import librosa import matplotlib.pyplot as plt import numpy as np import librosa.display # 1. Get the file path to the included audio example filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' # 2. Load the audio as a waveform `y` # Store the sampling rate as `sr` y, sr = librosa.load(filename, sr=None) plt.figure(figsize=(12, 8)) amplitude_to_db(np.abs(S)) plt.subplot(4, 2, 1) librosa.display.specshow(D, y_axis='linear') plt.colorbar(format='%+2.0f dB') plt.title('Linear-frequency power spectrogram')

import librosa filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' y, sr = librosa.load(filename,sr = None ) import librosa import matplotlib.pyplot as plt import librosa.display import numpy as np filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' tempo, beats = librosa.beat.beat_track(y=y, sr=sr) y_beats = librosa.clicks(frames=beats, sr=sr) y_beats = librosa.clicks(frames=beats, sr=sr, length=len(y)) times = librosa.frames_to_time(beats, sr=sr) y_beat_times = librosa.clicks(times=times, sr=sr) y_beat_times880 = librosa.clicks(times=times, sr=sr, click_freq=880, click_duration=0.5) plt.figure() S = librosa.feature.melspectrogram(y=y, sr=sr) ax = plt.subplot(2, 1, 2) librosa.display.specshow(librosa.power_to_db(S, ref=np.max), x_axis='time', y_axis='mel') plt.subplot(2, 1, 1, sharex=ax) librosa.display.waveshow(y_beat_times, sr=sr, label='Beat clicks') plt.legend() plt.xlim(15, 30) plt.tight_layout()

这段代码使用了Python中的librosa库，...最后，librosa.feature.melspectrogram函数用于提取梅尔频谱图，librosa.display.specshow函数和librosa.display.waveshow函数用于绘制出梅尔频谱图和节拍位置的波形图。

优化这段代码import numpy as np import wave import matplotlib.pyplot as plt import os COOKED_DIR = 'C:/Users/86137/Desktop/UrbanSound8K/audio/wyj' i = 0 for root, dirs, files in os.walk(COOKED_DIR): print("Root = ", root, "dirs = ", dirs, "files = ", files) for filename in files: print(filename) path_one = COOKED_DIR + filename print(path_one) f = wave.open(path_one, 'rb') params = f.getparams() # 一次性返回所有的音频参数，声道数、量化位数、采样频率、采样点数 nchannels, sampwidth, framerate, nframes = params[:4] # 声道/量化数/采样频率/采样点数 str_data = f.readframes(nframes) # 指定需要读取的长度(以取样点为单位)，返回字符串类型数据 waveData = np.frombuffer(str_data, dtype=np.int16) # 将字符串转化为int waveData = waveData * 1.0 / (max(abs(waveData))) # wave幅值归一化 plt.specgram(waveData, NFFT=512, Fs=framerate, noverlap=500, scale_by_freq=True, sides='default') plt.ylabel('Frequency') plt.xlabel('Time(s)') plt.axis('off') name = str(i) # 做名字 i += 1 plt.savefig("filepath"+name+".jpg", bbox_inches='tight', pad_inches=0) # 后两项为去除白边 plt.show()

import matplotlib.pyplot as plt import os COOKED_DIR = 'C:/Users/86137/Desktop/UrbanSound8K/audio/wyj' for filename in os.listdir(COOKED_DIR): path_one = os.path.join(COOKED_DIR, filename) print...

Traceback (most recent call last): File "D:\eda\789.py", line 17, in <module> librosa.display.waveshow(librosa.power_to_db(S, ref=np.max), axis='time', y_axis='mel')

import matplotlib.pyplot as plt import librosa.display import numpy as np filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' # 加载音频文件并转换为 numpy 数组 y, sr = librosa....

FutureWarning: waveshow() keyword argument 'x_axis' has been renamed to 'axis' in version 0.10.0. This alias will be removed in version 1.0. librosa.display.waveshow(librosa.power_to_db(S, ref=np.max), Traceback (most recent call last): File "D:\eda\123.py", line 29, in <module> librosa.display.waveshow(librosa.power_to_db(S, ref=np.max),

import matplotlib.pyplot as plt import librosa.display import numpy as np filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' # 加载音频文件并转换为 numpy 数组 y, sr = librosa....

