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Network flow visualization used by Dynatrace - SAL - LIT.AI.JKU in the NAD 2021 challenge
# Copyright 2021
# Dynatrace Research
# SAL Silicon Austria Labs
# LIT Artificial Intelligence Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Dict
import pandas as pd
import datashader as ds
import holoviews as hv
import holoviews.operation.datashader as hd
from IPython.core.display import display, HTML
hv.extension("bokeh")
def _get_data_matrix(df: pd.DataFrame, groups: List[str]) -> pd.DataFrame:
# merge those group columns into a group label and get the group x time grouping
df["group"] = df.groupby(groups).ngroup()
mat = df.groupby(["group", "time"])["label"].agg(["count", "median"]).astype(int)
# sort by group size and re-index groups
num_groups = mat.index.get_level_values(0).nunique()
# add the broadcasted group size to the "group" level
sizes = df.groupby("group").size()
_, sizes = mat.align(sizes, level=0, axis=0)
mat["size"] = sizes
# sort by the group size, then by group ids
mat = mat.reset_index().sort_values(by=["size", "group"])
# modify the group ids so that they are ascending again
mapping = dict(zip(mat["group"].unique(), range(num_groups)))
mat["group"] = mat["group"].map(mapping)
return mat
def shade_network_flow(df: pd.DataFrame, groups: List[str], colors: Dict[int, str], legend: bool = True):
if legend:
html = "<br/>".join([
f"<span style=\"background-color:black;color:{val};font-weight:bold;\">" +
f"{key} : {val}" +
f"</span>"
for key, val in colors.items()
])
display(HTML(html))
matrix = _get_data_matrix(df, groups)
points = hv.Points(matrix, ["group", "time"])
shaded = hd.datashade(points, aggregator=ds.count_cat("median"), color_key=colors)
render = hd.dynspread(shaded, threshold=0.5, max_px=4).opts(
bgcolor="black", xaxis=None, yaxis=None, width=1200, height=600)
return render
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ipython
bokeh>=2
numpy>=1.19
pandas>=1.2
holoviews>=1.14
datashader>=0.12
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