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bash\nset -euo pipefail\n\nLOG_PREFIX=\"[trend-model-codex-setup]\"\nlog() { printf \"%s %s\\n\" \"$LOG_PREFIX\" \"$*\"; }\nwarn() { printf \"%s [warn] %s\\n\" \"$LOG_PREFIX\" \"$*\" >&2; }\ndie() { printf \"%s [error] %s\\n\" \"$LOG_PREFIX\" \"$*\" >&2; exit 1; }\n\n# -----------------------------\n# Locate Trend_Model_Project repo root robustly.\n# Codex scripts sometimes run from /tmp, so don't rely on $0.\n# -----------------------------\nis_repo_root() {\n local d=\"$1\"\n [[ -f \"${d}/pyproject.toml\" ]] || return 1\n grep -q 'name = \"trend-model\"' \"${d}/pyproject.toml\" 2>/dev/null || return 1\n return 0\n}\n\nREPO_ROOT=\"\"\n\n# Prefer git if available\nif command -v git >/dev/null 2>&1; then\n git_root=\"$(git rev-parse --show-toplevel 2>/dev/null || true)\"\n if [[ -n \"${git_root}\" ]] && is_repo_root \"${git_root}\"; then\n REPO_ROOT=\"${git_root}\"\n fi\nfi\n\n# Walk upwards from PWD\nif [[ -z \"${REPO_ROOT}\" ]]; then\n d=\"${PWD}\"\n while [[ \"${d}\" != \"/\" ]]; do\n if is_repo_root \"${d}\"; then\n REPO_ROOT=\"${d}\"\n break\n fi\n d=\"$(dirname \"${d}\")\"\n done\nfi\n\n# Search common mount points (last resort)\nif [[ -z \"${REPO_ROOT}\" ]]; then\n for base in \"${PWD}\" \"/workspace\" \"/repo\" \"${HOME:-/home}\"; do\n [[ -d \"${base}\" ]] || continue\n while IFS= read -r f; do\n cand=\"$(cd \"$(dirname \"${f}\")\" && pwd)\"\n if is_repo_root \"${cand}\"; then\n REPO_ROOT=\"${cand}\"\n break 2\n fi\n done < <(find \"${base}\" -maxdepth 4 -type f -name pyproject.toml 2>/dev/null | head -n 100)\n done\nfi\n\n[[ -n \"${REPO_ROOT}\" ]] || die \"Could not locate Trend_Model_Project repo root (expected pyproject.toml with name='trend-model').\"\ncd \"${REPO_ROOT}\"\nlog \"Repo root: ${REPO_ROOT}\"\n\n# -----------------------------\n# Node is optional here: only needed for JavaScript workflow tests.\n# -----------------------------\nREQUIRED_NODE_MAJOR=20\nif command -v node >/dev/null 2>&1; then\n NODE_MAJOR=\"$(node -p \"parseInt(process.versions.node.split('.')[0], 10)\" 2>/dev/null || true)\"\n if [[ -n \"${NODE_MAJOR}\" ]] && (( NODE_MAJOR >= REQUIRED_NODE_MAJOR )); then\n log \"Node OK: $(node --version)\"\n else\n warn \"Node found but too old for JS workflow tests (need v${REQUIRED_NODE_MAJOR}+). Found: $(node --version 2>/dev/null || echo unknown)\"\n fi\nelse\n warn \"Node not found. That's fine unless you run JS workflow tests (Node v${REQUIRED_NODE_MAJOR}+).\"\nfi\n\n# -----------------------------\n# Python + venv\n# -----------------------------\nPYTHON_BIN=\"${PYTHON_BIN:-python3}\"\ncommand -v \"${PYTHON_BIN}\" >/dev/null 2>&1 || die \"python3 not found.\"\n\n\"${PYTHON_BIN}\" - <<'PY'\nimport sys\nif sys.version_info < (3, 11):\n raise SystemExit(f\"Python 3.11+ required; found {sys.version.split()[0]}\")\nprint(\"Python OK:\", sys.version.split()[0])\nPY\n\nENV_DIR=\"${ENV_DIR:-.venv}\"\nif [[ ! -d \"${ENV_DIR}\" ]]; then\n log \"Creating venv: ${ENV_DIR}\"\n \"${PYTHON_BIN}\" -m venv \"${ENV_DIR}\"\nfi\n# shellcheck disable=SC1091\nsource \"${ENV_DIR}/bin/activate\"\n\npython -m pip install --upgrade pip setuptools wheel >/dev/null\n\n# -----------------------------\n# Dependency install (lockfile-first, fallback if missing)\n# -----------------------------\nLOCK_FILE=\"${REPO_ROOT}/requirements.lock\"\n\nlog \"Installing uv...\"\npython -m pip install --upgrade uv >/dev/null\n\nif [[ -f \"${LOCK_FILE}\" ]]; then\n log \"Syncing locked dependencies (uv pip sync requirements.lock)...\"\n uv pip sync \"${LOCK_FILE}\"\n\n # Install the project (editable) without re-resolving deps.\n log \"Installing project (editable, no-deps): .[dev,app,notebooks]\"\n python -m pip install --no-deps -e \".[dev,app,notebooks]\"\nelse\n warn \"requirements.lock not found at ${LOCK_FILE}. Falling back to pip resolver (slower / less deterministic).\"\n python -m pip install -e \".[dev,app,notebooks]\"\nfi\n\n# -----------------------------\n# Convenience (best-effort)\n# -----------------------------\nchmod +x scripts/*.sh 2>/dev/null || true\nchmod +x scripts/trend scripts/trend-model 2>/dev/null || true\n\nif command -v pre-commit >/dev/null 2>&1 && [[ -d \".git\" ]]; then\n log \"Installing pre-commit hooks (best-effort)...\"\n pre-commit install --install-hooks >/dev/null 2>&1 || true\nfi\n\n# -----------------------------\n# Fast smoke checks\n# -----------------------------\nlog \"Smoke check: import core modules\"\npython -c \"import trend_analysis, trend_model\" >/dev/null\n\nlog \"Smoke check: CLI entrypoints (help)\"\ntrend-model --help >/dev/null 2>&1 || true\ntrend-analysis --help >/dev/null 2>&1 || true\n\nlog \"Setup complete.