def delete(update, context): chat_id = update.message.chat_id number_of_accounts = DataHandler.number_of_accounts(chat_id) if number_of_accounts == 0: reply(context, chat_id, 'You don\'t have any registered account(s) to delete') elif number_of_accounts == 1: DataHandler.delete_user(chat_id) lookup_dict.pop(chat_id) else: try: acc_num = int(context.args[0]) if acc_num > number_of_accounts: reply( context, chat_id, "You need to specify right account number as given in /listall" ) return current_default = DataHandler.get_default_acc(chat_id) id_to_delete = DataHandler.account_id(chat_id, acc_num) DataHandler.delete_account(id_to_delete) if id_to_delete == current_default: DataHandler.upsert_user(chat_id, 1) load_account(chat_id, force_reload=True) account_info_tuple = DataHandler.account_info(chat_id) reply( context, chat_id, 'Your current default account is now set to {username} @ {instance}' .format(username=account_info_tuple[0], instance=account_info_tuple[1])) except: reply(context, chat_id, '`usage:`\n`/delete <number>`')
class StreamPredict(StreamHandler): ''' Predicts on stream ''' def __init__(self, data_stem, model_name): self.model = self.load_model(model_name) self.d = DataHandler(data_stem) self.d.bandpass(0.1, 2.0) self.d.scale() super().__init__() def load_model(self, model_name): # get full filename full_name = './models/' + model_name + str( len([f for f in os.listdir('./models/') if model_name in f]) - 1) + '.pickle' print("Loading model: ", full_name) model = pickle.load(open(full_name, 'rb')) print("Model loaded!") return model def sample(self): ''' Pulls a single sample from inlet ''' # assert(hasattr(self, inlet)) # logging.debug("SAMPLE: pulling now") sample, timestamp = self.inlet.pull_sample() self.latest_timestamp = timestamp full_sample = [sample, timestamp] self.samples.append(full_sample) # PREDICT self.d.append_sample(full_sample) #self.d.y = np.append(self.d.y, None) self.d.bandpass(0.1, 2.0) self.d.scale() pred = self.model.pred(self.d.X[-1]) print("PREDICTION: ", pred) return full_sample
def format_data(filename, class_ids): data = dh.pickDataClass(filename, class_ids) number_per_class = data[0].count(class_ids[0]) test_instances = [31, 41] trainX, trainY, testX, testY = dh.splitData2TestTrain(data, number_per_class, test_instances) dh.write_2_file(trainX, trainY, testX, testY)
def _interpret_slices_(self, item, data=None): '''overrides member class function to allow for nRange items''' if type(item) in (list, tuple, str): # i.e. standard return DataHandler._interpret_slices_(self, item, data) else: return DataHandler._interpret_slices_( self, self._get_slices_from_nRange_(item), data)
def setdefault(update, context): chat_id = update.message.chat_id number_of_accounts = DataHandler.number_of_accounts(chat_id) if number_of_accounts == 0: reply(context, chat_id, 'You have not registered any mastodon account yet') return if number_of_accounts == 1: acc = DataHandler.account_info(chat_id) reply( context, chat_id, "Your only registered account is `{}` at `{}`".format( acc[0], acc[1])) return try: newDefault = int(context.args[0]) if newDefault <= number_of_accounts: DataHandler.upsert_user(chat_id, newDefault) accountObj = load_account(chat_id, force_reload=True) reply( context, chat_id, "Now you can toot to your account `{}` at `{}`".format( accountObj.user, accountObj.instance)) else: reply( context, chat_id, "You need to specify right account number as given in /listall" ) except: reply(context, chat_id, "`/setdefault` <number>")
def tick(self): self.tick_count += 1 try: tickdata = PriceFetcher.ReportMulti() DataHandler.appendData(tickdata, self.worksheet) except Exception as e: self.stop(exception=e)
def __init__( self, mode, ): self.workbook = DataHandler.initialize() self.worksheet = DataHandler.initializeWS(self.workbook) self.tick_count = 0 self.gave_order = False
def TrainServer(number): import time DataHandler.send_email(Email, Password, Recipient, "Training model ", "Dear user your model has started trianing") time.sleep(number) print("Training done") DataHandler.send_email(Email, Password, Recipient, "Training model ", "Dear user your model has finished trianing")
def get_articles_by_category(category): try: cur = DataHandler.build_cursor(f"SELECT * FROM Articles WHERE category = %s", (category)) return DataHandler.mysql_cursor_to_json(cur) except Exception as e: print(e) finally: cur.close()
def get_canidate_info(canidate): try: cur = DataHandler.Build_Cursor(f"SELECT * FROM Canidates WHERE Canidate=%s", (canidate)) return DataHandler.mysql_cursor_to_json(cur) except Exception as eL print(e) finally: cur.close()
def get_article_by_id(article_id): try: cur = DataHandler.Build_Cursor(f"SELECT * FROM Articles WHERE ID = %s", (article_id)) return DataHandler.mysql_cursor_to_json(cur) except Exception as e: print(e) finally: cur.close()
