def sum_array(self,array_1,array_2): if len(array_1) != len(array_2): raise Exception.message('both input arrays should be of the same size') result = [] for i in range(0,len(array_1)): result.append(array_1[i] + array_2[i]) return result
def evaluate(cpfcnpj, birthday, apikey=None): if validate_cpf(cpfcnpj): if birthday is None: raise Exception( "É necessário a data de nascimento para consultar um CPF.") elif validate_cnpj(cpfcnpj): pass else: raise Exception( "O documento informado não é um CPF ou CNPJ válido.") ws = WebService(apikey) return ws.post("SELECT FROM 'BIPBOPJS'.'CPFCNPJ'", { 'documento': cpfcnpj, 'nascimento': birthday }).find("./body/nome").text
def get_list_amount_activities_RescueTime_interval_with_mindwave( self, session_id, statistic_type): result_x = [] result_y = [] datetime_from_session, datetime_to_session = self.dasession.get_dates_session( session_id) list_dates = self.rescuetimeDataProcessing.get_dates_list( datetime_from_session, datetime_to_session) mindwave_data_list = Mindwave_data_processing.get_mindwave_data_filtered_smoothed( datetime_from_session, datetime_to_session, 10) for interval in list_dates: rescueTime_data = self.darescuetime.get_data_specific_period( interval[0], interval[1]) result_x.append(len(rescueTime_data)) sublist = Mindwave_data_processing.get_sublist( mindwave_data_list, datetime_from_session, datetime_to_session) if statistic_type == Calculate_statistics_enumeration.AVG: attention, meditation = Mindwave_data_processing.calculate_avg_attention_meditation( sublist) elif statistic_type == Calculate_statistics_enumeration.STD: attention, meditation = Mindwave_data_processing.calculate_std_attention_meditation( sublist) else: raise Exception.UnknownID("The statistic_type is unknown") result_y.append(attention) print "rescuetime amount" print "result_x" print result_x print "result_y" print result_y return result_x, result_y
def sum_array(self, array_1, array_2): if len(array_1) != len(array_2): raise Exception.message( 'both input arrays should be of the same size') result = [] for i in range(0, len(array_1)): result.append(array_1[i] + array_2[i]) return result
def Advise(self, pUnk): # Creates a connection to the client. Simply allocate a new cookie, # find the clients interface, and store it in a dictionary. try: interface = pUnk.QueryInterface(self._connect_interfaces_[0],pythoncom.IID_IDispatch) except pythoncom.com_error: raise Exception(scode=olectl.CONNECT_E_NOCONNECTION) self.cookieNo = self.cookieNo + 1 self.connections[self.cookieNo] = interface return self.cookieNo
def get_average_userfeedback_specific_period(start_datetime_activity_detail, end_datetime_activity_detail): gemiddelde_concentratie = 0.0 totaal = 0.0 userfeedback = DAUserFeedback_SQLite.get_data_specific_period( start_datetime_activity_detail, end_datetime_activity_detail) # gemiddelde berekenen van de userfeedback gegevens voor de periodes van activity_detail total_user_feedback_time = 0.0 for item in userfeedback: try: datetime_from_userfeedback = datetime.datetime.strptime( item[1], "%Y-%m-%d %H:%M:%S.%f") except: datetime_from_userfeedback = datetime.datetime.strptime( item[1], "%Y-%m-%d %H:%M:%S") datetime_to_userfeedback = datetime_from_userfeedback + datetime.timedelta( seconds=item[4]) if (datetime_from_userfeedback < end_datetime_activity_detail) and ( datetime_to_userfeedback > start_datetime_activity_detail): if datetime_from_userfeedback < start_datetime_activity_detail: datetime_from_userfeedback = start_datetime_activity_detail if datetime_to_userfeedback > end_datetime_activity_detail: datetime_to_userfeedback = end_datetime_activity_detail difference = datetime_to_userfeedback - datetime_from_userfeedback total_user_feedback_time += difference.seconds if total_user_feedback_time <= 0: raise Exception.ListEmpty('Geen user_feedback gegevens gevonden') for item in userfeedback: try: datetime_from_userfeedback = datetime.datetime.strptime( item[1], "%Y-%m-%d %H:%M:%S.%f") except: datetime_from_userfeedback = datetime.datetime.strptime( item[1], "%Y-%m-%d %H:%M:%S") datetime_to_userfeedback = datetime_from_userfeedback + datetime.timedelta( seconds=item[4]) if datetime_from_userfeedback < end_datetime_activity_detail: if datetime_from_userfeedback < start_datetime_activity_detail: datetime_from_userfeedback = start_datetime_activity_detail if datetime_to_userfeedback > end_datetime_activity_detail: datetime_to_userfeedback = end_datetime_activity_detail difference = datetime_to_userfeedback - datetime_from_userfeedback gemiddelde_concentratie += float( item[2] * (float(difference.seconds) / float(total_user_feedback_time))) return gemiddelde_concentratie
def checking_data(space, location): exception = Exception() if (not space): exception.no_data() if (not location): exception.no_data()
def checking_data(name, surname): exception = Exception() if(not name): exception.no_data() if(not surname): exception.no_data()