\"\nlog \"Useful commands:\"\nlog \" ./scripts/dev_check.sh --changed --fix\"\nlog \" ./scripts/run_tests.sh\"\nlog \" python scripts/generate_demo.py\"\nlog \" PYTHONPATH='./src' python -m trend_analysis.run_analysis -c config/demo.yml\"\nlog \" ./scripts/run_streamlit.sh # run the UI\"\n","mode","on","preset_allowlist","allowlist_domains","pypi.org, github.com, files.pythonhosted.org, deb.debian.org, security.ubuntu.com, raw.githubusercontent.com","allowlist_rules","denylist_domains","safe_methods_only","number",4880,"https://github.com/stranske/Trend_Model_Project/pull/4880","state","closed","merged","mergeable","draft","title","Codex belt for #4876","body","\n> **Source:** Issue #4876\n\n\n\n\n## Automated Status Summary\n#### Scope\nPR #4859 addressed issue #4856, but verification still failed. The remaining gaps are: (1) ensuring `trend_analysis.viz` is import-side-effect free (no implicit Streamlit/heavy viz imports), (2) removing `zip(..., strict=...)` from `quantile_bands(...)` to retain Python <3.10 compatibility, and (3) adding focused tests that exercise minimal construction/serialization of the viz figures plus edge-case coverage for quantile utilities.\n\n\n## Context for Agent\n\n### Related Issues/PRs\n- [#4859](https://github.com/stranske/Trend_Model_Project/issues/4859)\n- [#4856](https://github.com/stranske/Trend_Model_Project/issues/4856)\n\n\n#### Tasks\n### Package Structure\n\n- [x] Add a minimal package initializer at `src/trend_analysis/viz/__init__.py` that contains no imports of streamlit, fan, path_dist, or risk_return (keep it import-side-effect free).\n\n### Python Compatibility\n\n- [x] Update `trend_analysis.viz.utils.quantile_bands(...)` to remove any usage of `zip(..., strict=...)` and replace it with Python <3.10-compatible pairing/validation logic (including explicit length checks where needed).\n\n### Visualization Tests\n\n- [x] Create `tests/viz/test_fan_make.py` with a test that calls `trend_analysis.viz.fan.make(...)` using minimal valid inputs, asserts the result is `plotly.graph_objects.Figure`, and validates JSON-serializability via `json.loads(fig.to_json())`.\n- [x] Create `tests/viz/test_path_dist_make.py` with a test that calls `trend_analysis.viz.path_dist.make(...)` using minimal valid inputs, asserts `plotly.graph_objects.Figure`, and validates JSON-serializability via `json.loads(fig.to_json())`.\n- [x] Create `tests/viz/test_risk_return_make.py` with a test that calls `trend_analysis.viz.risk_return.make(...)` using minimal valid inputs, asserts `plotly.graph_objects.Figure`, and validates JSON-serializability via `json.loads(fig.to_json())`.\n\n### Utility Function Tests - quantile_bands\n\n- [x] Create `tests/viz/test_utils_quantile_bands.py` to cover `quantile_bands(...)`: (1) a normal expected pairing case and (2) an odd-length/mismatched quantiles case asserting a well-defined exception (type + message substring) or documented fallback behavior.\n\n### Utility Function Tests - _select_nav_paths\n\n- [x] Create a unit test for `_select_nav_paths` in `tests/viz/test_utils_quantile_bands.py` that passes empty input and asserts it raises a specific exception type with an expected message substring.\n- [x] Create a unit test for `_select_nav_paths` in `tests/viz/test_utils_quantile_bands.py` that passes unsorted quantile labels and asserts the output has correct shape and deterministic index order.\n- [x] Create a unit test for `_select_nav_paths` in `tests/viz/test_utils_quantile_bands.py` that passes duplicate quantile labels and asserts it raises a specific exception type with an expected message substring.\n\n### Utility Function Tests - _select_terminal_values\n\n- [x] Create a unit test for `_select_terminal_values` in `tests/viz/test_utils_quantile_bands.py` that passes a MultiIndex with unexpected level names and asserts it raises a specific exception type with an expected message substring.\n- [x] Create a unit test for `_select_terminal_values` in `tests/viz/test_utils_quantile_bands.py` that passes a MultiIndex with unexpected level order and asserts it returns correct numeric outputs with exact values.\n- [x] Create a unit test for `_select_terminal_values` in `tests/viz/test_utils_quantile_bands.py` that passes numeric values as strings and asserts they are coerced to numeric dtype with correct values.\n\n#### Acceptance criteria\n### Package Structure\n\n- [x] A file exists at `src/trend_analysis/viz/__init__.py`.