib中在循环语句中显示多张图片
caokaifa的博客
06-03 1万+
今天，运行MTCNN/TensorFlow 实现人脸检测程序，然后想要用matplotlib在循环语句中显示多张图片或自动关闭进入下一张图片显示，因为没事imshow()时需要手动关闭才能运行接下来的程序，这太麻烦了，现在可以给出自动关闭进入下一次显示的程序。from __future__ import absolute_import from __future__ import division...
Phase-Looked Loop是什么
06-24
import matplotlib.pyplot as plt def pll_simulation(input_signal, loop_filter_gain, vco_initial_frequency): # 初始化参数 output_signal = [] phase_error = 0 vco_frequency = vco_initial_frequency ...
## 题目重述 实验数据位于文件 `regress_data1.xlsx` 中，要求完成以下任务： 1. 绘制数据散点图，其中横轴为“人口”，纵轴为“收益”； 2. 构建线性回归模型 $ y = ax + b $，使用最小二乘法求解参数 $ a $ 和 $ b $，采用两种方法： - 使用 NumPy 内置函数； - 使用 Python 循环手动实现； 3. 构建相同模型，以最小平方误差为损失函数，使用梯度下降算法求解参数： - 批量梯度下降（Batch GD）； - 随机梯度下降（Stochastic GD）； - 小批量梯度下降（Mini-batch GD）； 4. 使用 `sklearn` 建立回归模型，并评估其性能（如 $ R^2 $、MSE 等）。 --- ## 给出答案（带注释的完整 Python 代码） ```python import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score import warnings # 忽略字体警告和收敛警告 warnings.filterwarnings("ignore") plt.rcParams['axes.unicode_minus'] = False # 正常显示负号 # ----------------------------- # 1. 加载数据 # ----------------------------- # 读取 Excel 文件中的数据 data = pd.read_excel('regress_data1.xlsx') # 提取“人口”和“收益”列 x = data['人口'].values # 形状: (n,) y = data['收益'].values # 形状: (n,) n = len(x) # ----------------------------- # 2. 绘制散点图 # ----------------------------- plt.figure(figsize=(10, 6)) plt.scatter(x, y, color='blue', alpha=0.7, label='数据点') plt.xlabel('人口') plt.ylabel('收益') plt.title('人口 vs 收益 散点图') plt.grid(True, linestyle='--', alpha=0.5) plt.legend() plt.show() # ----------------------------- # 3. 最小二乘法求解 y = ax + b # ----------------------------- # 方法一：使用 NumPy 的 polyfit 函数 a_numpy, b_numpy = np.polyfit(x, y, 1) # 方法二：使用循环手动实现最小二乘法 x_mean = np.mean(x) y_mean = np.mean(y) numerator = 0.0 denominator = 0.0 for i in range(n): numerator += (x[i] - x_mean) * (y[i] - y_mean) denominator += (x[i] - x_mean) ** 2 a_loop = numerator / denominator b_loop = y_mean - a_loop * x_mean print("【最小二乘法结果】") print(f"NumPy 函数法: a = {a_numpy:.4f}, b = {b_numpy:.4f}") print(f"循环实现法: a = {a_loop:.4f}, b = {b_loop:.4f}") # ----------------------------- # 4. 梯度下降算法 # ----------------------------- def add_intercept(X): """添加截距项（x0=1）""" return np.column_stack([np.ones(len(X)), X]) X = add_intercept(x) # (n, 2): 第一列为1，第二列为x y = y.reshape(-1, 1) # (n, 1) # 参数初始化 np.random.seed(42) theta = np.random.randn(2, 1) * 0.1 learning_rate = 0.0001 epochs = 10000 # 批量梯度下降 (BGD) theta_bgd = theta.copy() for _ in range(epochs): gradient = (1/n) * X.T @ (X @ theta_bgd - y) theta_bgd -= learning_rate * gradient a_bgd, b_bgd = theta_bgd[1, 0], theta_bgd[0, 0] # 随机梯度下降 (SGD) theta_sgd = theta.copy() for epoch in range(epochs): for i in range(n): xi = np.array([[1, x[i]]]) # (1, 2) yi = y[i] # (1, 1) gradient = xi.T @ (xi @ theta_sgd - yi) theta_sgd -= learning_rate * gradient a_sgd, b_sgd = theta_sgd[1, 0], theta_sgd[0, 0] # 小批量梯度下降 (MBGD)，batch_size=16 theta_mbgd = theta.copy() batch_size = 16 for epoch in range(epochs): indices = np.random.permutation(n) X_shuffled = X[indices] y_shuffled = y[indices] for start in range(0, n, batch_size): end = start + batch_size X_batch = X_shuffled[start:end] y_batch = y_shuffled[start:end] m = len(X_batch) gradient = (1/m) * X_batch.T @ (X_batch @ theta_mbgd - y_batch) theta_mbgd -= learning_rate * gradient a_mbgd, b_mbgd = theta_mbgd[1, 0], theta_mbgd[0, 0] print("\n【梯度下降结果】") print(f"批量梯度下降: a = {a_bgd:.4f}, b = {b_bgd:.4f}") print(f"随机梯度下降: a = {a_sgd:.4f}, b = {b_sgd:.4f}") print(f"小批量梯度下降: a = {a_mbgd:.4f}, b = {b_mbgd:.4f}") # ----------------------------- # 5. 使用 sklearn 建模与评估 # ----------------------------- model_sk = LinearRegression() model_sk.fit(x.reshape(-1, 1), y.ravel()) y_pred_sk = model_sk.predict(x.reshape(-1, 1)) mse = mean_squared_error(y, y_pred_sk) r2 = r2_score(y, y_pred_sk) print("\n【sklearn 模型结果】") print(f"斜率 a = {model_sk.coef_[0]:.4f}, 截距 b = {model_sk.intercept_:.4f}") print(f"均方误差 MSE = {mse:.4f}") print(f"决定系数 R² = {r2:.4f}") # ----------------------------- # 6. 