NXP MPL3115A2压力传感器：精确测高，I2C接口

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【干货】压力传感器.pdf

def scatter(self): self.scatter_frame = Frame(self.init_window_name) # 创建一个竖直滚动条 scrollbar = Scrollbar(self.scatter_frame, orient=tk.VERTICAL) scrollbar.pack(side=tk.RIGHT, fill=tk.Y) self.scatter_frame.place(x=10, y=460, width=750, height=310) fig = plt.figure() ax = fig.add_subplot(111) pd.plotting.scatter_matrix(self.df,alpha = 0.3,figsize = (10,10),grid = True,ax = ax) self.scatter_view = FigureCanvasTkAgg(fig,master = self.scatter_frame) self.scatter_view.draw() self.scatter_view.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1) # 将滚动条绑定到FigureCanvasTkAgg上 self.scatter_view.configure(yscrollcommand=scrollbar.set) scrollbar.config(command=self.scatter_view.yview)我想在self.scatter_view画布上加一个滚轮，让图片不要被压缩显示，怎么改代码？

self.scatter_view.mpl_connect('scroll_event', self.on_scroll) def on_scroll(self, event): # 获取当前滚轮的方向 if event.button == 'up': direction = 1 elif event.button == 'down': direction = -...

TypeError: _as_mpl_axes() missing 1 required positional argument: 'self'

这个错误提示通常在使用Matplotlib库进行绘图时遇到，特别是当你尝试调用某个特定方法（如`_as_mpl_axes()`）但没有正确提供所需的参数时。`_as_mpl_axes()`是Matplotlib内部的一个方法，可能是在DataFrame或Axes...

解释代码 c1 = np.array(mpl.colors.to_rgb(c1)) c2 = np.array(mpl.colors.to_rgb(c2))

这段代码使用Matplotlib库将颜色字符串转换为RGB数组。具体来说，将颜色c1和c2转换为RGB数组，以便在后续代码中使用。np.array()是将颜色转换为NumPy数组。mpl.colors.to_rgb()是将颜色转换为RGB值的函数。

ModuleNotFoundError: No module named 'mpl_toolkits.axes_gridl'

ModuleNotFoundError: No module named 'mpl_toolkits.axes_grid1' 是一个常见的Python错误，它表示在运行程序时找不到名为'mpl_toolkits.axes_grid1' 的模块。这个模块可能是matplotlib库中的一个扩展工具包，用于...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from mpl_toolkits.mplot3d import Axes3D from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler data=pd.read_csv('H:/analysis_results/mean_HN.csv') data.head() x=data.iloc[:,1:7] y=data.iloc[:,6] scaler=StandardScaler() scaler.fit(x) x_scaler=scaler.transform(x) print(x_scaler.shape) pca=PCA(n_components=3) x_pca=pca.fit_transform(x_scaler) print(x_pca.shape) #查看各个主成分对应的方差大小和占全部方差的比例 #可以看到前2个主成分已经解释了样本分布的90%的差异了 print('explained_variance_:',pca.explained_variance_) print('explained_variance_ratio_:',pca.explained_variance_ratio_) print('total explained variance ratio of first 6 principal components:',sum(pca.explained_variance_ratio_)) #将分析的结果保存成字典 result={ 'explained_variance_:',pca.explained_variance_, 'explained_variance_ratio_:',pca.explained_variance_ratio_, 'total explained variance ratio:',np.sum(pca.explained_variance_ratio_)} df=pd.DataFrame.from_dict(result,orient='index',columns=['value']) df.to_csv('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.csv') #可视化各个主成分贡献的方差 #fig1=plt.figure(figsize=(10,10)) #plt.rcParams['figure.dpi'] = 300#设置像素参数值 plt.rcParams['path.simplify'] = False#禁用抗锯齿效果 plt.figure() plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 plt.title('PCA_plot_HN') plt.xlabel('components_n',fontsize=16) plt.ylabel('explained_variance_',fontsize=16) #plt.savefig('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.png') plt.show()报错unhashable type: 'numpy.ndarray'，如何修改

这个错误通常是由于字典中存在不可哈希对象 (unhashable type)，比如 numpy 数组，而字典的键必须是可哈希的对象。你可以将 numpy 数组转换为列表，然后再保存到字典中，例如： ``` result={ ...

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # 创建数据点 x = np.linspace(-2, 2, 100) y = np.linspace(1, 3, 100) z1 = np.sqrt(4 * y2 - y2 - x2) z2 = np.sqrt(4 - x2 - y**2) # 绘制曲面1 fig1 = plt.figure() ax1 = fig1.add_subplot(111, projection='3d') ax1.plot_surface(x, y, z1, cmap='viridis') # 绘制曲面2 fig2 = plt.figure() ax2 = fig2.add_subplot(111, projection='3d') ax2.plot_surface(x, y, z2, cmap='magma') # 设置坐标轴标签 ax1.set_xlabel('X') ax1.set_ylabel('Y') ax1.set_zlabel('Z') ax2.set_xlabel('X') ax2.set_ylabel('Y') ax2.set_zlabel('Z') # 显示图形 plt.show() ValueError: Argument Z must be 2-dimensional.

