From 9e2e4638a7de89d5eca299811542955f3b70d9a2 Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Sat, 15 Nov 2025 02:33:21 +0100 Subject: [PATCH 1/8] add files Signed-off-by: Nitish Bharambe --- data/ev_profile.csv | 8760 +++++++++++++++++ .../Advanced Workshop.ipynb | 2092 ++++ .../ev_load_profile_generator.py | 77 + 3 files changed, 10929 insertions(+) create mode 100644 data/ev_profile.csv create mode 100644 pgm-advanced-usage-workshop/Advanced Workshop.ipynb create mode 100644 pgm-advanced-usage-workshop/ev_load_profile_generator.py diff --git a/data/ev_profile.csv b/data/ev_profile.csv new file mode 100644 index 0000000..4a79d0c --- /dev/null +++ b/data/ev_profile.csv @@ -0,0 +1,8760 @@ +0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 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`power-grid-model`. The tasks include:\n", + "\n", + "1. Load input\n", + "2. Data validation for large datasets\n", + "3. Columnar data memory usage\n", + "4. Selective output\n", + "5. Numpy tips\n", + " 1. Views vs copies\n", + " 2. Other tips\n", + " 3. PGM related recommendations\n", + "6. Graph operations \n", + "7. Topology caching\n", + "6. N 1 Scenario-Batch-Calculation\n", + "\n", + "The input data are CSV files in the `data/` folder:\n", + "* `node.csv`\n", + "* `line.csv`\n", + "* `source.csv`\n", + "* `sym_load.csv`\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b18d109c", + "metadata": {}, + "source": [ + "# Preparation\n", + "\n", + "First import everything we need for this workshop:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "fbc50a6d", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from typing import Dict\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from power_grid_model import (\n", + " PowerGridModel,\n", + " CalculationType,\n", + " CalculationMethod,\n", + " ComponentType,\n", + " DatasetType,\n", + " initialize_array,\n", + " attribute_dtype,\n", + " attribute_empty_value,\n", + " power_grid_meta_data\n", + ")\n", + "\n", + "from power_grid_model.validation import assert_valid_input_data, assert_valid_batch_data\n", + "\n", + "from power_grid_model_ds import Grid, PowerGridModelInterface\n", + "from power_grid_model_ds.enums import NodeType\n", + "from power_grid_model_ds.visualizer import visualize\n", + "\n", + "# For memory monitoring\n", + "import psutil\n", + "import gc\n", + "import os\n", + "from collections import Counter" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "50065790", + "metadata": {}, + "source": [ + "# Utilities\n", + "\n", + "Let's define a timer class to easily benchmark the calculations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "760a38b1", + "metadata": {}, + "outputs": [], + "source": [ + "class Timer:\n", + " def __init__(self, name: str):\n", + " self.name = name\n", + " self.start = None\n", + "\n", + " def __enter__(self):\n", + " self.start = time.perf_counter()\n", + "\n", + " def __exit__(self, *args):\n", + " print(\n", + " f\"Execution time for {self.name} is {(time.perf_counter() - self.start):0.6f} s\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "2d492557", + "metadata": {}, + "source": [ + "Now let's create a memory usage monitor to track memory consumption during calculations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1db379a", + "metadata": {}, + "outputs": [], + "source": [ + "import psutil\n", + "import gc\n", + "import os\n", + "\n", + "\n", + "class MemoryMonitor:\n", + " def __init__(self, name: str):\n", + " self.name = name\n", + " self.process = psutil.Process(os.getpid())\n", + " self.start_memory = None\n", + " self.peak_memory = None\n", + "\n", + " def __enter__(self):\n", + " # Force garbage collection to get a clean starting point\n", + " gc.collect()\n", + " # Get initial memory usage\n", + " self.start_memory = (\n", + " self.process.memory_info().rss / 1024 / 1024\n", + " ) # Convert to MB\n", + " self.peak_memory = self.start_memory\n", + " return self\n", + "\n", + " def __exit__(self, *args):\n", + " # Force garbage collection before final measurement\n", + " gc.collect()\n", + " # Get final memory usage\n", + " end_memory = self.process.memory_info().rss / 1024 / 1024 # Convert to MB\n", + " memory_diff = end_memory - self.start_memory\n", + " peak_diff = self.peak_memory - self.start_memory\n", + "\n", + " print(f\"Memory usage for {self.name}:\")\n", + " print(f\" Start: {self.start_memory:.2f} MB\")\n", + " print(f\" End: {end_memory:.2f} MB\")\n", + " print(f\" Net change: {memory_diff:+.2f} MB\")\n", + " print(f\" Peak increase: {peak_diff:+.2f} MB\")\n", + "\n", + " def update_peak(self):\n", + " \"\"\"Manually update peak memory usage during execution\"\"\"\n", + " current_memory = self.process.memory_info().rss / 1024 / 1024\n", + " if current_memory > self.peak_memory:\n", + " self.peak_memory = current_memory" + ] + }, + { + "cell_type": "markdown", + "id": "2fac670b", + "metadata": {}, + "source": [ + "You can also combine both Timer and MemoryMonitor for comprehensive performance analysis:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "403f4748", + "metadata": {}, + "outputs": [], + "source": [ + "class PerformanceMonitor:\n", + " \"\"\"Combined timer and memory monitor for comprehensive performance analysis\"\"\"\n", + "\n", + " def __init__(self, name: str):\n", + " self.name = name\n", + " self.timer = Timer(name)\n", + " self.memory_monitor = MemoryMonitor(name)\n", + "\n", + " def __enter__(self):\n", + " self.timer.__enter__()\n", + " self.memory_monitor.__enter__()\n", + " return self\n", + "\n", + " def __exit__(self, *args):\n", + " self.timer.__exit__(*args)\n", + " self.memory_monitor.__exit__(*args)\n", + " print(f\"--- End of {self.name} performance report ---\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b36e140", + "metadata": {}, + "outputs": [], + "source": [ + "class PerfLogger:\n", + " \"\"\"Simple in-memory performance logger that stores records in a pandas DataFrame.\"\"\"\n", + "\n", + " def __init__(self):\n", + " self.records = []\n", + "\n", + " def log(\n", + " self,\n", + " name: str,\n", + " start_time: float,\n", + " end_time: float,\n", + " start_mem: float,\n", + " end_mem: float,\n", + " peak_mem: float,\n", + " ):\n", + " self.records.append(\n", + " {\n", + " \"name\": name,\n", + " \"start_time\": start_time,\n", + " \"end_time\": end_time,\n", + " \"duration_s\": end_time - start_time,\n", + " \"start_mem_mb\": start_mem,\n", + " \"end_mem_mb\": end_mem,\n", + " \"net_mem_change_mb\": end_mem - start_mem,\n", + " \"peak_mem_mb\": peak_mem,\n", + " \"peak_increase_mb\": peak_mem - start_mem,\n", + " }\n", + " )\n", + "\n", + " def to_dataframe(self) -> pd.DataFrame:\n", + " return pd.DataFrame(self.records)\n", + "\n", + "\n", + "class LoggedPerformanceMonitor(PerformanceMonitor):\n", + " \"\"\"A PerformanceMonitor that logs results into a PerfLogger.\"\"\"\n", + "\n", + " def __init__(self, name: str, logger: PerfLogger):\n", + " super().__init__(name)\n", + " self.logger = logger\n", + " self._start_time = None\n", + " self._start_mem = None\n", + "\n", + " def __enter__(self):\n", + " # start timer and memory monitor\n", + " self._start_time = time.perf_counter()\n", + " self._start_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024\n", + " self.timer.__enter__()\n", + " self.memory_monitor.__enter__()\n", + " return self\n", + "\n", + " def __exit__(self, *args):\n", + " # capture end values and delegate to base\n", + " end_time = time.perf_counter()\n", + " self.timer.__exit__(*args)\n", + " self.memory_monitor.__exit__(*args)\n", + " end_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024\n", + " peak_mem = self.memory_monitor.peak_memory\n", + " # log the record\n", + " self.logger.log(\n", + " self.name, self._start_time, end_time, self._start_mem, end_mem, peak_mem\n", + " )\n", + "\n", + "\n", + "# Polling-backed variant to capture short-lived peaks\n", + "import threading\n", + "import time as _time\n", + "\n", + "\n", + "class PollingLoggedPerformanceMonitor(LoggedPerformanceMonitor):\n", + " \"\"\"Poll process memory on a short interval in a background thread to catch transient peaks.\"\"\"\n", + "\n", + " def __init__(self, name: str, logger: PerfLogger, poll_interval: float = 0.01):\n", + " super().__init__(name, logger)\n", + " self.poll_interval = poll_interval\n", + " self._stop_event = threading.Event()\n", + " self._thread = None\n", + "\n", + " def _poll(self):\n", + " proc = self.memory_monitor.process\n", + " while not self._stop_event.is_set():\n", + " cur = proc.memory_info().rss / 1024 / 1024\n", + " if cur > self.memory_monitor.peak_memory:\n", + " self.memory_monitor.peak_memory = cur\n", + " _time.sleep(self.poll_interval)\n", + "\n", + " def __enter__(self):\n", + " # start base enter first to initialize memory_monitor\n", + " super().__enter__()\n", + " self._stop_event.clear()\n", + " self._thread = threading.Thread(target=self._poll, daemon=True)\n", + " self._thread.start()\n", + " return self\n", + "\n", + " def __exit__(self, *args):\n", + " self._stop_event.set()\n", + " if self._thread is not None:\n", + " self._thread.join()\n", + " return super().__exit__(*args)\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0f33163a", + "metadata": {}, + "source": [ + "The following examples demonstrate how to use the Timer, MemoryMonitor, and PerformanceMonitor classes:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a61540cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Add Operation is 0.008699 s\n" + ] + } + ], + "source": [ + "# Example 1: Using Timer only\n", + "a = np.random.rand(1000000)\n", + "b = np.random.rand(1000000)\n", + "with Timer(\"Add Operation\"):\n", + " c = a + b" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bde7c586", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory usage for Large Array Creation:\n", + " Start: 188.52 MB\n", + " End: 227.30 MB\n", + " Net change: +38.78 MB\n", + " Peak increase: +0.00 MB\n" + ] + } + ], + "source": [ + "# Example 2: Using MemoryMonitor only\n", + "with MemoryMonitor(\"Large Array Creation\"):\n", + " large_array = np.random.rand(5000000) # ~40MB array\n", + " processed_array = large_array * 2 + 1\n", + " del large_array # Explicitly delete to free memory" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0115dac2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Matrix Operations is 0.199182 s\n", + "Memory usage for Matrix Operations:\n", + " Start: 227.30 MB\n", + " End: 240.82 MB\n", + " Net change: +13.52 MB\n", + " Peak increase: +0.00 MB\n", + "--- End of Matrix Operations performance report ---\n", + "\n" + ] + } + ], + "source": [ + "# Example 3: Using PerformanceMonitor for comprehensive analysis\n", + "with PerformanceMonitor(\"Matrix Operations\"):\n", + " # Create large matrices\n", + " matrix_a = np.random.rand(2000, 2000)\n", + " matrix_b = np.random.rand(2000, 2000)\n", + "\n", + " # Perform memory-intensive operations\n", + " result = np.dot(matrix_a, matrix_b)\n", + " eigenvalues = np.linalg.eigvals(result[:100, :100]) # Small subset for speed\n", + "\n", + " # Clean up\n", + " del matrix_a, matrix_b, result" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "05d3496c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory usage for Transient Peak Example:\n", + " Start: 240.82 MB\n", + " End: 248.26 MB\n", + " Net change: +7.44 MB\n", + " Peak increase: +60.81 MB\n" + ] + } + ], + "source": [ + "# Example 4: Demonstrate transient peak memory increase\n", + "with MemoryMonitor(\"Transient Peak Example\") as mm:\n", + " # Start with a small allocation\n", + " a = np.random.rand(1000000)\n", + " mm.update_peak() # record current memory as peak so far\n", + "\n", + " # Create a transient large allocation that should raise peak\n", + " temp = np.random.rand(8000000) # transient large array\n", + " mm.update_peak() # update peak after transient allocation\n", + "\n", + " # Use the temporary array briefly\n", + " s = temp.sum()\n", + "\n", + " # Remove the temporary allocation and force GC\n", + " del temp\n", + " gc.collect()\n", + "\n", + " # Final small operation\n", + " a = a * 2\n", + " del a\n", + "\n", + "# The MemoryMonitor should report a peak increase above the start memory." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7dbbd755", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Polled Transient Peak is 0.177470 s\n", + "Memory usage for Polled Transient Peak:\n", + " Start: 248.26 MB\n", + " End: 248.58 MB\n", + " Net change: +0.32 MB\n", + " Peak increase: +61.04 MB\n", + " name start_time end_time duration_s start_mem_mb end_mem_mb net_mem_change_mb peak_mem_mb peak_increase_mb\n", + "Polled Transient Peak 39863.123998 39863.301627 0.177629 248.257812 248.578125 0.320312 309.296875 61.039062\n" + ] + } + ], + "source": [ + "# Usage example: poll-backed logger to reliably capture transient peak\n", + "perf_logger = PerfLogger()\n", + "with PollingLoggedPerformanceMonitor(\n", + " \"Polled Transient Peak\", perf_logger, poll_interval=0.005\n", + "):\n", + " temp = np.random.rand(8000000)\n", + " s = temp.sum()\n", + " del temp\n", + " gc.collect()\n", + "\n", + "# Convert logs to DataFrame and display\n", + "perf_df = perf_logger.to_dataframe()\n", + "print(perf_df.to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5d41b75a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting: visualize duration and peak increase\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# If perf_df doesn't exist (run the previous logging cell), recreate from logger\n", + "try:\n", + " perf_df\n", + "except NameError:\n", + " perf_df = perf_logger.to_dataframe()\n", + "\n", + "if perf_df.empty:\n", + " print(\"No records to plot\")\n", + "else:\n", + " fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + " # Duration bar chart\n", + " axes[0].bar(perf_df[\"name\"], perf_df[\"duration_s\"], color=\"tab:blue\")\n", + " axes[0].set_title(\"Duration (s)\")\n", + " axes[0].set_ylabel(\"Seconds\")\n", + " axes[0].tick_params(axis=\"x\", rotation=45)\n", + "\n", + " # Peak increase bar chart\n", + " axes[1].bar(perf_df[\"name\"], perf_df[\"peak_increase_mb\"], color=\"tab:orange\")\n", + " axes[1].set_title(\"Peak Increase (MB)\")\n", + " axes[1].set_ylabel(\"MB\")\n", + " axes[1].tick_params(axis=\"x\", rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c4db246d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current memory usage: 251.70 MB\n", + "0.00766754150390625 MB\n", + "0.00016021728515625 MB\n", + "0.00766754150390625 MB\n", + "7.62939453125 MB\n" + ] + } + ], + "source": [ + "def print_memory_usage():\n", + " process = psutil.Process(os.getpid())\n", + " mem_info = process.memory_info()\n", + " print(f\"Current memory usage: {mem_info.rss / 1024 / 1024:.2f} MB\")\n", + "\n", + "\n", + "def print_memory_used_by_object(object):\n", + " print(f\"{object.__sizeof__() / 1024 / 1024} MB\")\n", + "\n", + "\n", + "def print_memory_used_by_dict(dictionary):\n", + " print(\n", + " f\"{sum(object.__sizeof__() / 1024 / 1024 for object in dictionary.values())} MB\"\n", + " )\n", + "\n", + "\n", + "def print_memory_used_by_array(array: np.ndarray):\n", + " print(f\"{array.nbytes / 1024 / 1024} MB\")\n", + "\n", + "\n", + "print_memory_usage()\n", + "print_memory_used_by_object([1] * 1000)\n", + "print_memory_used_by_object(\n", + " {\"a\": [1] * 1000}\n", + ") # This is because dictionary stores only references\n", + "print_memory_used_by_dict({\"a\": [1] * 1000})\n", + "print_memory_used_by_array(np.random.rand(1000, 1000))" + ] + }, + { + "cell_type": "markdown", + "id": "1f257ec6", + "metadata": {}, + "source": [ + "# Topology Data" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "8a2ba5ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ComponentType.node: 2001\n", + "ComponentType.line: 2000\n", + "ComponentType.source: 1\n", + "ComponentType.sym_load: 2000\n" + ] + } + ], + "source": [ + "def load_input_data() -> Dict[str, np.ndarray]:\n", + " input_data = {}\n", + " for component in [\n", + " ComponentType.node,\n", + " ComponentType.line,\n", + " ComponentType.source,\n", + " ComponentType.sym_load,\n", + " ]:\n", + " # Use pandas to read CSV data\n", + " df = pd.read_csv(f\"data/{component.value}.csv\")\n", + "\n", + " # Initialize array\n", + " input_data[component] = initialize_array(DatasetType.input, component, len(df))\n", + "\n", + " # Fill the attributes\n", + " for attr, values in df.items():\n", + " input_data[component][attr] = values\n", + "\n", + " # Print some debug info\n", + " print(f\"{component:9s}: {len(input_data[component]):4d}\")\n", + "\n", + " return input_data\n", + "\n", + "\n", + "input_data = load_input_data()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c56c97ce", + "metadata": {}, + "outputs": [], + "source": [ + "model = PowerGridModel(input_data=input_data)" + ] + }, + { + "cell_type": "markdown", + "id": "2740b1a3", + "metadata": {}, + "source": [ + "# Timeseries load profiles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5677f60", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate random load profile oh hourly data\n", + "n_timestamps = 24 * 7\n", + "dti = pd.date_range(\"2022-01-01\", periods=n_timestamps, freq=\"h\")\n", + "n_loads = len(input_data[ComponentType.sym_load])\n", + "load_id = input_data[ComponentType.sym_load][\"id\"]\n", + "load_p = input_data[ComponentType.sym_load][\"p_specified\"]\n", + "profile = np.tile(load_p, (n_timestamps, 1)) + 5e5 * np.random.randn(\n", + " n_timestamps, n_loads\n", + ")\n", + "df_load_profile = pd.DataFrame(profile, columns=load_id, index=dti)" + ] + }, + { + "cell_type": "markdown", + "id": "036537fe", + "metadata": {}, + "source": [ + "## PGM numpy arrays (Row based)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "2399bced", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.6905517578125\n", + "7.6904296875\n" + ] + } + ], + "source": [ + "# Initialize an empty load profile\n", + "load_profile_row_based = initialize_array(\n", + " DatasetType.update, ComponentType.sym_load, df_load_profile.shape\n", + ")\n", + "\n", + "load_profile_row_based[\"id\"] = df_load_profile.columns.to_numpy()\n", + "load_profile_row_based[\"p_specified\"] = df_load_profile.to_numpy()\n", + "load_profile_row_based[\"q_specified\"] = 0.0\n", + "\n", + "# Construct the update data\n", + "update_data = {ComponentType.sym_load: load_profile_row_based}\n", + "\n", + "print(load_profile_row_based.__sizeof__() / 1024 / 1024) # in MB\n", + "print(load_profile_row_based.nbytes / 1024 / 1024) # strictly data without overhead" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3548ce38", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation using Newton-Raphson row based data is 0.160462 s\n", + "56.412353515625\n" + ] + } + ], + "source": [ + "# Run Newton Raphson power flow (this may take a minute...)\n", + "with Timer(\"Batch Calculation using Newton-Raphson row based data\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data, calculation_method=CalculationMethod.newton_raphson\n", + " )\n", + "\n", + "print(sum(arr.__sizeof__() / 1024 / 1024 for arr in output_data.values()))" + ] + }, + { + "cell_type": "markdown", + "id": "e63af797", + "metadata": {}, + "source": [ + "### Columnar data format" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9c52f4f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5634765625\n" + ] + } + ], + "source": [ + "load_profile_columnar = df_load_profile.to_numpy(\n", + " dtype=attribute_dtype(DatasetType.update, ComponentType.sym_load, \"p_specified\"),\n", + " copy=False,\n", + " na_value=attribute_empty_value(\n", + " DatasetType.update, ComponentType.sym_load, \"p_specified\"\n", + " ),\n", + ")\n", + "\n", + "# Construct the update data\n", + "update_data_columnar = {ComponentType.sym_load: {\"p_specified\": load_profile_columnar}}\n", + "\n", + "print(load_profile_columnar.nbytes / 1024 / 1024) # in MB" + ] + }, + { + "cell_type": "markdown", + "id": "72fe185a", + "metadata": {}, + "source": [ + "Skipping ids is possible under certain conditions" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7be73996", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.155125 s\n" + ] + } + ], + "source": [ + "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_columnar,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "63f3197e", + "metadata": {}, + "source": [ + "### Selective Output\n", + "\n", + "Via output_component_types" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "2800a03b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.136163 s\n", + "output_data_selective contains following: \n", + "Keys: dict_keys([]). \n", + "Attributes: dict_keys(['loading'])\n", + "2.5635986328125\n" + ] + } + ], + "source": [ + "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", + " output_data_selective = model.calculate_power_flow(\n", + " update_data=update_data_columnar,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types={ComponentType.line: [\"loading\"]},\n", + " )\n", + "\n", + "print(\n", + " f\"output_data_selective contains following: \\n\"\n", + " f\"Keys: {output_data_selective.keys()}. \\n\"\n", + " f\"Attributes: {output_data_selective[ComponentType.line].keys()}\"\n", + ")\n", + "print(output_data_selective[ComponentType.line][\"loading\"].__sizeof__() / 1024 / 1024)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6261d8c5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "973a8ef8", + "metadata": {}, + "source": [ + "#### Clear memory" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "a006e388", + "metadata": {}, + "outputs": [], + "source": [ + "del load_profile_row_based\n", + "del output_data\n", + "del output_data_selective" + ] + }, + { + "cell_type": "markdown", + "id": "10693b81", + "metadata": {}, + "source": [ + "# Multiple Timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1de68fdd", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate random load profile oh hourly data\n", + "n_vehicles = 10000\n", + "vehicle_capacity_kwh = 60\n", + "n_vehicles_distributions = 10\n", + "ev_profile = pd.read_csv(\"data/ev_profile.csv\").to_numpy()\n", + "n_ev_profiles = ev_profile.shape[1]\n", + "n_vehicle_each_load_node_distributions = np.random.multinomial(\n", + " n=n_vehicles, pvals=np.ones(n_loads) / n_loads, size=n_vehicles_distributions\n", + ")\n", + "vehicles_profile_indptr = np.zeros((n_vehicles_distributions, n_loads + 1), dtype=int)\n", + "vehicles_profile_indptr[:, 1:] = np.cumsum(\n", + " n_vehicle_each_load_node_distributions, axis=1\n", + ")\n", + "\n", + "vehicles_profile_idx = np.random.randint(0, n_ev_profiles, n_vehicles)\n", + "\n", + "# n_loads = len(input_data[ComponentType.sym_load])\n", + "# load_id = input_data[ComponentType.sym_load][\"id\"]\n", + "# load_p = input_data[ComponentType.sym_load][\"p_specified\"]\n", + "# choose_random_loads = np.random.randint(low=0, high=n_loads, size=10)\n", + "# scaled_random_load_profile = load_p[choose_random_loads] / np.sum(load_p[choose_random_loads], axis=0)\n", + "\n", + "# profile = np.tile(scaled_random_load_profile, (n_scenarios, 1)) * vehicle_capacity_kwh + 1e3 * np.random.randn(n_scenarios, n_loads)\n", + "# dti = dti = pd.date_range(\"2022-01-01\", periods=n_scenarios, freq=\"h\")\n", + "# df_load_profile = pd.DataFrame(profile, columns=load_id, index=dti)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "092a7d8e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6, 5, 3, ..., 6, 6, 4], shape=(20000,))" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_vehicle_each_load_node_distributions.ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fa01e557", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 986, 1018, 1033, 988, 977, 965, 1027, 1019, 1038, 949])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.multinomial(n=n_vehicles, pvals=np.ones(n_ev_profiles) / n_ev_profiles)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3ce181d0", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only length-1 arrays can be converted to Python scalars", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m n_vehicle_to_profile_distributer = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrandom\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmultinomial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_vehicle_each_load_node_distributions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpvals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_ev_profiles\u001b[49m\u001b[43m)\u001b[49m\u001b[43m/\u001b[49m\u001b[43mn_ev_profiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m cumulative_sum = \u001b[32m0\u001b[39m\n\u001b[32m 4\u001b[39m result = np.zeros((n_timestamps, n_vehicles_distributions))\n", + "\u001b[36mFile \u001b[39m\u001b[32mnumpy/random/mtrand.pyx:4271\u001b[39m, in \u001b[36mnumpy.random.mtrand.RandomState.multinomial\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[31mTypeError\u001b[39m: only length-1 arrays can be converted to Python scalars" + ] + } + ], + "source": [ + "n_vehicle_to_profile_distributer = np.random.multinomial(\n", + " n=n_vehicle_each_load_node_distributions.ravel(),\n", + " pvals=np.ones(n_ev_profiles) / n_ev_profiles,\n", + ")\n", + "\n", + "cumulative_sum = 0\n", + "result = np.zeros((n_timestamps, n_vehicles_distributions))\n", + "for i in range(n_vehicles_distributions):\n", + " this_node_n_vehicles_idx = (\n", + " cumulative_sum + n_vehicle_each_load_node_distributions[i]\n", + " )\n", + " result[:, i] = np.sum(\n", + " ev_profile[:, vehicles_profile_idx[cumulative_sum:this_node_n_vehicles_idx]],\n", + " axis=1,\n", + " )\n", + " cumulative_sum = this_node_n_vehicles_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "236f8d95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "668.2586669921875" + ] + }, + "execution_count": 358, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = ev_profile[:, vehicles_profile_idx]\n", + "\n", + "x.nbytes / 1024 / 1024\n", + "# del x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e9e97e7", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only integer scalar arrays can be converted to a scalar index", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[359]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m sum_func = \u001b[38;5;28;01mlambda\u001b[39;00m x: np.sum(ev_profile[:, vehicles_profile_idx[vehicles_profile_indptr[x:x+\u001b[32m1\u001b[39m]]], axis=\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m x = \u001b[43msum_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvehicles_profile_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# vfunc = np.vectorize(squarer)\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# result = vfunc(sum_func)\u001b[39;00m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[359]\u001b[39m\u001b[32m, line 1\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(x)\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m sum_func = \u001b[38;5;28;01mlambda\u001b[39;00m x: np.sum(ev_profile[:, vehicles_profile_idx[\u001b[43mvehicles_profile_indptr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43mx\u001b[49m\u001b[43m+\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m]\u001b[49m]], axis=\u001b[32m1\u001b[39m)\n\u001b[32m 2\u001b[39m x = sum_func(vehicles_profile_idx)\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# vfunc = np.vectorize(squarer)\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# result = vfunc(sum_func)\u001b[39;00m\n", + "\u001b[31mTypeError\u001b[39m: only integer scalar arrays can be converted to a scalar index" + ] + } + ], + "source": [ + "sum_func = lambda x: np.sum(\n", + " ev_profile[:, vehicles_profile_idx[vehicles_profile_indptr[x : x + 1]]], axis=1\n", + ")\n", + "x = sum_func(vehicles_profile_idx)\n", + "# vfunc = np.vectorize(squarer)\n", + "# result = vfunc(sum_func)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "234f85fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 0., 0., ..., 0., 0., 0.],\n", + " [ 0., 0., 0., ..., 0., 0., 0.],\n", + " [ 0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [21., 7., 14., ..., 7., 0., 7.],\n", + " [14., 7., 7., ..., 7., 0., 7.],\n", + " [14., 7., 7., ..., 7., 0., 0.]], shape=(8759, 10000))" + ] + }, + "execution_count": 360, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38439a4b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1616, 828, 407, 1875, 1973, 1885, 1539, 347, 1741, 445])" + ] + }, + "execution_count": 361, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.choice(n_loads, size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4daf47b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe87d253", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'n_vehicle_each_load_node' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[362]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m plt.plot(\u001b[43mn_vehicle_each_load_node\u001b[49m)\n", + "\u001b[31mNameError\u001b[39m: name 'n_vehicle_each_load_node' is not defined" + ] + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "plt.plot(n_vehicle_each_load_node)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d7618f4", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "48146c6c", + "metadata": {}, + "source": [ + "Sample array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a873ead0", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 10))\n", + "loads[\"id\"] = range(10)\n", + "loads[\"status\"] = 1\n", + "loads[\"p_specified\"] = 1000\n", + "loads[\"q_specified\"] = 1000\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1977a53", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "721e9f2b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b1723736", + "metadata": {}, + "outputs": [], + "source": [ + "limit_related_output = {\n", + " ComponentType.line: [\"loading\"],\n", + " ComponentType.node: [\"u_pu\"],\n", + " ComponentType.transformer: [\"loading\"],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "1702d142", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "# Side topics related to numpy\n" + ] + }, + { + "cell_type": "markdown", + "id": "43f623ac", + "metadata": {}, + "source": [ + "## Correct dtype" + ] + }, + { + "cell_type": "markdown", + "id": "77c2b0b5", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "\n", + "Use of initialize_array is recommended. \n", + "However when you have a large dataset and you wish to translate the dtype into the one PGM uses, you can use `initialize_array(dataset_type, component_type, empty_shape, empty=True)` or `dtype = power_grid_meta_data[dataset_type][component_type].dtype` to find the dtype of the row based data. You can use this to change to the appropriate type which is required by PGM.\n", + "\n", + "For dtype of individual attribute which would be required in columnar data creation, you can use `attribute_dtype(data_type, component_type, attribute)`. The missing value can be filled by `attribute_empty_value(data_type, component_type, attribute)` \n", + "\n", + "Also check for contigious-ness. \n", + "\n", + "Check if a type-cast to this dtype creates a copy. Maybe such a copy is redundant." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72bde1c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype([('id', ' copy; else -> view\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "982330f6", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 366, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "x = np.arange(9)\n", + "x\n", + "y = x.reshape(3, 3)\n", + "y\n", + "y.base # .reshape() creates a view\n", + "z = y[[2, 1]]\n", + "z\n", + "z.base is None # advanced indexing creates a copy" + ] + }, + { + "cell_type": "markdown", + "id": "cf1fc55d", + "metadata": {}, + "source": [ + "## Garbage collection for views/copies\n", + "\n", + "### Higher than required memory usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f873008b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of returned array: (50, 2000)\n", + "0.762939453125 MB\n", + "Actual shape of memory: (168, 2000)\n", + "2.5634765625 MB\n" + ] + } + ], + "source": [ + "def calculate_and_give_first_50_loading(model, update_data):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " )\n", + " return output_data[ComponentType.line][\"loading\"][:50]\n", + "\n", + "\n", + "first_50_loading = calculate_and_give_first_50_loading(model, update_data)\n", + "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", + "print_memory_used_by_array(first_50_loading)\n", + "print(f\"Actual shape of memory: {first_50_loading.base.shape}\")\n", + "print_memory_used_by_array(first_50_loading.base)" + ] + }, + { + "cell_type": "markdown", + "id": "16af0e15", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "### Right Way to return values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "576a43a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of returned array: (50, 2000)\n", + "0.762939453125 MB\n" + ] + } + ], + "source": [ + "def calculate_and_give_first_50_loading(model, update_data):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " )\n", + " return output_data[ComponentType.line][\"loading\"][:50].copy()\n", + "\n", + "\n", + "first_50_loading = calculate_and_give_first_50_loading(model, update_data)\n", + "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", + "print_memory_used_by_array(first_50_loading)\n", + "assert first_50_loading.base is None" + ] + }, + { + "cell_type": "markdown", + "id": "aede02e5", + "metadata": {}, + "source": [ + "## Accessing and assigning to PGM arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "1f160355", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 10))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", + "\n", + "def copy_or_view(arr: np.ndarray) -> str:\n", + " return \"copy\" if arr.base is None else \"view\"" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "e7090991", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Single batch all components: `loads[:, 0]` gets a : view\n", + "All batch single components: `loads[0, :]` gets a : view\n", + "Sliced batch all components: `loads[:5, :]` gets a : view\n", + "Multiple batch via advanced indexing all components: `loads[[1,2,3], :]` gets a : copy\n", + "All batch Multiple components via advanced indexing: `loads[:, [1,2,3]]` gets a : view\n" + ] + } + ], + "source": [ + "\n", + "\n", + "print(\"Single batch all components: `loads[:, 0]` gets a :\", copy_or_view(loads[:, 0]))\n", + "print(\"All batch single components: `loads[0, :]` gets a :\", copy_or_view(loads[0, :]))\n", + "print(\"Sliced batch all components: `loads[:5, :]` gets a :\", copy_or_view(loads[:5, :]))\n", + "print(\"Multiple batch via advanced indexing all components: `loads[[1,2,3], :]` gets a :\", copy_or_view(loads[[1,2,3], :])) # Advanced indexing creates a copy\n", + "print(\"All batch Multiple components via advanced indexing: `loads[:, [1,2,3]]` gets a :\", copy_or_view(loads[:, [1,2,3]]))" + ] + }, + { + "cell_type": "markdown", + "id": "639e68c2", + "metadata": {}, + "source": [ + "### Attribute access\n", + "\n", + "Attribute access has similar behavior" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "9cfb9d33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Single batch all components: `loads['p_specified']` gets a : view\n", + "Single batch all components: `loads['p_specified'][:,0]` gets a : view\n", + "Single batch all components: `loads['p_specified'][0, :]` gets a : view\n", + "Sliced batch all components: `loads['p_specified'][:5, :]` gets a : view\n", + "Single batch all components: `loads['p_specified'][[1, 2, 3], :]` gets a : copy\n", + "Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a : view\n" + ] + } + ], + "source": [ + "print(\"Single batch all components: `loads['p_specified']` gets a :\", copy_or_view(loads[\"p_specified\"]))\n", + "print(\"Single batch all components: `loads['p_specified'][:,0]` gets a :\", copy_or_view(loads[\"p_specified\"][:, 0]))\n", + "print(\"Single batch all components: `loads['p_specified'][0, :]` gets a :\", copy_or_view(loads[\"p_specified\"][0, :]))\n", + "print(\"Sliced batch all components: `loads['p_specified'][:5, :]` gets a :\", copy_or_view(loads[\"p_specified\"][:5, :]))\n", + "print(\"Single batch all components: `loads['p_specified'][[1, 2, 3], :]` gets a :\", copy_or_view(loads[\"p_specified\"][[1, 2, 3], :]))\n", + "print(\"Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[\"p_specified\"][:, [1, 2, 3]]))" + ] + }, + { + "cell_type": "markdown", + "id": "418e9f07", + "metadata": {}, + "source": [ + "Multi field indexing" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "48d66913", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multiple attributes: `loads[['p_specified', 'q_specified']][:, [1, 2, 3]]` gets a : view\n" + ] + } + ], + "source": [ + "print(\"Multiple attributes: `loads[['p_specified', 'q_specified']][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[[\"p_specified\", \"q_specified\"]]))" + ] + }, + { + "cell_type": "markdown", + "id": "636da8aa", + "metadata": {}, + "source": [ + "A common source of confusion is when dealing with numpy arrays view and copy variables look the same. \n", + "\n", + "View related variables modify the original array whereas a copy related variable does not modify the original array" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "061825ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1.00000e+02 1.00000e+02 1.00000e+02]\n", + " [1.23456e+05 1.23456e+05 1.23456e+05]\n", + " [1.23456e+05 1.23456e+05 1.23456e+05]\n", + " ...\n", + " [1.00000e+02 1.00000e+02 1.00000e+02]\n", + " [1.00000e+02 1.00000e+02 1.00000e+02]\n", + " [1.00000e+02 1.00000e+02 1.00000e+02]]\n" + ] + } + ], + "source": [ + "\n", + "y = loads[\"p_specified\"][:, [7, 8, 9]]\n", + "loads[\"p_specified\"][:, [7, 8, 9]] = 123456\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "id": "7bd5c42a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[100. 