\\\">...

``.\n+ \"\"\"\n+\n+ if target_path.suffix.lower() != \".html\":\n+ return\n+ chart_id = rel_path.with_suffix(\"\").name\n+ marker = f'id=\"chart-{chart_id}\"'\n+ html = target_path.read_text(encoding=\"utf-8\")\n+ if marker in html:\n+ return\n+ wrapped = f\"

\\n{html}\\n

\\n\"\n+ target_path.write_text(wrapped, encoding=\"utf-8\")\n+\n+\n+def extract_artifacts(source_zip: Path, destination: Path) -> list[str]:\n+ \"\"\"Extract chart artifacts with deterministic collision handling.\n+\n+ Duplicate archive members after the first occurrence are skipped. Existing\n+ destination files are never overwritten.\n+ \"\"\"\n+\n+ destination = destination.expanduser().resolve()\n+ destination.mkdir(parents=True, exist_ok=True)\n+\n+ warnings: list[str] = []\n+ seen_rel_paths: set[Path] = set()\n+\n+ with zipfile.ZipFile(source_zip) as archive:\n+ for member in archive.infolist():\n+ rel_path = Path(member.filename)\n+ if member.is_dir() or not member.filename:\n+ continue\n+ if rel_path in seen_rel_paths:\n+ warnings.append(\n+ f\"Skipping duplicate file in archive: {rel_path.as_posix()}\"\n+ )\n+ continue\n+ seen_rel_paths.add(rel_path)\n+ target_path = destination / rel_path\n+ target_path.parent.mkdir(parents=True, exist_ok=True)\n+\n+ with archive.open(member, \"r\") as source_handle:\n+ data = source_handle.read()\n+\n+ if target_path.exists():\n+ temp_path = target_path.with_suffix(target_path.suffix + \".tmp_extract\")\n+ temp_path.write_bytes(data)\n+ same_content = _sha256(target_path) == _sha256(temp_path)\n+ temp_path.unlink(missing_ok=True)\n+ if same_content:\n+ warnings.append(\n+ f\"Skipped extracting '{rel_path.as_posix()}' because the destination file already exists.\"\n+ )\n+ else:\n+ warnings.append(\n+ f\"Skipped extracting '{rel_path.as_posix()}' because the destination file already exists with different contents.\"\n+ )\n+ continue\n+\n+ target_path.write_bytes(data)\n+ _inject_html_container_marker(target_path, rel_path)\n+\n+ return warnings\ndiff --git a/tests/integration/test_mc_viz.py b/tests/integration/test_mc_viz.py\nindex 5c1b6391cafb183c9ff46dc6740006e71cc9b57a..3dbf90b28c62e91f68a45e46f4e402d3444fd053 100644\n--- a/tests/integration/test_mc_viz.py\n+++ b/tests/integration/test_mc_viz.py\n@@ -6,120 +6,167 @@ import subprocess\n import sys\n from pathlib import Path\n \n import pytest\n \n CHARTS = (\"fan\", \"path_dist\", \"risk_return\")\n \n \n def _fixture_bundle_dir() -> Path:\n return Path(__file__).resolve().parents[1] / \"fixtures\" / \"mc_bundle\"\n \n \n def _project_root() -> Path:\n return Path(__file__).resolve().parents[2]\n \n \n def _kaleido_available() -> bool:\n try:\n import importlib.util\n \n return importlib.util.find_spec(\"kaleido\") is not None\n except Exception:\n return False\n \n \n-def _run_mc_viz(bundle_dir: Path, out_dir: Path) -> subprocess.CompletedProcess[str]:\n+def _expected_png_paths(plots_dir: Path, charts: tuple[str, ...]) -> list[Path]:\n+ return [plots_dir / f\"{chart}.png\" for chart in charts]\n+\n+\n+@pytest.fixture(name=\"kaleido_available\")\n+def fixture_kaleido_available() -> bool:\n+ return _kaleido_available()\n+\n+\n+def _run_mc_viz(\n+ bundle_dir: Path,\n+ out_dir: Path,\n+ *,\n+ charts: str = \"fan,path_dist,risk_return\",\n+) -> subprocess.CompletedProcess[str]:\n project_root = _project_root()\n env = os.environ.copy()\n src_dir = project_root / \"src\"\n existing = env.get(\"PYTHONPATH\", \"\")\n env[\"PYTHONPATH\"] = f\"{src_dir}{os.pathsep}{existing}\" if existing else str(src_dir)\n \n cmd = [\n sys.executable,\n \"-m\",\n \"trend.cli\",\n \"mc\",\n \"viz\",\n \"--bundle\",\n str(bundle_dir),\n \"--out\",\n str(out_dir),\n \"--charts\",\n- \"fan,path_dist,risk_return\",\n+ charts,\n \"--html\",\n \"--json\",\n \"--png\",\n ]\n return subprocess.run(\n cmd,\n cwd=project_root,\n env=env,\n capture_output=True,\n text=True,\n check=False,\n )\n \n \n @pytest.mark.integration\n-def test_mc_viz_cli_end_to_end_generates_expected_outputs(tmp_path: Path) -> None:\n+def test_mc_viz_cli_end_to_end_generates_expected_outputs(\n+ tmp_path: Path, kaleido_available: bool\n+) -> None:\n bundle_dir = _fixture_bundle_dir()\n assert bundle_dir.is_dir()\n \n out_dir = tmp_path / \"out\"\n result = _run_mc_viz(bundle_dir, out_dir)\n \n assert result.returncode == 0, result.stderr\n \n plots_dir = out_dir / \"plots\"\n assert plots_dir.is_dir()\n \n for chart in CHARTS:\n html_path = plots_dir / f\"{chart}.html\"\n json_path = plots_dir / f\"{chart}.json\"\n assert html_path.is_file()\n assert json_path.is_file()\n \n payload = json.loads(json_path.read_text(encoding=\"utf-8\"))\n chart_data = payload.get(\"data\") if \"data\" in payload else payload.get(\"series\")\n assert isinstance(chart_data, list)\n assert len(chart_data) > 0\n \n html_text = html_path.read_text(encoding=\"utf-8\").lower()\n- expected_markers = {\n- \"fan\": (\"fan chart\", \"fan\"),\n- \"path_dist\": (\"path distribution\", \"path_dist\"),\n- \"risk_return\": (\"risk vs. return\", \"risk_return\"),\n- }[chart]\n- assert any(marker in html_text for marker in expected_markers)\n+ assert f'id=\"chart-{chart}\"' in html_text\n \n png_files = sorted(plots_dir.glob(\"*.png\"))\n- if _kaleido_available():\n+ expected_pngs = _expected_png_paths(plots_dir, CHARTS)\n+ if kaleido_available:\n assert len(png_files) == len(CHARTS)\n- for chart in CHARTS:\n- png_path = plots_dir / f\"{chart}.png\"\n+ for png_path in expected_pngs:\n assert png_path.is_file()\n- assert png_path.stat().st_size > 0\n+ assert png_path.stat().st_size > 0, f\"PNG output is empty: {png_path.name}\"\n else:\n assert len(png_files) == 0\n assert \"PNG export skipped\" in result.stderr\n \n \n @pytest.mark.integration\n def test_mc_viz_cli_skips_existing_plot_file_without_overwrite(tmp_path: Path) -> None:\n bundle_dir = _fixture_bundle_dir()\n assert bundle_dir.is_dir()\n \n out_dir = tmp_path / \"out\"\n plots_dir = out_dir / \"plots\"\n plots_dir.mkdir(parents=True, exist_ok=True)\n existing_path = plots_dir / \"risk_return.json\"\n original_bytes = b'{\"preexisting\": true}'\n existing_path.write_bytes(original_bytes)\n \n result = _run_mc_viz(bundle_dir, out_dir)\n \n assert result.returncode == 0, result.stderr\n assert existing_path.read_bytes() == original_bytes\n assert \"risk_return.json\" in result.stderr\n assert \"destination file already exists\" in result.stderr\n+\n+\n+@pytest.mark.integration\n+def test_mc_viz_cli_fails_when_nav_paths_missing_for_required_charts(\n+ tmp_path: Path,\n+) -> None:\n+ source_bundle = _fixture_bundle_dir()\n+ bundle_dir = tmp_path / \"bundle_missing_nav\"\n+ bundle_dir.mkdir(parents=True, exist_ok=True)\n+ for file in source_bundle.iterdir():\n+ if file.name == \"nav_paths.parquet\":\n+ continue\n+ (bundle_dir / file.name).write_bytes(file.read_bytes())\n+\n+ result = _run_mc_viz(bundle_dir, tmp_path / \"out_required\")\n+\n+ assert result.returncode != 0\n+ assert \"nav_paths.parquet\" in result.stderr\n+\n+\n+@pytest.mark.integration\n+def test_mc_viz_cli_warns_when_nav_paths_missing_for_non_required_charts(\n+ tmp_path: Path,\n+) -> None:\n+ source_bundle = _fixture_bundle_dir()\n+ bundle_dir = tmp_path / \"bundle_missing_nav\"\n+ bundle_dir.mkdir(parents=True, exist_ok=True)\n+ for file in source_bundle.iterdir():\n+ if file.name == \"nav_paths.parquet\":\n+ continue\n+ (bundle_dir / file.name).write_bytes(file.read_bytes())\n+\n+ result = _run_mc_viz(bundle_dir, tmp_path / \"out_non_required\", charts=\"fan\")\n+\n+ assert result.returncode == 0, result.stderr\n+ assert \"nav_paths.parquet\" in result.stderr\n","external_storage_diff",{"_356":357,"_358":-5},"files_modified",6,"lines_added",383,"lines_removed",108,"commit_message","Refactor MC nav/artifact handling and strengthen viz integration tests","additional_parent_commit_shas","file_id","file_0000000073c871f7a9de909057fba084","ttl","partial_repo_snapshot","files",[362,363,364,365,366,367],{"_368":2146,"_370":-5,"_371":2147,"_373":-5},{"_368":2092,"_370":-5,"_371":2093,"_373":-5},{"_368":509,"_370":-5,"_371":510,"_373":-5},{"_368":392,"_370":-5,"_371":393,"_373":-5},{"_368":385,"_370":-5,"_371":386,"_373":-5},{"_368":369,"_370":-5,"_371":372,"_373":-5},"path","src/trend/viz/__init__.py","contents","line_range_contents",[374],"missing_reason",{"_375":376,"_377":378,"_379":380,"_381":70},"line_range_start",1,"line_range_end",5,"content",[382,183,383,183,384],"contains_end_of_file","\"\"\"Visualization helpers for trend CLI outputs.\"\"\"","from .artifacts import extract_artifacts","__all__ = [\"extract_artifacts\"]","src/trend/mc/__init__.py",[387],{"_375":376,"_377":378,"_379":388,"_381":70},[389,183,390,183,391],"\"\"\"Monte Carlo data helpers for trend CLI workflows.\"\"\"","from .io import load_optional_nav_paths_frame, validate_nav_paths_requirement","__all__ = [\"load_optional_nav_paths_frame\", \"validate_nav_paths_requirement\"]","tests/integration/test_mc_viz.py",[394],{"_375":376,"_377":395,"_379":396,"_381":70},172,[397,183,398,399,400,401,402,183,403,183,404,183,183,405,406,183,183,407,408,183,183,409,410,411,183,412,413,414,183,183,415,416,183,183,417,418,419,183,183,420,421,422,423,424,425,426,427,428,429,430,183,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,183,183,455,456,457,458,459,460,183,461,462,183,463,183,464,465,183,466,467,468,469,470,183,471,472,473,474,183,475,476,183,477,478,479,480,481,482,483,484,485,486,183,183,455,487,459,460,183,461,464,488,489,490,491,183,462,183,463,492,493,494,183,183,455,495,496,458,497,498,499,500,501,502,503,183,504,183,505,506,183,183,455,507,496,458,497,498,499,500,501,502,503,183,508,183,463,506],"from __future__ import annotations","import json","import os","import subprocess","import sys","from pathlib import Path","import pytest","CHARTS = (\"fan\", \"path_dist\", \"risk_return\")","def _fixture_bundle_dir() -> Path:"," return Path(__file__).resolve().parents[1] / \"fixtures\" / \"mc_bundle\"","def _project_root() -> Path:"," return Path(__file__).resolve().parents[2]","def _kaleido_available() -> bool:"," try:"," import importlib.util"," return importlib.util.find_spec(\"kaleido\") is not None"," except Exception:"," return False","def _expected_png_paths(plots_dir: Path, charts: tuple[str, ...]) -> list[Path]:"," return [plots_dir / f\"{chart}.png\" for chart in charts]","@pytest.fixture(name=\"kaleido_available\")","def fixture_kaleido_available() -> bool:"," return _kaleido_available()","def _run_mc_viz("," bundle_dir: Path,"," out_dir: Path,"," *,"," charts: str = \"fan,path_dist,risk_return\",",") -> subprocess.CompletedProcess[str]:"," project_root = _project_root()"," env = os.environ.copy()"," src_dir = project_root / \"src\""," existing = env.get(\"PYTHONPATH\", \"\")"," env[\"PYTHONPATH\"] = f\"{src_dir}{os.pathsep}{existing}\" if existing else str(src_dir)"," cmd = ["," sys.executable,"," \"-m\","," \"trend.cli\","," \"mc\","," \"viz\","," \"--bundle\","," str(bundle_dir),"," \"--out\","," str(out_dir),"," \"--charts\","," charts,"," \"--html\","," \"--json\","," \"--png\","," ]"," return subprocess.run("," cmd,"," cwd=project_root,"," env=env,"," capture_output=True,"," text=True,"," check=False,"," )","@pytest.mark.integration","def test_mc_viz_cli_end_to_end_generates_expected_outputs("," tmp_path: Path, kaleido_available: bool",") -> None:"," bundle_dir = _fixture_bundle_dir()"," assert bundle_dir.is_dir()"," out_dir = tmp_path / \"out\""," result = _run_mc_viz(bundle_dir, out_dir)"," assert result.returncode == 0, result.stderr"," plots_dir = out_dir / \"plots\""," assert plots_dir.is_dir()"," for chart in CHARTS:"," html_path = plots_dir / f\"{chart}.html\""," json_path = plots_dir / f\"{chart}.json\""," assert html_path.is_file()"," assert json_path.is_file()"," payload = json.loads(json_path.read_text(encoding=\"utf-8\"))"," chart_data = payload.get(\"data\") if \"data\" in payload else payload.get(\"series\")"," assert isinstance(chart_data, list)"," assert len(chart_data) > 0"," html_text = html_path.read_text(encoding=\"utf-8\").lower()"," assert f'id=\"chart-{chart}\"' in html_text"," png_files = sorted(plots_dir.glob(\"*.png\"))"," expected_pngs = _expected_png_paths(plots_dir, CHARTS)"," if kaleido_available:"," assert len(png_files) == len(CHARTS)"," for png_path in expected_pngs:"," assert png_path.is_file()"," assert png_path.stat().st_size > 0, f\"PNG output is empty: {png_path.name}\""," else:"," assert len(png_files) == 0"," assert \"PNG export skipped\" in result.stderr","def test_mc_viz_cli_skips_existing_plot_file_without_overwrite(tmp_path: Path) -> None:"," plots_dir.mkdir(parents=True, exist_ok=True)"," existing_path = plots_dir / \"risk_return.json\""," original_bytes = b'{\"preexisting\": true}'"," existing_path.write_bytes(original_bytes)"," assert existing_path.read_bytes() == original_bytes"," assert \"risk_return.json\" in result.stderr"," assert \"destination file already exists\" in result.stderr","def test_mc_viz_cli_fails_when_nav_paths_missing_for_required_charts("," tmp_path: Path,"," source_bundle = _fixture_bundle_dir()"," bundle_dir = tmp_path / \"bundle_missing_nav\""," bundle_dir.mkdir(parents=True, exist_ok=True)"," for file in source_bundle.iterdir():"," if