def checking_data(name, location): exception = Exception() if(not name): exception.no_data() if(not location): exception.no_data()
def get_sublist(mindwave_data, datetime_from, datetime_to): index_from = -1 index_to = -1 for i in range(0, len(mindwave_data)): if mindwave_data[i][0] >= datetime_from and index_from == -1: index_from = i else: if mindwave_data[i][1] <= datetime_to and index_from != -1: index_to = i if datetime_from == -1 or datetime_to == -1: raise Exception.ListEmpty( "datetime_from or datetime_to is outside the range of the mindwave data" ) return mindwave_data[index_from:index_to + 1]
def get_average_sample_interval(list_mindwave_data, start_index, start_date, end_date): sublist, end_index = get_list_interval_mindwave_data( list_mindwave_data, start_index, start_date, end_date) total = 0.0 attention_acc = 0.0 meditation_acc = 0.0 for index_sublist in range(0, len(sublist)): attention_acc += sublist[index_sublist][3] meditation_acc += sublist[index_sublist][4] total += 1.0 if total > 0: attention_avg = attention_acc / total meditation_avg = meditation_acc / total else: raise Exception.ListEmpty("no mindwave data for this interval") datetime_start_mindwave = sublist[0][1] datetime_end_mindwave = sublist[len(sublist) - 1][1] return attention_avg, meditation_avg, end_index, datetime_start_mindwave, datetime_end_mindwave
def EnumConnectionPoints(self): raise Exception(winerror.E_NOTIMPL)
def Unadvise(self, cookie): # Destroy a connection - simply delete interface from the map. try: del self.connections[cookie] except KeyError: raise Exception(scode=winerror.E_UNEXPECTED)
def GetConnectionInterface(self): raise Exception(winerror.E_NOTIMPL)
def detect_eye_boundaries(eye_frame_gray, x_position, y_position, set_brightness, set_contrast): """ #total_ipf_normal = 68.8214334285 #total_ipf_normal = 60.94624643 #total_ipf_normal = 61.1061384532 total_ipf_normal = 123.47399829497017 total_vpf_normal = 29.299850501006954 #brightness_initial = 100.0 #contrast_initial = 90.0 brightness_initial = 40.0 contrast_initial = 60.0 """ fig_y = None fig_x = None fig_x_iris = None subplot = 111 """ # plot fig_y = plt.figure() fig_x = plt.figure() fig_x_iris = plt.figure() subplot = 111 """ #treshold = 0.305 #treshold_y = 9 #treshold_x = 11 """ #eye_frame_gray = cv2.cvtColor(eye_frame, cv2.COLOR_BGR2GRAY) total_ipf_vpf_gray = calculate_total_ipf_vpf(eye_frame_gray) print str("total_ipf_vpf: " + str(total_ipf_vpf_gray)) print str("total_ipf test: " + str(calculate_total_ipf_check(eye_frame_gray))) #brightness = brightness_initial / total_ipf_vpf_gray[0] * total_ipf_normal * 0.2 #brightness /= (abs(brightness - brightness_initial) / brightness) #contrast = contrast_initial / total_ipf_vpf_gray[0] * total_ipf_normal * 0.2 #contrast *= (abs(contrast - contrast_initial)/ contrast) #brightness = 45 #contrast = 50 """ brightness = 0 contrast = 0 #brightness = set_brightness #contrast = set_contrast treshold_x = 2 treshold_y1 = 3 treshold_y2 = 2 alpha = 0.6 """ newGray = image_operations.update_brightcont(eye_frame_gray, brightness, contrast) print str("brightness: " + str(brightness) + "\t contrast: " + str(contrast)) """ #newGray = change_contrast(eye_frame_gray,10.0,2.0,255.0) #newGray = change_contrast_3de_poging(eye_frame_gray) #eye_frame_gray = newGray gray_original = eye_frame_gray """ y = 0 result = '' while y < len(eye_frame_gray): x = 0 result += "\n" + "\n" + "y = " + str(y) + "\n" + "\t" while x < len(eye_frame_gray[0]): result += str(eye_frame_gray[y][x]) + "\t" x += 1 y += 1 print result cv2.imshow('original',eye_frame_gray) """ # Dit stond er oorspronkelijk, als puur ipf en vpf gebruikt worden """ x1_initial = 2.0 x2_initial = float(len(eye_frame_gray[0]) - 1) y1_initial = math.floor(len(eye_frame_gray) * (1.0 / 5.0)) y2_initial = math.floor(len(eye_frame_gray) * (3.0 / 4.0)) """ x1_initial = 0.0 x2_initial = float(len(eye_frame_gray[0]) - 1) y1_initial = 0.0 y2_initial = math.floor(len(eye_frame_gray) - 1) y1 = calculate_horizontal_line(alpha, gray_original, x1_initial, x2_initial, y1_initial, y2_initial, fig_y, subplot, treshold_y1) y2 = calculate_horizontal_line(alpha, gray_original, x1_initial, x2_initial, y2_initial, y1, fig_y, subplot, treshold_y2) x1 = calculate_vertical_lines(alpha, gray_original, y1, y2, treshold_x, x1_initial, x2_initial, fig_x, subplot) x2 = calculate_vertical_lines(alpha, gray_original, y1, y2, treshold_x, x2_initial, x1, fig_x, subplot) if (y2 - y1) + (x2 - x1) == 0: raise Exception.NoEyesDetected('Unable to detect the eye') """ print '-----------------------------------------' # Mark mark_horizontal_line(eye_frame_gray,y1,x1_initial,x2_initial,0.5) mark_horizontal_line(eye_frame_gray,y2,x1_initial,x2_initial,0.5) mark_vertical_line(eye_frame_gray,x1,y1,y2,0) mark_vertical_line(eye_frame_gray,x2,y1,y2,0) cv2.imshow('test',eye_frame_gray) plt.show() """ return [[x1, y1], [x2, y2]]