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hotfix: Improve UI (#569)

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Co-authored-by: Philipp Horstenkamp <philipp@horstenkamp.de>
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Tristan Nolde 2024-01-17 16:22:46 +01:00 committed by GitHub
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7 changed files with 228 additions and 267 deletions
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@ -1,14 +1,14 @@
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[[package]]
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@ -6574,20 +6566,18 @@ sphinx = ">=2"
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@ -6646,38 +6636,34 @@ six = ">=1.5.2"
[[package]]
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[[package]]
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@ -6687,51 +6673,30 @@ description = "Database Abstraction Library"
optional = false
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@ -7343,13 +7308,13 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
[[package]]
name = "trio"
version = "0.23.2"
version = "0.24.0"
description = "A friendly Python library for async concurrency and I/O"
optional = false
python-versions = ">=3.8"
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[package.dependencies]

2
src/aki_prj23_transparenzregister/ui/company_elements.py
View File

@ -60,7 +60,7 @@ def format_date_de(value: Any) -> str:
Formatted date.
"""
if not value or value == "n.a." or isinstance(value, str):
return datetime.today().strftime("%d.%m.%Y")
return "n.a."
return value.strftime("%d.%m.%Y")

8
src/aki_prj23_transparenzregister/ui/data_elements.py
View File

@ -11,6 +11,8 @@ from sqlalchemy.orm import Session
from aki_prj23_transparenzregister.ui.session_handler import SessionHandler
from aki_prj23_transparenzregister.utils.sql import entities
CERTAINTY_MINIMUM = 0.65
def get_company_data(session: Session) -> pd.DataFrame:
"""Creates a session to the database and collects all available company data.
@ -161,7 +163,6 @@ def get_news_of_one_company(selected_company_id: int, session: Session) -> pd.Da
Returns:
A dataframe containing all news of the selected company.
"""
certainty_minimum = 0.9
data = (
session.query(
entities.News.title,
@ -174,7 +175,7 @@ def get_news_of_one_company(selected_company_id: int, session: Session) -> pd.Da
)
.join(entities.Sentiment, entities.News.id == entities.Sentiment.article_id)
.filter(entities.Sentiment.company_id == selected_company_id)
.filter(entities.News.overall_sentiment_certainty >= certainty_minimum)
.filter(entities.News.overall_sentiment_certainty >= CERTAINTY_MINIMUM)
.all()
)
return pd.DataFrame(data)
@ -192,7 +193,6 @@ def get_last_article_of_one_company(
Returns:
A dataframe containing all news of the selected company.
"""
certainty_minimum = 0.9
data = (
session.query(
entities.News.timestamp,
@ -200,7 +200,7 @@ def get_last_article_of_one_company(
)
.join(entities.Sentiment, entities.News.id == entities.Sentiment.article_id)
.filter(entities.Sentiment.company_id == selected_company_id)
.filter(entities.News.overall_sentiment_certainty >= certainty_minimum)
.filter(entities.News.overall_sentiment_certainty >= CERTAINTY_MINIMUM)
.order_by(entities.News.timestamp.desc())
.first()
)

25
src/aki_prj23_transparenzregister/ui/sentiment_elements.py
View File

@ -32,10 +32,7 @@ def sentiment_layout(selected_company_id: int, session: Session) -> html:
"""
articles = data_elements.get_news_of_one_company(selected_company_id, session)
# number of article sentiments for the rolling mean
n = 10
if articles.empty or (len(articles) < n):
if articles.empty:
return html.Div(
className="sentiment-wrapper",
children=[
@ -53,8 +50,13 @@ def sentiment_layout(selected_company_id: int, session: Session) -> html:
lambda _: _.value
)
rolling_window = 3
articles = articles.sort_values(by=["timestamp"], ascending=True)
articles["rolling_mean"] = articles["overall_sentiment_label"].rolling(n).mean()
articles["rolling_mean"] = (
articles["overall_sentiment_label"]
.rolling(rolling_window if len(articles) >= rolling_window else 1)
.mean()
)
sentiment_score = articles["rolling_mean"].iloc[-1] + 1
@ -68,7 +70,7 @@ def sentiment_layout(selected_company_id: int, session: Session) -> html:
"timestamp",
"title",
"times_named",
"source_domain",
"source_url",
"overall_sentiment_label",
]
]
@ -78,7 +80,7 @@ def sentiment_layout(selected_company_id: int, session: Session) -> html:
"timestamp": "Datum",
"title": "Titel",
"times_named": "Erwähnungen",
"source_domain": "Quelle",
"source_url": "Quelle",
"overall_sentiment_label": "Stimmung",
}
)
@ -98,7 +100,7 @@ def sentiment_layout(selected_company_id: int, session: Session) -> html:
className="sentiment-line",
children=[
dcc.Graph(
figure=sentiment_trend_figure(articles, n),
figure=sentiment_trend_figure(articles),
config={"displaylogo": False},
),
],
@ -183,24 +185,21 @@ def sentiment_layout(selected_company_id: int, session: Session) -> html:
html.Div(
className="sentiment-disclaimer",
children=[
f"*Für den aktuellen Stimmungstrend wurden die letzten {n} Artikel verwendet."
f"*Für den aktuellen Stimmungstrend wurden die letzten {len(articles)} Artikel verwendet."
],
),
)
def sentiment_trend_figure(articles: pd.DataFrame, n: int) -> go.Figure:
def sentiment_trend_figure(articles: pd.DataFrame) -> go.Figure:
"""Creates trend plot for the rolling mean sentiment trend.
Args:
articles: Dataframe containing all available news including the sentiments.
n: number of minimum articles used for the rolling mean.
Returns:
Plotly go figure.
"""
articles = articles.iloc[n:]
sentiment_fig = go.Figure()
# add trace for company 1
sentiment_fig.add_trace(

2
src/aki_prj23_transparenzregister/utils/networkx/network_2d.py
View File

@ -90,7 +90,6 @@ def create_2d_graph( # noqa PLR0913
y=edge_weight_y,
mode="text",
marker_size=0.5,
text=[0.45, 0.7, 0.34],
textposition="top center",
hoverinfo="none",
)
@ -100,7 +99,6 @@ def create_2d_graph( # noqa PLR0913
y=edge_weight_y,
mode="text",
marker_size=0.5,
text=[0.45, 0.7, 0.34],
textposition="top center",
hovertemplate="Relation: %{hovertext}<extra></extra>",
hovertext=edge_type_list,

3
src/aki_prj23_transparenzregister/utils/networkx/network_3d.py
View File

@ -94,9 +94,8 @@ def create_3d_graph( # noqa : PLR0913
z=edge_weight_z,
mode="text",
marker_size=1,
text=[0.45, 0.7, 0.34],
textposition="top center",
hovertemplate="weight: %{text}<extra></extra>",
hoverinfo="none",
)
# Getting the Positions from NetworkX and assign it to the variables.

2
tests/ui/sentiment_elements_test.py
View File

@ -23,7 +23,7 @@ def test_sentiment_trend_figure(news_db: Session) -> None:
articles = articles.sort_values(by=["timestamp"], ascending=True)
articles["rolling_mean"] = articles["overall_sentiment_label"].rolling(n).mean()
sentiment_elements.sentiment_trend_figure(articles, n)
sentiment_elements.sentiment_trend_figure(articles)
def test_sentiment_gauge_figure() -> None: