mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-09-13 07:21:16 +00:00
Visualize reworked v2 (#267)
* initial commit * Delete duplicate openapi.json * revert temp cahnges * test fixed * mypy fixes * mypy fixed * Financial Overview included * test data save path * almost done * mypy fix * Update visualize.py * ruff fix * config, label * improved start_hour vis * fix * fix2
This commit is contained in:
Normann
GitHub
@@ -11,6 +11,9 @@ from akkudoktoreos.optimization.genetic import (
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OptimizeResponse,
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optimization_problem,
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)
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from akkudoktoreos.utils.visualize import (
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prepare_visualize, # Import the new prepare_visualize
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)
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DIR_TESTDATA = Path(__file__).parent / "testdata"
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@@ -64,16 +67,12 @@ def test_optimize(fn_in: str, fn_out: str, ngen: int, is_full_run: bool):
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visualize_filename = str((DIR_TESTDATA / f"new_{fn_out}").with_suffix(".pdf"))
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def visualize_to_file(*args, **kwargs):
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from akkudoktoreos.visualize import visualisiere_ergebnisse
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# Write test output pdf to file, so we can look at it manually
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kwargs["filename"] = visualize_filename
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return visualisiere_ergebnisse(*args, **kwargs)
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with patch(
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"akkudoktoreos.optimization.genetic.visualisiere_ergebnisse", side_effect=visualize_to_file
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) as visualisiere_ergebnisse_patch:
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"akkudoktoreos.utils.visualize.prepare_visualize",
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side_effect=lambda parameters, results, *args, **kwargs: prepare_visualize(
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parameters, results, filename=visualize_filename, **kwargs
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),
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) as prepare_visualize_patch:
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# Call the optimization function
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ergebnis = opt_class.optimierung_ems(
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parameters=input_data, start_hour=start_hour, ngen=ngen
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@@ -92,4 +91,4 @@ def test_optimize(fn_in: str, fn_out: str, ngen: int, is_full_run: bool):
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compare_dict(ergebnis.model_dump(), expected_result.model_dump())
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# The function creates a visualization result PDF as a side-effect.
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visualisiere_ergebnisse_patch.assert_called_once()
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prepare_visualize_patch.assert_called_once()
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@@ -1,36 +1,36 @@
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import json
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import os
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import subprocess
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from pathlib import Path
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import pytest
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from matplotlib.testing.compare import compare_images
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from akkudoktoreos.config.config import get_config
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from akkudoktoreos.visualize import visualisiere_ergebnisse
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filename = "example_report.pdf"
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config = get_config()
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output_dir = config.data_output_path
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output_dir.mkdir(parents=True, exist_ok=True)
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output_file = os.path.join(output_dir, filename)
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DIR_TESTDATA = Path(__file__).parent / "testdata"
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DIR_IMAGEDATA = DIR_TESTDATA / "images"
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reference_file = DIR_TESTDATA / "test_example_report.pdf"
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@pytest.mark.parametrize(
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"fn_in, fn_out, fn_out_base",
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[("visualize_input_1.json", "visualize_output_1.pdf", "visualize_base_output_1.pdf")],
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)
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def test_visualisiere_ergebnisse(fn_in, fn_out, fn_out_base):
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with open(DIR_TESTDATA / fn_in, "r") as f:
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input_data = json.load(f)
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visualisiere_ergebnisse(**input_data)
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def test_generate_pdf_main():
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# Delete the old generated file if it exists
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if os.path.isfile(output_file):
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os.remove(output_file)
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config = get_config()
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output_dir = config.data_output_path
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output_dir.mkdir(parents=True, exist_ok=True)
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output_file = output_dir.joinpath(fn_out)
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# Execute the __main__ block of visualize.py by running it as a script
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script_path = Path(__file__).parent.parent / "src" / "akkudoktoreos" / "utils" / "visualize.py"
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subprocess.run(["python", str(script_path)], check=True)
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assert output_file.is_file()
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assert (
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compare_images(
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str(output_file),
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str(DIR_IMAGEDATA / fn_out_base),
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0,
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)
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is None
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)
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# Check if the file exists
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assert os.path.isfile(output_file)
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# Compare the generated file with the reference file
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comparison = compare_images(str(reference_file), str(output_file), tol=0)
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# Assert that there are no differences
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assert comparison is None, f"Images differ: {comparison}"
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BIN
tests/testdata/images/visualize_base_output_1.pdf
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tests/testdata/images/visualize_base_output_1.pdf
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BIN
tests/testdata/test_example_report.pdf
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BIN
tests/testdata/test_example_report.pdf
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175
tests/testdata/visualize_input_1.json
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175
tests/testdata/visualize_input_1.json
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@@ -1,175 +0,0 @@
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{
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"gesamtlast": [
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676.71, 876.19, 527.13, 468.88, 531.38, 517.95, 483.15, 472.28, 1011.68,
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995.0, 1053.07, 1063.91, 1320.56, 1132.03, 1163.67, 1176.82, 1216.22,
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1103.78, 1129.12, 1178.71, 1050.98, 988.56, 912.38, 704.61, 516.37, 868.05,
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694.34, 608.79, 556.31, 488.89, 506.91, 804.89, 1141.98, 1056.97, 992.46,
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1155.99, 827.01, 1257.98, 1232.67, 871.26, 860.88, 1158.03, 1222.72,
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1221.04, 949.99, 987.01, 733.99, 592.97
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],
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"pv_forecast": [
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5000.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0
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],
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"strompreise": [
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0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001,
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0.00001, 0.00001, 0.001, 0.00005, 0.00005, 0.00005, 0.00005, 0.001, 0.001,
