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Refactored Session handling for Network analysis
This commit is contained in:
Tim
180
poetry.lock
generated
180
poetry.lock
generated
@ -3439,36 +3439,43 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
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|
||||
feather = ["pyarrow (>=7.0.0)"]
|
||||
fss = ["fsspec (>=2022.05.0)"]
|
||||
gcp = ["gcsfs (>=2022.05.0)", "pandas-gbq (>=0.17.5)"]
|
||||
hdf5 = ["tables (>=3.7.0)"]
|
||||
html = ["beautifulsoup4 (>=4.11.1)", "html5lib (>=1.1)", "lxml (>=4.8.0)"]
|
||||
mysql = ["SQLAlchemy (>=1.4.36)", "pymysql (>=1.0.2)"]
|
||||
output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.8.10)"]
|
||||
parquet = ["pyarrow (>=7.0.0)"]
|
||||
performance = ["bottleneck (>=1.3.4)", "numba (>=0.55.2)", "numexpr (>=2.8.0)"]
|
||||
plot = ["matplotlib (>=3.6.1)"]
|
||||
postgresql = ["SQLAlchemy (>=1.4.36)", "psycopg2 (>=2.9.3)"]
|
||||
spss = ["pyreadstat (>=1.1.5)"]
|
||||
sql-other = ["SQLAlchemy (>=1.4.36)"]
|
||||
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
|
||||
xml = ["lxml (>=4.8.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pandas"
|
||||
version = "2.1.2"
|
||||
@ -3672,7 +3621,10 @@ files = [
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""}
|
||||
numpy = [
|
||||
{version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
tzdata = ">=2022.1"
|
||||
@ -3703,17 +3655,17 @@ xml = ["lxml (>=4.8.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pandas-stubs"
|
||||
version = "2.0.3.230814"
|
||||
version = "2.1.1.230928"
|
||||
description = "Type annotations for pandas"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "pandas_stubs-2.0.3.230814-py3-none-any.whl", hash = "sha256:4b3dfc027d49779176b7daa031a3405f7b839bcb6e312f4b9f29fea5feec5b4f"},
|
||||
{file = "pandas_stubs-2.0.3.230814.tar.gz", hash = "sha256:1d5cc09e36e3d9f9a1ed9dceae4e03eeb26d1b898dd769996925f784365c8769"},
|
||||
{file = "pandas_stubs-2.1.1.230928-py3-none-any.whl", hash = "sha256:992d97159e054ca3175ebe8321ac5616cf6502dd8218b03bb0eaf3c4f6939037"},
|
||||
{file = "pandas_stubs-2.1.1.230928.tar.gz", hash = "sha256:ce1691c71c5d67b8f332da87763f7f54650f46895d99964d588c3a5d79e2cacc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = {version = ">=1.25.0", markers = "python_version >= \"3.9\""}
|
||||
numpy = {version = ">=1.26.0", markers = "python_version < \"3.13\""}
|
||||
types-pytz = ">=2022.1.1"
|
||||
|
||||
[[package]]
|
||||
@ -5604,6 +5556,48 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
|
||||
testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
|
||||
torch = ["safetensors[numpy]", "torch (>=1.10)"]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.11.3"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
optional = false
|
||||
python-versions = "<3.13,>=3.9"
|
||||
files = [
|
||||
{file = "scipy-1.11.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:370f569c57e1d888304052c18e58f4a927338eafdaef78613c685ca2ea0d1fa0"},
|
||||
{file = "scipy-1.11.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:9885e3e4f13b2bd44aaf2a1a6390a11add9f48d5295f7a592393ceb8991577a3"},
|
||||
{file = "scipy-1.11.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e04aa19acc324a1a076abb4035dabe9b64badb19f76ad9c798bde39d41025cdc"},
|
||||
{file = "scipy-1.11.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3e1a8a4657673bfae1e05e1e1d6e94b0cabe5ed0c7c144c8aa7b7dbb774ce5c1"},
|
||||
{file = "scipy-1.11.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7abda0e62ef00cde826d441485e2e32fe737bdddee3324e35c0e01dee65e2a88"},
