def render_inter_view(source_path: str, epoch_num: int):
"""Render the cross-epoch infomartion chart of Citi Bike data.
This part would be displayed only if epoch_num > 1.
Args:
source_path (str): The root path of the dumped snapshots data for the corresponding experiment.
epoch_num (int): Total number of epoches,
i.e. the total number of data folders since there is a folder per epoch.
"""
helper.render_h1_title("Citi Bike Inter Epoch Data")
sample_ratio = helper.get_sample_ratio_selection_list(epoch_num)
# Get epoch sample number.
down_pooling_range = helper._get_sampled_epoch_range(epoch_num, sample_ratio)
attribute_option_candidates = (
CITIBIKEItemOption.quick_info + CITIBIKEItemOption.requirement_info + CITIBIKEItemOption.station_info
)
# Generate data.
data_summary = helper.read_detail_csv(
os.path.join(source_path, GlobalFileNames.stations_sum)
).iloc[down_pooling_range]
# Get formula and selected data.
data_formula = helper.get_filtered_formula_and_data(
GlobalScenarios.CITI_BIKE, data_summary, attribute_option_candidates
)
_generate_inter_view_panel(
data_formula["data"][data_formula["attribute_option"]],
down_pooling_range
)
def render_inter_view(source_path: str, epoch_num: int):
"""Show CIM inter-view plot.
Args:
source_path (str): The root path of the dumped snapshots data for the corresponding experiment.
epoch_num (int): Total number of epoches,
i.e. the total number of data folders since there is a folder per epoch.
"""
helper.render_h1_title("CIM Inter Epoch Data")
sample_ratio = helper.get_sample_ratio_selection_list(epoch_num)
# Get epoch sample list.
down_pooling_range = helper._get_sampled_epoch_range(epoch_num, sample_ratio)
attribute_option_candidates = (
CIMItemOption.quick_info + CIMItemOption.port_info + CIMItemOption.booking_info
)
# Generate data.
data = helper.read_detail_csv(os.path.join(source_path, GlobalFileNames.ports_sum)).iloc[down_pooling_range]
data["remaining_space"] = list(
map(
lambda x, y, z: x - y - z,
data["capacity"],
data["full"],
data["empty"]
)
)
# Get formula and selected data.
data_formula = helper.get_filtered_formula_and_data(GlobalScenarios.CIM, data, attribute_option_candidates)
_generate_inter_view_panel(
data_formula["data"][data_formula["attribute_option"]],
down_pooling_range
)
def render_intra_view(source_path: str, epoch_num: int, prefix: str):
"""Show Citi Bike intra-view plot.
Args:
source_path (str): The root path of the dumped snapshots data for the corresponding experiment.
epoch_num (int): Total number of epoches,
i.e. the total number of data folders since there is a folder per epoch.
prefix (str): Prefix of data folders.
"""
selected_epoch = 0
if epoch_num > 1:
selected_epoch = st.sidebar.select_slider(
label="Choose an Epoch:",
options=list(range(0, epoch_num))
)
helper.render_h1_title("Citi Bike Intra Epoch Data")
data_stations = pd.read_csv(os.path.join(source_path, f"{prefix}{selected_epoch}", "stations.csv"))
view_option = st.sidebar.selectbox(
label="By station/snapshot:",
options=CitiBikeIntraViewChoice._member_names_
)
stations_num = len(data_stations["id"].unique())
stations_index = np.arange(stations_num).tolist()
snapshot_num = len(data_stations["frame_index"].unique())
snapshots_index = np.arange(snapshot_num).tolist()
index_name_conversion = helper.read_detail_csv(
os.path.join(
source_path,
GlobalFileNames.name_convert
)
)
attribute_option_candidates = (
CITIBIKEItemOption.quick_info + CITIBIKEItemOption.requirement_info + CITIBIKEItemOption.station_info
)
st.sidebar.markdown("***")
# Filter by station index.
# Display the change of snapshot within 1 station.
render_top_k_summary(source_path, prefix, selected_epoch)
if view_option == CitiBikeIntraViewChoice.by_station.name:
_generate_intra_view_by_station(
data_stations, index_name_conversion, attribute_option_candidates, stations_index, snapshot_num
)
# Filter by snapshot index.
# Display all station information within 1 snapshot.
elif view_option == CitiBikeIntraViewChoice.by_snapshot.name:
_generate_intra_view_by_snapshot(
data_stations, index_name_conversion, attribute_option_candidates,
snapshots_index, snapshot_num, stations_num
)
def _render_intra_view_by_snapshot(source_path: str, option_epoch: int,
data_ports: pd.DataFrame,
snapshots_index: List[int],
index_name_conversion: pd.DataFrame,
attribute_option_candidates: List[str],
ports_num: int, prefix: str):
""" Show intra-view data by snapshot.