100. 100. 100. 100. 100. 100. 100. 100. 100.]\n", + " [100. 100. 100. 100. 100. 100. 100. 100. 100. 100.]\n", + " [100. 100. 100. 100. 100. 100. 100. 100. 100. 100.]]\n" + ] + } + ], + "source": [ + "x = loads[\"p_specified\"][[1, 2, 3], :]\n", + "loads[\"p_specified\"][[1, 2, 3], :] = 123456\n", + "print(x)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ad771a0", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "### Assignment via tuples" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "5cbd039b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[(1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])],\n", + " [(1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])]],\n", + " dtype={'names': ['id', 'node', 'status', 'type', 'p_specified', 'q_specified'], 'formats': ['\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize(grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e2ca4e4", + "metadata": {}, + "outputs": [], + "source": [ + "# Lines with most most number of dependent nodes\n", + "contingency_count = 5\n", + "\n", + "all_pairs = []\n", + "for node in grid.node.id:\n", + " paths = grid.graphs.active_graph.get_all_paths(node, grid.source.node[0])\n", + " for path in paths:\n", + " all_pairs += [(path[idx + 1], path[idx]) for idx in range(len(path) - 1)]\n", + "\n", + "\n", + "counts = Counter(all_pairs)\n", + "top_5_branches = counts.most_common(5)\n", + "from_nodes = [i[0][0] for i in top_5_branches]\n", + "to_nodes = [i[0][1] for i in top_5_branches]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff56f829", + "metadata": {}, + "outputs": [], + "source": [ + "# grid.node.node_type[0] = 0\n", + "# grid.node.filter(5)\n", + "line_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3f1ac0b", + "metadata": {}, + "outputs": [], + "source": [ + "combos_contingencies = np.eye(contingency_count, dtype=np.uint8)\n", + "combos_contingencies_timestamps = np.tile(combos_contingencies, (1, n_timestamps))\n", + "combos_contingencies_timestamps_reshaped = combos_contingencies_timestamps.reshape(\n", + " -1, contingency_count\n", + ")\n", + "# Worst case for PGM\n", + "# combos_contingencies_timestamps = combos_contingencies_timestamps.reshape(contingency_count, n_timestamps*contingency_count)\n", + "\n", + "line_update = initialize_array(\n", + " DatasetType.update,\n", + " ComponentType.line,\n", + " (contingency_count * n_timestamps, contingency_count),\n", + ")\n", + "line_update[\"id\"] = line_id\n", + "line_update[\"from_status\"] = 1 - combos_contingencies_timestamps_reshaped\n", + "line_update[\"to_status\"] = 1 - combos_contingencies_timestamps_reshaped" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b43456d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation with contingencies and load profiles is 1.154605 s\n", + "Execution time for Batch Calculation with contingencies and load profiles all output is 1.308957 s\n" + ] + } + ], + "source": [ + "load_profile_combined = initialize_array(\n", + " DatasetType.update,\n", + " ComponentType.sym_load,\n", + " (contingency_count * df_load_profile.shape[0], df_load_profile.shape[1]),\n", + ")\n", + "# load_profile[\"id\"] = df_load_profile.columns.to_numpy()\n", + "load_profile_combined[\"p_specified\"] = np.tile(\n", + " load_profile_columnar, (contingency_count, 1)\n", + ")\n", + "load_profile_combined[\"q_specified\"] = 0.0\n", + "\n", + "update_data_combined = {\n", + " ComponentType.line: line_update,\n", + " ComponentType.sym_load: load_profile_combined,\n", + "}\n", + "\n", + "with Timer(\"Batch Calculation with contingencies and load profiles\"):\n", + " output_data_combined = model.calculate_power_flow(\n", + " update_data=update_data_combined,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types={ComponentType.line: [\"loading\"]},\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 419, + "id": "06e5d27d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 419, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(output_data_combined[ComponentType.line][\"loading\"][:, 0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "power-grid-model-workshop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pgm-advanced-usage-workshop/ev_load_profile_generator.py b/pgm-advanced-usage-workshop/ev_load_profile_generator.py new file mode 100644 index 0000000..4bca171 --- /dev/null +++ b/pgm-advanced-usage-workshop/ev_load_profile_generator.py @@ -0,0 +1,77 @@ + +# EV load profile generator +import numpy as np + + +def generate_ev_load_profiles(n_load_nodes: int, n_timesteps: int, n_vehicles: int = 1000, + avg_kwh_per_vehicle: float = 50.0, charging_power_kw: float = 7.0, + arrival_window: tuple = (18, 23), seed: int | None = None): + """Generate synthetic EV charging load profiles. + Parameters: + - n_load_nodes: number of load nodes (distribution of vehicles across nodes) + - n_timesteps: number of time steps to generate (e.g., hours) + - n_vehicles: total number of vehicles in the fleet + - avg_kwh_per_vehicle: average energy required per vehicle during the charging event + - charging_power_kw: charging power per vehicle (kW) -> used to spread charging over timesteps + - arrival_window: tuple (start_hour, end_hour) where most arrivals occur (inclusive start, exclusive end) + - seed: random seed for reproducibility + Returns a numpy array of shape (n_timesteps, n_load_nodes) representing kW loads per node. + The model: randomly distribute vehicles across nodes, sample arrival times (hour of day) within window, + sample dwell/charging duration from a simple distribution, and accumulate charging power into timesteps. + """ + import numpy as _np + if seed is not None: + _np.random.seed(seed) + + # Distribute vehicles to nodes (multinomial) + p = _np.ones(n_load_nodes) / n_load_nodes + vehicles_per_node = _np.random.multinomial(n_vehicles, p) + + # Prepare output array in kW (timesteps x nodes) + profile = _np.zeros((n_timesteps, n_load_nodes), dtype=float) + + # For each node, generate arrival times and durations + for node_idx in range(n_load_nodes): + nv = vehicles_per_node[node_idx] + if nv == 0: + continue + + # arrivals: sample hours within arrival_window, but allow spread across whole timeseries days + # compute number of days covered by timesteps (assume hourly) + steps_per_day = 24 + n_days = int(_np.ceil(n_timesteps / steps_per_day)) + + # sample vehicle arrival day and hour for each vehicle + arrival_days = _np.random.randint(0, n_days, size=nv) + arrival_hours = _np.random.randint(arrival_window[0], arrival_window[1], size=nv) + arrival_steps = arrival_days * steps_per_day + arrival_hours + + # sample required kWh per vehicle around avg (lognormal-ish to avoid negatives) + kwh = _np.random.normal(loc=avg_kwh_per_vehicle, scale=avg_kwh_per_vehicle * 0.25, size=nv) + kwh = _np.clip(kwh, 1.0, None) + + # compute charging duration in hours (kwh / charging_power) and convert to integer timesteps + duration_hours = _np.ceil(kwh / charging_power_kw).astype(int) + + # accumulate charging power into profile (simple rectangular charging) + for a_step, dur in zip(arrival_steps, duration_hours): + if a_step >= n_timesteps: + continue + end = min(n_timesteps, a_step + dur) + profile[a_step:end, node_idx] += charging_power_kw + + return profile + +n_profiles = 10 + +n_timesteps = 24 * 365 # one year of hourly data +ev_profile = generate_ev_load_profiles(n_load_nodes=n_profiles, n_timesteps=n_timesteps, n_vehicles=5000, + avg_kwh_per_vehicle=40.0, charging_power_kw=7.0, + arrival_window=(17, 22), seed=42) + + +print('Generated EV profiles:') +print(' ev_profile shape (timesteps, nodes):', ev_profile.shape) + +np.savetxt("data/ev_profile.csv", ev_profile, delimiter=",") + From 7230f24f4711107088f350a87cd62b8a3526d358 Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Sat, 15 Nov 2025 03:08:41 +0100 Subject: [PATCH 2/8] add exercise Signed-off-by: Nitish Bharambe --- .../Advanced Workshop.ipynb | 49 +++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb index b4d0797..377f814 100644 --- a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb +++ b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb @@ -2066,6 +2066,55 @@ "source": [ "plt.plot(output_data_combined[ComponentType.line][\"loading\"][:, 0])" ] + }, + { + "cell_type": "markdown", + "id": "d7cfc8d4", + "metadata": {}, + "source": [ + "# Simulation design exercise" + ] + }, + { + "cell_type": "markdown", + "id": "98060838", + "metadata": {}, + "source": [ + "Question to participants: \n", + "* Can you give an example on how you can set up a network reconfiguration study based on the example above?\n", + "* What graph related operations are needed?\n", + " * Is it possible to do those via PGM-DS?\n", + "* What result attributes are to be checked? Do they need to be aggregated?" + ] + }, + { + "cell_type": "markdown", + "id": "1d0d4c20", + "metadata": {}, + "source": [ + "Question to participants: \n", + "* Can you give an example of how we can roughly calculate flexibility envelopes (based on technical limits and not economic ones) \n", + "* What result attributes are to be checked? Do they need to be aggregated?" + ] + }, + { + "cell_type": "markdown", + "id": "23b4016b", + "metadata": {}, + "source": [ + "Question to participants: \n", + "* Can you give an example of how we can implement a control related action via PGM? \n", + "* Draw a block diagram of the control?\n", + " * Is this scheme efficient?\n", + " * How do we create batches for such simulation. \n", + "* What result attributes are to be checked? Do they need to be aggregated?" + ] + }, + { + "cell_type": "markdown", + "id": "a76f4122", + "metadata": {}, + "source": [] } ], "metadata": { From c29b7b2b48ba472dfb4b1987be49ccfe7e006973 Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Sat, 15 Nov 2025 03:27:17 +0100 Subject: [PATCH 3/8] add remaining content Signed-off-by: Nitish Bharambe --- .../Advanced Workshop.ipynb | 398 +++++++++--------- 1 file changed, 203 insertions(+), 195 deletions(-) diff --git a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb index 377f814..44b4573 100644 --- a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb +++ b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 17, "id": "fbc50a6d", "metadata": {}, "outputs": [], @@ -85,14 +85,14 @@ "id": "50065790", "metadata": {}, "source": [ - "# Utilities\n", + "# Utilities (Ignore this section)\n", "\n", "Let's define a timer class to easily benchmark the calculations:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "760a38b1", "metadata": {}, "outputs": [], @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "c1db379a", "metadata": {}, "outputs": [], @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "403f4748", "metadata": {}, "outputs": [], @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "6b36e140", "metadata": {}, "outputs": [], @@ -321,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 22, "id": "a61540cd", "metadata": {}, "outputs": [ @@ -329,7 +329,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Add Operation is 0.008699 s\n" + "Execution time for Add Operation is 0.004897 s\n" ] } ], @@ -343,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 23, "id": "bde7c586", "metadata": {}, "outputs": [ @@ -352,9 +352,9 @@ "output_type": "stream", "text": [ "Memory usage for Large Array Creation:\n", - " Start: 188.52 MB\n", - " End: 227.30 MB\n", - " Net change: +38.78 MB\n", + " Start: 294.99 MB\n", + " End: 294.99 MB\n", + " Net change: +0.00 MB\n", " Peak increase: +0.00 MB\n" ] } @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 24, "id": "0115dac2", "metadata": {}, "outputs": [ @@ -377,11 +377,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Matrix Operations is 0.199182 s\n", + "Execution time for Matrix Operations is 0.255303 s\n", "Memory usage for Matrix Operations:\n", - " Start: 227.30 MB\n", - " End: 240.82 MB\n", - " Net change: +13.52 MB\n", + " Start: 294.99 MB\n", + " End: 295.18 MB\n", + " Net change: +0.18 MB\n", " Peak increase: +0.00 MB\n", "--- End of Matrix Operations performance report ---\n", "\n" @@ -405,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "id": "05d3496c", "metadata": {}, "outputs": [ @@ -414,10 +414,10 @@ "output_type": "stream", "text": [ "Memory usage for Transient Peak Example:\n", - " Start: 240.82 MB\n", - " End: 248.26 MB\n", - " Net change: +7.44 MB\n", - " Peak increase: +60.81 MB\n" + " Start: 295.18 MB\n", + " End: 302.68 MB\n", + " Net change: +7.50 MB\n", + " Peak increase: +68.45 MB\n" ] } ], @@ -448,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "id": "7dbbd755", "metadata": {}, "outputs": [ @@ -456,14 +456,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Polled Transient Peak is 0.177470 s\n", + "Execution time for Polled Transient Peak is 0.229323 s\n", "Memory usage for Polled Transient Peak:\n", - " Start: 248.26 MB\n", - " End: 248.58 MB\n", - " Net change: +0.32 MB\n", - " Peak increase: +61.04 MB\n", + " Start: 302.68 MB\n", + " End: 302.68 MB\n", + " Net change: +0.00 MB\n", + " Peak increase: +60.95 MB\n", " name start_time end_time duration_s start_mem_mb end_mem_mb net_mem_change_mb peak_mem_mb peak_increase_mb\n", - "Polled Transient Peak 39863.123998 39863.301627 0.177629 248.257812 248.578125 0.320312 309.296875 61.039062\n" + "Polled Transient Peak 48749.324769 48749.554265 0.229496 302.679688 302.679688 0.0 363.625 60.945312\n" ] } ], @@ -485,13 +485,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "id": "5d41b75a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -533,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "id": "c4db246d", "metadata": {}, "outputs": [ @@ -541,7 +541,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current memory usage: 251.70 MB\n", + "Current memory usage: 302.68 MB\n", "0.00766754150390625 MB\n", "0.00016021728515625 MB\n", "0.00766754150390625 MB\n", @@ -589,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "id": "8a2ba5ec", "metadata": {}, "outputs": [ @@ -614,7 +614,7 @@ " ComponentType.sym_load,\n", " ]:\n", " # Use pandas to read CSV data\n", - " df = pd.read_csv(f\"data/{component.value}.csv\")\n", + " df = pd.read_csv(f\"../data/{component.value}.csv\")\n", "\n", " # Initialize array\n", " input_data[component] = initialize_array(DatasetType.input, component, len(df))\n", @@ -634,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "id": "c56c97ce", "metadata": {}, "outputs": [], @@ -652,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "c5677f60", "metadata": {}, "outputs": [], @@ -719,7 +719,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson row based data is 0.160462 s\n", + "Execution time for Batch Calculation using Newton-Raphson row based data is 0.202111 s\n", "56.412353515625\n" ] } @@ -744,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "id": "9c52f4f6", "metadata": {}, "outputs": [ @@ -781,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 35, "id": "7be73996", "metadata": {}, "outputs": [ @@ -789,7 +789,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.155125 s\n" + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.181983 s\n" ] } ], @@ -813,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 36, "id": "2800a03b", "metadata": {}, "outputs": [ @@ -821,7 +821,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.136163 s\n", + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.140543 s\n", "output_data_selective contains following: \n", "Keys: dict_keys([]). \n", "Attributes: dict_keys(['loading'])\n", @@ -863,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 37, "id": "a006e388", "metadata": {}, "outputs": [], @@ -883,10 +883,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "1de68fdd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'data/ev_profile.csv'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[38]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 3\u001b[39m vehicle_capacity_kwh = \u001b[32m60\u001b[39m\n\u001b[32m 4\u001b[39m n_vehicles_distributions = \u001b[32m10\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m ev_profile = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdata/ev_profile.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m.to_numpy()\n\u001b[32m 6\u001b[39m n_ev_profiles = ev_profile.shape[\u001b[32m1\u001b[39m]\n\u001b[32m 7\u001b[39m n_vehicle_each_load_node_distributions = np.random.multinomial(\n\u001b[32m 8\u001b[39m n=n_vehicles, pvals=np.ones(n_loads) / n_loads, size=n_vehicles_distributions\n\u001b[32m 9\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 1014\u001b[39m dialect,\n\u001b[32m 1015\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 1022\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 1023\u001b[39m )\n\u001b[32m 1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 623\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1617\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1878\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1879\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1881\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1882\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1883\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1884\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1885\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1887\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1888\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 869\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 871\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 872\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 882\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n", + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'data/ev_profile.csv'" + ] + } + ], "source": [ "# Generate random load profile oh hourly data\n", "n_vehicles = 10000\n", @@ -917,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "092a7d8e", "metadata": {}, "outputs": [ @@ -938,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "fa01e557", "metadata": {}, "outputs": [ @@ -959,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "3ce181d0", "metadata": {}, "outputs": [ @@ -1179,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "b1723736", "metadata": {}, "outputs": [], @@ -1255,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "3b5bf1d0", "metadata": {}, "outputs": [ @@ -1284,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "e18d46cf", "metadata": {}, "outputs": [ @@ -1467,7 +1484,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": null, "id": "1f160355", "metadata": {}, "outputs": [], @@ -1481,7 +1498,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": null, "id": "e7090991", "metadata": {}, "outputs": [ @@ -1519,7 +1536,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": null, "id": "9cfb9d33", "metadata": {}, "outputs": [ @@ -1555,7 +1572,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": null, "id": "48d66913", "metadata": {}, "outputs": [ @@ -1583,7 +1600,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": null, "id": "061825ee", "metadata": {}, "outputs": [ @@ -1610,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": null, "id": "7bd5c42a", "metadata": {}, "outputs": [ @@ -1644,7 +1661,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": null, "id": "5cbd039b", "metadata": {}, "outputs": [ @@ -1700,19 +1717,7 @@ } }, "source": [ - "## Large Timeseries" - ] - }, - { - "cell_type": "markdown", - "id": "18b241b3", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "source": [ - "Use columnar data for P and Q specified" + "For Large Timeseries, use columnar data for P and Q specified" ] }, { @@ -1724,21 +1729,7 @@ } }, "source": [ - "## Contingency analysis\n", - "\n", - "For unequal sets use linear data + pointers" - ] - }, - { - "cell_type": "markdown", - "id": "346e8533", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "source": [ - "# Achieving control operations" + "For unequal update sets use linear data + pointers. Check sparse dataset representation." ] }, { @@ -1767,94 +1758,93 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "831c92b6", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ed78879", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d994194", - "metadata": { - "vscode": { - "languageId": "markdown" + "execution_count": 40, + "id": "d4b6742c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Validation of full batch data is 0.249144 s\n", + "Execution time for Validation of partial batch data is 0.009422 s\n" + ] } - }, - "outputs": [], - "source": [] + ], + "source": [ + "with Timer(\"Validation of full batch data\"):\n", + " assert_valid_batch_data(input_data=input_data, update_data=update_data, calculation_type=CalculationType.power_flow)\n", + "\n", + "with Timer(\"Validation of partial batch data\"):\n", + " assert_valid_batch_data(input_data=input_data, update_data={k: v[:5, :] for k, v in update_data.items()}, calculation_type=CalculationType.power_flow)" + ] }, { - "cell_type": "code", - "execution_count": null, - "id": "ac459edb", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "outputs": [], - "source": [] + "cell_type": "markdown", + "id": "788d8c8e", + "metadata": {}, + "source": [ + "# PGM DS library" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "07dd1858", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, + "execution_count": 42, + "id": "e8408fb2", + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "core_interface = PowerGridModelInterface(input_data=input_data)\n", + "grid = core_interface.create_grid_from_input_data()" + ] }, { - "cell_type": "code", - "execution_count": null, - "id": "683b86a0", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "outputs": [], - "source": [] + "cell_type": "markdown", + "id": "cc487324", + "metadata": {}, + "source": [ + "## Visualization of grid" + ] }, { "cell_type": "code", "execution_count": null, - "id": "5b5df1c5", + "id": "61c1c3d0", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + " \n", + " " + ], "text/plain": [ - "np.int32(0)" + "" ] }, - "execution_count": 462, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], - "source": [] + "source": [ + "visualize(grid)" + ] + }, + { + "cell_type": "markdown", + "id": "5a2b633f", + "metadata": {}, + "source": [ + "## Graph operations" + ] }, { "cell_type": "code", @@ -1875,70 +1865,75 @@ } ], "source": [ - "core_interface = PowerGridModelInterface(input_data=input_data)\n", - "grid = core_interface.create_grid_from_input_data()\n", + "\n", "grid.node.node_type[0] = NodeType.SUBSTATION_NODE\n", "\n", + "# Downstream nodes from node 1 with the substation node as reference\n", "grid.get_downstream_nodes(1)\n", "\n", + "# In case of multiple substations, find nearest one\n", "grid.get_nearest_substation_node(10)\n", - "# print(grid.graphs.active_graph.get_connected(0))\n", "\n", "# Find all paths between node 1 and node 10\n", "print(grid.graphs.active_graph.get_all_paths(1, 10))\n", "\n", + "# Find shortest path between node 1 and node 10\n", "print(grid.graphs.active_graph.get_shortest_path(1, 10))\n", "\n", + "# All components in this graph\n", "print(len(grid.graphs.active_graph.get_components()))\n", "\n", + "# All components connected to node 1\n", "print(len(grid.graphs.active_graph.get_connected(node_id=1)))\n", "\n", + "# All components connected to node 0, ignoring nodes 1 and 2\n", "print(len(grid.graphs.active_graph.get_connected(node_id=0, nodes_to_ignore=[1, 2])))\n" ] }, { "cell_type": "markdown", - "id": "cc487324", + "id": "a1fb9b25", "metadata": {}, "source": [ - "# Visualize" + "## Array operations" ] }, { "cell_type": "code", - "execution_count": null, - "id": "61c1c3d0", + "execution_count": 47, + "id": "3832ce10", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - " \n", - " " - ], "text/plain": [ - "" + "LineArray([[(2001, 0, 1, 1, 1, -2147483648, -2147483648, False, 0.10097627, 0.10430379, 3.24852006e-05, 0.09886571, 500.)\n", + " (2002, 1, 2, 1, 1, -2147483648, -2147483648, False, 0.0984731 , 0.10291788, 3.14336565e-05, 0.10919854, 500.)\n", + " (2003, 2, 3, 1, 1, -2147483648, -2147483648, False, 0.10927326, 0.09766883, 3.36881679e-05, 0.09503319, 500.)]]... + 2 more rows)" ] }, + "execution_count": 47, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "visualize(grid)" + "grid.line.filter(from_node=[0, 1, 2, 3, 4], to_node=[0, 1, 2, 3, 4, 5, 6, 7], mode_=\"AND\")" + ] + }, + { + "cell_type": "markdown", + "id": "569a7688", + "metadata": {}, + "source": [ + "## Example of graph usage\n", + "\n", + "Find top 10 ranked paths which most nodes depend on" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "0e2ca4e4", "metadata": {}, "outputs": [], @@ -1954,46 +1949,59 @@ "\n", "\n", "counts = Counter(all_pairs)\n", - "top_5_branches = counts.most_common(5)\n", - "from_nodes = [i[0][0] for i in top_5_branches]\n", - "to_nodes = [i[0][1] for i in top_5_branches]\n" + "top_5_branches_from_to_counts = counts.most_common(contingency_count)\n", + "from_nodes = [i[0][0] for i in top_5_branches_from_to_counts]\n", + "to_nodes = [i[0][1] for i in top_5_branches_from_to_counts]\n", + "\n", + "top_5_branches_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "ff56f829", + "cell_type": "markdown", + "id": "32149652", "metadata": {}, - "outputs": [], "source": [ - "# grid.node.node_type[0] = 0\n", - "# grid.node.filter(5)\n", - "line_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id" + "### Design batches for simulation in PGM" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "e3f1ac0b", "metadata": {}, "outputs": [], "source": [ "combos_contingencies = np.eye(contingency_count, dtype=np.uint8)\n", "combos_contingencies_timestamps = np.tile(combos_contingencies, (1, n_timestamps))\n", - "combos_contingencies_timestamps_reshaped = combos_contingencies_timestamps.reshape(\n", - " -1, contingency_count\n", - ")\n", - "# Worst case for PGM\n", - "# combos_contingencies_timestamps = combos_contingencies_timestamps.reshape(contingency_count, n_timestamps*contingency_count)\n", "\n", "line_update = initialize_array(\n", " DatasetType.update,\n", " ComponentType.line,\n", " (contingency_count * n_timestamps, contingency_count),\n", ")\n", - "line_update[\"id\"] = line_id\n", - "line_update[\"from_status\"] = 1 - combos_contingencies_timestamps_reshaped\n", - "line_update[\"to_status\"] = 1 - combos_contingencies_timestamps_reshaped" + "line_update[\"id\"] = top_5_branches_id\n", + "line_update[\"from_status\"] = 1 - combos_contingencies_timestamps.reshape(-1, contingency_count)\n", + "line_update[\"to_status\"] = 1 - combos_contingencies_timestamps.reshape(-1, contingency_count)" + ] + }, + { + "cell_type": "markdown", + "id": "4312000d", + "metadata": {}, + "source": [ + "Question to participants: What if contingencies are designed this way?\n", + "\n", + "```\n", + "combos_contingencies_timestamps = combos_contingencies_timestamps.reshape(contingency_count, n_timestamps*contingency_count)\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "14b7589b", + "metadata": {}, + "source": [ + "### Timeseries data from earlier cell" ] }, { @@ -2133,7 +2141,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.9" + "version": "3.14.0" } }, "nbformat": 4, From 75951ff63d5088d7e7fa963f4b0fb8230d1e4eb7 Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Sat, 15 Nov 2025 01:26:56 +0100 Subject: [PATCH 4/8] refine a bit --- .../Advanced Workshop.ipynb | 2236 ++++++++--------- pgm-advanced-usage-workshop/utils.py | 185 ++ .../utils_notebook.ipynb | 241 ++ 3 files changed, 1429 insertions(+), 1233 deletions(-) create mode 100644 pgm-advanced-usage-workshop/utils.py create mode 100644 pgm-advanced-usage-workshop/utils_notebook.ipynb diff --git a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb index 44b4573..62b71d8 100644 --- a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb +++ b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb @@ -8,25 +8,23 @@ "source": [ "# Introduction\n", "\n", - "In this assignment you will be given a series of tasks about using the library `power-grid-model`. The tasks include:\n", - "\n", - "1. Load input\n", - "2. Data validation for large datasets\n", - "3. Columnar data memory usage\n", - "4. Selective output\n", - "5. Numpy tips\n", - " 1. Views vs copies\n", - " 2. Other tips\n", - " 3. PGM related recommendations\n", - "6. Graph operations \n", - "7. Topology caching\n", - "6. N 1 Scenario-Batch-Calculation\n", - "\n", - "The input data are CSV files in the `data/` folder:\n", - "* `node.csv`\n", - "* `line.csv`\n", - "* `source.csv`\n", - "* `sym_load.csv`\n" + "In this assignment you will be given a series of tasks about using the library `power-grid-model`. The agenda includes:\n", + "\n", + "* Load input (From Power flow assignment)\n", + "* Numpy tips and PGM related recommendations\n", + "* Types of Dataset\n", + "* Miscellaneous PGM features\n", + "* Selective output\n", + "* Complex Simulations\n", + " * Multiple Timeseries\n", + " * Side topic: PGM DS library\n", + " * Contingency Analysis\n", + " * Topology caching\n", + " * Simulation design exercise\n", + " * Dynamic operating Envelope\n", + " * Simulation design exercise\n", + " * Additional content\n", + " * Simulation design exercise" ] }, { @@ -42,12 +40,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "id": "fbc50a6d", "metadata": {}, "outputs": [], "source": [ - "import time\n", "from typing import Dict\n", "\n", "import numpy as np\n", @@ -72,876 +69,727 @@ "from power_grid_model_ds.enums import NodeType\n", "from power_grid_model_ds.visualizer import visualize\n", "\n", - "# For memory monitoring\n", + "from utils import Timer\n", "import psutil\n", - "import gc\n", "import os\n", "from collections import Counter" ] }, { - "attachments": {}, + "cell_type": "code", + "execution_count": 2, + "id": "687985dc", + "metadata": {}, + "outputs": [], + "source": [ + "np.set_printoptions(precision=3, linewidth=100)" + ] + }, + { "cell_type": "markdown", - "id": "50065790", + "id": "bab4fd44", "metadata": {}, "source": [ - "# Utilities (Ignore this section)\n", + "# Topology Data and Timeseries load profiles \n", "\n", - "Let's define a timer class to easily benchmark the calculations:" + "(Reused from Power flow assignment)" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "760a38b1", + "execution_count": 3, + "id": "bb462396", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ComponentType.node: 2001\n", + "ComponentType.line: 2000\n", + "ComponentType.source: 1\n", + "ComponentType.sym_load: 2000\n" + ] + } + ], "source": [ - "class Timer:\n", - " def __init__(self, name: str):\n", - " self.name = name\n", - " self.start = None\n", + "def load_input_data() -> dict[str, np.ndarray]:\n", + " input_data = {}\n", + " for component in [\n", + " ComponentType.node,\n", + " ComponentType.line,\n", + " ComponentType.source,\n", + " ComponentType.sym_load,\n", + " ]:\n", + " # Use pandas to read CSV data\n", + " df = pd.read_csv(f\"../data/{component.value}.csv\")\n", + "\n", + " # Initialize array\n", + " input_data[component] = initialize_array(DatasetType.input, component, len(df))\n", + "\n", + " # Fill the attributes\n", + " for attr, values in df.items():\n", + " input_data[component][attr] = values\n", + "\n", + " # Print some debug info\n", + " print(f\"{component:9s}: {len(input_data[component]):4d}\")\n", + "\n", + " return input_data\n", "\n", - " def __enter__(self):\n", - " self.start = time.perf_counter()\n", "\n", - " def __exit__(self, *args):\n", - " print(\n", - " f\"Execution time for {self.name} is {(time.perf_counter() - self.start):0.6f} s\"\n", - " )" + "input_data = load_input_data()\n" ] }, { - "cell_type": "markdown", - "id": "2d492557", + "cell_type": "code", + "execution_count": 4, + "id": "cf581717", "metadata": {}, + "outputs": [], "source": [ - "Now let's create a memory usage monitor to track memory consumption during calculations:" + "model = PowerGridModel(input_data=input_data)" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "c1db379a", + "execution_count": 5, + "id": "98e89607", "metadata": {}, "outputs": [], "source": [ - "import psutil\n", - "import gc\n", - "import os\n", - "\n", - "\n", - "class MemoryMonitor:\n", - " def __init__(self, name: str):\n", - " self.name = name\n", - " self.process = psutil.Process(os.getpid())\n", - " self.start_memory = None\n", - " self.peak_memory = None\n", - "\n", - " def __enter__(self):\n", - " # Force garbage collection to get a clean starting point\n", - " gc.collect()\n", - " # Get initial memory usage\n", - " self.start_memory = (\n", - " self.process.memory_info().rss / 1024 / 1024\n", - " ) # Convert to MB\n", - " self.peak_memory = self.start_memory\n", - " return self\n", - "\n", - " def __exit__(self, *args):\n", - " # Force garbage collection before final measurement\n", - " gc.collect()\n", - " # Get final memory usage\n", - " end_memory = self.process.memory_info().rss / 1024 / 1024 # Convert to MB\n", - " memory_diff = end_memory - self.start_memory\n", - " peak_diff = self.peak_memory - self.start_memory\n", - "\n", - " print(f\"Memory usage for {self.name}:\")\n", - " print(f\" Start: {self.start_memory:.2f} MB\")\n", - " print(f\" End: {end_memory:.2f} MB\")\n", - " print(f\" Net change: {memory_diff:+.2f} MB\")\n", - " print(f\" Peak increase: {peak_diff:+.2f} MB\")\n", - "\n", - " def update_peak(self):\n", - " \"\"\"Manually update peak memory usage during execution\"\"\"\n", - " current_memory = self.process.memory_info().rss / 1024 / 1024\n", - " if current_memory > self.peak_memory:\n", - " self.peak_memory = current_memory" + "n_timestamps = 24 * 7" ] }, { - "cell_type": "markdown", - "id": "2fac670b", + "cell_type": "code", + "execution_count": 6, + "id": "b10f3052", "metadata": {}, + "outputs": [], "source": [ - "You can also combine both Timer and MemoryMonitor for comprehensive performance analysis:" + "# Generate random load profile oh hourly data\n", + "dti = pd.date_range(\"2022-01-01\", periods=n_timestamps, freq=\"h\")\n", + "n_loads = len(input_data[ComponentType.sym_load])\n", + "load_id = input_data[ComponentType.sym_load][\"id\"]\n", + "load_p = input_data[ComponentType.sym_load][\"p_specified\"]\n", + "profile = np.tile(load_p, (n_timestamps, 1)) + 5e5 * np.random.randn(\n", + " n_timestamps, n_loads\n", + ")\n", + "df_load_profile = pd.DataFrame(profile, columns=load_id, index=dti)" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "403f4748", + "execution_count": 7, + "id": "3daad8a8", "metadata": {}, "outputs": [], "source": [ - "class PerformanceMonitor:\n", - " \"\"\"Combined timer and memory monitor for comprehensive performance analysis\"\"\"\n", - "\n", - " def __init__(self, name: str):\n", - " self.name = name\n", - " self.timer = Timer(name)\n", - " self.memory_monitor = MemoryMonitor(name)\n", + "# Initialize an empty load profile\n", + "load_profile_row_based = initialize_array(\n", + " DatasetType.update, ComponentType.sym_load, df_load_profile.shape\n", + ")\n", "\n", - " def __enter__(self):\n", - " self.timer.__enter__()\n", - " self.memory_monitor.__enter__()\n", - " return self\n", + "load_profile_row_based[\"id\"] = df_load_profile.columns.to_numpy()\n", + "load_profile_row_based[\"p_specified\"] = df_load_profile.to_numpy()\n", + "load_profile_row_based[\"q_specified\"] = 0.0\n", "\n", - " def __exit__(self, *args):\n", - " self.timer.__exit__(*args)\n", - " self.memory_monitor.__exit__(*args)\n", - " print(f\"--- End of {self.name} performance report ---\\n\")" + "# Construct the update data\n", + "update_data_row_based = {ComponentType.sym_load: load_profile_row_based}\n" ] }, { - "cell_type": "code", - "execution_count": 21, - "id": "6b36e140", + "cell_type": "markdown", + "id": "588c60cd", "metadata": {}, - "outputs": [], "source": [ - "class PerfLogger:\n", - " \"\"\"Simple in-memory performance logger that stores records in a pandas DataFrame.\"\"\"\n", - "\n", - " def __init__(self):\n", - " self.records = []\n", - "\n", - " def log(\n", - " self,\n", - " name: str,\n", - " start_time: float,\n", - " end_time: float,\n", - " start_mem: float,\n", - " end_mem: float,\n", - " peak_mem: float,\n", - " ):\n", - " self.records.append(\n", - " {\n", - " \"name\": name,\n", - " \"start_time\": start_time,\n", - " \"end_time\": end_time,\n", - " \"duration_s\": end_time - start_time,\n", - " \"start_mem_mb\": start_mem,\n", - " \"end_mem_mb\": end_mem,\n", - " \"net_mem_change_mb\": end_mem - start_mem,\n", - " \"peak_mem_mb\": peak_mem,\n", - " \"peak_increase_mb\": peak_mem - start_mem,\n", - " }\n", - " )\n", - "\n", - " def to_dataframe(self) -> pd.DataFrame:\n", - " return pd.DataFrame(self.records)\n", - "\n", - "\n", - "class LoggedPerformanceMonitor(PerformanceMonitor):\n", - " \"\"\"A PerformanceMonitor that logs results into a PerfLogger.\"\"\"\n", - "\n", - " def __init__(self, name: str, logger: PerfLogger):\n", - " super().__init__(name)\n", - " self.logger = logger\n", - " self._start_time = None\n", - " self._start_mem = None\n", - "\n", - " def __enter__(self):\n", - " # start timer and memory monitor\n", - " self._start_time = time.perf_counter()\n", - " self._start_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024\n", - " self.timer.__enter__()\n", - " self.memory_monitor.__enter__()\n", - " return self\n", - "\n", - " def __exit__(self, *args):\n", - " # capture end values and delegate to base\n", - " end_time = time.perf_counter()\n", - " self.timer.__exit__(*args)\n", - " self.memory_monitor.