def __init__(self, server_ip, server_port, database):
self.client = MongoClient(server_ip, server_port, connect=True)
self.database = database
self.twitter_controller = TwitterController(self)
self.analysis_controller = AnalysisController(self.client, database)
self.dao_collection_tags = DAOTags(self.client, self.database)
class SilverEye:
def __init__(self, server_ip, server_port, database):
self.client = MongoClient(server_ip, server_port, connect=True)
self.database = database
self.twitter_controller = TwitterController(self)
self.analysis_controller = AnalysisController(self.client, database)
self.dao_collection_tags = DAOTags(self.client, self.database)
def start(self):
keywords = self.dao_collection_tags.get_array_of_the_name_of_classified_tags()
self.twitter_controller.start(keywords)
def stop(self):
self.twitter_controller.stop()
def analyze_tweet(self, tweet):
self.analysis_controller.analyze_tweet(tweet)
def analyse_temporal_time(self, init_time=0):
self.analysis_controller.analyse_temporal_lines(init_time)
def analyze_global_results(self):
self.analysis_controller.overall_analysis()
def start_hsne(self, data, data_file, hsne_file, label_file):
"""Initiate the embedding iteration in the analysis GUI"""
print("Created hSNE")
hsne = nptsne.HSne(True)
number_of_scales = self.model_gui.scales
print(f"Number of scales: {number_of_scales}")
if hsne_file is None:
print("hSNE from scratch")
hsne.create_hsne(data, number_of_scales)
hsne_file = data_file.with_suffix('.hsne')
hsne.save(str(hsne_file))
else:
print("existing hSNE")
hsne.load_hsne(data, str(hsne_file))
print("start analysis model")
self.data = data
self.analysis_model = hsne_analysis.AnalysisModel(
hsne, self.embedder_type)
top_analysis = self.analysis_model.top_analysis
all_analyses_per_scale = {
top_analysis.scale_id: {
top_analysis.id: top_analysis
}
}
self.__set_labels_and_color_norm(label_file)
# The AnalysisGui is non-blocking
# start with an analysis GUI containing all top scale landmarks
is_top_level = True
self.im_size = (0, 0)
if self.model_gui.demo_type in [
DemoType.LABELLED_DEMO, DemoType.HYPERSPECTRAL_DEMO
]:
self.im_size = (self.model_gui.im_size_x, self.model_gui.im_size_y)
top_analysis_gui = AnalysisController(self.model_gui.demo_type,
self.add_analysis,
self.remove_analysis,
self.analysis_stopped)
if self.model_gui.demo_type is DemoType.POINT_DEMO:
top_analysis_gui.set_metapath(self.labelcolor_filename)
top_analysis_gui.start_embedding(self.data, top_analysis,
self.model_gui.iterations,
self.im_size, False, self.labels,
self.color_norm)
print(f"Top analysis has {top_analysis.number_of_points} points")
self.analysis_guis[top_analysis.id] = top_analysis_gui
self.queue_new_analysis(top_analysis)
# Queue is used to pass changes in the analyses to the ModelGui
# The ModelGui is blocking
self.model_gui.set_analysis_model(self.analysis_model)
def add_analysis(self, analysis, selected_indexes):
"""Handle 2.) AnalysisEvent.ADDED
Callback to start a sub analysis in the analysis model"""
print("Drilling down to new analysis")
new_analysis = self.analysis_model.add_new_analysis(
analysis, selected_indexes)
print("Updated analysis hierarchy: ")
print(self.analysis_model.analysis_container)
print("Starting analysis GUI")
analysis_gui = AnalysisController(self.model_gui.demo_type,
self.add_analysis,
self.remove_analysis,
self.analysis_stopped)
if self.model_gui.demo_type is DemoType.POINT_DEMO:
analysis_gui.set_metapath(self.labelcolor_filename)
analysis_gui.start_embedding(self.data, new_analysis,
self.model_gui.iterations, self.im_size,
False, self.labels, self.color_norm)
self.analysis_guis[new_analysis.id] = analysis_gui
self.queue_new_analysis(new_analysis)