Args:
source_path (str): The root path of the dumped snapshots data for the corresponding experiment.
option_epoch (int): Index of selected epoch.
data_ports (pd.Dataframe): Filtered port data.
snapshots_index (List[int]): Index of selected snapshot.
index_name_conversion (pd.Dataframe): Relationship between index and name.
attribute_option_candidates (List[str]): All options for users to choose.
ports_num (int): Number of ports in current snapshot.
prefix (str): Prefix of data folders.
"""
selected_snapshot = st.sidebar.select_slider(label="snapshot index",
options=snapshots_index)
# Get sample ratio.
sample_ratio = helper.get_sample_ratio_selection_list(ports_num)
selected_port_sample_ratio = st.sidebar.select_slider(
label="Ports Sampling Ratio:", options=sample_ratio)
# Accumulated data.
helper.render_h1_title("Accumulated Data")
_render_intra_heat_map(source_path, GlobalScenarios.CIM, option_epoch,
selected_snapshot, prefix)
helper.render_h3_title(
f"SnapShot-{selected_snapshot}: Port Accumulated Attributes")
_generate_intra_panel_accumulated_by_snapshot(data_ports,
selected_snapshot, ports_num,
index_name_conversion,
selected_port_sample_ratio)
_generate_top_k_summary(data_ports, selected_snapshot,
index_name_conversion)
# Detailed data.
helper.render_h1_title("Detail Data")
_render_intra_panel_vessel(source_path, prefix, option_epoch,
selected_snapshot)
helper.render_h3_title(
f"Snapshot-{selected_snapshot}: Port Detail Attributes")
data_formula = helper.get_filtered_formula_and_data(
GlobalScenarios.CIM, data_ports, attribute_option_candidates)
_generate_intra_panel_by_snapshot(data_formula["data"], selected_snapshot,
ports_num, index_name_conversion,
selected_port_sample_ratio,
data_formula["attribute_option"])
def _render_intra_view_by_ports(
data_ports: pd.DataFrame, ports_index: int,
index_name_conversion: pd.DataFrame, attribute_option_candidates: List[str], snapshot_num: int
):
""" Show intra-view data by ports.
Args:
data_ports (pd.Dataframe): Filtered port data.
ports_index (int):Index of port of current data.
index_name_conversion (pd.Dataframe): Relationship of index and name.
attribute_option_candidates (List[str]): All options for users to choose.
snapshot_num (int): Number of snapshots on a port.
"""
selected_port = st.sidebar.select_slider(
label="Choose a Port:",
options=ports_index
)
sample_ratio = helper.get_sample_ratio_selection_list(snapshot_num)
selected_snapshot_sample_ratio = st.sidebar.select_slider(
label="Snapshot Sampling Ratio:",
options=sample_ratio
)
# Accumulated data.
helper.render_h1_title("CIM Accumulated Data")
helper.render_h3_title(
f"Port Accumulated Attributes: {selected_port} - {index_name_conversion.loc[int(selected_port)][0]}"
)
_generate_intra_panel_accumulated_by_ports(
data_ports, f"ports_{selected_port}", snapshot_num, selected_snapshot_sample_ratio
)
# Detailed data.
helper.render_h1_title("CIM Detail Data")
data_formula = helper.get_filtered_formula_and_data(
GlobalScenarios.CIM, data_ports, attribute_option_candidates
)
helper.render_h3_title(
f"Port Detail Attributes: {selected_port} - {index_name_conversion.loc[int(selected_port)][0]}"
)
_generate_intra_panel_by_ports(
data_formula["data"], f"ports_{selected_port}",
snapshot_num, selected_snapshot_sample_ratio, data_formula["attribute_option"]
)