__exit__(*args)\n", - " end_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024\n", - " peak_mem = self.memory_monitor.peak_memory\n", - " # log the record\n", - " self.logger.log(\n", - " self.name, self._start_time, end_time, self._start_mem, end_mem, peak_mem\n", - " )\n", - "\n", - "\n", - "# Polling-backed variant to capture short-lived peaks\n", - "import threading\n", - "import time as _time\n", - "\n", - "\n", - "class PollingLoggedPerformanceMonitor(LoggedPerformanceMonitor):\n", - " \"\"\"Poll process memory on a short interval in a background thread to catch transient peaks.\"\"\"\n", - "\n", - " def __init__(self, name: str, logger: PerfLogger, poll_interval: float = 0.01):\n", - " super().__init__(name, logger)\n", - " self.poll_interval = poll_interval\n", - " self._stop_event = threading.Event()\n", - " self._thread = None\n", - "\n", - " def _poll(self):\n", - " proc = self.memory_monitor.process\n", - " while not self._stop_event.is_set():\n", - " cur = proc.memory_info().rss / 1024 / 1024\n", - " if cur > self.memory_monitor.peak_memory:\n", - " self.memory_monitor.peak_memory = cur\n", - " _time.sleep(self.poll_interval)\n", - "\n", - " def __enter__(self):\n", - " # start base enter first to initialize memory_monitor\n", - " super().__enter__()\n", - " self._stop_event.clear()\n", - " self._thread = threading.Thread(target=self._poll, daemon=True)\n", - " self._thread.start()\n", - " return self\n", - "\n", - " def __exit__(self, *args):\n", - " self._stop_event.set()\n", - " if self._thread is not None:\n", - " self._thread.join()\n", - " return super().__exit__(*args)\n" + "# Side topics related to numpy\n" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "0f33163a", + "id": "bd25ed1b", "metadata": {}, "source": [ - "The following examples demonstrate how to use the Timer, MemoryMonitor, and PerformanceMonitor classes:" + "## Calcualting Memory usage" ] }, { "cell_type": "code", - "execution_count": 22, - "id": "a61540cd", + "execution_count": 8, + "id": "5c818b02", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Add Operation is 0.004897 s\n" + "Current memory usage: 170.23 MB\n", + "Memory used by object: 0.00766754150390625 MB\n", + "Memory used by object: 0.00016021728515625 MB\n", + "Memory used by dictionary: 0.00766754150390625 MB\n", + "Memory used by numpy array: 7.62939453125 MB\n" ] } ], "source": [ - "# Example 1: Using Timer only\n", - "a = np.random.rand(1000000)\n", - "b = np.random.rand(1000000)\n", - "with Timer(\"Add Operation\"):\n", - " c = a + b" + "def print_memory_usage():\n", + " process = psutil.Process(os.getpid())\n", + " mem_info = process.memory_info()\n", + " print(f\"Current memory usage: {mem_info.rss / 1024 / 1024:.2f} MB\")\n", + "\n", + "\n", + "def print_memory_used_by_object(object):\n", + " print(f\"Memory used by object: {object.__sizeof__() / 1024 / 1024} MB\")\n", + "\n", + "\n", + "def print_memory_used_by_dict(dictionary):\n", + " print(\n", + " f\"Memory used by dictionary: {sum(object.__sizeof__() / 1024 / 1024 for object in dictionary.values())} MB\"\n", + " )\n", + "\n", + "\n", + "def print_memory_used_by_array(array: np.ndarray):\n", + " print(f\"Memory used by numpy array: {array.nbytes / 1024 / 1024} MB\")\n", + "\n", + "\n", + "print_memory_usage()\n", + "\n", + "print_memory_used_by_object([1] * 1000)\n", + "print_memory_used_by_object(\n", + " {\"a\": [1] * 1000}\n", + ") # This is because dictionary stores only references\n", + "\n", + "print_memory_used_by_dict({\"a\": [1] * 1000})\n", + "\n", + "print_memory_used_by_array(np.random.rand(1000, 1000))" ] }, { - "cell_type": "code", - "execution_count": 23, - "id": "bde7c586", + "cell_type": "markdown", + "id": "94e707b9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Memory usage for Large Array Creation:\n", - " Start: 294.99 MB\n", - " End: 294.99 MB\n", - " Net change: +0.00 MB\n", - " Peak increase: +0.00 MB\n" - ] - } - ], "source": [ - "# Example 2: Using MemoryMonitor only\n", - "with MemoryMonitor(\"Large Array Creation\"):\n", - " large_array = np.random.rand(5000000) # ~40MB array\n", - " processed_array = large_array * 2 + 1\n", - " del large_array # Explicitly delete to free memory" + "## Correct dtype for PGM" ] }, { - "cell_type": "code", - "execution_count": 24, - "id": "0115dac2", + "cell_type": "markdown", + "id": "338d3c13", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Execution time for Matrix Operations is 0.255303 s\n", - "Memory usage for Matrix Operations:\n", - " Start: 294.99 MB\n", - " End: 295.18 MB\n", - " Net change: +0.18 MB\n", - " Peak increase: +0.00 MB\n", - "--- End of Matrix Operations performance report ---\n", - "\n" - ] - } - ], "source": [ - "# Example 3: Using PerformanceMonitor for comprehensive analysis\n", - "with PerformanceMonitor(\"Matrix Operations\"):\n", - " # Create large matrices\n", - " matrix_a = np.random.rand(2000, 2000)\n", - " matrix_b = np.random.rand(2000, 2000)\n", "\n", - " # Perform memory-intensive operations\n", - " result = np.dot(matrix_a, matrix_b)\n", - " eigenvalues = np.linalg.eigvals(result[:100, :100]) # Small subset for speed\n", + "Use of initialize_array is recommended for most uses. \n", + "However if you wish to translate the dtype into the one PGM uses, you can use `initialize_array(dataset_type, component_type, empty_shape, empty=True)` or `dtype = power_grid_meta_data[dataset_type][component_type].dtype` to find the dtype of the row based data. You can use this to change to the appropriate type which is required by PGM.\n", + "\n", + "For dtype of individual attribute which would be required in columnar data creation, you can use `attribute_dtype(data_type, component_type, attribute)`. The missing value can be filled by `attribute_empty_value(data_type, component_type, attribute)` \n", + "\n", + "Also check for contigious-ness. \n", "\n", - " # Clean up\n", - " del matrix_a, matrix_b, result" + "Check if a type-cast to this dtype creates a copy. Maybe such a copy is redundant." ] }, { "cell_type": "code", - "execution_count": 25, - "id": "05d3496c", + "execution_count": 9, + "id": "5674b0ac", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Memory usage for Transient Peak Example:\n", - " Start: 295.18 MB\n", - " End: 302.68 MB\n", - " Net change: +7.50 MB\n", - " Peak increase: +68.45 MB\n" - ] + "data": { + "text/plain": [ + "array([],\n", + " dtype={'names': ['id', 'node', 'status', 'type', 'p_specified', 'q_specified'], 'formats': ['" + "('id', 'node', 'status', 'type', 'p_specified', 'q_specified')" ] }, + "execution_count": 11, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# Plotting: visualize duration and peak increase\n", - "import matplotlib.pyplot as plt\n", + "initialize_array(DatasetType.input, ComponentType.sym_load, 0, empty=True).dtype.names" + ] + }, + { + "cell_type": "markdown", + "id": "83e8ef68", + "metadata": {}, + "source": [ + "## Views vs copies\n", "\n", - "# If perf_df doesn't exist (run the previous logging cell), recreate from logger\n", - "try:\n", - " perf_df\n", - "except NameError:\n", - " perf_df = perf_logger.to_dataframe()\n", + "Check numpy documentation for when certain functions or features create a view or copy. \n", "\n", - "if perf_df.empty:\n", - " print(\"No records to plot\")\n", - "else:\n", - " fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "* Views simplify assignment of values\n", + "* Uneccesary copies here are the first you can aim to eliminate to improve overall performance.\n", "\n", - " # Duration bar chart\n", - " axes[0].bar(perf_df[\"name\"], perf_df[\"duration_s\"], color=\"tab:blue\")\n", - " axes[0].set_title(\"Duration (s)\")\n", - " axes[0].set_ylabel(\"Seconds\")\n", - " axes[0].tick_params(axis=\"x\", rotation=45)\n", "\n", - " # Peak increase bar chart\n", - " axes[1].bar(perf_df[\"name\"], perf_df[\"peak_increase_mb\"], color=\"tab:orange\")\n", - " axes[1].set_title(\"Peak Increase (MB)\")\n", - " axes[1].set_ylabel(\"MB\")\n", - " axes[1].tick_params(axis=\"x\", rotation=45)\n", + "https://numpy.org/doc/stable/user/basics.copies.html\n" + ] + }, + { + "cell_type": "markdown", + "id": "64b08530", + "metadata": {}, + "source": [ + "Checking if view or copy\n", "\n", - " plt.tight_layout()\n", - " plt.show()\n" + "if `array.base is None` -> copy; else -> view\n" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "c4db246d", + "execution_count": 12, + "id": "ce90a14b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current memory usage: 302.68 MB\n", - "0.00766754150390625 MB\n", - "0.00016021728515625 MB\n", - "0.00766754150390625 MB\n", - "7.62939453125 MB\n" - ] - } - ], + "outputs": [], "source": [ - "def print_memory_usage():\n", - " process = psutil.Process(os.getpid())\n", - " mem_info = process.memory_info()\n", - " print(f\"Current memory usage: {mem_info.rss / 1024 / 1024:.2f} MB\")\n", - "\n", - "\n", - "def print_memory_used_by_object(object):\n", - " print(f\"{object.__sizeof__() / 1024 / 1024} MB\")\n", - "\n", - "\n", - "def print_memory_used_by_dict(dictionary):\n", - " print(\n", - " f\"{sum(object.__sizeof__() / 1024 / 1024 for object in dictionary.values())} MB\"\n", - " )\n", + "import numpy as np\n", + "x = np.arange(9)\n", "\n", + "y = x.reshape(3, 3)\n", "\n", - "def print_memory_used_by_array(array: np.ndarray):\n", - " print(f\"{array.nbytes / 1024 / 1024} MB\")\n", + "assert y.base is not None # .reshape() creates a view\n", "\n", + "z = y[[2, 1]]\n", "\n", - "print_memory_usage()\n", - "print_memory_used_by_object([1] * 1000)\n", - "print_memory_used_by_object(\n", - " {\"a\": [1] * 1000}\n", - ") # This is because dictionary stores only references\n", - "print_memory_used_by_dict({\"a\": [1] * 1000})\n", - "print_memory_used_by_array(np.random.rand(1000, 1000))" + "assert z.base is None # advanced indexing creates a copy" ] }, { "cell_type": "markdown", - "id": "1f257ec6", + "id": "b168a6e4", "metadata": {}, "source": [ - "# Topology Data" + "## Garbage collection for views/copies\n", + "\n", + "### Higher than required memory usage" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "8a2ba5ec", + "execution_count": 13, + "id": "a3cb6254", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "ComponentType.node: 2001\n", - "ComponentType.line: 2000\n", - "ComponentType.source: 1\n", - "ComponentType.sym_load: 2000\n" + "Shape of returned array: (50, 2000)\n", + "Memory used by numpy array: 0.762939453125 MB\n", + "Actual shape of memory: (168, 2000)\n", + "Memory used by numpy array: 25.634765625 MB\n" ] } ], "source": [ - "def load_input_data() -> Dict[str, np.ndarray]:\n", - " input_data = {}\n", - " for component in [\n", - " ComponentType.node,\n", - " ComponentType.line,\n", - " ComponentType.source,\n", - " ComponentType.sym_load,\n", - " ]:\n", - " # Use pandas to read CSV data\n", - " df = pd.read_csv(f\"../data/{component.value}.csv\")\n", - "\n", - " # Initialize array\n", - " input_data[component] = initialize_array(DatasetType.input, component, len(df))\n", + "def calculate_and_give_first_50_loading(model, update_data):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data,\n", + " calculation_method=CalculationMethod.newton_raphson\n", + " )\n", + " return output_data[ComponentType.line][\"loading\"][:50]\n", "\n", - " # Fill the attributes\n", - " for attr, values in df.items():\n", - " input_data[component][attr] = values\n", "\n", - " # Print some debug info\n", - " print(f\"{component:9s}: {len(input_data[component]):4d}\")\n", + "first_50_loading = calculate_and_give_first_50_loading(model, update_data_row_based)\n", "\n", - " return input_data\n", "\n", + "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", + "print_memory_used_by_array(first_50_loading)\n", "\n", - "input_data = load_input_data()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "c56c97ce", - "metadata": {}, - "outputs": [], - "source": [ - "model = PowerGridModel(input_data=input_data)" + "print(f\"Actual shape of memory: {first_50_loading.base.shape}\")\n", + "print_memory_used_by_array(first_50_loading.base)" ] }, { "cell_type": "markdown", - "id": "2740b1a3", + "id": "75552bf8", "metadata": {}, "source": [ - "# Timeseries load profiles" + "### Right Way to return values\n", + "\n", + "Use .copy() to convert a view into a copy." ] }, { "cell_type": "code", - "execution_count": 31, - "id": "c5677f60", + "execution_count": 14, + "id": "d15b6717", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of returned array: (50, 2000)\n", + "Memory used by numpy array: 0.762939453125 MB\n" + ] + } + ], "source": [ - "# Generate random load profile oh hourly data\n", - "n_timestamps = 24 * 7\n", - "dti = pd.date_range(\"2022-01-01\", periods=n_timestamps, freq=\"h\")\n", - "n_loads = len(input_data[ComponentType.sym_load])\n", - "load_id = input_data[ComponentType.sym_load][\"id\"]\n", - "load_p = input_data[ComponentType.sym_load][\"p_specified\"]\n", - "profile = np.tile(load_p, (n_timestamps, 1)) + 5e5 * np.random.randn(\n", - " n_timestamps, n_loads\n", - ")\n", - "df_load_profile = pd.DataFrame(profile, columns=load_id, index=dti)" + "def calculate_and_give_first_50_loading(model, update_data):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " threading=0\n", + " )\n", + " return output_data[ComponentType.line][\"loading\"][:50].copy()\n", + "\n", + "\n", + "first_50_loading = calculate_and_give_first_50_loading(model, update_data_row_based)\n", + "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", + "print_memory_used_by_array(first_50_loading)\n", + "assert first_50_loading.base is None" ] }, { "cell_type": "markdown", - "id": "036537fe", + "id": "0573d0f4", "metadata": {}, "source": [ - "## PGM numpy arrays (Row based)" + "### Contigious Data\n", + "\n", + "The input and update dataset needs to be a contigious dataset.\n", + "A transposed dataset for example would be copied first whereas a sliced dataset would not be.\n", + "A reshaped dataset mostly would be contigious.\n", + "Check arr.flags for contigiousness\n" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "2399bced", + "execution_count": 15, + "id": "093d3a22", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "7.6905517578125\n", - "7.6904296875\n" + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : True\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " WRITEBACKIFCOPY : False\n", + "\n", + " C_CONTIGUOUS : True\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " WRITEBACKIFCOPY : False\n", + "\n", + " C_CONTIGUOUS : False\n", + " F_CONTIGUOUS : True\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " WRITEBACKIFCOPY : False\n", + "\n", + "True\n" ] } ], "source": [ - "# Initialize an empty load profile\n", - "load_profile_row_based = initialize_array(\n", - " DatasetType.update, ComponentType.sym_load, df_load_profile.shape\n", - ")\n", + "loads = np.zeros((1000, 10),dtype=np.float64)\n", + "print(loads.flags)\n", + "print(loads.reshape(10, 20, 50).flags)\n", + "print(loads.T.flags)\n", "\n", - "load_profile_row_based[\"id\"] = df_load_profile.columns.to_numpy()\n", - "load_profile_row_based[\"p_specified\"] = df_load_profile.to_numpy()\n", - "load_profile_row_based[\"q_specified\"] = 0.0\n", - "\n", - "# Construct the update data\n", - "update_data = {ComponentType.sym_load: load_profile_row_based}\n", + "print(np.ascontiguousarray(loads) is loads)" + ] + }, + { + "cell_type": "markdown", + "id": "86c3eed3", + "metadata": {}, + "source": [ + "## Accessing and assigning to PGM arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "72ae61a4", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 10))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", "\n", - "print(load_profile_row_based.__sizeof__() / 1024 / 1024) # in MB\n", - "print(load_profile_row_based.nbytes / 1024 / 1024) # strictly data without overhead" + "def copy_or_view(arr: np.ndarray) -> str:\n", + " return \"copy\" if arr.base is None else \"view\"" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "3548ce38", + "execution_count": 17, + "id": "11f38b3f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson row based data is 0.202111 s\n", - "56.412353515625\n" + "Single batch all components: `loads[:, 0]` gets a : view\n", + "All batch single components: `loads[0, :]` gets a : view\n", + "Sliced batch all components: `loads[:5, :]` gets a : view\n", + "Multiple batch via advanced indexing all components: `loads[[1,2,3], :]` gets a : copy\n", + "All batch Multiple components via advanced indexing: `loads[:, [1,2,3]]` gets a : view\n" ] } ], "source": [ - "# Run Newton Raphson power flow (this may take a minute...)\n", - "with Timer(\"Batch Calculation using Newton-Raphson row based data\"):\n", - " output_data = model.calculate_power_flow(\n", - " update_data=update_data, calculation_method=CalculationMethod.newton_raphson\n", - " )\n", "\n", - "print(sum(arr.__sizeof__() / 1024 / 1024 for arr in output_data.values()))" + "\n", + "print(\"Single batch all components: `loads[:, 0]` gets a :\", copy_or_view(loads[:, 0]))\n", + "print(\"All batch single components: `loads[0, :]` gets a :\", copy_or_view(loads[0, :]))\n", + "print(\"Sliced batch all components: `loads[:5, :]` gets a :\", copy_or_view(loads[:5, :]))\n", + "print(\"Multiple batch via advanced indexing all components: `loads[[1,2,3], :]` gets a :\", copy_or_view(loads[[1,2,3], :])) # Advanced indexing creates a copy\n", + "print(\"All batch Multiple components via advanced indexing: `loads[:, [1,2,3]]` gets a :\", copy_or_view(loads[:, [1,2,3]])) # Deceptive: view of another copy" ] }, { "cell_type": "markdown", - "id": "e63af797", + "id": "38b16212", "metadata": {}, "source": [ - "### Columnar data format" + "### Attribute access\n", + "\n", + "Attribute access has similar behavior" ] }, { "cell_type": "code", - "execution_count": 34, - "id": "9c52f4f6", + "execution_count": 18, + "id": "3b3191ad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.5634765625\n" + "Single batch all components: `loads['p_specified']` gets a : view\n", + "Single batch all components: `loads['p_specified'][:,0]` gets a : view\n", + "Single batch all components: `loads['p_specified'][0, :]` gets a : view\n", + "Sliced batch all components: `loads['p_specified'][:5, :]` gets a : view\n", + "Single batch all components: `loads['p_specified'][[1, 2, 3], :]` gets a : copy\n", + "Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a : view\n" ] } ], "source": [ - "load_profile_columnar = df_load_profile.to_numpy(\n", - " dtype=attribute_dtype(DatasetType.update, ComponentType.sym_load, \"p_specified\"),\n", - " copy=False,\n", - " na_value=attribute_empty_value(\n", - " DatasetType.update, ComponentType.sym_load, \"p_specified\"\n", - " ),\n", - ")\n", - "\n", - "# Construct the update data\n", - "update_data_columnar = {ComponentType.sym_load: {\"p_specified\": load_profile_columnar}}\n", - "\n", - "print(load_profile_columnar.nbytes / 1024 / 1024) # in MB" + "print(\"Single batch all components: `loads['p_specified']` gets a :\", copy_or_view(loads[\"p_specified\"]))\n", + "print(\"Single batch all components: `loads['p_specified'][:,0]` gets a :\", copy_or_view(loads[\"p_specified\"][:, 0]))\n", + "print(\"Single batch all components: `loads['p_specified'][0, :]` gets a :\", copy_or_view(loads[\"p_specified\"][0, :]))\n", + "print(\"Sliced batch all components: `loads['p_specified'][:5, :]` gets a :\", copy_or_view(loads[\"p_specified\"][:5, :]))\n", + "print(\"Single batch all components: `loads['p_specified'][[1, 2, 3], :]` gets a :\", copy_or_view(loads[\"p_specified\"][[1, 2, 3], :]))\n", + "print(\"Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[\"p_specified\"][:, [1, 2, 3]])) " ] }, { "cell_type": "markdown", - "id": "72fe185a", + "id": "31bc7376", "metadata": {}, "source": [ - "Skipping ids is possible under certain conditions" + "Multi field indexing" ] }, { "cell_type": "code", - "execution_count": 35, - "id": "7be73996", + "execution_count": 19, + "id": "d4114e9f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.181983 s\n" + "Multiple attributes: `loads[['p_specified', 'q_specified']][:, [1, 2, 3]]` gets a : view\n" ] } ], "source": [ - "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", - " output_data = model.calculate_power_flow(\n", - " update_data=update_data_columnar,\n", - " calculation_method=CalculationMethod.newton_raphson,\n", - " )" + "print(\"Multiple attributes: `loads[['p_specified', 'q_specified']][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[[\"p_specified\", \"q_specified\"]]))" ] }, { "cell_type": "markdown", - "id": "63f3197e", + "id": "655f5b9d", "metadata": {}, "source": [ - "### Selective Output\n", - "\n", - "Via output_component_types" + "A common source of confusion is when dealing with numpy arrays view and copy variables. They look the same. \n", + "* Modifications on view modifies the view as well as the original array.\n", + "* Modifications on copy modifies only that copy.\n" ] }, { "cell_type": "code", - "execution_count": 36, - "id": "2800a03b", + "execution_count": 20, + "id": "388d5b98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.140543 s\n", - "output_data_selective contains following: \n", - "Keys: dict_keys([]). \n", - "Attributes: dict_keys(['loading'])\n", - "2.5635986328125\n" + "[[123456. 123456. 123456. 123456.]\n", + " [123456. 123456. 123456. 123456.]\n", + " [123456. 123456. 123456. 123456.]]\n" ] } ], "source": [ - "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", - " output_data_selective = model.calculate_power_flow(\n", - " update_data=update_data_columnar,\n", - " calculation_method=CalculationMethod.newton_raphson,\n", - " output_component_types={ComponentType.line: [\"loading\"]},\n", - " )\n", + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 4))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", "\n", - "print(\n", - " f\"output_data_selective contains following: \\n\"\n", - " f\"Keys: {output_data_selective.keys()}. \\n\"\n", - " f\"Attributes: {output_data_selective[ComponentType.line].keys()}\"\n", - ")\n", - "print(output_data_selective[ComponentType.line][\"loading\"].__sizeof__() / 1024 / 1024)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6261d8c5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "973a8ef8", - "metadata": {}, - "source": [ - "#### Clear memory" + "x = loads[\"p_specified\"][:3, :]\n", + "loads[\"p_specified\"][:3, :] = 123456\n", + "print(x)\n" ] }, { "cell_type": "code", - "execution_count": 37, - "id": "a006e388", + "execution_count": 21, + "id": "760d4347", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[100. 100. 100. 100.]\n", + " [100. 100. 100. 100.]\n", + " [100. 100. 100. 100.]]\n" + ] + } + ], "source": [ - "del load_profile_row_based\n", - "del output_data\n", - "del output_data_selective" + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 4))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", + "\n", + "x = loads[\"p_specified\"][[1, 2, 3], :]\n", + "loads[\"p_specified\"][[1, 2, 3], :] = 123456\n", + "print(x)\n" ] }, { "cell_type": "markdown", - "id": "10693b81", + "id": "95af8880", "metadata": {}, "source": [ - "# Multiple Timeseries" + "#### Deceiptive: It was a view of a copy" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "1de68fdd", + "execution_count": 22, + "id": "1c98c5b5", "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'data/ev_profile.csv'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[38]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 3\u001b[39m vehicle_capacity_kwh = \u001b[32m60\u001b[39m\n\u001b[32m 4\u001b[39m n_vehicles_distributions = \u001b[32m10\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m ev_profile = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdata/ev_profile.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m.to_numpy()\n\u001b[32m 6\u001b[39m n_ev_profiles = ev_profile.shape[\u001b[32m1\u001b[39m]\n\u001b[32m 7\u001b[39m n_vehicle_each_load_node_distributions = np.random.multinomial(\n\u001b[32m 8\u001b[39m n=n_vehicles, pvals=np.ones(n_loads) / n_loads, size=n_vehicles_distributions\n\u001b[32m 9\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 1014\u001b[39m dialect,\n\u001b[32m 1015\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 1022\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 1023\u001b[39m )\n\u001b[32m 1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 623\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1617\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1878\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1879\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1881\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1882\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1883\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1884\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1885\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1887\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1888\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/forks/power-grid-model-workshop/.venv/lib/python3.14/site-packages/pandas/io/common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 869\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 871\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 872\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 882\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n", - "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'data/ev_profile.csv'" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[100. 100. 100.]\n", + " [100. 100. 100.]\n", + " [100. 100. 100.]\n", + " ...\n", + " [100. 100. 100.]\n", + " [100. 100. 100.]\n", + " [100. 100. 100.]]\n" ] } ], "source": [ - "# Generate random load profile oh hourly data\n", - "n_vehicles = 10000\n", - "vehicle_capacity_kwh = 60\n", - "n_vehicles_distributions = 10\n", - "ev_profile = pd.read_csv(\"data/ev_profile.csv\").to_numpy()\n", - "n_ev_profiles = ev_profile.shape[1]\n", - "n_vehicle_each_load_node_distributions = np.random.multinomial(\n", - " n=n_vehicles, pvals=np.ones(n_loads) / n_loads, size=n_vehicles_distributions\n", - ")\n", - "vehicles_profile_indptr = np.zeros((n_vehicles_distributions, n_loads + 1), dtype=int)\n", - "vehicles_profile_indptr[:, 1:] = np.cumsum(\n", - " n_vehicle_each_load_node_distributions, axis=1\n", - ")\n", - "\n", - "vehicles_profile_idx = np.random.randint(0, n_ev_profiles, n_vehicles)\n", - "\n", - "# n_loads = len(input_data[ComponentType.sym_load])\n", - "# load_id = input_data[ComponentType.sym_load][\"id\"]\n", - "# load_p = input_data[ComponentType.sym_load][\"p_specified\"]\n", - "# choose_random_loads = np.random.randint(low=0, high=n_loads, size=10)\n", - "# scaled_random_load_profile = load_p[choose_random_loads] / np.sum(load_p[choose_random_loads], axis=0)\n", + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 4))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", "\n", - "# profile = np.tile(scaled_random_load_profile, (n_scenarios, 1)) * vehicle_capacity_kwh + 1e3 * np.random.randn(n_scenarios, n_loads)\n", - "# dti = dti = pd.date_range(\"2022-01-01\", periods=n_scenarios, freq=\"h\")\n", - "# df_load_profile = pd.DataFrame(profile, columns=load_id, index=dti)" + "x = loads[\"p_specified\"][:, [1, 2, 3]]\n", + "loads[\"p_specified\"][:, [1, 2, 3]] = 123456\n", + "print(x)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ab922bf6", + "metadata": {}, + "source": [ + "### Assignment via tuples" ] }, { "cell_type": "code", - "execution_count": null, - "id": "092a7d8e", + "execution_count": 23, + "id": "008513ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([6, 5, 3, ..., 6, 6, 4], shape=(20000,))" + "array([[(1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])],\n", + " [(1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])]],\n", + " dtype={'names': ['id', 'node', 'status', 'type', 'p_specified', 'q_specified'], 'formats': [' \u001b[39m\u001b[32m1\u001b[39m n_vehicle_to_profile_distributer = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrandom\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmultinomial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_vehicle_each_load_node_distributions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpvals\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_ev_profiles\u001b[49m\u001b[43m)\u001b[49m\u001b[43m/\u001b[49m\u001b[43mn_ev_profiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m cumulative_sum = \u001b[32m0\u001b[39m\n\u001b[32m 4\u001b[39m result = np.zeros((n_timestamps, n_vehicles_distributions))\n", - "\u001b[36mFile \u001b[39m\u001b[32mnumpy/random/mtrand.pyx:4271\u001b[39m, in \u001b[36mnumpy.random.mtrand.RandomState.multinomial\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[31mTypeError\u001b[39m: only length-1 arrays can be converted to Python scalars" - ] - } - ], "source": [ - "n_vehicle_to_profile_distributer = np.random.multinomial(\n", - " n=n_vehicle_each_load_node_distributions.ravel(),\n", - " pvals=np.ones(n_ev_profiles) / n_ev_profiles,\n", - ")\n", - "\n", - "cumulative_sum = 0\n", - "result = np.zeros((n_timestamps, n_vehicles_distributions))\n", - "for i in range(n_vehicles_distributions):\n", - " this_node_n_vehicles_idx = (\n", - " cumulative_sum + n_vehicle_each_load_node_distributions[i]\n", - " )\n", - " result[:, i] = np.sum(\n", - " ev_profile[:, vehicles_profile_idx[cumulative_sum:this_node_n_vehicles_idx]],\n", - " axis=1,\n", - " )\n", - " cumulative_sum = this_node_n_vehicles_idx" + "### Row based format" ] }, { "cell_type": "code", - "execution_count": null, - "id": "236f8d95", + "execution_count": 24, + "id": "2399bced", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "668.2586669921875" - ] - }, - "execution_count": 358, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory used by numpy array: 7.6904296875 MB\n" + ] } ], "source": [ - "x = ev_profile[:, vehicles_profile_idx]\n", - "\n", - "x.nbytes / 1024 / 1024\n", - "# del x" + "print_memory_used_by_array(update_data_row_based[ComponentType.sym_load])" ] }, { "cell_type": "code", - "execution_count": null, - "id": "2e9e97e7", + "execution_count": 25, + "id": "3548ce38", "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "only integer scalar arrays can be converted to a scalar index", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[359]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m sum_func = \u001b[38;5;28;01mlambda\u001b[39;00m x: np.sum(ev_profile[:, vehicles_profile_idx[vehicles_profile_indptr[x:x+\u001b[32m1\u001b[39m]]], axis=\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m x = \u001b[43msum_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvehicles_profile_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# vfunc = np.vectorize(squarer)\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# result = vfunc(sum_func)\u001b[39;00m\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[359]\u001b[39m\u001b[32m, line 1\u001b[39m, in \u001b[36m\u001b[39m\u001b[34m(x)\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m sum_func = \u001b[38;5;28;01mlambda\u001b[39;00m x: np.sum(ev_profile[:, vehicles_profile_idx[\u001b[43mvehicles_profile_indptr\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43mx\u001b[49m\u001b[43m+\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m]\u001b[49m]], axis=\u001b[32m1\u001b[39m)\n\u001b[32m 2\u001b[39m x = sum_func(vehicles_profile_idx)\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# vfunc = np.vectorize(squarer)\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# result = vfunc(sum_func)\u001b[39;00m\n", - "\u001b[31mTypeError\u001b[39m: only integer scalar arrays can be converted to a scalar index" + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation using Newton-Raphson row based data is 0.184756 s\n", + "Memory used by dictionary: 56.412353515625 MB\n" ] } ], "source": [ - "sum_func = lambda x: np.sum(\n", - " ev_profile[:, vehicles_profile_idx[vehicles_profile_indptr[x : x + 1]]], axis=1\n", - ")\n", - "x = sum_func(vehicles_profile_idx)\n", - "# vfunc = np.vectorize(squarer)\n", - "# result = vfunc(sum_func)" + "# Run Newton Raphson power flow (this may take a minute...)\n", + "with Timer(\"Batch Calculation using Newton-Raphson row based data\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_row_based,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " threading=0,\n", + " )\n", + "\n", + "print_memory_used_by_dict(output_data)" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "234f85fa", + "cell_type": "markdown", + "id": "e63af797", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 0., 0., ..., 0., 0., 0.],\n", - " [ 0., 0., 0., ..., 0., 0., 0.],\n", - " [ 0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [21., 7., 14., ..., 7., 0., 7.],\n", - " [14., 7., 7., ..., 7., 0., 7.],\n", - " [14., 7., 7., ..., 7., 0., 0.]], shape=(8759, 10000))" - ] - }, - "execution_count": 360, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "x" + "### Columnar data format" ] }, { "cell_type": "code", - "execution_count": null, - "id": "38439a4b", + "execution_count": 26, + "id": "9c52f4f6", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([1616, 828, 407, 1875, 1973, 1885, 1539, 347, 1741, 445])" - ] - }, - "execution_count": 361, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory used by numpy array: 2.5634765625 MB\n" + ] } ], "source": [ - "np.random.choice(n_loads, size=10)" + "load_profile_columnar = df_load_profile.to_numpy(\n", + " dtype=attribute_dtype(DatasetType.update, ComponentType.sym_load, \"p_specified\"),\n", + " copy=False,\n", + " na_value=attribute_empty_value(\n", + " DatasetType.update, ComponentType.sym_load, \"p_specified\"\n", + " ),\n", + ")\n", + "\n", + "# Construct the update data\n", + "update_data_columnar = {ComponentType.sym_load: {\"p_specified\": load_profile_columnar}}\n", + "\n", + "print_memory_used_by_array(load_profile_columnar) # in MB" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "f4daf47b", + "cell_type": "markdown", + "id": "72fe185a", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Skipping ids is possible under certain conditions" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "fe87d253", + "execution_count": 27, + "id": "7be73996", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'n_vehicle_each_load_node' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[362]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m plt.plot(\u001b[43mn_vehicle_each_load_node\u001b[49m)\n", - "\u001b[31mNameError\u001b[39m: name 'n_vehicle_each_load_node' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.252460 s\n" ] } ], "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "plt.plot(n_vehicle_each_load_node)" + "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_columnar,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " threading=0\n", + " )" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d7618f4", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "48146c6c", + "id": "63f3197e", "metadata": {}, "source": [ - "Sample array" + "### Selective Output\n", + "\n", + "It is possible to output only selected attributes via output_component_types. The effect on memory usage is huge." ] }, { "cell_type": "code", - "execution_count": null, - "id": "a873ead0", + "execution_count": 28, + "id": "2800a03b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.145284 s\n", + "output_data_selective contains following: \n", + "Keys: dict_keys([]). \n", + "Attributes: dict_keys(['loading'])\n", + "Memory used by numpy array: 2.5634765625 MB\n" + ] + } + ], "source": [ - "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 10))\n", - "loads[\"id\"] = range(10)\n", - "loads[\"status\"] = 1\n", - "loads[\"p_specified\"] = 1000\n", - "loads[\"q_specified\"] = 1000\n" + "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", + " output_data_selective = model.calculate_power_flow(\n", + " update_data=update_data_columnar,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types={ComponentType.line: [\"loading\"]},\n", + " threading=0\n", + " )\n", + "\n", + "print(\n", + " f\"output_data_selective contains following: \\n\"\n", + " f\"Keys: {output_data_selective.keys()}. \\n\"\n", + " f\"Attributes: {output_data_selective[ComponentType.line].keys()}\"\n", + ")\n", + "print_memory_used_by_array(output_data_selective[ComponentType.line][\"loading\"])" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "a1977a53", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "721e9f2b", + "cell_type": "markdown", + "id": "a0338fdb", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Lets define all limit related attributes to use in upcoming simulation" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "b1723736", + "execution_count": 29, + "id": "9831864d", "metadata": {}, "outputs": [], "source": [ @@ -1210,573 +1005,333 @@ }, { "cell_type": "markdown", - "id": "1702d142", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "source": [ - "# Side topics related to numpy\n" - ] - }, - { - "cell_type": "markdown", - "id": "43f623ac", - "metadata": {}, - "source": [ - "## Correct dtype" - ] - }, - { - "cell_type": "markdown", - "id": "77c2b0b5", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "source": [ - "\n", - "Use of initialize_array is recommended. \n", - "However when you have a large dataset and you wish to translate the dtype into the one PGM uses, you can use `initialize_array(dataset_type, component_type, empty_shape, empty=True)` or `dtype = power_grid_meta_data[dataset_type][component_type].dtype` to find the dtype of the row based data. You can use this to change to the appropriate type which is required by PGM.\n", - "\n", - "For dtype of individual attribute which would be required in columnar data creation, you can use `attribute_dtype(data_type, component_type, attribute)`. The missing value can be filled by `attribute_empty_value(data_type, component_type, attribute)` \n", - "\n", - "Also check for contigious-ness. \n", - "\n", - "Check if a type-cast to this dtype creates a copy. Maybe such a copy is redundant." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72bde1c3", + "id": "973a8ef8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dtype([('id', ' copy; else -> view\n" + "Columnar sparse dataset representation for example can be as follows:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "982330f6", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 366, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 31, + "id": "47aadfb0", + "metadata": {}, + "outputs": [], "source": [ - "import numpy as np\n", - "x = np.arange(9)\n", - "x\n", - "y = x.reshape(3, 3)\n", - "y\n", - "y.base # .reshape() creates a view\n", - "z = y[[2, 1]]\n", - "z\n", - "z.base is None # advanced indexing creates a copy" + "update_data_sparse = {\n", + " ComponentType.line: {\n", + " \"indptr\": [0, 1, 1, 2],\n", + " \"data\": {\n", + " \"p_specified\": [1000, 2000]\n", + " }\n", + " }\n", + "}" ] }, { "cell_type": "markdown", - "id": "cf1fc55d", + "id": "ec1c73c4", "metadata": {}, "source": [ - "## Garbage collection for views/copies\n", + "## Validating large batch data\n", "\n", - "### Higher than required memory usage" + "Since the validator for large batch dataset is expensive, we can choose to validate only some part of data.\n", + "The result of the validator is also difficult to interpret with many errors on a large dataset.\n", + "Maximum data errors typically occur in the input dataset.\n", + "Hence we can slice the update data to only certain batches and validate it." ] }, { "cell_type": "code", - "execution_count": null, - "id": "f873008b", + "execution_count": 32, + "id": "076b552b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Shape of returned array: (50, 2000)\n", - "0.762939453125 MB\n", - "Actual shape of memory: (168, 2000)\n", - "2.5634765625 MB\n" + "Execution time for Validation of full batch data is 0.957645 s\n", + "Execution time for Validation of partial batch data is 0.019265 s\n" ] } ], "source": [ - "def calculate_and_give_first_50_loading(model, update_data):\n", - " output_data = model.calculate_power_flow(\n", - " update_data=update_data,\n", - " calculation_method=CalculationMethod.newton_raphson,\n", - " output_component_types=limit_related_output,\n", + "with Timer(\"Validation of full batch data\"):\n", + " assert_valid_batch_data(\n", + " input_data=input_data,\n", + " update_data=update_data_row_based,\n", + " calculation_type=CalculationType.power_flow,\n", " )\n", - " return output_data[ComponentType.line][\"loading\"][:50]\n", "\n", - "\n", - "first_50_loading = calculate_and_give_first_50_loading(model, update_data)\n", - "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", - "print_memory_used_by_array(first_50_loading)\n", - "print(f\"Actual shape of memory: {first_50_loading.base.shape}\")\n", - "print_memory_used_by_array(first_50_loading.base)" + "with Timer(\"Validation of partial batch data\"):\n", + " assert_valid_batch_data(\n", + " input_data=input_data,\n", + " update_data={k: v[:5, :] for k, v in update_data_row_based.items()},\n", + " calculation_type=CalculationType.power_flow,\n", + " )" ] }, { "cell_type": "markdown", - "id": "16af0e15", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "source": [ - "### Right Way to return values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "576a43a1", + "id": "10693b81", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shape of returned array: (50, 2000)\n", - "0.762939453125 MB\n" - ] - } - ], "source": [ - "def calculate_and_give_first_50_loading(model, update_data):\n", - " output_data = model.calculate_power_flow(\n", - " update_data=update_data,\n", - " calculation_method=CalculationMethod.newton_raphson,\n", - " output_component_types=limit_related_output,\n", - " )\n", - " return output_data[ComponentType.line][\"loading\"][:50].copy()\n", + "# Complex simulations\n", "\n", + "Often times we wish to run multiple simulations at once. \n", + "These power flow calculations have multiple dimensions associated with them where the information regarding that particular simulation is put in.\n", + "The way to simulate these via PGM most effectively is to flatten all the excess dimension into one, ie. of batches. \n", "\n", - "first_50_loading = calculate_and_give_first_50_loading(model, update_data)\n", - "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", - "print_memory_used_by_array(first_50_loading)\n", - "assert first_50_loading.base is None" - ] - }, - { - "cell_type": "markdown", - "id": "aede02e5", - "metadata": {}, - "source": [ - "## Accessing and assigning to PGM arrays" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f160355", - "metadata": {}, - "outputs": [], - "source": [ - "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 10))\n", - "loads[:] = (99, 1, 100.0, 100.0)\n", "\n", - "def copy_or_view(arr: np.ndarray) -> str:\n", - " return \"copy\" if arr.base is None else \"view\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7090991", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Single batch all components: `loads[:, 0]` gets a : view\n", - "All batch single components: `loads[0, :]` gets a : view\n", - "Sliced batch all components: `loads[:5, :]` gets a : view\n", - "Multiple batch via advanced indexing all components: `loads[[1,2,3], :]` gets a : copy\n", - "All batch Multiple components via advanced indexing: `loads[:, [1,2,3]]` gets a : view\n" - ] - } - ], - "source": [ + "## Multiple Timeseries\n", "\n", + "We are now going to simulate multiple timeseries in a single power flow calculation and later the results would be aggregated from the output numpy arrays.\n", "\n", - "print(\"Single batch all components: `loads[:, 0]` gets a :\", copy_or_view(loads[:, 0]))\n", - "print(\"All batch single components: `loads[0, :]` gets a :\", copy_or_view(loads[0, :]))\n", - "print(\"Sliced batch all components: `loads[:5, :]` gets a :\", copy_or_view(loads[:5, :]))\n", - "print(\"Multiple batch via advanced indexing all components: `loads[[1,2,3], :]` gets a :\", copy_or_view(loads[[1,2,3], :])) # Advanced indexing creates a copy\n", - "print(\"All batch Multiple components via advanced indexing: `loads[:, [1,2,3]]` gets a :\", copy_or_view(loads[:, [1,2,3]]))" - ] - }, - { - "cell_type": "markdown", - "id": "639e68c2", - "metadata": {}, - "source": [ - "### Attribute access\n", "\n", - "Attribute access has similar behavior" + "Here we have a EV growth related scenario in a neighbourhood.\n", + "For this simulation we have yearly EV consumption profiles available for 10 different customers.\n", + "We shall subset these profiles and timestamps to align with the data in this notebook " ] }, { "cell_type": "code", - "execution_count": null, - "id": "9cfb9d33", + "execution_count": 33, + "id": "2c28f3fa", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Single batch all components: `loads['p_specified']` gets a : view\n", - "Single batch all components: `loads['p_specified'][:,0]` gets a : view\n", - "Single batch all components: `loads['p_specified'][0, :]` gets a : view\n", - "Sliced batch all components: `loads['p_specified'][:5, :]` gets a : view\n", - "Single batch all components: `loads['p_specified'][[1, 2, 3], :]` gets a : copy\n", - "Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a : view\n" - ] - } - ], + "outputs": [], "source": [ - "print(\"Single batch all components: `loads['p_specified']` gets a :\", copy_or_view(loads[\"p_specified\"]))\n", - "print(\"Single batch all components: `loads['p_specified'][:,0]` gets a :\", copy_or_view(loads[\"p_specified\"][:, 0]))\n", - "print(\"Single batch all components: `loads['p_specified'][0, :]` gets a :\", copy_or_view(loads[\"p_specified\"][0, :]))\n", - "print(\"Sliced batch all components: `loads['p_specified'][:5, :]` gets a :\", copy_or_view(loads[\"p_specified\"][:5, :]))\n", - "print(\"Single batch all components: `loads['p_specified'][[1, 2, 3], :]` gets a :\", copy_or_view(loads[\"p_specified\"][[1, 2, 3], :]))\n", - "print(\"Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[\"p_specified\"][:, [1, 2, 3]]))" + "# Hourly EV profile data for one year. Row = hours, column = profiles\n", + "ev_profile = pd.read_csv(\"../data/ev_profile.csv\").to_numpy() * 1000 # kw\n", + "# Select first n_timestamps and first n profiles\n", + "n_ev_profiles = 10\n", + "assert n_timestamps <= ev_profile.shape[0]\n", + "assert n_ev_profiles <= ev_profile.shape[1]\n", + "ev_profile = ev_profile[:n_timestamps, :n_ev_profiles]" ] }, { "cell_type": "markdown", - "id": "418e9f07", + "id": "27520d80", "metadata": {}, "source": [ - "Multi field indexing" + "\n", + "\n", + "Since the EV growth at each node is uncertain, we can sample different EV growth scenarios.\n", + "Lets consider: \n", + "* The maximal EV sales in a region is a known quantity (denoted by n_vehicles).\n", + "* The spread of EVs across each loaded nodes is equal (denoted by pvals of n_vehicle_each_node).\n", + "* We conduct n_experiments samples for monte carlo simulation\n", + "* Each EV profile is assigned to each EV equally random (denoted by pvals of distributions)." ] }, { "cell_type": "code", - "execution_count": null, - "id": "48d66913", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multiple attributes: `loads[['p_specified', 'q_specified']][:, [1, 2, 3]]` gets a : view\n" - ] - } - ], + "execution_count": 34, + "id": "1de68fdd", + "metadata": {}, + "outputs": [], "source": [ - "print(\"Multiple attributes: `loads[['p_specified', 'q_specified']][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[[\"p_specified\", \"q_specified\"]]))" + "n_vehicles = 100000\n", + "n_experiments = 11\n", + "\n", + "rng = np.random.default_rng(12345)\n", + "n_vehicle_each_node = rng.multinomial(\n", + " n=n_vehicles, pvals=[1 / n_loads] * n_loads, size=n_experiments\n", + ")\n", + "distributions = rng.multinomial(\n", + " n=n_vehicle_each_node, pvals=[1 / n_ev_profiles] * n_ev_profiles\n", + ")\n", + "ev_timeseries = np.transpose(distributions @ ev_profile.T, axes=(0, 2, 1))\n", + "combined_timeseries = df_load_profile.to_numpy()[np.newaxis, :, :] + ev_timeseries\n", + "p_ev_timeseries = combined_timeseries.reshape(-1, n_loads)\n", + "q_ev_timeseries = ev_timeseries.reshape(-1, n_loads)" ] }, { "cell_type": "markdown", - "id": "636da8aa", + "id": "264c840b", "metadata": {}, "source": [ - "A common source of confusion is when dealing with numpy arrays view and copy variables look the same. \n", - "\n", - "View related variables modify the original array whereas a copy related variable does not modify the original array" + "Create columnar update data and run calculation" ] }, { "cell_type": "code", - "execution_count": null, - "id": "061825ee", + "execution_count": 35, + "id": "57cc32d3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.00000e+02 1.00000e+02 1.00000e+02]\n", - " [1.23456e+05 1.23456e+05 1.23456e+05]\n", - " [1.23456e+05 1.23456e+05 1.23456e+05]\n", - " ...\n", - " [1.00000e+02 1.00000e+02 1.00000e+02]\n", - " [1.00000e+02 1.00000e+02 1.00000e+02]\n", - " [1.00000e+02 1.00000e+02 1.00000e+02]]\n" - ] - } - ], + "outputs": [], "source": [ "\n", - "y = loads[\"p_specified\"][:, [7, 8, 9]]\n", - "loads[\"p_specified\"][:, [7, 8, 9]] = 123456\n", - "print(y)" + "update_data_multi_timeseries = {\n", + " ComponentType.sym_load: {\n", + " \"p_specified\": p_ev_timeseries,\n", + " \"q_specified\": q_ev_timeseries,\n", + " }\n", + "}" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7bd5c42a", + "execution_count": 36, + "id": "9d7618f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[100. 100. 100. 100. 100. 100. 100. 100. 100. 100.]\n", - " [100. 100. 100. 100. 100. 100. 100. 100. 100. 100.]\n", - " [100. 100. 100. 100. 100. 100. 100. 100. 100. 100.]]\n" + "Execution time for Run multitimeseries PF is 1.340545 s\n", + "(11, 168, 2000)\n" ] } ], "source": [ - "x = loads[\"p_specified\"][[1, 2, 3], :]\n", - "loads[\"p_specified\"][[1, 2, 3], :] = 123456\n", - "print(x)\n" + "with Timer(\"Run multitimeseries PF\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_multi_timeseries,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " threading=0\n", + " )\n", + "\n", + "reshaped_loading_data = output_data[ComponentType.line][\"loading\"].reshape(n_experiments, n_timestamps, n_loads)\n", + "print(reshaped_loading_data.shape)" ] }, { "cell_type": "markdown", - "id": "5ad771a0", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, + "id": "418c6f70", + "metadata": {}, "source": [ - "### Assignment via tuples" + "Aggregate result on the basis of experiments of monte carlo simulation" ] }, { "cell_type": "code", - "execution_count": null, - "id": "5cbd039b", + "execution_count": 37, + "id": "d117ca19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[(1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])],\n", - " [(1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", - " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])]],\n", - " dtype={'names': ['id', 'node', 'status', 'type', 'p_specified', 'q_specified'], 'formats': ['" + "" ] }, "metadata": {}, @@ -1843,12 +1406,20 @@ "id": "5a2b633f", "metadata": {}, "source": [ - "## Graph operations" + "### Graph operations" + ] + }, + { + "cell_type": "markdown", + "id": "84cb32bc", + "metadata": {}, + "source": [ + "Some graphical operations possible via PGM-DS. Custom ones can be implemented in your simulation via rustworkx." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "ab287807", "metadata": {}, "outputs": [ @@ -1895,24 +1466,32 @@ "id": "a1fb9b25", "metadata": {}, "source": [ - "## Array operations" + "### Array operations" + ] + }, + { + "cell_type": "markdown", + "id": "1702164d", + "metadata": {}, + "source": [ + "Many array operations are possible. Such operations are possible via numpy too but are not as intuitive." ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 43, "id": "3832ce10", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "LineArray([[(2001, 0, 1, 1, 1, -2147483648, -2147483648, False, 0.10097627, 0.10430379, 3.24852006e-05, 0.09886571, 500.)\n", - " (2002, 1, 2, 1, 1, -2147483648, -2147483648, False, 0.0984731 , 0.10291788, 3.14336565e-05, 0.10919854, 500.)\n", - " (2003, 2, 3, 1, 1, -2147483648, -2147483648, False, 0.10927326, 0.09766883, 3.36881679e-05, 0.09503319, 500.)]]... + 2 more rows)" + "LineArray([[(2001, 0, 1, 1, 1, -2147483648, -2147483648, False, 0.101, 0.104, 3.249e-05, 0.099, 500.)\n", + " (2002, 1, 2, 1, 1, -2147483648, -2147483648, False, 0.098, 0.103, 3.143e-05, 0.109, 500.)\n", + " (2003, 2, 3, 1, 1, -2147483648, -2147483648, False, 0.109, 0.098, 3.369e-05, 0.095, 500.)]]... + 2 more rows)" ] }, - "execution_count": 47, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1926,14 +1505,16 @@ "id": "569a7688", "metadata": {}, "source": [ - "## Example of graph usage\n", + "## Contingency Analysis with timeseries\n", + "\n", + "Here we perform a selective contingency analysis on specific branches for all timesteps.\n", "\n", - "Find top 10 ranked paths which most nodes depend on" + "Use graph operations to find top 5 ranked branches which most nodes depend on." ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 44, "id": "0e2ca4e4", "metadata": {}, "outputs": [], @@ -1941,19 +1522,19 @@ "# Lines with most most number of dependent nodes\n", "contingency_count = 5\n", "\n", - "all_pairs = []\n", + "all_branches = []\n", "for node in grid.node.id:\n", " paths = grid.graphs.active_graph.get_all_paths(node, grid.source.node[0])\n", " for path in paths:\n", - " all_pairs += [(path[idx + 1], path[idx]) for idx in range(len(path) - 1)]\n", + " all_branches += [(path[idx + 1], path[idx]) for idx in range(len(path) - 1)]\n", "\n", "\n", - "counts = Counter(all_pairs)\n", - "top_5_branches_from_to_counts = counts.most_common(contingency_count)\n", - "from_nodes = [i[0][0] for i in top_5_branches_from_to_counts]\n", - "to_nodes = [i[0][1] for i in top_5_branches_from_to_counts]\n", + "counts = Counter(all_branches)\n", + "top_branches_from_to_counts = counts.most_common(contingency_count)\n", + "from_nodes = [i[0][0] for i in top_branches_from_to_counts]\n", + "to_nodes = [i[0][1] for i in top_branches_from_to_counts]\n", "\n", - "top_5_branches_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id" + "top_branches_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id" ] }, { @@ -1961,12 +1542,12 @@ "id": "32149652", "metadata": {}, "source": [ - "### Design batches for simulation in PGM" + "Design batches for simulation in PGM" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 45, "id": "e3f1ac0b", "metadata": {}, "outputs": [], @@ -1979,34 +1560,22 @@ " ComponentType.line,\n", " (contingency_count * n_timestamps, contingency_count),\n", ")\n", - "line_update[\"id\"] = top_5_branches_id\n", + "line_update[\"id\"] = top_branches_id\n", "line_update[\"from_status\"] = 1 - combos_contingencies_timestamps.reshape(-1, contingency_count)\n", "line_update[\"to_status\"] = 1 - combos_contingencies_timestamps.reshape(-1, contingency_count)" ] }, - { - "cell_type": "markdown", - "id": "4312000d", - "metadata": {}, - "source": [ - "Question to participants: What if contingencies are designed this way?\n", - "\n", - "```\n", - "combos_contingencies_timestamps = combos_contingencies_timestamps.reshape(contingency_count, n_timestamps*contingency_count)\n", - "```\n" - ] - }, { "cell_type": "markdown", "id": "14b7589b", "metadata": {}, "source": [ - "### Timeseries data from earlier cell" + "Run contingency analysis on timeseries by using Timeseries data from earlier cell" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "b43456d7", "metadata": {}, "outputs": [ @@ -2014,8 +1583,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation with contingencies and load profiles is 1.154605 s\n", - "Execution time for Batch Calculation with contingencies and load profiles all output is 1.308957 s\n" + "Execution time for Batch Calculation with contingencies and load profiles is 2.314557 s\n" ] } ], @@ -2041,28 +1609,37 @@ " update_data=update_data_combined,\n", " calculation_method=CalculationMethod.newton_raphson,\n", " output_component_types={ComponentType.line: [\"loading\"]},\n", + " threading=0\n", " )" ] }, + { + "cell_type": "markdown", + "id": "6c73a578", + "metadata": {}, + "source": [ + "Plot loading on first line for all situations" + ] + }, { "cell_type": "code", - "execution_count": 419, + "execution_count": 47, "id": "06e5d27d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 419, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2075,42 +1652,235 @@ "plt.plot(output_data_combined[ComponentType.line][\"loading\"][:, 0])" ] }, + { + "cell_type": "code", + "execution_count": 48, + "id": "1d863a5f", + "metadata": {}, + "outputs": [], + "source": [ + "del output_data_combined, update_data_combined" + ] + }, { "cell_type": "markdown", - "id": "d7cfc8d4", + "id": "afe04047", "metadata": {}, "source": [ - "# Simulation design exercise" + "#### Question regarding topology caching\n", + "\n", + "Instead of designing the simulation like we did in this way:\n", + "\n", + "```\n", + "1 0 0 0 timestep 1\n", + "1 0 0 0 timestep 2\n", + "1 0 0 0 timestep 3\n", + "...\n", + "\n", + "0 1 0 0 timestep 1\n", + "0 1 0 0 timestep 2\n", + "0 1 0 0 timestep 3\n", + "...\n", + "\n", + "```\n", + "\n", + "What if contingencies are designed this way?\n", + "\n", + "\n", + "```\n", + "0 1 1 1 timestep 1\n", + "1 0 1 1\n", + "1 1 0 1\n", + "1 1 1 0\n", + "\n", + "0 1 1 1 timestep 2\n", + "1 0 1 1\n", + "1 1 0 1\n", + "1 1 1 0\n", + "```\n" ] }, { "cell_type": "markdown", - "id": "98060838", + "id": "0c8aa053", "metadata": {}, "source": [ - "Question to participants: \n", + "### Simulation design exercise\n", + "\n", "* Can you give an example on how you can set up a network reconfiguration study based on the example above?\n", "* What graph related operations are needed?\n", " * Is it possible to do those via PGM-DS?\n", "* What result attributes are to be checked? Do they need to be aggregated?" ] }, + { + "cell_type": "markdown", + "id": "1cc830d3", + "metadata": {}, + "source": [ + "## Dynamic operating envelope" + ] + }, + { + "cell_type": "markdown", + "id": "28763a05", + "metadata": {}, + "source": [ + "Calculate the operating envelope for all timesteps at node 10 based on voltage limits.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ccf1a371", + "metadata": {}, + "source": [ + "Find the sym load connected to node 10" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "f6293765", + "metadata": {}, + "outputs": [], + "source": [ + "sym_load_node_10 = grid.sym_load.filter(node=10).id\n", + "sym_load_node_10_idx = model.get_indexer(ids=sym_load_node_10, component_type=ComponentType.sym_load)[0]" + ] + }, + { + "cell_type": "markdown", + "id": "a1a77c96", + "metadata": {}, + "source": [ + "* Create a grid of P and Q values between a max and minimum range.\n", + "* Create multiple timeseries for each point in the grid\n", + "* Add the values in the grid to the load at node 10. Rest timeseries would remain the same." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "b924a3a8", + "metadata": {}, + "outputs": [], + "source": [ + "min_p = -1e7\n", + "max_p = 1e7\n", + "min_q = -1e7\n", + "max_q = 1e7\n", + "resolution = 11\n", + "\n", + "p_linear_values = np.linspace(start=min_p, stop=max_p, num=resolution)\n", + "q_linear_values = np.linspace(start=min_q, stop=max_q, num=resolution)\n", + "\n", + "p_values, q_values = np.meshgrid(p_linear_values, q_linear_values, copy=False)\n", + "\n", + "\n", + "p_envelope = np.tile(df_load_profile.to_numpy(), (resolution, resolution, 1, 1))\n", + "p_envelope[:, :, :, sym_load_node_10_idx] += p_values[:, :, np.newaxis]\n", + "\n", + "q_envelope = np.zeros(p_envelope.shape)\n", + "q_envelope[:, :, :, sym_load_node_10_idx] += q_values[:, :, np.newaxis]\n" + ] + }, + { + "cell_type": "markdown", + "id": "a92e58c8", + "metadata": {}, + "source": [ + "Create update data and run simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "d2ad55f3", + "metadata": {}, + "outputs": [], + "source": [ + "update_data_envelope = {\n", + " ComponentType.sym_load: {\n", + " \"p_specified\": p_envelope.reshape(-1, n_loads),\n", + " \"q_specified\": q_envelope.reshape(-1, n_loads),\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "6a152a12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Batch Calculation with grid of P and Q to calculate an envelope of operation is 42.895948 s\n" + ] + }, + { + "data": { + "text/plain": [ + "(121, 168, 2001)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with Timer(\"Batch Calculation with grid of P and Q to calculate an envelope of operation\"):\n", + " output_data_envelope = model.calculate_power_flow(\n", + " update_data=update_data_envelope,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " threading=0\n", + " )\n", + "\n", + "nodal_voltages = output_data_envelope[ComponentType.node][\"u_pu\"].reshape(resolution * resolution, n_timestamps, -1)\n", + "nodal_voltages.shape" + ] + }, + { + "cell_type": "markdown", + "id": "b3b602c5", + "metadata": {}, + "source": [ + "Next Step to calculate the envelope:\n", + "\n", + "* Calculate the index mask where the voltage does not cross above or below the voltage limit for each timestep\n", + "* Apply this mask on the grid of P and Q for each time step." + ] + }, { "cell_type": "markdown", "id": "1d0d4c20", "metadata": {}, "source": [ - "Question to participants: \n", - "* Can you give an example of how we can roughly calculate flexibility envelopes (based on technical limits and not economic ones) \n", + "### Simulation Design Exercise\n", + "\n", + "* Can you give anadditional examples of calculating flexibility envelopes? Maybe with more details? (based on technical limits and not economic ones) \n", "* What result attributes are to be checked? Do they need to be aggregated?" ] }, + { + "cell_type": "markdown", + "id": "d7cfc8d4", + "metadata": {}, + "source": [ + "# Additional Content" + ] + }, { "cell_type": "markdown", "id": "23b4016b", "metadata": {}, "source": [ - "Question to participants: \n", + "#### Additional simulation design exercise\n", + "\n", "* Can you give an example of how we can implement a control related action via PGM? \n", "* Draw a block diagram of the control?\n", " * Is this scheme efficient?\n", @@ -2120,7 +1890,7 @@ }, { "cell_type": "markdown", - "id": "a76f4122", + "id": "9de122f7", "metadata": {}, "source": [] } @@ -2141,7 +1911,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.0" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/pgm-advanced-usage-workshop/utils.py b/pgm-advanced-usage-workshop/utils.py new file mode 100644 index 0000000..59b1f8d --- /dev/null +++ b/pgm-advanced-usage-workshop/utils.py @@ -0,0 +1,185 @@ + +# For memory monitoring +import psutil +import gc +import os +from collections import Counter + +import psutil +import gc +import os +import pandas as pd + +# Polling-backed variant to capture short-lived peaks +import threading +import time as _time + + +class Timer: + def __init__(self, name: str): + self.name = name + self.start = None + + def __enter__(self): + self.start = _time.perf_counter() + + def __exit__(self, *args): + print( + f"Execution time for {self.name} is {(_time.perf_counter() - self.start):0.6f} s" + ) + + + + +class MemoryMonitor: + def __init__(self, name: str): + self.name = name + self.process = psutil.Process(os.getpid()) + self.start_memory = None + self.peak_memory = None + + def __enter__(self): + # Force garbage collection to get a clean starting point + gc.collect() + # Get initial memory usage + self.start_memory = ( + self.process.memory_info().rss / 1024 / 1024 + ) # Convert to MB + self.peak_memory = self.start_memory + return self + + def __exit__(self, *args): + # Force garbage collection before final measurement + gc.collect() + # Get final memory usage + end_memory = self.process.memory_info().rss / 1024 / 1024 # Convert to MB + memory_diff = end_memory - self.start_memory + peak_diff = self.peak_memory - self.start_memory + + print(f"Memory usage for {self.name}:") + print(f" Start: {self.start_memory:.2f} MB") + print(f" End: {end_memory:.2f} MB") + print(f" Net change: {memory_diff:+.2f} MB") + print(f" Peak increase: {peak_diff:+.2f} MB") + + def update_peak(self): + """Manually update peak memory usage during execution""" + current_memory = self.process.memory_info().rss / 1024 / 1024 + if current_memory > self.peak_memory: + self.peak_memory = current_memory + + +class PerformanceMonitor: + """Combined timer and memory monitor for comprehensive performance analysis""" + + def __init__(self, name: str): + self.name = name + self.timer = Timer(name) + self.memory_monitor = MemoryMonitor(name) + + def __enter__(self): + self.timer.__enter__() + self.memory_monitor.__enter__() + return self + + def __exit__(self, *args): + self.timer.__exit__(*args) + self.memory_monitor.__exit__(*args) + print(f"--- End of {self.name} performance report ---\n") + + +class PerfLogger: + """Simple in-memory performance logger that stores records in a pandas DataFrame.""" + + def __init__(self): + self.records = [] + + def log( + self, + name: str, + start_time: float, + end_time: float, + start_mem: float, + end_mem: float, + peak_mem: float, + ): + self.records.append( + { + "name": name, + "start_time": start_time, + "end_time": end_time, + "duration_s": end_time - start_time, + "start_mem_mb": start_mem, + "end_mem_mb": end_mem, + "net_mem_change_mb": end_mem - start_mem, + "peak_mem_mb": peak_mem, + "peak_increase_mb": peak_mem - start_mem, + } + ) + + def to_dataframe(self) -> pd.DataFrame: + return pd.DataFrame(self.records) + + +class LoggedPerformanceMonitor(PerformanceMonitor): + """A PerformanceMonitor that logs results into a PerfLogger.""" + + def __init__(self, name: str, logger: PerfLogger): + super().__init__(name) + self.logger = logger + self._start_time = None + self._start_mem = None + + def __enter__(self): + # start timer and memory monitor + self._start_time = _time.perf_counter() + self._start_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024 + self.timer.__enter__() + self.memory_monitor.__enter__() + return self + + def __exit__(self, *args): + # capture end values and delegate to base + end_time = _time.perf_counter() + self.timer.__exit__(*args) + self.memory_monitor.__exit__(*args) + end_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024 + peak_mem = self.memory_monitor.peak_memory + # log the record + self.logger.log( + self.name, self._start_time, end_time, self._start_mem, end_mem, peak_mem + ) + + + + +class PollingLoggedPerformanceMonitor(LoggedPerformanceMonitor): + """Poll process memory on a short interval in a background thread to catch transient peaks.""" + + def __init__(self, name: str, logger: PerfLogger, poll_interval: float = 0.01): + super().__init__(name, logger) + self.poll_interval = poll_interval + self._stop_event = threading.Event() + self._thread = None + + def _poll(self): + proc = self.memory_monitor.process + while not self._stop_event.is_set(): + cur = proc.memory_info().rss / 1024 / 1024 + if cur > self.memory_monitor.peak_memory: + self.memory_monitor.peak_memory = cur + __time.sleep(self.poll_interval) + + def __enter__(self): + # start base enter first to initialize memory_monitor + super().__enter__() + self._stop_event.clear() + self._thread = threading.Thread(target=self._poll, daemon=True) + self._thread.start() + return self + + def __exit__(self, *args): + self._stop_event.set() + if self._thread is not None: + self._thread.join() + return super().__exit__(*args) diff --git a/pgm-advanced-usage-workshop/utils_notebook.ipynb b/pgm-advanced-usage-workshop/utils_notebook.ipynb new file mode 100644 index 0000000..e224be9 --- /dev/null +++ b/pgm-advanced-usage-workshop/utils_notebook.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "c05df45c", + "metadata": {}, + "outputs": [], + "source": [ + "from utils import (\n", + " Timer,\n", + " PerformanceMonitor,\n", + " MemoryMonitor,\n", + " PerfLogger,\n", + " LoggedPerformanceMonitor,\n", + " PollingLoggedPerformanceMonitor,\n", + ")\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9567c5da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Add Operation is 0.005363 s\n" + ] + } + ], + "source": [ + "# Example 1: Using Timer only\n", + "a = np.random.rand(1000000)\n", + "b = np.random.rand(1000000)\n", + "with Timer(\"Add Operation\"):\n", + " c = a + b\n", + "del a, b, c" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "efaa3563", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory usage for Large Array Creation:\n", + " Start: 117.07 MB\n", + " End: 155.22 MB\n", + " Net change: +38.15 MB\n", + " Peak increase: +0.00 MB\n" + ] + } + ], + "source": [ + "# Example 2: Using MemoryMonitor only\n", + "with MemoryMonitor(\"Large Array Creation\"):\n", + " large_array = np.random.rand(5000000) # ~40MB array\n", + " processed_array = large_array * 2 + 1\n", + " del large_array # Explicitly delete to free memory" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f501714e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Matrix Operations is 0.350366 s\n", + "Memory usage for Matrix Operations:\n", + " Start: 155.22 MB\n", + " End: 166.23 MB\n", + " Net change: +11.01 MB\n", + " Peak increase: +0.00 MB\n", + "--- End of Matrix Operations performance report ---\n", + "\n" + ] + } + ], + "source": [ + "# Example 3: Using PerformanceMonitor for comprehensive analysis\n", + "with PerformanceMonitor(\"Matrix Operations\"):\n", + " # Create large matrices\n", + " matrix_a = np.random.rand(2000, 2000)\n", + " matrix_b = np.random.rand(2000, 2000)\n", + "\n", + " # Perform memory-intensive operations\n", + " result = np.dot(matrix_a, matrix_b)\n", + " eigenvalues = np.linalg.eigvals(result[:100, :100]) # Small subset for speed\n", + "\n", + " # Clean up\n", + " del matrix_a, matrix_b, result" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "26b92c96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memory usage for Transient Peak Example:\n", + " Start: 166.23 MB\n", + " End: 173.88 MB\n", + " Net change: +7.64 MB\n", + " Peak increase: +68.68 MB\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'gc' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[32m 14\u001b[39m \u001b[38;5;66;03m# Remove the temporary allocation and force GC\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m temp\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m \u001b[43mgc\u001b[49m.collect()\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# Final small operation\u001b[39;00m\n\u001b[32m 19\u001b[39m a = a * \u001b[32m2\u001b[39m\n", + "\u001b[31mNameError\u001b[39m: name 'gc' is not defined" + ] + } + ], + "source": [ + "# Example 4: Demonstrate transient peak memory increase\n", + "with MemoryMonitor(\"Transient Peak Example\") as mm:\n", + " # Start with a small allocation\n", + " a = np.random.rand(1000000)\n", + " mm.update_peak() # record current memory as peak so far\n", + "\n", + " # Create a transient large allocation that should raise peak\n", + " temp = np.random.rand(8000000) # transient large array\n", + " mm.update_peak() # update peak after transient allocation\n", + "\n", + " # Use the temporary array briefly\n", + " s = temp.sum()\n", + "\n", + " # Remove the temporary allocation and force GC\n", + " del temp\n", + " gc.collect()\n", + "\n", + " # Final small operation\n", + " a = a * 2\n", + " del a\n", + "\n", + "# The MemoryMonitor should report a peak increase above the start memory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b063cc24", + "metadata": {}, + "outputs": [], + "source": [ + "# Usage example: poll-backed logger to reliably capture transient peak\n", + "perf_logger = PerfLogger()\n", + "with PollingLoggedPerformanceMonitor(\n", + " \"Polled Transient Peak\", perf_logger, poll_interval=0.005\n", + "):\n", + " temp = np.random.rand(8000000)\n", + " s = temp.sum()\n", + " del temp\n", + " gc.collect()\n", + "\n", + "# Convert logs to DataFrame and display\n", + "perf_df = perf_logger.to_dataframe()\n", + "print(perf_df.to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60715e98", + "metadata": {}, + "outputs": [], + "source": [ + "# Plotting: visualize duration and peak increase\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# If perf_df doesn't exist (run the previous logging cell), recreate from logger\n", + "try:\n", + " perf_df\n", + "except NameError:\n", + " perf_df = perf_logger.to_dataframe()\n", + "\n", + "if perf_df.empty:\n", + " print(\"No records to plot\")\n", + "else:\n", + " fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + " # Duration bar chart\n", + " axes[0].bar(perf_df[\"name\"], perf_df[\"duration_s\"], color=\"tab:blue\")\n", + " axes[0].set_title(\"Duration (s)\")\n", + " axes[0].set_ylabel(\"Seconds\")\n", + " axes[0].tick_params(axis=\"x\", rotation=45)\n", + "\n", + " # Peak increase bar chart\n", + " axes[1].bar(perf_df[\"name\"], perf_df[\"peak_increase_mb\"], color=\"tab:orange\")\n", + " axes[1].set_title(\"Peak Increase (MB)\")\n", + " axes[1].set_ylabel(\"MB\")\n", + " axes[1].tick_params(axis=\"x\", rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "power-grid-model-workshop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9d6ece45748760d1d0b681d0d436e217bc320f4b Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Wed, 26 Nov 2025 21:23:59 +0100 Subject: [PATCH 5/8] clean more Signed-off-by: Nitish Bharambe --- .../Advanced Workshop.ipynb | 250 ++++++++++++------ pgm-advanced-usage-workshop/utils.py | 12 +- .../utils_notebook.ipynb | 88 +++--- 3 files changed, 231 insertions(+), 119 deletions(-) diff --git a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb index 62b71d8..762d764 100644 --- a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb +++ b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb @@ -150,6 +150,14 @@ "model = PowerGridModel(input_data=input_data)" ] }, + { + "cell_type": "markdown", + "id": "6791a9c9", + "metadata": {}, + "source": [ + "Lets use these number timestamps for all calculations in the notebook." + ] + }, { "cell_type": "code", "execution_count": 5, @@ -172,10 +180,9 @@ "n_loads = len(input_data[ComponentType.sym_load])\n", "load_id = input_data[ComponentType.sym_load][\"id\"]\n", "load_p = input_data[ComponentType.sym_load][\"p_specified\"]\n", - "profile = np.tile(load_p, (n_timestamps, 1)) + 5e5 * np.random.randn(\n", + "profile = np.tile(load_p, (n_timestamps, 1)) + 1e5 * np.random.randn(\n", " n_timestamps, n_loads\n", - ")\n", - "df_load_profile = pd.DataFrame(profile, columns=load_id, index=dti)" + ")" ] }, { @@ -187,11 +194,11 @@ "source": [ "# Initialize an empty load profile\n", "load_profile_row_based = initialize_array(\n", - " DatasetType.update, ComponentType.sym_load, df_load_profile.shape\n", + " DatasetType.update, ComponentType.sym_load, profile.shape\n", ")\n", "\n", - "load_profile_row_based[\"id\"] = df_load_profile.columns.to_numpy()\n", - "load_profile_row_based[\"p_specified\"] = df_load_profile.to_numpy()\n", + "load_profile_row_based[\"id\"] = load_id\n", + "load_profile_row_based[\"p_specified\"] = profile\n", "load_profile_row_based[\"q_specified\"] = 0.0\n", "\n", "# Construct the update data\n", @@ -224,7 +231,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current memory usage: 170.23 MB\n", + "Current memory usage: 170.34 MB\n", "Memory used by object: 0.00766754150390625 MB\n", "Memory used by object: 0.00016021728515625 MB\n", "Memory used by dictionary: 0.00766754150390625 MB\n", @@ -851,7 +858,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson row based data is 0.184756 s\n", + "Execution time for Batch Calculation using Newton-Raphson row based data is 0.211790 s\n", "Memory used by dictionary: 56.412353515625 MB\n" ] } @@ -891,18 +898,10 @@ } ], "source": [ - "load_profile_columnar = df_load_profile.to_numpy(\n", - " dtype=attribute_dtype(DatasetType.update, ComponentType.sym_load, \"p_specified\"),\n", - " copy=False,\n", - " na_value=attribute_empty_value(\n", - " DatasetType.update, ComponentType.sym_load, \"p_specified\"\n", - " ),\n", - ")\n", - "\n", "# Construct the update data\n", - "update_data_columnar = {ComponentType.sym_load: {\"p_specified\": load_profile_columnar}}\n", + "update_data_columnar = {ComponentType.sym_load: {\"p_specified\": profile}}\n", "\n", - "print_memory_used_by_array(load_profile_columnar) # in MB" + "print_memory_used_by_array(profile) # in MB" ] }, { @@ -923,7 +922,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.252460 s\n" + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.256051 s\n" ] } ], @@ -956,7 +955,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.145284 s\n", + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.253432 s\n", "output_data_selective contains following: \n", "Keys: dict_keys([]). \n", "Attributes: dict_keys(['loading'])\n", @@ -1082,8 +1081,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Validation of full batch data is 0.957645 s\n", - "Execution time for Validation of partial batch data is 0.019265 s\n" + "Execution time for Validation of full batch data is 0.892844 s\n", + "Execution time for Validation of partial batch data is 0.031841 s\n" ] } ], @@ -1113,6 +1112,7 @@ "Often times we wish to run multiple simulations at once. \n", "These power flow calculations have multiple dimensions associated with them where the information regarding that particular simulation is put in.