def change_cellref(self, item0, item1=None): '''input a range in the form of two ints, two tuples, or a range of cells''' if item1 is not None: DataHandler.change_cellref(self, item0, item1) else: start, end = self._interpret_nRange_(item0) (self.start_row, self.start_col) = start (self.end_row, self.end_col) = end
def dataExportSlot(self): print 'Exporting . . . ' filename = QtGui.QFileDialog.getSaveFileName(self, 'Dialog Title', root, selectedFilter='*.mat') if filename.endsWith('.mat'): name = str(self.tableWidgetData.currentItem().text()) DataHandler.exportAsMat(filename=filename, hdf5name=name)
def onOkClickedSlot(self): if(self.comboBox.currentText().__str__()=='MAT'): print 'Calling Mat Saver' DataHandler.exportAsMat(self.lineEdit.text().__str__(),self.dataName) elif(self.comboBox.currentText().__str__()=='HDF5'): print 'Calling HDF5 Saver' shutil.copyfile(root+'/data/'+self.dataName+'.hdf5',self.lineEdit.text().__str__()) self.signalRefreshTrigger.emit('\''+self.dataName+'\' has been exported with '+self.comboBox.currentText().__str__()+' format to location \''+self.lineEdit.text().__str__()+'\'') self.closedSlot()
def main(args): if len(args) > 1: print "Output for %s"%args[1] OperationsTable = DH.make_operations(args[1]) DBMan(OperationsTable).execute() else: inputFiles = os.listdir("inputs/") inputFiles.remove("__init__.py") for file in inputFiles: print "Output for %s"%file OperationsTable = DH.make_operations("inputs/%s"%file) DBMan(OperationsTable).execute() print "===========================================\n"
def create_item_dictionary(self, click_list): self.items = {} # if there is only a single click in a session if self.number_of_clicks == 1: actual_item = click_list[0]['id_item'] self.items[actual_item] = ItemData( click_list[0]['id_item'], click_list[0]['category'], 1, SessionData.default_clicktime ) return for index in range(0, self.number_of_clicks - 1): actual_item = click_list[index]['id_item'] actual_timestamp = click_list[index]['timestamp'] next_timestamp = click_list[index + 1]['timestamp'] actual_time = DataHandler.timestamp_diff(next_timestamp, actual_timestamp) # If item does not exist in session, add new item if actual_item not in self.items.keys(): self.items[actual_item] = ItemData( click_list[index]['id_item'], click_list[index]['category'], 1, actual_time ) # If item already exists, update its values else: self.items[actual_item].update_item(1, actual_time) # manually one more time for the last indexed item without clicktime actual_item = click_list[-1]['id_item'] actual_time = (DataHandler.timestamp_diff(click_list[0]['timestamp'], click_list[-1]['timestamp'])/(len(click_list)-1)) if actual_item not in self.items.keys(): self.items[actual_item] = ItemData( actual_item, click_list[-1]['category'], 1, actual_time ) else: self.items[actual_item].update_item(1, actual_time)
def deleteall(update, context): chat_id = update.message.chat_id try: assert (context.args[0] == 'yes') except: reply( context, chat_id, '`NOTE: delete all registered accounts \nusage:\n/deleteall yes`') else: DataHandler.delete_user(chat_id) try: lookup_dict.pop(chat_id) except KeyError: pass
def main(): with requests.Session() as s: response = s.post(settings["base_url"] + settings["default_ext"], qt_user) error_check(response) for count, link in enumerate(settings["links"]): file_name = settings["files"][count] response = s.get(settings["base_url"] + link) error_check(response) data = DataHandler.DataHandler().convert(response, link) if DataHandler.DataHandler().compare(data, file_name): DataHandler.DataHandler().updater(data, file_name) DataHandler.DataHandler().data_sender(data, settings["email"], settings["key"], file_name)
def main(): root = tk.Tk() root.title("Smog simulation") DataHandler.getSmogLevel() app = App(root) app.pack() def onClosing(): if messagebox.askokcancel("Quit", "Do you want to quit?"): exit() root.protocol("WM_DELETE_WINDOW", onClosing) root.mainloop()
def main(username, algos): userClasses = dict([(name, cls) for name, cls in Algorithms.__dict__.items() if name in Algorithms.usernames]) countriesData = DataHandler.GetData() if username == NO_ARGUMENT: createAlgoSelect() for username in userClasses: usermod = userClasses[username] algomod = dict([(name, cls) for name, cls in usermod.__dict__.items() if name in usermod.classNames]) for algo in algomod: runAlgo(username, algo, algomod[algo], countriesData) elif username in userClasses: usermod = userClasses[username] algomod = dict([(name, cls) for name, cls in usermod.__dict__.items() if name in usermod.classNames]) if algos[0] == NO_ARGUMENT: for algo in algomod: runAlgo(username, algo, algomod[algo], countriesData) else: for algo in algos: if algo in algomod: runAlgo(username, algo, algomod[algo], countriesData) else: print(algo + " algorithm not found!") else: print(username + " module not found!")