绘制所有回归线对比图 # ----------------------------- plt.figure(figsize=(12, 7)) plt.scatter(x, y, color='blue', alpha=0.6, label='原始数据') x_plot = np.linspace(x.min(), x.max(), 100) plt.plot(x_plot, a_numpy * x_plot + b_numpy, color='green', label='最小二乘法（NumPy）', linewidth=2) plt.plot(x_plot, a_bgd * x_plot + b_bgd, color='orange', linestyle='-.', label='批量梯度下降', linewidth=2) plt.plot(x_plot, model_sk.coef_[0] * x_plot + model_sk.intercept_, color='red', label='Sklearn 模型', linewidth=2) plt.xlabel('人口') plt.ylabel('收益') plt.title('不同方法拟合的线性回归模型对比') plt.legend() plt.grid(True, alpha=0.5) plt.tight_layout() plt.show() 修缮代码使可视化图片的文字可以显示成功
10-27
print(f"循环实现法: a = {a_loop:.4f}, b = {b_loop:.4f}") # ----------------------------- # 4. 梯度下降算法 # ----------------------------- def add_intercept(X): return np.column_stack([np.ones(len(X...
A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "D:\python\Anaconda3\envs\d2l\lib\runpy.py", line 197, in _run_module_as_main return _run_code(code, main_globals, None, File "D:\python\Anaconda3\envs\d2l\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel_launcher.py", line 18, in <module> app.launch_new_instance() File "D:\python\Anaconda3\envs\d2l\lib\site-packages\traitlets\config\application.py", line 1075, in launch_instance app.start() File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\kernelapp.py", line 739, in start self.io_loop.start() File "D:\python\Anaconda3\envs\d2l\lib\site-packages\tornado\platform\asyncio.py", line 211, in start self.asyncio_loop.run_forever() File "D:\python\Anaconda3\envs\d2l\lib\asyncio\base_events.py", line 601, in run_forever self._run_once() File "D:\python\Anaconda3\envs\d2l\lib\asyncio\base_events.py", line 1905, in _run_once handle._run() File "D:\python\Anaconda3\envs\d2l\lib\asyncio\events.py", line 80, in _run self._context.run(self._callback, *self._args) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\kernelbase.py", line 519, in dispatch_queue await self.process_one() File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\kernelbase.py", line 508, in process_one await dispatch(*args) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\kernelbase.py", line 400, in dispatch_shell await result File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\ipkernel.py", line 368, in execute_request await super().execute_request(stream, ident, parent) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\kernelbase.py", line 767, in execute_request reply_content = await reply_content File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\ipkernel.py", line 455, in do_execute res = shell.run_cell( File "D:\python\Anaconda3\envs\d2l\lib\site-packages\ipykernel\zmqshell.py", line 602, in run_cell return super().run_cell(*args, **kwargs) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py", line 3048, in run_cell result = self._run_cell( File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py", line 3103, in _run_cell result = runner(coro) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner coro.send(None) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py", line 3308, in run_cell_async has_raised = await self.run_ast_nodes(code_ast.body, cell_name, File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py", line 3490, in run_ast_nodes if await self.run_code(code, result, async_=asy): File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py", line 3550, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "d:\Temp\ipykernel_10160\1106343824.py", line 1, in <module> get_ipython().run_line_magic('matplotlib', 'inline') File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py", line 2456, in run_line_magic result = fn(*args, **kwargs) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\magics\pylab.py", line 99, in matplotlib gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui) File "D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py", line 3630, in enable_matplotlib from matplotlib_inline.backend_inline import configure_inline_support File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib_inline\__init__.py", line 