\n", "The way to simulate these via PGM most effectively is to flatten all the excess dimension into one, ie. of batches. \n", + "This makes the entire simulation including and most likely excluding PGM most efficient, readable and less bug-prone.\n", "\n", "\n", "## Multiple Timeseries\n", @@ -1133,7 +1133,12 @@ "outputs": [], "source": [ "# Hourly EV profile data for one year. Row = hours, column = profiles\n", - "ev_profile = pd.read_csv(\"../data/ev_profile.csv\").to_numpy() * 1000 # kw\n", + "ev_profile = (\n", + " pd.read_csv(\"../data/ev_profile.csv\").to_numpy(\n", + " dtype=np.float64, copy=False, na_value=np.nan\n", + " )\n", + " * 1000\n", + ") # kw\n", "# Select first n_timestamps and first n profiles\n", "n_ev_profiles = 10\n", "assert n_timestamps <= ev_profile.shape[0]\n", @@ -1161,22 +1166,41 @@ "execution_count": 34, "id": "1de68fdd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of n_vehicle_each_node: (11, 2000)\n", + "Shape of distributions: (11, 2000, 10)\n", + "Shape of ev_timeseries: (11, 168, 2000)\n", + "Shape of combined_timeseries: (11, 168, 2000)\n" + ] + } + ], "source": [ "n_vehicles = 100000\n", "n_experiments = 11\n", "\n", "rng = np.random.default_rng(12345)\n", + "\n", "n_vehicle_each_node = rng.multinomial(\n", " n=n_vehicles, pvals=[1 / n_loads] * n_loads, size=n_experiments\n", ")\n", + "print(f\"Shape of n_vehicle_each_node: {n_vehicle_each_node.shape}\")\n", + "\n", "distributions = rng.multinomial(\n", " n=n_vehicle_each_node, pvals=[1 / n_ev_profiles] * n_ev_profiles\n", ")\n", + "print(f\"Shape of distributions: {distributions.shape}\")\n", + "\n", "ev_timeseries = np.transpose(distributions @ ev_profile.T, axes=(0, 2, 1))\n", - "combined_timeseries = df_load_profile.to_numpy()[np.newaxis, :, :] + ev_timeseries\n", - "p_ev_timeseries = combined_timeseries.reshape(-1, n_loads)\n", - "q_ev_timeseries = ev_timeseries.reshape(-1, n_loads)" + "print(f\"Shape of ev_timeseries: {ev_timeseries.shape}\")\n", + "\n", + "ev_timeseries_q = ev_timeseries * np.tan(np.arccos(0.95))\n", + "\n", + "combined_timeseries = profile[np.newaxis, :, :] + ev_timeseries\n", + "print(f\"Shape of combined_timeseries: {combined_timeseries.shape}\")\n" ] }, { @@ -1194,11 +1218,10 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "update_data_multi_timeseries = {\n", " ComponentType.sym_load: {\n", - " \"p_specified\": p_ev_timeseries,\n", - " \"q_specified\": q_ev_timeseries,\n", + " \"p_specified\": combined_timeseries.reshape(-1, n_loads),\n", + " \"q_specified\": ev_timeseries.reshape(-1, n_loads),\n", " }\n", "}" ] @@ -1213,8 +1236,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Run multitimeseries PF is 1.340545 s\n", - "(11, 168, 2000)\n" + "Execution time for Run multitimeseries PF is 1.509123 s\n", + "Shape of reshaped_loading_data: (11, 168, 2000)\n" ] } ], @@ -1228,7 +1251,7 @@ " )\n", "\n", "reshaped_loading_data = output_data[ComponentType.line][\"loading\"].reshape(n_experiments, n_timestamps, n_loads)\n", - "print(reshaped_loading_data.shape)" + "print(f\"Shape of reshaped_loading_data: {reshaped_loading_data.shape}\")\n" ] }, { @@ -1248,13 +1271,13 @@ { "data": { "text/plain": [ - "array([[1.692, 1.593, 1.361, ..., 0.687, 0.379, 0.262],\n", - " [1.747, 1.609, 1.451, ..., 0.586, 0.409, 0.173],\n", - " [1.784, 1.615, 1.522, ..., 0.565, 0.363, 0.199],\n", + "array([[1.689, 1.512, 1.334, ..., 0.519, 0.351, 0.181],\n", + " [1.676, 1.498, 1.331, ..., 0.545, 0.367, 0.184],\n", + " [1.702, 1.521, 1.346, ..., 0.5 , 0.338, 0.179],\n", " ...,\n", - " [2.044, 1.879, 1.692, ..., 0.571, 0.392, 0.19 ],\n", - " [1.656, 1.441, 1.225, ..., 0.544, 0.359, 0.236],\n", - " [1.618, 1.549, 1.401, ..., 0.526, 0.349, 0.126]], shape=(168, 2000))" + " [1.753, 1.577, 1.396, ..., 0.552, 0.359, 0.182],\n", + " [1.723, 1.554, 1.369, ..., 0.552, 0.368, 0.185],\n", + " [1.714, 1.536, 1.358, ..., 0.533, 0.354, 0.188]], shape=(168, 2000))" ] }, "execution_count": 37, @@ -1294,7 +1317,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Run multi-timeseries PF in loop is 1.808167 s\n" + "Execution time for Run multi-timeseries PF in loop is 3.535915 s\n" ] } ], @@ -1307,7 +1330,7 @@ " ComponentType.sym_load: {\n", " \"id\": load_id_loop,\n", " \"p_specified\": combined_timeseries[experiment, :, :],\n", - " \"q_specified\": ev_timeseries[experiment, :, :] * np.tan(np.arccos(0.95)),\n", + " \"q_specified\": ev_timeseries_q[experiment, :, :],\n", " }\n", " }\n", " output_data = model.calculate_power_flow(\n", @@ -1327,7 +1350,7 @@ "metadata": {}, "source": [ "\n", - "The difference between this method and one big batch is indeed not very large (~10-20%), but this is a significant for performance improvement. \n", + "The difference between this method and one big batch is indeed not very large, but this is a significant for performance improvement. \n", "\n", "The combining/seprating numpy arrays is a lot more wasteful.\n", "\n", @@ -1390,7 +1413,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1427,11 +1450,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]\n", - "([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 9)\n", - "1\n", - "2000\n", - "1990\n" + "1. [2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "2. id | u_rated | node_type | feeder_branch_id | feeder_node_id \n", + " 0 | 10500.0 | 1 | -2147483648 | -2147483648 \n", + "3. [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]\n", + "4. ([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 9)\n", + "5. 1\n", + "6. 2000\n", + "7. 1990\n" ] } ], @@ -1440,25 +1466,25 @@ "grid.node.node_type[0] = NodeType.SUBSTATION_NODE\n", "\n", "# Downstream nodes from node 1 with the substation node as reference\n", - "grid.get_downstream_nodes(1)\n", + "print(f\"1. {grid.get_downstream_nodes(1)}\")\n", "\n", "# In case of multiple substations, find nearest one\n", - "grid.get_nearest_substation_node(10)\n", + "print(f\"2. {grid.get_nearest_substation_node(10)}\")\n", "\n", "# Find all paths between node 1 and node 10\n", - "print(grid.graphs.active_graph.get_all_paths(1, 10))\n", + "print(f\"3. {grid.graphs.active_graph.get_all_paths(1, 10)}\")\n", "\n", "# Find shortest path between node 1 and node 10\n", - "print(grid.graphs.active_graph.get_shortest_path(1, 10))\n", + "print(f\"4. {grid.graphs.active_graph.get_shortest_path(1, 10)}\")\n", "\n", "# All components in this graph\n", - "print(len(grid.graphs.active_graph.get_components()))\n", + "print(f\"5. {len(grid.graphs.active_graph.get_components())}\")\n", "\n", "# All components connected to node 1\n", - "print(len(grid.graphs.active_graph.get_connected(node_id=1)))\n", + "print(f\"6. {len(grid.graphs.active_graph.get_connected(node_id=1))}\")\n", "\n", "# All components connected to node 0, ignoring nodes 1 and 2\n", - "print(len(grid.graphs.active_graph.get_connected(node_id=0, nodes_to_ignore=[1, 2])))\n" + "print(f\"7. {len(grid.graphs.active_graph.get_connected(node_id=0, nodes_to_ignore=[1, 2]))}\")\n" ] }, { @@ -1517,8 +1543,18 @@ "execution_count": 44, "id": "0e2ca4e4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Branch list [(0, 1), (1, 2), (0, 1), (2, 3), (1, 2), (0, 1), (3, 4), (2, 3), (1, 2), (0, 1), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (5, 6), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (6, 7), (5, 6), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (7, 8), (6, 7), (5, 6), (4, 5), (3, 4), (2, 3), 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(1987, 1988), (1986, 1987), (1985, 1986), (1984, 1985), (1983, 1984), (1982, 1983), (1981, 1982), (0, 1981), (0, 1991), (1991, 1992), (0, 1991), (1992, 1993), (1991, 1992), (0, 1991), (1993, 1994), (1992, 1993), (1991, 1992), (0, 1991), (1994, 1995), (1993, 1994), (1992, 1993), (1991, 1992), (0, 1991), (1995, 1996), (1994, 1995), (1993, 1994), (1992, 1993), (1991, 1992), (0, 1991), (1996, 1997), (1995, 1996), (1994, 1995), (1993, 1994), (1992, 1993), (1991, 1992), (0, 1991), (1997, 1998), (1996, 1997), (1995, 1996), (1994, 1995), (1993, 1994), (1992, 1993), (1991, 1992), (0, 1991), (1998, 1999), (1997, 1998), (1996, 1997), (1995, 1996), (1994, 1995), (1993, 1994), (1992, 1993), (1991, 1992), (0, 1991), (1999, 2000), (1998, 1999), (1997, 1998), (1996, 1997), (1995, 1996), (1994, 1995), (1993, 1994), (1992, 1993), (1991, 1992), (0, 1991)]\n", + "top 5 branches [2001 2011 2021 2031 2041]\n" + ] + } + ], "source": [ + "# TODO fix from/to\n", "# Lines with most most number of dependent nodes\n", "contingency_count = 5\n", "\n", @@ -1528,13 +1564,16 @@ " for path in paths:\n", " all_branches += [(path[idx + 1], path[idx]) for idx in range(len(path) - 1)]\n", "\n", + "print(f\"Branch list {all_branches}\")\n", "\n", "counts = Counter(all_branches)\n", "top_branches_from_to_counts = counts.most_common(contingency_count)\n", "from_nodes = [i[0][0] for i in top_branches_from_to_counts]\n", "to_nodes = [i[0][1] for i in top_branches_from_to_counts]\n", "\n", - "top_branches_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id" + "top_branches_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id\n", + "\n", + "print(f\"top 5 branches {top_branches_id}\")" ] }, { @@ -1553,6 +1592,7 @@ "outputs": [], "source": [ "combos_contingencies = np.eye(contingency_count, dtype=np.uint8)\n", + "\n", "combos_contingencies_timestamps = np.tile(combos_contingencies, (1, n_timestamps))\n", "\n", "line_update = initialize_array(\n", @@ -1583,7 +1623,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation with contingencies and load profiles is 2.314557 s\n" + "Execution time for Batch Calculation with contingencies and load profiles is 3.640553 s\n" ] } ], @@ -1591,12 +1631,13 @@ "load_profile_combined = initialize_array(\n", " DatasetType.update,\n", " ComponentType.sym_load,\n", - " (contingency_count * df_load_profile.shape[0], df_load_profile.shape[1]),\n", + " (contingency_count * profile.shape[0], profile.shape[1]),\n", ")\n", - "# load_profile[\"id\"] = df_load_profile.columns.to_numpy()\n", + "\n", "load_profile_combined[\"p_specified\"] = np.tile(\n", - " load_profile_columnar, (contingency_count, 1)\n", + " profile, (contingency_count, 1)\n", ")\n", + "\n", "load_profile_combined[\"q_specified\"] = 0.0\n", "\n", "update_data_combined = {\n", @@ -1608,7 +1649,7 @@ " output_data_combined = model.calculate_power_flow(\n", " update_data=update_data_combined,\n", " calculation_method=CalculationMethod.newton_raphson,\n", - " output_component_types={ComponentType.line: [\"loading\"]},\n", + " output_component_types=limit_related_output,\n", " threading=0\n", " )" ] @@ -1630,7 +1671,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 47, @@ -1639,7 +1680,7 @@ }, { "data": { - "image/png": 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D5Q7TOKsPxi8dykrE8rnwgHnxiz+YbiOLAqMVo8jEnSRfDyvMrBsa7HbhwaznubWUU2i53XhQrpSif181RhqtWGZWEl7Tx+xYVq+ZwffH8Kmr6Ltf9hvO0WUMi+QSSqngXrCojsZtMGvDAerUsKquzVRVVeWB2MqF+bFhnR6zpjphEH2q9nBbLG3Orwfptsn6la3lQd/sntZ9d+mJU2EoY2bJWv7bEUqrWIHOrXuW0jqrwqrv2CYFGTvF7ndnhYcMLLCwAO/wm2JBPbnzlAfsL1fvESZlVVptJH5gfV6dRwMmLBTpwU4DOfVmXePAT1FFHgDM2kfrJAukdub6Ek99+YsI+CzeRh2w63SAsdvuzxMW0B3vLReZCaqZv2zyVwUuOsnAcSo6VLsyq+OiGnT5/jCKleLjq4/HmVw8uFkVjnPy/Yznqbo3OSuN3S9Xj/tJd01V10ceW62MCMkGl4jzc3UuDM12ySI3YB8mFhZ2sb6/+LdAYSAdMzUYwaJwCfHv5trxC+nK13/UfU+72DWr+LdkKTbHKaGU+/NSlW5YWIBnUHWW8kAguyq0tEBeGNHp4M/7spvZ2LFTKvct70rVOZvFiphtb0cop24XT1N8ToHBzVwsih8coPfuLV0MAc6n1TN6i3a2iwHRUlS/Wr1XeV3l2bmqloWTVj3t8IZRXSM3cQ5mg7u2X6OQYJHGD147yyzo1iiYzNpaPk/V151ekrbLK6DrB+0iSjbMY+XUdqvFD40DqVkzG5tVJQLdWlhU97TeJVR6jPcXF7tBrm5fT3eO8j6MFhYnfYfKwiJPcOTfld1P2SpLKDkYwRJC3wELCwAKjFYIRv6tqjq2g/kFloM/p0pOXbqDZm/YT52fnSVScd2fl3r/OguLyn1hETgXjEsolJVynQmW4tdPnfEpA1KLtyHlzP+N2ZtFIS/OprnohR/ohRkbwhB0G7itPFMO1sLiNHBWFWtilo6qOqrZAKedo1XcjywM5Sq5nIV266SlIvD6rveXi2Bs1feRz9OuTVRxHPKAqXMRWVlYDAOpmevLOGNXiUCz7BWz2b7K5eGz6Tv4uDpXqXRMzoziYFguu8/WmK7PzRJu4VCCbmWxbVeyRZ8l5HNd7yWsgoW8AywswDOoOh2rH67WKVp1yJwqyQ+NwROX+Fd/djpLMnMDyFuqzkF37oXesbCYHVsbJFSCxqf4rFwdeOqy4iDJfXknxaz933O20IgrWoTkS1ed5nHZwqJYb8pJ7+pUNKmOb2ph8Tm/XlobWolW+S25nb9fX1wsLW36epq+Zp/4mwOF22VX0Z+nSequ3bG0qqqyNUAW5la3YEqAhcUXdguL2VdRTXac9B2yqJLvLW5zbmutvZm/f7aG/ndn96CDbuX92/38zYSUGxeNLPpCmezIbRQOK3UowMICPINyoDRJx9VtE8ZzUBV/MhMsckeuNElLHzO+H0xpBatuwq6egtlaKrp9+MwtC9pLdh2t3CmrcJpNVHw+gQeTgyeDdQk5FU2qNjXL6lLt0SxOQWsCK8FiF5grF1BT3bN2LiGze0P7W7YGyF8jwVUMi3o748uyuLJb8NFUsKhcsjZ9h89l32G3bUWbwnFyuQU70Sw3ScBkp8jmRFRtYnFqqntNJ1J05xVbewsEC/AMqh+xXUAg/67CWSeAza2cisqzqRlri2ewqtRfRrbM2rmEIh3D8tf3lofsEtJeV81Wna4ZZMy0MWLsfDkV1mwNG9XR9ILFWR0WI04XQFQNgpzF5jRby2zCXejSwqJCruKqckXoBYuPFm09TA9MXSUKHDLyqckuJe2cdFk10r7cxbA4s5KorFY/78p1VIvFSoSaBdTK5+GmGq+dIGELy4G8k2LdnaUlpQbkc5DFvN3Ab+VOLnLYd8ifszpzedkO5TGlP2OsVyBYgHewSyNVzvzJ58paYVcqgWer909ZJQLzOFvFOMDJg5VsGtUtdKhwrRhFgKNaHAasTLG8UJkV8qBkGofhH0gV7znsrORKoyrkNuFO/Zmv14s1bIxr5Ji5+k5IKevBWliczhLNNuMAZKdZQtbtbCVYrM/RrhicfM9yXaJBby6iz1bupk9X7rbcVvtbH9xc+r5V+ESAYHEYh2JWdPCV7wLrtPjcTHaKrNuz+B4jx6iEoXwNOUuo37/mi5WNH/9sbUDbyosj2sVR6QP2g+s7zujqypjfL2/9GLj0hHwMX4gTrXACwQI8bmEpntmzKV61aqmYJbn4EdkFxvFAoC3epsoqkWdJ+josvgATtzyIGTvloCwsFDxyx2q2dgxvwtV0VRYW7rWcnLNdITy58/1hwwHLQUt1uBOniixjWJw0q9PidWbWGsc1OkxjWEhY8azWk7H7HvIyASrrgmy1WLcnN+CelQcwue1VFhZZGFq5QR27hHzOLF5pyUkhZwnx60dPnlbHx9nEv7mNb+P3tfR/juUqft+nLGhp5xKyqsPiC7OFRXl8E7EXa5cQgm6BZ1C7Ioj+NmWVqBZa86wUE8Hi/BjGTtWIKgJfNonLfmjZPC7/qHkg5ToOciplWGJYQgh2kztIsziTL37eTc9N30DVKyWbWFjsG5oHYsvzkDpfndvC4UVUuYTs1pexOgcrzDpn1T2i2tLMwsKZVK1Hf6tbJ8uI3ffQLaaoOE85LkRuZ23QldtMfl8VwyILK/2yDUWUlJhkniVkcu7GAddMuKmK5JkN1maChbOo1uzOpWqKe5o/42beoJrsyOcu3xc1KicHWrokC4vdwC9XVDaKm0KnMSw6C4uzz5QeU32QYCzD4QQWFuAZlAWkfD4hVhht9iLDn3ATw2K3oqpd0K3OD21iQeEZKX/mxv8u9kwdFvl4xkqjGixWmMOKRf6KY1goZGRrlZNidk5cQg//b7Wr/TjNEjKN9XF4smaXa96mQ7bizu7+kFOdVYOLPJCq1osyu7+173xcFoYlwnv6mr20dPvvAduaDehuVsVWkVLR+fCkFiwkxAqjWriSm82VdVZR/0S/BELg5MKs79Cuiarv4nN9QlrjJ9gYljPSfeH2tyvfUvJn3cT8RAIIFuAZlPUYHNSQcPMTsnMJqd7XmXVlwSK9bpz5783Rx2QExLAEVYcleOTvEEyJ/2JhSCFzxsGquVao1hLimAE3OA26NYutUMdDBG5nWs3UwVe1a47DdhaWM9btrHcJyeKm+O+T0n2uWWvum7LSUvgZg1JDSWs2c72ZNYtqH04CW131HQqXkGzJUn1d+R6SY1i0czNal7g/04L9zQRpkdMYLJsYHitkUc5LppTuh2IKXELAM6jSG+1+nE5dFVaCRB5EVbUUdIFzJqmJxqBWY00KJxYW1TouwVpYOKCVV4NWEZRgcRkrZIa+zdy5coyfiXRp/lOFJrE+0ud5ITq2AKriguzqsFhht4n8vspipHPz6NqZHG0rW8I0S1axC1SKZwhYH0u/X9N6Pz79AG1W2yZVsrDwb/D/Fv4mavwEU+nW7DOh9h3yucuDvPYzNus7tNeNQosFjvG+CSg66XNvYeHv6VTocAD//pIYnOLPmp9LtIFgAR63sNh8yGUMi3EWyGmeskiQy5BrmEX6y1aTM4YOXu5si7c1ChZ1jZIUKSYglOJPvO5RsLVSVIhsrBAEC19bdoHI7aBbhM7hfvTtHFzQrZnlJGA7RcVf44DB2TeqNbDEdiHUDXLT1vJxWACw61SV+aO9b+SU1B5a+8r3tuYSMv407ES4k98ln7upS0iKYXl/0W+6FbcD9uPQ6mU8X1cxLIZJCLezLP71ZU8CXULHpKDb0hIC+hPgviPRtp3JEfosRfWE0Ah/n2GGRST1x4ZgAcA0TdXWJcT/BTlL4lTaLs/O0tW0UMew2Ef6G2f+xlMKDJwLPGfehzEOU+6I7SwsHMz57br9dFW7upbbmWUJWeE2uFm1eOS2Q8eoTkaqSQyLfudml1SeNcqLNPo/50AOOM0SMhtIWch8snwXtW9QxVSsWM2E3a7WbIfcji9+u1FUGa5XJU35vrbbBJsYFn1MVpHSxWUXW+GkND/vQhZMRpcQ12Jhsbu6JBbFjGBW1hYWFjd1WCQl8fPOHLFwpBygrgpylfsFOXZMuyZG8cxtbQyuDwzY9wVV3drKFbrzyHHR1n1a1XG8z1gAwQI8g5s1RTTW7z0qFucLZkXVnzYfCkjFtXUJmUT6y3/zjNT4wzbWXVB15irTvpvUxI7PfB+QjRAulxB3pBPmbnH1GdnFxWJFTvcMNFlTWFxCTsZ5p51ugUkH/8BHq2jzgXy6sFkNy8+b3bt2h5+yZAet2FG8dpMTZGsIixWG1xoycw0YMWb+FO9T4RIy3IDGAdCphUV+vcjCJbRix+/0xg+bxW/yCpuB1K5wnIrFWw/Th0uKF0J0gnzekxf+FhBLpBLCp2z6jkALS1HA79zYdxQFkdbM96LVwp28/peThRUhWAAIwSU0acF2V8dIliwoTiexckcku1Pk2ZFu5n+mMEB8BFhYVILFLnDQYRCLPFipMMsSsmN8yWDoFP6OiRYyS24/pz52/UAahAvRlUtI3cGzWGHmlwheM8wmtHZHf/TTNeQGuzRtfTsHvq+qwKpyvdlZWJzGsBj3YTbzX7DlsH8bO5GtEgt2xx/zpbmLSX0MyVKluIqq++qMWZZQybbG82arobGdnVhnbZfjEBYW+8+pMjFlYuwRQpYQ8A4qH2u48/7tsoTUGRelr8mpqNrMlgda+WPcwdutzqzySmgdDFtIvv9lv+ikddUqHboTuAZMuF1CwWDXscqdObeHvM6QcUDQvrcqGFSPA5eQdNwfNx0UgbOhZBO5DdoN51ISrttZcex8aTkF/0Ba6D6Gxfi95Lc37T9KK3eUWEINdXOctLMvxGU9woGyoKJ8DtL3KE1r9qnbuWRfRrHJfYexzI+T+Dd5DSZemZ7PRe8ScnY/265xhBgWAKwql4b3B8L9iNWKo3Z1KpZsK14jRP5xG3/k3OkYZ05OsoS0bR7+eDV9vWYv/bljfXrsynN15+5klmQ3gEVLsHC7WYkno4VFfv7rfn2GE38lNo6pgkFl3NRhmfvrQRoycYn4W7WCt5MgRSvkqryRDFyURZzyfaldVb+nJSXr3uiDbhUuIcPn7ES5fKzL/zlP/Lv07730LiEOBrVwVThtM5V1LtztzOLCqu+Q2zlBcQ+p2lnVdxiD693UcLp2/ALacvAYPd7vXOrcqFrpZ4p8lpWV7QLNveISgoUFeIZgTfxu4MJX3HmamZjVsSUmVR/9nY7+fc5oMHbCxhRQpWAp6dxYrGj1RQJiYRwEjNq5CIJ1Cbml3ZPf0aqdOaadnD57xXp9Fe17y4MCr4tjNGE7uV2048zfdDAsriMzTpxWxxKFe5L6tw9X0pvzzN11+jos1jNpTRDK3/3346dpza7cgJT7gPoghl2q4jTYmqUPunXmqrBrM9U+wl3kjO+3m/67xHQSJbezKuhW1R8Yfxufrthlm9ZcZFISgWGxwny1eq+hYKDPUd9hZhW0OnY0gWABniHnxKmoKHqOQZi/6ZCyE1T9IM1mHWazpMXbjtB7i/TBfBxwt/y3I/7VcpVZQjZBt9wpObOwWHdMqkq2kYBP/d4PV5iaoo1ZDFYWDbMgxcElFhI5BoDrz2w+cNR0X1o7291adgvU2XEoX93OkejztSrFTtqZMbsmqrRm5qo35gfEVnCtDrY4agN4YNBt8XNZvPM+5HZnF4OTdrYbKFVrWEXCfcFxS2LJDcWuVVlCdu1sfJ+DeVcYkgjyTp6mhVsO+4PpnfQdrC2NSQFOlqSw61/iTrDMmzePrrrqKsrKyhKmsWnTplluf/PNN4vtjI9WrVr5txkzZkzA+y1atAjuG4G45bCig4/UD2Tj/qM0bVXgyrV267KotlUtyqhlIGnwjOfa8Qup1ytzLSws1tYdp5270Z3iNrAunBw6eoreW1ScUWHlyrCbaZuJw/V78wLWYLns5bnU65V5pqXvSwWL+fF4EHZawt9tO0fqnubS+fbtXPKazT2t+u7GBLpbJi2j6/6zkD5Zsdsy1kKONeKB3LiyuZ1Li/mxZDkDJ0sVuF0zyi3s2plpWCCVkb+Hqg6LjCYIVf0NZz7K/GfuVrr+rUV0y6SlFu5k/XFYGLpJa9YwiqXA846zGJZjx45Ru3bt6JZbbqFrrrnGdvvXXnuNnn/+ef/zM2fOiM8PGDBAtx0LmO+//770xCogvKa8oZqRRkrQc70KFZpJVcZMJPDrYt0ghUXDbHanWTfsYmX8+5E2HPfDFlq9y7oeBbNwa3F2hRkHpNTiSMPxMs98vV75Htey0OCvaSXG2I11VkoFV1YPFgy8wGBx3FLp69o+rO6ti1/8wby0vkPM2jnUYF4z7np/hW07a9YQs8Gc3bJ8fqr3zWI3Pl+1W8RbGQdSrZ1lV69w/Un3dJfnZtF5DapQqKiKIUaqnbW4J8t2LjHBmIlwFjeqbEKrvmPR1uIYGJUB1Xgco2B55JM1dEGz6mTHMhvBEsyireHEtSro27eveDglMzNTPDTYIvP777/T0KFD9SdSoQLVqWOdaw/KLjzTOnKswHOK3qrT+XlXrlh1d8wfS62FbopWBfOa3UzTCfJKsN7BegbY9blZNPSCRq7uB21bfUF5ZxaWnUesU8NDaedQLTehoH1lM6vhiP+tFvV2ep1bO+A9s+BYrR2N7elfL0cKjubjGq+hm5ozbohlO2sDu9k9/fXqvTRr/X4a2bc0qD6UTJ1Cw2dYWxqtLj9ttp7IOKGwvMWw/Pe//6VevXpRw4YNda9v2rRJuJmaNGlCN9xwA+3Y4bygD4h/ck6cVlodYu0zZVQWFP97hT76+2elK6tq2FkCVN9LmdbtAcEWDYrTLq2/6zs/bXc1a9asCUZLiTbwxureipSrwgnad7Y6h60Hj+nWktEwC1TXfh7Gn4lmWZFdQlzbJlr3tCfa2aLv4FiY/yiKMdrFoPlUfYdBTBbHCoX/+8e6P4qqYNmzZw998803dNttt+le79q1K02aNIlmzJhB48ePp23bttFFF11ER4+qA+cKCgooLy9P9wDxDZeVV+EJwWKT6ieX9nf6w1b1SXmKwMFYzhKjSXFAsb0YcWVh8a9MTAFBjOKYMWraUNOlQ8EuhsXKmmKWFqu1s3H2fbSk7oj8ORal0bqnY9nOfkuWTd9R46yUgNdEUK8Fhaq+o+Se1uBaLpEQbOEuM+FpwfLuu+9SlSpVqH///rrX2cXEMS1t27alPn360PTp0yknJ4c++ugj5X7Gjh3rdzXxIzs7O0rfAETSwqLCC+O1Xal7ed0WDbtOWSXEco7r24AH2ljPaKIFf015cTgz3Ax2/pm/T93OcucbzY441OyjUNC+p1wmPtTlG7R9GtuQi5gFWFiES6io7LdziRPSru+oml66FpHGUYP4CKbvSDTEsISLWPdHURMsfDNPnDiRbrrpJkpODrxIMixqzjnnHNq8ebPy/ZEjR1Jubq7/sXPnzgidNYgWchVIL/1AVLMXI2elVnAfw6LqdE5Ep9PxItw/2LUz42bWaJYKraXAyuNmqDVX3MB1TWIFD3YsIOxm8W6KC5q1M8fwsKUmMIaFykE7F/9rd0+rqjVrlil3guVUQIB0JOJNyk0My9y5c4UAufXWW223zc/Ppy1btlDduupVZ1NSUigjI0P3APGNk8EqVqhqPMicVJjKrSwB36zZS0PfWRp4HEOnI2onlBPBwl/Trp0ZN+2x8/fjdNlLc0xno3LHH0v3QTTh5ss7YT0gMidsBI0Ma71hk5fRDxsDC/HxNZVdQmxxiJaFJZZo95bdPa1qZ1XGk8bE+dto1OfrlJMd2cJlLM0fLuIuS4jFhGz54HiTVatWUbVq1ahBgwbC+rF7926aPHlyQLAtx6q0bt06YJ8PPfSQqO3Cgbgc5zJ69GhKSkqi66+/PtjvBeIMs5oZcSFYXGbd3GmSfqpyi3HV3PIAd/CqGB4jbga7UZ+vVabKawUK5f5cq3FRHtrZiTB0c0/vPHJcl9IbKFhK9/XA1J+pR/OaVNbRtEO4+46nvlIv2JhnEIa8cGRNRXxMubOwLFu2jDp06CAezPDhw8Xfo0aNEs/37t0bkOHDbptPPvnE1Lqya9cuIU6aN29O1113HVWvXp0WLVpENWuW/RsbODODxhL7WVJ40oSNfmitHLiXqV7J2r0bzEBaO6O0o62aXjFol5BZpVmtneUMDnmNqEiRZKy85oLkCuExhvN4o2pn46m5uaePWFROzj1xKiBYd47CElNmLSzHo9d3FBja+fNVeyjcxDoJwrWFpUePHpYBapztY4QDY48fP276mSlTprg9DVBOYliC5eJzatK8Xw+GJ7bCxoR+yCTDKVyBx17mpm4N6dXvN4W8H+5SNAtLkxqVaX9ecZuelVpRF4twNAyWOBbHbDJ3suheOEmvmBT0+fdtXScsA5CwZJW4X2tUTvG3s7FH33HEvL92PZC6cC95ARaHod4b3M6c+WZ3vffkhF7vR7MaqlcvDy/hXp/JLVhLCHgCu8h4t6REYEZao7LammDlc3ZD/km9HzoeqF81PewWlnpVS7Ou0ixWew4FDgh1snptOElLDv67qLJJQnW9qdLxI+HqjcZAGk4qhGAJ02DjHYsVu59zuGLU8k+eiYowjHUSAAQL8AThmDmHu9PRfLaamNKqUl7ZJjIVmdnbEc1slXBQNzM1LPuRg27b1s/0u1DMBvn6kqgJ6nglpdGjSaWU4JcbyQiTuJDbOSO1InVtXE38PaRbo4i0Mw9w0RaGsXTd6S2zp/2TpydLqmHf0LVBRFyrhT7zOjnl2iUEQDzEsISj02HmbjwoOnme6V/VLou6NqlGdTJSafqab5Tb//pMX6qYlECNR053fSwOKI232Wi4BAt37stL1jFhl9CqUZeL1EyzYNhpd19AVdIqUu9X54nKrG7hmW20XRWhWIvCZQ3h8UaLIWFL1r/+0kGsubR2dy5NWrA9YPsHep0j7vupy3bSE9MCKzo7auc4EyxhsbCIdj7gb+fB3RrSZS1qUe2MVHp/cWAV91b1MuntwZ2Ee7n787M923cUxvhSwsICPIFdgSW3pFSwHhya1KjkagXcgZ2zhW+bXSAVksx/NryN2SJxdqzdnUcfLomvJSmqVwpPJgKvbp138gw1qp5O3ZtWpyrpyZaDNLczX4dUm+tsxmOfraGTURaHobiEMhS1foLhxKkz9EPJQPqXrg2oYlIiZVVJM7X+VEhKEG1dOSW4c+e1iX477F5Qxor05CRbYdmkZiVHlohv1u4Tf/+lSwPRJ2RXSzcNnuZCffweHz8Ypq/ZRzN/CVxBuqxZWCBYgCcI9+/gwd7niNl/63qBNXp41d/ZD/Wgx65sYbufbSWdbbvs0gU83dCgWrqYoTrluekbKJ7ISKsgApw114KRr/92IX1+9wWO27lN/SqUKM1wE2xmwTygarw0oJ3j8+bOnQVipGFrW7gsLH+/8lxh3VPxcJ/mtPGZKwKyqoz8duS4+K3xb0AW7aaChWu8GyYAE27s6OrcP1q2i+IFbufxN3YUKcEq8dCpYVWa/WAPGtJNvxaeysLy2+HiwOX22farUReUZAvJ7Xx9lwbUzsFnNcIR/G4HBAsAYRYsPHjyrHHBo5fR3T2amW53+8VN6er21mJC63RYeAQDd3oVw+Se8iI8c5x8Sxeacvv5pgMed7qvDmzvqJ0bOmxnzeUnm+/DlfobTipLQkAWYm5pVqsyDbu4CS0ceZlpe/BgN+ehS53dz9XTdZZAWVgZ98ukVixt2+omwedewey7OKFv67rifl3yWE/6U4d6pu3x5NWtqUMDczHB7pm9uSf8bW2HVnlYThao5MG+ozDGQbeIYQFlBu5guBO5qm2xCOEOubKqbL6kjpIt3DtyjQk25wb7A5etALGExVM4MprYbXPXpc2odVap1cnMDaY1r52Y0NrZKAzNZv4VS2b+mgWg+DVvtLMM33+hlIh/4dq2worVpGZlfztzUxsFflJJ+wfbznI76l9PCJj5B+uGixb8mz5d6O4+H3ZRY9EmAzpl+9tZVXhNjo2z6ju0mDwWejUr27tNNfekLGrPeLDvKESWEACli4WFQrVKyTS4WyOqKkXcX9C0Bg3qnE1jrmqpTCV0MivnGZux03nlOmfuh+JOxxs/s1CyVGS4U72uUza1zNK7256/pg1d06Ee9WxRy/9aUslAaCcMNepW0bs8xvyxldK6pXXscofulXaWXT+VU0ILlj2/SXW6orV+iRJ2sfVuWVsMssaB1KmVqW6mPvvn7FqVReyQEe23IltY5L+9SEoQrjeOTbupWyNKlT477KIm1K9tXXriDy3VgsVBW2dlpgWIeXbfOVlskgsbcoyRl/qOIriEAAgPqrkID2zPX9uWbr6gsbL4kZMOgWeUxk7nmvPq0//u6Gb7WV6d1iszf45bCIfZ3OyTg7o0oFcGttcFl2oz9IoOB1JjnEfjGpVo3ohLdecuIw8gXpqNasjBqsGcncpw1bZ+FXpzcCdqUScj4Ltzezi53dKSEwN+Jx8MOz8g3krLPJEtLF50vYVag0nVzjxIj/vLeTrXkNwPOBHhsgDSuPvSZgETHtVilFzVOVzlGUJF+/3Femkzb995oNwQDuHuNEbArYXFbBsnM/pil5D3LCzhSvtWIQ9u2nGcCiTztlZ/Xhacmpso1siCTW7zYJLHrD6TIlk65OvpRIQnJ6mtEEZxrc38ZasK38+hCF5vCpYER/tzO9lx2neoLSze6zsK4RICIBCz/sOqowxmQHDS8Zp1TE4+y4XgvDLzlwNAnWSsmH0/O3EpD6TaDNGpS8i8rRPj1MIiCRaT7a3uo0TLgTTQkuW0rStWsBeAjFZDxXgss5gXL6Cyati3MzkSLPL6U06shmbXItlwLiohwMtHeEUYVoZLCIBStJ8B+4yrpFek92/tqtzOqjO26tzNCGWWZDyXsde0URZ08srMXw5AHn75OVTrrBT622XmWVRmbW3Xacmf06xeTn3xZm0tf55jlUpft890iTbpyaXtfFZqBbro7Boi88Qs0yzYe1puK3m7UAZSo5C5ul29ABHKx/KKq0Lm8X7nioqxz/RvHdZ2lq0csrBwcr85uZ+Zt4d0UltYPNJ3nFXSd8S6EjeyhICn6NemLr1xfQdTEy13ALwOjIpg+tBwmXV5Vs11E1R+6GDcL/yRcFtf5TLrjWpUosWP9RTt/PrszabBi6q2tjsveTCr4FawOLBmyXVA5A5dC/B1iyrrJhSyqqTSqp3Ff6dVrCDSvplRn69z1c5uZv6ydcnJQGrmNpHb86+XNKHMkrou8nXhsvOJHrmnZW67qAndemFj08VKg21nU3dymPqO5rXPoh7NawUeq9BH6YZYo1i1M5eJCOdCr8HiDfkGgM+ZP9lqUArGwuKk0zGbTcoDg3EbrpvBXHZuraBm/pWkWXq4kAuFscCyq8hrNqjZ+bGTpO9bmr0SWgyLLHhkAdi5UVX/38HO+p26q5zSsHolnYWtOBU5QedOcBpz4TS2Qv5dOBGHZtvI7S9bBmU3S2pyUlCTg0oRuKdV7WXWRQTbzjJ6C4uzDEO7181cmR0bVg3KzVkpAu2sLcGxL/ckxRJYWICnsPt5WvURwQxXobgRzAZR5oPbuoqy/td0rE8fKNYOsSM9JSnsC0JqtTys/PxOOng7l5BWE0SesYdqYalg0tZ/6dpQDDbnN6kW9DonLHTCOW/kNY40ckoWwLMyp1sPpObHkcWF2xgW05m/tB+5nfl+mTS0sxiwedHEYCYHTu9pNxYvDgY2ZtiYiQ/LdnZ2ONeCJdmkZo38WaPQnv3gJfTTlsOiHMPoL9RWuWj3HXVKBItWDC9WQLCAuKrDYuXTDWYNH6fptnadjvHYtTJS/anUwcz8gxkQWtQ5izbsO2r6PruB3AgWs23sBItq0HMqWMyuhxykKAsi3v+N5xeXSd9yMJ+CIZQKtHYDo7YysjEAV8bqWjgNunWbJeQkuNkowmW3RTC/teLYHntpyJadUyXqkycUVnETXEr/5GlncjPYdjYVLA6shuYWlsA4L3lioU0ugimJkO6wnWXa1MukNbtzTd/XloSItYUFLiHgWVQFlqzCFCIVdGs2PsudEfv1QzmGkWBWuLWLlaktVe6UO2+2BrkTLHbnETjohR7DYj6Q+rcJMoYl3AGkcuEyjk/QuK/n2cq1rawHUnLmEtIF3QYfDCq/bnU/BZOR53RhP/m4CRa2D75XLjmnZkBbsDC8qUTEOm7nxMjEsJhZdeS+Q752RoJJa04LonCe3YRRKzS4N/ekZV8XaSBYgKeQf7tcYKlt/UzHFpZgxp1Q4hfkQdRqELfyQ/PKr3Me6mG6GFo44c7vjb90oKf7t6Z6JUF0TPdmNeg/N3V03NnadVjy99WuifO05gTbfZoNpHb+/p8evUw5aAYbrGsGt9s3911E91zajO7tebb/da7A/OmdgQtBWlWOdR7D4rIOi0uXkJvfGgcZcxaakUoOYyv0rqhEy+/PVWjvvrQpfXHPhbr3+B43YrWvoCwsEbJkOb2nB3XOVq7hFUzqsZUwZGpnplCfVrVFYkEsM4UgWIAnMPuNGQc6q44yGNO+k8JxZrMPvWAJzsJScLqIaitW4NVM4mbUUKxPwum+nEJrxR/aZjmefZq1zSkb649uhuxwjRvj9kFZWGwGEFmkhWphsVrpm49zbt0MeqhP8wA3kEqQBW9hUX/OUQyLk4HUYhC3GuDrVU1TXm+OrXCCfH25oq+ZZaZz42p0VmpFerhPC2pep9SSZYZVOzt1ccmB007uabN2cipYrK6lz0dUNT3Zdd+h4vKWtYVL2epe+89NncRSGbGsdIwYFuAxEiwHoXDXJQjFJSR3NHIFTKuZcO2MFMo5ftrv8sk7cVrZYdnNYrKrpQWkGGorJ3d+9ns6eLT4PQ5GZf80r0ljRaqiEzIbEE/aCBaVAAi1RoqTDl6uFcIr3XIbOum8rQaMqukVlYsX8krfLDTvm7LKL1K4hhDHVHRpXM3VwGhlwre0sEjftzBM1Zt1rgqLdpGvB6fL8/2/pyS+gYWO6h5w6hKSP9upYVWRft9mzHf+13iWz9f3zh5NHe2vdL+hB93KiV6hrPPj1JJl7DuOnyr0L6zI8VGqz9pNKMyOM+P+i6nRo1/7X2N3G4uY3q3qkFeAYAGewOcwCNPKihJcDIv9Z5wYQK1cQvKAdF/Pc+jPHevTOY9/I55zNL9d7Am//diV59J5DavSewt/o9qZqbRyx+8B26n2MuV2+/WOxDkqBhMzl5Bdh6i6DvwdQ6l3YpVCrmrn1vUy6f9u7Upjvlyny9JSHV9u/6EXNKKWdTOE2Jmxbh9d1KwGPTt9vfJ48uc+u6u7CLQOhmAtLPLsW46tcFaaPzRXhXw92KWZX3CG2j81Uzxnkaj6rPw9ezSvSZ0bVROC5JWZv9KQ7o3orvdXBPzGeT/yvngRU3ldMCv4c7KQs2qWxGAsLE76DpMb3mmskNxmLw9oL8Sw1newYFH9Frh+ixwsO7h7Q7r47Jr08ncbhQgZ8+UvAZ9Rff13S+oHeQm4hICnMP5wjB2rlfk+mBWfw1WDw8olJHc6fDjurDT3BM9gVF/phT+31c0MuSjWeQ2qisUFH7miheXAH4wtw41LyA6V350tBaHMSK2yKlTb8KDJ53+uwcxtvEe4OJp8T/U6tzYN6JRNf+naQFirtAqfKmSXSSiZRsHGVsjHdFuB1UlshdV3ktN1OTaKLUvyAKz6ncrbNKtZWcSodW1Snab+tZuoOeLfn8GlKLeBm194YN8Revyb2xgWn5MMQ4e/y8SSvkOjYfV0pdi5R6peXSklie7q0UwI+HeGdjEVe3YxLF4BFhbgCcxnIvofklUnqlrxVIY7Qnkm6jSt2UlUvNUmuk6npPP9cNj59OaPW+j2i5oGmP25hHv3pqUuHFU/6wtT5oaVWyJYMWc2MPD+gjFXu4mtkFfJ1twHO44cp24l7Wm8Tg/3bk6z1x8wFQhOM2WCse7Zud6Mx7BCt8ZNSBVY7S1Z4vMGUcT38EsD2tG+3BPUtGZlWrz1iO59rk68QrIKGttVfu7mGljBwk3KKrfsO5zHsLgULCb9gtzOVr2LbrKTUPyZL+65gP63fBc90OscOlmymrbGwE7ZIpDfbduF0ndEEwgW4CkSQhg0VSueyogOyyhYQpglObWwyGJA60AaVE+nZ/oHrj2kdWZyR6McoBWHSwih85HjIaxec4JZk4ZWpM+Zz1/j9Bmff/b/934tTbczuhysBtJAbK6RQ1TuuFCyV0IJupVft/pOqt8Nuzr9n00MvJfk72IUD/KxjIeV33PjUiy2Ap1xJMAcW1gkd4ujyY5J7yG3s1XlaFXf0bZ+FfFgDhw9GSBEdZa/eFEiDoFLCHgCJ6ZTO06YrBOi8Y9riwUCz0xK91/6g/70ru4iyNLxyUkUWrqEnPmrNRKMM07FZ1QCKZhiXqXnGD4Ly2Utaot/ORZEdS2f/GMreurqVq72qartYoVZsK1P0WZ6wWI8rsMZagg9aaqVhcXms1pmx8Ul9Ujkdr6qXRa9PThwUT2na9w4DbpVn3dCwL2kCzQ13KvyPW68jYN1txmtQFaDt10qMNfQYZ75U+uA/bOri2OY3CC3hbV11tpFV8FgzeRNnJQACGffEU0gWICnMP5w7OIorulQvJosc8LGwvKnDvVp1ajL6b5eZysHZR5gVaumOpnUOXUJOelAilfDlToqRWeiOpy272D80UqXkNT2DyrqaphR86wUWjOmtzBdqwY5fp9dNW7SieVNHQkWF64n+TyMbW01yMlvhWJhkQelId30Ked2M+Sv7r1QtLWc5q4VjuPMr14ta4v6GWHPEnIZ38RtbCXCrVxCMq5iWAznKF9njk26Qsp+ses7Hrj8HNF3XN2+XsD9zP92aFCV/jmwXXAZhg6tsxUU18N4jYoFuDtxz4S5dmLEgGABnsDsN3vHJU31PybDhhyE6rTTYaoY6hbIM0X+u2PDaqJjCieqGBYreBO5o1HF0MivPd7vXJENwP8GC393Y5E+ucPnAMkMiwBUI1wfw1ilU9sfH4sfa8b0ofMaFJu27ZDbzZFLyEUtCiuXkDxIsGWIxdbLAwIHJjemd222rro/2FLyqnRP2+2W25jbWsY/kJa09/gbOtKLUhB38TYJoQXd2lhYjK4QPhcrC4v8npZOe+15pS6mYHjRcJ3k71OzcgpNkIolnjhVFHTfoVlaeEK09O+9dNv4HEzMrASLXDU5UXEzGEUMb2IlwI1wVlyj6uk6d56XgWABnsL488qulk7rn77C0WdP2riEVGiduiwSjB1TqKWo5Rm0MehXBXcyOsGi2EZ+jTOIFo68zL9KcLCT/Wl3XSAKnqkEC+/TKtbCCdogqZmseX9WAacycr9cIRTBYpPWHGBhkd7jLK0lj/Wka0s6d3lLN+WBeLb+9yvPVc6i+fCygAnGVK+JCW0dGv4OXFTQ7arYlkG3NuX/jT8ZPid9urL+fbndtUrBL1+nslg4/y1y2vT3wy9Wfh/j79Au/s2qrWRhXt3Qzk5O16pLsHMnJxle43Z0I+5HX9WKfnioR4Do9SoQLMATWP2unQ5qxoh5J/D6OlxYjdNZrX7UoSAPQKcduCp4kLIblI0dYTh80DygyMc1ppcGs0aJTM9za4tl6rm9VbPLG7o2MP2s/P2cWDPclA+3DLqVV55O4loy6mO7DW6UA5rlv7k5QhWGFzSrIQTKhWfXND0/8zosztrZbYo6t10Fhy4hbuNwxVSYLRBpDHR1Yp01wi5kLk8g9x3G02YLhh1FLoNuLV1CxmUsHFl048QfhCwh4DVC+e3IBZOcwrMjjrUw+9HyAHtpi9JVaoPBWB/EbdCtSs1ZfdNwdT/GNXYqmaw27BSuHzOiT3NdW8vC6xnF+i/BuoTMYlhUWRtyvFCAYLFI85W/h9sYFv16OUm6mX9GWmiChd1Kyx/vpTs/+fSm3W1+vzu2sNi6hAL3a5X5Jh8qnMOnmRg1ChY3LkSNzPSKNP+RSw3tXPo3u7TOkRa/DLWGU6KqIKPCIqiPFaIyRRn7OiBucWjqVW3FC51xbMGTLjNPnMwwMsJsKnXSMXIno0vlVG0UhRVTjQMWm+i5na2Ehdu2lgWE1XWQT8VJgKBplpBLl5DewmJR2j0h+LaVByUeSDtkc9pqpi4o1C3GtpS/l5WlzHGlW5dBt7yStlU5et35hlGxmB1TcwlxeX+2kqjW13KC1T1rVXQwmCrZCYpDJaosLBbtHO/AwgI8hZOO31hymzubG7s2iIhpM9y7dJK9wlk+ckek8ttbdXKhtIMu88XQ2bWokyFiOMLZzg5CegLOJZTjqw5n1cHLzwMsLPLfri0sicp1nHggZWH0uYUVJBicWqicpsTaWlgMDc2ZS0lyQG+QCyu61elmYrSwpNCeyuoXLpy6Ca3icnTu5EJn7mSnGVflwsIyb948uuqqqygrK0s0zrRp0yy3nzNnjt8nKT/27dun227cuHHUqFEjSk1Npa5du9KSJUvcfxsQtzjth/i3rTK/R8oPG+79OomtMI4T6qDbyFtYVANWuNvDrv5FpI4rowsGtbBMqJYcCBZdrFBS4EAa7u/rNC082XEdFnfnJ1xCDkWT1Z7d3vdmVZfloNtI3VtOw3ysRLu8ntfpkmKIVnCzWsVklTvBcuzYMWrXrp0QGG7YuHEj7d271/+oVas0LmDq1Kk0fPhwGj16NK1YsULsv0+fPnTgQGnJbFA+cFJDJJo/wnAdqkmNSv5l3G1x0IFGwSMUlXZ2amFxeir3lqyj8nCf5o7PQWdhMQzEVhaHUMY5fcaMXPWUIoN0rlaxKbq0ZosvyCm8quKAwbqEdKcaxtvOLIbDKtA1XDgVQlaVbuX7pFGNdAfHtLYKljuXUN++fcXDLSxQqlRR11x45ZVXaNiwYTR06FDxfMKECfT111/TxIkT6dFHH3V9LBB/uBmAo/kjDJdJdfp9F1HO8dNUJ9N+Rd8EB20TDcESjXZ2mqbq9DoMv/wcuq5TNtWvmub4eFbBoAmGQTdcmNUk0Sws4SbcLqGWWRkild6YLm1eh8V6CQSnkxa3970TC0ukcNp12FkZ1z7ZhwpOFzpKPea203/nsiVYohZ02759e6pbty5dfvnl9NNPP/lfP3XqFC1fvpx69SotuJOYmCieL1y4ULmvgoICysvL0z1AGcHB7yuUlXHdEq5DsS/aiVhRHVMVQGqZJRTCOSdE3cLiVLA4n9Vy7R6z2e3AztkBr6VLacTG8U0+O6P1JZR2Nhu8IzWQOhUssoXF7trUzUwzLTkgp64zPIjK7WyVVRVOC4vZd7WyakQ/hsX6/copFai6VMnYCj6k3M5OshLjiYgLFhYpbDH55JNPxCM7O5t69OghXD/MoUOHqLCwkGrX1pvK+bkxzkVj7NixlJmZ6X/wPkF849Q3zdtF08ISrRoFPFs1O6aqc7WyTBgHi2CJimApiu51UNXU0dW6MBxHdh0Y77vqlZwNIirM3CORGkitgqnNYlOcikkVvDjfPZc20+03XUqLt1w52WK/dauoLWeO1uyR+pioWFgcbhdKOzPfD7+E5N+JHPfitiAeZ0y5cfWVOcHSvHlz+utf/0odO3ak7t27CzcP//vPf/4z6H2OHDmScnNz/Y+dO3eG9ZxB7HDyI7+/ZF2baJSTjpY24tmqm2Na9XHP/qkN3XphY5px/0UhjWxcKZSRO8Bw44uya44tXefUrhzwmtlg7rNwL7TLriJiZf51fYeQLSxahdSLpWJvkcJpDEuobkcuYCcPpDoLi9UtpbjW79zcWQigP7Sp6+ocjMLo5u6N/NlBkcbpPRuqYGlWq/R+5q8ri3t2Jbnhg2FdRbE71Zpq5TatuUuXLjR//nzxd40aNSgpKYn279+v24af16mjrkOQkpIiHqDs4PQ3y9txCvOFzWpQg2r2QWihEs20QM7QYBNut6alHX0wFimOK3jiDy1DPp/aGam0aGRPquxiDaFIxbAEuXC0kovOrkm/7s+nKiUrc8uCzDjAyaenGuh5jaVgMBar+/GRS0WMU5ZLC4JTZEuRZQxLGK09XHTR1PXmsOaOBhdvDLWAI/9kRv2hJQ3u1pAalwTBe2GyE05jT0uDZeTkaXcuIV7eI9TK3mVOsKxatUq4ipjk5GRhfZk1axb1799fvFZUVCSe33PPPbE4PeBxeAYRjQ6n+FgUNdi0O3fTQbquk73lKBpBt4zTuJt4Smt+qHdzseDbZSUl1VMsXEKyMAxn7JRsrGGRkJ5cQTwihTwoWmfohMclxDSqUYn+fcN5futRJen7BRt0Gyp8DZvU1FvYIkaULCzM13+7kNbtzgvIQgxmuRIv4/oXkp+fT5s3b/Y/37ZtmxAg1apVowYNGgh3ze7du2ny5Mni/VdffZUaN25MrVq1opMnT9Lbb79Ns2fPpu+++86/D05pHjJkCHXq1ElYX/gznD6tZQ2Bso/2m/XauhbRPJ8G1dPppurOKm5GSa9EHKd9dTgtXbxWz03dGjlyCUWqoWULSzSCyGXh5TQ2KRyi+ErJhZOe4szC4rEuIAoWltAbulVWpngYCWZRRy/j2tC6bNky6tChg3hoYoP/HjVqlHjONVZ27NihywJ68MEHqU2bNnTJJZfQzz//TN9//z317NnTv83AgQPppZdeEvvgbCIWQDNmzAgIxAXAa53Oi39uK/598o+tohpr0DZMgbVGoj1WOO2s+3fIEv9e0Kx62M9BXhHXOJBWq6xO2w2VaNfK0Lu2rLt9LcanU6OqYT2HSpKFxapqazRcvdHATmRrQcmR6Ds0ChSVtZvULLZOyyuzl1kLC2f4WPmdJ02apHs+YsQI8bCD3T9wAYEEj7hCnJqnB3TKpr5t6orUw3DDM//8gjPK99jPXCsjlf7UoR7FM06vJwcl//JUH0p1uHK3G+R9qpYjeLzfuWGPLdGtXBwFk4IsDO300Tf3XSyWkAh11WirbKwCRWzFe7d2paXbj9Af2xWL03ATbauk3VV9qE9zuqNH04j0HVYWlsm3dKH/W/gbDSkJQI4nsJYQ8ARedXE4qRUWqQ6HZ/75BeYrxT7aN/yZDtE2x7u57pGK8dCviBv4/m0XNQn7MXUl/6NsYbFzc7JoC7dYMbq+VLEVF55dQzzKCk5cfZEUK2ZBt/WrptPIK8+leASrNQNPYTdgRlvYxHLxMLOiXGWJaBTwcuMSikUMVTRiWAqjbZq0IRaxFU4z0sJFWYnF8RIQLMATRLszcUosg4DlgbSsEo6Aw3BaWGKBl5ZAiBZu023jkbK2UrIXKPs9IogrIpnSGAyxXDts+OXFC/hde17kC+TFCi+Mo61LApjlGItoEo2Kwl5oZ6ZFnbPEv31bq2tslSVi2Xc81Psc14uAxgOIYQFAwaDO2TRl6U7/6r+xoF/butShwWVUJyOytVBkot3HjvljKxo2eRnd1aMpxYrMtIq08onLKTmCFX2DLZUfLjo3Lq5a3LQkQyRWfHHPhZRz/JQIGC+rdGlcjZZsO0JXRSh42Alc0PBP59WnrAjXUYo2ECwgvmJYojRVHHtNG/p7v3MdrZAaSSJV+dQrcKGrNWN6x7ydq5qsOhwNrBYCDBcZqRVFlhVXU44lLApjJVaiZWWaMux8On66MOIBtXaubG1doLIEBAvwBE47k2jFlPBxYj2Ilhe/e3ls51isPh7JSrpAfz1jKVbKMohhAZ7CrOt++upWYu0XrVAbiAy8cGLV9IphWYsImMNFu3jdlx7NI7/YYXnnr5c0oZpnpdDtl4Q/PR1ElwSf18LHgyAvL48yMzPFys0ZGfFXvQ8Q9f7nXLEg3Qe3daXu0iqvMnyreq10f1kE7RwdtK4XbR15cE+XjfEbdivgLSz6FHQ40QHtHB3QztEDbV02gEsIeIL4t/MBAACIJBAswFN4rQ4LAAAAbwDBAjwBDCwAAACsgGABngKuZgAAACogWIAnKAPJagAAACIIBAvwFDCwAAAAUAHBAjwB7CsAAACsgGABAAAAgOeBYAGeMrGgwBMAAAAVECwAAAAA8DwQLMBTMSwwsAAAAFABwQIAAAAAzwPBAjwFDCwAAABUQLAAT4DCcQAAAKyAYAGeAjEsAAAAVECwAE8A+woAAAArIFiAx4CJBQAAQCAQLMATIIQFAACAFRAswFMghgUAAIAKCBbgCXyIYgEAAGABBAvwFDCwAAAACItgmTdvHl111VWUlZUlFqqbNm2a5faffvopXX755VSzZk3KyMigbt260bfffqvbZsyYMWJf8qNFixZuTw3EMYhhAQAAEFbBcuzYMWrXrh2NGzfOscBhwTJ9+nRavnw5XXrppULwrFy5Urddq1ataO/evf7H/Pnz3Z4aKANgtWYAAAAqKpBL+vbtKx5OefXVV3XPn3vuOfr888/pyy+/pA4dOpSeSIUKVKdOHbenA8oIsLAAAADwVAxLUVERHT16lKpVq6Z7fdOmTcLN1KRJE7rhhhtox44dpvsoKCigvLw83QOUDWBfAQAA4AnB8tJLL1F+fj5dd911/te6du1KkyZNohkzZtD48eNp27ZtdNFFFwlho2Ls2LGUmZnpf2RnZ0fxGwAAAACgTAuWDz74gJ588kn66KOPqFatWv7X2cU0YMAAatu2LfXp00fEu+Tk5IjtVIwcOZJyc3P9j507d0bxW4BIghAWAAAAYYlhCZYpU6bQbbfdRh9//DH16tXLctsqVarQOeecQ5s3b1a+n5KSIh6g7IDVmgEAAMTcwvLhhx/S0KFDxb/9+vWz3Z5dRlu2bKG6detG4/SAh0hAFAsAAIBwWFhYTMiWD443WbVqlQiibdCggXDX7N69myZPnux3Aw0ZMoRee+01Eauyb98+8XpaWpqIP2EeeughkercsGFD2rNnD40ePZqSkpLo+uuvd3t6IE6BfQUAAEBYLSzLli0T6chaSvLw4cPF36NGjRLPuYaKnOHz5ptv0pkzZ+juu+8WFhPtcd999/m32bVrlxAnzZs3F8G41atXp0WLFolicwAAAAAAri0sPXr0sIw34GwfmTlz5jiKbwHlG+2WQtAtAAAAFVhLCAAAAACeB4IFeAKs1gwAAMAKCBYAAAAAeB4IFuAJEMMCAADACggWAAAAAHgeCBbgKVA4DgAAgAoIFuAJEHILAADACggW4CkQwwIAAEAFBAvwBFj7EAAAgBUQLMBTwMICAABABQQL8AgwsQAAADAHggV4CmQJAQAAUAHBAjwBYlgAAABYAcECPAViWAAAAKiAYAGeAAYWAAAAVkCwAE8BAwsAAAAVECzAE/gQxAIAAMACCBbgKRDDAgAAQAUEC/AEsK8AAACwAoIFAAAAAJ4HggV4gtIQFviEAAAABALBAgAAAADPA8ECPJUlhKBbAAAAKiBYAAAAAOB5IFiAJ9BCWGBgAQAAoAKCBQAAAACeB4IFeMrEkoAgFgAAAAogWAAAAADgeSBYgKeAfQUAAIAKCBbgCVCaHwAAgBUQLMBTIIQFAACACggW4KnCcQAAAEBYBMu8efPoqquuoqysLJHRMW3aNNvPzJkzh8477zxKSUmhZs2a0aRJkwK2GTduHDVq1IhSU1Opa9eutGTJErenBsoACYhiAQAAEA7BcuzYMWrXrp0QGE7Ytm0b9evXjy699FJatWoV3X///XTbbbfRt99+699m6tSpNHz4cBo9ejStWLFC7L9Pnz504MABt6cH4hTYVwAAAFiR4AvBFs8Wls8++4z69+9vus0jjzxCX3/9Na1du9b/2qBBgygnJ4dmzJghnrNFpXPnzvTGG2+I50VFRZSdnU333nsvPfroo7bnkZeXR5mZmZSbm0sZGRnBfh0QQ1qOmkHHTxXSjyMupexq6bE+HQAAAFHAzfgd8RiWhQsXUq9evXSvsfWEX2dOnTpFy5cv122TmJgonmvbGCkoKBBfUn6A+AYhLAAAAGIqWPbt20e1a9fWvcbPWWScOHGCDh06RIWFhcpt+LMqxo4dKxSZ9mBrDAAAAADKLnGZJTRy5EhhPtIeO3fujPUpgRDxIYoFAACABRUowtSpU4f279+ve42fs68qLS2NkpKSxEO1DX9WBWcb8QOUPVCHBQAAQEwsLN26daNZs2bpXps5c6Z4nUlOTqaOHTvqtuGgW36ubQPKPohhAQAAEFbBkp+fL9KT+aGlLfPfO3bs8LtrBg8e7N/+jjvuoK1bt9KIESNow4YN9O9//5s++ugjeuCBB/zbcErzW2+9Re+++y6tX7+e7rzzTpE+PXToULenB+IcrNYMAAAgLC6hZcuWiZoqsthghgwZIgrC7d271y9emMaNG4u0ZhYor732GtWvX5/efvttkSmkMXDgQDp48CCNGjVKBNq2b99epDwbA3FB2QUGFgAAABGrw+IVUIcl/jnn8W/o1Jki+unRy6helbRYnw4AAIDyVocFAEeUyGY4hAAAAKiAYAEAAACA54FgAZ6qw4KYWwAAACogWAAAAADgeSBYgCfQQr8TEMUCAABAAQQLAAAAADwPBAvwBFpuPWJYAAAAqIBgAQAAAIDngWABngIGFgAAACogWIAnKAMFlwEAAEQQCBbgLWBiAQAAoACCBXgC2FcAAABYAcECPAXqsAAAAFABwQI8AUJYAAAAWAHBAjwF6rAAAABQAcECAAAAAM8DwQI8BQwsAAAAVECwgJiDGiwAAADsgGABniIBQSwAAAAUQLCAmAMDCwAAADsgWAAAAADgeSBYQMyRDSxwCAEAAFABwQIAAAAAzwPBAjyVJYSYWwAAACogWAAAAADgeSBYgMdiWGBiAQAAEAgECwAAAAA8DwQL8FYdFhhYAAAAKIBgAQAAAIDngWABMccnRbEgSwgAAIAKCBYAAAAAlE3BMm7cOGrUqBGlpqZS165dacmSJabb9ujRQyxoZ3z069fPv83NN98c8P4VV1wR3DcCcR3DAgMLAAAAFRXIJVOnTqXhw4fThAkThFh59dVXqU+fPrRx40aqVatWwPaffvopnTp1yv/88OHD1K5dOxowYIBuOxYo77zzjv95SkqK21MDAAAAQBnFtYXllVdeoWHDhtHQoUOpZcuWQrikp6fTxIkTldtXq1aN6tSp43/MnDlTbG8ULCxQ5O2qVq0a/LcCcQtb1wAAAICQBAtbSpYvX069evUq3UFioni+cOFCR/v473//S4MGDaJKlSrpXp8zZ46w0DRv3pzuvPNOYYkxo6CggPLy8nQPAAAAAJRdXAmWQ4cOUWFhIdWuXVv3Oj/ft2+f7ec51mXt2rV02223BbiDJk+eTLNmzaJ//OMfNHfuXOrbt684loqxY8dSZmam/5Gdne3mawAPA/sKAACAsMSwhAJbV9q0aUNdunTRvc4WFw1+v23bttS0aVNhdenZs2fAfkaOHCniaDTYwgLRUkYKxwEAAAChWlhq1KhBSUlJtH//ft3r/JzjTqw4duwYTZkyhW699Vbb4zRp0kQca/Pmzcr3Od4lIyND9wBlA4SwAAAACFmwJCcnU8eOHYXrRqOoqEg879atm+VnP/74YxF7cuONN9oeZ9euXSKGpW7dum5OD5SBwnEAAABAWLKE2BXz1ltv0bvvvkvr168XAbJsPeGsIWbw4MHCZaNyB/Xv35+qV6+uez0/P58efvhhWrRoEW3fvl2In6uvvpqaNWsm0qVB+QKrNQMAAAhLDMvAgQPp4MGDNGrUKBFo2759e5oxY4Y/EHfHjh0ic0iGa7TMnz+fvvvuu4D9sYtp9erVQgDl5ORQVlYW9e7dm55++mnUYiknIIYFAACAHQk+X/wPFxx0y9lCubm5iGeJQ44VnKFWo78Vf294+gpKrZgU61MCAADgsfEbawmBmBP3ihkAAEDEgWABAAAAgOeBYAExpwx4JQEAAEQYCBYAAAAAeB4IFhBzZPsKCscBAABQAcECAAAAAM8DwQJijhzCgsJxAAAAVECwAAAAAMDzQLCA2CNbWGBgAQAAoACCBQAAAACeB4IFeGq1ZhhYAAAAqIBgAQAAAIDngWAB3soSQhALAAAABRAsAAAAAPA8ECzAU8C+AgAAQAUEC4g5WPoQAACAHRAswFMghAUAAIAKCBYQc3xy1C0AAACgAIIFeApkCQEAAFABwQJiDuwrAAAA7IBgAQAAAIDngWABMQchLAAAAOyAYAEAAACA54FgAZ5Z/BDxtgAAAMyAYAEAAACA54FgAbGnJIYFBhYAAABmQLAAAAAAwPNAsICYoyUJoWgcAAAAMyBYAAAAAOB5IFiAZ+qwwL4CAADADAgWAAAAAJRNwTJu3Dhq1KgRpaamUteuXWnJkiWm206aNEnEJsgP/pxxtd5Ro0ZR3bp1KS0tjXr16kWbNm0K5tRAHII6LAAAAMIuWKZOnUrDhw+n0aNH04oVK6hdu3bUp08fOnDggOlnMjIyaO/evf7Hb7/9pnv/hRdeoNdff50mTJhAixcvpkqVKol9njx50u3pAQAAAKAM4lqwvPLKKzRs2DAaOnQotWzZUoiM9PR0mjhxouln2KpSp04d/6N27do668qrr75Kjz/+OF199dXUtm1bmjx5Mu3Zs4emTZsW/DcDcRjDAhMLAACAMAiWU6dO0fLly4XLxr+DxETxfOHChaafy8/Pp4YNG1J2drYQJevWrfO/t23bNtq3b59un5mZmcLVZLVPAAAAAJQfXAmWQ4cOUWFhoc5CwvBzFh0qmjdvLqwvn3/+Ob333ntUVFRE3bt3p127don3tc+52WdBQQHl5eXpHiB+8S/WDAMLAACAWGUJdevWjQYPHkzt27enSy65hD799FOqWbMm/ec//wl6n2PHjhVWGO3BlhsAAAAAlF1cCZYaNWpQUlIS7d+/X/c6P+fYFCdUrFiROnToQJs3bxbPtc+52efIkSMpNzfX/9i5c6ebrwE8CgwsAAAAwiJYkpOTqWPHjjRr1iz/a+zi4edsSXECu5TWrFkjUpiZxo0bC2Ei75NdPJwtZLbPlJQUkXkkP0D8woHXAAAAgBUVyCWc0jxkyBDq1KkTdenSRWT4HDt2TGQNMez+qVevnnDbME899RSdf/751KxZM8rJyaEXX3xRpDXfdttt/gyi+++/n5555hk6++yzhYB54oknKCsri/r37+/29EAcgzosAAAAwiZYBg4cSAcPHhSF3jgolmNTZsyY4Q+a3bFjh8gc0vj9999FGjRvW7VqVWGhWbBggUiJ1hgxYoQQPbfffrsQNRdeeKHYp7HAHCibwMACAADAjgRfGbDHswuJg285ngXuofhj55HjdNELP1BaxSRa//QVsT4dAAAAHhy/sZYQAAAAADwPBAvwDIhhAQAAYAYEC4g58e+UBAAAEGkgWAAAAADgeSBYQMzxlRTnh0cIAACAGRAsAAAAAPA8ECzAMzEsXEQQAAAAUAHBAgAAAADPA8ECYo6WJAT7CgAAADMgWAAAAADgeSBYQMzxrw4BEwsAAAATIFgAAAAA4HkgWEDMQQwLAAAAOyBYAAAAAOB5IFhAzEEdFgAAAHZAsAAAAADA80CwAA9QspYQDCwAAABMgGABAAAAgOeBYAExB2VYAAAA2AHBAgAAAADPA8ECPAOyhAAAAJgBwQI8UzgOAAAAMAOCBXgG2FcAAACYAcECPBN0CwAAAJgBwQIAAAAAzwPBAmKOD4XjAAAA2ADBAgAAAADPA8ECPBTDAhMLAAAANRAsAAAAAPA8ECzAO6X5YWABAABgAgQLAAAAADwPBAvwTpZQrE8EAABA2RIs48aNo0aNGlFqaip17dqVlixZYrrtW2+9RRdddBFVrVpVPHr16hWw/c033yzWkZEfV1xxRTCnBgAAAIAyiGvBMnXqVBo+fDiNHj2aVqxYQe3ataM+ffrQgQMHlNvPmTOHrr/+evrhhx9o4cKFlJ2dTb1796bdu3frtmOBsnfvXv/jww8/DP5bgbgCMSwAAADCLlheeeUVGjZsGA0dOpRatmxJEyZMoPT0dJo4caJy+/fff5/uuusuat++PbVo0YLefvttKioqolmzZum2S0lJoTp16vgfbI0BAAAAAHAtWE6dOkXLly8Xbh2NxMRE8ZytJ044fvw4nT59mqpVqxZgialVqxY1b96c7rzzTjp8+LDpPgoKCigvL0/3APFPAqJYAAAAhEOwHDp0iAoLC6l27dq61/n5vn37HO3jkUceoaysLJ3oYXfQ5MmThdXlH//4B82dO5f69u0rjqVi7NixlJmZ6X+wmwkAAAAAZZcK0TzY888/T1OmTBHWFA7Y1Rg0aJD/7zZt2lDbtm2padOmYruePXsG7GfkyJEijkaDLSwQLfELYlgAAACE1cJSo0YNSkpKov379+te5+ccd2LFSy+9JATLd999JwSJFU2aNBHH2rx5s/J9jnfJyMjQPQAAAABQdnElWJKTk6ljx466gFktgLZbt26mn3vhhRfo6aefphkzZlCnTp1sj7Nr1y4Rw1K3bl03pwfiFNRhAQAAEPYsIXbFcG2Vd999l9avXy8CZI8dOyayhpjBgwcLl40Gx6Q88cQTIouIa7dwrAs/8vPzxfv878MPP0yLFi2i7du3C/Fz9dVXU7NmzUS6NAAAAACA6xiWgQMH0sGDB2nUqFFCeHC6MltOtEDcHTt2iMwhjfHjx4vsoj//+c+6/XAdlzFjxggX0+rVq4UAysnJEQG5XKeFLTLs+gHlKYYFNhYAAABqEnw+bbiIXzjolrOFcnNzEc8Sh/y8M4euHvcT1auSRj89elmsTwcAAIAHx2+sJQQAAAAAzwPBAmJO3Jv4AAAARBwIFuAZEMICAADADAgWEHPKQBgVAACACAPBAgAAAADPA8ECYo5mX4FLCAAAgBkQLAAAAADwPBAswDuF41CcHwAAgAkQLAAAAADwPBAswAOULH4IAwsAAAATIFgAAAAA4HkgWICHYlgAAAAANRAsAAAAAPA8ECzAQ3VYYGMBAACgBoIFAAAAAJ4HggXEHMSwAAAAsAOCBQAAAACeB4IFeGe1ZphYAAAAmADBAgAAAADPA8ECvJMlFOPzAAAA4F0gWAAAAADgeSBYQMzxh7CgDgsAAAATIFgAAAAA4HkgWEDM8WmrNcf6RAAAAHgWCBYAAAAAeB4IFgAAAAB4HggWEHv8QbexPhEAAABeBYIFAAAAAJ4HggV4qHAcTCwAAADUQLAAAAAAwPNAsAAPFY6L9ZkAAAAoU4Jl3Lhx1KhRI0pNTaWuXbvSkiVLLLf/+OOPqUWLFmL7Nm3a0PTp0wNW6x01ahTVrVuX0tLSqFevXrRp06ZgTg0AAAAAZRDXgmXq1Kk0fPhwGj16NK1YsYLatWtHffr0oQMHDii3X7BgAV1//fV066230sqVK6l///7isXbtWv82L7zwAr3++us0YcIEWrx4MVWqVEns8+TJk6F9OxBXheMAAAAAMxJ8bN5wAVtUOnfuTG+88YZ4XlRURNnZ2XTvvffSo48+GrD9wIED6dixY/TVV1/5Xzv//POpffv2QqDw4bOysujBBx+khx56SLyfm5tLtWvXpkmTJtGgQYNszykvL48yMzPF5zIyMtx8HeABftx0kG767xJqUecsmnH/xbE+HQAAAFHCzfhdwc2OT506RcuXL6eRI0f6X0tMTBQunIULFyo/w6+zRUaGrSfTpk0Tf2/bto327dsn9qHBJ8/CiD+rEiwFBQXiIX/hSHCmsIienb4+IvsGpez+/YT4F4sfAgAACItgOXToEBUWFgrrhww/37Bhg/IzLEZU2/Pr2vvaa2bbGBk7diw9+eSTFGmKfETv/LQ94scBxZyV4up2BAAAUI6IyxGCLTyy1YYtLOyWCjeJCUR3X9o07PsFgSQmJNAf2mbF+jQAAACUBcFSo0YNSkpKov379+te5+d16tRRfoZft9pe+5df4ywheRuOc1GRkpIiHpGmQlIiPdynRcSPAwAAAIAwZgklJydTx44dadasWf7XOOiWn3fr1k35GX5d3p6ZOXOmf/vGjRsL0SJvwxYTzhYy2ycAAAAAyheuXULsihkyZAh16tSJunTpQq+++qrIAho6dKh4f/DgwVSvXj0RZ8Lcd999dMkll9DLL79M/fr1oylTptCyZcvozTff9Ada3n///fTMM8/Q2WefLQTME088ITKHOP0ZAAAAAMC1YOE05YMHD4pCbxwUy26bGTNm+INmd+zYITKHNLp3704ffPABPf744/TYY48JUcIZQq1bt/ZvM2LECCF6br/9dsrJyaELL7xQ7JMLzQEAAAAAuK7D4kVQhwUAAAAo2+M31hICAAAAgOeBYAEAAACA54FgAQAAAIDngWABAAAAgOeBYAEAAACA54FgAQAAAIDngWABAAAAgOeBYAEAAACA54FgAQAAAEDZK83vRbRivVwxDwAAAADxgTZuOym6XyYEy9GjR8W/2dnZsT4VAAAAAAQxjnOJ/jK/llBRURHt2bOHzjrrLLH6c7jVHwuhnTt3Yp2iOADXK37AtYofcK3ii7w4ul4sQVisZGVl6RZOLrMWFv6S9evXj+gx+KJ7/cKDUnC94gdcq/gB1yq+yIiT62VnWdFA0C0AAAAAPA8ECwAAAAA8DwSLDSkpKTR69GjxL/A+uF7xA65V/IBrFV+klNHrVSaCbgEAAABQtoGFBQAAAACeB4IFAAAAAJ4HggUAAAAAngeCBQAAAACeB4LFhnHjxlGjRo0oNTWVunbtSkuWLIn1KZUrxo4dS507dxZVjGvVqkX9+/enjRs36rY5efIk3X333VS9enWqXLkyXXvttbR//37dNjt27KB+/fpRenq62M/DDz9MZ86cifK3KX88//zzovr0/fff738N18s77N69m2688UZxLdLS0qhNmza0bNky//uckzFq1CiqW7eueL9Xr160adMm3T6OHDlCN9xwgyhQVqVKFbr11lspPz8/Bt+mbFNYWEhPPPEENW7cWFyLpk2b0tNPP61bg6fMXy/OEgJqpkyZ4ktOTvZNnDjRt27dOt+wYcN8VapU8e3fvz/Wp1Zu6NOnj++dd97xrV271rdq1SrflVde6WvQoIEvPz/fv80dd9zhy87O9s2aNcu3bNky3/nnn+/r3r27//0zZ874Wrdu7evVq5dv5cqVvunTp/tq1KjhGzlyZIy+VflgyZIlvkaNGvnatm3ru++++/yv43p5gyNHjvgaNmzou/nmm32LFy/2bd261fftt9/6Nm/e7N/m+eef92VmZvqmTZvm+/nnn31//OMffY0bN/adOHHCv80VV1zha9eunW/RokW+H3/80desWTPf9ddfH6NvVXZ59tlnfdWrV/d99dVXvm3btvk+/vhjX+XKlX2vvfZaubleECwWdOnSxXf33Xf7nxcWFvqysrJ8Y8eOjel5lWcOHDjA0wnf3LlzxfOcnBxfxYoVxY9XY/369WKbhQsXiuc84CUmJvr27dvn32b8+PG+jIwMX0FBQQy+Rdnn6NGjvrPPPts3c+ZM3yWXXOIXLLhe3uGRRx7xXXjhhabvFxUV+erUqeN78cUX/a/x9UtJSfF9+OGH4vkvv/wirt3SpUv923zzzTe+hIQE3+7duyP8DcoX/fr1891yyy2616655hrfDTfcUG6uF1xCJpw6dYqWL18uTGrymkX8fOHChTE9t/JMbm6u+LdatWriX75Gp0+f1l2nFi1aUIMGDfzXif9lU3ft2rX92/Tp00csELZu3bqof4fyALt82KUjXxcG18s7fPHFF9SpUycaMGCAcLt16NCB3nrrLf/727Zto3379umuFa/5wq5x+VqxW4H3o8Hbc1+5ePHiKH+jsk337t1p1qxZ9Ouvv4rnP//8M82fP5/69u1bbq5XmVj8MBIcOnRI+AzlTpPh5xs2bIjZeZVneFVujoW44IILqHXr1uI1/oEmJyeLH6HxOvF72jaq66i9B8LLlClTaMWKFbR06dKA93C9vMPWrVtp/PjxNHz4cHrsscfE9frb3/4mrs+QIUP8ba26FvK1YrEjU6FCBTGhwLUKL48++qgQ7Szwk5KSxPj07LPPingUpjxcLwgWEFez9rVr14pZBfAmvJz9fffdRzNnzhSB6sDbEwCeaT/33HPiOVtY+Pc1YcIEIViAt/joo4/o/fffpw8++IBatWpFq1atEhO4rKyscnO94BIyoUaNGkLFGrMX+HmdOnVidl7llXvuuYe++uor+uGHH6h+/fr+1/lasPsuJyfH9Drxv6rrqL0Hwge7fA4cOEDnnXeemLnxY+7cufT666+Lv3m2h+vlDTiTpGXLlrrXzj33XJGhJbe1VR/I//L1luFsLs5EwbUKLw8//LCwsgwaNEi4TG+66SZ64IEHRCZlebleECwmsFm0Y8eOwmcoz0j4ebdu3WJ6buUJDgxnsfLZZ5/R7NmzRUqfDF+jihUr6q4Tpz1zp6tdJ/53zZo1uh8qWwA4rc/YYYPQ6Nmzp2hrnv1pD57Fs9la+xvXyxuwa9VYIoDjIxo2bCj+5t8aD2LytWKXBMc6yNeKxScLVQ3+nXJfybETIHwcP35cxJrI8KSa27rcXK9YR/16Pa2ZI6wnTZokoqtvv/12kdYsZy+AyHLnnXeKNL05c+b49u7d638cP35clybLqc6zZ88WabLdunUTD2OabO/evUVq9IwZM3w1a9ZEmmyUkLOEGFwv76SdV6hQQaTLbtq0yff+++/70tPTfe+9954uTZb7vM8//9y3evVq39VXX61Mk+3QoYNIjZ4/f77IDouXNNl4YsiQIb569er505o//fRTke4/YsSIcnO9IFhs+Ne//iU6V67HwmnOnLsOogdratWDa7No8I/xrrvu8lWtWlV0uH/605+EqJHZvn27r2/fvr60tDTxI3/wwQd9p0+fjsE3Kn8YBQuul3f48ssvhTjkiVmLFi18b775pu59TpV94oknfLVr1xbb9OzZ07dx40bdNocPHxYDHtcE4dTzoUOHirR2EF7y8vLE74jHo9TUVF+TJk18f//733Wp/mX9eiXw/2Jt5QEAAAAAsAIxLAAAAADwPBAsAAAAAPA8ECwAAAAA8DwQLAAAAADwPBAsAAAAAPA8ECwAAAAA8DwQLAAAAADwPBAsAAAAAPA8ECwAAAAA8DwQLAAAAADwPBAsAAAAAPA8ECwAAAAAIK/z/5SL6Bf/yg3sAAAAAElFTkSuQmCC", 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", "text/plain": [ "
