n = 0 for i in data: WY = i[0:200] ZH = i[200:400] # print(len(WY),len(ZH)) plt.plot(WY[0: 100], ZH[0: 100], color=seismic(5 / 5.0), lw=3) plt.plot(WY[100: 200], ZH[100: 200], color=seismic(0 / 5.0), lw=3) plt.xticks([]) plt.yticks([]) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) Y_ALL = [] Y_ALL.extend(list(map(float, ZH))) Y_MAX, Y_MIN = np.max(Y_ALL), np.min(Y_ALL) ax.set_ylim(np.min([0, Y_MIN - (Y_MAX - Y_MIN) * 0.1]), Y_MAX + (Y_MAX - Y_MIN) * 0.1) plt.savefig("G:/功图/0/" + cond[:-4] + '/' + str(n), dpi=50) # plt.savefig(newpath + "GT/" + cond + "/" + data['IMGNAME'][i], dpi=50) plt.clf() # plt.show() n = n + 1这段代码是一段绘图代码，当i中存在空值时绘图会中断，怎样将i中存在空值的整条i删除或跳过

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nodejs-haveibeenpwned:HaveIBeenPwnd.com的API方法（非官方）

const hibp = require ( 'haveibeenpwned' ) ( ) ; hibp . breachedAccount ( 'foo@bar.com' , ( err , data ) => { if ( err ) { return console . log ( err ) ; } console . log ( data ) ; } ) ; 安装 npm i...

I-WBAN: Enabling Information-centric data retrieval in heterogeneous WBAN

r = open('sensor-data.txt') r.close r.replace("/n","") r = r.split() i = 5 z = {} c = 0 x = 47.08 for i in r: z.append(i) i += 6 for a in z: if a > c: c = a a += 1 else: a += 1 for b in z: if b < x: b = x x += 1 else: x += 1 for y in z: m += y l = len(z) M = m/l print(a,b,M)

for value in z.values(): if value > c: c = value if value < x: x = value m = 0 for value in z.values(): m += value l = len(z) M = m / l print("最大值：", c) print("最小值：", x) print("平均值：...

for i in range(len(data)):

这段代码通常用于循环遍历一个序列或者列表 data，其中 range(len(data)) 会生成一个从 0 到 len(data)-1 的整数序列，然后 for 循环会依次遍历这个序列中的每一个元素，将每个元素赋值给变量 i。...

def calcu(cmd='求平均值',data): if cmd=='求平均值': s=sum(data)/len(data) return s return s elif cmd=='求和': return sum(data) else: s=1 for i in data: s=i return s res=calcu('求和',3,3,100) print(res)

这段代码的作用是定义了一个函数 calcu，它接受一个 cmd 参数和不定长的 data 参数。根据 cmd 的值，函数会执行不同的操作并返回结果。如果 cmd 是 '求平均值'，则计算 data 中所有元素的平均值并返回...

data = [{'dwbm':23,"spzxl":"95",'wxzyType': 'wh',}] for i in data: print(i['dwbm']) 优化代码

你的代码有一个小问题，应该...for i in data: print(i['dwbm']) 这段代码可以输出23，即字典中键'dwbm'对应的值。如果你要输出键'spzxl'或'wxzyType'对应的值，只需要将print语句中的'dwbm'改为相应的键名即可。

datas = [] for a in result: data = [] for i in keyword: data.append("") if a[0] == '{}': datas.append(data) continue else: a = a[0] a = ast.literal_eval(a) a = a['企业触发词'][0] keyvalues = a['text'] f = "" try: relations = a['relations'] # print(relations) for ii in relations: # print(ii) f = ii # break relations = a['relations'][f] cc = keyword.index(f) str = '' for i in relations: # print(i['text']) str = str +'、'+ i['text'] data[cc] =str[1:] #print(data[cc]) data[0] = keyvalues datas.append(data) #print(datas) #data[0] = keyvalues # print() except: datas.append("") pass

for i in keyword: data.append("") # 创建一个空的data列表 if a[0] == '{}': datas.append(data) continue else: a = a[0] a = ast.literal_eval(a) a = a['企业触发词'][0] keyvalues = a['text'] f =...