1, in <module> from . import backend_inline, config # noqa File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib_inline\backend_inline.py", line 6, in <module> import matplotlib File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\__init__.py", line 161, in <module> from . import _api, _version, cbook, _docstring, rcsetup File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\rcsetup.py", line 27, in <module> from matplotlib.colors import Colormap, is_color_like File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\colors.py", line 56, in <module> from matplotlib import _api, _cm, cbook, scale File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\scale.py", line 22, in <module> from matplotlib.ticker import ( File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\ticker.py", line 138, in <module> from matplotlib import transforms as mtransforms File "D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\transforms.py", line 49, in <module> from matplotlib._path import ( --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) AttributeError: _ARRAY_API not found --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[1], line 1 ----> 1 get_ipython().run_line_magic('matplotlib', 'inline') 2 import os 3 import pandas as pd File D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py:2456, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth) 2454 kwargs['local_ns'] = self.get_local_scope(stack_depth) 2455 with self.builtin_trap: -> 2456 result = fn(*args, **kwargs) 2458 # The code below prevents the output from being displayed 2459 # when using magics with decorator @output_can_be_silenced 2460 # when the last Python token in the expression is a ';'. 2461 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False): File D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\magics\pylab.py:99, in PylabMagics.matplotlib(self, line) 97 print("Available matplotlib backends: %s" % backends_list) 98 else: ---> 99 gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui) 100 self._show_matplotlib_backend(args.gui, backend) File D:\python\Anaconda3\envs\d2l\lib\site-packages\IPython\core\interactiveshell.py:3630, in InteractiveShell.enable_matplotlib(self, gui) 3609 def enable_matplotlib(self, gui=None): 3610 """Enable interactive matplotlib and inline figure support. 3611 3612 This takes the following steps: (...) 3628 display figures inline. 3629 """ -> 3630 from matplotlib_inline.backend_inline import configure_inline_support 3632 from IPython.core import pylabtools as pt 3633 gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select) File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib_inline\__init__.py:1 ----> 1 from . import backend_inline, config # noqa 3 __version__ = "0.2.1" 5 # we can't ''.join(...) otherwise finding the version number at build time requires 6 # import which introduces IPython and matplotlib at build time, and thus circular 7 # dependencies. File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib_inline\backend_inline.py:6 1 """A matplotlib backend for publishing figures via display_data""" 3 # Copyright (c) IPython Development Team. 4 # Distributed under the terms of the BSD 3-Clause License. ----> 6 import matplotlib 7 from IPython.core.getipython import get_ipython 8 from IPython.core.interactiveshell import InteractiveShell File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\__init__.py:161 157 from packaging.version import parse as parse_version 159 # cbook must import matplotlib only within function 160 # definitions, so it is safe to import from it here. --> 161 from . import _api, _version, cbook, _docstring, rcsetup 162 from matplotlib.cbook import sanitize_sequence 163 from matplotlib._api import MatplotlibDeprecationWarning File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\rcsetup.py:27 25 from matplotlib import _api, cbook 26 from matplotlib.cbook import ls_mapper ---> 27 from matplotlib.colors import Colormap, is_color_like 28 from