def load_and_preprocess_data(baseName,dimension, lowerLimit, upperLimit): data = File.ler('../data/'+baseName+'.txt') dh:DataHandler = DataHandler(data, dimension, 60, 20,20) X_train, y_train, val_set, val_target, X_test, y_test, arima_train, arima_val, arima_test= dh.redimensiondata(data, dimension, 60, 20,20) y = [[]] x = np.concatenate( (X_train,X_test) ) y = np.matrix( np.concatenate( (np.array(y_train),np.array(y_test)) )) data = np.concatenate((x, y.T), axis=1) x, y = Padronizar.dividir(data, dimension, 1) # data = Arquivo.ler('../data/projeto base completa.csv') # x, y = Padronizar.dividir(data, 4, 1) # print("y") # print(y[y.columns[0]]) # minmaxscaler = MinMaxScaler(feature_range=(0,1)) # dataNX, listMin, listMax = Padronizar.normalizarLinear(x, lowerLimit, upperLimit) # dataNY, listMinY, listMaxY = Padronizar.normalizarLinear(y, lowerLimit, upperLimit) # scalerX,scalerY, dataNormalizadoX, dataNormalizadoY = Padronizar.normalizar(x,y) X_train, X_test, y_train, y_test = train_test_split(x, y, train_size = 0.6, test_size = 0.4) X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, train_size = 0.5, test_size = 0.5) X_train = addBias(X_train.values) X_val = addBias(X_val.values) X_test = addBias(X_test.values) # WE WILL USE ONLY THE FIRST COLUMN y_train = y_train[y_train.columns[0]].values y_test = y_test[y_test.columns[0]].values y_val = y_val[y_val.columns[0]].values return X_train, y_train, X_val, y_val, X_test, y_test
def GetBestModel(clamp, lr, ss, n, steps, dx, v0x, vf, obs_t, obs_offset): data_handler = dh.DataHandler(10, "optimal_nn.csv", "eval_nn.csv", True, 2) T_opt, _, _, x = data_handler.getOptimalSolution(dx, v0x, vf, obs_t, obs_offset) obs_x = x[13] obs_y = x[14] best_cost = float('inf') best_mu = float('inf') * np.ones(4) best_sig = float('inf') * np.ones(4) for i in range(n): net = wdnn.WaypointDistributionNN(len(x), lr, clamp) count = 0 while count < steps: count += 1 mu, S = net(x) mu = mu[0, :] S = S[0, :, :] sig = np.diag(S) wpts = mltnrm(mu, S, ss) Cs = [] C_tot = 0 for i in range(ss): T, T_col = data_handler.Evaluate(dx, v0x, vf, wpts[i, :], obs_x, obs_y) C = data_handler.GetCost(T_opt, T_col, T) Cs += [C / ss] C_tot += C C_avg = C_tot / ss if C_avg < best_cost: best_cost = C_avg best_mu[:] = mu best_sig[:] = sig net.update(-np.array(Cs), wpts, np.vstack([x] * ss)) return best_cost, best_mu, best_sig
def testGame(envName, verbose=True): env = Definitions.makeSeededEnvironment(envName) net = Net.Net(env.observation_space, env.action_space) net = net.to(Definitions.device) policy = PolicyLearner.PolicyLearner(net) dataHandler = DataHandler.DataHandler(policy, env) sumReward = 0. maxReward = -1e9 nIters = 20 for i in range(nIters): dataHandler.render(episodes=3) confidenceMul = i policy.setConfidenceMul(confidenceMul) for j in range(10): reward = dataHandler.generate(episodes=10) dataHandler.reset(keepSize=40000) dataHandler.train(batchSize=8, useFraction=0.1) if verbose: print(envName, " iteration:", str(i + 1) + "/" + str(nIters), " reward:", reward, " trained on:", len(dataHandler.inputs), " confidence multipiler:", confidenceMul) sumReward += reward maxReward = max(maxReward, reward) Definitions.saveModel(net, envName, i + 1, reward) avgReward = sumReward / nIters if verbose: print("%s Avg: %.2f Max: %.2f" % (envName, avgReward, maxReward)) print() env.close() return avgReward, maxReward