解释一下这段代码：import tensorflow as tf from im_dataset import train_image, train_label, test_image, test_label from Resnet18 import ResNet18 import os import matplotlib.pyplot as plt import argparse import numpy as np train_db = tf.data.Dataset.from_tensor_slices((train_image, train_label)) test_db = tf.data.Dataset.from_tensor_slices((test_image, test_label)) model = ResNet18([2, 2, 2, 2]) model.build(input_shape=(128, 32, 32, 1)) model.compile(optimizer = tf.keras.optimizers.Adam(lr = 1e-3), loss = tf.keras.losses.MSE, metrics = ['MSE']) checkpoint_save_path = "./checkpoint/InceptionNet_im_3/checkpoint.ckpt" if os.path.exists(checkpoint_save_path+'.index'): print('------------------load the model---------------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,save_weights_only=True,save_best_only=True) history = model.fit(train_db, epochs=500,batch_size=128, validation_data=test_db, validation_freq=1, callbacks=[cp_callback]) model.summary() acc = history.history['loss'] val_acc = history.history['val_loss'] plt.plot(acc, label='Training MSE') plt.plot(val_acc, label='Validation MSE') plt.title('Training and Validation MSE') plt.legend() plt.show()

这段代码实现了使用 ResNet18 模型对图像进行分类和训练。首先通过导入 TensorFlow 和相关模块，加载训练和测试数据。之后，使用 ResNet18 模型构建一个神经网络，并编译模型，设置优化器和损失函数。...

帮我优化一下代码 import matplotlib.pyplot as plt from matplotlib.offsetbox import OffsetImage, AnnotationBbox import pandas as pd import tkinter as tk from tkinter import filedialog import csv import numpy as np filepath = filedialog.askopenfilename() readData = pd.read_csv(filepath, encoding = 'gb2312') # 读取csv数据 print(readData) xdata = readData.iloc[:, 2].tolist() # 获取dataFrame中的第3列，并将此转换为list ydata = readData.iloc[:, 3].tolist() # 获取dataFrame中的第4列，并将此转换为list Color_map = { '0x0': 'r', '0x10': 'b', '0x20': 'pink', '0x30': 'm', '0x40': 'm', '0x50': 'm', '0x60': 'g', '0x70': 'orange', '0x80': 'orange', '0x90': 'm', '0xa0': 'b', '0xb0': 'g', '0xc0': 'g', '0xd0': 'orange', '0xe0': 'orange', '0xf0': 'orange', } plt.ion() fig = plt.figure(num = "蓝牙钥匙连接状态", figsize= (10.8,10.8),frameon= True) gs = fig.add_gridspec(1, 1) ax = fig.add_subplot(gs[0, 0]) colors = readData.iloc[:, 1].map(Color_map) plt.title("Connecting Status For Bluetooth Key") #plt.rcParams['figure.figsize']=(15, 15) ax.axis('equal') a,b = (0.,0.) r = [5,10] for r1 in r: theta = np.arange(0,r1np.pi,0.05) ax.plot(a+r1np.cos(theta),b+r1np.sin(theta),linestyle='-.',c = 'darkgrey') ax.spines['bottom'].set_position(('data', 0)) ax.spines['left'].set_position(('data', 0)) ax.spines['right'].set_position(('data', 0)) ax.spines['top'].set_position(('data', 0)) arr_img = plt.imread('D:\\2022\\测试工作\\蓝牙钥匙测试\\定位\\室内定位(v3.6.21).rar-1656500746516.室内定位(v3.6.21)\\车型图2.png') imagebox = OffsetImage(arr_img, zoom=0.3) ab = AnnotationBbox(imagebox, [0, 0],xybox=(0, 0),pad=0) ax.add_artist(ab) ticks = np.arange(-10,10,2) plt.xticks(ticks) plt.yticks(ticks) #plt.figure(figsize=(15,15)) plt.scatter(xdata, ydata, s=150, edgecolors = None, linewidths=0, alpha=0.3,c = colors) # 画散点图，:r表示点用*表示，颜色为红色 plt.legend() plt.ioff() plt.show() # 画图

import matplotlib.pyplot as plt from matplotlib.offsetbox import OffsetImage, AnnotationBbox import pandas as pd import tkinter as tk from tkinter import filedialog import numpy as np # 使用tkinter...