matplotlib._fontconfig_pattern import parse_fontconfig_pattern 29 from matplotlib._enums import JoinStyle, CapStyle File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\colors.py:56 54 import matplotlib as mpl 55 import numpy as np ---> 56 from matplotlib import _api, _cm, cbook, scale 57 from ._color_data import BASE_COLORS, TABLEAU_COLORS, CSS4_COLORS, XKCD_COLORS 60 class _ColorMapping(dict): File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\scale.py:22 20 import matplotlib as mpl 21 from matplotlib import _api, _docstring ---> 22 from matplotlib.ticker import ( 23 NullFormatter, ScalarFormatter, LogFormatterSciNotation, LogitFormatter, 24 NullLocator, LogLocator, AutoLocator, AutoMinorLocator, 25 SymmetricalLogLocator, AsinhLocator, LogitLocator) 26 from matplotlib.transforms import Transform, IdentityTransform 29 class ScaleBase: File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\ticker.py:138 136 import matplotlib as mpl 137 from matplotlib import _api, cbook --> 138 from matplotlib import transforms as mtransforms 140 _log = logging.getLogger(__name__) 142 __all__ = ('TickHelper', 'Formatter', 'FixedFormatter', 143 'NullFormatter', 'FuncFormatter', 'FormatStrFormatter', 144 'StrMethodFormatter', 'ScalarFormatter', 'LogFormatter', (...) 150 'MultipleLocator', 'MaxNLocator', 'AutoMinorLocator', 151 'SymmetricalLogLocator', 'AsinhLocator', 'LogitLocator') File D:\python\Anaconda3\envs\d2l\lib\site-packages\matplotlib\transforms.py:49 46 from numpy.linalg import inv 48 from matplotlib import _api ---> 49 from matplotlib._path import ( 50 affine_transform, count_bboxes_overlapping_bbox, update_path_extents) 51 from .path import Path 53 DEBUG = False ImportError: numpy.core.multiarray failed to import
最新发布
12-02
print(f"matplotlib-inline: {matplotlib_inline.__version__}") # 应为0.2.1 ``` #### 步骤4：处理其他依赖 如果d2l或scipy仍然报错，可以尝试升级它们到兼容版本： ```bash # 升级d2l到最新版本（可能已经适配了...
%load_ext autoreload %autoreload 2 %matplotlib inline A module that was compiled using NumPy 1.x cannot be run in NumPy 2.0.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "E:\anaconda\lib\runpy.py", line 197, in _run_module_as_main return _run_code(code, main_globals, None, File "E:\anaconda\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "E:\anaconda\lib\site-packages\ipykernel_launcher.py", line 17, in <module> app.launch_new_instance() File "E:\anaconda\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance app.start() File "E:\anaconda\lib\site-packages\ipykernel\kernelapp.py", line 712, in start self.io_loop.start() File "E:\anaconda\lib\site-packages\tornado\platform\asyncio.py", line 199, in start self.asyncio_loop.run_forever() File "E:\anaconda\lib\asyncio\base_events.py", line 601, in run_forever self._run_once() File "E:\anaconda\lib\asyncio\base_events.py", line 1905, in _run_once handle._run() File "E:\anaconda\lib\asyncio\events.py", line 80, in _run self._context.run(self._callback, *self._args) File "E:\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 510, in dispatch_queue await self.process_one() File "E:\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 499, in process_one await dispatch(*args) File "E:\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 406, in dispatch_shell await result File "E:\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 730, in execute_request reply_content = await reply_content File "E:\anaconda\lib\site-packages\ipykernel\ipkernel.py", line 390, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "E:\anaconda\lib\site-packages\ipykernel\zmqshell.py", line 528, in run_cell return super().run_cell(*args, **kwargs) File "E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2914, in run_cell result = self._run_cell( File "E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2960, in _run_cell return runner(coro) File "E:\anaconda\lib\site-packages\IPython\core\async_helpers.py", line 78, in _pseudo_sync_runner coro.send(None) File "E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3185, in run_cell_async