Compare commits
3 Commits
Author | SHA1 | Date | |
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408cff442c | |||
67b88aa2a1 | |||
7b0238b4a5 |
@ -20,7 +20,7 @@ steps:
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when:
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- event: [push, tag]
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deploy:
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image: quay.io/wollud1969/k8s-admin-helper:0.1.3
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image: quay.io/wollud1969/k8s-admin-helper:0.2.1
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environment:
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KUBE_CONFIG_CONTENT:
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from_secret: kube_config
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@ -19,10 +19,11 @@ kubectl create namespace $NAMESPACE \
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-o yaml | \
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kubectl -f - apply
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SECRETS_FILE=`mktemp`
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gpg --decrypt --passphrase $GPG_PASSPHRASE --yes --batch --homedir /tmp/.gnupg --output $SECRETS_FILE secrets.asc
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. $SECRETS_FILE
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rm $SECRETS_FILE
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# SECRETS_FILE=`mktemp`
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# gpg --decrypt --passphrase $GPG_PASSPHRASE --yes --batch --homedir /tmp/.gnupg --output $SECRETS_FILE secrets.asc
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# . $SECRETS_FILE
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# rm $SECRETS_FILE
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eval "`cat secrets.asc | /usr/local/bin/decrypt-secrets.sh`"
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kubectl create secret generic pv-stats \
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--dry-run=client \
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16
src/run.py
16
src/run.py
@ -10,6 +10,7 @@ import plotly.express as px
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import pandas as pd
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import psycopg
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import sqlalchemy
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import time
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try:
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redis_url = os.environ['REDIS_URL']
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@ -50,10 +51,15 @@ def index():
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dbh = psycopg.connect()
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engine = sqlalchemy.create_engine("postgresql+psycopg://", creator=lambda: dbh)
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start_time = time.time()
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df = pd.read_sql("SELECT month, cast(year AS varchar), current_energy AS value FROM pv_energy_by_month", con=engine)
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step1_time = time.time()
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duration1 = step1_time - start_time
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fig_1 = px.bar(df, x='month', y='value', color='year', barmode='group')
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step2_time = time.time()
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duration2 = step2_time - start_time
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fig_1.update_layout(
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title="Jahreswerte Exportierte Energie",
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title=f"Jahreswerte Exportierte Energie {duration1:.3f}, {duration2:.3f}",
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xaxis_title="",
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yaxis_title="",
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legend_title="Jahr",
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@ -66,22 +72,26 @@ def index():
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)
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graph_html_1 = fig_1.to_html(full_html=False, default_height='33%')
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start_time = time.time()
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df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) - '1 day'::interval AND time < date_trunc('day', now()) GROUP BY bucket ORDER BY bucket", con=engine)
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duration = time.time() - start_time
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fig_2 = px.line(df, x='bucket', y='avg_power')
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fig_2.update_layout(
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xaxis_title="",
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yaxis_title="",
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title="Export gestern",
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title=f"Export gestern {duration}",
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yaxis=dict(ticksuffix=" W")
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)
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graph_html_2 = fig_2.to_html(full_html=False, default_height='33%')
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start_time = time.time()
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df = pd.read_sql("SELECT time_bucket('5 minutes', time) AS bucket, AVG(power) AS avg_power FROM pv_power_v WHERE time >= date_trunc('day', now()) AND time < date_trunc('day', now()) + '1 day'::interval GROUP BY bucket ORDER BY bucket", con=engine)
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duration = time.time() - start_time
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fig_3 = px.line(df, x='bucket', y='avg_power')
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fig_3.update_layout(
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xaxis_title="",
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yaxis_title="",
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title="Export heute",
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title=f"Export heute {duration}",
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yaxis=dict(ticksuffix=" W")
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)
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graph_html_3 = fig_3.to_html(full_html=False, default_height='33%')
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