def _generate_intra_view_by_snapshot(data_stations: pd.DataFrame,
index_name_conversion: pd.DataFrame,
attribute_option_candidates: List[str],
snapshots_index: List[int],
snapshot_num: int, stations_num: int):
"""Show Citi Bike intra-view data by snapshot.
Args:
data_stations (pd.Dataframe): Filtered Data.
index_name_conversion (pd.Dataframe): Relationship between index and name.
attribute_option_candidates (List[str]): All options for users to choose.
snapshots_index (List[int]): Sampled snapshot index list.
snapshot_num (int): Number of snapshots.
stations_num (int): Number of stations.
"""
# Get selected snapshot index.
selected_snapshot = st.sidebar.select_slider(label="snapshot index",
options=snapshots_index)
helper.render_h3_title(f"Snapshot-{selected_snapshot}: Detail Data")
# Get according data with selected snapshot.
data_filtered = data_stations[data_stations["frame_index"] ==
selected_snapshot]
# Get increasing rate.
sample_ratio = helper.get_sample_ratio_selection_list(stations_num)
# Get sample rate (between 0-1).
selected_station_sample_ratio = st.sidebar.select_slider(
label="Station Sampling Ratio", options=sample_ratio)
# Get formula input and output.
data_formula = helper.get_filtered_formula_and_data(
GlobalScenarios.CITI_BIKE, data_filtered, attribute_option_candidates)
# Get sampled data and get station name.
down_pooling_sample_list = helper.get_sample_index_list(
stations_num, selected_station_sample_ratio)
attribute_option = data_formula["attribute_option"]
attribute_option.append("name")
if selected_station_sample_ratio == 0:
empty_head = attribute_option
empty_head.append("Station Name")
data_filtered = pd.DataFrame(columns=empty_head)
else:
data_filtered = data_formula["data"][attribute_option]
data_filtered = data_filtered.iloc[down_pooling_sample_list]
data_filtered["name"] = data_filtered["name"].apply(
lambda x: int(x[9:]))
data_filtered["Station Name"] = data_filtered["name"].apply(
lambda x: index_name_conversion.loc[int(x)])
data_melt = data_filtered.melt(["Station Name", "name"],
var_name="Attributes",
value_name="Count")
intra_snapshot_line_chart = alt.Chart(data_melt).mark_bar().encode(
x=alt.X("name:N", axis=alt.Axis(title="Name")),
y="Count:Q",
color="Attributes:N",
tooltip=["Attributes", "Count", "Station Name"],
).properties(width=700, height=380)
st.altair_chart(intra_snapshot_line_chart)
def _generate_intra_panel_by_ports(
data: pd.DataFrame, option_port_name: str,
snapshot_num: int, snapshot_sample_num: float, attribute_option: List[str] = None
):
"""Generate intra-view plot.
View info within different resource holders(In this senario, ports) in the same epoch.
Change snapshot sampling num freely.
Args:
data (pd.Dataframe): Filtered data within selected conditions.
option_port_name (str): Condition for filtering the name attribute in the data.
snapshot_num (int): Number of snapshots.
snapshot_sample_num (float): Number of sampled snapshots.
attribute_option (List[str]): Translated user-selecteded option.
"""
if attribute_option is not None:
attribute_option.append("frame_index")
else:
attribute_option = ["frame_index"]
attribute_temp_option = attribute_option
attribute_temp_option.append("name")
data_acc = data[attribute_temp_option]
down_pooling_sample_list = helper.get_sample_index_list(snapshot_num, snapshot_sample_num)
port_filtered = data_acc[data_acc["name"] == option_port_name][attribute_option].reset_index(drop=True)
attribute_option.remove("name")
data_filtered = port_filtered.loc[down_pooling_sample_list]
data_filtered = data_filtered[attribute_option]
data_filtered.rename(
columns={"frame_index": "snapshot_index"},
inplace=True
)
data_melt = data_filtered.melt(
"snapshot_index",
var_name="Attributes",
value_name="Count"
)
port_line_chart = alt.Chart(data_melt).mark_line().encode(
x=alt.X("snapshot_index", axis=alt.Axis(title="Snapshot Index")),
y="Count",
color="Attributes",
tooltip=["Attributes", "Count", "snapshot_index"]
).properties(
width=700,
height=380
)
st.altair_chart(port_line_chart)
port_bar_chart = alt.Chart(data_melt).mark_bar().encode(
x=alt.X("snapshot_index:N", axis=alt.Axis(title="Snapshot Index")),
y="Count:Q",
color="Attributes:N",
tooltip=["Attributes", "Count", "snapshot_index"]
).properties(
width=700,
height=380)
st.altair_chart(port_bar_chart)
def _generate_intra_view_by_station(data_stations: pd.DataFrame,
index_name_conversion: pd.DataFrame,
attribute_option_candidates: List[str],
stations_index: List[int],
snapshot_num: int):
""" Show Citi Bike intra-view data by station.
Args:
data_stations (pd.Dataframe): Filtered station data.
index_name_conversion (pd.Dataframe): Relationship between index and name.
attribute_option_candidates (List[str]): All options for users to choose.
stations_index (List[int]): List of station index.
snapshot_num (int): Number of snapshots.
"""
selected_station = st.sidebar.select_slider(label="station index",
options=stations_index)
helper.render_h3_title(
index_name_conversion.loc[int(selected_station)][0] + " Detail Data")
# Filter data by station index.
data_filtered = data_stations[data_stations["name"] ==
f"stations_{selected_station}"]
snapshot_sample_ratio_list = helper.get_sample_ratio_selection_list(
snapshot_num)
selected_snapshot_sample_ratio = st.sidebar.select_slider(
label="Snapshot Sampling Ratio:", options=snapshot_sample_ratio_list)
# Get formula input and output.
data_formula = helper.get_filtered_formula_and_data(
GlobalScenarios.CITI_BIKE, data_filtered, attribute_option_candidates)
attribute_option = data_formula["attribute_option"]
attribute_option.append("frame_index")
data_filtered = data_formula["data"][attribute_option].reset_index(
drop=True)
down_pooling_sample_list = helper.get_sample_index_list(
snapshot_num, selected_snapshot_sample_ratio)
data_filtered = data_filtered.iloc[down_pooling_sample_list]
data_filtered.rename(columns={"frame_index": "snapshot_index"},
inplace=True)
data_melt = data_filtered.melt("snapshot_index",
var_name="Attributes",
value_name="Count")
intra_station_line_chart = alt.Chart(data_melt).mark_line().encode(
x=alt.X("snapshot_index", axis=alt.Axis(title="Snapshot Index")),
y="Count",
color="Attributes",
tooltip=["Attributes", "Count",
"snapshot_index"]).properties(width=700, height=380)
st.altair_chart(intra_station_line_chart)
intra_station_bar_chart = alt.Chart(data_melt).mark_bar().encode(
x=alt.X("snapshot_index:N", axis=alt.Axis(title="Snapshot Index")),
y="Count:Q",
color="Attributes:N",
tooltip=["Attributes", "Count",
"snapshot_index"]).properties(width=700, height=380)
st.altair_chart(intra_station_bar_chart)
def _generate_intra_panel_by_snapshot(data: pd.DataFrame,
snapshot_index: int,
ports_num: int,
index_name_conversion: pd.DataFrame,
sample_ratio: List[float],
attribute_option: List[str] = None):
"""Generate intra-view plot.
View info within different snapshot in the same epoch.
Args:
data (pd.Dataframe): Filtered data within selected conditions.
snapshot_index (int): user-selected snapshot index.
ports_num (int): Number of ports.
index_name_conversion (pd.Dataframe): Relationship between index and name.
sample_ratio (List[float]): Sampled port index list.
attribute_option (List[str]): Translated user-selected options.
"""
if attribute_option is not None:
attribute_option.append("name")
else:
attribute_option = ["name"]
attribute_temp_option = attribute_option
attribute_temp_option.append("frame_index")
data_acc = data[attribute_temp_option]
down_pooling_sample_list = helper.get_sample_index_list(
ports_num, sample_ratio)
snapshot_filtered = data_acc[data_acc["frame_index"] ==
snapshot_index][attribute_option].reset_index(
drop=True)
data_rename = pd.DataFrame(columns=attribute_option)
for index in down_pooling_sample_list:
data_rename = pd.concat([
data_rename,
snapshot_filtered[snapshot_filtered["name"] == f"ports_{index}"]
],
axis=0)
data_rename = data_rename.reset_index(drop=True)
attribute_option.remove("frame_index")
data_rename["name"] = data_rename["name"].apply(lambda x: int(x[6:]))
data_rename = data_rename[attribute_option]
data_rename["Port Name"] = data_rename["name"].apply(
lambda x: index_name_conversion.loc[int(x)][0])
data_melt = data_rename.melt(["name", "Port Name"],
var_name="Attributes",
value_name="Count")
intra_bar_chart = alt.Chart(data_melt).mark_bar().encode(
x=alt.X("name:N", axis=alt.Axis(title="Name")),
y="Count:Q",
color="Attributes:N",
tooltip=["Attributes", "Count", "Port Name"]).properties(width=700,
height=380)
st.altair_chart(intra_bar_chart)
def _generate_intra_panel_accumulated_by_ports(
data: pd.DataFrame, option_port_name: str, snapshot_num: int, snapshot_sample_num: float
):
"""Generate intra-view accumulated plot by ports.
Args:
data (pd.Dataframe): Filtered data within selected conditions.
option_port_name (str): Condition for filtering the name attribute in the data.
snapshot_num (int): Number of snapshots.
snapshot_sample_num (float): Number of sampled snapshots.
