def handle_metric(self):
value = core.get_value("metrics")
core.clear_plot("Metrics plot")
bar_positions = [2 * idx + 1 for idx in range(len(self.alg_result))]
if len(self.alg_result):
elapsed_times = [
self.alg_result[idx]["elapsed_time"]
for idx in range(len(self.alg_result))
]
expanded_nodes = [
algorithm["alg_result"]["expanded"]
for algorithm in self.alg_result
]
if value == 0:
core.add_bar_series("Metrics plot", "Elapsed time",
bar_positions, elapsed_times)
core.set_plot_ylimits("Metrics plot", 0,
max(elapsed_times) * 1.2)
if value == 1:
core.add_bar_series("Metrics plot", "Expanded nodes",
bar_positions, expanded_nodes)
core.set_plot_ylimits("Metrics plot", 0,
max(expanded_nodes) * 1.2)
x_ticks = [[alg, idx]
for alg, idx in zip(self.selected_algorithms, bar_positions)
]
core.reset_xticks("Metrics plot")
core.set_xticks("Metrics plot", x_ticks)
def create(sender, data):
core.set_value("company_id",
"{} Model Created".format(model_company.split('_')[0]))
core.clear_plot("Pred")
predict, original = data
core.add_line_series("Pred",
"Prediction",
predict.index.tolist(),
predict[0].tolist(),
color=[255, 50, 50, 100])
def update_chart(self, data: List[float], labels: List[str]) -> None:
"""
Updates data and labels and updates Pie chart
:param data: List of floats
:param labels: List of labels
:return: None but updates chart
"""
self.data = data
self.labels = labels
c.clear_plot(self.plot_label)
self.chart()
def display_graph(self):
core.clear_plot(self.plotName)
core.add_scatter_series(self.plotName,
name="0",
x=self.dataX2,
y=self.dataY2,
update_bounds=True)
core.add_scatter_series(self.plotName,
name="1",
x=self.dataX1,
y=self.dataY1,
update_bounds=True)
def update_data(self):
"""
Updates data in table and plot
"""
dev.log_debug("Called update data")
# Setting table data
c.set_table_data(self._table_name, data=self.table_data)
# Clearing plot and setting new scatter plot
c.clear_plot(self._plot_name)
c.add_scatter_series(
self._plot_name,
name="Example Scatter",
x=self.x_values,
y=self.y_values,
)
# Updating plot limits
x_lim, y_lim = self.recalculate_plot_limits()
c.set_plot_xlimits(self._plot_name, **x_lim)
c.set_plot_ylimits(self._plot_name, **y_lim)
def plot_callback(sender, data):
if data == "clear":
selected_companies.clear()
elif data != None:
if data not in selected_companies:
selected_companies.append(data)
else:
selected_companies.remove(data)
core.clear_plot("Plot")
cap_callback(None, selected_cap)
for i in range(0, len(selected_companies)):
company = selected_companies[i]
stocks = pd.read_csv("dataset/" + company)
data_x = list(range(1, len(stocks.index) + 1))
data_y = stocks['High'].tolist()
core.add_line_series("Plot",
company.split('_')[0],
data_x,
data_y,
color=colors[i % len(colors)])
def cap_callback(sender, data):
global selected_cap
selected_cap = data
core.clear_plot("Cap")
for i in range(0, len(selected_companies)):
company = selected_companies[i]
stocks = pd.read_csv("dataset/" + company, parse_dates=['Date'])
stocks['Date'] = pd.to_datetime(stocks['Date'],
unit='D',
errors='coerce')
if data == "daily":
data_x = list(range(1, len(stocks.index) + 1))
data_y = (stocks['High'] * stocks['Volume']).tolist()
core.add_line_series("Cap",
company.split('_')[0],
data_x,
data_y,
color=colors[i % len(colors)])
core.add_line_series("Cap",
"large-cap", [data_x[0], data_x[-1]],
[10000000000, 10000000000],
weight=3,
color=[255, 50, 50, 100])
core.add_line_series("Cap",
"mid-cap", [data_x[0], data_x[-1]],
[2000000000, 2000000000],
weight=3,
color=[200, 50, 50, 100])
core.add_line_series("Cap",
"small-cap", [data_x[0], data_x[-1]],
[300000000, 300000000],
weight=3,
color=[150, 50, 50, 100])
elif data == "monthly":
monthly_stocks = stocks.groupby(pd.Grouper(key="Date",
freq='1M')).mean()
data_x = list(range(1, len(monthly_stocks.index) + 1))
data_y = (monthly_stocks['High'] *
monthly_stocks['Volume']).tolist()
core.add_line_series("Cap",
company.split('_')[0],
data_x,
data_y,
color=colors[i % len(colors)])
core.add_line_series("Cap",
"large-cap", [data_x[0], data_x[-1]],
[10000000000, 10000000000],
weight=3,
color=[255, 50, 50, 100])
core.add_line_series("Cap",
"mid-cap", [data_x[0], data_x[-1]],
[2000000000, 2000000000],
weight=3,
color=[200, 50, 50, 100])
core.add_line_series("Cap",
"small-cap", [data_x[0], data_x[-1]],
[300000000, 300000000],
weight=3,
color=[150, 50, 50, 100])
elif data == "quarterly":
quarterly_stocks = stocks.groupby(pd.Grouper(key="Date",
freq='3M')).mean()
data_x = list(range(1, len(quarterly_stocks.index) + 1))
data_y = (quarterly_stocks['High'] *
quarterly_stocks['Volume']).tolist()
core.add_line_series("Cap",
company.split('_')[0],
data_x,
data_y,
color=colors[i % len(colors)])
core.add_line_series("Cap",
"large-cap", [data_x[0], data_x[-1]],
[10000000000, 10000000000],
weight=3,
color=[255, 50, 50, 100])
core.add_line_series("Cap",
"mid-cap", [data_x[0], data_x[-1]],
[2000000000, 2000000000],
weight=3,
color=[200, 50, 50, 100])
core.add_line_series("Cap",
"small-cap", [data_x[0], data_x[-1]],
[300000000, 300000000],
weight=3,
color=[150, 50, 50, 100])
elif data == "yearly":
yearly_stocks = stocks.groupby(pd.Grouper(key="Date",
freq='1Y')).mean()
data_x = list(range(1, len(yearly_stocks.index) + 1))
data_y = (yearly_stocks['High'] * yearly_stocks['Volume']).tolist()
core.add_line_series("Cap",
company.split('_')[0],
data_x,
data_y,
color=colors[i % len(colors)])
core.add_line_series("Cap",
"large-cap", [data_x[0], data_x[-1]],
[10000000000, 10000000000],
weight=3,
color=[255, 50, 50, 100])
core.add_line_series("Cap",
"mid-cap", [data_x[0], data_x[-1]],
[2000000000, 2000000000],
weight=3,
color=[200, 50, 50, 100])
core.add_line_series("Cap",
"small-cap", [data_x[0], data_x[-1]],
[300000000, 300000000],
weight=3,
color=[150, 50, 50, 100])
def clear(self) -> None:
dpgcore.clear_plot(self.id)
def draw_chart(self, data, labels):
self.data = data
self.labels = labels
core.clear_plot(self.plot_label)
self.chart()