|
||||
{file = "scipy-1.11.3-cp310-cp310-win_amd64.whl", hash = "sha256:033c3fd95d55012dd1148b201b72ae854d5086d25e7c316ec9850de4fe776929"},
|
||||
{file = "scipy-1.11.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:925c6f09d0053b1c0f90b2d92d03b261e889b20d1c9b08a3a51f61afc5f58165"},
|
||||
{file = "scipy-1.11.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5664e364f90be8219283eeb844323ff8cd79d7acbd64e15eb9c46b9bc7f6a42a"},
|
||||
{file = "scipy-1.11.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:00f325434b6424952fbb636506f0567898dca7b0f7654d48f1c382ea338ce9a3"},
|
||||
{file = "scipy-1.11.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5f290cf561a4b4edfe8d1001ee4be6da60c1c4ea712985b58bf6bc62badee221"},
|
||||
{file = "scipy-1.11.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:91770cb3b1e81ae19463b3c235bf1e0e330767dca9eb4cd73ba3ded6c4151e4d"},
|
||||
{file = "scipy-1.11.3-cp311-cp311-win_amd64.whl", hash = "sha256:e1f97cd89c0fe1a0685f8f89d85fa305deb3067d0668151571ba50913e445820"},
|
||||
{file = "scipy-1.11.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:dfcc1552add7cb7c13fb70efcb2389d0624d571aaf2c80b04117e2755a0c5d15"},
|
||||
{file = "scipy-1.11.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:0d3a136ae1ff0883fffbb1b05b0b2fea251cb1046a5077d0b435a1839b3e52b7"},
|
||||
{file = "scipy-1.11.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bae66a2d7d5768eaa33008fa5a974389f167183c87bf39160d3fefe6664f8ddc"},
|
||||
{file = "scipy-1.11.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2f6dee6cbb0e263b8142ed587bc93e3ed5e777f1f75448d24fb923d9fd4dce6"},
|
||||
{file = "scipy-1.11.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:74e89dc5e00201e71dd94f5f382ab1c6a9f3ff806c7d24e4e90928bb1aafb280"},
|
||||
{file = "scipy-1.11.3-cp312-cp312-win_amd64.whl", hash = "sha256:90271dbde4be191522b3903fc97334e3956d7cfb9cce3f0718d0ab4fd7d8bfd6"},
|
||||
{file = "scipy-1.11.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a63d1ec9cadecce838467ce0631c17c15c7197ae61e49429434ba01d618caa83"},
|
||||
{file = "scipy-1.11.3-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:5305792c7110e32ff155aed0df46aa60a60fc6e52cd4ee02cdeb67eaccd5356e"},
|
||||
{file = "scipy-1.11.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9ea7f579182d83d00fed0e5c11a4aa5ffe01460444219dedc448a36adf0c3917"},
|
||||
{file = "scipy-1.11.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c77da50c9a91e23beb63c2a711ef9e9ca9a2060442757dffee34ea41847d8156"},
|
||||
{file = "scipy-1.11.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:15f237e890c24aef6891c7d008f9ff7e758c6ef39a2b5df264650eb7900403c0"},
|
||||
{file = "scipy-1.11.3-cp39-cp39-win_amd64.whl", hash = "sha256:4b4bb134c7aa457e26cc6ea482b016fef45db71417d55cc6d8f43d799cdf9ef2"},
|
||||
{file = "scipy-1.11.3.tar.gz", hash = "sha256:bba4d955f54edd61899776bad459bf7326e14b9fa1c552181f0479cc60a568cd"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.21.6,<1.28.0"
|
||||
|
||||
[package.extras]
|
||||
dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
|
||||
doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
|
||||
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
|
||||
[[package]]
|
||||
name = "seaborn"
|
||||
version = "0.13.0"
|
||||
@ -7373,5 +7367,5 @@ web-server = ["dash", "dash-auth", "dash-bootstrap-components", "matplotlib", "s
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "3b7621baf170bf8c0c5ede80e52d42a5a227c85757a9f4fa2b8648d944496007"
