pre-release 0.10.0

rl_coach/dashboard_components/signals_file_base.py

@@ -0,0 +1,129 @@
+import numpy as np
+from bokeh.models import ColumnDataSource
+
+from rl_coach.dashboard_components.signals import Signal
+from rl_coach.dashboard_components.globals import x_axis, x_axis_options, show_spinner
+
+
+class SignalsFileBase:
+    def __init__(self, plot):
+        self.plot = plot
+        self.full_csv_path = ""
+        self.dir = ""
+        self.filename = ""
+        self.signals_averaging_window = 1
+        self.show_bollinger_bands = False
+        self.csv = None
+        self.bokeh_source = None
+        self.bokeh_source_orig = None
+        self.last_modified = None
+        self.signals = {}
+        self.separate_files = False
+        self.last_reload_data_fix = False
+
+    def load_csv(self):
+        pass
+
+    def update_x_axis_index(self):
+        global x_axis
+        self.bokeh_source_orig.data['index'] = self.bokeh_source_orig.data[x_axis[0]]
+        self.bokeh_source.data['index'] = self.bokeh_source.data[x_axis[0]]
+
+    def toggle_y_axis(self, signal_name=None):
+        if signal_name and signal_name in self.signals.keys():
+            self.signals[signal_name].toggle_axis()
+        else:
+            for signal in self.signals.values():
+                if signal.selected:
+                    signal.toggle_axis()
+
+    def update_source_and_signals(self):
+        # create bokeh data sources
+        self.bokeh_source_orig = ColumnDataSource(self.csv)
+
+        if self.bokeh_source is None:
+            self.bokeh_source = ColumnDataSource(self.csv)
+            self.update_x_axis_index()
+        else:
+            self.update_x_axis_index()
+            # smooth the data if necessary
+            self.change_averaging_window(self.signals_averaging_window, force=True)
+
+        # create all the signals
+        if len(self.signals.keys()) == 0:
+            self.signals = {}
+            unique_signal_names = []
+            for name in self.csv.columns:
+                if len(name.split('/')) == 1:
+                    unique_signal_names.append(name)
+                else:
+                    unique_signal_names.append('/'.join(name.split('/')[:-1]))
+            unique_signal_names = list(set(unique_signal_names))
+            for signal_name in unique_signal_names:
+                self.signals[signal_name] = Signal(signal_name, self, self.plot)
+
+    def load(self):
+        self.load_csv()
+        self.update_source_and_signals()
+
+    def reload_data(self):
+        # this function is a workaround to reload the data of all the signals
+        # if the data doesn't change, bokeh does not refresh the line
+        temp_data = self.bokeh_source.data.copy()
+        for col in self.bokeh_source.data.keys():
+            if not self.last_reload_data_fix:
+                temp_data[col] = temp_data[col][:-1]
+        self.last_reload_data_fix = not self.last_reload_data_fix
+        self.bokeh_source.data = temp_data
+
+    def change_averaging_window(self, new_size, force=False, signals=None):
+        if force or self.signals_averaging_window != new_size:
+            self.signals_averaging_window = new_size
+            win = np.ones(new_size) / new_size
+            temp_data = self.bokeh_source_orig.data.copy()
+            for col in self.bokeh_source.data.keys():
+                if col == 'index' or col in x_axis_options \
+                        or (signals and not any(col in signal for signal in signals)):
+                    temp_data[col] = temp_data[col][:-new_size]
+                    continue
+                temp_data[col] = np.convolve(self.bokeh_source_orig.data[col], win, mode='same')[:-new_size]
+            self.bokeh_source.data = temp_data
+
+            # smooth bollinger bands
+            for signal in self.signals.values():
+                if signal.has_bollinger_bands:
+                    signal.set_bands_source()
+
+    def hide_all_signals(self):
+        for signal_name in self.signals.keys():
+            self.set_signal_selection(signal_name, False)
+
+    def set_signal_selection(self, signal_name, val):
+        self.signals[signal_name].set_selected(val)
+
+    def change_bollinger_bands_state(self, new_state):
+        self.show_bollinger_bands = new_state
+        for signal in self.signals.values():
+            signal.change_bollinger_bands_state(new_state)
+
+    def file_was_modified_on_disk(self):
+        pass
+
+    def get_range_of_selected_signals_on_axis(self, axis, selected_signal=None):
+        max_val = -float('inf')
+        min_val = float('inf')
+        for signal in self.signals.values():
+            if (selected_signal and signal.name == selected_signal) or (signal.selected and signal.axis == axis):
+                max_val = max(max_val, signal.max_val)
+                min_val = min(min_val, signal.min_val)
+        return min_val, max_val
+
+    def get_selected_signals(self):
+        signals = []
+        for signal in self.signals.values():
+            if signal.selected:
+                signals.append(signal)
+        return signals
+
+    def show_files_separately(self, val):
+        pass