解释以下这段代码：import tensorflow as tf gpus =tf.config.experimental.list_physical_devices(device_type='GPU') tf.config.experimental.set_virtual_device_configuration(gpus[0],[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)]) #import scipy.io as sio import pickle import os,random import matplotlib.pyplot as plt #import scipy.stats from tensorflow import losses from tensorflow.keras import Model from tensorflow.keras import layers import matplotlib.pyplot as plt import tensorflow as tf import numpy as np #import scipy.io as sio #import scipy.stats import math import os import pdb from tensorflow import losses from model import ResNet18 from re_dataset_real import train_image1,train_label1,test_image1,test_label1,val_image1,val_label1 from re_dataset_imag import train_image2,train_label2,test_image2,test_label2,val_image2,val_label2 def phsical_loss(y_true, y_pred): y_true =tf.cast(y_true, y_pred.dtype) loss_real=tf.keras.losses.MSE(y_true[0],y_pred[0]) loss_img= tf.keras.losses.MSE(y_true[1],y_pred[1]) amp_ture=tf.pow(y_true[0],2)+tf.pow(y_true[1],2) amp_pred=tf.pow(y_pred[0],2)+tf.pow(y_pred[1],2) loss_amp=tf.keras.losses.MSE(amp_ture,amp_pred) return loss_real+loss_img+loss_amp#两个子模型各加一个完整约束 def angle_loss(y_true, y_pred): y_true = tf.cast(y_true, y_pred.dtype) img_ture=tf.atan2(y_true[1],y_true[0]) img_pred=tf.atan2(y_pred[1],y_pred[0]) return tf.keras.losses.MAE(img_ture,img_pred) def amp_loss(y_true, y_pred): y_true = tf.cast(y_true, y_pred.dtype) amp_ture=tf.pow(y_true[0],2)+tf.pow(y_true[1],2) amp_pred=tf.pow(y_pred[0],2)+tf.pow(y_pred[1],2) loss_phsical=tf.keras.losses.MSE(amp_ture,amp_pred) return loss_phsical model_in=tf.keras.Input((16,16,1)) model_real_out=ResNet18([2,2,2,2])(model_in) model_img_out=ResNet18([2,2,2,2])(model_in) model_all=tf.keras.Model(model_in,[model_real_out,model_img_out]) model_all.compile(loss=phsical_loss, optimizer=tf.keras.optimizers.Adam(tf.keras.optimizers.schedules.InverseTimeDecay( 0.001, decay_steps=250*25, decay_rate=1, staircase=False)), metrics=['mse']) checkpoint_save_path= "C:\\Users\\Root\\Desktop\\bysj\\model_all.ckpt" if os.path.exists(checkpoint_save_path + '.index'): print('------------------load model all---------------------') model_all.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True,save_best_only=True)

这段代码主要是实现了一个包含两个子模型的神经网络，其中每个子模型都是由 ResNet18 构成的。此外，还定义了三个损失函数 phsical_loss、angle_loss 和 amp_loss，分别用于计算物理约束、角度约束和幅度约束。...

Traceback (most recent call last): File "D:\eda\123.py", line 11, in <module> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) ^ NameError: name 'y' is not defined

import matplotlib.pyplot as plt import librosa.display import numpy as np filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' # 加载音频文件并转换为 numpy 数组 y, sr = librosa....

已知程序 import xarray as xr from collections import namedtuple import numpy as np from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter import matplotlib.ticker as mticker import cartopy.feature as cfeature import cartopy.crs as ccrs import matplotlib.pyplot as plt import matplotlib.cm as cm import matplotlib.colors as mcolors def region_mask(lon, lat, extents): lonmin, lonmax, latmin, latmax = extents return ( (lon >= lonmin) & (lon <= lonmax) & (lat >= latmin) & (lat <= latmax) ) Point = namedtuple('Point', ['x', 'y']) Pair = namedtuple('Pair', ['start', 'end']) time = '2023-05-04' filepath_DPR = r"C:\pythontest\zFactor\test1.nc4" extents = [110, 122, 25, 38] with xr.open_dataset(filepath_DPR) as f: lon_DPR = f['FS_Longitude'][:] lat_DPR = f['FS_Latitude'][:] zFactorFinalNearSurface = f['FS_SLV_zFactorFinalNearSurface'][:] nscan, nray = lon_DPR.shape midray = nray // 2 mask = region_mask(lon_DPR[:, midray], lat_DPR[:, midray], extents) index = np.s_[mask] lon_DPR = lon_DPR[index] lat_DPR = lat_DPR[index] zFactorFinalNearSurface = zFactorFinalNearSurface[index] for data in [ zFactorFinalNearSurface, ]: data.values[data <= -9999] = np.nan proj = ccrs.PlateCarree() fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection=proj) ax.coastlines(resolution='50m', lw=0.5) ax.add_feature(cfeature.OCEAN.with_scale('50m')) ax.add_feature(cfeature.LAND.with_scale('50m')) ax.set_xticks(np.arange(-180, 181, 5), crs=proj) ax.set_yticks(np.arange(-90, 91, 5), crs=proj) ax.xaxis.set_minor_locator(mticker.AutoMinorLocator(2)) ax.yaxis.set_minor_locator(mticker.AutoMinorLocator(2)) ax.xaxis.set_major_formatter(LongitudeFormatter()) ax.yaxis.set_major_formatter(LatitudeFormatter()) ax.set_extent(extents, crs=proj) ax.tick_params(labelsize='large') def make_zF_cmap(levels): '''制作雷达反射率的colormap.''' nbin = len(levels) - 1 cmap = cm.get_cmap('jet', nbin) norm = mcolors.BoundaryNorm(levels, nbin) return cmap, norm levels_zF = [0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45] cmap_zF, norm_zF = make_zF_cmap(levels_zF) im = ax.contourf( lon_DPR, lat_DPR, zFactorFinalNearSurface, levels_zF, # 三个物理量为 (500, 49)就是在500*49的格点上赋予这三个物理量 cmap=cmap_zF, norm=norm_zF, extend='both', transform=proj ) cbar = fig.colorbar(im, ax=ax, ticks=levels_zF) cbar.set_label('zFactor (dBZ)', fontsize='large') cbar.ax.tick_params(labelsize='large') ax.set_title(f'DPR zFactor on {time}', fontsize='x-large') plt.show()如何将其中的zFactorFinal变量变为二维