|
||||
python-versions = ">=3.11,<3.13"
|
||||
content-hash = "0119b3fb52b3b7ccd99631b5d9418d7c0e139096feacd0c56f8bb2345c2861f0"
|
||||
|
||||
@ -66,9 +66,10 @@ matplotlib = "^3.8.1"
|
||||
pgeocode = "^0.4.1"
|
||||
psycopg2-binary = "^2.9.7"
|
||||
pymongo = "^4.6.0"
|
||||
python = "^3.11"
|
||||
python = ">=3.11,<3.13"
|
||||
python-dotenv = "^1.0.0"
|
||||
rapidfuzz = "^3.5.2"
|
||||
scipy = "^1.11.3"
|
||||
seaborn = "^0.13.0"
|
||||
selenium = "^4.15.2"
|
||||
spacy = "^3.6.1"
|
||||
@ -112,7 +113,7 @@ SQLAlchemy = {version = "*", extras = ["mypy"]}
|
||||
black = "*"
|
||||
loguru-mypy = "*"
|
||||
mypy = "*"
|
||||
networkx-stubs = "^0.0.1"
|
||||
networkx-stubs = "*"
|
||||
pandas-stubs = "*"
|
||||
pip-audit = "*"
|
||||
pip-licenses = "*"
|
||||
@ -125,7 +126,7 @@ types-setuptools = "*"
|
||||
types-six = "*"
|
||||
types-tabulate = "*"
|
||||
types-tqdm = "*"
|
||||
types-urllib3 = "^1.26.25.14"
|
||||
types-urllib3 = "*"
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.4.2"
|
||||
|
||||
@ -1,10 +1,15 @@
|
||||
"""Content of home page."""
|
||||
from functools import lru_cache
|
||||
|
||||
import dash
|
||||
import dash_daq as daq
|
||||
import networkx as nx
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
from cachetools import TTLCache, cached
|
||||
from dash import Input, Output, callback, dash_table, dcc, html
|
||||
from loguru import logger
|
||||
|
||||
from aki_prj23_transparenzregister.utils.networkx.network_2d import (
|
||||
create_2d_graph,
|
||||
@ -19,19 +24,10 @@ from aki_prj23_transparenzregister.utils.networkx.network_base import (
|
||||
from aki_prj23_transparenzregister.utils.networkx.networkx_data import (
|
||||
create_edge_and_node_list,
|
||||
filter_relation_type,
|
||||
filter_relation_with_more_than_one_connection,
|
||||
get_all_company_relations,
|
||||
get_all_person_relations,
|
||||
)
|
||||
|
||||
dash.register_page(__name__, path="/")
|
||||
|
||||
# Get Data
|
||||
person_relation = filter_relation_type(get_all_person_relations(), "NACHFOLGER")
|
||||
company_relation = filter_relation_with_more_than_one_connection(
|
||||
get_all_company_relations(), "id_company_to", "id_company_from"
|
||||
)
|
||||
|
||||
dash.register_page(
|
||||
__name__,
|
||||
path="/",
|
||||
@ -44,38 +40,40 @@ dash.register_page(
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
# Plotly figure
|
||||
def networkGraph(EGDE_VAR: None) -> go.Figure:
|
||||
# find_all_company_relations()
|
||||
|
||||
graph, metrices = initialize_network(nodes = nodes, edges = edges)
|
||||
# print(graph)
|
||||
metric = None
|
||||
network = create_3d_graph(graph, nodes, edges, metrics, metric)
|
||||
# Create Edge and Node List from data
|
||||
nodes, edges = create_edge_and_node_list(person_relation, company_relation)
|
||||
# nodes, edges = create_edge_and_node_list(person_relation, company_relation)
|
||||
# Initialize the Network and receive the Graph and a DataFrame with Metrics
|
||||
graph, metrics = initialize_network(nodes=nodes, edges=edges)
|
||||
# graph, metrics = initialize_network(nodes=nodes, edges=edges)
|
||||
metric = "None"
|
||||
layout = "Spring"
|
||||
# switch_node_annotaion_value = False
|
||||
# layout = "Spring"
|
||||
# # switch_node_annotaion_value = False
|
||||
switch_edge_annotaion_value = False
|
||||
egde_thickness = 1
|
||||
network = create_3d_graph(
|
||||
graph,
|
||||
nodes,
|
||||
edges,
|
||||
metrics,
|
||||
metric,
|
||||
layout,
|
||||
switch_edge_annotaion_value,
|
||||
egde_thickness,
|
||||
)