"""
info_selector = CIMItemOption.basic_info + CIMItemOption.acc_info
data_acc = data[info_selector]
info_selector.pop(0)
down_pooling_sample_list = helper.get_sample_index_list(snapshot_num, snapshot_sample_num)
port_filtered = data_acc[data_acc["name"] == option_port_name][info_selector].reset_index(drop=True)
port_filtered.rename(
columns={"frame_index": "snapshot_index"},
inplace=True
)
data_filtered = port_filtered.loc[down_pooling_sample_list]
data_melt = data_filtered.melt(
"snapshot_index",
var_name="Attributes",
value_name="Count"
)
port_line_chart = alt.Chart(data_melt).mark_line().encode(
x=alt.X("snapshot_index", axis=alt.Axis(title="Snapshot Index")),
y="Count",
color="Attributes",
tooltip=["Attributes", "Count", "snapshot_index"]
).properties(
width=700,
height=380
)
st.altair_chart(port_line_chart)
port_bar_chart = alt.Chart(data_melt).mark_bar().encode(
x=alt.X("snapshot_index:N", axis=alt.Axis(title="Snapshot Index")),
y="Count:Q",
color="Attributes:N",
tooltip=["Attributes", "Count", "snapshot_index"]
).properties(
width=700,
height=380)
st.altair_chart(port_bar_chart)
def _generate_intra_panel_vessel(data_vessel: pd.DataFrame, snapshot_index: int, vessels_num: int):
"""Generate vessel data plot.
Args:
data_vessel (pd.Dataframe): Data of vessel information within selected snapshot index.
snapshot_index (int): User-selected snapshot index.
vessels_num (int): Number of vessels.
"""
helper.render_h3_title(f"SnapShot-{snapshot_index}: Vessel Attributes")
# Get sampled(and down pooling) index.
sample_ratio = helper.get_sample_ratio_selection_list(vessels_num)
selected_vessel_sample_ratio = st.sidebar.select_slider(
label="Vessels Sampling Ratio:",
options=sample_ratio
)
down_pooling_sample_list = helper.get_sample_index_list(vessels_num, selected_vessel_sample_ratio)
data_vessel = data_vessel[
data_vessel["frame_index"] == snapshot_index
][CIMItemOption.vessel_info].reset_index(drop=True)
data_rename = pd.DataFrame(columns=CIMItemOption.vessel_info)
for index in down_pooling_sample_list:
data_rename = pd.concat(
[data_rename, data_vessel[data_vessel["name"] == f"vessels_{index}"]],
axis=0
)
data_filtered = data_rename.reset_index(drop=True)
data_filtered["name"] = data_filtered["name"].apply(lambda x: int(x[8:]))
data_melt = data_filtered.melt(
"name",
var_name="Attributes",
value_name="Count"
)
intra_vessel_bar_chart = alt.Chart(data_melt).mark_bar().encode(
x=alt.X("name:N", axis=alt.Axis(title="Vessel Index")),
y="Count:Q",
color="Attributes:N",
tooltip=["Attributes", "Count", "name"]
).properties(
width=700,
height=380
)
st.altair_chart(intra_vessel_bar_chart)
def _generate_intra_panel_accumulated_by_snapshot(
data: pd.DataFrame, snapshot_index: int, ports_num: int,
index_name_conversion: pd.DataFrame, sample_ratio: List[float]
):
"""Generate intra-view accumulated plot by snapshot.
Args:
data (pd.Dataframe): Filtered data within selected conditions.
snapshot_index (int): user-selected snapshot index.
ports_num (int): Number of ports.
index_name_conversion (pd.Dataframe): Relationship between index and name.
sample_ratio (List[float]): Sampled port index list.
"""
info_selector = CIMItemOption.basic_info + CIMItemOption.acc_info
data_acc = data[info_selector]
info_selector.pop(1)
down_pooling_sample_list = helper.get_sample_index_list(ports_num, sample_ratio)
snapshot_filtered = data_acc[data_acc["frame_index"] == snapshot_index][info_selector].reset_index(drop=True)
data_rename = pd.DataFrame(columns=info_selector)
for index in down_pooling_sample_list:
data_rename = pd.concat(
[data_rename, snapshot_filtered[snapshot_filtered["name"] == f"ports_{index}"]],
axis=0
)
data_rename = data_rename.reset_index(drop=True)
data_rename["name"] = data_rename["name"].apply(lambda x: int(x[6:]))
data_rename["Port Name"] = data_rename["name"].apply(lambda x: index_name_conversion.loc[int(x)][0])
data_melt = data_rename.melt(
["name", "Port Name"],
var_name="Attributes",
value_name="Count"
)
intra_bar_chart = alt.Chart(data_melt).mark_bar().encode(
x=alt.X("name:N", axis=alt.Axis(title="Name")),
y="Count:Q",
color="Attributes:N",
tooltip=["Attributes", "Count", "Port Name"]
).properties(
width=700,
height=380
)
st.altair_chart(intra_bar_chart)
def test_get_sample_index_list(self):
"""Test method get_sample_index_list(a,b)"""
self.assertEqual([0, 10, 20, 30, 40], get_sample_index_list(50, 0.1))