在这段代码中，zFactorFinalNearSurface的...reshape方法中第一个参数是新的shape，这里是(nscan*nray, 1)，表示将数据变成nscan*nray行，1列的形状。注意reshape方法返回的是一个新的数组，不会改变原始数组的值。

import tensorflow as tf from im_dataset import train_image, train_label, test_image, test_label from AlexNet8 import AlexNet8 from baseline import baseline from InceptionNet import Inception10 from Resnet18 import ResNet18 import os import matplotlib.pyplot as plt import argparse import numpy as np parse = argparse.ArgumentParser(description="CVAE model for generation of metamaterial") hyperparameter_set = parse.add_argument_group(title='HyperParameter Setting') dim_set = parse.add_argument_group(title='Dim setting') hyperparameter_set.add_argument("--num_epochs",type=int,default=200,help="Number of train epochs") hyperparameter_set.add_argument("--learning_rate",type=float,default=4e-3,help="learning rate") hyperparameter_set.add_argument("--image_size",type=int,default=16*16,help="vector size of image") hyperparameter_set.add_argument("--batch_size",type=int,default=16,help="batch size of database") dim_set.add_argument("--z_dim",type=int,default=20,help="dim of latent variable") dim_set.add_argument("--feature_dim",type=int,default=32,help="dim of feature vector") dim_set.add_argument("--phase_curve_dim",type=int,default=41,help="dim of phase curve vector") dim_set.add_argument("--image_dim",type=int,default=16,help="image size: [image_dim,image_dim,1]") args = parse.parse_args() def preprocess(x, y): x = tf.io.read_file(x) x = tf.image.decode_png(x, channels=1) x = tf.cast(x,dtype=tf.float32) /255. x1 = tf.concat([x, x], 0) x2 = tf.concat([x1, x1], 1) x = x - 0.5 y = tf.convert_to_tensor(y) y = tf.cast(y,dtype=tf.float32) return x2, y train_db = tf.data.Dataset.from_tensor_slices((train_image, train_label)) train_db = train_db.shuffle(100).map(preprocess).batch(args.batch_size) test_db = tf.data.Dataset.from_tensor_slices((test_image, test_label)) test_db = test_db.map(preprocess).batch(args.batch_size) model = ResNet18([2, 2, 2, 2]) model.build(input_shape=(args.batch_size, 32, 32, 1)) model.compile(optimizer = tf.keras.optimizers.Adam(lr = 1e-3), loss = tf.keras.losses.MSE, metrics = ['MSE']) checkpoint_save_path = "./checkpoint/InceptionNet_im_3/checkpoint.ckpt" if os.path.exists(checkpoint_save_path+'.index'): print('------------------load the model---------------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,save_weights_only=True,save_best_only=True) history = model.fit(train_db, epochs=500, validation_data=test_db, validation_freq=1, callbacks=[cp_callback]) model.summary() acc = history.history['loss'] val_acc = history.history['val_loss'] plt.plot(acc, label='Training MSE') plt.plot(val_acc, label='Validation MSE') plt.title('Training and Validation MSE') plt.legend() plt.show()

这段代码是使用 TensorFlow 构建了一个 ResNet18 模型，对图像进行分类任务。其中使用了 argparse 库来设置超参数，使用了 tf.data....同时还使用了 matplotlib.pyplot 库来绘制训练和验证的 MSE（均方误差）曲线。