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0.001, 0.001, 0.001, 0.00001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,
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0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001,
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0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001
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],
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"ergebnisse": {
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"Last_Wh_pro_Stunde": [
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12493.71, 10502.19, 12775.13, 15356.88, 11468.38, 4037.95, 6047.15,
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3112.2799999999997, 3211.68, 995.0, 1053.07, 1063.91, 1320.56, 1132.03,
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1163.67, 1176.82, 1216.22, 1103.78, 1129.12, 1178.71, 1050.98, 988.56,
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2035.7436363636361, 704.61, 516.37, 868.05, 694.34, 608.79, 556.31,
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488.89, 506.91, 804.89, 1141.98, 1056.97, 992.46, 1155.99, 827.01,
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1257.98, 1232.67, 871.26, 860.88, 1158.03, 1222.72, 1221.04, 949.99,
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987.01, 733.99, 592.97
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],
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"Netzeinspeisung_Wh_pro_Stunde": [
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3679.44, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0
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],
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"Netzbezug_Wh_pro_Stunde": [
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12493.71, 10502.19, 12775.13, 15356.88, 11468.38, 4037.95, 6047.15,
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3112.2799999999997, 3211.68, 995.0, 1053.07, 1063.91, 0.0, 1132.03,
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1163.67, 1176.82, 1216.22, 1103.78, 1129.12, 1178.71, 1050.98, 0.0,
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2035.7436363636361, 704.61, 516.37, 868.05, 694.34, 608.79, 556.31,
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488.89, 506.91, 804.89, 1141.98, 1056.97, 992.46, 1155.99, 827.01,
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1257.98, 1232.67, 871.26, 860.88, 1158.03, 0.0, 1221.04, 0.0, 0.0, 0.0,
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592.97
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],
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"Kosten_Euro_pro_Stunde": [
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0.1249371, 0.10502190000000002, 0.1277513, 0.1535688, 0.1146838,
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0.0403795, 0.060471500000000004, 0.0311228, 0.0321168, 0.00995, 1.05307,
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0.05319550000000001, 0.0, 0.0566015, 0.058183500000000006, 1.17682,
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1.21622, 1.10378, 1.12912, 1.1787100000000001, 0.010509800000000001, 0.0,
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2.035743636363636, 0.7046100000000001, 0.51637, 0.86805,
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0.6943400000000001, 0.6087899999999999, 0.55631, 0.48889,
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0.5069100000000001, 0.80489, 1.14198, 1.05697, 0.99246, 1.15599, 0.82701,
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1.25798, 1.2326700000000002, 0.87126, 0.86088, 1.15803, 0.0, 1.22104, 0.0,
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0.0, 0.0, 0.59297
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],
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"akku_soc_pro_stunde": [
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25.0, 31.666666666666664, 38.333333333333336, 55.00000000000001,
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61.66666666666667, 75.0, 81.66666666666667, 91.66666666666666, 100.0,
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100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
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100.0, 100.0, 95.7448347107438, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
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100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
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100.0, 100.0, 100.0, 100.0, 94.73691460055097, 94.73691460055097,
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90.64777031680441, 86.39927685950414, 83.23988464187329, 83.23988464187329
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],
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"Einnahmen_Euro_pro_Stunde": [
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2575608,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0
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],
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"Gesamtbilanz_Euro": 27.732796636363638,
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"EAuto_SoC_pro_Stunde": [
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30.294999999999998, 43.405, 60.885, 78.365, 93.66, 93.66, 100.0, 100.0,
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100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
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100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
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100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0,
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100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0
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],
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"Gesamteinnahmen_Euro": 0.2575608,
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"Gesamtkosten_Euro": 27.990357436363638,
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"Verluste_Pro_Stunde": [
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843.0, 654.0, 792.0, 1152.0, 723.0, 480.0, 440.2105263157896, 360.0,
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300.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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134.80363636363631, 153.18595041322305, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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166.73454545454547, 0.0, 129.54409090909098, 134.59227272727276,
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100.08954545454549, 0.0
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],
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"Gesamt_Verluste": 6563.160567638104,
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"Home_appliance_wh_per_hour": [
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null,
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null,
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null,
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null,
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null,
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null,
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null,
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]
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},
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"ac": [
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0.6, 0.4, 0.4, 1.0, 0.4, 0.8, 0.4, 0.6, 1.0, 0.2, 0.2, 0.2, 0.6, 0.0, 0.2,
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0.6, 0.0, 0.0, 0.8, 0.8, 0.4, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.8,
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0.2, 0.4, 0.6, 1.0, 0.0, 1.0, 0.8, 0.4, 0.4, 1.0, 0.2, 0.6, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0
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],
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"dc": [
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
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],
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"discharge": [
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0
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],
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"temperature": [
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18.3, 17.8, 16.9, 16.2, 15.6, 15.1, 14.6, 14.2, 14.3, 14.8, 15.7, 16.7,
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17.4, 18.0, 18.6, 19.2, 19.1, 18.7, 18.5, 17.7, 16.2, 14.6, 13.6, 13.0,
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12.6, 12.2, 11.7, 11.6, 11.3, 11.0, 10.7, 10.2, 11.4, 14.4, 16.4, 18.3,
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19.5, 20.7, 21.9, 22.7, 23.1, 23.1, 22.8, 21.8, 20.2, 19.1, 18.0, 17.4
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],
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"start_hour": 0,
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"einspeiseverguetung_euro_pro_wh": [
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0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
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0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
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0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
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0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
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0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007,
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0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007, 0.00007
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],
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"filename": "visualize_output_1.pdf",
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"extra_data": null
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}
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