\n- [x] `src/trend_analysis/viz/__init__.py` contains no import statements that reference `streamlit`, `trend_analysis.viz.fan`, `trend_analysis.viz.path_dist`, or `trend_analysis.viz.risk_return` (including `import X` and `from X import Y`).\n- [x] A test in `tests/test_viz_package_import.py` verifies that after `import trend_analysis.viz` in a fresh interpreter context, `\"streamlit\" not in sys.modules` evaluates to True.\n\n### Python Compatibility\n\n- [x] The function `trend_analysis.viz.utils.quantile_bands(...)` contains no usage of `zip(..., strict=...)`.\n- [x] `trend_analysis.viz.utils.quantile_bands(...)` raises `ValueError` on quantile pairing mismatch, and tests assert both `ValueError` type and a specific message substring (e.g., \"mismatch\" or \"odd length\").\n- [x] For an input with N quantiles (where N is even), `quantile_bands(...)` produces N/2 bands, and the test asserts `len(result) == N/2` and validates the band structure (e.g., each band has lower and upper quantile labels).\n\n### Visualization Tests\n\n- [x] A test file exists at `tests/viz/test_fan_make.py` and contains at least one test that calls `trend_analysis.viz.fan.make(...)` with minimal valid inputs.\n- [x] The `fan.make(...)` test asserts the returned object is an instance of `plotly.graph_objects.Figure`.\n- [x] The `fan.make(...)` test validates JSON serializability by calling `json.loads(fig.to_json())` without raising.\n- [x] A test file exists at `tests/viz/test_path_dist_make.py` and contains at least one test that calls `trend_analysis.viz.path_dist.make(...)` with minimal valid inputs.\n- [x] The `path_dist.make(...)` test asserts the returned object is an instance of `plotly.graph_objects.Figure` and validates JSON-serializability via `json.loads(fig.to_json())`.\n- [x] A test file exists at `tests/viz/test_risk_return_make.py` and contains at least one test that calls `trend_analysis.viz.risk_return.make(...)` with minimal valid inputs.\n- [x] The `risk_return.make(...)` test asserts the returned object is an instance of `plotly.graph_objects.Figure` and validates JSON-serializability via `json.loads(fig.to_json())`.\n\n### Utility Function Tests\n\n- [x] `tests/viz/test_utils_quantile_bands.py` exists and includes unit tests for `_select_nav_paths` that cover: (1) empty input raising a specific exception type and asserting a message substring, (2) unsorted quantile labels producing output with a deterministic index/column order (asserted), and (3) duplicate quantile labels raising a specific exception type and asserting a message substring.\n- [x] `tests/viz/test_utils_quantile_bands.py` includes unit tests for `_select_terminal_values` that cover: (1) MultiIndex with unexpected level names raising a specific exception type and asserting a message substring, (2) MultiIndex with unexpected level order still returning correct numeric outputs (assert exact values), and (3) numeric values passed as strings are coerced to numeric dtype (assert dtype is numeric) and values match expected.\n\n","base","head","base_sha","60317a91289e5748b8a4ba4e90edff06591e102d","head_sha","f28681caa84de5938297d431123cfb2e2d863a43","head_repo_id","head_repo_full_name","merge_commit_sha","67d727a5ee6edea2a4555be8d55187210a67585a","comments","diff","user",{"_273":274,"_161":274,"_324":-5,"_277":278,"_53":275},"commits",7,"changed_files",9,"additions",395,"deletions",17,"email","events",[],[],"messages",[],{"_104":660,"_108":105,"_376":661},{"_104":359,"_360":361},{"_104":333,"_334":335,"_336":337,"_338":339,"_340":-5},"pr","pr_title","Follow-up: enforce quantile band mismatch errors and add viz selector edge tests","pr_message","### Motivation\n\n- Ensure `trend_analysis.viz` utilities are Python <3.10 compatible by removing reliance on strict `zip` semantics and making quantile-pairing behavior explicit and deterministic. \n- Make fan chart construction robust when a median quantile is present so band pairing and median trace rendering remain well-defined. \n- Add focused unit tests that exercise quantile pairing and the internal NAV/terminal selectors to catch edge cases and preserve stable error messages.\n\n### Description\n\n- Replaced permissive pairing logic in `src/trend_analysis/viz/utils.py` `quantile_bands(...)` with explicit half-splitting and validation that raises `ValueError` for odd-length or mismatched inputs, and added explicit lower/upper ordering checks (no `zip(..., strict=...)`).\n- Updated `src/trend_analysis/viz/fan.py` `make(...)` to remove a median quantile from the band pairing when the quantile count is odd while still rendering the median trace, so band pairing uses only symmetric quantiles.