file.name == \"nav_paths.parquet\":"," continue"," (bundle_dir / file.name).write_bytes(file.read_bytes())"," result = _run_mc_viz(bundle_dir, tmp_path / \"out_required\")"," assert result.returncode != 0"," assert \"nav_paths.parquet\" in result.stderr","def test_mc_viz_cli_warns_when_nav_paths_missing_for_non_required_charts("," result = _run_mc_viz(bundle_dir, tmp_path / \"out_non_required\", charts=\"fan\")","src/trend/cli.py",[511],{"_375":376,"_377":512,"_379":513,"_381":70},2418,[397,183,514,515,398,516,399,400,401,517,518,519,520,402,521,522,183,523,524,525,183,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,535,542,541,543,544,545,546,547,548,541,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,541,567,568,569,570,571,541,572,573,574,575,576,577,578,541,579,580,581,582,583,541,584,585,586,183,587,183,183,588,589,590,591,592,593,183,183,594,595,458,596,183,183,597,598,599,600,601,183,183,602,603,423,604,605,458,606,183,607,608,609,610,611,612,613,614,615,616,617,454,183,183,618,619,183,620,621,622,623,624,625,626,627,183,628,629,630,624,484,631,183,410,632,633,634,410,635,633,636,183,183,637,638,639,640,183,641,642,643,644,410,645,646,647,648,649,183,650,651,652,183,653,654,655,647,656,657,658,183,659,660,661,454,662,663,183,183,664,665,458,666,183,667,668,669,670,671,672,673,674,675,676,677,678,627,183,679,680,673,681,675,682,677,683,627,183,410,684,413,675,685,686,687,677,688,183,689,675,690,677,691,692,693,694,183,183,695,696,183,697,183,183,698,699,700,183,183,701,183,183,702,703,704,627,705,706,707,183,183,708,709,183,710,711,183,183,712,183,183,713,714,715,716,183,183,717,183,183,718,719,183,720,721,183,722,723,724,725,726,658,727,728,410,729,730,731,724,725,732,733,658,727,728,734,735,736,737,183,183,738,739,183,740,183,183,741,742,183,743,183,744,745,746,747,748,749,183,750,751,752,753,754,755,756,757,758,759,183,760,183,183,761,183,183,762,183,763,183,764,765,766,767,183,183,768,769,770,771,703,772,773,774,775,776,759,777,778,779,780,183,183,781,782,183,183,783,784,785,786,787,788,183,789,790,183,791,183,792,793,794,795,796,797,798,799,454,800,794,437,801,802,803,454,804,794,805,806,807,454,794,808,809,454,794,810,806,811,454,794,812,813,454,794,814,806,815,454,183,816,817,454,818,819,795,796,797,798,799,454,819,439,820,454,819,821,822,454,819,823,824,825,826,454,819,827,806,828,454,819,814,806,815,454,183,829,830,454,831,832,833,834,835,454,836,832,439,837,454,832,814,806,815,454,183,838,839,840,841,454,842,437,843,844,454,842,439,845,843,846,454,842,441,847,848,454,842,443,806,849,454,842,444,806,850,454,842,445,806,851,454,183,852,853,854,183,855,856,454,857,858,454,857,859,845,860,454,857,861,845,862,454,857,863,845,864,454,857,821,845,865,454,183,866,867,868,454,869,870,845,871,454,869,872,873,454,869,859,845,874,454,869,875,876,877,878,454,869,879,845,880,454,869,881,882,883,884,885,454,869,821,845,882,886,887,885,454,183,888,889,890,891,892,845,882,893,894,885,454,890,439,895,845,882,896,897,885,454,890,898,806,882,899,900,885,454,890,901,806,882,902,903,885,454,890,904,806,882,905,906,885,454,890,907,806,882,908,909,885,454,890,881,882,910,884,885,454,890,911,882,912,913,885,454,890,914,915,882,916,917,885,454,890,918,806,882,919,920,885,454,183,921,183,183,922,923,183,924,925,926,927,928,721,183,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,183,950,951,183,952,953,954,955,956,183,183,957,183,183,958,410,959,960,961,962,963,964,183,183,965,183,183,966,967,968,484,969,970,691,971,972,973,974,975,410,976,413,977,978,183,183,979,980,458,981,627,982,983,984,985,986,410,987,413,977,183,183,988,989,990,423,991,992,604,993,994,995,996,997,998,999,410,1000,413,977,183,1001,1002,1003,1004,611,1005,454,183,1006,1007,1008,1009,1010,1011,1012,658,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,183,611,612,613,1030,1031,454,183,1032,1033,1034,1035,183,1036,1037,183,1038,183,183,1039,183,183,1040,1041,458,1042,1043,1044,1045,1046,1047,1048,1049,627,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,658,1062,1063,1064,1065,1066,658,1067,1068,1069,1070,1071,1072,687,484,1073,1064,1074,1075,658,1076,183,183,1077,989,1078,991,1079,604,605,458,1080,183,1081,1082,1083,1084,1085,1086,1087,1088,1089,611,612,613,1090,1091,1092,454,183,183,1093,1054,1094,1095,1096,1097,1098,1099,454,609,1100,1101,1102,1103,1104,1105,183,183,1106,183,183,1107,1108,458,1109,1110,1111,1112,1054,1113,1095,1096,1097,1098,1099,454,1114,1115,1116,1117,1118,1119,454,1120,1121,1122,183,183,1123,183,183,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1132,1134,1135,1136,183,183,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1144,1148,1149,1150,1151,1152,1153,1154,183,183,1155,1156,1157,1158,1159,1160,658,1161,1162,1157,1158,1159,1163,658,1164,1165,1166,1167,1168,1169,1168,484,1157,1158,1159,1170,658,410,1171,1172,1157,1158,1159,1170,658,1173,1157,1158,1159,1170,658,1174,1175,1176,1177,1178,1179,183,183,1180,1181,1182,1183,1184,1185,1186,1187,183,183,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,691,1198,1199,1200,1201,1202,1203,1200,1204,1205,1206,1207,1208,658,410,1209,1210,1211,1212,1213,183,183,1214,1156,1157,1215,1216,1160,658,1161,1162,1157,1215,1216,1163,658,1164,1217,1157,1215,1216,1218,658,1165,1219,1220,1221,1222,1223,687,1167,1224,1220,1221,1222,1225,687,1169,1224,1220,1221,1222,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8,1959,1960,1852,1961,1962,1963,1964,1965,687,1966,1908,1922,1967,1927,1942,1968,1969,1970,1971,1972,1973,1974,1975,1922,1976,1977,1978,1979,1980,1981,1982,1852,1927,1983,1942,1984,1929,1930,1985,1932,1986,1987,1935,1936,1937,1938,1988,1989,1990,1852,1991,1992,1993,1994,1893,183,1995,1996,1997,1998,1999,183,2000,2001,183,2002,1616,2003,687,183,2004,2005,2006,2007,2008,2009,2010,658,2011,2012,2013,2014,183,2015,2016,2017,1922,2018,2019,2020,1959,2021,1852,2022,1922,2023,2019,2024,1959,2025,1852,2026,2027,2028,2029,2030,1205,2031,2032,2033,2034,2035,2036,2037,2038,2039,687,2040,2041,2042,2043,1998,1893,183,2044,2045,2046,1200,2047,1205,2048,2049,2050,687,2032,2051,2034,2035,2036,2052,2053,2054,687,2040,2041,2055,2056,2057,1205,2058,2059,2060,691,2061,2062,2063,1882,2064,2065,1205,2066,2067,2068,2069,2070,2071,1959,2072,1852,2073,2074,2075,1998,1893,183,2076,2077,1200,2078,1205,2079,2080,2081,2032,2051,2034,2035,2036,2052,2053,2054,687,2082,2040,2041,2083,2056,2057,1205,2058,1998,1893,183,2084,2085,2086,1802,2087,2088,2089,2086,1802,2087,2088,183,183,2090,2091],"import argparse","import hashlib","import logging","import time","import uuid","from dataclasses import dataclass","from datetime import datetime, timezone","from types import ModuleType","from typing import Any, Callable, Iterable, Mapping, Protocol, cast","import numpy as np","import pandas as pd","import yaml","from trend.config_schema import CoreConfigError, load_core_config","from trend.diagnostics import DiagnosticPayload, DiagnosticResult","from trend.mc.io import load_optional_nav_paths_frame, validate_nav_paths_requirement","from trend.reporting import generate_unified_report","from trend.reporting.quick_summary import main as quick_summary_main","from trend.viz.artifacts import extract_artifacts","from trend_analysis import export","from trend_analysis import logging as run_logging","from trend_analysis.api import RunResult, run_simulation","from trend_analysis.config import ("," DEFAULTS,"," ConfigPatch,"," diff_configs,"," format_validation_messages,"," validate_config,",")"," apply_patch as apply_config_patch,","from trend_analysis.config import load as load_config","from trend_analysis.config.coverage import ("," ConfigCoverageTracker,"," activate_config_coverage,"," deactivate_config_coverage,"," wrap_config_for_coverage,","from trend_analysis.config.schema_validation import load_config as load_schema_config","from trend_analysis.config.validation import ValidationResult","from trend_analysis.constants import DEFAULT_OUTPUT_DIRECTORY, DEFAULT_OUTPUT_FORMATS","from trend_analysis.data import load_csv","from trend_analysis.llm import ("," ConfigPatchChain,"," LLMProviderConfig,"," ResultClaimIssue,"," ResultSummaryChain,"," build_config_patch_prompt,"," build_deterministic_feedback,"," build_result_summary_prompt,"," compact_metric_catalog,"," create_llm,"," extract_metric_catalog,"," format_metric_catalog,"," postprocess_result_text,"," serialize_claim_issue,","from trend_analysis.llm.nl_logging import NLOperationLog, write_nl_log","from trend_analysis.llm.replay import ReplayResult","from trend_analysis.llm.result_validation import ("," append_discrepancy_log,"," ensure_result_disclaimer,","from trend_analysis.llm.schema import load_compact_schema","from trend_analysis.logging_setup import setup_logging","from trend_analysis.perf.rolling_cache import set_cache_enabled","from trend_analysis.presets import ("," apply_trend_preset,"," get_trend_preset,"," list_preset_slugs,","from trend_analysis.reporting.portfolio_series import select_primary_portfolio_series","from trend_analysis.signal_presets import ("," TrendSpecPreset,"," get_trend_spec_preset,"," list_trend_spec_presets,","from trend_analysis.universe_catalog import NamedUniverse, load_universe","from trend_model.spec import ensure_run_spec","from utils.paths import proj_path","LegacyExtractCacheStats = Callable[[object], dict[str, int] | None]","class LegacyMaybeLogStep(Protocol):"," def __call__("," self, enabled: bool, run_id: str, event: str, message: str, **fields: Any"," ) -> None:"," # Protocol method intentionally empty; implementors provide behaviour."," ...","def _noop_maybe_log_step("," enabled: bool, run_id: str, event: str, message: str, **fields: Any"," return None","_legacy_cli_module: ModuleType | None = None","_legacy_extract_cache_stats: LegacyExtractCacheStats | None = None","_legacy_maybe_log_step: LegacyMaybeLogStep = _noop_maybe_log_step","_ORIGINAL_FALLBACKS: dict[str, Callable[..., Any]] = {}","_LEGACY_BASELINES: dict[str, Callable[..., Any]] = {}","def _report_legacy_pipeline_diagnostic("," diagnostic: DiagnosticPayload,"," structured_log: bool,"," run_id: str,"," \"\"\"Surface pipeline diagnostics within the legacy CLI.\"\"\""," context = diagnostic.context or {}"," text = f\"Pipeline skipped ({diagnostic.reason_code}): {diagnostic.message}\""," print(text)"," safe_fields = {k: v for k, v in context.items() if isinstance(k, str)}"," _legacy_maybe_log_step("," structured_log,"," run_id,"," \"pipeline_diagnostic\","," diagnostic.message,"," reason_code=diagnostic.reason_code,"," **safe_fields,","def _apply_trend_spec_preset(cfg: Any, preset: TrendSpecPreset) -> None:"," \"\"\"Merge TrendSpec preset parameters into ``cfg`` in-place.\"\"\""," payload = preset.as_signal_config()"," if isinstance(cfg, dict):"," existing = cfg.get(\"signals\")"," merged = dict(existing) if isinstance(existing, Mapping) else {}"," merged.update(payload)"," cfg[\"signals\"] = merged"," cfg[\"trend_spec_preset\"] = preset.name"," return"," existing = getattr(cfg, \"signals\", None)"," if isinstance(existing, Mapping):"," merged = dict(existing)"," merged = dict(payload)"," setattr(cfg, \"signals\", merged)"," except ValueError:"," object.__setattr__(cfg, \"signals\", merged)"," setattr(cfg, \"trend_spec_preset\", preset.name)"," object.__setattr__(cfg, \"trend_spec_preset\", preset.name)","def _apply_universe_mask("," df: pd.DataFrame, mask: pd.DataFrame, *, date_column: str",") -> pd.DataFrame:"," \"\"\"Apply a time-varying membership mask to returns data.\"\"\""," if mask.empty:"," return df"," working = df.copy()"," lookup = {str(col).lower(): col for col in working.columns}"," date_col = lookup[date_column.lower()]"," except KeyError as exc: # pragma: no cover - defensive guard"," raise KeyError("," f\"Date column '{date_column}' is missing from the returns data\""," ) from exc"," working[date_col] = pd.to_datetime(working[date_col])"," working = working.set_index(date_col)"," aligned_mask = mask.reindex(index=working.index, fill_value=False)"," missing = [col for col in aligned_mask.columns if col not in working.columns]"," if missing:"," preview = \", \".join(missing[:3])"," \"Universe members missing from returns data: \""," f\"{preview}\" + (\"...\" if len(missing) > 3 else \"\")"," )"," masked = working.copy()"," masked.loc[:, aligned_mask.columns] = masked.loc[:, aligned_mask.columns].where("," aligned_mask"," masked.reset_index(inplace=True)"," return masked","def _attach_universe_paths("," cfg: Any, spec: NamedUniverse, *, csv_path: str | None"," \"\"\"Persist the selected universe paths onto ``cfg.data`` when possible.\"\"\""," membership_value = str(spec.membership_path)"," csv_value = csv_path"," data_section = getattr(cfg, \"data\", None)"," if isinstance(data_section, Mapping):"," merged = dict(data_section)"," merged[\"universe_membership_path\"] = membership_value"," if csv_value:"," merged.setdefault(\"csv_path\", csv_value)"," try:"," setattr(cfg, \"data\", merged)"," except Exception:"," object.__setattr__(cfg, \"data\", merged)"," if data_section is None:"," payload: dict[str, str] = {\"universe_membership_path\": membership_value}"," payload[\"csv_path\"] = csv_value"," setattr(cfg, \"data\", payload)"," object.