|
||||
network = None
|
||||
# create_3d_graph(
|
||||
# graph,
|
||||
# nodes,
|
||||
# edges,
|
||||
# metrics,
|
||||
# metric,
|
||||
# layout,
|
||||
# switch_edge_annotaion_value,
|
||||
# egde_thickness,
|
||||
# )
|
||||
|
||||
# Get the possible Filter values for the Dropdowns.
|
||||
person_relation_type_filter = get_all_person_relations()["relation_type"].unique()
|
||||
company_relation_type_filter = get_all_company_relations()["relation_type"].unique()
|
||||
|
||||
|
||||
def person_relation_type_filter() -> np.ndarray:
|
||||
"""Returns an Numpy Array of String with Person telation types."""
|
||||
logger.debug("Updating Person Dropdown")
|
||||
return get_all_person_relations()["relation_type"].unique()
|
||||
|
||||
|
||||
def company_relation_type_filter() -> np.ndarray:
|
||||
"""Returns an Numpy Array of String with Company relation types."""
|
||||
logger.debug("Updating Person Dropdown")
|
||||
return get_all_company_relations()["relation_type"].unique()
|
||||
|
||||
|
||||
def update_table(
|
||||
@ -90,6 +88,7 @@ def update_table(
|
||||
Returns:
|
||||
tuple[dict, list]: _description_
|
||||
"""
|
||||
logger.debug("Updateing Table")
|
||||
table_df = metrics.sort_values(metric_dropdown_value, ascending=False).head(10)
|
||||
table_df = table_df[["designation", "category", metric_dropdown_value]]
|
||||
table_df.to_dict("records")
|
||||
@ -97,228 +96,245 @@ def update_table(
|
||||
return table_df.to_dict("records"), columns # type: ignore
|
||||
|
||||
|
||||
top_companies_dict, top_companies_columns = update_table("closeness", metrics)
|
||||
|
||||
layout = html.Div(
|
||||
children=html.Div(
|
||||
children=[
|
||||
html.Div(
|
||||
className="top_companytable_style",
|
||||
children=[
|
||||
html.H1(
|
||||
title="Top Ten Nodes in Graph by Metric", style={"align": "mid"}
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Filter Metric:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
[
|
||||
"eigenvector",
|
||||
"degree",
|
||||
"betweenness",
|
||||
"closeness",
|
||||
],
|
||||
"closeness",
|
||||
id="dropdown_table_metric",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
dash_table.DataTable(
|
||||
top_companies_dict,
|
||||
top_companies_columns,
|
||||
id="metric_table",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="networkx_style",
|
||||
children=[
|
||||
html.H1(className="header", children=["Social Graph"]),
|
||||
# html.Div(
|
||||
# className="filter-wrapper",
|
||||
# children=[
|
||||
# html.Div(
|
||||
# className="filter-wrapper-item",
|
||||
# children=[
|
||||
# html.H5(
|
||||
# className="filter-description",
|
||||
# children=["Data Source:"],
|
||||
# ),
|
||||
# dcc.Dropdown(
|
||||
# ["Company Data only", "Person Data only", "Company & Person Data"],
|
||||
# "Company Data only",
|
||||
# id="dropdown_data_soruce_filter",
|
||||
# className="dropdown_style",
|
||||
# ),
|
||||
# ],
|
||||
# ),
|
||||
# ],
|
||||
# ),
|
||||
html.Div(
|
||||
className="filter-wrapper",
|
||||
id="company_dropdown",
|
||||
# style="visibility: hidden;",
|
||||
children=[
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Company Relation Type Filter:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
company_relation_type_filter,
|
||||
company_relation_type_filter[0],
|
||||
id="dropdown_company_relation_filter",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
# style="visibility: visible;",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Person Relation Type Filter:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
person_relation_type_filter,
