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Fix: Rerun Examples and Fix Minor Issues (NREL#275)
* put default value back to month start to ensure examples run correctly * fix new issue with duplicated column/index names * don't download monthly reanalysis if it already exists * fix offset string bug and update changelog * rerun examples to remove fixed warnings and ensure they work
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‎CHANGELOG.md

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@@ -15,6 +15,11 @@ All notable changes to this project will be documented in this file. If you make
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- L -> ms
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- U -> us
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- N -> ns
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- Replaced the "ME" default time basis with "MS" to maintain consistency with the examples.
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- Fixes a bug in the frequency validation where a monthly frequency offset is attempted to be
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converted into seconds. Prior to Pandas 2.0 this was supported, but "M" would return 1 minute,
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so OpenOA will no longer attempt to convert "ME" or "MS", which are unsupported or incorrect,
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respectively.
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- Python 3.11 is now supported.
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- Updates the dependency requirements to minimize the number of required packages, and have a more
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expansive list of modifiers. Users can now use any combination of
@@ -33,6 +38,8 @@ All notable changes to this project will be documented in this file. If you make
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- The turbine capacity value used for power curve filtering in `TurbineLongTermGrossEnergy` is
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changed to the rated power from the asset table instead of the maximum power from SCADA. This
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makes the power curve filtering more robust to turbine power outliers above rated power.
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- Fixed a minor bug in the Cubico example workflow that caused the download of reanalysis data
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without checking for its existence, unlike what is done with the project data.
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## [3.0.1 - 2023-12-22]
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‎examples/00_intro_to_plant_data.ipynb

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‎examples/01_utils_examples.ipynb

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‎examples/02a_plant_aep_analysis.ipynb

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‎examples/02b_plant_aep_analysis_cubico.ipynb

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‎examples/03_turbine_ideal_energy.ipynb

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{
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"WARNING:py.warnings:/Users/rhammond/GitHub_Public/OpenOA/openoa/utils/plot.py:1514: DeprecationWarning: invalid escape sequence '\\c'\n",
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" label=f\"Max. Power Vane Angle = {round(curve_fit_params_ws[:,i,1].mean(),1)}$^\\circ$\", # noqa: W605\n",
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"\n",
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"WARNING:py.warnings:/Users/rhammond/GitHub_Public/OpenOA/openoa/analysis/electrical_losses.py:374: DeprecationWarning: invalid escape sequence '\\m'\n",
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" label=f\"Electrical Losses\\n$\\mu$={mean:.2%}, $\\sigma$={std:.2%}\", # noqa: W605\n",
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"\n",
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"WARNING:py.warnings:/Users/rhammond/GitHub_Public/OpenOA/openoa/analysis/electrical_losses.py:374: DeprecationWarning: invalid escape sequence '\\s'\n",
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" label=f\"Electrical Losses\\n$\\mu$={mean:.2%}, $\\sigma$={std:.2%}\", # noqa: W605\n",
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"\n"
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}
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],
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"outputs": [],
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"source": [
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"# Import required packages\n",
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"import numpy as np\n",
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"Mean long-term turbine ideal energy is 13.5 GWh/year\n",
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"Uncertainty in long-term turbine ideal energy is 0.1 GWh/year, or 1.1% percent\n"
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"Uncertainty in long-term turbine ideal energy is 0.2 GWh/year, or 1.7% percent\n"
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‎examples/04_electrical_losses.ipynb

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"/var/folders/q5/tfpytqxn0r396dfg7rk5sj8rwq9tvv/T/ipykernel_77703/3508303544.py:5: DeprecationWarning: \n",
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"Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
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"(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
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"but was not found to be installed on your system.\n",
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"/Users/rhammond/GitHub_Public/OpenOA/examples/project_ENGIE.py:106: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
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"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
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"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
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"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
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"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
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"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
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"/Users/rhammond/GitHub_Public/OpenOA/examples/project_ENGIE.py:106: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
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"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
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"A typical example is when you are setting values in a column of a DataFrame, like:\n",
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"\n",
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"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
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"100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3000/3000 [00:01<00:00, 1762.28it/s]\n"
238+
"100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3000/3000 [00:01<00:00, 1958.02it/s]\n"
305239
]
306240
}
307241
],
@@ -326,7 +260,7 @@
326260
"name": "stdout",
327261
"output_type": "stream",
328262
"text": [
329-
"Electrical losses are 2.01%\n",
263+
"Electrical losses are 1.99%\n",
330264
"Uncertainty in the electrical loss estimate is 0.69%\n"
331265
]
332266
}
@@ -361,7 +295,7 @@
361295
"name": "python",
362296
"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
364-
"version": "3.9.18"
298+
"version": "3.11.7"
365299
},
366300
"toc": {
367301
"base_numbering": 1,

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