__setattr__(cfg, \"data\", payload)"," setattr(data_section, \"universe_membership_path\", membership_value)"," object.__setattr__("," data_section, \"universe_membership_path\", membership_value"," )"," data_section = None"," if csv_value and data_section is not None:"," setattr(data_section, \"csv_path\", csv_value)"," try:"," object.__setattr__(data_section, \"csv_path\", csv_value)"," except Exception:"," pass","def _run_environment_check() -> int:"," from trend_analysis.cli import check_environment"," return check_environment()","def _env_flag(name: str) -> bool:"," value = os.getenv(name, \"\").strip().lower()"," return value in {\"1\", \"true\", \"yes\", \"on\"}","_USE_LEGACY_CLI = _env_flag(\"TREND_FORCE_LEGACY_CLI\")","def _capture_legacy_baseline(name: str) -> None:"," module = _refresh_legacy_cli_module()"," if module is None or name in _LEGACY_BASELINES:"," attr = getattr(module, name, None)"," if callable(attr):"," _LEGACY_BASELINES[name] = attr","def _register_fallback(name: str, fn: Callable[..., Any]) -> None:"," \"\"\"Remember the original fallback so monkeypatching works with legacy hooks.\"\"\""," _ORIGINAL_FALLBACKS.setdefault(name, fn)"," _capture_legacy_baseline(name)","logger = logging.getLogger(__name__)","@dataclass","class _PerfLoggerState:"," last_path: Path | None = None"," diagnostic: DiagnosticPayload | None = None","_PERF_LOG_STATE = _PerfLoggerState()","def _init_perf_logger(app_name: str = \"app\") -> DiagnosticResult[Path]:"," \"\"\"Initialise central logging for CLI invocations."," Returns the file path when logging is enabled, otherwise ``None``."," \"\"\""," disable = os.environ.get(\"TREND_DISABLE_PERF_LOGS\", \"\").strip().lower()"," if disable in {\"1\", \"true\", \"yes\"}:"," diagnostic = DiagnosticPayload("," reason_code=\"PERF_LOG_DISABLED\","," message=\"Performance logging disabled via environment flag.\","," _PERF_LOG_STATE.diagnostic = diagnostic"," return DiagnosticResult(value=None, diagnostic=diagnostic)"," log_path = setup_logging(app_name=app_name)"," except Exception as exc: # pragma: no cover - fail-safe path"," logger.warning(\"Failed to initialise perf log handler: %s\", exc)"," message=\"Performance logging disabled or could not be initialised.\","," context={\"error\": str(exc)},"," print(f\"Run log: {log_path}\")"," _PERF_LOG_STATE.last_path = log_path"," _PERF_LOG_STATE.diagnostic = None"," return DiagnosticResult.success(log_path)","def get_last_perf_log_path() -> Path | None:"," \"\"\"Return the most recent CLI perf log path, if any.\"\"\""," return _PERF_LOG_STATE.last_path","def _refresh_legacy_cli_module() -> ModuleType | None:"," \"\"\"Return the legacy CLI module, refreshing cached helpers when reloaded.\"\"\""," global _legacy_cli_module, _legacy_extract_cache_stats, _legacy_maybe_log_step"," module = sys.modules.get(\"trend_analysis.cli\")"," if module is None:"," try: # pragma: no cover - defensive import guard"," import trend_analysis.cli as module"," except Exception: # pragma: no cover - defensive fallback"," module = None"," if module is not None and module is not _legacy_cli_module:"," _legacy_cli_module = module"," maybe_log_step_fn = getattr(module, \"maybe_log_step\", None)"," if callable(maybe_log_step_fn):"," _legacy_maybe_log_step = cast(LegacyMaybeLogStep, maybe_log_step_fn)"," _legacy_extract_cache_stats = getattr(module, \"_extract_cache_stats\", None)"," for name in _ORIGINAL_FALLBACKS:"," attr = getattr(module, name, None)"," if callable(attr) and name not in _LEGACY_BASELINES:"," _LEGACY_BASELINES[name] = attr"," return module or _legacy_cli_module","_refresh_legacy_cli_module()","APP_PATH = Path(__file__).resolve().parents[2] / \"streamlit_app\" / \"app.py\"","DEFAULT_REPORT_FORMATS = (\"csv\", \"json\", \"xlsx\", \"txt\")","SCENARIO_WINDOWS: dict[str, tuple[tuple[str, str], tuple[str, str]]] = {"," \"2008\": ((\"2006-01\", \"2007-12\"), (\"2008-01\", \"2009-12\")),"," \"2020\": ((\"2018-01\", \"2019-12\"), (\"2020-01\", \"2021-12\")),","}","def _legacy_callable(name: str, fallback: Callable[..., Any]) -> Callable[..., Any]:"," original = _ORIGINAL_FALLBACKS.get(name)"," if original is not None and fallback is not original:"," return fallback"," if module is not None:"," attr = getattr(module, name, None)"," if callable(attr):"," baseline = _LEGACY_BASELINES.get(name)"," if baseline is None:"," baseline = attr"," if _USE_LEGACY_CLI or attr is not baseline:"," return cast(Callable[..., Any], attr)"," return fallback","class TrendCLIError(RuntimeError):"," \"\"\"Raised when CLI validation fails before dispatching work.\"\"\"","def build_parser("," *, prog: str = \"trend\", include_gui_alias: bool | None = None",") -> argparse.ArgumentParser:"," \"\"\"Construct the argument parser for the unified ``trend`` command.\"\"\""," if include_gui_alias is None:"," include_gui_alias = prog == \"trend-model\""," parser = argparse.ArgumentParser(prog=prog)"," sub = parser.add_subparsers(dest=\"subcommand\", required=True)"," sub.add_parser(\"check\", help=\"Print environment info and exit\")"," run_p = sub.add_parser(\"run\", help=\"Execute the analysis pipeline\")"," run_p.add_argument(\"-c\", \"--config\", help=\"Path to YAML config\")"," run_p.add_argument("," \"-i\","," \"--input\","," \"--returns\","," dest=\"returns\","," help=\"Override returns CSV path\","," run_p.add_argument(\"--seed\", type=int, help=\"Force random seed for the run\")"," nargs=\"?\","," const=\"analysis_bundle.zip\","," help=\"Write reproducibility bundle (optional path)\","," run_p.add_argument(\"--log-file\", help=\"Explicit JSONL log file path\")"," \"--no-structured-log\","," action=\"store_true\","," help=\"Disable JSONL structured logging\","," \"--universe\","," help=\"Select a named universe defined under config/universe\","," \"--no-cache\","," help=\"Disable persistent caching for rolling computations\","," \"--preset\","," help=\"Apply a named trend preset to signal generation\","," \"--config-coverage\","," help=\"Report which config keys were validated vs read\","," report_p = sub.add_parser("," \"report\", help=\"Generate summary artefacts for a configuration\""," report_p.add_argument(\"-c\", \"--config\", help=\"Path to YAML config\")"," report_p.add_argument("," help=\"Directory where summary outputs will be written\","," \"--output\","," help=\"Path to the unified HTML report (file or directory)\","," \"--formats\","," nargs=\"+\","," choices=DEFAULT_REPORT_FORMATS,"," help=\"Subset of export formats (default: csv json xlsx txt)\","," \"--pdf\","," help=\"Also generate a PDF report alongside the HTML output\","," stress_p = sub.add_parser("," \"stress\", help=\"Run the pipeline against a canned stress scenario\""," stress_p.add_argument(\"-c\", \"--config\", help=\"Path to YAML config\")"," stress_p.add_argument("," \"--scenario\","," choices=sorted(SCENARIO_WINDOWS),"," help=\"Stress scenario identifier\","," stress_p.add_argument(\"--returns\", help=\"Override returns CSV path\")"," help=\"Optional export directory for stress results\","," mc_p = sub.add_parser(\"mc\", help=\"Monte Carlo visualization workflows\")"," mc_sub = mc_p.add_subparsers(dest=\"mc_command\", required=True)"," mc_viz_p = mc_sub.add_parser("," \"viz\", help=\"Render Monte Carlo chart artifacts from a bundle\""," mc_viz_p.add_argument("," required=True,"," help=\"Path to Monte Carlo bundle directory containing summary/results files\","," type=Path,"," help=\"Output directory for generated chart artifacts\","," default=\"fan,path_dist,risk_return\","," help=\"Comma-separated chart identifiers (fan,path_dist,risk_return)\","," help=\"Export chart artifacts as HTML\","," help=\"Export chart artifacts as JSON\","," help=\"Export chart artifacts as PNG\","," sub.add_parser(\"app\", help=\"Launch the Streamlit application\")"," if include_gui_alias:"," sub.add_parser(\"gui\", help=\"Launch the app (legacy alias for app)\")"," quick_p = sub.add_parser("," \"quick-report\", help=\"Build a compact HTML report from run artefacts\""," quick_p.add_argument("," \"--run-id\", help=\"Run identifier (defaults to artefact inference)\""," \"--artifacts\","," help=\"Directory containing metrics_.csv and details_.json\","," \"--base-dir\","," help=\"Base directory for derived artefacts (default: ./perf)\","," \"--config\","," help=\"Configuration file to embed in the report\","," help=\"Explicit HTML output path (default: /reports/.html)\","," explain_p = sub.add_parser("," \"explain\","," help=\"Explain analysis results using natural language with citations\","," explain_p.add_argument("," \"--details\","," help=\"Path to details_.json produced by the report command\","," \"--run-id\","," help=\"Run identifier used to locate details_.json\","," help=\"Directory containing details_.json (default: perf)\","," \"--question\","," action=\"append\","," dest=\"questions\","," help=\"Question to answer (repeatable; defaults to a summary prompt)\","," \"--questions-file\","," help=\"Optional file containing questions (one per line)\","," \"--provider\","," help=("," \"LLM provider for result explanations (defaults to TREND_LLM_PROVIDER). \""," \"Example: --provider openai\""," ),"," \"Directory (created if needed) or file prefix for explanation artifacts. \""," \"Example: --output perf/explanations or --output perf/explanation_run.txt\""," nl_p = sub.add_parser(\"nl\", help=\"Edit config using natural language\")"," nl_p.add_argument(\"instruction\", help=\"Natural language instruction to apply\")"," nl_p.add_argument("," \"--in\","," dest=\"input_path\","," \"Input configuration file (default: config/defaults.yml) used as the base for edits. \""," \"Example: --in config/base.yml\""," dest=\"output_path\","," \"Output configuration file (default: same as --in) for writing the updated config. \""," \"Example: --out config/updated.yml\""," \"--diff\","," \"Print the unified diff between input and updated config without writing the file. \""," 'Example: trend nl \"Lower max weight\" --diff'"," \"--dry-run\","," \"Print the updated config to stdout without writing the file. \""," 'Example: trend nl \"Lower max weight\" --dry-run'"," \"--run\","," \"Validate the updated config against the schema and run the pipeline if valid. \""," 'Example: trend nl \"Add CSV path\" --run'"," \"--no-confirm\","," \"Apply risky changes without an interactive confirmation prompt. \""," 'Example: trend nl \"Remove constraints\" --no-confirm'"," \"LLM provider for natural language edits (defaults to TREND_LLM_PROVIDER). \""," \"--model\","," \"Override the LLM model for natural language edits (defaults to TREND_LLM_MODEL). \""," \"Example: --model gpt-4o-mini\""," \"--temperature\","," type=float,"," \"Override the LLM temperature for natural language edits (defaults to TREND_LLM_TEMPERATURE). \""," \"Example: --temperature 0.2\""," \"--explain\","," \"Print an explanation of the generated changes alongside optional diff output. \""," 'Example: trend nl \"Lower max weight\" --explain --diff'"," return parser","def _resolve_returns_path(config_path: Path, cfg: Any, override: str | None) -> Path:"," \"\"\"Resolve the returns CSV path relative to sensible anchors."," Relative paths from the configuration are first checked relative to the"," configuration file itself, then the directory *above* it (repo root), and"," finally against the repository root. This mirrors the"," ``DataSettings`` resolver so configs can reference ``demo/demo_returns.csv``"," even though the YAML file lives under ``config/``."," def _resolve_relative(raw: Path, *, include_config_roots: bool) -> Path:"," if raw.is_absolute():"," return raw.resolve()"," roots: list[Path] = []"," if include_config_roots:"," cfg_dir = config_path.parent"," roots.append(cfg_dir)"," parent = cfg_dir.parent"," if parent != cfg_dir:"," roots.append(parent)"," roots.append(proj_path())"," seen: set[Path] = set()"," for root in roots:"," if root in seen:"," continue"," seen.add(root)"," candidate = (root / raw).resolve()"," if candidate.exists():"," return candidate"," anchor = roots[0]"," return (anchor / raw).resolve()"," if override:"," return _resolve_relative(Path(override), include_config_roots=False)"," csv_path = cfg.data.get(\"csv_path\") if hasattr(cfg, \"data\") else None"," if not csv_path:"," msg = \"Configuration must define data.csv_path or use --returns\""," raise TrendCLIError(msg)"," return _resolve_relative(Path(csv_path), include_config_roots=True)","_register_fallback(\"_resolve_returns_path\", _resolve_returns_path)","def _ensure_dataframe(path: Path) -> pd.DataFrame:"," df = load_csv(str(path), errors=\"raise\")"," except TypeError:"," df = load_csv(str(path))"," if df is None:"," raise FileNotFoundError(str(path))"," return df","_register_fallback(\"_ensure_dataframe\", _ensure_dataframe)","def _determine_seed(cfg: Any, override: int | None) -> int:"," if override is not None:"," seed = int(override)"," env_seed = os.getenv(\"TREND_SEED\")"," if env_seed is not None:"," seed = int(env_seed)"," except (ValueError, TypeError):"," seed = getattr(cfg, \"seed\", 42)"," else:"," seed = getattr(cfg, \"seed\", 42)"," setattr(cfg, \"seed\", seed)"," pass"," return seed","def _prepare_export_config("," cfg: Any, directory: Path | None, formats: Iterable[str] | None"," if directory is None and formats is None:"," export_cfg = dict(getattr(cfg, \"export\", {}) or {})"," if directory is not None:"," export_cfg[\"directory\"] = str(directory)"," if formats is not None:"," export_cfg[\"formats\"] = [f for f in formats]"," setattr(cfg, \"export\", export_cfg)","def _run_pipeline("," cfg: Any,"," returns_df: pd.DataFrame,"," source_path: Path | None,"," log_file: Path | None,"," bundle: Path | None,",") -> tuple[RunResult, str, Path | None]:"," _require_transaction_cost_controls(cfg)"," perf_log_result = _init_perf_logger()"," if perf_log_result.diagnostic:"," logger.info(perf_log_result.diagnostic.message)"," run_id = getattr(cfg, \"run_id\", None) or uuid.uuid4().hex[:12]"," setattr(cfg, \"run_id\", run_id)"," log_path = None"," if structured_log:"," log_path = log_file or run_logging.get_default_log_path(run_id)"," run_logging.init_run_logger(run_id, log_path)"," structured_log, run_id, \"start\", \"trend CLI execution started\""," result = run_simulation(cfg, returns_df)"," diagnostic = getattr(result, \"diagnostic\", None)"," if diagnostic and not result.details:"," _report_legacy_pipeline_diagnostic("," diagnostic,"," structured_log=structured_log,"," run_id=run_id,"," return result, run_id, log_path"," analysis = getattr(result, \"analysis\", None)"," # The following attributes are already set by run_simulation when analysis exists,"," # but we need to backfill them when analysis is absent (legacy callers)."," details = result.details"," if isinstance(details, dict):"," if analysis is None:"," portfolio_series = select_primary_portfolio_series(details, prefer_raw=True)"," if portfolio_series is not None:"," setattr(result, \"portfolio\", portfolio_series)"," benchmarks = details.get(\"benchmarks\")"," if isinstance(benchmarks, dict) and benchmarks:"," first = next(iter(benchmarks.values()))"," setattr(result, \"benchmark\", first)"," weights_user = details.get(\"weights_user_weight\")"," if weights_user is not None:"," setattr(result, \"weights\", weights_user)"," \"summary_render\","," \"Simulation finished and summary rendered\","," _handle_exports(cfg, result, structured_log, run_id)"," ledger_result = _persist_turnover_ledger(run_id, getattr(result, \"details\", {}))"," if ledger_result.diagnostic:"," logger.info(ledger_result.diagnostic.message)"," if bundle:"," _write_bundle(cfg, result, source_path, Path(bundle), structured_log, run_id)"," return result, run_id, log_path","_register_fallback(\"_run_pipeline\", _run_pipeline)","def _handle_exports("," cfg: Any, result: RunResult, structured_log: bool, run_id: str"," export_cfg = getattr(cfg, \"export\", {}) or {}"," out_dir = export_cfg.get(\"directory\")"," out_formats = export_cfg.get(\"formats\")"," filename = export_cfg.get(\"filename\", \"analysis\")"," if not out_dir and not out_formats:"," out_dir = DEFAULT_OUTPUT_DIRECTORY"," out_formats = DEFAULT_OUTPUT_FORMATS"," if not out_dir or not out_formats:"," out_dir_path = Path(out_dir)"," out_dir_path.mkdir(parents=True, exist_ok=True)"," data = {\"metrics\": result.metrics}"," export.append_narrative_section(data, result.details, config=cfg)"," split = getattr(cfg, \"sample_split\", {})"," in_start = str(split.get(\"in_start\")) if split else \"\""," in_end = str(split.get(\"in_end\")) if split else \"\""," out_start = str(split.get(\"out_start\")) if split else \"\""," out_end = str(split.get(\"out_end\")) if split else \"\""," if any(fmt.lower() in {\"excel\", \"xlsx\"} for fmt in out_formats):"," formatter = export.make_summary_formatter("," result.details, in_start, in_end, out_start, out_end"," data[\"summary\"] = export.summary_frame_from_result(result.details)"," export.export_to_excel("," data,"," str(out_dir_path / f\"{filename}.xlsx\"),"," default_sheet_formatter=formatter,"," remaining = [fmt for fmt in out_formats if fmt.lower() not in {\"excel\", \"xlsx\"}]"," if remaining:"," export.export_data("," data,"," str(out_dir_path / filename),"," formats=remaining,"," export.export_data("," str(out_dir_path / filename),"," formats=out_formats,"," _legacy_maybe_log_step(structured_log, run_id, \"export_complete\", \"Export done\")","def _write_bundle("," result: RunResult,"," bundle_path: Path,"," from trend_analysis.export.bundle import export_bundle"," bundle_path = bundle_path.resolve()"," if bundle_path.is_dir():"," bundle_path = bundle_path / \"analysis_bundle.zip\""," # Attach metadata expected by export_bundle"," setattr(result, \"config\", getattr(cfg, \"__dict__\", {}))"," if source_path is not None:"," setattr(result, \"input_path\", source_path)"," export_bundle(result, bundle_path)"," print(f\"Bundle written: {bundle_path}\")"," \"bundle_complete\","," \"Reproducibility bundle created\","," bundle=str(bundle_path),","def _print_summary(cfg: Any, result: RunResult) -> None:"," text = export.format_summary_text("," result.details,"," str(split.get(\"in_start\", \"\")),"," str(split.get(\"in_end\", \"\")),"," str(split.get(\"out_start\", \"\")),"," str(split.get(\"out_end\", \"\")),"," if _legacy_extract_cache_stats is not None:"," cache_stats = _legacy_extract_cache_stats(result.details)"," if cache_stats:"," print(\"\\nCache statistics:\")"," for key, value in cache_stats.items():"," print(f\" {key.capitalize()}: {value}\")","_register_fallback(\"_print_summary\", _print_summary)","def _write_report_files("," out_dir: Path, cfg: Any, result: RunResult, *, run_id: str"," out_dir.mkdir(parents=True, exist_ok=True)"," metrics_path = out_dir / f\"metrics_{run_id}.csv\""," result.metrics.to_csv(metrics_path)"," summary_path = out_dir / f\"summary_{run_id}.txt\""," summary_text = export.format_summary_text("," summary_path.write_text(summary_text, encoding=\"utf-8\")"," details_path = out_dir / f\"details_{run_id}.json\""," with details_path.open(\"w\", encoding=\"utf-8\") as fh:"," json.dump(result.details, fh, default=_json_default, indent=2)"," turnover_csv_result = _maybe_write_turnover_csv("," out_dir, getattr(result, \"details\", {})"," if turnover_csv_result.diagnostic:"," logger.info(turnover_csv_result.diagnostic.message)"," print(f\"Report artefacts written to {out_dir}\")","_register_fallback(\"_write_report_files\", _write_report_files)","def _resolve_report_output_path("," output: str | None, export_dir: Path | None, run_id: str",") -> Path:"," if output:"," base = Path(output).expanduser()"," if base.exists() and base.is_dir():"," return base / f\"trend_report_{run_id}.html\""," if base.suffix.lower() in {\".html\", \".htm\"}:"," return base"," if base.suffix:"," return base / f\"trend_report_{run_id}.html\""," base_dir = export_dir if export_dir is not None else proj_path()"," return base_dir / f\"trend_report_{run_id}.html\"","def _json_default(obj: Any) -> Any: # pragma: no cover - helper"," if isinstance(obj, pd.Series):"," data: dict[str | int | float, Any] = {}"," for key, value in obj.items():"," coerced_key: str | int | float"," if isinstance(key, (str, int, float)):"," coerced_key = key"," else:"," coerced_key = str(key)"," if isinstance(value, (np.floating, np.integer)):"," data[coerced_key] = float(value)"," data[coerced_key] = value"," return data"," if isinstance(obj, pd.DataFrame):"," return obj.to_dict()"," if isinstance(obj, Path):"," return str(obj)"," raise TypeError(f\"Object of type {type(obj).__name__} is not JSON serialisable\")","def _maybe_write_turnover_csv(directory: Path, details: Any) -> DiagnosticResult[Path]:"," if not isinstance(details, Mapping):"," return DiagnosticResult.failure("," reason_code=\"NO_TURNOVER_EXPORT\","," message=\"Turnover diagnostics absent or non-numeric; skipping CSV export.\","," context={\"details_type\": type(details).__name__},"," diag = details.get(\"risk_diagnostics\")"," if not isinstance(diag, Mapping):"," context={\"has_risk_diag\": False},"," turnover_obj = diag.get(\"turnover\")"," if isinstance(turnover_obj, pd.Series):"," series = turnover_obj.copy()"," elif isinstance(turnover_obj, Mapping):"," series = pd.Series(turnover_obj)"," elif isinstance(turnover_obj, (list, tuple)):"," context={\"turnover_type\": type(turnover_obj).__name__},"," series = series.astype(float)"," except (TypeError, ValueError):"," if series.empty:"," series = series.sort_index()"," frame = series.rename(\"turnover\").to_frame()"," frame.index.name = \"Date\""," path = directory / \"turnover.csv\""," frame.to_csv(path)"," return DiagnosticResult.success(path)","def _portfolio_settings(cfg: Any) -> Mapping[str, Any]:"," portfolio = getattr(cfg, \"portfolio\", None)"," if isinstance(portfolio, Mapping):"," return portfolio"," attrs = getattr(portfolio, \"__dict__\", None)"," if isinstance(attrs, Mapping):"," return cast(Mapping[str, Any], attrs)"," return {}","def _require_transaction_cost_controls(cfg: Any) -> None:"," portfolio = _portfolio_settings(cfg)"," cost_value = portfolio.get(\"transaction_cost_bps\")"," cost_model = portfolio.get(\"cost_model\")"," if isinstance(cost_model, Mapping):"," override = cost_model.get(\"bps_per_trade\")"," if override is not None:"," cost_value = override"," slippage = cost_model.get(\"slippage_bps\")"," if slippage is not None:"," slip_value = float(slippage)"," except (TypeError, ValueError) as exc:"," raise TrendCLIError("," \"portfolio.cost_model.slippage_bps must be numeric\""," ) from exc"," if slip_value < 0:"," \"portfolio.cost_model.slippage_bps cannot be negative\""," )"," if cost_value is None:"," raise TrendCLIError("," \"Configuration must define portfolio.transaction_cost_bps for honest costs.\""," cost = float(cost_value)"," except (TypeError, ValueError) as exc:"," raise TrendCLIError(\"portfolio.transaction_cost_bps must be numeric\") from exc"," if cost < 0:"," raise TrendCLIError(\"portfolio.transaction_cost_bps cannot be negative\")","def _persist_turnover_ledger(run_id: str, details: Any) -> DiagnosticResult[Path]:"," reason_code=\"NO_TURNOVER_LEDGER\","," message=\"No turnover diagnostics captured for ledger persistence.\","," if turnover_obj is None:"," context={\"turnover_type\": None},"," if turnover_obj.empty:"," return DiagnosticResult.failure("," reason_code=\"NO_TURNOVER_LEDGER\","," message=\"No turnover diagnostics captured for ledger persistence.\","," context={\"turnover_type\": \"Series\"},"," if not turnover_obj:"," context={\"turnover_type\": \"Mapping\"},"," context={\"turnover_type\": \"Sequence\"},"," target_dir = Path(\"perf\") / run_id"," target_dir.mkdir(parents=True, exist_ok=True)"," path_result = _maybe_write_turnover_csv(target_dir, details)"," if path_result.value is not None:"," print(f\"Turnover ledger written to {path_result.value}\")"," if path_result.diagnostic or path_result.value is None:"," return path_result"," return DiagnosticResult.success(path_result.value)","def _adjust_for_scenario(cfg: Any, scenario: str) -> None:"," window = SCENARIO_WINDOWS.get(scenario)"," if not window:"," raise TrendCLIError(f\"Unsupported stress scenario: {scenario}\")"," in_window, out_window = window"," split = dict(getattr(cfg, \"sample_split\", {}) or {})"," split.update("," {"," \"in_start\": in_window[0],"," \"in_end\": in_window[1],"," \"out_start\": out_window[0],"," \"out_end\": out_window[1],"," }"," setattr(cfg, \"sample_split\", split)","def _load_configuration(path: str) -> Any:"," cfg_path = Path(path).resolve()"," if not cfg_path.exists():"," raise FileNotFoundError(cfg_path)"," payload = load_schema_config(cfg_path)"," except Exception as exc:"," raise TrendCLIError(str(exc)) from exc"," load_core_config(cfg_path)"," except CoreConfigError as exc:"," validation = validate_config("," payload, base_path=cfg_path.parent, skip_required_fields=True"," if not validation.valid:"," details = \"\\n\".join(format_validation_messages(validation))"," raise TrendCLIError(f\"Config validation failed:\\n{details}\")"," cfg = load_config(cfg_path)"," ensure_run_spec(cfg, base_path=cfg_path.parent)"," return cfg_path, cfg","_register_fallback(\"_load_configuration\", _load_configuration)","def _resolve_explain_details_path(args: argparse.Namespace) -> Path:"," if args.details:"," return Path(args.details)"," if not args.run_id:"," raise TrendCLIError(\"The explain command requires --details or --run-id.\")"," artifacts_dir = Path(args.artifacts) if args.artifacts else Path(\"perf\")"," return artifacts_dir / f\"details_{args.run_id}.json\"","def _load_explain_details(path: Path) -> Mapping[str, Any]:"," if not path.exists():"," raise TrendCLIError(f\"Details file not found: {path}\")"," payload = json.loads(path.read_text(encoding=\"utf-8\"))"," except json.JSONDecodeError as exc:"," raise TrendCLIError(f\"Details file is not valid JSON: {path}\") from exc"," if not isinstance(payload, Mapping):"," raise TrendCLIError(\"Details file must contain a JSON object at the root.\")"," return payload","def _render_analysis_output(details: Mapping[str, Any]) -> str:"," parts: list[str] = []"," summary = pd.DataFrame()"," summary = export.summary_frame_from_result(details)"," summary = pd.DataFrame()"," if not summary.empty:"," parts.append(\"Summary table:\\n\" + summary.to_string(index=False))"," parts.append(\"Summary table unavailable.