|
||||
person_relation_type_filter[0],
|
||||
id="dropdown_person_relation_filter",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Choose Graph Metric:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
[
|
||||
"None",
|
||||
"eigenvector",
|
||||
"degree",
|
||||
"betweenness",
|
||||
"closeness",
|
||||
],
|
||||
"None",
|
||||
id="dropdown",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Choose Layout:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
[
|
||||
"Spring",
|
||||
# "Bipartite",
|
||||
"Circular",
|
||||
"Kamada Kawai",
|
||||
# "Planar",
|
||||
"Random",
|
||||
"Shell (only 2D)",
|
||||
# "Spectral",
|
||||
"Spiral (only 2D)",
|
||||
# "Multipartite"
|
||||
],
|
||||
"Spring",
|
||||
id="dropdown_layout",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Adjust Edge Thickness"],
|
||||
),
|
||||
dcc.Slider(
|
||||
1,
|
||||
4,
|
||||
1,
|
||||
value=1,
|
||||
id="slider",
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper",
|
||||
children=[
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Switch to 2D Diagramm"],
|
||||
),
|
||||
html.Div(
|
||||
className="switch-style",
|
||||
children=[
|
||||
daq.BooleanSwitch(id="switch", on=False)
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
# html.Div(
|
||||
# className="filter-wrapper-item",
|
||||
# children=[
|
||||
# html.H5(
|
||||
# className="filter-description",
|
||||
# children=["Enable Node Annotation"],
|
||||
# ),
|
||||
# html.Div(
|
||||
# className="switch-style",
|
||||
# children=[daq.BooleanSwitch(id="switch_node_annotation", on=False)],
|
||||
# ),
|
||||
# ],
|
||||
# ),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Enable Edge Annotation"],
|
||||
),
|
||||
html.Div(
|
||||
className="switch-style",
|
||||
children=[
|
||||
daq.BooleanSwitch(
|
||||
id="switch_edge_annotation", on=False
|
||||
)
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
dcc.Graph(figure=network, id="my-graph", className="graph-style"),
|
||||
],
|
||||
),
|
||||
]
|
||||
def layout() -> list[html.Div]:
|
||||
"""Generates the Layout of the Homepage."""
|
||||
logger.debug("Layouting Homepage")
|
||||
person_relation_types = person_relation_type_filter()
|
||||
company_relation_types = company_relation_type_filter()
|
||||
top_companies_dict, top_companies_columns, figure = update_figure(
|
||||
"None",
|
||||
False,
|
||||
False,
|
||||
company_relation_types[0],
|
||||
person_relation_types[0],
|
||||
"Spring",
|
||||
1,
|
||||
"degree",
|
||||
)
|
||||
return html.Div(
|
||||
children=html.Div(
|
||||
children=[
|
||||
html.Div(
|
||||
className="top_companytable_style",
|
||||
children=[
|
||||
html.H1(
|
||||
title="Top Ten Nodes in Graph by Metric",
|
||||
style={"align": "mid"},
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Filter Metric:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
[
|
||||
"eigenvector",
|
||||
"degree",
|
||||
"betweenness",
|
||||
"closeness",
|
||||
],
|
||||
"closeness",
|
||||
id="dropdown_table_metric",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
dash_table.DataTable(
|
||||
top_companies_dict,
|
||||
top_companies_columns,
|
||||
id="metric_table",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="networkx_style",
|
||||
children=[
|
||||
html.H1(className="header", children=["Social Graph"]),
|
||||
# html.Div(
|
||||
# className="filter-wrapper",
|
||||
# children=[
|
||||
# html.Div(
|
||||
# className="filter-wrapper-item",
|
||||
# children=[
|
||||
# html.H5(
|
||||
# className="filter-description",
|