\n- Added `tests/viz/test_utils_quantile_bands.py` with unit tests covering `quantile_bands(...)` even/odd behavior, `_select_nav_paths(...)` empty/unsorted/duplicate-label cases, and `_select_terminal_values(...)` MultiIndex name validation, nonstandard level-order handling, and string-to-numeric coercion.\n\n### Testing\n\n- Ran byte-compile to validate syntax with `python -m compileall src/trend_analysis/viz tests/viz/test_utils_quantile_bands.py tests/viz/test_fan_make.py tests/viz/test_path_dist_make.py tests/viz/test_risk_return_make.py` which completed successfully.\n- Ran `pytest -q tests/test_viz_package_import.py tests/viz/test_fan_make.py tests/viz/test_path_dist_make.py tests/viz/test_risk_return_make.py tests/viz/test_utils_quantile_bands.py`, which failed during collection due to a missing runtime dependency (`pandas`) in the execution environment, so tests could not be executed end-to-end here.\n- Pre-commit checks reformatted the modified files with `black`, while `mypy` reported type errors in unrelated modules (preventing a full pre-commit pass in this environment).","output_diff",{"_104":338,"_341":342,"_139":140,"_343":-5,"_313":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":-5},"pre_apply_patch","repo_id","/workspace/Trend_Model_Project","preapply_patch_base_commit_sha","diff --git a/src/trend_analysis/viz/fan.py b/src/trend_analysis/viz/fan.py\nindex dfe0cf39af2116e19631ff4f7c8f8146f3cca9d7..81f3ba5e1162ca96e4e662a5461834f7b5cc1d13 100644\n--- a/src/trend_analysis/viz/fan.py\n+++ b/src/trend_analysis/viz/fan.py\n@@ -67,51 +67,56 @@ def _select_nav_paths(\n ensure_non_empty(\"nav_paths\", frame)\n return frame\n \n \n def _median_quantile(quantiles: Iterable[float]) -> float | None:\n \"\"\"Pick the quantile closest to 0.5 (median).\"\"\"\n \n q_values = tuple(float(q) for q in quantiles)\n if not q_values:\n return None\n return min(q_values, key=lambda q: abs(q - 0.5))\n \n \n def make(\n nav_paths: pd.DataFrame | dict[str, Sequence[float]],\n *,\n quantiles: Iterable[float] = DEFAULT_QUANTILES,\n max_paths: int | None = 200,\n show_paths: bool = False,\n title: str | None = \"Fan Chart\",\n ) -> go.Figure:\n \"\"\"Create a fan chart from simulated NAV paths.\"\"\"\n \n frame = _select_nav_paths(nav_paths, max_paths=max_paths)\n quantiles_frame = quantiles_over_columns(frame, quantiles)\n- bands = quantile_bands(quantiles)\n+\n+ median_q = _median_quantile(quantiles_frame.columns)\n+ band_quantiles = list(quantiles_frame.columns)\n+ if len(band_quantiles) % 2 != 0 and median_q is not None:\n+ band_quantiles.remove(median_q)\n+ bands = quantile_bands(band_quantiles)\n \n fig = go.Figure()\n x_vals = quantiles_frame.index\n \n base_color = DEFAULT_COLORS[0]\n if bands:\n alpha_step = 0.6 / max(len(bands), 1)\n else:\n alpha_step = 0.2\n \n for idx, band in enumerate(bands):\n upper = quantiles_frame[band.upper]\n lower = quantiles_frame[band.lower]\n fill_alpha = min(0.15 + alpha_step * idx, 0.7)\n fill_color = hex_to_rgba(base_color, fill_alpha)\n \n fig.add_trace(\n go.Scatter(\n x=x_vals,\n y=upper,\n mode=\"lines\",\n line=dict(width=0),\n showlegend=False,\n hoverinfo=\"skip\",\n )\ndiff --git a/src/trend_analysis/viz/utils.py b/src/trend_analysis/viz/utils.py\nindex 9cc6e3dfe811fa9fb8d307852e141c8e9e440c46..288c67ee610d408473c272b17da882bea6dd0df7 100644\n--- a/src/trend_analysis/viz/utils.py\n+++ b/src/trend_analysis/viz/utils.py\n@@ -81,47 +81,57 @@ def validate_quantiles(quantiles: Iterable[float]) -> tuple[float, ...]:\n if any(q < 0.0 or q > 1.0 for q in q_values):\n raise ValueError(\"quantiles must be between 0 and 1\")\n return q_values\n \n \n def quantiles_over_columns(\n data: pd.DataFrame,\n quantiles: Iterable[float],\n ) -> pd.DataFrame:\n \"\"\"Compute quantiles for each row across columns.\"\"\"\n \n ensure_non_empty(\"data\", data)\n q_values = validate_quantiles(quantiles)\n quantile_series = {q: data.quantile(q, axis=1) for q in q_values}\n frame = pd.DataFrame(quantile_series)\n frame.columns = [float(q) for q in frame.columns]\n return frame\n \n \n def quantile_bands(\n quantiles: Iterable[float],\n ) -> tuple[QuantileBand, ...]:\n \"\"\"Return symmetric quantile bands sorted from widest to narrowest.