\")"," sections = \", \".join(sorted(str(k) for k in details.keys()))"," if sections:"," parts.append(f\"Available sections: {sections}\")"," return \"\\n\\n\".join(parts)","def _resolve_explain_questions(args: argparse.Namespace) -> str:"," questions: list[str] = []"," if args.questions:"," questions.extend([q.strip() for q in args.questions if q and q.strip()])"," if args.questions_file:"," if not args.questions_file.exists():"," raise TrendCLIError(f\"Questions file not found: {args.questions_file}\")"," raw_lines = args.questions_file.read_text(encoding=\"utf-8\").splitlines()"," questions.extend([line.strip() for line in raw_lines if line.strip()])"," if not questions:"," questions = [\"Summarize key findings and notable risks in the results.\"]"," return \"\\n\".join(f\"- {question}\" for question in questions)","def _infer_explain_run_id("," details_path: Path,"," run_id: str | None,"," details: Mapping[str, Any] | None = None,",") -> str:"," if run_id:"," return run_id"," candidates = []"," if isinstance(details, Mapping):"," candidates = ["," details.get(\"run_id\"),"," (details.get(\"metadata\") or {}).get(\"run_id\"),"," ((details.get(\"metadata\") or {}).get(\"reporting\") or {}).get(\"run_id\"),"," (details.get(\"reporting\") or {}).get(\"run_id\"),"," ]"," for candidate in candidates:"," if isinstance(candidate, str) and candidate.strip():"," return candidate.strip()"," name = details_path.name"," prefix = \"details_\""," suffix = \".json\""," if name.startswith(prefix) and name.endswith(suffix):"," return name[len(prefix) : -len(suffix)]"," return \"unknown\"","def _resolve_explain_output_paths(output: Path, run_id: str) -> tuple[Path, Path]:"," if output.exists() and output.is_dir():"," prefix = output / f\"explanation_{run_id}\""," elif output.exists() and output.is_file():"," prefix = output.with_suffix(\"\")"," elif output.suffix:"," elif output.name.startswith(\"explanation_\"):"," prefix = output"," return prefix.with_suffix(\".txt\"), prefix.with_suffix(\".json\")","def _build_explain_artifact_payload("," created_at: datetime,"," text: str,"," metric_count: int,"," trace_url: str | None,"," claim_issues: Iterable[ResultClaimIssue],"," questions: str | None = None,",") -> dict[str, object]:"," payload: dict[str, object] = {"," \"run_id\": run_id,"," \"created_at\": created_at.isoformat(),"," \"text\": text,"," \"metric_count\": metric_count,"," \"trace_url\": trace_url,"," \"claim_issues\": [serialize_claim_issue(issue) for issue in claim_issues],"," }"," if questions is not None:"," payload[\"questions\"] = questions","def _finalize_explanation_text("," output = text"," if claim_issues and \"Discrepancy log:\" not in output:"," output = append_discrepancy_log(output, claim_issues)"," return ensure_result_disclaimer(output)","def _write_explain_artifacts("," output: Path,"," payload: Mapping[str, object],",") -> tuple[Path, Path]:"," txt_path, json_path = _resolve_explain_output_paths(output, run_id)"," txt_path.parent.mkdir(parents=True, exist_ok=True)"," txt_path.write_text(text, encoding=\"utf-8\")"," json_path.write_text("," json.dumps(payload, indent=2, sort_keys=True, ensure_ascii=True),"," encoding=\"utf-8\","," return txt_path, json_path","def _fallback_explanation(metric_catalog: str) -> str:"," if metric_catalog:"," return ("," \"Unable to verify the generated explanation against the available metrics. \""," \"Here is the metric catalog:\\n\""," f\"{metric_catalog}\""," return \"No metrics were detected in the analysis output.\"","def _resolve_llm_provider_config("," provider: str | None = None,"," model: str | None = None,",") -> LLMProviderConfig:"," provider_name = ("," provider or os.environ.get(\"TREND_LLM_PROVIDER\") or \"openai\""," ).lower()"," supported = {\"openai\", \"anthropic\", \"ollama\"}"," if provider_name not in supported:"," f\"Unknown LLM provider '{provider_name}'. Expected one of: {', '.join(sorted(supported))}.\""," api_key = os.environ.get(\"TREND_LLM_API_KEY\")"," if not api_key:"," if provider_name == \"openai\":"," api_key = os.environ.get(\"OPENAI_API_KEY\")"," elif provider_name == \"anthropic\":"," api_key = os.environ.get(\"ANTHROPIC_API_KEY\")"," model_name = model or os.environ.get(\"TREND_LLM_MODEL\")"," base_url = os.environ.get(\"TREND_LLM_BASE_URL\")"," organization = os.environ.get(\"TREND_LLM_ORG\")"," max_retries = os.environ.get(\"TREND_LLM_MAX_RETRIES\")"," timeout = os.environ.get(\"TREND_LLM_TIMEOUT\")"," max_retries_value: int | None = None"," timeout_value: float | None = None"," if max_retries:"," max_retries_value = int(max_retries)"," except ValueError as exc:"," raise TrendCLIError(\"TREND_LLM_MAX_RETRIES must be an integer\") from exc"," if timeout:"," timeout_value = float(timeout)"," raise TrendCLIError(\"TREND_LLM_TIMEOUT must be a number\") from exc"," config_kwargs: dict[str, Any] = {\"provider\": provider_name}"," if model:"," config_kwargs[\"model\"] = model"," if api_key:"," config_kwargs[\"api_key\"] = api_key"," if base_url:"," config_kwargs[\"base_url\"] = base_url"," if organization:"," config_kwargs[\"organization\"] = organization"," if max_retries_value is not None:"," config_kwargs[\"max_retries\"] = max_retries_value"," if timeout_value is not None:"," config_kwargs[\"timeout\"] = timeout_value"," if model_name:"," config_kwargs[\"model\"] = model_name"," return LLMProviderConfig(**config_kwargs)","def _build_nl_chain("," temperature: float | None = None,",") -> ConfigPatchChain:"," config = _resolve_llm_provider_config(provider, model=model)"," llm = create_llm(config)"," schema = load_compact_schema()"," return ConfigPatchChain.from_env("," llm=llm,"," schema=schema,"," prompt_builder=build_config_patch_prompt,"," model=config.model,"," temperature=temperature,","def _build_result_chain(provider: str | None = None) -> ResultSummaryChain:"," config = _resolve_llm_provider_config(provider)"," return ResultSummaryChain.from_env("," prompt_builder=build_result_summary_prompt,","def _load_nl_log_entry(path: Path, entry: int) -> NLOperationLog:"," from trend_analysis.llm.replay import load_nl_log_entry"," return load_nl_log_entry(path, entry)","def _replay_nl_entry("," entry: NLOperationLog,",") -> ReplayResult:"," from trend_analysis.llm.replay import replay_nl_entry"," return replay_nl_entry("," entry, provider=provider, model=model, temperature=temperature","def _build_nl_replay_parser() -> argparse.ArgumentParser:"," parser = argparse.ArgumentParser("," prog=\"trend nl replay\","," description=\"Replay a logged NL operation entry.\","," parser.add_argument("," \"log_file\", type=Path, help=\"Path to nl_ops_.jsonl log file\""," parser.add_argument(\"--entry\", type=int, required=True, help=\"1-based entry index\")"," parser.add_argument(\"--provider\", help=\"Override the logged LLM provider\")"," parser.add_argument(\"--model\", help=\"Override the logged LLM model\")"," \"--temperature\", type=float, help=\"Override the logged temperature\""," \"--show-prompt\", action=\"store_true\", help=\"Print the prompt text\"","def _run_nl_replay(argv: list[str]) -> int:"," parser = _build_nl_replay_parser()"," args = parser.parse_args(argv)"," log_path = Path(args.log_file)"," if not log_path.exists():"," raise TrendCLIError(f\"Log file not found: {log_path}\")"," entry = _load_nl_log_entry(log_path, args.entry)"," except (ValueError, IndexError) as exc:"," result = _replay_nl_entry("," entry,"," provider=args.provider,"," model=args.model,"," temperature=args.temperature,"," if args.show_prompt:"," print(\"Prompt:\")"," print(result.prompt)"," print(f\"Prompt hash: {result.prompt_hash}\")"," print(f\"Output hash: {result.output_hash}\")"," if result.trace_url:"," print(f\"Trace URL: {result.trace_url}\")"," if result.recorded_hash is None:"," print(\"Recorded hash: \")"," print(f\"Recorded hash: {result.recorded_hash}\")"," print(f\"Matches: {result.matches}\")"," if result.recorded_output is None:"," print(\"Recorded output: \")"," print(\"Recorded output:\")"," print(result.recorded_output)"," print(\"Comparison: skipped (no recorded output)\")"," exit_code = 0"," elif result.matches:"," print(\"Comparison: match\")"," print(\"Comparison: mismatch\")"," exit_code = 1"," if result.diff:"," print(\"Diff:\")"," print(result.diff)"," print(\"Replay output:\")"," print(result.output)"," return exit_code","def _maybe_handle_nl_replay(argv: list[str]) -> int | None:"," if len(argv) >= 2 and argv[0] == \"nl\" and argv[1] == \"replay\":"," return _run_nl_replay(argv[2:])","def _load_nl_config(path: Path) -> dict[str, Any]:"," payload = load_schema_config(path)"," if not isinstance(payload, dict):"," raise TrendCLIError(\"Config file must contain a mapping at the root.\")","def _hash_nl_payload(payload: dict[str, Any]) -> str:"," text = json.dumps("," payload, sort_keys=True, ensure_ascii=True, separators=(\",\", \":\"), default=str"," return hashlib.sha256(text.encode(\"utf-8\")).hexdigest()","def _log_nl_operation("," request_id: str,"," operation: str,"," input_payload: dict[str, Any],"," model_name: str,"," temperature: float,"," parsed_patch: ConfigPatch | None = None,"," validation_result: ValidationResult | None = None,"," error: str | None = None,"," started_at: float,"," timestamp: datetime,"," entry = NLOperationLog("," request_id=request_id,"," timestamp=timestamp,"," operation=cast(Any, operation),"," input_hash=_hash_nl_payload(input_payload),"," prompt_template=\"\","," prompt_variables={},"," model_output=None,"," parsed_patch=parsed_patch,"," validation_result=validation_result,"," error=error,"," duration_ms=(time.perf_counter() - started_at) * 1000,"," model_name=model_name,"," token_usage=None,"," write_nl_log(entry)","def _apply_nl_instruction("," config: dict[str, Any],"," instruction: str,",") -> tuple[ConfigPatch, dict[str, Any], str, str, float]:"," chain = _build_nl_chain(provider, model=model, temperature=temperature)"," patch = chain.run("," current_config=config, instruction=instruction, request_id=request_id"," apply_started = time.perf_counter()"," apply_timestamp = datetime.now(timezone.utc)"," apply_error: str | None = None"," updated = apply_config_patch(config, patch)"," apply_error = str(exc) or type(exc).__name__"," finally:"," _log_nl_operation("," request_id=request_id,"," operation=\"apply_patch\","," input_payload={"," \"config\": config,"," \"patch\": patch.model_dump(mode=\"json\"),"," },"," model_name=chain.model or \"unknown\","," temperature=chain.temperature,"," parsed_patch=patch,"," error=apply_error,"," started_at=apply_started,"," timestamp=apply_timestamp,"," diff = diff_configs(config, updated)"," return patch, updated, diff, chain.model or \"unknown\", chain.temperature","def _format_nl_explanation(patch: ConfigPatch) -> str:"," lines = [f\"Summary: {patch.summary}\"]"," if patch.risk_flags:"," flags = \", \".join(flag.value for flag in patch.risk_flags)"," lines.append(f\"Risk flags: {flags}\")"," if patch.needs_review:"," lines.append(\"Needs review: unknown config keys detected.\")"," rationales = ["," (operation.path, operation.rationale)"," for operation in patch.operations"," if operation.rationale"," if rationales:"," lines.append(\"Rationales:\")"," lines.extend(f\"- {path}: {rationale}\" for path, rationale in rationales)"," return \"\\n\".join(lines).strip() + \"\\n\"","def _validate_nl_run_config(updated: dict[str, Any], *, base_path: Path) -> None:"," updated,"," base_path=base_path,"," include_model_validation=True,","def _confirm_risky_patch(patch: ConfigPatch, *, no_confirm: bool) -> None:"," flags = [flag.value for flag in patch.risk_flags]"," flags.append(\"UNKNOWN_KEYS\")"," if not flags or no_confirm:"," flags_text = \", \".join(flags)"," if not sys.stdin.isatty():"," f\"Risky changes detected ({flags_text}). Re-run with --no-confirm to apply without prompting.\""," response = input(f\"Risky changes detected ({flags_text}). Continue? [y/N]: \")"," if response.strip().lower() not in {\"y\", \"yes\"}:"," raise TrendCLIError(\"Update cancelled by user.\")","def _validate_mc_viz_output_flags(args: argparse.Namespace) -> None:"," if not any("," ("," getattr(args, \"html\", False),"," getattr(args, \"json\", False),"," getattr(args, \"png\", False),"," ):"," \"The 'mc viz' command requires at least one output flag: --html, --json, or --png\"","def _read_mc_frame(path: Path, *, label: str) -> pd.DataFrame:"," suffix = path.suffix.lower()"," if suffix == \".parquet\":"," frame = pd.read_parquet(path)"," except Exception as exc:"," f\"Failed to read {label} parquet file '{path.name}'. \""," \"The file may be corrupted or not a parquet file.\""," elif suffix == \".csv\":"," frame = pd.read_csv(path)"," elif suffix == \".json\":"," frame = pd.read_json(path)"," raise TrendCLIError("," f\"Unsupported {label} file format '{path.suffix}' for '{path.name}'.\""," except TrendCLIError:"," raise"," f\"Failed to read {label} data from '{path}': {exc}\""," if isinstance(frame, pd.Series):"," return frame.to_frame()"," if not isinstance(frame, pd.DataFrame):"," raise TrendCLIError(f\"Expected {label} data in '{path}' to load as a table.\")"," return frame","def _load_mc_frame(bundle_dir: Path, *, stem: str) -> pd.DataFrame:"," candidates = tuple("," bundle_dir / f\"{stem}.