has_raised = await self.run_ast_nodes(code_ast.body, cell_name, File "E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3377, in run_ast_nodes if (await self.run_code(code, result, async_=asy)): File "E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3457, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "C:\Users\John\AppData\Local\Temp\ipykernel_7032\4138125057.py", line 3, in <module> get_ipython().run_line_magic('matplotlib', 'inline') File "E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2364, in run_line_magic result = fn(*args, **kwargs) File "E:\anaconda\lib\site-packages\decorator.py", line 232, in fun return caller(func, *(extras + args), **kw) File "E:\anaconda\lib\site-packages\IPython\core\magic.py", line 187, in <lambda> call = lambda f, *a, **k: f(*a, **k) File "E:\anaconda\lib\site-packages\IPython\core\magics\pylab.py", line 99, in matplotlib gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui) File "E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3533, in enable_matplotlib from matplotlib_inline.backend_inline import configure_inline_support File "E:\anaconda\lib\site-packages\matplotlib_inline\__init__.py", line 1, in <module> from . import backend_inline, config # noqa File "E:\anaconda\lib\site-packages\matplotlib_inline\backend_inline.py", line 6, in <module> import matplotlib File "E:\anaconda\lib\site-packages\matplotlib\__init__.py", line 109, in <module> from . import _api, _version, cbook, docstring, rcsetup File "E:\anaconda\lib\site-packages\matplotlib\rcsetup.py", line 27, in <module> from matplotlib.colors import Colormap, is_color_like File "E:\anaconda\lib\site-packages\matplotlib\colors.py", line 56, in <module> from matplotlib import _api, cbook, scale File "E:\anaconda\lib\site-packages\matplotlib\scale.py", line 23, in <module> from matplotlib.ticker import ( File "E:\anaconda\lib\site-packages\matplotlib\ticker.py", line 136, in <module> from matplotlib import transforms as mtransforms File "E:\anaconda\lib\site-packages\matplotlib\transforms.py", line 46, in <module> from matplotlib._path import ( --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) AttributeError: _ARRAY_API not found --------------------------------------------------------------------------- ImportError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_7032\4138125057.py in <module> 1 get_ipython().run_line_magic('load_ext', 'autoreload') 2 get_ipython().run_line_magic('autoreload', '2') ----> 3 get_ipython().run_line_magic('matplotlib', 'inline') E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py in run_line_magic(self, magic_name, line, _stack_depth) 2362 kwargs['local_ns'] = self.get_local_scope(stack_depth) 2363 with self.builtin_trap: -> 2364 result = fn(*args, **kwargs) 2365 return result 2366 E:\anaconda\lib\site-packages\decorator.py in fun(*args, **kw) 230 if not kwsyntax: 231 args, kw = fix(args, kw, sig) --> 232 return caller(func, *(extras + args), **kw) 233 fun.__name__ = func.__name__ 234 fun.__doc__ = func.__doc__ E:\anaconda\lib\site-packages\IPython\core\magic.py in <lambda>(f, *a, **k) 185 # but it's overkill for just that one bit of state. 186 def magic_deco(arg): --> 187 call = lambda f, *a, **k: f(*a, **k) 188 189 if callable(arg): E:\anaconda\lib\site-packages\IPython\core\magics\pylab.py in matplotlib(self, line) 97 print("Available matplotlib backends: %s" % backends_list) 98 else: ---> 99 gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui) 100 self._show_matplotlib_backend(args.gui, backend) 101 E:\anaconda\lib\site-packages\IPython\core\interactiveshell.py in enable_matplotlib(self, gui) 3531 """ 3532 from IPython.core import pylabtools as pt -> 3533 from matplotlib_inline.backend_inline import configure_inline_support 3534 gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select) 3535 E:\anaconda\lib\site-packages\matplotlib_inline\__init__.py in <module> ----> 1 from . import backend_inline, config # noqa 2 __version__ = "0.1.6" # noqa E:\anaconda\lib\site-packages\matplotlib_inline\backend_inline.py in <module> 4 # Distributed under the terms of the BSD 3-Clause License. 