" ] @@ -1659,7 +1700,7 @@ "metadata": {}, "outputs": [], "source": [ - "del output_data_combined, update_data_combined" + "del output_data_combined" ] }, { @@ -1763,7 +1804,16 @@ "execution_count": 50, "id": "b924a3a8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p_values shape: (11, 11) and q_values shape: (11, 11)\n", + "p_envelope shape: (11, 11, 168, 2000)\n" + ] + } + ], "source": [ "min_p = -1e7\n", "max_p = 1e7\n", @@ -1775,9 +1825,10 @@ "q_linear_values = np.linspace(start=min_q, stop=max_q, num=resolution)\n", "\n", "p_values, q_values = np.meshgrid(p_linear_values, q_linear_values, copy=False)\n", + "print(f\"p_values shape: {p_values.shape} and q_values shape: {q_values.shape}\")\n", "\n", - "\n", - "p_envelope = np.tile(df_load_profile.to_numpy(), (resolution, resolution, 1, 1))\n", + "p_envelope = np.tile(profile, (resolution, resolution, 1, 1))\n", + "print(f\"p_envelope shape: {p_envelope.shape}\")\n", "p_envelope[:, :, :, sym_load_node_10_idx] += p_values[:, :, np.newaxis]\n", "\n", "q_envelope = np.zeros(p_envelope.shape)\n", @@ -1817,18 +1868,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation with grid of P and Q to calculate an envelope of operation is 42.895948 s\n" + "Execution time for Batch Calculation with grid of P and Q to calculate an envelope of operation is 38.109268 s\n", + "nodal_voltages shape: (11, 11, 168, 2001)\n" ] - }, - { - "data": { - "text/plain": [ - "(121, 168, 2001)" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -1840,8 +1882,8 @@ " threading=0\n", " )\n", "\n", - "nodal_voltages = output_data_envelope[ComponentType.node][\"u_pu\"].reshape(resolution * resolution, n_timestamps, -1)\n", - "nodal_voltages.shape" + "nodal_voltages = output_data_envelope[ComponentType.node][\"u_pu\"].reshape(resolution, resolution, n_timestamps, -1)\n", + "print(f\"nodal_voltages shape: {nodal_voltages.shape}\")" ] }, { @@ -1855,6 +1897,52 @@ "* Apply this mask on the grid of P and Q for each time step." ] }, + { + "cell_type": "code", + "execution_count": 53, + "id": "1a298bf9", + "metadata": {}, + "outputs": [], + "source": [ + "u_low_lim = 0.95\n", + "u_high_lim = 1.03\n", + "\n", + "max_voltage_scenario = np.max(nodal_voltages, axis=3) > u_high_lim\n", + "min_voltage_scenario = np.min(nodal_voltages, axis=3) < u_low_lim\n", + "points_outside_limits = np.logical_or(max_voltage_scenario, min_voltage_scenario)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "70460504", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(points_outside_limits[:,:, 0])" + ] + }, { "cell_type": "markdown", "id": "1d0d4c20", diff --git a/pgm-advanced-usage-workshop/utils.py b/pgm-advanced-usage-workshop/utils.py index 59b1f8d..ffea1fb 100644 --- a/pgm-advanced-usage-workshop/utils.py +++ b/pgm-advanced-usage-workshop/utils.py @@ -12,7 +12,7 @@ # Polling-backed variant to capture short-lived peaks import threading -import time as _time +import time class Timer: @@ -21,11 +21,11 @@ def __init__(self, name: str): self.start = None def __enter__(self): - self.start = _time.perf_counter() + self.start = time.perf_counter() def __exit__(self, *args): print( - f"Execution time for {self.name} is {(_time.perf_counter() - self.start):0.6f} s" + f"Execution time for {self.name} is {(time.perf_counter() - self.start):0.6f} s" ) @@ -132,7 +132,7 @@ def __init__(self, name: str, logger: PerfLogger): def __enter__(self): # start timer and memory monitor - self._start_time = _time.perf_counter() + self._start_time = time.perf_counter() self._start_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024 self.timer.__enter__() self.memory_monitor.__enter__() @@ -140,7 +140,7 @@ def __enter__(self): def __exit__(self, *args): # capture end values and delegate to base - end_time = _time.perf_counter() + end_time = time.perf_counter() self.timer.__exit__(*args) self.memory_monitor.__exit__(*args) end_mem = self.memory_monitor.process.memory_info().rss / 1024 / 1024 @@ -168,7 +168,7 @@ def _poll(self): cur = proc.memory_info().rss / 1024 / 1024 if cur > self.memory_monitor.peak_memory: self.memory_monitor.peak_memory = cur - __time.sleep(self.poll_interval) + time.sleep(self.poll_interval) def __enter__(self): # start base enter first to initialize memory_monitor diff --git a/pgm-advanced-usage-workshop/utils_notebook.ipynb b/pgm-advanced-usage-workshop/utils_notebook.ipynb index e224be9..7830960 100644 --- a/pgm-advanced-usage-workshop/utils_notebook.ipynb +++ b/pgm-advanced-usage-workshop/utils_notebook.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "c05df45c", "metadata": {}, "outputs": [], @@ -15,12 +15,13 @@ " LoggedPerformanceMonitor,\n", " PollingLoggedPerformanceMonitor,\n", ")\n", - "import numpy as np" + "import numpy as np\n", + "import gc" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "9567c5da", "metadata": {}, "outputs": [ @@ -28,7 +29,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Add Operation is 0.005363 s\n" + "Execution time for Add Operation is 0.003038 s\n" ] } ], @@ -43,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "efaa3563", "metadata": {}, "outputs": [ @@ -52,8 +53,8 @@ "output_type": "stream", "text": [ "Memory usage for Large Array Creation:\n", - " Start: 117.07 MB\n", - " End: 155.22 MB\n", + " Start: 109.99 MB\n", + " End: 148.14 MB\n", " Net change: +38.15 MB\n", " Peak increase: +0.00 MB\n" ] @@ -69,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "f501714e", "metadata": {}, "outputs": [ @@ -77,11 +78,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Matrix Operations is 0.350366 s\n", + "Execution time for Matrix Operations is 0.482387 s\n", "Memory usage for Matrix Operations:\n", - " Start: 155.22 MB\n", - " End: 166.23 MB\n", - " Net change: +11.01 MB\n", + " Start: 148.18 MB\n", + " End: 159.18 MB\n", + " Net change: +11.00 MB\n", " Peak increase: +0.00 MB\n", "--- End of Matrix Operations performance report ---\n", "\n" @@ -105,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "26b92c96", "metadata": {}, "outputs": [ @@ -114,21 +115,10 @@ "output_type": "stream", "text": [ "Memory usage for Transient Peak Example:\n", - " Start: 166.23 MB\n", - " End: 173.88 MB\n", - " Net change: +7.64 MB\n", - " Peak increase: +68.68 MB\n" - ] - }, - { - "ename": "NameError", - "evalue": "name 'gc' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[32m 14\u001b[39m \u001b[38;5;66;03m# Remove the temporary allocation and force GC\u001b[39;00m\n\u001b[32m 15\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m temp\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m \u001b[43mgc\u001b[49m.collect()\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# Final small operation\u001b[39;00m\n\u001b[32m 19\u001b[39m a = a * \u001b[32m2\u001b[39m\n", - "\u001b[31mNameError\u001b[39m: name 'gc' is not defined" + " Start: 159.20 MB\n", + " End: 159.20 MB\n", + " Net change: +0.01 MB\n", + " Peak increase: +68.67 MB\n" ] } ], @@ -159,10 +149,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "b063cc24", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Execution time for Polled Transient Peak is 0.295633 s\n", + "Memory usage for Polled Transient Peak:\n", + " Start: 159.22 MB\n", + " End: 159.22 MB\n", + " Net change: +0.00 MB\n", + " Peak increase: +61.06 MB\n", + " name start_time end_time duration_s start_mem_mb end_mem_mb net_mem_change_mb peak_mem_mb peak_increase_mb\n", + "Polled Transient Peak 1.904327e+06 1.904328e+06 0.295648 159.21875 159.21875 0.0 220.277344 61.058594\n" + ] + } + ], "source": [ "# Usage example: poll-backed logger to reliably capture transient peak\n", "perf_logger = PerfLogger()\n", @@ -181,10 +186,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "60715e98", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plotting: visualize duration and peak increase\n", "import matplotlib.pyplot as plt\n", @@ -215,6 +231,14 @@ " plt.tight_layout()\n", " plt.show()\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8df8d2fd", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 10d3dd1e128ae2b2e4f97df3b5624ffe6d1d4c8d Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Sat, 29 Nov 2025 18:33:10 +0100 Subject: [PATCH 6/8] minor details Signed-off-by: Nitish Bharambe --- .../Advanced Workshop.ipynb | 166 ++++++++++++------ pgm-advanced-usage-workshop/utils.py | 14 +- .../utils_notebook.ipynb | 44 +++-- 3 files changed, 151 insertions(+), 73 deletions(-) diff --git a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb index 762d764..658a5fb 100644 --- a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb +++ b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb @@ -24,7 +24,10 @@ " * Dynamic operating Envelope\n", " * Simulation design exercise\n", " * Additional content\n", - " * Simulation design exercise" + " * Simulation design exercise\n", + "\n", + "Throughout this notebook there are big notebook cells of code. \n", + "To have a deeper understanding of the simulations, comment all + uncomment line by line by confirming the debugging print statements." ] }, { @@ -231,7 +234,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current memory usage: 170.34 MB\n", + "Current memory usage: 170.72 MB\n", "Memory used by object: 0.00766754150390625 MB\n", "Memory used by object: 0.00016021728515625 MB\n", "Memory used by dictionary: 0.00766754150390625 MB\n", @@ -858,7 +861,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson row based data is 0.211790 s\n", + "Execution time for Batch Calculation using Newton-Raphson row based data is 0.648063 s\n", "Memory used by dictionary: 56.412353515625 MB\n" ] } @@ -922,7 +925,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.256051 s\n" + "Execution time for Batch Calculation using Newton-Raphson columnar data is 1.087428 s\n" ] } ], @@ -955,7 +958,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.253432 s\n", + "Execution time for Batch Calculation using Newton-Raphson columnar data is 0.816132 s\n", "output_data_selective contains following: \n", "Keys: dict_keys([]). \n", "Attributes: dict_keys(['loading'])\n", @@ -1081,8 +1084,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Validation of full batch data is 0.892844 s\n", - "Execution time for Validation of partial batch data is 0.031841 s\n" + "Execution time for Validation of full batch data is 4.033818 s\n", + "Execution time for Validation of partial batch data is 0.120680 s\n" ] } ], @@ -1130,7 +1133,15 @@ "execution_count": 33, "id": "2c28f3fa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of ev_profile: (168, 10)\n" + ] + } + ], "source": [ "# Hourly EV profile data for one year. Row = hours, column = profiles\n", "ev_profile = (\n", @@ -1139,11 +1150,14 @@ " )\n", " * 1000\n", ") # kw\n", + "\n", "# Select first n_timestamps and first n profiles\n", "n_ev_profiles = 10\n", + "\n", "assert n_timestamps <= ev_profile.shape[0]\n", "assert n_ev_profiles <= ev_profile.shape[1]\n", - "ev_profile = ev_profile[:n_timestamps, :n_ev_profiles]" + "ev_profile = ev_profile[:n_timestamps, :n_ev_profiles] # Hint: Slicing\n", + "print(f\"Shape of ev_profile: {ev_profile.shape}\") # Prints: (n_timestamps * n_ev_profiles) " ] }, { @@ -1187,20 +1201,20 @@ "n_vehicle_each_node = rng.multinomial(\n", " n=n_vehicles, pvals=[1 / n_loads] * n_loads, size=n_experiments\n", ")\n", - "print(f\"Shape of n_vehicle_each_node: {n_vehicle_each_node.shape}\")\n", + "print(f\"Shape of n_vehicle_each_node: {n_vehicle_each_node.shape}\") # Prints: (n_experiments * n_loads) \n", "\n", "distributions = rng.multinomial(\n", " n=n_vehicle_each_node, pvals=[1 / n_ev_profiles] * n_ev_profiles\n", ")\n", - "print(f\"Shape of distributions: {distributions.shape}\")\n", + "print(f\"Shape of distributions: {distributions.shape}\") # Prints: (n_experiments, n_loads, n_ev_profiles) \n", "\n", "ev_timeseries = np.transpose(distributions @ ev_profile.T, axes=(0, 2, 1))\n", - "print(f\"Shape of ev_timeseries: {ev_timeseries.shape}\")\n", + "print(f\"Shape of ev_timeseries: {ev_timeseries.shape}\") # Prints: (n_experiments, n_timestamps, n_loads) \n", "\n", - "ev_timeseries_q = ev_timeseries * np.tan(np.arccos(0.95))\n", + "ev_timeseries_q = ev_timeseries * np.tan(np.arccos(0.95)) # assume power factor of 0.95\n", "\n", "combined_timeseries = profile[np.newaxis, :, :] + ev_timeseries\n", - "print(f\"Shape of combined_timeseries: {combined_timeseries.shape}\")\n" + "print(f\"Shape of combined_timeseries: {combined_timeseries.shape}\") # Prints: (n_experiments, n_timestamps, n_loads) \n" ] }, { @@ -1220,7 +1234,7 @@ "source": [ "update_data_multi_timeseries = {\n", " ComponentType.sym_load: {\n", - " \"p_specified\": combined_timeseries.reshape(-1, n_loads),\n", + " \"p_specified\": combined_timeseries.reshape(-1, n_loads), # All update data Should be 2D eventually\n", " \"q_specified\": ev_timeseries.reshape(-1, n_loads),\n", " }\n", "}" @@ -1236,7 +1250,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Run multitimeseries PF is 1.509123 s\n", + "Execution time for Run multitimeseries PF is 7.146938 s\n", "Shape of reshaped_loading_data: (11, 168, 2000)\n" ] } @@ -1251,7 +1265,7 @@ " )\n", "\n", "reshaped_loading_data = output_data[ComponentType.line][\"loading\"].reshape(n_experiments, n_timestamps, n_loads)\n", - "print(f\"Shape of reshaped_loading_data: {reshaped_loading_data.shape}\")\n" + "print(f\"Shape of reshaped_loading_data: {reshaped_loading_data.shape}\") # Prints: (n_experiments, n_timestamps, n_loads) \n" ] }, { @@ -1271,13 +1285,13 @@ { "data": { "text/plain": [ - "array([[1.689, 1.512, 1.334, ..., 0.519, 0.351, 0.181],\n", - " [1.676, 1.498, 1.331, ..., 0.545, 0.367, 0.184],\n", - " [1.702, 1.521, 1.346, ..., 0.5 , 0.338, 0.179],\n", + "array([[1.71 , 1.558, 1.378, ..., 0.506, 0.335, 0.171],\n", + " [1.71 , 1.527, 1.349, ..., 0.551, 0.35 , 0.17 ],\n", + " [1.684, 1.508, 1.333, ..., 0.54 , 0.35 , 0.183],\n", " ...,\n", - " [1.753, 1.577, 1.396, ..., 0.552, 0.359, 0.182],\n", - " [1.723, 1.554, 1.369, ..., 0.552, 0.368, 0.185],\n", - " [1.714, 1.536, 1.358, ..., 0.533, 0.354, 0.188]], shape=(168, 2000))" + " [1.79 , 1.632, 1.446, ..., 0.53 , 0.352, 0.198],\n", + " [1.686, 1.539, 1.364, ..., 0.53 , 0.353, 0.182],\n", + " [1.721, 1.55 , 1.384, ..., 0.521, 0.345, 0.182]], shape=(168, 2000))" ] }, "execution_count": 37, @@ -1286,7 +1300,7 @@ } ], "source": [ - "np.mean(reshaped_loading_data, axis=0)" + "np.mean(reshaped_loading_data, axis=0) # Shape: (n_timestamps, n_loads) " ] }, { @@ -1317,7 +1331,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Run multi-timeseries PF in loop is 3.535915 s\n" + "Execution time for Run multi-timeseries PF in loop is 6.871816 s\n" ] } ], @@ -1378,10 +1392,30 @@ "execution_count": 40, "id": "e8408fb2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id | from_node | to_node | from_status | to_status | feeder_branch_id | feeder_node_id | is_feeder | r1 | x1 | c1 | tan1 | i_n \n", + " 2001 | 0 | 1 | 1 | 1 | -2147483648 | -2147483648 | False |0.100..|0.104..|3.2..|0.098..| 500.0 \n", + " 2002 | 1 | 2 | 1 | 1 | -2147483648 | -2147483648 | False |0.098..|0.102..|3.1..|0.109..| 500.0 \n", + " 2003 | 2 | 3 | 1 | 1 | -2147483648 | -2147483648 | False |0.109..|0.097..|3.3..|0.095..| 500.0 \n", + " 2004 | 3 | 4 | 1 | 1 | -2147483648 | -2147483648 | False |0.101..|0.108..|2.9..|0.100..| 500.0 \n", + " 2005 | 4 | 5 | 1 | 1 | -2147483648 | -2147483648 | False |0.090..|0.106..|3.3..|0.101..| 500.0 \n", + " (..1990 hidden rows..) \n", + " 3996 | 1995 | 1996 | 1 | 1 | -2147483648 | -2147483648 | False |0.095..|0.101..|3.0..|0.108..| 500.0 \n", + " 3997 | 1996 | 1997 | 1 | 1 | -2147483648 | -2147483648 | False |0.097..|0.105..|3.1..|0.103..| 500.0 \n", + " 3998 | 1997 | 1998 | 1 | 1 | -2147483648 | -2147483648 | False |0.108..|0.098..|3.2..|0.092..| 500.0 \n", + " 3999 | 1998 | 1999 | 1 | 1 | -2147483648 | -2147483648 | False |0.103..|0.094..|3.3..|0.104..| 500.0 \n", + " 4000 | 1999 | 2000 | 1 | 1 | -2147483648 | -2147483648 | False |0.108..|0.106..|3.2..|0.105..| 500.0 \n" + ] + } + ], "source": [ "core_interface = PowerGridModelInterface(input_data=input_data)\n", - "grid = core_interface.create_grid_from_input_data()" + "grid = core_interface.create_grid_from_input_data()\n", + "print(grid.line)" ] }, { @@ -1413,7 +1447,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1451,8 +1485,7 @@ "output_type": "stream", "text": [ "1. [2, 3, 4, 5, 6, 7, 8, 9, 10]\n", - "2. id | u_rated | node_type | feeder_branch_id | feeder_node_id \n", - " 0 | 10500.0 | 1 | -2147483648 | -2147483648 \n", + "2. [0]\n", "3. [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]\n", "4. ([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 9)\n", "5. 1\n", @@ -1469,7 +1502,7 @@ "print(f\"1. {grid.get_downstream_nodes(1)}\")\n", "\n", "# In case of multiple substations, find nearest one\n", - "print(f\"2. {grid.get_nearest_substation_node(10)}\")\n", + "print(f\"2. {grid.get_nearest_substation_node(10).id}\")\n", "\n", "# Find all paths between node 1 and node 10\n", "print(f\"3. {grid.graphs.active_graph.get_all_paths(1, 10)}\")\n", @@ -1586,12 +1619,13 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "e3f1ac0b", "metadata": {}, "outputs": [], "source": [ - "combos_contingencies = np.eye(contingency_count, dtype=np.uint8)\n", + "# TODO Other way possible\n", + "# combos_contingencies = np.eye(contingency_count, dtype=np.uint8)\n", "\n", "combos_contingencies_timestamps = np.tile(combos_contingencies, (1, n_timestamps))\n", "\n", @@ -1623,7 +1657,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation with contingencies and load profiles is 3.640553 s\n" + "Execution time for Batch Calculation with contingencies and load profiles is 9.043806 s\n" ] } ], @@ -1631,7 +1665,7 @@ "load_profile_combined = initialize_array(\n", " DatasetType.update,\n", " ComponentType.sym_load,\n", - " (contingency_count * profile.shape[0], profile.shape[1]),\n", + " (contingency_count * n_timestamps, n_loads),\n", ")\n", "\n", "load_profile_combined[\"p_specified\"] = np.tile(\n", @@ -1671,7 +1705,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 47, @@ -1680,7 +1714,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1741,6 +1775,24 @@ "```\n" ] }, + { + "cell_type": "markdown", + "id": "60a37750", + "metadata": {}, + "source": [ + "Another alternative to is to cache internal state of PowerGridModel object." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "71ae9bce", + "metadata": {}, + "outputs": [], + "source": [ + "model_topology_2 = model.copy()" + ] + }, { "cell_type": "markdown", "id": "0c8aa053", @@ -1780,13 +1832,22 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "id": "f6293765", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n" + ] + } + ], "source": [ "sym_load_node_10 = grid.sym_load.filter(node=10).id\n", - "sym_load_node_10_idx = model.get_indexer(ids=sym_load_node_10, component_type=ComponentType.sym_load)[0]" + "sym_load_node_10_idx = model.get_indexer(ids=sym_load_node_10, component_type=ComponentType.sym_load)[0]\n", + "print(sym_load_node_10_idx)" ] }, { @@ -1801,7 +1862,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "id": "b924a3a8", "metadata": {}, "outputs": [ @@ -1809,7 +1870,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "p_values shape: (11, 11) and q_values shape: (11, 11)\n", + "p_values shape: (11, 11)\n", + "q_values shape: (11, 11)\n", "p_envelope shape: (11, 11, 168, 2000)\n" ] } @@ -1825,10 +1887,12 @@ "q_linear_values = np.linspace(start=min_q, stop=max_q, num=resolution)\n", "\n", "p_values, q_values = np.meshgrid(p_linear_values, q_linear_values, copy=False)\n", - "print(f\"p_values shape: {p_values.shape} and q_values shape: {q_values.shape}\")\n", + "print(f\"p_values shape: {p_values.shape}\")\n", + "print(f\"q_values shape: {q_values.shape}\")\n", "\n", "p_envelope = np.tile(profile, (resolution, resolution, 1, 1))\n", "print(f\"p_envelope shape: {p_envelope.shape}\")\n", + "\n", "p_envelope[:, :, :, sym_load_node_10_idx] += p_values[:, :, np.newaxis]\n", "\n", "q_envelope = np.zeros(p_envelope.shape)\n", @@ -1845,7 +1909,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "id": "d2ad55f3", "metadata": {}, "outputs": [], @@ -1860,7 +1924,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "id": "6a152a12", "metadata": {}, "outputs": [ @@ -1868,7 +1932,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation with grid of P and Q to calculate an envelope of operation is 38.109268 s\n", + "Execution time for Batch Calculation with grid of P and Q to calculate an envelope of operation is 47.157588 s\n", "nodal_voltages shape: (11, 11, 168, 2001)\n" ] } @@ -1899,7 +1963,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "id": "1a298bf9", "metadata": {}, "outputs": [], @@ -1914,23 +1978,23 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "id": "70460504", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 54, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/pgm-advanced-usage-workshop/utils.py b/pgm-advanced-usage-workshop/utils.py index ffea1fb..8d96bab 100644 --- a/pgm-advanced-usage-workshop/utils.py +++ b/pgm-advanced-usage-workshop/utils.py @@ -1,16 +1,20 @@ +""" +Sample code to demonstrate tIme and memory Usage. + +Disclaimer: +The tools below except Timer are AI generated and not scrutinized adequately. +Their purpose is to demonstrate only possibilities. +""" -# For memory monitoring import psutil import gc import os -from collections import Counter import psutil import gc import os import pandas as pd -# Polling-backed variant to capture short-lived peaks import threading import time @@ -29,8 +33,6 @@ def __exit__(self, *args): ) - - class MemoryMonitor: def __init__(self, name: str): self.name = name @@ -151,8 +153,6 @@ def __exit__(self, *args): ) - - class PollingLoggedPerformanceMonitor(LoggedPerformanceMonitor): """Poll process memory on a short interval in a background thread to catch transient peaks.""" diff --git a/pgm-advanced-usage-workshop/utils_notebook.ipynb b/pgm-advanced-usage-workshop/utils_notebook.ipynb index 7830960..7e53e93 100644 --- a/pgm-advanced-usage-workshop/utils_notebook.ipynb +++ b/pgm-advanced-usage-workshop/utils_notebook.ipynb @@ -1,5 +1,19 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "8e504d19", + "metadata": {}, + "source": [ + "# Example memory and time usage utils\n", + "\n", + "The examples below demonstrate features included in the ./utils.py file for memory and time duration monitoring.\n", + "\n", + "Disclaimer: \n", + "All tools and this example except related to Timer are AI generated and not scrutinized adequately.\n", + "Their purpose is to demonstrate only possibilities. " + ] + }, { "cell_type": "code", "execution_count": 1, @@ -29,7 +43,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Add Operation is 0.003038 s\n" + "Execution time for Add Operation is 0.003431 s\n" ] } ], @@ -53,8 +67,8 @@ "output_type": "stream", "text": [ "Memory usage for Large Array Creation:\n", - " Start: 109.99 MB\n", - " End: 148.14 MB\n", + " Start: 109.91 MB\n", + " End: 148.06 MB\n", " Net change: +38.15 MB\n", " Peak increase: +0.00 MB\n" ] @@ -78,11 +92,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Matrix Operations is 0.482387 s\n", + "Execution time for Matrix Operations is 0.716264 s\n", "Memory usage for Matrix Operations:\n", - " Start: 148.18 MB\n", - " End: 159.18 MB\n", - " Net change: +11.00 MB\n", + " Start: 148.09 MB\n", + " End: 159.09 MB\n", + " Net change: +10.99 MB\n", " Peak increase: +0.00 MB\n", "--- End of Matrix Operations performance report ---\n", "\n" @@ -115,10 +129,10 @@ "output_type": "stream", "text": [ "Memory usage for Transient Peak Example:\n", - " Start: 159.20 MB\n", - " End: 159.20 MB\n", + " Start: 159.10 MB\n", + " End: 159.11 MB\n", " Net change: +0.01 MB\n", - " Peak increase: +68.67 MB\n" + " Peak increase: +68.68 MB\n" ] } ], @@ -157,14 +171,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Polled Transient Peak is 0.295633 s\n", + "Execution time for Polled Transient Peak is 0.331507 s\n", "Memory usage for Polled Transient Peak:\n", - " Start: 159.22 MB\n", - " End: 159.22 MB\n", + " Start: 159.12 MB\n", + " End: 159.12 MB\n", " Net change: +0.00 MB\n", " Peak increase: +61.06 MB\n", " name start_time end_time duration_s start_mem_mb end_mem_mb net_mem_change_mb peak_mem_mb peak_increase_mb\n", - "Polled Transient Peak 1.904327e+06 1.904328e+06 0.295648 159.21875 159.21875 0.0 220.277344 61.058594\n" + "Polled Transient Peak 2.152501e+06 2.152501e+06 0.331531 159.117188 159.117188 0.0 220.175781 61.058594\n" ] } ], @@ -192,7 +206,7 @@ "outputs": [ { "data": { - "image/png": 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2ASVKZJ7v2LEjyvfo+bDXK53ds15/+vRpuXTpUrhr9BehAdiz7gkAAOAOYhN7AQAA2EticaJr165JSEiIqWIKS58fO3Ysyvdowimq6/W89XXruWddE1FwcLA5rDSTZ83s2Uto8AO73RsAAIjd/5Zb7+3konO7x17OiJMkOOH8TgEASLDuOD9OcmpSKr4YNWqUDBkyJNJ5LWUHAAAJl88k+3/G3bt3TVW2qyJOAgDARY32cXqc5NSkVIYMGcTT01MuX74c7rw+9/X1jfI9ev5511t/6jndfS/sNdp3Kip9+/Y1DT+ttLfCjRs3JH369OLh4fEfviEAV6GZfv1/wIKCghJU7xgA9qMzfxpoaS/LhCI2sRdxEoAXIU4CENs4yalJqaRJk0rJkiVlw4YNZgc9a6Cjzz/88MMo31O+fHnzerdu3Wzn1q9fb86rnDlzmqBKr7EmofQ/kroLX6dOnaK8Z7JkycwRVpo0aeLsewJwHRpoEWwBsEpoFVKxib2IkwBEF3ESgJjGSU5fvqczb23atJFSpUpJmTJlZNKkSWZ3vXbt2pnXW7duLVmzZjWl4+rjjz+WKlWqyPjx46Vu3bqyaNEi2bt3r8ycOdO8rjN2mrAaPny45M2b1ySpBgwYYLJz1uALAADAXb0o9gIAAHAUpyelmjdvLlevXpWBAweaRuRa3bR27VpbA86zZ8+aXWGsXn31VVmwYIH0799fPv30U5N4WrlypRQuXNh2Ta9evUxw1bFjR7l165ZUrFjR3NPLy8sp3xEAACC+eFHsBQAA4CgeloS0ZQwAOInuPKUVm9pbJeIyFgAAAHdGnAQgtkhKAQAAAAAAwOH+XRcHAAAAAAAAOAhJKQAAAAAAADgcSSkAAAAAAAA4HEkpAAAAAAAAOBxJKQBwcexnAQAAEDXiJMC5SEoBgIvz8PCQ3377TQ4cOODsoQAAAMQrxEmAc5GUAgAXExQUZHscGhoqV65ckU6dOsmDBw+cOi4AAABnI04C4heSUgDgQlauXCk5cuSQTZs2meeJEiWS1KlTS0hIiHh7e5vgCwAAwB0RJwHxD0kpAHAh9erVk6ZNm0rz5s1ly5Yt5tz9+/dNaXratGlN8AUAAOCOiJOA+If/1QGAC0mcOLEsWrRIqlWrJo0bNzY9EqzBVooUKZw9PAAAAKchTgLiHw8L2w0AgMvRMvS3335bNm7cKJMnT5Zhw4ZJsWLFpGTJkpI0aVK5c+eOPHnyxJxr2LChs4cLAADgMMRJQPxBUgoAXNTTp0+lVatWsnTpUilYsKDpoaAzhNov4fbt26Z3wvjx46Vw4cLOHioAAIBDEScB8QNJKQBI4PQ/41p2fvr0aXn8+LEJpgoUKGBe01m+zp07y+LFi2XNmjVSsWLFcLOEnp6eThw5AACAfREnAfEbPaUAwAUCrVWrVklAQIApMdfS88GDB8ulS5ckSZIkMn36dKldu7a88cYbsm7dOtt7aeYJAABcGXESEP/xvzQASMA00Fq7dq20bt1aunXrJjt27JDPP/9chg4dKqNHj5YLFy6YWT4tTS9evLiZDXz48KHtvQAAAK6KOAmI/1i+BwAJ2I0bN6RLly6m30G/fv3kzJkzUqtWLcmZM6fZUaZjx47St29fyZYtm5kt1OAra9aszh42AACA3REnAfEflVIAkIDpDjF16tSRd955R65du2bK0qtUqSK//PKLjBkzRr766itTon7x4kUz40egBQAA3AVxEhD/kZQCgAQsZcqUUr9+fcmePbtp0pkmTRoZOXKkeS158uRmZvCHH35w9jABAAAcjjgJiP8SO3sAAICYNevct2+fHD9+XK5cuSJNmzY1JedKd5XRazQAsz7v3bu3NGjQwGxrDAAA4KqIk4CEiZ5SAJCALF++XLp27Sq5c+c2jTl3794tM2fOlLffflt+/PFHE1g1btzYbHm8efNm2b59u5kFBAAAcHXESUDCw/I9AEggDhw4YHaFGT58uGzdutUEXo8ePZKzZ89KaGio1KtXT7799lsJDg4WHx8f+f333wm0AACAWyBOAhImKqUAIJ7RwClRoshzBtrzYPbs2bJq1So5ceKE1KhRwzTvnDFjhnn93r17piRdA7DEiRObAwAAwJUQJwGuhUopAIiHgVZQUJAsWbJEvvjiCzl16pQ8ffrU9EY4d+6cOXQ7Yw20vvzyS/M+nQ3s1q2bCbS8vLwItAAAgMshTgJcD0kpAIhngdahQ4ekUqVKMmjQIPn444+lWrVqsmzZMqlcubKZ4cufP79Ur17dNvOndu7caYIxDbYAAABcDXES4JpISgFAPAq0Dh8+LOXLl5d27drJunXr5OLFi5IjRw4ZPHiw2RmmQoUK4ufnZ7Y21iadunPMp59+KnPmzJFRo0aZrY4BAABcCXES4LroKQUA8YQ24vT395c2bdrI3Llzbed1d5jatWvLnj17zG4yn3zyiWng+ffff5vZwPv378vChQulRIkSTh0/AACAvRAnAa6JxbQAEE9kzpxZsmbNKsePH5f9+/dLoUKFJFmyZPLgwQPT/0D7JaRIkULGjx9vStB37NghefPmlSxZsphZQQAAAFdFnAS4JiqlACAeCAkJEU9PTxNYvfLKKya40l1k9Pyrr74qb7/9tnz22Wei/8n28PBw9nABAAAchjgJcF0kpQAgntAZPt0NRgOu4sWLm+Dr7t27Ur9+fdvuMc/aBhkAAMCVEScBron/xQJAPKGBlgZcyZMnl4MHD5odZK5evSrt27e3XcPsHwAAcEfESYBrolIKAJwkbIm5tSw97Ezgw4cPTVNODb5mz55tZgUJtgAAgDsgTgLcA5VSAOAgWlKudItia6B16NAhc84aaIWdCdStjbWRp17fpEkTsw0yAACAKyJOAtwTSSkAcBDtcXDy5El555135N69e7JkyRIzq7dv377nlqjv2rVLMmbMKKlSpXLKuAEAAOyNOAlwTyzfAwAH+vvvvyVPnjxSqlQpM7un5eZt2rR55m4x1hJ1AAAAV0ecBLgfKqUAwEG0H0KuXLlk6tSpsnfvXilcuLC89tpr5jUNtKKaI7AGWswfAAAAV0acBLgnklIA4CDWLYq9vLxk3Lhxcu7cOWnbtq2cOHHCnI84A6jBmRWNOwEAgCsjTgLcE8v3AMDOrCXnd+/eDdfv4K+//pJy5cpJ2bJlZcqUKZI7d25zfsOGDVKjRg0njhgAAMAxiJMA90ZSCgAc4KeffpIZM2aYxp3aG6FatWqSPXt2E3CVL1/eBF3dunWT33//XUaPHm16KmTJksXZwwYAALA74iTAfZGUAgA72759u1SvXl26du1qtja+fPmyCbACAwMlb968ZqeZgIAAMzt4/fp1WbVqlbzyyivOHjYAAIDdEScB7o2kFADY0T///CNz586V1KlTm+BKTZ48WRYtWmQaePbq1cvsMnP16lXTO8HPz098fX2dPWwAAAC7I04CQKNzALCT48ePS4sWLWTevHmSIkUK2/mPPvrInD98+LCMHz/elKZnzJhRSpQoQaAFAADcAnESAEVSCgDsJF++fFKpUiV58OCBacp548aNcAHX22+/LZs2bZJp06bJ06dPnTpWAAAARyJOAqBYvgcAcbx7TESDBw+W5cuXS5MmTUy/hAwZMthemzlzptSuXVv8/f0dPFoAAADHIU4CEBWSUgAQh4HWxo0bZcWKFaYRZ8GCBaVfv37mdf25du1aqVu3rpn9CxtwAQAAuDLiJADPwvI9AIgDGmhpkNWoUSOznbE25xwyZIh5fv/+fRkxYoTZOWbdunUyatQoE4wBAAC4A+IkAM9CUgoA4oDuCNO/f38ZNmyYzJkzRz788EPx8fEx5ebe3t7mmpEjR0qZMmVk3759Ehoa6uwhAwAAOARxEoBnISkFADE0ZswY+fvvv8OdCw4OFi8vL9MLQbc3zp8/v+mNMGnSJEmUKJFs27bNXPfFF1/IkiVLzC4yAAAAroY4CUBMkJQCgBjQEvOffvpJHj9+HO584sSJ5datWyaQql69utSrV0+mTp1qXvvjjz9k4MCBsmvXLvM8U6ZMThk7AACAPREnAYgpklIAEAMpUqQwTTp1hm/r1q1y8uRJc97Pz89sa/zee+9JkSJFZMaMGSYAU9999508fPhQcuTI4eTRAwAA2A9xEoCY+r//EgAAos3T01OePHliStB11m/Dhg2SO3duadWqlZnte/TokSxdulRSpUpldpKZN2+ebNmyRXx9fZ09dAAAALsiTgIQEx4W3Z8TABDt7Yw1mNK+CFevXpX69evL3bt3ZfXq1ZIrVy5ZtWqVLF682DzXACxNmjQyefJkKVq0qLOHDwAAYDfESQBig6QUAMQg0Prtt99k586d0rhxYylQoIAJuOrUqSMPHjyQH3/80QRcISEhcvHiRTMDqM079ScAAICrIk4CEFv0lAKAaAZay5cvlwYNGphtiq1bFevuMFp6rjOCOht46tQpU7b+0ksvma2OCbQAAIArI04C8F9QKQUA0aA7wmgwNXbsWGnbtq3tvM4AasB148YNCQgIkAsXLphtjXPmzOnU8QIAADgKcRKA2KJSCgCi4fDhw5I3b14TaGmvhBUrVpjZQN3SeMSIEZIuXTpZs2aNvPzyy2bGEAAAwF0QJwGILXbfA4BoSJYsmdlBZtCgQWaGT7c81nOvv/66jBo1SmrUqCHlypUzO8xofwQAAAB3QZwEILZISgHAc3aP0cfe3t6m5FxL09evXy/FihWT1q1bS/ny5eWvv/4yjTtTp05t3kugBQAAXBlxEoC4RFIKAKIItHSr4lmzZpleCG3atJEOHTrIlClT5N69e5IyZUrb9d99953ZUUbL0gEAAFwZcRKAuEZSCgDCBFl6bNmyRVq1amWO9OnTS5cuXeSPP/6QHj16iL+/v7n+559/Nse3335rtj/29fV19lcAAACwC+IkAPZCUgoAdCtSDw/zU3eFOXDggAwZMkS6d+9uzmmjznbt2pnHGnD5+fmZfglBQUGydetWKVSokFPHDgAAYE/ESQDshaQUAIhIaGionDt3zszwpU2bVvr162d7rXHjxmaGUAMuDcoGDhxoGnk+fPhQfHx8nDpuAAAAeyNOAmAvJKUA4P833syePbvMmDFD3n//fTl06JDcuHHD1gOhSZMm5hr9mTRpUhkzZgyBFgAAcAvESQDshaQUAIShjTp1NrBTp06SJ08e+fDDDyVNmjTmtUaNGskPP/wgefPmFU9PT2cPFQAAwKGIkwDENZJSANy6YafO9F26dEnu378vdevWNbN7OgP49OlT6dq1q7nuo48+ss321atXz9lDBwAAsCviJACOQlIKgNsGWitWrDAzfVmzZpWjR49KjRo1TNPOatWqmZ1klD7XrYz79OlDGToAAHB5xEkAHImkFAC3o4HWhg0bpGPHjjJ69Ghp37697Nu3T0qXLi2PHz82s3+1a9c2AZc26RwxYoTZTQYAAMDVEScBcCQPi6bCAcBNZv3Uo0ePzK4w2pBz1KhRcurUKQkICDDBlm5znDJlShk5cqTUqlXLvOfmzZtmpxkAAABXRJwEwFlISgFwC9qUU4MrnekrUKCA7N27V3x9fSVz5swmqCpatKh89dVXsnv3bqlcubKUKlXKBGT6WthADQAAwNUQJwFwlkRO+2QAsHNwpa5du2a2LNZA6+effzazfPv375cyZcrIyy+/LNu2bTNl6NoLQd2+fVvKly8viRMnlnz58plzBFoAAMCVECcBiC9ISgFw2dk+DarKlStnGnDqzjEXL16UiRMnSsWKFcXLy8sWjN29e9dco7Zu3SqvvvqqrF27VrJnz+7kbwIAABC3iJMAxCc0OgfgkoHWwYMHTXm57hqjZeV58+aVNGnSyNixY8111lLzKlWqSN++feWtt96S5MmTy4kTJ2Tjxo22YAwAAMBVECcBiG+olALgcoHWsWPHpGrVqtK7d2/57LPPTMPOtm3bmvJ0nQW0BltPnjwRf39/2bRpkzRu3Fjq1KkjO3bskOLFizv7qwAAAMQp4iQA8RGVUgBcKtA6dOiQmfnTUnPtd6B09u/jjz82AVavXr1MuXmzZs3MDKAGXNozYdiwYc7+CgAAAHZBnAQgviIpBSDB0yDKWopeqVIlM9uXLVs2mTt3rty/f1+GDx8uefLkkcDAQBNgtW/f3rxPAy4NyNg1BgAAuCriJADxGUkpAAmeBkpabv7KK6/IJ598ImPGjDHPHz58KIsWLTKv6wxfrly5pFu3bub5Bx98IMHBwdKqVStnDx8AAMBuiJMAxGceFk19A0ACFxISIr/++qsEBATYzmnANWvWLFm4cKGZ7bOWnv/9999mVnDDhg1y5MgRSZkyJTOAAADAZREnAYivSEoBcDnW/6xpAKVbHM+cOTNSwHXmzBnx9vaWzJkzO3m0AAAAjkOcBCA+YfkeAJcTdjbP19dXOnbsaB4vW7bM9E6YMGGC2U0GAADA3RAnAYhPSEoBSHBGjx4tGTNmtDXifBFrwKWB1ubNm+Xq1avm/QAAAK6GOAlAQsLyPQAJjm5b/MUXX8i3334rb731VrTfd/nyZfH09JQMGTLYdXwAAADOQpwEICGhUgpAgvP555+bpptt2rQxfRE04IpOA076IgAAAFdHnAQgISEpBSDB0MBKj0SJEknv3r3lxo0b0rlzZ0mWLJlpzgkAAOCuiJMAJESJnD0AAIguneXTQGv58uVStWpVuX79ujx58kTeeecd+e6775w9PAAAAKchTgKQEJGUApCg7N+/X1q3bm1m/qZMmSIHDhwwjTzbtWsnCxYscPbwAAAAnIY4CUBCw/I9AAnKhQsXJGfOnNKkSRNJly6dZMqUSSZPniyhoaHy3nvviZeXl3kNAADA3RAnAUhoqJQCEO+F3ST08ePHcvToUVOOrp4+fWpK1Tt06GDOac+EpUuXOnG0AAAAjkOcBCAhIykFIN4HWWF3jGnQoIGUKVNGunTpYrYuTpz4/wo+06dPLy1btpQ+ffpI4cKFnTZmAAAARyBOAuAKPCxhU+sAEE/of5o0yPr9999l27ZtcufOHRNEaUC1bNkys92xBliTJk0ST09PmTFjhvz666/y22+/SfLkyZ09fAAAALshTgLgKugpBSBe0kDr+++/N405GzZsKPfu3TO7yfz8888yf/58uX//vsybN09y5colL7/8stlh5pdffiHQAgAALo84CYCroFIKQLwREhJiZvPUqVOnpHbt2tKzZ0/p1KmTHD9+XMqXL29mAKdOnWp7z7p168Tb29s09cyWLZsTRw8AAGA/xEkAXBE9pQA4nW5RHBQUZAItDbjUxYsXJUWKFCbQ+ueff6RWrVry5ptv2gItLVdXAQEBUrlyZQItAADgkoiTALgyklIAnOrYsWPy2Wefydtvv222MbbOAGp5ua+vr+zevVsqVaokderUsQVaBw8elIULF5r3AgAAuCriJACujqQUAKfKnz+/2QkmSZIk8s4778j58+fN+bRp05otjcuVK2cCLW3QaQ3Evv76a/nzzz8lY8aMTh49AACA/RAnAXB1JKUAOMW4ceNk4sSJ5nHz5s3l/fffl9DQUGndurUpUdfeB9qgU7cyTpQokSlD379/v/To0UPmzp1r21UGAADA1RAnAXAX7L4HwOF0Rxid6Zs+fbppvvnBBx/IG2+8YV6bNm2aCbg00KpRo4YpP+/evbv88MMP4uPjY/onbNq0SYoUKeLsrwEAABDniJMAuBN23wPgFNoXQYOtSZMmyZgxY0yjTrV06VITcOlWxxpwZc+e3Vx748YNSZo0qSlF15J1AAAAV0WcBMBdkJQC4DS6c4w25Zw8eXKUAZeaP38+O8YAAAC3Q5wEwB2wfA+Aw2lPBO1/4OfnZ3okqF69epmfGnBZS9RnzpwpjRo1ktWrV0uWLFmcOmYAAABHIE4C4E5ISgFwGC3M1HLzhw8fmscpU6Y0s3vaC0EDsIgBV3BwsCxZskSePHni7KEDAADYFXESAHfE8j0ADg20fvzxR7OjzPXr101DzsDAQHnttdckJCRERo0aJVOmTJHPPvvMNjN49+5dSZUqlbOHDwAAYDfESQDcFZVSABxCA621a9dK06ZN5ZNPPhF/f38TePXr10/++usv+fjjj03gpVsb6wxgkiRJ5N133yXQAgAALo84CYC7olIKgN1m+yJub/zWW29J7ty5ZcKECbbzffr0kRUrVsjEiRPl9ddfl3/++Ue+/fZbadasmeTLl88JowcAALAf4iQA+BdJKQB2ac5569Ytsz2xypUrl/lZpUoVKV++vIwePdr0QUiWLJk5by1LX79+vXmujz09PZ34LQAAAOIecRIAhJcownMA+M+B1pEjR6R+/fomuKpZs6Z07drVFnRt3LjRPNZA6/Hjx+Zx1apVTfD19OlT85xACwAAuBriJACIjKQUgDgNtP73v/+ZWb6iRYvK2LFjpXLlyrJs2TIZOXKk2TXmzJkz0qpVK/OepEmTmp/Hjx+XtGnTmnsAAAC4GuIkAIgay/cAxJmTJ09KkSJFTIPOoUOHmnO6rXHdunXNbN+GDRtkzZo1pkGnbnFcvHhxefDggaxatUp27Nhh3gsAAOCKiJMAIDIqpQDECZ29mzNnjtkFJkOGDLbz3t7eUq1aNXny5Im5RsvVf//9dylcuLDpp6C7x+zatYtACwAAuCziJACIGpVSAOLMhQsXTCn6zp07pUGDBvLpp5/KtWvXJGfOnNK/f3/p3bt3pPfQrBMAALgD4iQAiIykFIA4denSJRkxYoTs379fKlSoIAsXLpTGjRvL5MmTzevW/+RYt0KOaltkAAAAV0ScBADhkZQCEOcuXrxoGnYuX75csmbNKnv27DHnddeYxIkTO3t4AAAATkOcBAD/oqcUgDjn5+dnytCbNWtmSs7HjBljzmugxc4xAADAnREnAcC/qJQCYPcS9QMHDkiNGjVkyJAhzh4SAABAvECcBABUSgGwI19fX+nXr5/kzZtXtm/fLtevX3f2kAAAAOIF4iQAoFIKgANcvnzZ/MycObOzhwIAABCvECcBcGckpQAAAAAAAOBwLN8DAAAAAACAw5GUAgAAAAAAgMORlAIAAAAAAIDDkZQCAAAAAACAw5GUAgAAAAAAgMORlAIAAAAAAIDDkZQCAAAAAACAw5GUAgAAAAAAgMORlAIAAAAAAIDDkZQCAAAAAACAw5GUAgAAAAAAgDja/wMs4XT8DrPbiQAAAABJRU5ErkJggg==", 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", 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" ] From ac03e0e517a102086ee1f649452c32a0fb5b2669 Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Mon, 1 Dec 2025 07:22:53 +0100 Subject: [PATCH 7/8] add assignment Signed-off-by: Nitish Bharambe --- .../Advanced Workshop Assignment.ipynb | 1730 +++++++++++++++++ .../Advanced Workshop.ipynb | 443 +++-- 2 files changed, 2029 insertions(+), 144 deletions(-) create mode 100644 pgm-advanced-usage-workshop/Advanced Workshop Assignment.ipynb diff --git a/pgm-advanced-usage-workshop/Advanced Workshop Assignment.