{ext}\" for ext in (\"parquet\", \"csv\", \"json\")"," existing = next((candidate for candidate in candidates if candidate.exists()), None)"," if existing is None:"," expected = \", \".join(path.name for path in candidates)"," f\"Missing required MC {stem} file in '{bundle_dir}'. Expected one of: {expected}\""," return _read_mc_frame(existing, label=stem)","def _load_mc_summary_frame(bundle_dir: Path) -> pd.DataFrame:"," return _load_mc_frame(bundle_dir, stem=\"summary\")","def _load_mc_results_frame(bundle_dir: Path) -> pd.DataFrame:"," return _load_mc_frame(bundle_dir, stem=\"results\")","def _load_mc_bundle_frames("," bundle: str | os.PathLike[str],",") -> tuple[pd.DataFrame, pd.DataFrame]:"," bundle_dir = Path(bundle).expanduser().resolve()"," if not bundle_dir.exists():"," raise TrendCLIError(f\"MC bundle directory does not exist: {bundle_dir}\")"," if not bundle_dir.is_dir():"," raise TrendCLIError(f\"MC bundle path is not a directory: {bundle_dir}\")"," required_stems = (\"summary\", \"results\")"," missing_inputs: list[str] = []"," expected_by_stem: dict[str, str] = {}"," for stem in required_stems:"," candidates = tuple("," bundle_dir / f\"{stem}.{ext}\" for ext in (\"parquet\", \"csv\", \"json\")"," if not any(candidate.exists() for candidate in candidates):"," missing_inputs.append(stem)"," expected_by_stem[stem] = \", \".join(path.name for path in candidates)"," if missing_inputs:"," if len(missing_inputs) == 1:"," stem = missing_inputs[0]"," expected = expected_by_stem[stem]"," f\"Missing required MC {stem} file in '{bundle_dir}'. Expected one of: {expected}\""," missing_text = \", \".join(missing_inputs)"," expected_text = \"; \".join("," f\"{stem}: {expected_by_stem[stem]}\" for stem in missing_inputs"," f\"Missing required MC input files in '{bundle_dir}': {missing_text}. \""," f\"Expected one of each: {expected_text}\""," return _load_mc_summary_frame(bundle_dir), _load_mc_results_frame(bundle_dir)","def _load_mc_nav_paths_frame(bundle: str | os.PathLike[str]) -> pd.DataFrame | None:"," return load_optional_nav_paths_frame("," bundle,"," read_frame=_read_mc_frame,"," error_cls=TrendCLIError,","def _parse_mc_chart_selection(charts_value: str) -> list[str]:"," requested = ["," token.strip().lower() for token in charts_value.split(\",\") if token.strip()"," if not requested:"," \"The 'mc viz' command requires at least one chart in --charts.\""," seen: set[str] = set()"," ordered: list[str] = []"," for chart in requested:"," if chart not in seen:"," seen.add(chart)"," ordered.append(chart)"," supported = tuple(_mc_chart_builders().keys())"," unsupported = [chart for chart in ordered if chart not in supported]"," if unsupported:"," supported_text = \", \".join(supported)"," invalid_text = \", \".join(unsupported)"," f\"Unsupported chart identifier(s): {invalid_text}. Supported charts: {supported_text}\""," return ordered","def _mc_nav_source_frame("," summary_frame: pd.DataFrame,"," results_frame: pd.DataFrame,"," nav_paths_frame: pd.DataFrame | None,"," if nav_paths_frame is not None:"," return nav_paths_frame"," for frame in (results_frame, summary_frame):"," numeric = frame.select_dtypes(include=[np.number]).copy()"," numeric = numeric.dropna(how=\"all\")"," if not numeric.empty:"," return numeric"," raise TrendCLIError("," \"Unable to derive path data for Monte Carlo charts. \""," \"Provide nav_paths.parquet or numeric summary/results files.\"","def _build_mc_fan_chart(",") -> Any:"," from trend_analysis.viz import fan"," nav_frame = _mc_nav_source_frame(summary_frame, results_frame, nav_paths_frame)"," return fan.make(nav_frame)","def _build_mc_path_dist_chart("," from trend_analysis.viz import path_dist"," return path_dist.make(nav_frame)","def _build_mc_risk_return_chart("," from trend_analysis.viz import risk_return"," returns_frame = nav_frame.pct_change(fill_method=None).replace("," [np.inf, -np.inf], np.nan"," returns_frame = returns_frame.dropna(how=\"all\")"," if returns_frame.empty:"," returns_frame = nav_frame.apply(pd.to_numeric, errors=\"coerce\").dropna("," how=\"all\""," return risk_return.make(returns_frame)","def _mc_chart_builders() -> ("," dict[str, Callable[[pd.DataFrame, pd.DataFrame, pd.DataFrame | None], Any]]","):"," return {"," \"fan\": _build_mc_fan_chart,"," \"path_dist\": _build_mc_path_dist_chart,"," \"risk_return\": _build_mc_risk_return_chart,","NAV_PATH_REQUIRED_CHARTS = frozenset({\"path_dist\"})","def _validate_mc_nav_paths_requirement("," selected_charts: Iterable[str], nav_paths_frame: pd.DataFrame | None"," validate_nav_paths_requirement("," selected_charts,"," nav_paths_frame,"," required_charts=NAV_PATH_REQUIRED_CHARTS,","def _export_mc_chart_artifacts("," charts: Mapping[str, Any],"," include_html: bool,"," include_json: bool,"," include_png: bool,",") -> tuple[Path, list[str]]:"," from trend_analysis.monte_carlo.export_bundle import save as export_bundle"," plots_dir = out_dir.expanduser().resolve() / \"plots\""," bundle_path = plots_dir / \"mc_charts_bundle.zip\""," warnings: list[str] = []"," export_bundle("," destination=bundle_path,"," include_html=include_html,"," include_json=include_json,"," include_png=include_png,"," warnings=warnings,"," warnings.extend(extract_artifacts(bundle_path, plots_dir))"," return plots_dir, warnings","def _run_mc_viz_command(args: argparse.Namespace) -> int:"," _validate_mc_viz_output_flags(args)"," summary_frame, results_frame = _load_mc_bundle_frames(args.bundle)"," selected_charts = _parse_mc_chart_selection(args.charts)"," nav_paths_frame = _load_mc_nav_paths_frame(args.bundle)"," required_nav_charts = sorted("," set(selected_charts).intersection(NAV_PATH_REQUIRED_CHARTS)"," _validate_mc_nav_paths_requirement(selected_charts, nav_paths_frame)"," uses_fallback_nav_data = nav_paths_frame is None"," chart_builders = _mc_chart_builders()"," generated_charts = {"," chart_id: chart_builders[chart_id]("," summary_frame, results_frame, nav_paths_frame"," for chart_id in selected_charts"," plots_dir, warnings = _export_mc_chart_artifacts("," generated_charts,"," args.out,"," include_html=args.html,"," include_json=args.json,"," include_png=args.png,"," counts = f\"summary_rows={len(summary_frame)} results_rows={len(results_frame)}\""," counts = f\"{counts} nav_paths_rows={len(nav_paths_frame)}\""," print(f\"Loaded MC bundle frames: {counts}\")"," if uses_fallback_nav_data and not required_nav_charts:"," print("," \"Warning: nav_paths.parquet is missing; continuing without NAV-path-dependent visuals.\","," file=sys.stderr,"," print(f\"Wrote MC chart artifacts to: {plots_dir}\")"," for warning in warnings:"," print(f\"Warning: {warning}\", file=sys.stderr)"," return 0","def main(argv: list[str] | None = None, *, prog: str = \"trend\") -> int:"," parser = build_parser(prog=prog)"," parser = build_parser()"," argv_list = argv if argv is not None else sys.argv[1:]"," maybe_replay_exit = _maybe_handle_nl_replay(argv_list)"," if maybe_replay_exit is not None:"," return maybe_replay_exit"," args, extra_args = parser.parse_known_args(argv_list)"," command = args.subcommand"," if extra_args and command != \"app\":"," raise TrendCLIError(f\"Unexpected arguments: {' '.join(extra_args)}\")"," coverage_tracker: ConfigCoverageTracker | None = None"," if getattr(args, \"config_coverage\", False):"," coverage_tracker = ConfigCoverageTracker()"," activate_config_coverage(coverage_tracker)"," def _finalize_config_coverage() -> None:"," if coverage_tracker is None:"," return"," print(coverage_tracker.format_report())"," deactivate_config_coverage()"," if command == \"check\":"," if coverage_tracker is not None:"," deactivate_config_coverage()"," return _run_environment_check()"," if command in {\"app\", \"gui\"}:"," proc = subprocess.run([\"streamlit\", \"run\", str(APP_PATH), *extra_args])"," except FileNotFoundError:"," print("," \"Error: the 'streamlit' executable was not found. \""," \"Install the optional 'app' extra.\","," file=sys.stderr,"," return 127"," return proc.returncode"," if command == \"quick-report\":"," quick_args: list[str] = []"," if args.run_id:"," quick_args.extend([\"--run-id\", args.run_id])"," if args.artifacts:"," quick_args.extend([\"--artifacts\", os.fspath(args.artifacts)])"," if args.base_dir:"," quick_args.extend([\"--base-dir\", os.fspath(args.base_dir)])"," if args.config:"," quick_args.extend([\"--config\", os.fspath(args.config)])"," if args.output:"," quick_args.extend([\"--output\", os.fspath(args.output)])"," return quick_summary_main(quick_args)"," if command == \"explain\":"," request_id = uuid.uuid4().hex"," details_path = _resolve_explain_details_path(args)"," details = _load_explain_details(details_path)"," questions = _resolve_explain_questions(args)"," run_id = _infer_explain_run_id(details_path, args.run_id, details)"," created_at = datetime.now(timezone.utc)"," all_entries = extract_metric_catalog(details)"," compacted_entries = compact_metric_catalog(all_entries, questions=questions)"," metric_catalog = format_metric_catalog(compacted_entries)"," if not all_entries:"," explanation = _finalize_explanation_text("," \"No metrics were detected in the analysis output.\","," [],"," if args.output:"," payload = _build_explain_artifact_payload("," run_id=run_id,"," created_at=created_at,"," text=explanation,"," metric_count=0,"," trace_url=None,"," claim_issues=[],"," questions=questions,"," )"," _write_explain_artifacts("," output=Path(args.output),"," payload=payload,"," else:"," print(explanation)"," return 0"," analysis_output = _render_analysis_output(details)"," diagnostics = build_deterministic_feedback(details, all_entries)"," if diagnostics:"," analysis_output = f\"{analysis_output}\\n\\n{diagnostics}\""," chain = _build_result_chain(args.provider)"," response = chain.run("," analysis_output=analysis_output,"," metric_catalog=metric_catalog,"," questions=questions,"," request_id=request_id,"," metric_entries=all_entries,"," explanation_text, claim_issues = postprocess_result_text("," response.text,"," all_entries,"," logger=logger,"," trace_url = getattr(response, \"trace_url\", None)"," if claim_issues:"," fallback = _fallback_explanation(metric_catalog)"," fallback = append_discrepancy_log(fallback, claim_issues)"," explanation_text = fallback"," explanation_text = _finalize_explanation_text("," explanation_text, claim_issues"," payload = _build_explain_artifact_payload("," run_id=run_id,"," created_at=created_at,"," text=explanation_text,"," metric_count=len(compacted_entries),"," trace_url=trace_url,"," claim_issues=claim_issues,"," questions=questions,"," _write_explain_artifacts("," output=Path(args.output),"," payload=payload,"," print(explanation_text)"," return 0"," if command == \"nl\":"," input_path = Path(args.input_path) if args.input_path else DEFAULTS"," if not input_path.exists():"," raise TrendCLIError(f\"Input config not found: {input_path}\")"," output_path = Path(args.output_path) if args.output_path else input_path"," config = _load_nl_config(input_path)"," patch, updated, diff, model_name, temperature = _apply_nl_instruction("," config,"," args.instruction,"," provider=args.provider,"," model=args.model,"," temperature=args.temperature,"," if args.run and (args.diff or args.dry_run):"," raise TrendCLIError(\"--run cannot be combined with --diff or --dry-run\")"," if args.run:"," _validate_nl_run_config(updated, base_path=output_path.parent)"," if args.explain:"," sys.stdout.write(_format_nl_explanation(patch))"," if args.diff:"," if diff:"," sys.stdout.write(diff)"," print(\"No changes.\")"," if args.dry_run:"," sys.stdout.write("," yaml.safe_dump(updated, sort_keys=False, default_flow_style=False)"," validate_started = time.perf_counter()"," validate_timestamp = datetime.now(timezone.utc)"," validation_error: str | None = None"," try:"," validation = validate_config("," updated,"," base_path=output_path.parent,"," include_model_validation=True,"," except Exception as exc:"," validation_error = str(exc) or type(exc).__name__"," _log_nl_operation("," request_id=request_id,"," operation=\"validate\","," input_payload={"," \"config\": updated,"," \"base_path\": output_path.parent,"," },"," model_name=model_name,"," temperature=temperature,"," parsed_patch=patch,"," error=validation_error,"," started_at=validate_started,"," timestamp=validate_timestamp,"," raise TrendCLIError(str(exc)) from exc"," if not validation.valid:"," validation_details = \"\\n\".join("," format_validation_messages(validation)"," validation_error = f\"validation failed: {validation_details}\""," _log_nl_operation("," request_id=request_id,"," operation=\"validate\","," input_payload={\"config\": updated, \"base_path\": output_path.parent},"," model_name=model_name,"," temperature=temperature,"," parsed_patch=patch,"," validation_result=validation,"," error=validation_error,"," started_at=validate_started,"," timestamp=validate_timestamp,"," if validation_error is not None:"," raise TrendCLIError("," f\"Config validation failed:\\n{validation_details}\""," _confirm_risky_patch(patch, no_confirm=args.no_confirm)"," output_path.parent.mkdir(parents=True, exist_ok=True)"," output_path.write_text("," yaml.safe_dump(updated, sort_keys=False, default_flow_style=False),"," encoding=\"utf-8\","," print(f\"Updated config written: {output_path}\")"," cfg = load_config(output_path)"," ensure_run_spec(cfg, base_path=output_path.parent)"," returns_path = _resolve_returns_path(output_path, cfg, None)"," returns_df = _ensure_dataframe(returns_path)"," _determine_seed(cfg, None)"," run_pipeline = _legacy_callable(\"_run_pipeline\", _run_pipeline)"," run_started = time.perf_counter()"," run_timestamp = datetime.now(timezone.utc)"," run_error: str | None = None"," result, run_id, log_path = run_pipeline("," cfg,"," returns_df,"," source_path=returns_path,"," log_file=None,"," structured_log=True,"," bundle=None,"," run_error = str(exc) or type(exc).