def test_generate_inspections_summary(self): expect_json = ''' [{ "日付": "2020-03-20", "小計": 67 }, { "日付": "2020-03-21", "小計": 111 }, { "日付": "2020-03-22", "小計": 182 }, { "日付": "2020-03-23", "小計": 205 }] '''.strip() null_data = self.__generate_null_data(datetime.datetime(2020, 3, 24)) expect = json.loads(expect_json) expect.extend(null_data) dh = handler.DataHandler( patients_csvfile=self.patients_csvfile, data_summary_csvfile=self.data_summary_csvfile, total_sickbeds=TOTAL_SICKBEDS) result = dh.generate_inspections_summary() self.assertListEqual(result, expect)
def test_generate_main_summary(self): expect_json = ''' { "attr": "累計", "value": 19, "children": [ { "attr": "入院中", "value": 18 }, { "attr": "死亡", "value": 0 }, { "attr": "退院", "value": 1 } ] } '''.strip() dh = handler.DataHandler( patients_csvfile=self.patients_csvfile, data_summary_csvfile=self.data_summary_csvfile) result = dh.generate_main_summary() expect = json.loads(expect_json) expect["date"] = self.datetime_now_str self.assertDictEqual(result, expect)
def __init__(self, **kwargs): super().__init__(**kwargs) self.cols = 2 self.add_widget(Label(text="Company Name:", font_size=30)) self.Ticker = TextInput(multiline=False, font_size=30) self.add_widget(self.Ticker) self.add_widget(Label(text="Start Refrence (yyyy-mm-dd):", font_size=30)) self.Start = TextInput(multiline=False, font_size=30) self.add_widget(self.Start) self.add_widget(Label(text="Stop Refrence(yyyy-mm-dd):", font_size=30)) self.Stop = TextInput(multiline=False, font_size=30) self.add_widget(self.Stop) self.Predict = Button(text="Predict", font_size=30) self.Predict.bind(on_release=self.PredictButton) self.add_widget(self.Predict) self.Updates = Button(text="Version Information", font_size=30) self.Updates.bind(on_press=self.InfoPopup) self.add_widget(self.Updates) self.OutputPrediction = Label(text="Decision", font_size=30) self.add_widget(self.OutputPrediction) self.Probability = Label(text="Probability", font_size=30) self.add_widget(self.Probability) try: self.DH = DataHandler.BuySellHandler() self.Predictor = Model.StockPredictingModel((window_size,7)) self.Predictor.Load() except: raise AssertionError("Load Failed")
def test_generate_querents(self): expect_json = ''' [ { "日付": "2020-03-20", "小計": 100 }, { "日付": "2020-03-21", "小計": 117 }, { "日付": "2020-03-22", "小計": 99 }, { "日付": "2020-03-23", "小計": 311 } ] '''.strip() expect = json.loads(expect_json) dh = handler.DataHandler( patients_csvfile=self.patients_csvfile, data_summary_csvfile=self.data_summary_csvfile) result = dh.generate_querents() self.assertListEqual(result, expect)
def test_generate_patients_summary_by_date(self): expect_json = ''' [{ "日付": "2020-03-17", "小計": 1 }, { "日付": "2020-03-18", "小計": 0 }, { "日付": "2020-03-19", "小計": 2 }, { "日付": "2020-03-20", "小計": 3 }] '''.strip() # テストデータのため2020-03-21から本日までの日付のデータを作成する null_data = self.__generate_null_data(datetime.datetime(2020, 3, 21)) expect = json.loads(expect_json) expect.extend(null_data) dh = handler.DataHandler( patients_csvfile=self.patients_csvfile, data_summary_csvfile=self.data_summary_csvfile) result = dh.generate_patients_summary_by_date() self.assertListEqual(result, expect)
def __init__(self, size_v, size_h, filters_no, conv_kernel, typeB='scalar', typeC='matrix'): """ :param size_v: vertical size of input image :param size_h: horizontal size of input image :param filters_no: how many feature maps :param conv_kernel: size of convolutional kernel (tuple) :param typeB: scalar or matrix version of feature map biases? :param typeC: scalar or matrix version of visible layer bias? """ # RBM parameters self.insize_v = size_v self.insize_h = size_h self.filters_no = filters_no self.conv_kernel = conv_kernel # neurons, weigths and biases self.v = np.ndarray((size_v, size_h), dtype=np.float32) # int32? self.h = [ np.ndarray( (size_v - conv_kernel[0] + 1, size_h - conv_kernel[1] + 1), dtype=np.int8) for i in range(filters_no) ] self.W = [ np.random.normal(0, 0.01, conv_kernel) for i in range(filters_no) ] if typeB not in ('scalar', 'matrix') or typeC not in ('scalar', 'matrix'): raise ValueError( 'Wrong input arguments. typeB and typeC must be either \'scalar\' or \'matrix\'' ) self.typeB = typeB self.typeC = typeC if self.typeB == 'scalar': self.b = [np.random.normal(0, 0.01) for i in range(filters_no)] else: self.b = [ np.random.normal( 0, 0.01, (size_v - conv_kernel[0] + 1, size_h - conv_kernel[1] + 1)) for i in range(filters_no) ] if self.typeC == 'scalar': self.c = np.random.normal(0, 0.01) else: self.c = np.random.normal(0, 0.01, (size_v, size_h)) self.dh = DataHandler.DataHandler() self.imgInfo = None self.iterations = 0 self.mse = [] logger.info('Created CCRBM. {}'.format(self))
def init(inFile, outFile): global MPIFX3, MPIHandler, MPIProcessor global dev, handler, processor MPIFX3 = mp.Queue() MPIHandler = mp.Queue() MPIProcessor = mp.Queue() dev = FX3.Emulator(MPIFX3, inFile) pipe = dev.getPipe() buffSize = dev.getBufferSize() handler = DataHandler.DataHandler(MPIHandler, pipe, buffSize, filename=outFile) realtime = handler.getRealtimeQueue() handler.enableRealtime() processor = DataProcessor.DataProcessor(MPIProcessor, realtime, [[0, 2], [3, 3]], legacy=False, fs=2.5E6, bufferSize=buffSize, calcFlow=True, numProcessors=2)
def pushButtonOkClicked(self): if self.lineEditData.text()=='' or self.lineEditFolderData.text()=='': QtGui.QMessageBox.about(self,'Incomplete Conversion','Data Left Out\nCheck Form again') #For TEXT option if(self.comboBox.currentIndex()==0 and str(self.lineEditData.text())!='' and self.lineEditFolderData.text()!=''): if(os.path.exists(self.lineEditFolderData.text()+'/test.txt')==0): QtGui.QMessageBox.about(self,'File Not Found',self.lineEditFolderData.text()+'/test.txt\n Does not Exist') return DataHandler.text2HDF5(str(self.lineEditData.text()), self.lineEditFolderData.text()+'/test.txt',self.root+'/Data',hasLabel=self.checkbox.isChecked()) return #Jaley End if(self.comboBox.currentIndex()==2 and str(self.lineEditData.text())!='' and self.lineEditFolderData.text()!=''): if(os.path.exists(self.lineEditFolderData.text()+'/test.mat')==0): QtGui.QMessageBox.about(self,'File Not Found',self.lineEditFolderData.text()+'/test.mat\n Does not Exist') return DataHandler.mat2HDF5(str(self.lineEditData.text()), self.lineEditFolderData.text()+'/test.mat',self.root+'/Data',hasLabel=self.checkbox.isChecked()) return if(self.comboBox.currentIndex()==1 and str(self.lineEditData.text())!='' and self.lineEditFolderData.text()!=''): if(os.path.exists(self.lineEditFolderData.text()+'/test_leveldb')==0): QtGui.QMessageBox.about(self,'File Not Found',self.lineEditFolderData.text()+'/test_leveldb\n Does not Exist') return DataHandler.leveldb2HDF5(str(self.lineEditData.text()), self.lineEditFolderData.text()+'/test_leveldb',self.root+'/Data',hasLabel=self.checkbox.isChecked()) return