使用Begin/End将下面的python代码转换成伪代码 h, w = img.shape[:2] pixelSequence = img.reshape([h * w, ]) # [262144,] for i in range(len(pixelSequence)): if 0 < pixelSequence[i] < Max: pixelSequence[i] -= 1 Z = zhi() n = 0 for i in range(len(pixelSequence)): if pixelSequence[i] == Max: if Hidden_Data[n] == 1 or Hidden_Data[n] == '1': pixelSequence[i] -= 1 else: pass n += 1 if n == len(Hidden_Data): break Marked_Image = pixelSequence.reshape(h, w) return Marked_Image

for i in range(len(pixelSequence)): if 0 [i] < Max: pixelSequence[i] -= 1 Z = zhi() n = 0 for i in range(len(pixelSequence)): if pixelSequence[i] == Max: if Hidden_Data[n] == 1 or Hidden_Data...

data = [{'dwbm':23,"spzxl":"95",'wxzyType': 'wh',}] for i in data: print(i['dwbm'],i['spzxl'])优化代码

同样的，你需要将方括号...for i in data: print(i['dwbm'], i['spzxl']) 这段代码可以同时输出字典中键'dwbm'和' spzxl'对应的值。如果你还要输出键'wxzyType'对应的值，可以在print语句中继续添加相应的代码。

for i in range(0, len(ori_data) - seq_len): _x = ori_data[i:i + seq_len] temp_data.append(_x)

在每次循环中，通过切片操作ori_data[i:i + seq_len]，将从索引i开始，长度为seq_len的子序列提取出来，并赋值给变量_x。然后，将_x添加到temp_data列表中，用于存储所有提取出的子序列。循环结束后，temp_data...

data={'gong1':[56,87,98], 'gong2’:[77,65,45], 'gong3’:|87, 66,77] } score=[]for i in data.values: score.append(round (sum(i)/len(i),2)) n=0 forij in data.items): j.append(score[n]) n+=1 print(data'gong3'1)

for i in data.values(): score.append(round(sum(i) / len(i), 2)) n = 0 j = [] for j in data.items(): j.append(score[n]) n += 1 print(data['gong3'][1]) 这段代码的功能是计算 data 中每个列表的...

import numpy as np import matplotlib.pyplot as plt with open('01_nve_limit.lammpstrj', 'r') as f: data = f.readlines() #print(float(data[0])) for _ in range(9): #next(f) try: while True: a = next(f) print(a) except StopIteration: pass for line in f: #print(line) print(float(data[0])) data = np.loadtxt(f) print(data.reshape(-1,5)) b = np.logical_and(data[ : , 4 ] >= -35 ,data[ : , 4 ] <= 601) data = data[mask] data = data[np.argsort(data[:,4])] x = np.arange(-35, 600, 1) y = np.zeros_like(x) index = 0 for i in range(len(x)): while index < len(data) and data[index,4] < x[i]: index += 1 while index < len(data) and data[index,4] < x[i]+1: y[i] += data[index,5] index += 1 y[i] /= 879.4显示报错：b = np.logical_and(data[ : , 4 ] >= -35 ,data[ : , 4 ] <= 601) IndexError: too many indices怎么解决

for i in range(len(x)): while index < len(data) and data[index,4] [i]: index += 1 while index < len(data) and data[index,4] [i]+1: y[i] += data[index,5] index += 1 y[i] /= 879.4 plt.plot(x, ...