from mpl_toolkits.mplot3d import Axes3D # 创建数据点 x = np.linspace(-2, 2, 100) y = np.linspace(1, 3, 100) X, Y = np.meshgrid(x, y) Z1 = np.sqrt(4 * Y**2 - Y**2 - X**2) Z2 = np.sqrt(4 - X**2 - Y**2)...

fig, ax = plt.subplots() ax.plot(new_name_lst, x_list, label='velocity_x') ax.plot(new_name_lst, y_list, label='velocity_y') ax.plot(new_name_lst, list3, label='velocity') # leg = ax.legend(loc='upper left', fancybox=True, shadow=True) leg.get_frame().set_alpha(0.5) lines = [l for l in ax.lines] lined = dict() for legline, origline in zip(leg.get_lines(), lines): legline.set_picker(True) lined[legline] = origline def onpick(event): legline = event.artist origline = lined[legline] visible = not origline.get_visible() origline.set_visible(visible) if visible: legline.set_alpha(1.0) else: legline.set_alpha(0.2) fig.canvas.draw() fig.canvas.mpl_connect('pick_event', onpick) plt.show()这个代码加一个跟随鼠标的虚线

你可以尝试添加以下代码来实现跟随鼠标的...fig.canvas.mpl_connect('pick_event', onpick) plt.show() ``` 这个代码会在图形中添加一个跟随鼠标的垂直虚线，当鼠标拾取到某个线条时，虚线会跟随鼠标移动到该线条上。

优化这个代码import xarray as xr import netCDF4 as nc import pandas as pd import numpy as np import datetime import matplotlib.pyplot as plt import cartopy.mpl.ticker as cticker import cartopy.crs as ccrs import cartopy.feature as cfeature ds = xr.open_dataset('C:/Users/cindy/Desktop/SP.nc', engine='netcdf4') # 读取原始数据 ds_temp = xr.open_dataset('C:/Users/cindy/Desktop/SP.nc') # 区域提取* south_asia = ds_temp.sel(latitude=slice(38, 28), longitude=slice(75, 103)) indian_ocean = ds_temp.sel(latitude=slice(5, -15), longitude=slice(60, 100)) # 高度插值 south_asia_200hpa = south_asia.t.interp(level=200) indian_ocean_200hpa = indian_ocean.t.interp(level=200) south_asia_400hpa = south_asia.t.interp(level=400) indian_ocean_400hpa = indian_ocean.t.interp(level=400) # 区域平均 TTP = south_asia_400hpa.mean(dim=('latitude', 'longitude'))#.values TTIO = indian_ocean_400hpa.mean(dim=('latitude', 'longitude'))# TTP_200hpa = south_asia_200hpa.mean(dim=('latitude', 'longitude')) TTIO_200hpa = indian_ocean_200hpa.mean(dim=('latitude', 'longitude')) tlup=(TTP-TTIO)-(TTP_200hpa-TTIO_200hpa)-(-5.367655815) # 定义画图区域和投影方式 fig = plt.figure(figsize=[10, 8]) ax = plt.axes(projection=ccrs.PlateCarree()) # 添加地图特征 ax.set_extent([60, 140, -15, 60], crs=ccrs.PlateCarree()) ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidths=0.5) ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='lightgray') ax.add_feature(cfeature.OCEAN.with_scale('50m'), facecolor='white') # 画距平场 im = ax.contourf(TTP_200hpa, TTP, tlup, cmap='coolwarm', levels=np.arange(-4, 4.5, 0.5), extend='both') # 添加色标 cbar = plt.colorbar(im, ax=ax, shrink=0.8) cbar.set_label('Temperature anomaly (°C)') # 添加经纬度坐标轴标签 ax.set_xticks(np.arange(60, 105, 10), crs=ccrs.PlateCarree()) ax.set_yticks(np.arange(-10, 40, 10), crs=ccrs.PlateCarree()) lon_formatter = cticker.LongitudeFormatter() lat_formatter = cticker.LatitudeFormatter() ax.xaxis.set_major_formatter(lon_formatter) ax.yaxis.set_major_formatter(lat_formatter) # 添加标题和保存图片 plt.title('Temperature anomaly at 400hPa over South Asia and the Indian Ocean') plt.savefig('temperature_anomaly.png', dpi=300) plt.show()