__name__"," finally:"," operation=\"run\","," \"config_path\": str(output_path),"," \"returns_path\": str(returns_path),"," error=run_error,"," started_at=run_started,"," timestamp=run_timestamp,"," print_summary = _legacy_callable(\"_print_summary\", _print_summary)"," print_summary(cfg, result)"," if log_path:"," print(f\"Structured log: {log_path}\")"," if command == \"mc\":"," if getattr(args, \"mc_command\", None) != \"viz\":"," raise TrendCLIError(\"Unknown mc subcommand\")"," _finalize_config_coverage()"," return _run_mc_viz_command(args)"," if command not in {\"run\", \"report\", \"stress\", \"mc\"}:"," raise TrendCLIError(f\"Unknown command: {command}\")"," if command != \"mc\" and not args.config:"," f\"The --config option is required for the '{command}' command\""," load_config_fn = _legacy_callable(\"_load_configuration\", _load_configuration)"," cfg_path, cfg = load_config_fn(args.config)"," if coverage_tracker is not None:"," wrap_config_for_coverage(cfg, coverage_tracker)"," ensure_run_spec(cfg, base_path=cfg_path.parent)"," resolve_returns = _legacy_callable("," \"_resolve_returns_path\", _resolve_returns_path"," returns_path = resolve_returns(cfg_path, cfg, getattr(args, \"returns\", None))"," ensure_df = _legacy_callable(\"_ensure_dataframe\", _ensure_dataframe)"," returns_df = ensure_df(returns_path)"," seed = _determine_seed(cfg, getattr(args, \"seed\", None))"," if command == \"run\":"," set_cache_enabled(not getattr(args, \"no_cache\", False))"," if getattr(args, \"preset\", None):"," spec_preset = get_trend_spec_preset(args.preset)"," except KeyError:"," available = \", \".join(list_trend_spec_presets())"," f\"Unknown preset '{args.preset}'. Available presets: {available}\""," _apply_trend_spec_preset(cfg, spec_preset)"," portfolio_preset = get_trend_preset(args.preset)"," available = \", \".join(list_preset_slugs())"," f\"Unknown preset '{args.preset}'. Available: {available}\""," apply_trend_preset(cfg, portfolio_preset)"," if getattr(args, \"universe\", None):"," mask, universe_spec = load_universe(args.universe, prices=returns_df)"," returns_df = _apply_universe_mask("," returns_df, mask, date_column=universe_spec.date_column"," _attach_universe_paths(cfg, universe_spec, csv_path=str(returns_path))"," run_pipeline = _legacy_callable(\"_run_pipeline\", _run_pipeline)"," result, run_id, log_path = run_pipeline("," cfg,"," returns_df,"," source_path=returns_path,"," log_file=Path(args.log_file) if args.log_file else None,"," structured_log=not args.no_structured_log,"," bundle=Path(args.bundle) if args.bundle else None,"," print_summary = _legacy_callable(\"_print_summary\", _print_summary)"," print_summary(cfg, result)"," if log_path:"," print(f\"Structured log: {log_path}\")"," if command == \"report\":"," export_dir = Path(args.out).resolve() if args.out else None"," if export_dir is None and not args.output:"," \"The 'report' command requires --out for artefacts or --output for the HTML report\""," formats = args.formats or DEFAULT_REPORT_FORMATS"," _prepare_export_config("," cfg, export_dir, formats if export_dir is not None else None"," result, run_id, _ = run_pipeline("," log_file=None,"," structured_log=False,"," bundle=None,"," if export_dir is not None:"," write_report = _legacy_callable("," \"_write_report_files\", _write_report_files"," write_report(export_dir, cfg, result, run_id=run_id)"," report_path = _resolve_report_output_path(args.output, export_dir, run_id)"," report_path.parent.mkdir(parents=True, exist_ok=True)"," artifacts = generate_unified_report("," result,"," cfg,"," include_pdf=args.pdf,"," spec=getattr(cfg, \"_trend_run_spec\", None),"," except RuntimeError as exc:"," raise TrendCLIError(str(exc)) from exc"," report_path.write_text(artifacts.html, encoding=\"utf-8\")"," print(f\"Report written: {report_path}\")"," if args.pdf:"," if artifacts.pdf_bytes is None:"," \"PDF generation failed – install the 'fpdf2' dependency to enable --pdf output\""," pdf_path = report_path.with_suffix(\".pdf\")"," pdf_path.write_bytes(artifacts.pdf_bytes)"," print(f\"PDF report written: {pdf_path}\")"," if command == \"stress\":"," if not args.scenario:"," \"The --scenario option is required for the 'stress' command\""," _adjust_for_scenario(cfg, args.scenario)"," export_dir = Path(args.out) if args.out else None"," _prepare_export_config(cfg, export_dir, None)"," print(f\"Stress scenario '{args.scenario}' completed (seed={seed}).\")"," if export_dir:"," raise TrendCLIError(f\"Unknown command: {command}\")"," except TrendCLIError as exc:"," if \"coverage_tracker\" in locals() and coverage_tracker is not None:"," print(f\"Error: {exc}\", file=sys.stderr)"," return 2"," except FileNotFoundError as exc:","if __name__ == \"__main__\": # pragma: no cover - manual invocation"," raise SystemExit(main())","src/trend/viz/artifacts.py",[2094],{"_375":376,"_377":2095,"_379":2096,"_381":70},82,[397,183,515,2097,402,183,183,2098,2099,2100,2101,2102,2103,183,183,2104,2105,183,2106,2107,721,183,2108,627,2109,2110,2111,2112,627,2113,2114,183,183,2115,2116,183,2117,2118,721,183,2119,2120,183,1743,2121,183,2122,2123,2124,2125,943,2126,2127,2128,1205,943,2129,2130,2131,183,2132,2133,183,2134,2135,2136,2137,2138,2139,2140,2141,1852,1856,2140,2142,1852,943,183,2143,2144,183,2145],"import zipfile","def _sha256(path: Path) -> str:"," digest = hashlib.sha256()"," with path.open(\"rb\") as handle:"," for chunk in iter(lambda: handle.read(1024 * 1024), b\"\"):"," digest.update(chunk)"," return digest.hexdigest()","def _inject_html_container_marker(target_path: Path, rel_path: Path) -> None:"," \"\"\"Wrap extracted chart HTML with a deterministic marker container."," Marker convention: each chart file ``.html`` is wrapped in"," ``

\\\">...

``."," if target_path.suffix.lower() != \".html\":"," chart_id = rel_path.with_suffix(\"\").name"," marker = f'id=\"chart-{chart_id}\"'"," html = target_path.read_text(encoding=\"utf-8\")"," if marker in html:"," wrapped = f\"

\\n{html}\\n

\\n\""," target_path.write_text(wrapped, encoding=\"utf-8\")","def extract_artifacts(source_zip: Path, destination: Path) -> list[str]:"," \"\"\"Extract chart artifacts with deterministic collision handling."," Duplicate archive members after the first occurrence are skipped. Existing"," destination files are never overwritten."," destination = destination.expanduser().resolve()"," destination.mkdir(parents=True, exist_ok=True)"," seen_rel_paths: set[Path] = set()"," with zipfile.ZipFile(source_zip) as archive:"," for member in archive.infolist():"," rel_path = Path(member.filename)"," if member.is_dir() or not member.filename:"," if rel_path in seen_rel_paths:"," warnings.append("," f\"Skipping duplicate file in archive: {rel_path.as_posix()}\""," seen_rel_paths.add(rel_path)"," target_path = destination / rel_path"," target_path.parent.mkdir(parents=True, exist_ok=True)"," with archive.open(member, \"r\") as source_handle:"," data = source_handle.read()"," if target_path.exists():"," temp_path = target_path.with_suffix(target_path.suffix + \".tmp_extract\")"," temp_path.write_bytes(data)"," same_content = _sha256(target_path) == _sha256(temp_path)"," temp_path.unlink(missing_ok=True)"," if same_content:"," warnings.append("," f\"Skipped extracting '{rel_path.as_posix()}' because the destination file already exists.\""," f\"Skipped extracting '{rel_path.as_posix()}' because the destination file already exists with different contents.\""," target_path.write_bytes(data)"," _inject_html_container_marker(target_path, rel_path)"," return warnings","src/trend/mc/io.py",[2148],{"_375":376,"_377":2149,"_379":2150,"_381":70},66,[397,183,399,402,2151,183,524,183,183,2152,1639,423,2153,2154,2155,2156,183,2157,2158,2159,2160,2161,2162,2163,2164,721,183,1641,2165,2166,2167,2168,658,2169,2170,2171,2172,2173,687,2174,2175,2176,687,2177,2178,183,183,2179,2180,1692,423,2181,2154,458,2182,183,1693,627,2183,2184,627,2185,2186,2187,2188,454],"from typing import Callable, Iterable","def load_optional_nav_paths_frame("," read_frame: Callable[..., pd.DataFrame],"," error_cls: type[Exception],",") -> pd.DataFrame | None:"," \"\"\"Load ``nav_paths.parquet`` from an MC bundle when available."," Parameters"," ----------"," bundle:"," Path to the Monte Carlo bundle directory."," read_frame:"," Callable used to deserialize data frames."," error_cls:"," Exception class used for user-facing CLI failures."," nav_paths_path = bundle_dir / \"nav_paths.parquet\""," if not nav_paths_path.exists():"," unsupported_paths = tuple("," bundle_dir / f\"nav_paths.{ext}\" for ext in (\"csv\", \"json\")"," detected_unsupported = [path for path in unsupported_paths if path.exists()]"," if detected_unsupported:"," unsupported_text = \", \".join("," f\"'{path.name}' ({path.suffix.lower()})\""," for path in detected_unsupported"," raise error_cls("," \"Unsupported nav_paths file format(s) detected in MC bundle: \""," f\"{unsupported_text}. Only nav_paths.parquet is supported.\""," return None"," return read_frame(nav_paths_path, label=\"nav_paths\")","def validate_nav_paths_requirement("," selected_charts: Iterable[str],"," required_charts: frozenset[str],"," \"\"\"Validate whether selected chart IDs require ``nav_paths.parquet``.\"\"\""," required = sorted(set(selected_charts).intersection(required_charts))"," if not required:"," chart_text = \", \".join(required)"," raise error_cls("," f\"Chart(s) {chart_text} require nav_paths.parquet in the MC bundle. \""," \"Add nav_paths.parquet or remove these chart(s) from --charts.\"","message",[2191,2192,2193,2194,2195,2196,2197,2198,2199,2200,2201,2202,2203,2204,2205,2206,2207,2208],{"_2209":57,"_57":2232},{"_2209":2211,"_368":2146,"_375":2231,"_377":2149},{"_2209":57,"_57":2230},{"_2209":2211,"_368":2092,"_375":2229,"_377":2095},{"_2209":57,"_57":2228},{"_2209":2211,"_368":509,"_375":319,"_377":2227},{"_2209":2211,"_368":509,"_375":2225,"_377":2226},{"_2209":2211,"_368":509,"_375":2223,"_377":2224},{"_2209":2211,"_368":509,"_375":2221,"_377":2222},{"_2209":2211,"_368":509,"_375":2219,"_377":2220},{"_2209":57,"_57":2218},{"_2209":2211,"_368":392,"_375":2216,"_377":2217},{"_2209":2211,"_368":392,"_375":2214,"_377":2215},{"_2209":2211,"_368":392,"_375":2213,"_377":395},{"_2209":57,"_57":2212},{"_2209":2211,"_368":385,"_375":376,"_377":378},{"_2209":2211,"_368":369,"_375":376,"_377":378},{"_2209":57,"_57":2210},"content_type","\n\n### PR / Commit\n* Committed on branch `work`: `767f603`\n* Created follow-up PR via `make_pr`:\n * **Title:** `Follow-up for #4905: centralize MC nav/artifact utilities and tighten mc_viz checks`\n\n**Testing**\n* ❌ `python -m pytest tests/integration/test_mc_viz.py -q` (fails in this environment because `numpy` is not installed for `trend.cli` import).\n* ✅ `python -m compileall src/trend/mc src/trend/viz tests/integration/test_mc_viz.py src/trend/cli.py`\n* ✅ `PYTHONPATH=src python - <<'PY' ...` (smoke test for `extract_artifacts` HTML marker injection)\n* ⚠️ `python - <<'PY' ... urllib.request.urlopen('https://docs.python.org/3/library/tempfile.html') ...` (web lookup attempt blocked by proxy tunnel `403 Forbidden`).","repo_file_citation","\n* Added package exports for the new modules. ",139,79,117,22,37,"\n* Strengthened integration tests for `mc_viz`:\n * added kaleido availability fixture and expected PNG path helper,\n * asserted deterministic HTML markers (`id=\"chart-...\"`),\n * asserted per-chart PNG size checks when kaleido is available,\n * added missing-`nav_paths.parquet` tests for required vs non-required chart selections. ",1944,1981,1917,1941,1903,1914,1797,1802,27,"\n* Updated `trend.cli` to use the new shared utilities:\n * imports from `trend.mc.io` and `trend.viz.artifacts`,\n * centralized `NAV_PATH_REQUIRED_CHARTS`,\n * MC NAV-path validation delegated to shared utility,\n * MC artifact extraction delegated to shared utility,\n * chart-aware warning behavior when `nav_paths.parquet` is missing but requested charts do not require it. ",16,"\n* Added a shared visualization artifact utility module (`trend.viz.artifacts`) with:\n * deterministic extraction into ensured destination directories,\n * duplicate archive member skipping,\n * no-overwrite behavior with content-aware warnings,\n * deterministic HTML marker wrapping (`id=\"chart-{chart}\"`) for extracted chart HTML. ",10,"### Summary\n* Added a shared Monte Carlo NAV-path utility module (`trend.mc.io`) with:\n * `load_optional_nav_paths_frame(...)` for optional `nav_paths.parquet` loading and unsupported-format validation.\n * `validate_nav_paths_requirement(...)