import numpy as np import pandas as pd data = pd.read_csv("data/预处理.csv", header=None); data = np.array(data) cluster = [] # 按第二个到第十四个标签分类 for i in range(178): if len(cluster) == 0: cluster.append([0]) else: m = 0 for j in range(len(cluster)): n = 0 for x in range(1, 14): if data[i][x] == data[j][x]: n = n + 1 if n == 2: cluster[j].append(i) m = 1 if m == 0: cluster.append([i]) for i in range(len(cluster)): for j in range(len(cluster)): if cluster[i] == cluster[j] and i != j: cluster[j] = 0 for i in range(len(cluster)): print( cluster[i]) xiu1 = [] for t in range(178): if data[t][0] ==1: xiu1.append(t) xiu2 = [] for t in range(178): if data[t][0] == 2: xiu2.append(t) xiu3 = [] for t in range(178): if data[t][0] == 3: xiu3.append(t) print(xiu1) print(xiu2) print(xiu3)修改并完善代码，只用简单的for循环不用decision，使他能分别输出xiu1,xiu2,xiu3在所有13个条件属性的下近似，

approx = sum(data[t][col] for t in xiu1) / len(xiu1) # 计算平均值作为近似值 approximations_xiu1.append(approx) # 计算xiu2在所有13个条件属性下的近似值 approximations_xiu2 = [] for col in range(1, 14...

#功图批量绘制 import os import numpy as np import pandas as pd from PIL import Image from matplotlib import pyplot as plt plt.figure(figsize=(4, 2), dpi=50, frameon=False) ax = plt.axes([0, 0, 1, 1]) grey = plt.get_cmap('Greys') seismic = plt.get_cmap('bwr') datapath = "G:/功图excel/0" conds = os.listdir(datapath) for cond in conds: data = pd.read_csv("G:/功图excel/0/" + cond) os.mkdir(r"G:/功图/0/" + cond[:-4]) # print(data) # 首先将pandas读取的数据转化为array data = np.array(data) # 然后转化为list形式 data = data.tolist() # print(data) n = 0 for i in data: if np.isnan(i).any(): # 检查数据是否包含 NaN 值 continue # 如果包含，则跳过该迭代 WY = i[0:200] ZH = i[200:400] # print(len(WY),len(ZH)) plt.plot(WY[0: 100], ZH[0: 100], color=seismic(5 / 5.0), lw=3) plt.plot(WY[100: 200], ZH[100: 200], color=seismic(0 / 5.0), lw=3) plt.xticks([]) plt.yticks([]) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.spines['left'].set_visible(False) ax.spines['bottom'].set_visible(False) Y_ALL = [] Y_ALL.extend(list(map(float, ZH))) Y_MAX, Y_MIN = np.max(Y_ALL), np.min(Y_ALL) ax.set_ylim(np.min([0, Y_MIN - (Y_MAX - Y_MIN) * 0.1]), Y_MAX + (Y_MAX - Y_MIN) * 0.1) plt.savefig("G:/功图/0/" + cond[:-4] + '/' + str(n), dpi=50) # plt.savefig(newpath + "GT/" + cond + "/" + data['IMGNAME'][i], dpi=50) plt.clf() # plt.show() n = n + 1，以上这段代码画出来的图第一张无边框，其余都有边框。怎样让所有图都无边框

ZH = i[200:400] plt.plot(WY[0: 100], ZH[0: 100], color=seismic(5 / 5.0), lw=3) plt.plot(WY[100: 200], ZH[100: 200], color=seismic(0 / 5.0), lw=3) plt.xticks([]) plt.yticks([]) Y_ALL = [] Y_ALL....

import numpy as np import pandas as pd data = pd.read_csv("data/预处理.csv", header=None); data = np.array(data) cluster = [] 按第二个到第十四个标签分类 for i in range(178): if len(cluster) == 0: cluster.append([0]) else: m = 0 for j in range(len(cluster)): n = 0 for x in range(1, 14): if data[i][x] == data[j][x]: n = n + 1 if n == 2: cluster[j].append(i) m = 1 if m == 0: cluster.append([i]) for i in range(len(cluster)): for j in range(len(cluster)): if cluster[i] == cluster[j] and i != j: cluster[j] = 0 for i in range(len(cluster)): print( cluster[i]) xiu1 = [] for t in range(178): if data[t][0] ==1: xiu1.append(t) xiu2 = [] for t in range(178): if data[t][0] == 2: xiu2.append(t) xiu3 = [] for t in range(178): if data[t][0] == 3: xiu3.append(t) print(xiu1) print(xiu2) print(xiu3)修改并完善代码，只用简单的for循环不用decision，使他能分别输出xiu1,xiu2,xiu3在所有13个条件属性下的下近似集合，

for i in range(len(cluster)): for j in range(len(cluster)): if cluster[i] == cluster[j] and i != j: cluster[j] = 0 # 定义函数来判断两个集合是否相似 def is_similar(set1, set2): for item in set1: ...