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop Assignment.ipynb new file mode 100644 index 0000000..f82d9f3 --- /dev/null +++ b/pgm-advanced-usage-workshop/Advanced Workshop Assignment.ipynb @@ -0,0 +1,1730 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "a727ce38", + "metadata": {}, + "source": [ + "# Introduction\n", + "\n", + "In this assignment you will be given a series of tasks about using the library `power-grid-model`. The agenda includes:\n", + "\n", + "* Load input (From Power flow assignment)\n", + "* Numpy tips and PGM related recommendations\n", + "* Types of Dataset\n", + "* Miscellaneous PGM features\n", + "* Selective output\n", + "* Complex Simulations\n", + " * Multiple Timeseries\n", + " * Side topic: PGM DS library\n", + " * Contingency Analysis\n", + " * Topology caching\n", + " * Simulation design exercise\n", + " * Dynamic operating Envelope\n", + " * Simulation design exercise\n", + " * Additional content\n", + " * Simulation design exercise\n", + "\n", + "Throughout this notebook there are big notebook cells of code. \n", + "To have a deeper understanding of the simulations, it is recommended to comment it all and uncomment line by line along with confirming the debugging print statements." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b18d109c", + "metadata": {}, + "source": [ + "# Preparation\n", + "\n", + "First import everything we need for this workshop:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbc50a6d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from power_grid_model import (\n", + " PowerGridModel,\n", + " CalculationType,\n", + " CalculationMethod,\n", + " ComponentType,\n", + " DatasetType,\n", + " initialize_array,\n", + " attribute_dtype,\n", + " attribute_empty_value,\n", + " power_grid_meta_data\n", + ")\n", + "\n", + "from power_grid_model.validation import assert_valid_input_data, assert_valid_batch_data\n", + "\n", + "from power_grid_model_ds import PowerGridModelInterface\n", + "from power_grid_model_ds.enums import NodeType\n", + "from power_grid_model_ds.visualizer import visualize\n", + "\n", + "from utils import Timer\n", + "import psutil\n", + "import os\n", + "from collections import Counter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "687985dc", + "metadata": {}, + "outputs": [], + "source": [ + "np.set_printoptions(precision=3, linewidth=100)" + ] + }, + { + "cell_type": "markdown", + "id": "bab4fd44", + "metadata": {}, + "source": [ + "# Topology Data and Timeseries load profiles \n", + "\n", + "(Reused from Power flow assignment)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb462396", + "metadata": {}, + "outputs": [], + "source": [ + "def load_input_data() -> dict[str, np.ndarray]:\n", + " input_data = {}\n", + " for component in [\n", + " ComponentType.node,\n", + " ComponentType.line,\n", + " ComponentType.source,\n", + " ComponentType.sym_load,\n", + " ]:\n", + " # Use pandas to read CSV data\n", + " df = pd.read_csv(f\"../data/{component.value}.csv\")\n", + "\n", + " # Initialize array\n", + " input_data[component] = initialize_array(DatasetType.input, component, len(df))\n", + "\n", + " # Fill the attributes\n", + " for attr, values in df.items():\n", + " input_data[component][attr] = values\n", + "\n", + " # Print some debug info\n", + " print(f\"{component:9s}: {len(input_data[component]):4d}\")\n", + "\n", + " return input_data\n", + "\n", + "\n", + "input_data = load_input_data()\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec1e571c", + "metadata": {}, + "source": [ + "Create a PowerGridModel instance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf581717", + "metadata": {}, + "outputs": [], + "source": [ + "model = PowerGridModel(input_data=input_data)" + ] + }, + { + "cell_type": "markdown", + "id": "6791a9c9", + "metadata": {}, + "source": [ + "Lets use these number timestamps for all calculations in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98e89607", + "metadata": {}, + "outputs": [], + "source": [ + "n_timestamps = 24 * 7" + ] + }, + { + "cell_type": "markdown", + "id": "fbe924e2", + "metadata": {}, + "source": [ + "We use same timeseries profile as in the Power Flow assignment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b10f3052", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate random load profile oh hourly data\n", + "dti = pd.date_range(\"2022-01-01\", periods=n_timestamps, freq=\"h\")\n", + "n_loads = len(input_data[ComponentType.sym_load])\n", + "load_id = input_data[ComponentType.sym_load][\"id\"]\n", + "load_p = input_data[ComponentType.sym_load][\"p_specified\"]\n", + "profile = np.tile(load_p, (n_timestamps, 1)) + 1e5 * np.random.randn(\n", + " n_timestamps, n_loads\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3daad8a8", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize an empty load profile\n", + "load_profile_row_based = initialize_array(\n", + " DatasetType.update, ComponentType.sym_load, profile.shape\n", + ")\n", + "\n", + "load_profile_row_based[\"id\"] = load_id\n", + "load_profile_row_based[\"p_specified\"] = profile\n", + "load_profile_row_based[\"q_specified\"] = 0.0\n", + "\n", + "# Construct the update data\n", + "update_data_row_based = {ComponentType.sym_load: load_profile_row_based}\n" + ] + }, + { + "cell_type": "markdown", + "id": "588c60cd", + "metadata": {}, + "source": [ + "# Side topics related to numpy\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd25ed1b", + "metadata": {}, + "source": [ + "## Calcualting Memory usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c818b02", + "metadata": {}, + "outputs": [], + "source": [ + "def print_memory_usage():\n", + " process = psutil.Process(os.getpid())\n", + " mem_info = process.memory_info()\n", + " print(f\"Current memory usage: {mem_info.rss / 1024 / 1024:.2f} MB\")\n", + "\n", + "\n", + "def print_memory_used_by_object(object):\n", + " print(f\"Memory used by object: {object.__sizeof__() / 1024 / 1024} MB\")\n", + "\n", + "\n", + "def print_memory_used_by_dict(dictionary):\n", + " print(\n", + " f\"Memory used by dictionary: {sum(object.__sizeof__() / 1024 / 1024 for object in dictionary.values())} MB\"\n", + " )\n", + "\n", + "\n", + "def print_memory_used_by_array(array: np.ndarray):\n", + " print(f\"Memory used by numpy array: {array.nbytes / 1024 / 1024} MB\")\n", + "\n", + "\n", + "print_memory_usage()\n", + "\n", + "print_memory_used_by_object([1] * 1000)\n", + "print_memory_used_by_object(\n", + " {\"a\": [1] * 1000}\n", + ") # This is because dictionary stores only references\n", + "\n", + "print_memory_used_by_dict({\"a\": [1] * 1000})\n", + "\n", + "print_memory_used_by_array(np.random.rand(1000, 1000))" + ] + }, + { + "cell_type": "markdown", + "id": "94e707b9", + "metadata": {}, + "source": [ + "## Correct dtype for PGM arrays" + ] + }, + { + "cell_type": "markdown", + "id": "338d3c13", + "metadata": {}, + "source": [ + "\n", + "Use of initialize_array is recommended for most uses.\n", + "The reasoning for this recommendation should be clear by the end of this workshop.\n", + "\n", + "However if you wish to translate the dtype into the one PGM uses, you can use `initialize_array(dataset_type, component_type, empty_shape, empty=True)` or `dtype = power_grid_meta_data[dataset_type][component_type].dtype` to find the dtype of the row based data. You can use this to change to the appropriate type which is required by PGM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5674b0ac", + "metadata": {}, + "outputs": [], + "source": [ + "initialize_array(DatasetType.input, ComponentType.sym_load, 0, empty=True)\n", + "# power_grid_meta_data[DatasetType.input][ComponentType.sym_load].dtype" + ] + }, + { + "cell_type": "markdown", + "id": "1c2ac8f3", + "metadata": {}, + "source": [ + "For dtype of individual attribute which would be required in columnar data creation, you can use `attribute_dtype(data_type, component_type, attribute)`. The missing value can be filled by `attribute_empty_value(data_type, component_type, attribute)` \n", + "\n", + "Tip: Check if a type-cast to this dtype creates a copy. Maybe such a copy is redundant." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ce0be48", + "metadata": {}, + "outputs": [], + "source": [ + "attribute_dtype(DatasetType.input, ComponentType.transformer, \"sn\")" + ] + }, + { + "cell_type": "markdown", + "id": "cd3c9251", + "metadata": {}, + "source": [ + "All available attributes are visible via following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eecf5cd9", + "metadata": {}, + "outputs": [], + "source": [ + "initialize_array(DatasetType.input, ComponentType.sym_load, 0, empty=True).dtype.names" + ] + }, + { + "cell_type": "markdown", + "id": "83e8ef68", + "metadata": {}, + "source": [ + "## Views, copies and Memory location\n", + "\n", + "A view is a windowed look into an array. Creating views is very inexpensive. \n", + "A copy here is meant by an array which owns the data within it.\n", + "\n", + "Check numpy documentation for when certain functions or features create a view or copy. \n", + "\n", + "* Views simplify assignment of values to arrays.\n", + "* Uneccesary copies here are the first you can aim to eliminate to improve overall performance.\n", + "* Beware of unintentional memory usage because of views.\n", + "\n", + "\n", + "https://numpy.org/doc/stable/user/basics.copies.html\n" + ] + }, + { + "cell_type": "markdown", + "id": "64b08530", + "metadata": {}, + "source": [ + "\n", + "* Checking if view or copy: if `array.base is None` -> copy; else -> view\n", + "* Finding out view belongs to which original array: if `array.base is original_array`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce90a14b", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "x = np.arange(9)\n", + "y = x.reshape(3, 3)\n", + "assert y.base is not None # .reshape() creates a view\n", + "\n", + "z = y[[2, 1]]\n", + "assert z.base is None # advanced indexing creates a copy" + ] + }, + { + "cell_type": "markdown", + "id": "b168a6e4", + "metadata": {}, + "source": [ + "## Garbage collection for views/copies\n", + "\n", + "### Holding base array\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3cb6254", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_and_give_first_50_loading(model, update_data):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data,\n", + " calculation_method=CalculationMethod.newton_raphson\n", + " )\n", + " return output_data[ComponentType.line][\"loading\"][:50]\n", + "\n", + "\n", + "first_50_loading = calculate_and_give_first_50_loading(model, update_data_row_based)\n", + "\n", + "\n", + "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", + "print_memory_used_by_array(first_50_loading)\n", + "\n", + "print(f\"Actual shape of memory: {first_50_loading.base.shape}\")\n", + "print_memory_used_by_array(first_50_loading.base)" + ] + }, + { + "cell_type": "markdown", + "id": "75552bf8", + "metadata": {}, + "source": [ + "Use .copy() to convert a view into a copy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d15b6717", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_and_give_first_50_loading(model, update_data):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " threading=0\n", + " )\n", + " return output_data[ComponentType.line][\"loading\"][:50].copy()\n", + "\n", + "\n", + "first_50_loading = calculate_and_give_first_50_loading(model, update_data_row_based)\n", + "print(f\"Shape of returned array: {first_50_loading.shape}\")\n", + "print_memory_used_by_array(first_50_loading)\n", + "assert first_50_loading.base is None" + ] + }, + { + "cell_type": "markdown", + "id": "0573d0f4", + "metadata": {}, + "source": [ + "## Data Contiguity\n", + "\n", + "What is Contiguity in numpy arrays?\n", + "- Contiguity means all array elements are laid out sequentially in memory.\n", + " - C contiguous: row major where rows are stored one after another. ie. All elements are in order of right to left dimension. Rightmost dimension is fast changing. (This is default in numpy and also required by PGM.)\n", + " - F contiguous: column-major where columns stored one after another. ie. All elements are in order of left to right dimension. Leftmost dimension is fast changing.\n", + "\n", + "The input and update dataset needs to be a C contigious dataset before PGM can make use of it. While PGM output is C contigious. PGM internally converts it to be C contigious if its not.\n", + "\n", + "A basic sliced dataset would retain contiguity. Whereas a transposed or advanced sliced array would not.\n", + "A reshaped dataset mostly would be C contigious.\n", + "\n", + "Check `arr.flags` for Contiguity.\n", + "This requirement for PGM and for further uses can be pre-planned.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "093d3a22", + "metadata": {}, + "outputs": [], + "source": [ + "loads = np.zeros((1000, 10),dtype=np.float64)\n", + "print(f\"1. {loads.flags}\")\n", + "print(f\"2. {loads.reshape(10, 20, 50).flags}\")\n", + "print(f\"3. {loads.T.flags}\")\n", + "print(f\"5. {np.transpose(loads.reshape(10, 20, 50), axes=(2, 0, 1)).flags}\")\n", + "\n", + "print(f\"4. {np.ascontiguousarray(loads) is loads}\")" + ] + }, + { + "cell_type": "markdown", + "id": "bd9a03ce", + "metadata": {}, + "source": [ + "`initialize_array` handles contiguity and dtype requirement for row based data without user having to think about it. \n", + "For columnar data however the responsibility lies with the user." + ] + }, + { + "cell_type": "markdown", + "id": "097bc587", + "metadata": {}, + "source": [ + "Questions:\n", + "- Does a view own its data?\n", + "- Give reasoning for flags of 3." + ] + }, + { + "cell_type": "markdown", + "id": "86c3eed3", + "metadata": {}, + "source": [ + "## Accessing and assigning to PGM arrays" + ] + }, + { + "cell_type": "markdown", + "id": "77e9f0d2", + "metadata": {}, + "source": [ + "PGM component has layout of (scenarios, components in a scenario)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72ae61a4", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 10))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", + "\n", + "def copy_or_view(arr: np.ndarray) -> str:\n", + " return \"copy\" if arr.base is None else \"view\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11f38b3f", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Single batch all components: `loads[:, 0]` gets a :\", copy_or_view(loads[:, 0]))\n", + "print(\"All batch single components: `loads[0, :]` gets a :\", copy_or_view(loads[0, :]))\n", + "print(\"Sliced batch all components: `loads[:5, :]` gets a :\", copy_or_view(loads[:5, :]))\n", + "print(\"Multiple batch via advanced indexing all components: `loads[[1,2,3], :]` gets a :\", copy_or_view(loads[[1,2,3], :])) # Advanced indexing creates a copy\n", + "print(\"All batch Multiple components via advanced indexing: `loads[:, [1,2,3]]` gets a :\", copy_or_view(loads[:, [1,2,3]])) # Deceptive: view of another copy" + ] + }, + { + "cell_type": "markdown", + "id": "38b16212", + "metadata": {}, + "source": [ + "### Attribute access\n", + "\n", + "Attribute access has similar behavior" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b3191ad", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Single batch all components: `loads['p_specified']` gets a :\", copy_or_view(loads[\"p_specified\"]))\n", + "print(\"Single batch all components: `loads['p_specified'][:,0]` gets a :\", copy_or_view(loads[\"p_specified\"][:, 0]))\n", + "print(\"Single batch all components: `loads['p_specified'][0, :]` gets a :\", copy_or_view(loads[\"p_specified\"][0, :]))\n", + "print(\"Sliced batch all components: `loads['p_specified'][:5, :]` gets a :\", copy_or_view(loads[\"p_specified\"][:5, :]))\n", + "print(\"Single batch all components: `loads['p_specified'][[1, 2, 3], :]` gets a :\", copy_or_view(loads[\"p_specified\"][[1, 2, 3], :]))\n", + "print(\"Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[\"p_specified\"][:, [1, 2, 3]])) " + ] + }, + { + "cell_type": "markdown", + "id": "49f6fd1c", + "metadata": {}, + "source": [ + "Its recommended to index with field first (`loads[\"p_specified\"][...]` instead of `loads[...][\"p_specified\"]`) because advanced indexing on structured arrays can create copies." + ] + }, + { + "cell_type": "markdown", + "id": "31bc7376", + "metadata": {}, + "source": [ + "Multi field indexing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4114e9f", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Multiple attributes: `loads[['p_specified', 'q_specified']][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[[\"p_specified\", \"q_specified\"]]))" + ] + }, + { + "cell_type": "markdown", + "id": "655f5b9d", + "metadata": {}, + "source": [ + "A common source of confusion is when dealing with numpy arrays view and copy variables. They look the same. \n", + "* Modifications on view modifies the view as well as the original array.\n", + "* Modifications on copy modifies only that copy.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "388d5b98", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 4))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", + "\n", + "x = loads[\"p_specified\"][:3, :]\n", + "loads[\"p_specified\"][:3, :] = 123456\n", + "print(x)\n", + "print(x.base is loads) # x is a view of loads\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "760d4347", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 4))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", + "\n", + "x = loads[\"p_specified\"][[1, 2, 3], :]\n", + "loads[\"p_specified\"][[1, 2, 3], :] = 123456\n", + "print(x)\n", + "print(x.base is loads) # x is a copy of the loadings of scenario 1 2 3" + ] + }, + { + "cell_type": "markdown", + "id": "95af8880", + "metadata": {}, + "source": [ + "#### Deceiptive: It was a view of a copy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c98c5b5", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.update, ComponentType.sym_load, (1000, 4))\n", + "loads[:] = (99, 1, 100.0, 100.0)\n", + "\n", + "x = loads[\"p_specified\"][:, [1, 2, 3]]\n", + "loads[\"p_specified\"][:, [1, 2, 3]] = 123456\n", + "print(x)\n", + "print(x.base is loads)" + ] + }, + { + "cell_type": "markdown", + "id": "ab922bf6", + "metadata": {}, + "source": [ + "### Assignment to arrays\n", + "\n", + "Assigning means setting values to the array.\n", + "\n", + "#### Via tuple" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "008513ea", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.input, ComponentType.asym_load, (10, 6))\n", + "loads[\"p_specified\"] = 1000\n", + "loads[\"p_specified\"] = [[1000], [1000], [1000], [1000], [1000], [1000]]\n", + "# fields are in this order: ('id', 'node', 'status', 'type', 'p_specified', 'q_specified')\n", + "loads[:, 0] = (1, 2, 1, 0, 123, 0)\n", + "loads[:] = (1, 2, 1, 0, 123, 0)\n", + "loads[:2]\n" + ] + }, + { + "cell_type": "markdown", + "id": "45e96401", + "metadata": {}, + "source": [ + "Reshaping the destination arrays is also possible since a view gets created.\n", + "Its possible to assign values this way." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5be80e4", + "metadata": {}, + "outputs": [], + "source": [ + "loads = initialize_array(DatasetType.input, ComponentType.asym_load, (10, 6))\n", + "reshaped_loads = loads.reshape(2, 5, 6)\n", + "reshaped_loads[0, :, :] = (1, 2, 1, 0, 123, 0)\n", + "reshaped_loads[1, :, :] = (3, 4, 5, 0, 456, 0)\n", + "loads[[2, -2]]" + ] + }, + { + "cell_type": "markdown", + "id": "696572d9", + "metadata": {}, + "source": [ + "Use functions with out parameter to write results directly into pre-allocated arrays. for eg. arrays created via initialize_array" + ] + }, + { + "cell_type": "markdown", + "id": "036537fe", + "metadata": {}, + "source": [ + "# Types of datasets " + ] + }, + { + "cell_type": "markdown", + "id": "3f3d4a87", + "metadata": {}, + "source": [ + "### Row based format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2399bced", + "metadata": {}, + "outputs": [], + "source": [ + "print_memory_used_by_array(update_data_row_based[ComponentType.sym_load])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3548ce38", + "metadata": {}, + "outputs": [], + "source": [ + "# Run Newton Raphson power flow (this may take a minute...)\n", + "with Timer(\"Batch Calculation using Newton-Raphson row based data\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_row_based,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " threading=0,\n", + " )\n", + "\n", + "print_memory_used_by_dict(output_data)" + ] + }, + { + "cell_type": "markdown", + "id": "e63af797", + "metadata": {}, + "source": [ + "### Columnar data format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c52f4f6", + "metadata": {}, + "outputs": [], + "source": [ + "# Construct the update data\n", + "update_data_columnar = {ComponentType.sym_load: {\"p_specified\": profile}}\n", + "\n", + "print_memory_used_by_array(profile) # in MB" + ] + }, + { + "cell_type": "markdown", + "id": "2796441d", + "metadata": {}, + "source": [ + "PGM Columnar data is all attributes array are fed in indvidually as separate arrays.\n", + "This enables us to skip empty or redundant attribute data.\n", + "Skipping ids is also possible under certain conditions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7be73996", + "metadata": {}, + "outputs": [], + "source": [ + "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_columnar,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " threading=0\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "63f3197e", + "metadata": {}, + "source": [ + "### Selective Output\n", + "\n", + "It is possible to output only selected attributes via output_component_types. The effect on memory usage is huge." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2800a03b", + "metadata": {}, + "outputs": [], + "source": [ + "with Timer(\"Batch Calculation using Newton-Raphson columnar data\"):\n", + " output_data_selective = model.calculate_power_flow(\n", + " update_data=update_data_columnar,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types={ComponentType.line: [\"loading\"]},\n", + " threading=0\n", + " )\n", + "\n", + "print(\n", + " f\"output_data_selective contains following: \\n\"\n", + " f\"Keys: {output_data_selective.keys()}. \\n\"\n", + " f\"Attributes: {output_data_selective[ComponentType.line].keys()}\"\n", + ")\n", + "print_memory_used_by_array(output_data_selective[ComponentType.line][\"loading\"])" + ] + }, + { + "cell_type": "markdown", + "id": "a0338fdb", + "metadata": {}, + "source": [ + "Lets define all limit related attributes to use in upcoming simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9831864d", + "metadata": {}, + "outputs": [], + "source": [ + "limit_related_output = {\n", + " ComponentType.line: [\"loading\"],\n", + " ComponentType.node: [\"u_pu\"],\n", + " ComponentType.transformer: [\"loading\"],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "973a8ef8", + "metadata": {}, + "source": [ + "#### Clear memory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a006e388", + "metadata": {}, + "outputs": [], + "source": [ + "del load_profile_row_based, output_data, output_data_selective" + ] + }, + { + "cell_type": "markdown", + "id": "eba5b93e", + "metadata": {}, + "source": [ + "# Miscellanious PGM features\n", + "\n", + "## Sparse datasets" + ] + }, + { + "cell_type": "markdown", + "id": "88e731c5", + "metadata": {}, + "source": [ + "For unequal update sets use linear data + pointers. \n", + "This way of providing update would likely be a niche.\n", + "\n", + "Columnar sparse dataset representation for example can be as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47aadfb0", + "metadata": {}, + "outputs": [], + "source": [ + "update_data_sparse = {\n", + " ComponentType.line: {\n", + " \"indptr\": [0, 1, 1, 2],\n", + " \"data\": {\n", + " \"p_specified\": [1000, 2000]\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "ec1c73c4", + "metadata": {}, + "source": [ + "## Validating large batch data\n", + "\n", + "Since the validator for large batch dataset is expensive, we can choose to validate only some part of data.\n", + "The result of the validator is also difficult to interpret with many errors on a large dataset.\n", + "Maximum data errors typically occur in the input dataset.\n", + "Hence we can slice the update data to only certain batches and validate it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "076b552b", + "metadata": {}, + "outputs": [], + "source": [ + "with Timer(\"Validation of full batch data\"):\n", + " assert_valid_batch_data(\n", + " input_data=input_data,\n", + " update_data=update_data_row_based,\n", + " calculation_type=CalculationType.power_flow,\n", + " )\n", + "\n", + "with Timer(\"Validation of partial batch data\"):\n", + " assert_valid_batch_data(\n", + " input_data=input_data,\n", + " update_data={k: v[:5, :] for k, v in update_data_row_based.items()},\n", + " calculation_type=CalculationType.power_flow,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "10693b81", + "metadata": {}, + "source": [ + "# Complex simulations\n", + "\n", + "Often times we wish to run multiple simulations at once. \n", + "These power flow calculations have multiple dimensions associated with them where the information regarding that particular simulation is put in.\n", + "The way to simulate these via PGM most effectively is to flatten all the excess dimension into one, ie. of batches. \n", + "This makes the entire simulation including and most likely excluding PGM most efficient, readable and less bug-prone.\n", + "\n", + "\n", + "## Multiple Timeseries\n", + "\n", + "We are now going to simulate multiple timeseries in a single power flow calculation and later the results would be aggregated from the output numpy arrays.\n", + "\n", + "\n", + "Here we have a EV growth related scenario in a neighbourhood.\n", + "For this simulation we have yearly EV consumption profiles available for 10 different customers.\n", + "We shall subset these profiles and timestamps to align with the data in this notebook " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c28f3fa", + "metadata": {}, + "outputs": [], + "source": [ + "# Hourly EV profile data for one year. Row = hours, column = profiles\n", + "ev_profile = (\n", + " pd.read_csv(\"../data/ev_profile.csv\").to_numpy(\n", + " dtype=np.float64, copy=False, na_value=np.nan\n", + " )\n", + " * 1000\n", + ") # kw\n", + "\n", + "# Select first n_timestamps and first n profiles\n", + "n_ev_profiles = 10\n", + "\n", + "assert n_timestamps <= ev_profile.shape[0]\n", + "assert n_ev_profiles <= ev_profile.shape[1]\n", + "ev_profile = ev_profile[:n_timestamps, :n_ev_profiles] # Hint: Slicing\n", + "print(f\"Shape of ev_profile: {ev_profile.shape}\") # Prints: (n_timestamps * n_ev_profiles) " + ] + }, + { + "cell_type": "markdown", + "id": "27520d80", + "metadata": {}, + "source": [ + "\n", + "\n", + "Since the EV growth at each node is uncertain, we can sample different EV growth scenarios.\n", + "Lets consider: \n", + "* The maximal EV sales in a region is a known quantity (denoted by n_vehicles).\n", + "* The spread of EVs across each loaded nodes is equal (denoted by pvals of n_vehicle_each_node).\n", + "* We conduct n_experiments samples for monte carlo simulation\n", + "* Each EV profile is assigned to each EV equally random (denoted by pvals of distributions)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1de68fdd", + "metadata": {}, + "outputs": [], + "source": [ + "n_vehicles = 100000\n", + "n_experiments = 11\n", + "\n", + "rng = np.random.default_rng(12345)\n", + "\n", + "n_vehicle_each_node = rng.multinomial(\n", + " n=..., pvals=[1 / ...] * ..., size=...\n", + ")\n", + "print(f\"Shape of n_vehicle_each_node: {n_vehicle_each_node.shape}\") # Prints: (n_experiments * n_loads) \n", + "\n", + "distributions = rng.multinomial(\n", + " n=n_vehicle_each_node, pvals=[1 / ...] * ..., size=None\n", + ")\n", + "print(f\"Shape of distributions: {distributions.shape}\") # Prints: (n_experiments, n_loads, n_ev_profiles) \n", + "\n", + "ev_timeseries = np.transpose(... @ (...).T, axes=(0, 2, 1))\n", + "print(f\"Shape of ev_timeseries: {ev_timeseries.shape}\") # Prints: (n_experiments, n_timestamps, n_loads) \n", + "\n", + "ev_timeseries_q = ev_timeseries * ... # assume power factor of 0.95\n", + "\n", + "# Hint: broadcasting (n_timestamps, n_loads) to (n_experiments, n_timestamps, n_loads) \n", + "combined_timeseries = profile[...] + ev_timeseries \n", + "print(f\"Shape of combined_timeseries: {combined_timeseries.shape}\") # Prints: (n_experiments, n_timestamps, n_loads) \n" + ] + }, + { + "cell_type": "markdown", + "id": "264c840b", + "metadata": {}, + "source": [ + "Create columnar update data and run calculation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57cc32d3", + "metadata": {}, + "outputs": [], + "source": [ + "update_data_multi_timeseries = {\n", + " ComponentType.sym_load: {\n", + " \"p_specified\": combined_timeseries.reshape(..., ...), # All update data Should be 2D eventually\n", + " \"q_specified\": ev_timeseries.reshape(..., ...),\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d7618f4", + "metadata": {}, + "outputs": [], + "source": [ + "with Timer(\"Run multitimeseries PF\"):\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_multi_timeseries,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " threading=0\n", + " )\n", + "\n", + "reshaped_loading_data = output_data[ComponentType.line][\"loading\"].reshape(..., ..., ...)\n", + "print(f\"Shape of reshaped_loading_data: {reshaped_loading_data.shape}\") # Prints: (n_experiments, n_timestamps, n_loads) \n" + ] + }, + { + "cell_type": "markdown", + "id": "418c6f70", + "metadata": {}, + "source": [ + "Aggregate result on the basis of experiments of monte carlo simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d117ca19", + "metadata": {}, + "outputs": [], + "source": [ + "np.mean(reshaped_loading_data, axis=...) # Shape: (n_timestamps, n_loads) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62bf3183", + "metadata": {}, + "outputs": [], + "source": [ + "del output_data, reshaped_loading_data" + ] + }, + { + "cell_type": "markdown", + "id": "700eb7ce", + "metadata": {}, + "source": [ + "When PF is run in loop\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d85e9d3", + "metadata": {}, + "outputs": [], + "source": [ + "load_id_loop = np.tile(input_data[ComponentType.sym_load][\"id\"], (n_timestamps, 1))\n", + "output_datas = []\n", + "with Timer(\"Run multi-timeseries PF in loop\"):\n", + " for experiment in range(n_experiments):\n", + " update_data_loop = {\n", + " ComponentType.sym_load: {\n", + " \"id\": load_id_loop,\n", + " \"p_specified\": combined_timeseries[experiment, :, :],\n", + " \"q_specified\": ev_timeseries_q[experiment, :, :],\n", + " }\n", + " }\n", + " output_data = model.calculate_power_flow(\n", + " update_data=update_data_loop,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " threading=0,\n", + " )\n", + " output_datas.append(output_datas)\n", + "\n", + "del output_datas" + ] + }, + { + "cell_type": "markdown", + "id": "63ea122b", + "metadata": {}, + "source": [ + "\n", + "The difference between this method and one big batch is indeed not very large, but this is a significant for performance improvement. \n", + "\n", + "The combining/seprating numpy arrays is a lot more wasteful.\n", + "\n", + "The calculating of mean on `output_datas` list is more difficult than a numpy array.\n", + "\n", + "Always keep the components within a single scenario as the rightmost dimension (aka. fast moving dimension). \n", + "In this example it would be `n_loads`.\n", + "A transposing of array would likely be required otherwise which is an extra copy." + ] + }, + { + "cell_type": "markdown", + "id": "788d8c8e", + "metadata": {}, + "source": [ + "## PGM DS library" + ] + }, + { + "cell_type": "markdown", + "id": "7eb8b3b4", + "metadata": {}, + "source": [ + "Converting to and from a PGM DS grid can be done via `PowerGridModelInterface`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8408fb2", + "metadata": {}, + "outputs": [], + "source": [ + "core_interface = PowerGridModelInterface(input_data=input_data)\n", + "grid = core_interface.create_grid_from_input_data()\n", + "print(grid.line)" + ] + }, + { + "cell_type": "markdown", + "id": "cc487324", + "metadata": {}, + "source": [ + "### Visualization of grid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61c1c3d0", + "metadata": {}, + "outputs": [], + "source": [ + "visualize(grid)" + ] + }, + { + "cell_type": "markdown", + "id": "5a2b633f", + "metadata": {}, + "source": [ + "### Graph operations" + ] + }, + { + "cell_type": "markdown", + "id": "84cb32bc", + "metadata": {}, + "source": [ + "Some graphical operations possible via PGM-DS. Custom ones can be implemented in your simulation via rustworkx." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab287807", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "grid.node.node_type[0] = NodeType.SUBSTATION_NODE\n", + "\n", + "# Downstream nodes from node 1 with the substation node as reference\n", + "print(f\"1. {grid.get_downstream_nodes(node_id=1)}\")\n", + "\n", + "# In case of multiple substations, find nearest one\n", + "print(f\"2. {grid.get_nearest_substation_node(node_id=10).id}\")\n", + "\n", + "# Find all paths between node 1 and node 10\n", + "print(f\"3. {grid.graphs.active_graph.get_all_paths(ext_start_node_id=1, ext_end_node_id=10)}\")\n", + "\n", + "# Find shortest path between node 1 and node 10\n", + "print(f\"4. {grid.graphs.active_graph.get_shortest_path(ext_start_node_id=1, ext_end_node_id=10)}\")\n", + "\n", + "# All components in this graph\n", + "print(f\"5. {len(grid.graphs.active_graph.get_components())}\")\n", + "\n", + "# All components connected to node 1\n", + "print(f\"6. {len(grid.graphs.active_graph.get_connected(node_id=1))}\")\n", + "\n", + "# All components connected to node 0, ignoring nodes 1 and 2\n", + "print(f\"7. {len(grid.graphs.active_graph.get_connected(node_id=0, nodes_to_ignore=[1, 2]))}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "a1fb9b25", + "metadata": {}, + "source": [ + "### Array operations" + ] + }, + { + "cell_type": "markdown", + "id": "1702164d", + "metadata": {}, + "source": [ + "Many array operations are possible. Such operations are possible via numpy too but are not as intuitive." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3832ce10", + "metadata": {}, + "outputs": [], + "source": [ + "grid.line.filter(from_node=[0, 1, 2, 3, 4], to_node=[0, 1, 2, 3, 4, 5, 6, 7], mode_=\"AND\")" + ] + }, + { + "cell_type": "markdown", + "id": "f1b2bcd9", + "metadata": {}, + "source": [ + "### Extended arrays\n", + "\n", + "Extra information can be stored along with arrays via a way described in https://power-grid-model-ds.readthedocs.io/en/stable/examples/model/grid_extensions_examples.html\n", + "\n", + "Note that concatenating new fields to row based arrays would be inefficient as they would need to be reconstructed before using in PGM." + ] + }, + { + "cell_type": "markdown", + "id": "569a7688", + "metadata": {}, + "source": [ + "## Contingency Analysis with timeseries\n", + "\n", + "Here we perform a selective contingency analysis on specific branches for all timesteps.\n", + "\n", + "Use graph operations to find top 5 ranked branches which most nodes depend on.\n", + "\n", + "(Assumption: Grid is radial and nodes are assigned with from_ndoe at source side.