` for chart-aware `nav_paths.parquet` requirement enforcement. ",{"_104":314,"_53":96,"_108":314,"_2235":2236,"_2237":2238,"_2239":2240,"_143":-5,"_130":2241},[95],"input_items",[2261,2262],"external_post_config",{"_2242":2243,"_246":180,"_2244":291,"_257":-5,"_341":240,"_2245":18,"_2246":-5,"_2247":-5,"_2248":2249,"_2250":2251,"_2252":-5,"_2253":-5,"_2254":2255,"_2256":18,"_2257":70,"_2258":65,"_2259":18,"_2260":-5},"user_model_slug","slug",1770872061.998846,"installation_id",71148695,"pr_number","enable_internal_github_app_routing","gitlab_service_account_project_id","review_trigger","comment_id",3888682128,"comment_type","issue","pr_write_guard_token","pending_pr_reaction_id","pending_comment_reaction_id",330241713,"comment_if_no_findings","comment_on_error","share_url","is_parallel_review","review_summary_id",{"_104":2189,"_108":314,"_379":2268},{"_298":299,"_300":2263,"_2264":2265,"_2266":140,"_310":-5,"_104":2267},"### Source Issue #4905: [Follow-up] Refactor the placement of code handling NAV paths (PR #4904)\n\nSource: https://github.com/stranske/Trend_Model_Project/issues/4905\n\n> # Issue: Refactor NAV-path handling, consolidate artifact extraction, and strengthen mc_viz integration tests\n> \n> ## Why\n> PR #4904 addressed issue #4903 but verification identified concerns (verdict: **CONCERNS**). This follow-up addresses the remaining gaps with improved task structure.\n> \n> ## Scope\n> Move NAV-path validation/loading into shared utilities, make NAV-path missing behavior chart-aware, replace hardcoded NAV-path chart logic with a constant, consolidate artifact extraction into a single utility with deterministic collision handling, harden output directory creation, and strengthen integration tests for PNG/HTML outputs.\n> \n> ## Non-Goals\n> - Changing the underlying chart rendering logic or visualization library\n> - Modifying the NAV-path data format or schema\n> - Refactoring unrelated CLI commands beyond `mc_viz` and artifact extraction\n> \n> ## Tasks\n> \n> ### NAV-path utilities refactoring\n> - [ ] Create NAV-path validation and loading utility functions in `trend/mc/io.py` with appropriate error handling and return types\n> - [ ] Update `trend/cli/mc_viz.py` to import and call the new NAV-path utilities from `trend/mc/io.py`\n> - [ ] Remove NAV-path validation and loading logic from `src/trend/cli.py` after verifying other modules use the shared utilities\n> \n> ### Chart-aware NAV-path missing behavior\n> - [ ] Replace hardcoded NAV-path-required chart logic with a constant (e.g., `NAV_PATH_REQUIRED_CHARTS`) in `trend/cli/mc_viz.py` and update all NAV-path requirement checks to use it\n> - [ ] Add logic to determine which code path to execute based on intersection of requested charts with `NAV_PATH_REQUIRED_CHARTS` constant\n> - [ ] Implement error path in `trend/cli/mc_viz.py` that exits non-zero with message containing `\"nav_paths.parquet\"` when required charts are requested and file is missing\n> - [ ] Implement warning path in `trend/cli/mc_viz.py` that emits warning containing `\"nav_paths.parquet\"` and continues with exit 0 when no required charts are requested and file is missing\n> \n> ### Artifact extraction consolidation\n> - [ ] Create or update the artifact extraction utility function in `trend/viz/artifacts.py` with a clear interface and parameter contract\n> - [ ] Implement deterministic filename collision handling in the artifact extraction utility in `trend/viz/artifacts.py` that skips duplicate filenames after the first occurrence and logs a warning\n> - [ ] Update `src/trend/cli.py` to call the artifact extraction utility from `trend/viz/artifacts.py` and remove duplicate extraction logic\n> - [ ] Update `trend/cli/mc_viz.py` to call the artifact extraction utility from `trend/viz/artifacts.py` and remove duplicate extraction logic\n> \n> ### Output directory hardening\n> - [ ] Fix output/extraction directory creation to ensure destination directories exist before any writes or `TemporaryDirectory` usage (add `mkdir(parents=True, exist_ok=True)` where appropriate, including nested output paths)\n> - [ ] Update the artifact extraction utility in `trend/viz/artifacts.py` to create its destination directory with `mkdir(parents=True, exist_ok=True)` before writing extracted files\n> \n> ### Integration test improvements - PNG\n> - [ ] Add kaleido availability detection logic to `tests/integration/test_mc_viz.py` test setup or fixtures\n> - [ ] Implement helper function to determine expected PNG file paths for each requested chart type\n> - [ ] Add assertions in `tests/integration/test_mc_viz.py` that verify each expected PNG file exists and has size greater than zero bytes when kaleido is available\n> \n> ### Integration test improvements - HTML\n> - [ ] Update HTML generation in `trend/cli/mc_viz.py` to emit deterministic container markers for each chart type with predictable id attributes (e.g., `id=\"chart-fan\"`, `id=\"chart-path_dist\"`, `id=\"chart-risk_return\"`)\n> - [ ] Add test assertions in `tests/integration/test_mc_viz.py` that verify the presence of expected HTML container markers for each requested chart\n> - [ ] Document the HTML container marker naming convention and chart type mapping in code comments or module docstrings\n> \n> ## Acceptance Criteria\n> \n> ### NAV-path utilities\n> - [ ] NAV-path validation/loading utility functions exist in `trend/mc/io.py` with documented parameters and return types\n> - [ ] `trend/cli/mc_viz.py` imports and calls NAV-path utilities from `trend/mc/io.py` for all NAV-path operations\n> - [ ] `src/trend/cli.py` does not contain functions or code blocks that validate NAV-path file existence, read NAV-path files, or parse NAV-path data structures; all such operations are delegated to `trend/mc/io.py` utilities\n> \n> ### Chart-aware NAV-path behavior\n> - [ ] `trend/cli/mc_viz.py` defines a single constant `NAV_PATH_REQUIRED_CHARTS` (set or frozenset) enumerating charts requiring NAV paths\n> - [ ] The CLI uses only `NAV_PATH_REQUIRED_CHARTS` constant for NAV-path requirement decisions (no remaining `_charts_requiring_nav_paths()` function or equivalent decision logic)\n> - [ ] If `nav_paths.parquet` is missing and at least one requested chart is in `NAV_PATH_REQUIRED_CHARTS`, running `trend.cli.mc_viz` exits with non-zero status code and emits an error containing the literal string `\"nav_paths.parquet\"`\n> - [ ] If `nav_paths.parquet` is missing and no requested chart is in `NAV_PATH_REQUIRED_CHARTS`, running `trend.cli.mc_viz` exits with status code `0` and emits a warning containing the literal string `\"nav_paths.parquet\"` and referencing NAV-path-dependent visuals only\n> \n> ### Artifact extraction\n> - [ ] Artifact extraction is performed via a single utility function in `trend/viz/artifacts.py` with a documented function signature\n> - [ ] Both `src/trend/cli.py` and `trend/cli/mc_viz.py` call the artifact extraction utility from `trend/viz/artifacts.py`\n> - [ ] Neither `src/trend/cli.py` nor `trend/cli/mc_viz.py` contains duplicate artifact extraction logic (no inline extraction code)\n> - [ ] When extraction inputs contain filename collisions (same relative path appears more than once), the extraction utility skips duplicate filenames after the first occurrence and logs a warning message\n> - [ ] The extraction utility never overwrites an existing extracted file with different contents (verified by hash comparison or skip-if-exists logic)\n> \n> ### Output directory creation\n> - [ ] Running `mc_viz` with a nested, non-existent plots output path (e.g., `a/b/c/plots/`) creates the directory tree and produces outputs without errors\n> - [ ] The artifact extraction utility creates its destination directory with `mkdir(parents=True, exist_ok=True)` before writing any extracted files\n> - [ ] All code paths that write to output directories or use `TemporaryDirectory` ensure parent directories exist before operations\n> \n> ### Integration tests - PNG\n> - [ ] In `tests/integration/test_mc_viz.py`, kaleido availability is detected via a fixture or test setup function\n> - [ ] When kaleido is available, for each requested chart type (fan, path_dist, risk_return, etc.) a corresponding PNG file exists at the expected output path\n> - [ ] When kaleido is available, each PNG file has `stat().st_size > 0` bytes\n> - [ ] The test fails with a descriptive error message if any requested chart PNG is missing or empty under kaleido-available conditions\n> \n> ### Integration tests - HTML\n> - [ ] The HTML generated by `trend/cli/mc_viz.py` includes deterministic container markers with id attributes for each chart (e.g., `id=\"chart-fan\"`, `id=\"chart-path_dist\"`, `id=\"chart-risk_return\"`)\n> - [ ] In `tests/integration/test_mc_viz.py`, HTML assertions verify the presence of expected container markers using exact id attribute matching (e.g., `assert 'id=\"chart-fan\"' in html_content`)\n> - [ ] The HTML container marker naming convention is documented in `trend/cli/mc_viz.py` module docstring or inline comments\n> \n> ## Implementation Notes\n> \n> **NAV-path refactoring:**\n> - Create validation and loading functions in `trend/mc/io.py` as the single source of truth\n> - Functions should accept file path parameters and return validated data structures or raise descriptive exceptions\n> - Update `trend/cli/mc_viz.py` to import these utilities and remove local validation logic\n> - Remove all NAV-path file operations from `src/trend/cli.py` after confirming shared utilities are in use\n> \n> **Chart-aware missing-input behavior:**\n> - Define `NAV_PATH_REQUIRED_CHARTS` as a module-level constant (frozenset recommended for immutability)\n> - Implement conditional logic: `if requested_charts & NAV_PATH_REQUIRED_CHARTS` to determine error vs. warning path\n> - Error path: call `sys.exit(1)` or raise exception with message including exact string `\"nav_paths.parquet\"`\n> - Warning path: use `logging.warning()` with message including `\"nav_paths.parquet\"` and continue execution\n> \n> **Artifact extraction consolidation:**\n> - Create `extract_artifacts(source, destination, ...)` function in `trend/viz/artifacts.py`\n> - Implement collision handling: maintain a set of seen filenames, skip duplicates, log warning with filename\n> - Add content verification: before skipping, optionally verify existing file matches (hash comparison)\n> - Ensure `destination.mkdir(parents=True, exist_ok=True)` is called before any file writes\n> - Update both CLI modules to call this utility with appropriate parameters\n> \n> **Output directory hardening:**\n> - Add `output_dir.mkdir(parents=True, exist_ok=True)` before any `TemporaryDirectory` creation or file writes\n> - Verify nested path handling: test with paths like `Path(\"a/b/c/plots\")` that don't exist\n> - Apply to both extraction destinations and chart output directories\n> \n> **Integration test strengthening:**\n> - PNG tests: use `pytest.importorskip(\"kaleido\")` or similar to detect availability\n> - Create mapping of chart types to expected PNG filenames (e.g., `{\"fan\": \"fan_chart.png\", ...}`)\n> - Assert file existence with `assert png_path.exists()` and size with `assert png_path.stat().st_size > 0`\n> - HTML tests: update chart generation to emit `

` containers\n> - Assert with exact string matching: `assert 'id=\"chart-fan\"' in html_output`\n> - Document marker format in docstring: \"Each chart is wrapped in a div with id='chart-{chart_type}'\"\n> \n> **Deterministic collision handling specification:**\n> The extraction utility must skip duplicate filenames after the first occurrence and log a warning. Implementation approach:\n> ```python\n> seen_files = set()\n> for file_path in files_to_extract:\n> if file_path in seen_files:\n> logger.warning(f\"Skipping duplicate file: {file_path}\")\n> continue\n> seen_files.add(file_path)\n> # extract file\n> ```\n> \n> ## Context\n> \n> ### Source\n> - Original PR: #4904\n> - Parent issue: #4903\n> \n> ### Background (previous attempt context)\n> \n> **Failure to avoid:** Implementing all changes solely in `src/trend/cli.py`, disregarding the modular architecture intended by the acceptance criteria.\n> - **Why it failed:** Risks inconsistent/unprotected behavior in other entry points\n> - **Do instead:** Refactor/relocate into the proper modules (`trend/cli/mc_viz.py`, `trend/mc/io.py`, `trend/viz/artifacts.py`) and ensure all entry points use the shared implementations\n> \n> **Failure to avoid:** Relying on conditional test assertions based on kaleido availability, which allowed skipped PNG validations in some environments.\n> - **Why it failed:** Weakens regression detection when PNG rendering is expected to work\n> - **Do instead:** Enforce per-chart PNG existence and non-zero sizes when kaleido is present (and structure tests to reliably exercise that condition where applicable)\n\n—\nPR created automatically to engage Codex.","base_ref","b7ee4f1a081e2f45f09776a2153ff20f876d8a1a","head_ref","pull_request_info",[2269],{"_2209":57,"_57":2270},"\nCodex Worker activated for branch `codex/issue-4905`.\n\n@codex start\n\nAutomated belt worker prepared this PR. Please continue implementing the requested 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