>>> import numpy as np >>> import cv2 >>> import matplotlib as plt >>> import os >>> import time >>> def save_imginfo_to_txt(filepath, txtpath): ... files = os.listdir(filepath) ... res = [] ... for file in files: ... filename = filepath + "I:\\18Breakageratecalculation\\SVM run\\pos" + file ... img = cv2.imread(filename) ... res.append([filename, 1, 0, 0, img.shape[0], img.shape[1]]) ... save_txt = txtpath + "I:\\18Breakageratecalculation\\SVM run\\pos" + "file_name.txt" ... file = open(save_txt, 'a') ... for i in res: ... file.write(' '.join([str(j) for j in i])) ... file.write("\n") ... file.close() ... >>> filepath = r"I:\18Breakageratecalculation\SVM run\pos\" File "<stdin>", line 1 filepath = r"I:\18Breakageratecalculation\SVM run\pos\" ^ SyntaxError: unterminated string literal (detected at line 1) >>> filepath = r"I:\18Breakageratecalculation\SVM run\pos" >>> txtpath = r"I:\18Breakageratecalculation\SVM run" >>> save_imginfo_to_txt(filepath, txtpath) [ WARN:0@385.483] imread_('I:\18Breakageratecalculation\SVM run\posI:\18Breakageratecalculation\SVM run\pos1.png'): can't open/read file: check file path/integrity Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<stdin>", line 7, in save_imginfo_to_txt AttributeError: 'NoneType' object has no attribute 'shape'

import matplotlib as plt import os import time def save_imginfo_to_txt(filepath, txtpath): files = os.listdir(filepath) res = [] for file in files: filename = filepath + "\\" + file img = cv2....

import pandas as pd import matplotlib import numpy as np import matplotlib.pyplot as plt import jieba as jb import re from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.feature_selection import chi2 import numpy as np from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.naive_bayes import MultinomialNB def sigmoid(x): return 1 / (1 + np.exp(-x)) import numpy as np #定义删除除字母,数字，汉字以外的所有符号的函数 def remove_punctuation(line): line = str(line) if line.strip()=='': return '' rule = re.compile(u"[^a-zA-Z0-9\u4E00-\u9FA5]") line = rule.sub('',line) return line def stopwordslist(filepath): stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()] return stopwords df = pd.read_csv('./online_shopping_10_cats/online_shopping_10_cats.csv') df=df[['cat','review']] df = df[pd.notnull(df['review'])] d = {'cat':df['cat'].value_counts().index, 'count': df['cat'].value_counts()} df_cat = pd.DataFrame(data=d).reset_index(drop=True) df['cat_id'] = df['cat'].factorize()[0] cat_id_df = df[['cat', 'cat_id']].drop_duplicates().sort_values('cat_id').reset_index(drop=True) cat_to_id = dict(cat_id_df.values) id_to_cat = dict(cat_id_df[['cat_id', 'cat']].values) #加载停用词 stopwords = stopwordslist("./online_shopping_10_cats/chineseStopWords.txt") #删除除字母,数字，汉字以外的所有符号 df['clean_review'] = df['review'].apply(remove_punctuation) #分词，并过滤停用词 df['cut_review'] = df['clean_review'].apply(lambda x: " ".join([w for w in list(jb.cut(x)) if w not in stopwords])) tfidf = TfidfVectorizer(norm='l2', ngram_range=(1, 2)) features = tfidf.fit_transform(df.cut_review) labels = df.cat_id X_train, X_test, y_train, y_test = train_test_split(df['cut_review'], df['cat_id'], random_state = 0) count_vect = CountVectorizer() X_train_counts = count_vect.fit_transform(X_train) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) 已经写好以上代码，请补全train和test函数

以下是train和test函数的代码： python def train(X_train_tfidf, y_train): clf = MultinomialNB().fit(X_train_tfidf, y_train) return clf def test(clf, X_test): X_test_counts = count_vect.transform...

用matplotlib实现天气预测

import matplotlib.pyplot as plt # 加载数据 def data_load(filepath): features = pd.read_csv(filepath) return features # 选择需要可视化的特征列 def select_features(features): selected_features = ...

python利用ASE和NUMPY分析多个POSCAR结构文件中 C原子的分布共同点，具体代码，输入和输出文件

import matplotlib.pyplot as plt # 输入文件夹路径 input_dir = 'path/to/input/directory' # 输出文件路径 output_file = 'path/to/output/file' # 读取所有POSCAR文件的结构信息 structures = [] for filename ...

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Traceback (most recent call last): File "D:\eda\789.py", line 17, in <module> librosa.display.waveshow(librosa.power_to_db(S, ref=np.max), axis='time', y_axis='mel')

Traceback (most recent call last): File "D:\eda\123.py", line 11, in <module> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) ^ NameError: name 'y' is not defined

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