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e2ca4e4", + "metadata": {}, + "outputs": [], + "source": [ + "# Lines with most most number of dependent nodes\n", + "n_contingencies = 5\n", + "\n", + "all_branches = []\n", + "for node in grid.node.id:\n", + " paths = grid.graphs.active_graph.get_all_paths(ext_start_node_id=..., ext_end_node_id=...)\n", + " for path in paths:\n", + " all_branches += [(path[idx], path[idx + 1]) for idx in range(len(path) - 1)]\n", + "\n", + "print(f\"Path list with length {len(all_branches)}\")\n", + "print(all_branches)\n", + "\n", + "counts = Counter(all_branches)\n", + "top_branches_from_to_counts = counts.most_common(n=...)\n", + "nodes_from = [i[0][0] for i in top_branches_from_to_counts]\n", + "nodes_to = [i[0][1] for i in top_branches_from_to_counts]\n", + "\n", + "top_branches_id = grid.line.filter(from_node=..., to_node=..., mode_=\"AND\").id\n", + "\n", + "print(f\"top {n_contingencies} branches {top_branches_id}\")" + ] + }, + { + "cell_type": "markdown", + "id": "32149652", + "metadata": {}, + "source": [ + "Design batches for simulation in PGM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3f1ac0b", + "metadata": {}, + "outputs": [], + "source": [ + "combos_contingencies = 1 - np.eye(..., dtype=np.uint8)\n", + "\n", + "line_update = initialize_array(\n", + " DatasetType.update,\n", + " ComponentType.line,\n", + " (... * ..., ...),\n", + ")\n", + "line_update[\"id\"] = top_branches_id\n", + "line_update_reshaped = line_update.reshape(n_contingencies, n_timestamps, n_contingencies)\n", + "line_update_reshaped[\"from_status\"] = combos_contingencies[:, np.newaxis, :]\n", + "line_update_reshaped[\"to_status\"] = combos_contingencies[:, np.newaxis, :]" + ] + }, + { + "cell_type": "markdown", + "id": "14b7589b", + "metadata": {}, + "source": [ + "Run contingency analysis on timeseries by using Timeseries data from earlier cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2afaaa39", + "metadata": {}, + "outputs": [], + "source": [ + "load_profile_combined = initialize_array(\n", + " DatasetType.update,\n", + " ComponentType.sym_load,\n", + " (n_contingencies * n_timestamps, n_loads),\n", + ")\n", + "\n", + "load_profile_combined[\"p_specified\"] = np.broadcast_to(\n", + " profile, (n_contingencies, n_timestamps, n_loads)\n", + ").reshape(-1, n_loads)\n", + "# Alternatively:\n", + "# load_profile_combined[\"p_specified\"].reshape(n_contingencies, n_timestamps, n_loads)[:] = profile\n", + "\n", + "load_profile_combined[\"q_specified\"] = 0.0\n", + "\n", + "update_data_combined = {\n", + " ComponentType.line: line_update,\n", + " ComponentType.sym_load: load_profile_combined,\n", + "}\n", + "\n", + "with Timer(\"Batch Calculation with contingencies and load profiles\"):\n", + " output_data_combined = model.calculate_power_flow(\n", + " update_data=update_data_combined,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " threading=0\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6c73a578", + "metadata": {}, + "source": [ + "Plot loading on first line for all situations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06e5d27d", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(output_data_combined[ComponentType.line][\"loading\"][:, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d863a5f", + "metadata": {}, + "outputs": [], + "source": [ + "del output_data_combined" + ] + }, + { + "cell_type": "markdown", + "id": "afe04047", + "metadata": {}, + "source": [ + "#### Question regarding topology caching\n", + "\n", + "Instead of designing the simulation like we did in this way:\n", + "\n", + "```\n", + "1 0 0 0 timestep 1\n", + "1 0 0 0 timestep 2\n", + "1 0 0 0 timestep 3\n", + "...\n", + "\n", + "0 1 0 0 timestep 1\n", + "0 1 0 0 timestep 2\n", + "0 1 0 0 timestep 3\n", + "...\n", + "\n", + "```\n", + "\n", + "What if contingencies are designed this way?\n", + "\n", + "\n", + "```\n", + "0 1 1 1 timestep 1\n", + "1 0 1 1\n", + "1 1 0 1\n", + "1 1 1 0\n", + "\n", + "0 1 1 1 timestep 2\n", + "1 0 1 1\n", + "1 1 0 1\n", + "1 1 1 0\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "60a37750", + "metadata": {}, + "source": [ + "Another alternative to is to cache internal state of PowerGridModel object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71ae9bce", + "metadata": {}, + "outputs": [], + "source": [ + "model_topology_2 = model.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "0c8aa053", + "metadata": {}, + "source": [ + "### Simulation design exercise\n", + "\n", + "* Can you give an example on how you can set up a network reconfiguration study based on the example above?\n", + "* What graph related operations are needed?\n", + " * Is it possible to do those via PGM-DS?\n", + "* What result attributes are to be checked? Do they need to be aggregated?" + ] + }, + { + "cell_type": "markdown", + "id": "1cc830d3", + "metadata": {}, + "source": [ + "## Dynamic operating envelope" + ] + }, + { + "cell_type": "markdown", + "id": "28763a05", + "metadata": {}, + "source": [ + "Calculate the operating envelope for all timesteps at node 10 based on voltage limits.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ccf1a371", + "metadata": {}, + "source": [ + "Find the sym load connected to node 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6293765", + "metadata": {}, + "outputs": [], + "source": [ + "sym_load_node_10 = grid.sym_load.filter(node=10).id\n", + "sym_load_node_10_idx = model.get_indexer(ids=sym_load_node_10, component_type=ComponentType.sym_load)[0]\n", + "print(sym_load_node_10_idx)" + ] + }, + { + "cell_type": "markdown", + "id": "a1a77c96", + "metadata": {}, + "source": [ + "* Create a grid of P and Q values between a max and minimum range.\n", + "* Create multiple timeseries for each point in the grid\n", + "* Add the values in the grid to the load at node 10. Rest timeseries would remain the same." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b924a3a8", + "metadata": {}, + "outputs": [], + "source": [ + "min_p = -1e7\n", + "max_p = 1e7\n", + "min_q = -1e7\n", + "max_q = 1e7\n", + "resolution = 11\n", + "\n", + "p_linear_values = np.linspace(start=..., stop=..., num=...)\n", + "q_linear_values = np.linspace(start=..., stop=..., num=...)\n", + "\n", + "p_values, q_values = np.meshgrid(p_linear_values, q_linear_values, copy=False)\n", + "print(f\"p_values shape: {p_values.shape}\") # Prints: (resolution, resolution)\n", + "print(f\"q_values shape: {q_values.shape}\") # Prints: (resolution, resolution)\n", + "\n", + "p_envelope = np.tile(profile, (resolution, resolution, 1, 1))\n", + "print(f\"p_envelope shape: {p_envelope.shape}\") # Prints: (resolution, resolution, n_timestamps, n_loads)\n", + "\n", + "p_envelope[:, :, :, sym_load_node_10_idx] += p_values[..., ..., ...]\n", + "\n", + "q_envelope = np.zeros(p_envelope.shape)\n", + "q_envelope[:, :, :, sym_load_node_10_idx] += q_values[..., ..., ...]\n" + ] + }, + { + "cell_type": "markdown", + "id": "a92e58c8", + "metadata": {}, + "source": [ + "Create update data and run simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2ad55f3", + "metadata": {}, + "outputs": [], + "source": [ + "update_data_envelope = {\n", + " ComponentType.sym_load: {\n", + " \"p_specified\": p_envelope.reshape(..., ...),\n", + " \"q_specified\": q_envelope.reshape(..., ...),\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a152a12", + "metadata": {}, + "outputs": [], + "source": [ + "with Timer(\"Batch Calculation with grid of P and Q to calculate an envelope of operation\"):\n", + " output_data_envelope = model.calculate_power_flow(\n", + " update_data=update_data_envelope,\n", + " calculation_method=CalculationMethod.newton_raphson,\n", + " output_component_types=limit_related_output,\n", + " threading=0\n", + " )\n", + "\n", + "nodal_voltages = output_data_envelope[ComponentType.node][\"u_pu\"].reshape(..., ..., ..., ...)\n", + "print(f\"nodal_voltages shape: {nodal_voltages.shape}\") # Prints: (resolution, resolution, n_timestamps, n_nodes)" + ] + }, + { + "cell_type": "markdown", + "id": "b3b602c5", + "metadata": {}, + "source": [ + "Next Step to calculate the envelope:\n", + "\n", + "* Calculate the index mask where the voltage does not cross above or below the voltage limit for each timestep\n", + "* Apply this mask on the grid of P and Q for each time step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a298bf9", + "metadata": {}, + "outputs": [], + "source": [ + "u_low_lim = 0.95\n", + "u_high_lim = 1.03\n", + "\n", + "max_voltage_scenario = np.max(nodal_voltages, axis=...) > ...\n", + "min_voltage_scenario = np.min(nodal_voltages, axis=...) < ...\n", + "points_outside_limits = np.logical_or(max_voltage_scenario, min_voltage_scenario)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70460504", + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(points_outside_limits[:,:, 0])" + ] + }, + { + "cell_type": "markdown", + "id": "1d0d4c20", + "metadata": {}, + "source": [ + "### Simulation Design Exercise\n", + "\n", + "* Can you give anadditional examples of calculating flexibility envelopes? Maybe with more details? (based on technical limits and not economic ones) \n", + "* What result attributes are to be checked? Do they need to be aggregated?" + ] + }, + { + "cell_type": "markdown", + "id": "d7cfc8d4", + "metadata": {}, + "source": [ + "# Additional Content" + ] + }, + { + "cell_type": "markdown", + "id": "23b4016b", + "metadata": {}, + "source": [ + "#### Additional simulation design exercise\n", + "\n", + "* Can you give an example of how we can implement a control related action via PGM? \n", + "* Draw a block diagram of the control?\n", + " * Is this scheme efficient?\n", + " * How do we create batches for such simulation. \n", + "* What result attributes are to be checked? Do they need to be aggregated?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "power-grid-model-workshop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb index 658a5fb..16375de 100644 --- a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb +++ b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb @@ -27,7 +27,7 @@ " * Simulation design exercise\n", "\n", "Throughout this notebook there are big notebook cells of code. \n", - "To have a deeper understanding of the simulations, comment all + uncomment line by line by confirming the debugging print statements." + "To have a deeper understanding of the simulations, it is recommended to comment it all and uncomment line by line along with confirming the debugging print statements." ] }, { @@ -48,8 +48,6 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Dict\n", - "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -68,7 +66,7 @@ "\n", "from power_grid_model.validation import assert_valid_input_data, assert_valid_batch_data\n", "\n", - "from power_grid_model_ds import Grid, PowerGridModelInterface\n", + "from power_grid_model_ds import PowerGridModelInterface\n", "from power_grid_model_ds.enums import NodeType\n", "from power_grid_model_ds.visualizer import visualize\n", "\n", @@ -143,6 +141,14 @@ "input_data = load_input_data()\n" ] }, + { + "cell_type": "markdown", + "id": "ec1e571c", + "metadata": {}, + "source": [ + "Create a PowerGridModel instance" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -171,6 +177,14 @@ "n_timestamps = 24 * 7" ] }, + { + "cell_type": "markdown", + "id": "fbe924e2", + "metadata": {}, + "source": [ + "We use same timeseries profile as in the Power Flow assignment" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -234,7 +248,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Current memory usage: 170.72 MB\n", + "Current memory usage: 195.36 MB\n", "Memory used by object: 0.00766754150390625 MB\n", "Memory used by object: 0.00016021728515625 MB\n", "Memory used by dictionary: 0.00766754150390625 MB\n", @@ -280,7 +294,7 @@ "id": "94e707b9", "metadata": {}, "source": [ - "## Correct dtype for PGM" + "## Correct dtype for PGM arrays" ] }, { @@ -289,14 +303,10 @@ "metadata": {}, "source": [ "\n", - "Use of initialize_array is recommended for most uses. \n", - "However if you wish to translate the dtype into the one PGM uses, you can use `initialize_array(dataset_type, component_type, empty_shape, empty=True)` or `dtype = power_grid_meta_data[dataset_type][component_type].dtype` to find the dtype of the row based data. You can use this to change to the appropriate type which is required by PGM.\n", - "\n", - "For dtype of individual attribute which would be required in columnar data creation, you can use `attribute_dtype(data_type, component_type, attribute)`. The missing value can be filled by `attribute_empty_value(data_type, component_type, attribute)` \n", - "\n", - "Also check for contigious-ness. \n", + "Use of initialize_array is recommended for most uses.\n", + "The reasoning for this recommendation should be clear by the end of this workshop.\n", "\n", - "Check if a type-cast to this dtype creates a copy. Maybe such a copy is redundant." + "However if you wish to translate the dtype into the one PGM uses, you can use `initialize_array(dataset_type, component_type, empty_shape, empty=True)` or `dtype = power_grid_meta_data[dataset_type][component_type].dtype` to find the dtype of the row based data. You can use this to change to the appropriate type which is required by PGM." ] }, { @@ -322,6 +332,16 @@ "# power_grid_meta_data[DatasetType.input][ComponentType.sym_load].dtype" ] }, + { + "cell_type": "markdown", + "id": "1c2ac8f3", + "metadata": {}, + "source": [ + "For dtype of individual attribute which would be required in columnar data creation, you can use `attribute_dtype(data_type, component_type, attribute)`. The missing value can be filled by `attribute_empty_value(data_type, component_type, attribute)` \n", + "\n", + "Tip: Check if a type-cast to this dtype creates a copy. Maybe such a copy is redundant." + ] + }, { "cell_type": "code", "execution_count": 10, @@ -348,7 +368,7 @@ "id": "cd3c9251", "metadata": {}, "source": [ - "All available attributes are visible via" + "All available attributes are visible via following:" ] }, { @@ -377,12 +397,16 @@ "id": "83e8ef68", "metadata": {}, "source": [ - "## Views vs copies\n", + "## Views, copies and Memory location\n", + "\n", + "A view is a windowed look into an array. Creating views is very inexpensive. \n", + "A copy here is meant by an array which owns the data within it.\n", "\n", "Check numpy documentation for when certain functions or features create a view or copy. \n", "\n", - "* Views simplify assignment of values\n", + "* Views simplify assignment of values to arrays.\n", "* Uneccesary copies here are the first you can aim to eliminate to improve overall performance.\n", + "* Beware of unintentional memory usage because of views.\n", "\n", "\n", "https://numpy.org/doc/stable/user/basics.copies.html\n" @@ -393,9 +417,9 @@ "id": "64b08530", "metadata": {}, "source": [ - "Checking if view or copy\n", "\n", - "if `array.base is None` -> copy; else -> view\n" + "* Checking if view or copy: if `array.base is None` -> copy; else -> view\n", + "* Finding out view belongs to which original array: if `array.base is original_array`" ] }, { @@ -405,15 +429,12 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "x = np.arange(9)\n", "\n", + "x = np.arange(9)\n", "y = x.reshape(3, 3)\n", - "\n", "assert y.base is not None # .reshape() creates a view\n", "\n", "z = y[[2, 1]]\n", - "\n", "assert z.base is None # advanced indexing creates a copy" ] }, @@ -424,7 +445,7 @@ "source": [ "## Garbage collection for views/copies\n", "\n", - "### Higher than required memory usage" + "### Holding base array\n" ] }, { @@ -468,8 +489,6 @@ "id": "75552bf8", "metadata": {}, "source": [ - "### Right Way to return values\n", - "\n", "Use .copy() to convert a view into a copy." ] }, @@ -509,12 +528,20 @@ "id": "0573d0f4", "metadata": {}, "source": [ - "### Contigious Data\n", + "## Data Contiguity\n", + "\n", + "What is Contiguity in numpy arrays?\n", + "- Contiguity means all array elements are laid out sequentially in memory.\n", + " - C contiguous: row major where rows are stored one after another. ie. All elements are in order of right to left dimension. Rightmost dimension is fast changing. (This is default in numpy and also required by PGM.)\n", + " - F contiguous: column-major where columns stored one after another. ie. All elements are in order of left to right dimension. Leftmost dimension is fast changing.\n", "\n", - "The input and update dataset needs to be a contigious dataset.\n", - "A transposed dataset for example would be copied first whereas a sliced dataset would not be.\n", - "A reshaped dataset mostly would be contigious.\n", - "Check arr.flags for contigiousness\n" + "The input and update dataset needs to be a C contigious dataset before PGM can make use of it. While PGM output is C contigious. PGM internally converts it to be C contigious if its not.\n", + "\n", + "A basic sliced dataset would retain contiguity. Whereas a transposed or advanced sliced array would not.\n", + "A reshaped dataset mostly would be C contigious.\n", + "\n", + "Check `arr.flags` for Contiguity.\n", + "This requirement for PGM and for further uses can be pre-planned.\n" ] }, { @@ -527,38 +554,65 @@ "name": "stdout", "output_type": "stream", "text": [ - " C_CONTIGUOUS : True\n", + "1. C_CONTIGUOUS : True\n", " F_CONTIGUOUS : False\n", " OWNDATA : True\n", " WRITEABLE : True\n", " ALIGNED : True\n", " WRITEBACKIFCOPY : False\n", "\n", - " C_CONTIGUOUS : True\n", + "2. C_CONTIGUOUS : True\n", " F_CONTIGUOUS : False\n", " OWNDATA : False\n", " WRITEABLE : True\n", " ALIGNED : True\n", " WRITEBACKIFCOPY : False\n", "\n", - " C_CONTIGUOUS : False\n", + "3. C_CONTIGUOUS : False\n", " F_CONTIGUOUS : True\n", " OWNDATA : False\n", " WRITEABLE : True\n", " ALIGNED : True\n", " WRITEBACKIFCOPY : False\n", "\n", - "True\n" + "5. C_CONTIGUOUS : False\n", + " F_CONTIGUOUS : False\n", + " OWNDATA : False\n", + " WRITEABLE : True\n", + " ALIGNED : True\n", + " WRITEBACKIFCOPY : False\n", + "\n", + "4. True\n" ] } ], "source": [ "loads = np.zeros((1000, 10),dtype=np.float64)\n", - "print(loads.flags)\n", - "print(loads.reshape(10, 20, 50).flags)\n", - "print(loads.T.flags)\n", + "print(f\"1. {loads.flags}\")\n", + "print(f\"2. {loads.reshape(10, 20, 50).flags}\")\n", + "print(f\"3. {loads.T.flags}\")\n", + "print(f\"5. {np.transpose(loads.reshape(10, 20, 50), axes=(2, 0, 1)).flags}\")\n", "\n", - "print(np.ascontiguousarray(loads) is loads)" + "print(f\"4. {np.ascontiguousarray(loads) is loads}\")" + ] + }, + { + "cell_type": "markdown", + "id": "bd9a03ce", + "metadata": {}, + "source": [ + "`initialize_array` handles contiguity and dtype requirement for row based data without user having to think about it. \n", + "For columnar data however the responsibility lies with the user." + ] + }, + { + "cell_type": "markdown", + "id": "097bc587", + "metadata": {}, + "source": [ + "Questions:\n", + "- Does a view own its data?\n", + "- Give reasoning for flags of 3." ] }, { @@ -569,6 +623,14 @@ "## Accessing and assigning to PGM arrays" ] }, + { + "cell_type": "markdown", + "id": "77e9f0d2", + "metadata": {}, + "source": [ + "PGM component has layout of (scenarios, components in a scenario)" + ] + }, { "cell_type": "code", "execution_count": 16, @@ -602,8 +664,6 @@ } ], "source": [ - "\n", - "\n", "print(\"Single batch all components: `loads[:, 0]` gets a :\", copy_or_view(loads[:, 0]))\n", "print(\"All batch single components: `loads[0, :]` gets a :\", copy_or_view(loads[0, :]))\n", "print(\"Sliced batch all components: `loads[:5, :]` gets a :\", copy_or_view(loads[:5, :]))\n", @@ -649,6 +709,14 @@ "print(\"Single batch all components: `loads['p_specified'][:, [1, 2, 3]]` gets a :\", copy_or_view(loads[\"p_specified\"][:, [1, 2, 3]])) " ] }, + { + "cell_type": "markdown", + "id": "49f6fd1c", + "metadata": {}, + "source": [ + "Its recommended to index with field first (`loads[\"p_specified\"][...]` instead of `loads[...][\"p_specified\"]`) because advanced indexing on structured arrays can create copies." + ] + }, { "cell_type": "markdown", "id": "31bc7376", @@ -697,7 +765,8 @@ "text": [ "[[123456. 123456. 123456. 123456.]\n", " [123456. 123456. 123456. 123456.]\n", - " [123456. 123456. 123456. 123456.]]\n" + " [123456. 123456. 123456. 123456.]]\n", + "True\n" ] } ], @@ -707,7 +776,8 @@ "\n", "x = loads[\"p_specified\"][:3, :]\n", "loads[\"p_specified\"][:3, :] = 123456\n", - "print(x)\n" + "print(x)\n", + "print(x.base is loads) # x is a view of loads\n" ] }, { @@ -722,7 +792,8 @@ "text": [ "[[100. 100. 100. 100.]\n", " [100. 100. 100. 100.]\n", - " [100. 100. 100. 100.]]\n" + " [100. 100. 100. 100.]]\n", + "False\n" ] } ], @@ -732,7 +803,8 @@ "\n", "x = loads[\"p_specified\"][[1, 2, 3], :]\n", "loads[\"p_specified\"][[1, 2, 3], :] = 123456\n", - "print(x)\n" + "print(x)\n", + "print(x.base is loads) # x is a copy of the loadings of scenario 1 2 3" ] }, { @@ -759,7 +831,8 @@ " ...\n", " [100. 100. 100.]\n", " [100. 100. 100.]\n", - " [100. 100. 100.]]\n" + " [100. 100. 100.]]\n", + "False\n" ] } ], @@ -769,7 +842,8 @@ "\n", "x = loads[\"p_specified\"][:, [1, 2, 3]]\n", "loads[\"p_specified\"][:, [1, 2, 3]] = 123456\n", - "print(x)\n" + "print(x)\n", + "print(x.base is loads)" ] }, { @@ -777,7 +851,11 @@ "id": "ab922bf6", "metadata": {}, "source": [ - "### Assignment via tuples" + "### Assignment to arrays\n", + "\n", + "Assigning means setting values to the array.\n", + "\n", + "#### Via tuple" ] }, { @@ -793,11 +871,13 @@ " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])],\n", " [(1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", + " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.]),\n", " (1, 2, 1, 0, [123., 123., 123.], [0., 0., 0.])]],\n", " dtype={'names': ['id', 'node', 'status', 'type', 'p_specified', 'q_specified'], 'formats': [']). \n", "Attributes: dict_keys(['loading'])\n", @@ -993,7 +1129,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "9831864d", "metadata": {}, "outputs": [], @@ -1015,7 +1151,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "id": "a006e388", "metadata": {}, "outputs": [], @@ -1046,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "id": "47aadfb0", "metadata": {}, "outputs": [], @@ -1076,7 +1212,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "id": "076b552b", "metadata": {}, "outputs": [ @@ -1084,8 +1220,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Validation of full batch data is 4.033818 s\n", - "Execution time for Validation of partial batch data is 0.120680 s\n" + "Execution time for Validation of full batch data is 0.325254 s\n", + "Execution time for Validation of partial batch data is 0.050361 s\n" ] } ], @@ -1130,7 +1266,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "id": "2c28f3fa", "metadata": {}, "outputs": [ @@ -1177,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "id": "1de68fdd", "metadata": {}, "outputs": [ @@ -1213,7 +1349,7 @@ "\n", "ev_timeseries_q = ev_timeseries * np.tan(np.arccos(0.95)) # assume power factor of 0.95\n", "\n", - "combined_timeseries = profile[np.newaxis, :, :] + ev_timeseries\n", + "combined_timeseries = profile[np.newaxis, :, :] + ev_timeseries # Hint: broadcasting (n_timestamps, n_loads) to (n_experiments, n_timestamps, n_loads) \n", "print(f\"Shape of combined_timeseries: {combined_timeseries.shape}\") # Prints: (n_experiments, n_timestamps, n_loads) \n" ] }, @@ -1227,7 +1363,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "id": "57cc32d3", "metadata": {}, "outputs": [], @@ -1242,7 +1378,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "id": "9d7618f4", "metadata": {}, "outputs": [ @@ -1250,7 +1386,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Run multitimeseries PF is 7.146938 s\n", + "Execution time for Run multitimeseries PF is 0.350957 s\n", "Shape of reshaped_loading_data: (11, 168, 2000)\n" ] } @@ -1278,23 +1414,23 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "id": "d117ca19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1.71 , 1.558, 1.378, ..., 0.506, 0.335, 0.171],\n", - " [1.71 , 1.527, 1.349, ..., 0.551, 0.35 , 0.17 ],\n", - " [1.684, 1.508, 1.333, ..., 0.54 , 0.35 , 0.183],\n", + "array([[1.698, 1.509, 1.347, ..., 0.534, 0.346, 0.18 ],\n", + " [1.719, 1.542, 1.363, ..., 0.528, 0.349, 0.181],\n", + " [1.711, 1.543, 1.379, ..., 0.513, 0.344, 0.182],\n", " ...,\n", - " [1.79 , 1.632, 1.446, ..., 0.53 , 0.352, 0.198],\n", - " [1.686, 1.539, 1.364, ..., 0.53 , 0.353, 0.182],\n", - " [1.721, 1.55 , 1.384, ..., 0.521, 0.345, 0.182]], shape=(168, 2000))" + " [1.773, 1.584, 1.406, ..., 0.554, 0.346, 0.181],\n", + " [1.751, 1.569, 1.388, ..., 0.542, 0.365, 0.187],\n", + " [1.72 , 1.543, 1.366, ..., 0.53 , 0.352, 0.183]], shape=(168, 2000))" ] }, - "execution_count": 37, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1305,7 +1441,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "id": "62bf3183", "metadata": {}, "outputs": [], @@ -1323,7 +1459,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "id": "5d85e9d3", "metadata": {}, "outputs": [ @@ -1331,7 +1467,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Run multi-timeseries PF in loop is 6.871816 s\n" + "Execution time for Run multi-timeseries PF in loop is 0.464040 s\n" ] } ], @@ -1368,7 +1504,11 @@ "\n", "The combining/seprating numpy arrays is a lot more wasteful.\n", "\n", - "The calculating of mean on `output_datas` list is more difficult than a numpy array." + "The calculating of mean on `output_datas` list is more difficult than a numpy array.\n", + "\n", + "Always keep the components within a single scenario as the rightmost dimension (aka. fast moving dimension). \n", + "In this example it would be `n_loads`.\n", + "A transposing of array would likely be required otherwise which is an extra copy." ] }, { @@ -1389,7 +1529,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "id": "e8408fb2", "metadata": {}, "outputs": [ @@ -1428,7 +1568,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "id": "61c1c3d0", "metadata": {}, "outputs": [ @@ -1447,7 +1587,7 @@ " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1476,7 +1616,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "id": "ab287807", "metadata": {}, "outputs": [ @@ -1499,16 +1639,16 @@ "grid.node.node_type[0] = NodeType.SUBSTATION_NODE\n", "\n", "# Downstream nodes from node 1 with the substation node as reference\n", - "print(f\"1. {grid.get_downstream_nodes(1)}\")\n", + "print(f\"1. {grid.get_downstream_nodes(node_id=1)}\")\n", "\n", "# In case of multiple substations, find nearest one\n", - "print(f\"2. {grid.get_nearest_substation_node(10).id}\")\n", + "print(f\"2. {grid.get_nearest_substation_node(node_id=10).id}\")\n", "\n", "# Find all paths between node 1 and node 10\n", - "print(f\"3. {grid.graphs.active_graph.get_all_paths(1, 10)}\")\n", + "print(f\"3. {grid.graphs.active_graph.get_all_paths(ext_start_node_id=1, ext_end_node_id=10)}\")\n", "\n", "# Find shortest path between node 1 and node 10\n", - "print(f\"4. {grid.graphs.active_graph.get_shortest_path(1, 10)}\")\n", + "print(f\"4. {grid.graphs.active_graph.get_shortest_path(ext_start_node_id=1, ext_end_node_id=10)}\")\n", "\n", "# All components in this graph\n", "print(f\"5. {len(grid.graphs.active_graph.get_components())}\")\n", @@ -1517,7 +1657,7 @@ "print(f\"6. {len(grid.graphs.active_graph.get_connected(node_id=1))}\")\n", "\n", "# All components connected to node 0, ignoring nodes 1 and 2\n", - "print(f\"7. {len(grid.graphs.active_graph.get_connected(node_id=0, nodes_to_ignore=[1, 2]))}\")\n" + "print(f\"7. {len(grid.graphs.active_graph.get_connected(node_id=0, nodes_to_ignore=[1, 2]))}\")" ] }, { @@ -1538,7 +1678,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "id": "3832ce10", "metadata": {}, "outputs": [ @@ -1550,7 +1690,7 @@ " (2003, 2, 3, 1, 1, -2147483648, -2147483648, False, 0.109, 0.098, 3.369e-05, 0.095, 500.)]]... + 2 more rows)" ] }, - "execution_count": 43, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1559,6 +1699,18 @@ "grid.line.filter(from_node=[0, 1, 2, 3, 4], to_node=[0, 1, 2, 3, 4, 5, 6, 7], mode_=\"AND\")" ] }, + { + "cell_type": "markdown", + "id": "f1b2bcd9", + "metadata": {}, + "source": [ + "### Extended arrays\n", + "\n", + "Extra information can be stored along with arrays via a way described in https://power-grid-model-ds.readthedocs.io/en/stable/examples/model/grid_extensions_examples.html\n", + "\n", + "Note that concatenating new fields to row based arrays would be inefficient as they would need to be reconstructed before using in PGM." + ] + }, { "cell_type": "markdown", "id": "569a7688", @@ -1568,12 +1720,14 @@ "\n", "Here we perform a selective contingency analysis on specific branches for all timesteps.\n", "\n", - "Use graph operations to find top 5 ranked branches which most nodes depend on." + "Use graph operations to find top 5 ranked branches which most nodes depend on.\n", + "\n", + "(Assumption: Grid is radial and nodes are assigned with from_ndoe at source side.)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "id": "0e2ca4e4", "metadata": {}, "outputs": [ @@ -1581,32 +1735,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Branch list [(0, 1), (1, 2), (0, 1), (2, 3), (1, 2), (0, 1), (3, 4), (2, 3), (1, 2), (0, 1), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (5, 6), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (6, 7), (5, 6), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (7, 8), (6, 7), (5, 6), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (8, 9), (7, 8), (6, 7), (5, 6), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (9, 10), (8, 9), (7, 8), (6, 7), (5, 6), (4, 5), (3, 4), (2, 3), (1, 2), (0, 1), (0, 11), (11, 12), (0, 11), (12, 13), (11, 12), (0, 11), (13, 14), (12, 13), (11, 12), (0, 11), (14, 15), (13, 14), (12, 13), (11, 12), (0, 11), (15, 16), (14, 15), (13, 14), (12, 13), (11, 12), (0, 11), (16, 17), (15, 16), (14, 15), (13, 14), (12, 13), (11, 12), (0, 11), (17, 18), (16, 17), (15, 16), (14, 15), (13, 14), (12, 13), (11, 12), (0, 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- " all_branches += [(path[idx + 1], path[idx]) for idx in range(len(path) - 1)]\n", + " all_branches += [(path[idx], path[idx + 1]) for idx in range(len(path) - 1)]\n", "\n", - "print(f\"Branch list {all_branches}\")\n", + "print(f\"Path list with length {len(all_branches)}\")\n", + "print(all_branches)\n", "\n", "counts = Counter(all_branches)\n", - "top_branches_from_to_counts = counts.most_common(contingency_count)\n", - "from_nodes = [i[0][0] for i in top_branches_from_to_counts]\n", - "to_nodes = [i[0][1] for i in top_branches_from_to_counts]\n", + "top_branches_from_to_counts = counts.most_common(n=n_contingencies)\n", + "nodes_from = [i[0][0] for i in top_branches_from_to_counts]\n", + "nodes_to = [i[0][1] for i in top_branches_from_to_counts]\n", "\n", - "top_branches_id = grid.line.filter(from_node=from_nodes, to_node=to_nodes, mode_=\"AND\").id\n", + "top_branches_id = grid.line.filter(from_node=nodes_from, to_node=nodes_to, mode_=\"AND\").id\n", "\n", - "print(f\"top 5 branches {top_branches_id}\")" + "print(f\"top {n_contingencies} branches {top_branches_id}\")" ] }, { @@ -1619,24 +1774,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "e3f1ac0b", "metadata": {}, "outputs": [], "source": [ - "# TODO Other way possible\n", - "# combos_contingencies = np.eye(contingency_count, dtype=np.uint8)\n", - "\n", - "combos_contingencies_timestamps = np.tile(combos_contingencies, (1, n_timestamps))\n", + "combos_contingencies = 1 - np.eye(n_contingencies, dtype=np.uint8)\n", "\n", "line_update = initialize_array(\n", " DatasetType.update,\n", " ComponentType.line,\n", - " (contingency_count * n_timestamps, contingency_count),\n", + " (n_contingencies * n_timestamps, n_contingencies),\n", ")\n", "line_update[\"id\"] = top_branches_id\n", - "line_update[\"from_status\"] = 1 - combos_contingencies_timestamps.reshape(-1, contingency_count)\n", - "line_update[\"to_status\"] = 1 - combos_contingencies_timestamps.reshape(-1, contingency_count)" + "line_update_reshaped = line_update.reshape(n_contingencies, n_timestamps, n_contingencies)\n", + "line_update_reshaped[\"from_status\"] = combos_contingencies[:, np.newaxis, :]\n", + "line_update_reshaped[\"to_status\"] = combos_contingencies[:, np.newaxis, :]" ] }, { @@ -1649,15 +1802,15 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "b43456d7", + "execution_count": 47, + "id": "2afaaa39", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation with contingencies and load profiles is 9.043806 s\n" + "Execution time for Batch Calculation with contingencies and load profiles is 0.328704 s\n" ] } ], @@ -1665,12 +1818,14 @@ "load_profile_combined = initialize_array(\n", " DatasetType.update,\n", " ComponentType.sym_load,\n", - " (contingency_count * n_timestamps, n_loads),\n", + " (n_contingencies * n_timestamps, n_loads),\n", ")\n", "\n", - "load_profile_combined[\"p_specified\"] = np.tile(\n", - " profile, (contingency_count, 1)\n", - ")\n", + "load_profile_combined[\"p_specified\"] = np.broadcast_to(\n", + " profile, (n_contingencies, n_timestamps, n_loads)\n", + ").reshape(-1, n_loads)\n", + "# Alternatively:\n", + "# load_profile_combined[\"p_specified\"].reshape(n_contingencies, n_timestamps, n_loads)[:] = profile\n", "\n", "load_profile_combined[\"q_specified\"] = 0.0\n", "\n", @@ -1698,23 +1853,23 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "id": "06e5d27d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 47, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1729,7 +1884,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "id": "1d863a5f", "metadata": {}, "outputs": [], @@ -1785,7 +1940,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "id": "71ae9bce", "metadata": {}, "outputs": [], @@ -1832,7 +1987,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "id": "f6293765", "metadata": {}, "outputs": [ @@ -1862,7 +2017,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "id": "b924a3a8", "metadata": {}, "outputs": [ @@ -1887,11 +2042,11 @@ "q_linear_values = np.linspace(start=min_q, stop=max_q, num=resolution)\n", "\n", "p_values, q_values = np.meshgrid(p_linear_values, q_linear_values, copy=False)\n", - "print(f\"p_values shape: {p_values.shape}\")\n", - "print(f\"q_values shape: {q_values.shape}\")\n", + "print(f\"p_values shape: {p_values.shape}\") # Prints: (resolution, resolution)\n", + "print(f\"q_values shape: {q_values.shape}\") # Prints: (resolution, resolution)\n", "\n", "p_envelope = np.tile(profile, (resolution, resolution, 1, 1))\n", - "print(f\"p_envelope shape: {p_envelope.shape}\")\n", + "print(f\"p_envelope shape: {p_envelope.shape}\") # Prints: (resolution, resolution, n_timestamps, n_loads)\n", "\n", "p_envelope[:, :, :, sym_load_node_10_idx] += p_values[:, :, np.newaxis]\n", "\n", @@ -1909,7 +2064,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "id": "d2ad55f3", "metadata": {}, "outputs": [], @@ -1924,7 +2079,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "id": "6a152a12", "metadata": {}, "outputs": [ @@ -1932,7 +2087,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Execution time for Batch Calculation with grid of P and Q to calculate an envelope of operation is 47.157588 s\n", + "Execution time for Batch Calculation with grid of P and Q to calculate an envelope of operation is 3.471581 s\n", "nodal_voltages shape: (11, 11, 168, 2001)\n" ] } @@ -1947,7 +2102,7 @@ " )\n", "\n", "nodal_voltages = output_data_envelope[ComponentType.node][\"u_pu\"].reshape(resolution, resolution, n_timestamps, -1)\n", - "print(f\"nodal_voltages shape: {nodal_voltages.shape}\")" + "print(f\"nodal_voltages shape: {nodal_voltages.shape}\") # Prints: (resolution, resolution, n_timestamps, n_nodes)" ] }, { @@ -1963,7 +2118,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "id": "1a298bf9", "metadata": {}, "outputs": [], @@ -1978,23 +2133,23 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "id": "70460504", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 55, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2063,7 +2218,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.14.0" } }, "nbformat": 4, From 81fac93eff875f0bf760dfce0cc39e418c6edbb0 Mon Sep 17 00:00:00 2001 From: Nitish Bharambe Date: Mon, 1 Dec 2025 07:43:31 +0100 Subject: [PATCH 8/8] update TOC Signed-off-by: Nitish Bharambe --- .../Advanced Workshop.ipynb | 22 ++++++------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb index 16375de..20d5460 100644 --- a/pgm-advanced-usage-workshop/Advanced Workshop.ipynb +++ b/pgm-advanced-usage-workshop/Advanced Workshop.ipynb @@ -10,21 +10,13 @@ "\n", "In this assignment you will be given a series of tasks about using the library `power-grid-model`. The agenda includes:\n", "\n", - "* Load input (From Power flow assignment)\n", - "* Numpy tips and PGM related recommendations\n", - "* Types of Dataset\n", - "* Miscellaneous PGM features\n", - "* Selective output\n", - "* Complex Simulations\n", - " * Multiple Timeseries\n", - " * Side topic: PGM DS library\n", - " * Contingency Analysis\n", - " * Topology caching\n", - " * Simulation design exercise\n", - " * Dynamic operating Envelope\n", - " * Simulation design exercise\n", - " * Additional content\n", - " * Simulation design exercise\n", + "* [Introduction](#introduction)\n", + "* [Preparation](#preparation)\n", + "* [Topology Data and Timeseries load profiles](#topology-data-and-timeseries-load-profiles)\n", + "* [Side topics related to numpy](#side-topics-related-to-numpy)\n", + "* [Types of datasets](#types-of-datasets)\n", + "* [Miscellanious PGM features](#miscellanious-pgm-features)\n", + "* [Complex simulations](#complex-simulations)\n", "\n", "Throughout this notebook there are big notebook cells of code. \n", "To have a deeper understanding of the simulations, it is recommended to comment it all and uncomment line by line along with confirming the debugging print statements."