\"\"\"\n \n q_values = sorted(validate_quantiles(quantiles))\n+ if len(q_values) % 2 != 0:\n+ raise ValueError(\"quantile pairing mismatch: odd length\")\n+\n+ midpoint = len(q_values) // 2\n+ lower_quantiles = q_values[:midpoint]\n+ upper_quantiles = list(reversed(q_values[midpoint:]))\n+ if len(lower_quantiles) != len(upper_quantiles):\n+ raise ValueError(\"quantile pairing mismatch\")\n+\n bands: list[QuantileBand] = []\n- for idx, lower in enumerate(q_values):\n- upper = q_values[-(idx + 1)]\n+ for lower, upper in zip(lower_quantiles, upper_quantiles):\n if lower >= upper:\n- break\n+ raise ValueError(\n+ \"quantile pairing mismatch: lower quantile must be less than upper quantile\"\n+ )\n bands.append(QuantileBand(lower=lower, upper=upper))\n return tuple(bands)\n \n \n def hex_to_rgba(color: str, alpha: float) -> str:\n \"\"\"Convert a hex color to an rgba color string.\"\"\"\n \n if not color.startswith(\"#\") or len(color) not in {4, 7}:\n raise ValueError(\"color must be a hex string like #abc or #aabbcc\")\n if not 0.0 <= alpha <= 1.0:\n raise ValueError(\"alpha must be between 0 and 1\")\n \n hex_value = color.lstrip(\"#\")\n if len(hex_value) == 3:\n hex_value = \"\".join(ch * 2 for ch in hex_value)\n r, g, b = (int(hex_value[i : i + 2], 16) for i in range(0, 6, 2))\n return f\"rgba({r}, {g}, {b}, {alpha})\"\ndiff --git a/tests/viz/test_utils_quantile_bands.py b/tests/viz/test_utils_quantile_bands.py\nindex 333f7377876556a82e06ba3ea3a41fdfe41050b5..1cfe32cd5935340ab4d6328e3394a39c612e0290 100644\n--- a/tests/viz/test_utils_quantile_bands.py\n+++ b/tests/viz/test_utils_quantile_bands.py\n@@ -1,18 +1,98 @@\n-\"\"\"Tests for quantile band pairing behavior.\"\"\"\n+\"\"\"Tests for quantile pairing and path/terminal selectors.\"\"\"\n \n+from __future__ import annotations\n+\n+import pandas as pd\n+import pytest\n+from pandas.api.types import is_numeric_dtype\n+\n+from trend_analysis.viz.fan import _select_nav_paths\n+from trend_analysis.viz.path_dist import _select_terminal_values\n from trend_analysis.viz.utils import quantile_bands\n \n \n def test_quantile_bands_even_pairing():\n bands = quantile_bands([0.1, 0.25, 0.75, 0.9])\n \n+ assert len(bands) == 2\n assert [(band.lower, band.upper) for band in bands] == [\n (0.1, 0.9),\n (0.25, 0.75),\n ]\n \n \n-def test_quantile_bands_odd_length():\n- bands = quantile_bands([0.05, 0.5, 0.95])\n+def test_quantile_bands_odd_length_raises_value_error():\n+ with pytest.raises(ValueError, match=\"odd length\"):\n+ quantile_bands([0.05, 0.5, 0.95])\n+\n+\n+def test_select_nav_paths_empty_input_raises_value_error():\n+ with pytest.raises(ValueError, match=\"cannot be empty\"):\n+ _select_nav_paths({}, max_paths=None)\n+\n+\n+def test_select_nav_paths_unsorted_index_is_sorted_deterministically():\n+ frame = pd.DataFrame(\n+ {\n+ \"q90\": [1.1, 1.3, 1.2],\n+ \"q10\": [0.9, 0.7, 0.8],\n+ },\n+ index=pd.to_datetime([\"2020-01-03\", \"2020-01-01\", \"2020-01-02\"]),\n+ )\n+\n+ result = _select_nav_paths(frame, max_paths=None)\n+\n+ assert result.shape == (3, 2)\n+ assert list(result.index) == sorted(frame.index.tolist())\n+ assert list(result.columns) == [\"q90\", \"q10\"]\n+\n+\n+def test_select_nav_paths_duplicate_labels_raise_value_error():\n+ frame = pd.DataFrame([[1.0, 1.1], [1.05, 1.15]], columns=[\"q50\", \"q50\"])\n+\n+ with pytest.raises(ValueError, match=\"duplicate path labels\"):\n+ _select_nav_paths(frame, max_paths=None)\n+\n+\n+def test_select_terminal_values_unexpected_multiindex_names_raises():\n+ columns = pd.MultiIndex.from_product(\n+ [(\"NAV\",), (\"p1\", \"p2\")], names=[\"bucket\", \"path\"]\n+ )\n+ frame = pd.DataFrame([[1.0, 1.1], [1.2, 1.3]], columns=columns)\n+\n+ with pytest.raises(ValueError, match=\"must be named 'asset' and/or 'path'\"):\n+ _select_terminal_values(frame, max_paths=None)\n+\n+\n+def test_select_terminal_values_unexpected_level_order_returns_expected_values():\n+ columns = pd.MultiIndex.from_tuples(\n+ [(\"p1\", \"NAV\"), (\"p2\", \"NAV\")],\n+ names=[\"path\", \"asset\"],\n+ )\n+ frame = pd.DataFrame(\n+ [[1.0, 2.0], [1.5, 2.5]],\n+ index=pd.to_datetime([\"2020-01-31\", \"2020-02-29\"]),\n+ columns=columns,\n+ )\n+\n+ result = _select_terminal_values(frame, max_paths=None)\n+\n+ assert result.tolist() == [1.5, 2.5]\n+ assert result.index.tolist() == [\"p1\", \"p2\"]\n+\n+\n+def test_select_terminal_values_string_numbers_are_coerced_to_numeric_dtype():\n+ columns = pd.MultiIndex.from_tuples(\n+ [(\"NAV\", \"path_a\"), (\"NAV\", \"path_b\")],\n+ names=[\"asset\", \"path\"],\n+ )\n+ frame = pd.DataFrame(\n+ [[\"1.00\", \"2.00\"], [\"1.75\", \"2.25\"]],\n+ index=pd.to_datetime([\"2020-01-31\", \"2020-02-29\"]),\n+ columns=columns,\n+ )\n+\n+ result = _select_terminal_values(frame, max_paths=None)\n \n- assert [(band.lower, band.upper) for band in bands] == [(0.05, 0.95)]\n+ assert is_numeric_dtype(result.dtype)\n+ assert result.tolist() == [1.75, 2.25]\n","external_storage_diff",{"_356":357,"_358":-5},"files_modified",3,"lines_added",103,"lines_removed",8,"commit_message","Fix