def vis_detections(im, class_name, dets, thresh=0.5): """Draw detected bounding boxes.""" inds = np.where(dets[:, -1] >= thresh)[0] if len(inds) == 0: return plt.use('Agg') im = im[:, :, (2, 1, 0)] fig, ax = plt.subplots(figsize=(12, 12)) ax.imshow(im, aspect='equal') for i in inds: bbox = dets[i, :4] score = dets[i, -1] ax.add_patch( plt.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1], fill=False, edgecolor='red', linewidth=3.5)) ax.text(bbox[0], bbox[1] - 2, '{:s} {:.3f}'.format(class_name, score), bbox=dict(facecolor='blue', alpha=0.5), fontsize=14, color='white') ax.set_title(('{} detections with ' 'p({} | box) >= {:.1f}').format(class_name, class_name, thresh), fontsize=14) plt.axis('off') plt.tight_layout() plt.draw()
def scatter_graph(purchases): # Setting up the scatter graph using matplotlib plt.use("ggplot") graph_x = [] graph_y = [] graph_transactions = 0 for j in range(len(purchases)): # Appending the variables to x and y (matplotlib) graph_x.append(j) graph_y.append(purchases[j]) # Plotting the matplotlib scatter plot plt.figure(1) plt.title("Scatter (All transactions)") plt.scatter(graph_x, graph_y, s=5)
def find_phase_regression(d, phs_truth=""): print(np.mean(d)) plt.use('Agg') plt.plot(d) plt.ylabel('some numbers') plt.show() # Find frequency D = np.abs(np.fft.fftshift(np.fft.fft(d * np.hamming(len(d))))) f = np.arange(-0.5, 0.5 - 1.0 / len(d), 1.0 / len(d)) pos = D.argmax() # Create x (synthetic sin wave) x = np.sin(2 * np.pi * np.arange(-1, len(D)) * np.abs(f[pos])) # Compute w w = LS_local(np.asarray(d), x, 2) # Initialize ph0 = 0 ph1 = -2 * np.pi * np.abs(f[pos]) # Compute ph ph = np.arctan((w[0] * np.sin(ph0) + w[1] * np.sin(ph1)) / (w[0] * np.cos(ph0) + w[1] * np.cos(ph1))) if np.sign((w[0] * np.cos(ph0) + w[1] * np.cos(ph1))) < 0: ph = ph - np.pi ph = modmPitoPi(ph) if phs_truth: # determine Empty String phs_truth = modmPitoPi(phs_truth) # Check print( str(phs_truth) + " = " + str(ph) + " ?" ) # These should be (approximately) equal or off by an integer multiple of 2*pi else: #print(str(ph)) pass # Return return ph, ErrStd
def __init__(self, dataY, dataFrame, plotType): import matplotlib as plt plt.use("WXAgg") plt.interactive(False) self.plotType = plotType self.dataFrame = dataFrame self.figure = pl.figure() self.axis = self.figure.add_subplot(111) # create a long tooltip with newline to get around wx bug (in v2.6.3.3) # where newlines aren't recognized on subsequent self.tooltip.SetTip() calls self.tooltip = wx.ToolTip(tip="tip with a long %s line and a newline\n" % (" " * 100)) gcfm().canvas.SetToolTip(self.tooltip) self.tooltip.Enable(False) self.tooltip.SetDelay(0) self.figure.canvas.mpl_connect("motion_notify_event", self._onMotion) self.dataX = range(len(dataY)) self.dataY = dataY self.xTicks = dataFrame.index pl.xticks(self.dataX, self.xTicks) self.axis.plot(self.dataX, self.dataY, linestyle="-", marker="o", markersize=15, label="myplot")
import networkx as nx import matplotlib.pyplot as plt plt.use("Agg") G = nx.Graph() G.add_node(1) nx.draw(G) G = nx.path_graph(4) cities = {0: "Toronto", 1: "London", 2: "Berlin", 3: "New York"} H = nx.relabel_nodes(G, cities) print("Nodes of graph: ") print(H.nodes()) print("Edges of graph: ") print(H.edges()) nx.draw(H) plt.savefig("path_graph_cities.png") plt.show()
# -*- coding: utf-8 -*- """ Created on Mon Sep 16 14:12:33 2019 @author: gynjkm """ #imports required packages import matplotlib.pyplot as plt plt.use('TkAgg') import matplotlib.animation import agentframework_zombies import csv import random #defines our arguments and creating the lists of sheep and zombiesheep num_of_agents = 100 num_of_iterations = 150 neighbourhood = 15 num_of_zombsheep = 2 agents = [] zombsheep = []
parser.add_argument('--confirmexit', '-x', default=False, help='confirm exit') parser.add_argument('--prompt', '-p', default='>>> ', help='input prompt') parser.add_argument( '--resultprefix', '-r', default=None, help='execution result prefix, include {} for execution count number') parser.add_argument('--showassign', '-a', default=False, help='display the result of assignments') args = parser.parse_args() if args.backend is not None: print(f"Using matplotlub backend {args.backend}") plt.use(args.backend) # load some robot models puma = models.DH.Puma560() panda = models.DH.Panda() # print the banner # https://patorjk.com/software/taag/#p=display&f=Cybermedium&t=Robotics%20Toolbox%0A print( r"""____ ____ ___ ____ ___ _ ____ ____ ___ ____ ____ _ ___ ____ _ _ |__/ | | |__] | | | | | [__ | | | | | | |__] | | \/ | \ |__| |__] |__| | | |___ ___] | |__| |__| |___ |__] |__| _/\_ for Python from roboticstoolbox import *