def clustersgte10(): # clusters with 10 or more measurements clusters_gte10 = set([i for i in clusters if clusters.count(i) >= 10]) mvir_out,mvir_p_out,mvir_m_out,cvir_out,cvir_p_out,cvir_m_out = ([],[],[],[],[],[]) z_out,methods_out = ([],[]) for i in clusters_gte10: tmp_mvir,tmp_mvir_p,tmp_mvir_m,tmp_cvir,tmp_cvir_p,tmp_cvir_m = ([],[],[],[],[],[]) tmp_z,tmp_methods = ([],[]) for j in range(len(clusters)): if clusters[j] == i: if math.isnan(float(mvir[j])) or math.isnan(float(cvir[j])): continue else: tmp_mvir.append(mvir[j]) tmp_mvir_p.append(mvir_plus[j]) tmp_mvir_m.append(mvir_minus[j]) tmp_cvir.append(cvir[j]) tmp_cvir_p.append(cvir_plus[j]) tmp_cvir_m.append(cvir_minus[j]) tmp_z.append(redshift[j]) tmp_methods.append(methods[j]) mvir_out.append(tmp_mvir) mvir_p_out.append(tmp_mvir_p) mvir_m_out.append(tmp_mvir_m) cvir_out.append(tmp_cvir) cvir_p_out.append(tmp_cvir_p) cvir_m_out.append(tmp_cvir_m) z_out.append(tmp_z) methods_out.append(tmp_methods) nplots = len(clusters_gte10) # subplots do not auto-update; need to do manually f, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True) f.set_size_inches(7,7,forward=True) # loop structure only works for 3 clusters in a 2x2 grid format for i,j in enumerate(clusters_gte10): if i <= 1: axes[0][i].set_xlim(1e14,2e16) axes[0][i].set_ylim(1,30) axes[0][i].text(3e14,1.5,'{}'.format(j)) axes[0][i].text(3e14,1.25,'z={}'.format(z_out[i][0])) axes[0][i].set_xscale('log') axes[0][i].set_yscale('log') else: axes[1][0].set_xlim(1e14,2e16) axes[1][0].set_ylim(1,30) axes[1][0].text(3e14,1.5,'{}'.format(j)) axes[1][0].text(3e14,1.25,'z={}'.format(z_out[i][0])) axes[1][0].set_xscale('log') axes[1][0].set_yscale('log') print j for k in range(len(mvir_out[i])): print "Measurement number {}".format(k+1) flag = DH.scatter_flag(k,mvir_out[i],mvir_p_out[i],mvir_m_out[i], cvir_out[i],cvir_p_out[i],cvir_m_out[i]) col = DH.colorselect(methods_out[i][k]) if i <= 1: DH.plotwithflag(axes[0][i],flag,col,mvir_out[i][k]*1e14,mvir_p_out[i][k]*1e14,mvir_m_out[i][k]*1e14, cvir_out[i][k],cvir_p_out[i][k],cvir_m_out[i][k],z_out[i][k],witherrors=True) else: DH.plotwithflag(axes[1][0],flag,col,mvir_out[i][k]*1e14,mvir_p_out[i][k]*1e14,mvir_m_out[i][k]*1e14, cvir_out[i][k],cvir_p_out[i][k],cvir_m_out[i][k],z_out[i][k],witherrors=True) axes[1][1].scatter(1e6,1e6,color=DH.colorselect('X-ray'),label='X-ray') axes[1][1].scatter(1e6,1e6,color=DH.colorselect('WL'),label='WL') axes[1][1].scatter(1e6,1e6,color=DH.colorselect('SL'),label='SL') axes[1][1].scatter(1e6,1e6,color=DH.colorselect('WL+SL'),label='WL+SL') axes[1][1].scatter(1e6,1e6,color=DH.colorselect('CM'),label='CM') axes[1][1].scatter(1e6,1e6,color=DH.colorselect('LOSVD'),label='LOSVD') axes[1][1].set_xlim(1e14,2e16) axes[1][1].set_ylim(1,30) # over-plot cm relations # CO07_1 mlistCO07_1_1689,clistCO07_1_1689,zCO07_1_1689 = DH.cmrelation_co07_1(1e14,2e16,z_out[0][0]) mlistCO07_1_p_1689,clistCO07_1_p_1689,zCO07_1_1689 = DH.cmrelation_co07_1(1e14,2e16,z_out[0][0],c0=20.9,alpha=-0.02) mlistCO07_1_m_1689,clistCO07_1_m_1689,zCO07_1_1689 = DH.cmrelation_co07_1(1e14,2e16,z_out[0][0],c0=8.7,alpha=-0.26) mlistCO07_1_2137,clistCO07_1_2137,zCO07_1_2137 = DH.cmrelation_co07_1(1e14,2e16,z_out[1][0]) mlistCO07_1_p_2137,clistCO07_1_p_2137,zCO07_1_2137 = DH.cmrelation_co07_1(1e14,2e16,z_out[1][0],c0=20.9,alpha=-0.02) mlistCO07_1_m_2137,clistCO07_1_m_2137,zCO07_1_2137 = DH.cmrelation_co07_1(1e14,2e16,z_out[1][0],c0=8.7,alpha=-0.26) mlistCO07_1_1835,clistCO07_1_1835,zCO07_1_1835 = DH.cmrelation_co07_1(1e14,2e16,z_out[2][0]) mlistCO07_1_p_1835,clistCO07_1_p_1835,zCO07_1_1835 = DH.cmrelation_co07_1(1e14,2e16,z_out[2][0],c0=20.9,alpha=-0.02) mlistCO07_1_m_1835,clistCO07_1_m_1835,zCO07_1_1835 = DH.cmrelation_co07_1(1e14,2e16,z_out[2][0],c0=8.7,alpha=-0.26) # GR14_1 mlistGR14_1_1689,clistGR14_1_1689,zGR14_1_1689 = DH.cmrelation_gr14_1(1e14,2e16,z_out[0][0]) mlistGR14_1_p_1689,clistGR14_1_p_1689,zGR14_1_1689 = DH.cmrelation_gr14_1(1e14,2e16,z_out[0][0],c0=4.797,alpha=-0.049) mlistGR14_1_m_1689,clistGR14_1_m_1689,zGR14_1_1689 = DH.cmrelation_gr14_1(1e14,2e16,z_out[0][0],c0=4.753,alpha=-0.063) mlistGR14_1_2137,clistGR14_1_2137,zGR14_1_2137 = DH.cmrelation_gr14_1(1e14,2e16,z_out[1][0]) mlistGR14_1_p_2137,clistGR14_1_p_2137,zGR14_1_2137 = DH.cmrelation_gr14_1(1e14,2e16,z_out[1][0],c0=4.797,alpha=-0.049) mlistGR14_1_m_2137,clistGR14_1_m_2137,zGR14_1_2137 = DH.cmrelation_gr14_1(1e14,2e16,z_out[1][0],c0=4.753,alpha=-0.063) mlistGR14_1_1835,clistGR14_1_1835,zGR14_1_1835 = DH.cmrelation_gr14_1(1e14,2e16,z_out[2][0]) mlistGR14_1_p_1835,clistGR14_1_p_1835,zGR14_1_1835 = DH.cmrelation_gr14_1(1e14,2e16,z_out[2][0],c0=4.797,alpha=-0.049) mlistGR14_1_m_1835,clistGR14_1_m_1835,zGR14_1_1835 = DH.cmrelation_gr14_1(1e14,2e16,z_out[2][0],c0=4.753,alpha=-0.063) # BU01_1 mlistBU01_1_1689,clistBU01_1_1689,zBU01_1_1689 = DH.cmrelation_bu01_1(1e14,2e16,z_out[0][0]) mlistBU01_1_2137,clistBU01_1_2137,zBU01_1_2137 = DH.cmrelation_bu01_1(1e14,2e16,z_out[1][0]) mlistBU01_1_1835,clistBU01_1_1835,zBU01_1_1835 = DH.cmrelation_bu01_1(1e14,2e16,z_out[2][0]) # HE07_1 mlistHE07_1_1689,clistHE07_1_1689,zHE07_1_1689 = DH.cmrelation_he07_1(1e14,2e16,z_out[0][0]) mlistHE07_1_2137,clistHE07_1_2137,zHE07_1_2137 = DH.cmrelation_he07_1(1e14,2e16,z_out[1][0]) mlistHE07_1_1835,clistHE07_1_1835,zHE07_1_1835 = DH.cmrelation_he07_1(1e14,2e16,z_out[2][0]) # PR11_1 #mlistPR11_1_1689,clistPR11_1_1689,zPR11_1_1689 = DH.cmrelation_pr11_1(1e14,2e16,z_out[0][0]) #mlistPR11_1_2137,clistPR11_1_2137,zPR11_1_2137 = DH.cmrelation_pr11_1(1e14,2e16,z_out[1][0]) #mlistPR11_1_1835,clistPR11_1_1835,zPR11_1_1835 = DH.cmrelation_pr11_1(1e14,2e16,z_out[2][0]) # Abell 1689 axes[0][0].plot(mlistCO07_1_1689,clistCO07_1_1689,color='b',linewidth=2,linestyle='--') #axes[0][0].plot(mlistBU01_1_1689,clistBU01_1_1689,color='y',linewidth=2,linestyle='--') #axes[0][0].plot(mlistHE07_1_1689,clistHE07_1_1689,color='orange',linewidth=2,linestyle='--') axes[0][0].plot(mlistGR14_1_1689,clistGR14_1_1689,color='green',linewidth=2,linestyle='--') #axes[0][0].plot(mlistPR11_1_1689,clistPR11_1_1689,color='green',linewidth=2,linestyle='--') axes[0][0].fill_between(mlistCO07_1_1689,clistCO07_1_p_1689,clistCO07_1_m_1689,alpha=0.25,color='blue') axes[0][0].fill_between(mlistGR14_1_1689,clistGR14_1_p_1689,clistGR14_1_m_1689,alpha=0.25,color='green') # MS 2137 