quantile band pairing compatibility and add viz utility edge tests","additional_parent_commit_shas","file_id","file_0000000017c471f7be661eb64639f234","ttl","partial_repo_snapshot","files",[362,363,364],{"_365":567,"_367":-5,"_368":568,"_370":-5},{"_365":438,"_367":-5,"_368":439,"_370":-5},{"_365":366,"_367":-5,"_368":369,"_370":-5},"path","tests/viz/test_utils_quantile_bands.py","contents","line_range_contents",[371],"missing_reason",{"_372":373,"_374":375,"_376":377,"_378":70},"line_range_start",1,"line_range_end",98,"content",[379,183,380,183,381,382,383,183,384,385,386,183,183,387,388,183,389,390,391,392,393,183,183,394,395,396,183,183,397,398,399,183,183,400,401,402,403,404,405,406,407,183,408,183,409,410,411,183,183,412,413,183,414,415,183,183,416,417,418,407,419,183,420,421,183,183,422,423,424,425,407,401,426,427,428,407,183,429,183,430,431,183,183,432,423,433,434,407,401,435,427,428,407,183,429,183,436,437],"contains_end_of_file","\"\"\"Tests for quantile pairing and path/terminal selectors.\"\"\"","from __future__ import annotations","import pandas as pd","import pytest","from pandas.api.types import is_numeric_dtype","from trend_analysis.viz.fan import _select_nav_paths","from trend_analysis.viz.path_dist import _select_terminal_values","from trend_analysis.viz.utils import quantile_bands","def test_quantile_bands_even_pairing():"," bands = quantile_bands([0.1, 0.25, 0.75, 0.9])"," assert len(bands) == 2"," assert [(band.lower, band.upper) for band in bands] == ["," (0.1, 0.9),"," (0.25, 0.75),"," ]","def test_quantile_bands_odd_length_raises_value_error():"," with pytest.raises(ValueError, match=\"odd length\"):"," quantile_bands([0.05, 0.5, 0.95])","def test_select_nav_paths_empty_input_raises_value_error():"," with pytest.raises(ValueError, match=\"cannot be empty\"):"," _select_nav_paths({}, max_paths=None)","def test_select_nav_paths_unsorted_index_is_sorted_deterministically():"," frame = pd.DataFrame("," {"," \"q90\": [1.1, 1.3, 1.2],"," \"q10\": [0.9, 0.7, 0.8],"," },"," index=pd.to_datetime([\"2020-01-03\", \"2020-01-01\", \"2020-01-02\"]),"," )"," result = _select_nav_paths(frame, max_paths=None)"," assert result.shape == (3, 2)"," assert list(result.index) == sorted(frame.index.tolist())"," assert list(result.columns) == [\"q90\", \"q10\"]","def test_select_nav_paths_duplicate_labels_raise_value_error():"," frame = pd.DataFrame([[1.0, 1.1], [1.05, 1.15]], columns=[\"q50\", \"q50\"])"," with pytest.raises(ValueError, match=\"duplicate path labels\"):"," _select_nav_paths(frame, max_paths=None)","def test_select_terminal_values_unexpected_multiindex_names_raises():"," columns = pd.MultiIndex.from_product("," [(\"NAV\",), (\"p1\", \"p2\")], names=[\"bucket\", \"path\"]"," frame = pd.DataFrame([[1.0, 1.1], [1.2, 1.3]], columns=columns)"," with pytest.raises(ValueError, match=\"must be named 'asset' and/or 'path'\"):"," _select_terminal_values(frame, max_paths=None)","def test_select_terminal_values_unexpected_level_order_returns_expected_values():"," columns = pd.MultiIndex.from_tuples("," [(\"p1\", \"NAV\"), (\"p2\", \"NAV\")],"," names=[\"path\", \"asset\"],"," [[1.0, 2.0], [1.5, 2.5]],"," index=pd.to_datetime([\"2020-01-31\", \"2020-02-29\"]),"," columns=columns,"," result = _select_terminal_values(frame, max_paths=None)"," assert result.tolist() == [1.5, 2.5]"," assert result.index.tolist() == [\"p1\", \"p2\"]","def test_select_terminal_values_string_numbers_are_coerced_to_numeric_dtype():"," [(\"NAV\", \"path_a\"), (\"NAV\", \"path_b\")],"," names=[\"asset\", \"path\"],"," [[\"1.00\", \"2.00\"], [\"1.75\", \"2.25\"]],"," assert is_numeric_dtype(result.dtype)"," assert result.tolist() == [1.75, 2.25]","src/trend_analysis/viz/fan.py",[440],{"_372":373,"_374":441,"_376":442,"_378":70},175,[443,183,380,183,444,183,381,445,183,446,447,448,449,450,451,452,453,183,454,183,183,455,456,457,458,459,460,183,461,183,462,463,464,465,466,467,468,469,470,471,472,473,474,475,183,476,477,478,479,480,183,481,482,183,483,484,183,485,486,487,488,489,490,491,183,492,493,494,183,183,495,496,183,497,498,499,500,183,183,501,456,457,502,503,504,505,506,507,183,508,509,183,510,511,512,513,514,183,515,516,183,517,518,519,520,521,183,522,523,524,525,526,183,527,528,529,530,531,532,533,534,535,536,527,528,529,537,531,532,538,539,540,534,535,536,183,510,541,542,527,528,529,543,531,544,545,535,536,183,546,547,548,549,550,551,552,553,554,555,556,557,558,535,183,559,560,561,562,563,564,407,565,183,183,566],"\"\"\"Fan chart visualizations.