Next Steps: - Plot Data to Graph, Add Styling - try, catch - point to audioFiles directory - make program run faster audioFiles = ["PTSD_female.wav", "Anxiety_female.wav", "Depression_male.wav", "Depression_male2_JB.wav", "Depression_male3_JB.wav", "Control_male.wav", "Control_Male2.wav", "Depression_female.wav", "Depression_female2.wav", "Depression_female3.wav", "PTSD_male.wav", "Anxiety_male.wav", "Anxiety_male2.wav"] ''' from pyAudioAnalysis import audioBasicIO from pyAudioAnalysis import audioFeatureExtraction import matplotlib.pyplot as plt import os, sys import matplotlib.pyplot as style style.use('ggplot') #Define Global Variables path = "/Users/kamilahmitchell/Desktop/C++, Python & Vsts/Neurolex/traumaDemo/Audio Files" dir = os.listdir(path) i = 0 def readFiles(dir): audioFiles = [] for file in dir: if file.endswith(".wav"): audioFiles.append(file) print audioFiles #Trace return audioFiles def findFormants(audioFiles, i, dir):
plt.ylabel("E") if flag_start == False: command = input("Press Enter, then start.") flag_start = True plt.pause(0.001) plt.clf() flag_plt = True #Visualization """ E_nx_for_plt=[ E_nx[0,0,0] E_nx[1,0,0] ... E_nx[i,0,0] E ] """ Ex_for_plt = np.zeros([n_meshx, n_meshy, n_meshz]) for i in range(0, n_meshx - 1): for j in range(0, n_meshy - 1): Ex_for_plt[i, j] = E_nx[i, j, 0] x_plt = np.arange(-area_x + dx, area_x - dx, dx) y_plt = np.arange(-area_y + dx, area_y - dx, dy) X, Y = np.meshgrid(x_plt, y_plt) plt.use('Agg') plt.pcolor(X, Y, Ex_for_plt) plt.colorbar() plt.show()
# collocations=False, # font_path='simhei.ttf', # icon_name='fas fa-heart', # size=653, # # palette='matplotlib.Inferno_9', # output_name='./005827.png') # Image(filename='./005827.png') # 用更为量化的方法,计算出每个评论的情感评分 senta = hub.Module(name="senta_bilstm") texts = df['标题'].tolist() input_data = {'text': texts} res = senta.sentiment_classify(data=input_data) df['投资者情绪'] = [x['positive_probs'] for x in res] # 重采样至15分钟 df['时间'] = pd.to_datetime(df['时间']) df.index = df['时间'] data = df.resample('15min').mean().reset_index() # 通过AkShare这一开源API接口获取上证指数分时数据,AkShare是基于Python的财经数据接口库, # 可以实现对股票、期货、期权、基金、外汇、债券、指数、 # 数字货币等金融产品的基本面数据、历史行情数据的快速采集和清洗。 sz_index = ak.stock_zh_a_minute(symbol='sh000001', period='15', adjust="qfq") sz_index['日期'] = pd.to_datetime(sz_index['day']) sz_index['收盘价'] = sz_index['close'].astype('float') data = data.merge(sz_index, left_on='时间', right_on='日期', how='inner') plt.use('Qt5Agg') data.index = data['时间'] data[['投资者情绪', '收盘价']].plot(secondary_y=['close']) plt.show()
im1 = imageio.imread('chelsea.png') im2 = imageio.imread('chelsea_morph1.png') #im2 = imageio.imread('https://dl.dropboxusercontent.com/u/1463853/images/chelsea_morph1.png') # Select one channel (grayscale), and make float im1 = im1[:,:,1].astype('float32') im2 = im2[:,:,1].astype('float32') # Get default params and adjust params = pyelastix.get_default_params() params.NumberOfResolutions = 3 print(params) # Register! im3, field = pyelastix.register(im1, im2, params) # Visualize the result fig = plt.figure(1); plt.clf() plt.subplot(231); plt.imshow(im1) plt.subplot(232); plt.imshow(im2) plt.subplot(234); plt.imshow(im3) plt.subplot(235); plt.imshow(field[0]) plt.subplot(236); plt.imshow(field[1]) # Enter mainloop if hasattr(plt, 'use'): plt.use().Run() # visvis else: plt.show() # mpl
#!/usr/bin/env python import time import datetime import numpy as np import matplotlib.pyplot as plt plt.use('GTKAgg') import matplotlib.ticker as mticker import matplotlib.dates as mdates eachStock = 'TSLA', 'AAPL' def graphData(stock): try: stockfile = stock+'.txt' date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile, delimiter=',',unpack=True,converters={ 0: smdates.strpdate2num('%Y%m%d')}) fig = plt.figure() ax1 = plt.subplot(1,1,1) ax1.plot(date, openp) ax1.plot(date, highp) ax1.plot(date, lowp) ax1.plot(date, closep) plt.show() except Exception, e: print 'failed main loop',str(e) for stock in eachStock: graphData(stock)
feature_file_path = "F:\Yassien_PhD\Experiment_4\All_Categories_Data_25_Basic_Features_With_10_Time_Intervals\Arts, Crafts & Sewing.txt" target = [] features = [] with open(feature_file_path, 'r') as filep: for item in filep: review = item.split(' ') target.append(review[0]) feat_vect = [] for i in range(2, len(review)): feat_vect.append(float(review[i].split(':')[1])) features.append(feat_vect) data = np.array(features) # clustering thresh = 0.7 clusters = hcluster.fclusterdata(data, thresh, criterion="distance") #k_means = cluster.KMeans(n_clusters=30) #k_means.fit(data) print(type(clusters)) for clus in clusters: print(clus) # plotting plt.use("ggplot") plt.scatter(*numpy.transpose(data), c=clusters) plt.axis("equal") title = "threshold: %f, number of clusters: %d" % (thresh, len(set(clusters))) plt.title(title) plt.show()