axes[0][1].plot(mlistCO07_1_2137,clistCO07_1_2137,color='b',linewidth=2,linestyle='--') #axes[0][1].plot(mlistBU01_1_2137,clistBU01_1_2137,color='y',linewidth=2,linestyle='--') #axes[0][1].plot(mlistHE07_1_2137,clistHE07_1_2137,color='orange',linewidth=2,linestyle='--') axes[0][1].plot(mlistGR14_1_2137,clistGR14_1_2137,color='green',linewidth=2,linestyle='--') #axes[0][1].plot(mlistPR11_1_2137,clistPR11_1_2137,color='green',linewidth=2,linestyle='--') axes[0][1].fill_between(mlistCO07_1_2137,clistCO07_1_p_2137,clistCO07_1_m_2137,alpha=0.25,color='blue') axes[0][1].fill_between(mlistGR14_1_2137,clistGR14_1_p_2137,clistGR14_1_m_2137,alpha=0.25,color='green') # Abell 1835 axes[1][0].plot(mlistCO07_1_1835,clistCO07_1_1835,color='b',linewidth=2,linestyle='--') #axes[1][0].plot(mlistBU01_1_1835,clistBU01_1_1835,color='y',linewidth=2,linestyle='--') #axes[1][0].plot(mlistHE07_1_1835,clistHE07_1_1835,color='orange',linewidth=2,linestyle='--') axes[1][0].plot(mlistGR14_1_1835,clistGR14_1_1835,color='green',linewidth=2,linestyle='--') #axes[1][0].plot(mlistPR11_1_1835,clistPR11_1_1835,color='green',linewidth=2,linestyle='--') axes[1][0].fill_between(mlistCO07_1_1835,clistCO07_1_p_1835,clistCO07_1_m_1835,alpha=0.25,color='blue') axes[1][0].fill_between(mlistGR14_1_1835,clistGR14_1_p_1835,clistGR14_1_m_1835,alpha=0.25,color='green') axes[1][1].plot(1e6,1e6,color='b',linewidth=2,linestyle='--',label='CO07') #axes[1][1].plot(1e6,1e6,color='y',linewidth=2,linestyle='--',label='BU01') #axes[1][1].plot(1e6,1e6,color='orange',linewidth=2,linestyle='--',label='HE07') axes[1][1].plot(1e6,1e6,color='green',linewidth=2,linestyle='--',label='GR14') #axes[1][1].plot(1e6,1e6,color='green',linewidth=2,linestyle='--',label='PR11') axes[1][1].set_xscale('log') axes[1][1].set_yscale('log') f.subplots_adjust(hspace=0) f.subplots_adjust(wspace=0) f.text(0.5, 0.04, r'$\mathrm{M_{vir} (M_{\odot})}$', ha='center', va='center', fontsize=18) f.text(0.06, 0.5, r'$\mathrm{c_{vir} (1+z)}$', ha='center', va='center', rotation='vertical', fontsize=18) plt.legend(loc=0,scatterpoints=1,frameon=False) plt.show()
def dataExportSlot(self): print 'Exporting . . . ' filename = QtGui.QFileDialog.getSaveFileName(self, 'Dialog Title',root, selectedFilter='*.mat') if filename.endsWith('.mat'): name=str(self.tableWidgetData.currentItem().text()) DataHandler.exportAsMat(filename=filename,hdf5name=name)
start_test_list = [330, 45, 45, 75, 60, 80, 50, 45, 45, 45, 45] #indices = ['^NSEI', '^DJI', '^FTSE', '^AXJO', '^HSI', '^N225', '^IXIC']#, '000001.SS'] #market_name = ['nse', 'dji', 'ftse', 'aus', 'hsi', 'nikkei', 'nasdaq']#, 'sanghai'] indices = ['^NSEI', '^BSESN', '^AXJO', '^HSI', '^N225']#, '000001.SS'] market_name = ['nse', 'bse', 'aus', 'hsi', 'nikkei']#, 'sanghai'] if __name__ == '__main__': final_result='' for start_date,e,s in zip(start_date_list, end_date_list, start_test_list): end_date, start_test = get_date(start_date, e,s) d = DataHandler() data_frames = d.fetch_and_save_data(indices, market_name, start_date, end_date) # plot([data_frames[0]], 'Adj Close', market_name[0]) for data_frame in data_frames: d.daily_return(data_frame) # for index, data_frame in zip(range(len(data_frames)), data_frames): # plot([d.daily_return(data_frame)], 'Daily Return', market_name[index].upper()+' Index') # print(data_frames[0].head(5)) #d.plot_data([data_frames[0],data_frames[7]], ['Daily Return'], market_names=market_name) #plt.show()