for i in range(len(data_df)-2): x = datagyh[i] y = datagyh[i + 1]

这段代码中，首先通过len(data_df)获取数据集的长度，然后使用range(len(data_df)-2)循环遍历数据集中的每个元素（i从0到len(data_df)-3）。在循环中，变量x被赋值为datagyh[i]，变量y被赋值为datagyh[i ...

data = [['西红柿', '排骨', '鸡蛋'], ['西红柿', '茄子'], ['鸡蛋', '袜子'], ['西红柿', '排骨', '茄子'], ['西红柿', '排骨', '袜子', '酸奶'], ['鸡蛋', '茄子', '酸奶'], ['排骨', '鸡蛋', '茄子'], ['土豆', '鸡蛋', '袜子'], ['西红柿', '排骨', '鞋子', '土豆']] # 获取所有可能出现的物品 items=['土豆','排骨','茄子','袜子','西红柿','酸奶','鞋子','鸡蛋'] # 构建布尔矩阵 matrix = [] for d in data: row = [] for item in items: if item in d: row.append(1) else: row.append(0) matrix.append(row) # 输出布尔矩阵 print('ID', end='\t') for item in items: print(item, end='\t') print() for i in range(len(data)): print('I' + str(i+1), end='\t') for j in range(len(items)): print(matrix[i][j], end='\t') print() # 计算关联规则 rules = [('西红柿','排骨'), ('排骨','西红柿'), ('袜子','鸡蛋'), ('茄子','排骨'), ('茄子','西红柿'), ('茄子','鸡蛋')] # 计算支持度同时购买的概率 def support(itemset, data): count = 0 for d in data: if all(item in d for item in itemset): count += 1 return count / len(data) # 计算置信度购买一个xxx def confidence(rule, data): antecedent = rule[0] consequent = rule[1] antecedent_count = 0 rule_count = 0 for d in data: if all(item in d for item in antecedent): antecedent_count += 1 if all(item in d for item in rule): rule_count += 1 return rule_count / antecedent_count # 输出支持度和置信度 for rule in rules: print(f"Rule: {rule} \t Support: {support(rule, data):.4f} \t Confidence: {confidence(rule, data):.4f}")这个代码哪里有问题怎么改

for i in range(len(data)): print('I' + str(i+1), end='\t') for j in range(len(items)): print(matrix[i][j], end='\t') print() # 计算关联规则 rules = [(('西红柿', '排骨'), '鸡蛋'), (('排骨', '西红柿...

from proxypool.schemas import Proxy def is_valid_proxy(data): if data.contains(':'): ip = data.split(':')[0] port = data.split(':')[1] return is_ip_valid(ip) and is_port_valid(port) else: return is_ip_valid(data) def is_ip_valid(ip): a = ip.split('.') if len(a) != 4: return False for x in a: if not x.isdigit(): return False i = int(x) if i < 0 or i > 255: return False return True def is_port_valid(port): return port.isdigit() def convert_proxy_or_proxies(data): if not data: return None if isinstance(data, list): result = [] for item in data: # skip invalid item item = item.strip() if not is_valid_proxy(item): continue host, port = item.split(':') result.append(Proxy(host=host, port=int(port))) return result if isinstance(data, str) and is_valid_proxy(data): host, port = data.split(':') return Proxy(host=host, port=int(port))给这段代码加注释

for item in data: # 跳过无效的代理数据 item = item.strip() if not is_valid_proxy(item): continue host, port = item.split(':') result.append(Proxy(host=host, port=int(port))) return result if ...

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