||||
# children=["Data Source:"],
|
||||
# ),
|
||||
# dcc.Dropdown(
|
||||
# ["Company Data only", "Person Data only", "Company & Person Data"],
|
||||
# "Company Data only",
|
||||
# id="dropdown_data_soruce_filter",
|
||||
# className="dropdown_style",
|
||||
# ),
|
||||
# ],
|
||||
# ),
|
||||
# ],
|
||||
# ),
|
||||
html.Div(
|
||||
className="filter-wrapper",
|
||||
id="company_dropdown",
|
||||
# style="visibility: hidden;",
|
||||
children=[
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Company Relation Type Filter:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
company_relation_types,
|
||||
company_relation_types[0],
|
||||
id="dropdown_company_relation_filter",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
# style="visibility: visible;",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Person Relation Type Filter:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
person_relation_types,
|
||||
person_relation_types[0],
|
||||
id="dropdown_person_relation_filter",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Choose Graph Metric:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
[
|
||||
"None",
|
||||
"eigenvector",
|
||||
"degree",
|
||||
"betweenness",
|
||||
"closeness",
|
||||
],
|
||||
"None",
|
||||
id="dropdown",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Choose Layout:"],
|
||||
),
|
||||
dcc.Dropdown(
|
||||
[
|
||||
"Spring",
|
||||
# "Bipartite",
|
||||
"Circular",
|
||||
"Kamada Kawai",
|
||||
# "Planar",
|
||||
"Random",
|
||||
"Shell (only 2D)",
|
||||
# "Spectral",
|
||||
"Spiral (only 2D)",
|
||||
# "Multipartite"
|
||||
],
|
||||
"Spring",
|
||||
id="dropdown_layout",
|
||||
className="dropdown_style",
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Adjust Edge Thickness"],
|
||||
),
|
||||
dcc.Slider(
|
||||
1,
|
||||
4,
|
||||
1,
|
||||
value=1,
|
||||
id="slider",
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
html.Div(
|
||||
className="filter-wrapper",
|
||||
children=[
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Switch to 2D Diagramm"],
|
||||
),
|
||||
html.Div(
|
||||
className="switch-style",
|
||||
children=[
|
||||
daq.BooleanSwitch(id="switch", on=False)
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
# html.Div(
|
||||
# className="filter-wrapper-item",
|
||||
# children=[
|
||||
# html.H5(
|
||||
# className="filter-description",
|
||||
# children=["Enable Node Annotation"],
|
||||
# ),
|
||||
# html.Div(
|
||||
# className="switch-style",
|
||||
# children=[daq.BooleanSwitch(id="switch_node_annotation", on=False)],
|
||||
# ),
|
||||
# ],
|
||||
# ),
|
||||
html.Div(
|
||||
className="filter-wrapper-item",
|
||||
children=[
|
||||
html.H5(
|
||||
className="filter-description",
|
||||
children=["Enable Edge Annotation"],
|
||||
),
|
||||
html.Div(
|
||||
className="switch-style",
|
||||
children=[
|
||||
daq.BooleanSwitch(
|
||||
id="switch_edge_annotation",
|
||||
on=False,
|
||||
)
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
dcc.Graph(
|
||||
figure=figure, id="my-graph", className="graph-style"
|
||||
),
|
||||
],
|
||||
),
|
||||
]
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
# @lru_cache(200)
|
||||
@lru_cache(200)
|
||||
def update_graph_data(
|
||||
person_relation_type: str = "HAFTENDER_GESELLSCHAFTER",
|
||||
company_relation_type: str = "GESCHAEFTSFUEHRER",
|
||||
@ -332,6 +348,7 @@ def update_graph_data(
|
||||
Returns:
|
||||
tuple[nx.Graph, pd.DataFrame, dict, list]: _description_
|
||||
"""
|
||||
logger.debug("Updating Graph data")
|
||||
