def render_top_k_summary(source_path: str, prefix: str, epoch_index: int):
""" Show top-k summary plot.
Args:
source_path (str): The root path of the dumped snapshots data for the corresponding experiment.
prefix (str): Prefix of data folders.
epoch_index (int): The index of selected epoch.
"""
helper.render_h3_title("Cike Bike Top K")
data = helper.read_detail_csv(
os.path.join(
source_path,
f"{prefix}{epoch_index}",
GlobalFileNames.stations_sum
)
)
# Convert index to station name.
index_name_conversion = helper.read_detail_csv(os.path.join(source_path, GlobalFileNames.name_convert))
data["station name"] = list(
map(
lambda x: index_name_conversion.loc[int(x[9:])][0],
data["name"]
)
)
# Generate top summary.
top_number = st.select_slider(
"Select Top K",
list(range(1, 6))
)
top_attributes = ["bikes", "trip_requirement", "fulfillment", "fulfillment_ratio"]
for item in top_attributes:
helper.generate_by_snapshot_top_summary("station name", data, int(top_number), item)
def render_intra_view(source_path: str, epoch_num: int, prefix: str):
"""Show CIM 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 = st.sidebar.select_slider(
label="Choose an Epoch:",
options=list(range(0, epoch_num))
)
# Get data of selected epoch.
data_ports = helper.read_detail_csv(os.path.join(source_path, f"{prefix}{selected_epoch}", "ports.csv"))
data_ports["remaining_space"] = list(
map(
lambda x, y, z: x - y - z,
data_ports["capacity"],
data_ports["full"],
data_ports["empty"]
)
)
# Basic data.
ports_num = len(data_ports["name"].unique())
ports_index = np.arange(ports_num).tolist()
snapshot_num = len(data_ports["frame_index"].unique())
snapshots_index = np.arange(snapshot_num).tolist()
# Items for user to select.
attribute_option_candidates = (
CIMItemOption.quick_info + CIMItemOption.booking_info + CIMItemOption.port_info
)
# Name conversion.
index_name_conversion = helper.read_detail_csv(os.path.join(source_path, GlobalFileNames.name_convert))
st.sidebar.markdown("***")
option_view = st.sidebar.selectbox(
label="By ports/snapshot:",
options=CIMIntraViewChoice._member_names_
)
if option_view == CIMIntraViewChoice.by_port.name:
_render_intra_view_by_ports(
data_ports, ports_index, index_name_conversion,
attribute_option_candidates, snapshot_num
)
elif option_view == CIMIntraViewChoice.by_snapshot.name:
_render_intra_view_by_snapshot(
source_path, selected_epoch, data_ports, snapshots_index,
index_name_conversion, attribute_option_candidates, ports_num, prefix
)
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_panel_vessel(source_path: str, prefix: str,
option_epoch: int, snapshot_index: int):
"""Show vessel info of selected snapshot.
Args:
source_path (str): The root path of the dumped snapshots data for the corresponding experiment.
prefix (str): Prefix of data folders.
option_epoch (int): Selected index of epoch.
snapshot_index (int): Index of selected snapshot folder.
"""
data_vessel = helper.read_detail_csv(
os.path.join(source_path, f"{prefix}{option_epoch}", "vessels.csv"))
vessels_num = len(data_vessel["name"].unique())
_generate_intra_panel_vessel(data_vessel, snapshot_index, vessels_num)