import cartopy.mpl.ticker as cticker, cartopy.crs as ccrs, cartopy.feature as cfeature ``` 2. 可以在代码中删除不必要的变量，例如 `ds` 和 `ds_temp` 都是指向同一个文件的数据集，因此只需要保留一个即可。...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from mpl_toolkits.mplot3d import Axes3D from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler data=pd.read_csv('H:/analysis_results/mean_HN.csv') data.head() x=data.iloc[:,1:7] y=data.iloc[:,6] scaler=StandardScaler() scaler.fit(x) x_scaler=scaler.transform(x) print(x_scaler.shape) pca=PCA(n_components=3) x_pca=pca.fit_transform(x_scaler) print(x_pca.shape) #查看各个主成分对应的方差大小和占全部方差的比例 #可以看到前2个主成分已经解释了样本分布的90%的差异了 print('explained_variance_:',pca.explained_variance_) print('explained_variance_ratio_:',pca.explained_variance_ratio_) print('total explained variance ratio of first 6 principal components:',sum(pca.explained_variance_ratio_)) #可视化各个主成分贡献的方差 #fig1=plt.figure(figsize=(10,10)) #plt.rcParams['figure.dpi'] = 300#设置像素参数值 plt.rcParams['path.simplify'] = False#禁用抗锯齿效果 plt.figure() plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 plt.title('PCA_plot_HN') plt.xlabel('components_n',fontsize=16) plt.ylabel('explained_variance_',fontsize=16) plt.show() plt.pause(0.5) plt.savefig('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.png')保存的图像中，一片空白，如何修改

plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 plt.title('PCA_plot_HN') plt.xlabel('components_n',fontsize=16...

cannot import name 'AXES3D' from 'mpl_toolkits.mplot3d' (D:\xiazai\anaconda\Lib\site-packages\mpl_toolkits\mplot3d\init.py)

这个错误通常是由于安装的 matplotlib 版本与 mpl_toolkits.mplot3d 中所需的模块不兼容造成的。你可以尝试以下方法来解决这个问题： 1. 确保你的 matplotlib 版本是最新的。可以使用以下命令来更新 matplotlib： `...

在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作

import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm ...

mpl_toolkits.basemap 没有basemap mac

2. 使用pip安装mpl_toolkits.basemap：虽然macOS没有官方支持的预编译包，但仍然可以使用pip安装mpl_toolkits.basemap。这需要一些额外的步骤和依赖项，包括proj4和geos库的手动安装。这可能需要一些时间和技巧，...

mpl.rcParams['axes.unicode_minus'] = False mpl.rcParams['font.sans-serif'] = ['SimHei']

1. `mpl.rcParams['axes.unicode_minus'] = False`：将 Matplotlib 图表中的负号（减号）显示为正常的形式，避免出现中文乱码的问题。 2. `mpl.rcParams['font.sans-serif'] = ['SimHei']`：设置 Matplotlib 图表中...

from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from cartopy.io.shapereader import Reader

这两行代码导入了`cartopy.mpl.gridliner`模块中的`LONGITUDE_FORMATTER`和`LATITUDE_FORMATTER`，以及`cartopy.io.shapereader`模块中的`Reader`。 `cartopy.mpl.gridliner`模块提供了用于绘制地图网格线的功能，...

import random fig = plt.figure(figsize=(13,13)) ax = fig.add_subplot(111,projection='3d') colors = ['r', 'g', 'b', 'c', 'm'] for i in new_df.index: ax.scatter(new_df.loc[i,'Total Cases'],new_df.loc[i,'Total Deaths'],new_df.loc[i,'Total Recovered'],c=random.choice(colors)) ax.text(new_df.loc[i,'Total Cases'],new_df.loc[i,'Total Deaths'],new_df.loc[i,'Total Recovered'], new_df.loc[i,'Country/Region'], fontsize=10) # 设置刻度 ax.set_xlim([0,new_df['Total Cases'].max() + 1]) ax.set_ylim([0,new_df['Total Deaths'].max() + 1]) ax.set_zlim([0,new_df['Total Recovered'].max() + 1]) # 设置坐标轴标签 ax.set_xlabel('Total Cases',fontsize=15) ax.set_ylabel('Total Deaths',fontsize=15) ax.set_zlabel('Total Recovered',fontsize=15) ax.set_title('3D scatterplot',fontsize=25) plt.show()这段代码无法运行

from mpl_toolkits.mplot3d import Axes3D import random # 导入数据集 df = pd.read_csv('covid19_data.csv') # 对数据进行处理 new_df = df.groupby(['Country/Region']).sum().reset_index() # 创建3D散点图 ...

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NXP MPL3115A2压力传感器：精确测高，I2C接口

【干货】压力传感器.pdf

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stm32驱动mpl3115a2传感器

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TypeError: _as_mpl_axes() missing 1 required positional argument: 'self'

解释代码 c1 = np.array(mpl.colors.to_rgb(c1)) c2 = np.array(mpl.colors.to_rgb(c2))

ModuleNotFoundError: No module named 'mpl_toolkits.axes_gridl'

cannot import name 'AXES3D' from 'mpl_toolkits.mplot3d' (D:\xiazai\anaconda\Lib\site-packages\mpl_toolkits\mplot3d\__init__.py)

mpl_toolkits.basemap 没有basemap mac

mpl.rcParams['axes.unicode_minus'] = False mpl.rcParams['font.sans-serif'] = ['SimHei']

from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER from cartopy.io.shapereader import Reader

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cannot import name 'AXES3D' from 'mpl_toolkits.mplot3d' (D:\xiazai\anaconda\Lib\site-packages\mpl_toolkits\mplot3d\init.py)