\"\"\"","from typing import Iterable, Sequence","import plotly.graph_objects as go","from .utils import ("," DEFAULT_COLORS,"," coerce_frame,"," ensure_non_empty,"," hex_to_rgba,"," quantile_bands,"," quantiles_over_columns,",")","DEFAULT_QUANTILES: tuple[float, ...] = (0.1, 0.25, 0.5, 0.75, 0.9)","def _select_nav_paths("," nav_paths: pd.DataFrame | dict[str, Sequence[float]],"," *,"," max_paths: int | None,",") -> pd.DataFrame:"," \"\"\"Normalize NAV paths into a numeric DataFrame with a clean index.\"\"\""," frame = coerce_frame(nav_paths, name=\"nav_paths\")"," if isinstance(frame.columns, pd.MultiIndex):"," names = [name or \"\" for name in frame.columns.names]"," if \"asset\" in names:"," asset_level = names.index(\"asset\")"," assets = frame.columns.get_level_values(asset_level)"," preferred = None"," for candidate in (\"NAV\", \"nav\", \"wealth\"):"," if candidate in assets:"," preferred = candidate"," break"," if preferred is None and len(assets) > 0:"," preferred = assets[0]"," if preferred is not None:"," frame = frame.xs(preferred, level=asset_level, axis=1)"," if \"path\" in names:"," path_level = names.index(\"path\")"," frame.columns = frame.columns.get_level_values(path_level)"," else:"," frame.columns = [\"_\".join(map(str, col)) for col in frame.columns]"," if frame.columns.has_duplicates:"," raise ValueError(\"nav_paths has duplicate path labels\")"," if max_paths is not None and frame.shape[1] > max_paths:"," frame = frame.iloc[:, :max_paths]"," frame = frame.sort_index()"," if not isinstance(frame.index, pd.DatetimeIndex):"," converted = pd.to_datetime(frame.index, errors=\"coerce\")"," if converted.notna().any():"," frame = frame.copy()"," frame.index = converted"," frame = frame[frame.index.notna()]"," frame = frame.apply(pd.to_numeric, errors=\"coerce\").dropna(how=\"all\")"," ensure_non_empty(\"nav_paths\", frame)"," return frame","def _median_quantile(quantiles: Iterable[float]) -> float | None:"," \"\"\"Pick the quantile closest to 0.5 (median).\"\"\""," q_values = tuple(float(q) for q in quantiles)"," if not q_values:"," return None"," return min(q_values, key=lambda q: abs(q - 0.5))","def make("," quantiles: Iterable[float] = DEFAULT_QUANTILES,"," max_paths: int | None = 200,"," show_paths: bool = False,"," title: str | None = \"Fan Chart\",",") -> go.Figure:"," \"\"\"Create a fan chart from simulated NAV paths.\"\"\""," frame = _select_nav_paths(nav_paths, max_paths=max_paths)"," quantiles_frame = quantiles_over_columns(frame, quantiles)"," median_q = _median_quantile(quantiles_frame.columns)"," band_quantiles = list(quantiles_frame.columns)"," if len(band_quantiles) % 2 != 0 and median_q is not None:"," band_quantiles.remove(median_q)"," bands = quantile_bands(band_quantiles)"," fig = go.Figure()"," x_vals = quantiles_frame.index"," base_color = DEFAULT_COLORS[0]"," if bands:"," alpha_step = 0.6 / max(len(bands), 1)"," else:"," alpha_step = 0.2"," for idx, band in enumerate(bands):"," upper = quantiles_frame[band.upper]"," lower = quantiles_frame[band.lower]"," fill_alpha = min(0.15 + alpha_step * idx, 0.7)"," fill_color = hex_to_rgba(base_color, fill_alpha)"," fig.add_trace("," go.Scatter("," x=x_vals,"," y=upper,"," mode=\"lines\","," line=dict(width=0),"," showlegend=False,"," hoverinfo=\"skip\","," )"," )"," y=lower,"," fill=\"tonexty\","," fillcolor=fill_color,"," name=band.label(),"," if median_q is not None:"," median = quantiles_frame[median_q]"," y=median,"," line=dict(color=base_color, width=2),"," name=\"Median\","," if show_paths:"," path_color = hex_to_rgba(DEFAULT_COLORS[-1], 0.25)"," for col in frame.columns:"," fig.add_trace("," go.Scatter("," x=frame.index,"," y=frame[col],"," mode=\"lines\","," line=dict(color=path_color, width=1),"," name=str(col),"," showlegend=False,"," hoverinfo=\"skip\","," )"," fig.update_layout("," title=title,"," xaxis_title=\"Date\","," yaxis_title=\"Value\","," legend_title=\"Quantile Band\","," template=\"plotly_white\","," return fig","__all__ = [\"make\"]","src/trend_analysis/viz/utils.py",[569],{"_372":373,"_374":570,"_376":571,"_378":70},137,[572,183,380,183,573,574,183,381,183,575,576,577,578,579,580,581,453,183,183,582,583,584,183,585,586,183,587,588,589,590,183,183,591,592,183,593,594,595,596,597,596,183,183,598,599,183,600,601,520,602,183,603,604,605,606,183,183,607,608,457,609,610,459,611,183,612,613,520,614,183,615,494,183,183,616,617,183,497,498,618,619,620,621,183,183,622,623,624,459,625,183,626,627,628,629,630,494,183,183,631,624,632,633,183,634,635,636,183,637,638,639,640,641,183,642,643,644,645,646,535,647,648,183,183,649,650,183,651,652,653,654,183,655,656,657,658,659],"\"\"\"Shared visualization utilities for Plotly figures.