# Get Data
|
||||
person_df = get_all_person_relations()
|
||||
company_df = get_all_company_relations()
|
||||
@ -369,6 +386,7 @@ def update_graph_data(
|
||||
allow_duplicate=True,
|
||||
)
|
||||
# @lru_cache(20)
|
||||
@cached(cache=TTLCache(maxsize=100, ttl=500))
|
||||
def update_figure( # noqa: PLR0913
|
||||
selected_metric: str,
|
||||
switch_value: bool,
|
||||
@ -396,6 +414,7 @@ def update_figure( # noqa: PLR0913
|
||||
Returns:
|
||||
Network Graph(Plotly Figure): Plotly Figure in 3 or 2D
|
||||
"""
|
||||
logger.debug("Update Figure")
|
||||
_ = c_relation_filter_value, p_relation_filter_value
|
||||
|
||||
graph, metrics, nodes, edges = update_graph_data(
|
||||
|
||||
@ -5,16 +5,14 @@ import networkx as nx
|
||||
import pandas as pd
|
||||
from sqlalchemy.orm import aliased
|
||||
|
||||
from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider
|
||||
from aki_prj23_transparenzregister.ui.session_handler import SessionHandler
|
||||
from aki_prj23_transparenzregister.utils.networkx.network_base import (
|
||||
initialize_network_with_reduced_metrics,
|
||||
initialize_network_without_metrics,
|
||||
)
|
||||
from aki_prj23_transparenzregister.utils.sql import connector, entities
|
||||
from aki_prj23_transparenzregister.utils.sql.connector import get_session
|
||||
from aki_prj23_transparenzregister.utils.sql import entities
|
||||
|
||||
# Gets the Session Key for the DB Connection.
|
||||
session = get_session(JsonFileConfigProvider("secrets.json"))
|
||||
|
||||
# Alias for Company table for the base company
|
||||
to_company = aliased(entities.Company, name="to_company")
|
||||
@ -32,7 +30,8 @@ def find_all_company_relations() -> pd.DataFrame:
|
||||
Returns:
|
||||
pd.DataFrame: _description_
|
||||
"""
|
||||
session = connector.get_session(JsonFileConfigProvider("./secrets.json"))
|
||||
session = SessionHandler.session
|
||||
assert session # noqa: S101
|
||||
query_companies = session.query(entities.Company) # .all()
|
||||
query_relations = session.query(entities.CompanyRelation) # .all()
|
||||
|
||||
@ -70,7 +69,8 @@ def find_top_companies() -> pd.DataFrame:
|
||||
Returns:
|
||||
pd.DataFrame: _description_
|
||||
"""
|
||||
session = connector.get_session(JsonFileConfigProvider("./secrets.json"))
|
||||
session = SessionHandler.session
|
||||
assert session # noqa: S101
|
||||
query_companies = session.query(entities.Company) # .all()
|
||||
|
||||
companies_df: pd.DataFrame = pd.read_sql(str(query_companies), session.bind) # type: ignore
|
||||
@ -87,6 +87,8 @@ def get_all_company_relations() -> pd.DataFrame:
|
||||
Returns:
|
||||
DataFrame: DataFrame with all Relations between Companies.
|
||||
"""
|
||||
session = SessionHandler.session
|
||||
assert session # noqa: S101
|
||||
# Query to fetch relations between companies
|
||||
relations_company_query = (
|
||||
session.query(
|
||||
@ -124,6 +126,8 @@ def get_all_person_relations() -> pd.DataFrame:
|
||||
Returns:
|
||||
DataFrame: DataFrame with all Relations between Persons and Companies.
|
||||
"""
|
||||
session = SessionHandler.session
|
||||
assert session # noqa: S101
|
||||
relations_person_query = (
|
||||
session.query(
|
||||
entities.Company.id.label("id_company"),
|
||||
@ -285,6 +289,8 @@ def find_company_relations(
|
||||
Returns:
|
||||
Two Dataframes
|
||||
"""
|
||||
session = SessionHandler.session
|
||||
assert session # noqa: S101
|
||||
relations_company_query = (
|
||||
session.query(
|
||||
to_company.id.label("id_company_to"),
|
||||
|
||||
Reference in New Issue
Block a user