5 ----> 6 import matplotlib 7 from matplotlib import colors 8 from matplotlib.backends import backend_agg E:\anaconda\lib\site-packages\matplotlib\__init__.py in <module> 107 # cbook must import matplotlib only within function 108 # definitions, so it is safe to import from it here. --> 109 from . import _api, _version, cbook, docstring, rcsetup 110 from matplotlib.cbook import MatplotlibDeprecationWarning, sanitize_sequence 111 from matplotlib.cbook import mplDeprecation # deprecated E:\anaconda\lib\site-packages\matplotlib\rcsetup.py in <module> 25 from matplotlib import _api, cbook 26 from matplotlib.cbook import ls_mapper ---> 27 from matplotlib.colors import Colormap, is_color_like 28 from matplotlib.fontconfig_pattern import parse_fontconfig_pattern 29 from matplotlib._enums import JoinStyle, CapStyle E:\anaconda\lib\site-packages\matplotlib\colors.py in <module> 54 import matplotlib as mpl 55 import numpy as np ---> 56 from matplotlib import _api, cbook, scale 57 from ._color_data import BASE_COLORS, TABLEAU_COLORS, CSS4_COLORS, XKCD_COLORS 58 E:\anaconda\lib\site-packages\matplotlib\scale.py in <module> 21 import matplotlib as mpl 22 from matplotlib import _api, docstring ---> 23 from matplotlib.ticker import ( 24 NullFormatter, ScalarFormatter, LogFormatterSciNotation, LogitFormatter, 25 NullLocator, LogLocator, AutoLocator, AutoMinorLocator, E:\anaconda\lib\site-packages\matplotlib\ticker.py in <module> 134 import matplotlib as mpl 135 from matplotlib import _api, cbook --> 136 from matplotlib import transforms as mtransforms 137 138 _log = logging.getLogger(__name__) E:\anaconda\lib\site-packages\matplotlib\transforms.py in <module> 44 45 from matplotlib import _api ---> 46 from matplotlib._path import ( 47 affine_transform, count_bboxes_overlapping_bbox, update_path_extents) 48 from .path import Path ImportError: numpy.core.multiarray failed to import
08-14
for pkg in packages: try: subprocess.check_call([ sys.executable, "-m", "pip", "install", f"{pkg}==3.8.4" if pkg=="matplotlib" else pkg, "--force-reinstall", "--no-binary=:all:" ]) except: ...
matplotlib 通过for循环画多张图
u011828519的博客
12-13 1万+
思路 利用plt.clf()在每次画完图片后，更新画布。 代码 #导入两个包 import matplotlib.pyplot as plt import numpy as np #数据准备 x = np.arange(27) x = np.reshape(x, (3,9)) #建立for循环语句，绘制x的前三列 for i in range(3): plt.plot(x[:,i]) # plt.show() #保存图片的时候不要plt.show() plt.savefig
matplotlib实现根据实时数据动态更新图形
实践求真知
11-22 3万+
一 代码 from time import sleep from threading importThread import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets importButton fig, ax = plt.subplots() #设置图形显示位置 plt.subplo
python中plt.imshow,Python：如何将plt.imshow（）嵌套在For循环中？
weixin_39534100的博客
03-25 919
from__future__importdivision#Avoids the floor of the mathematical result of division if the args are ints or longsimportnumpyasnpimportmatplotlib.pyplotaspltimportmatplotlib.colorsasmcimportSSFM2Dimpo...
python correlation plot_for循环python中的plot图例
weixin_42721619的博客
02-09 2074
下一个算法给了我一个很好的9个资产之间的关联图(vDT是一个带日期的列表,mPR是45个资产x 1042个时间点的关联矩阵,asX是一个资产列表):plt.figure(figsize=(10, 5))plt.plot(vDT, mPR[0,:], 'xkcd:gold', label=asX[0] + asX[1])plt.plot(vDT, mPR[1,:], 'xkcd:tan', labe...
使用for循环画子图matplotlib
qq_44840703的博客
08-07 2225
使用for循环画子图，重点在于如何在循环中添加子图。
python matplotlib这么同时显示多张图片在同一个图中
热门推荐
qwe2508的博客
03-02 6万+
CIFAR10读进来的图像的的维度是（50000,32,32,3），插入以下代码就可以用了， img_test = images_test[:32, :, :, :] 选取前32张显示 plt.figure() for i in range(1,32): plt.subplot(4,8,i) plt.imshow(img_test[i-1]) plt.show() 这...
Matplotlib之plot.show方法
guangyugu的博客
07-16 1万+
在使用Matplotlib画图时： 尽量不要使用show方法，不到万不得已千万别用，阻塞函数。 可以使用pause函数代替show。pause可以再一段时间间隔内刷新，并显示GUI界面，主要是不阻塞。...
Python 使用matplotlib模块连续显示图片
赫凯的博客
11-07 1万+
我也不知道为什么这样写有的需要show()，循环的时候就不用，求大给我讲解，另外，循环显示多张图片时该如何写才能优雅 显示一张简单的代码如下 from matplotlib import pyplot as plt import numpy as np plt.imshow(np.random.randint(0, 2, (2, 2))) plt.show() 连续的一张图变化这样写 from...
评论
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值