\"\"\"","from dataclasses import dataclass","from typing import Iterable, Mapping, Sequence","DEFAULT_COLORS: tuple[str, ...] = ("," \"#2E86AB\","," \"#F6C85F\","," \"#6F4E7C\","," \"#9FD356\","," \"#CA6B4B\","," \"#5E5E5E\",","@dataclass(frozen=True)","class QuantileBand:"," \"\"\"Represents a quantile band for fan charts.\"\"\""," lower: float"," upper: float"," def label(self) -> str:"," pct_low = int(round(self.lower * 100))"," pct_high = int(round(self.upper * 100))"," return f\"{pct_low}-{pct_high}%\"","def ensure_non_empty(name: str, data: object) -> None:"," \"\"\"Raise when a required object is empty.\"\"\""," if data is None:"," raise ValueError(f\"{name} cannot be None\")"," if isinstance(data, (pd.Series, pd.DataFrame)) and data.empty:"," raise ValueError(f\"{name} cannot be empty\")"," if isinstance(data, (Sequence, Mapping)) and not data:","def coerce_series(data: Sequence[float] | pd.Series, name: str) -> pd.Series:"," \"\"\"Return a non-empty Series with a stable name.\"\"\""," if isinstance(data, pd.Series):"," series = data.copy()"," series = pd.Series(list(data), name=name)"," ensure_non_empty(name, series)"," if series.name is None:"," series.name = name"," return series","def coerce_frame("," data: pd.DataFrame | Mapping[str, Sequence[float]],"," index: Sequence[object] | None = None,"," name: str = \"data\","," \"\"\"Return a non-empty DataFrame from a mapping or DataFrame.\"\"\""," if isinstance(data, pd.DataFrame):"," frame = data.copy()"," frame = pd.DataFrame(data, index=index)"," ensure_non_empty(name, frame)","def validate_quantiles(quantiles: Iterable[float]) -> tuple[float, ...]:"," \"\"\"Normalize quantiles and ensure they are within ``[0, 1]``.\"\"\""," raise ValueError(\"quantiles cannot be empty\")"," if any(q < 0.0 or q > 1.0 for q in q_values):"," raise ValueError(\"quantiles must be between 0 and 1\")"," return q_values","def quantiles_over_columns("," data: pd.DataFrame,"," quantiles: Iterable[float],"," \"\"\"Compute quantiles for each row across columns.\"\"\""," ensure_non_empty(\"data\", data)"," q_values = validate_quantiles(quantiles)"," quantile_series = {q: data.quantile(q, axis=1) for q in q_values}"," frame = pd.DataFrame(quantile_series)"," frame.columns = [float(q) for q in frame.columns]","def quantile_bands(",") -> tuple[QuantileBand, ...]:"," \"\"\"Return symmetric quantile bands sorted from widest to narrowest.\"\"\""," q_values = sorted(validate_quantiles(quantiles))"," if len(q_values) % 2 != 0:"," raise ValueError(\"quantile pairing mismatch: odd length\")"," midpoint = len(q_values) // 2"," lower_quantiles = q_values[:midpoint]"," upper_quantiles = list(reversed(q_values[midpoint:]))"," if len(lower_quantiles) != len(upper_quantiles):"," raise ValueError(\"quantile pairing mismatch\")"," bands: list[QuantileBand] = []"," for lower, upper in zip(lower_quantiles, upper_quantiles):"," if lower >= upper:"," raise ValueError("," \"quantile pairing mismatch: lower quantile must be less than upper quantile\""," bands.append(QuantileBand(lower=lower, upper=upper))"," return tuple(bands)","def hex_to_rgba(color: str, alpha: float) -> str:"," \"\"\"Convert a hex color to an rgba color string.\"\"\""," if not color.startswith(\"#\") or len(color) not in {4, 7}:"," raise ValueError(\"color must be a hex string like #abc or #aabbcc\")"," if not 0.0 <= alpha <= 1.0:"," raise ValueError(\"alpha must be between 0 and 1\")"," hex_value = color.lstrip(\"#\")"," if len(hex_value) == 3:"," hex_value = \"\".join(ch * 2 for ch in hex_value)"," r, g, b = (int(hex_value[i : i + 2], 16) for i in range(0, 6, 2))"," return f\"rgba({r}, {g}, {b}, {alpha})\"","message",[662,663,664,665,666,667,668,669],{"_670":57,"_57":680},{"_670":672,"_365":567,"_372":678,"_374":679},{"_670":57,"_57":677},{"_670":672,"_365":438,"_372":676,"_374":375},{"_670":672,"_365":438,"_372":570,"_374":675},{"_670":57,"_57":674},{"_670":672,"_365":366,"_372":673,"_374":375},{"_670":57,"_57":671},"content_type","\n\n**Testing**\n* ⚠️ `pytest -q tests/test_viz_package_import.py tests/viz/test_fan_make.py tests/viz/test_path_dist_make.py tests/viz/test_risk_return_make.py tests/viz/test_utils_quantile_bands.py` (fails in this environment because `pandas` is not installed).\n* ✅ `python -m compileall src/trend_analysis/viz tests/viz/test_utils_quantile_bands.py tests/viz/test_fan_make.py tests/viz/test_path_dist_make.py tests/viz/test_risk_return_make.py`\n* ⚠️ `curl -I https://docs.python.org/3/library/functions.html | head` (network/proxy tunnel returned `403`, so external web lookup was attempted but blocked).\n\nCommitted on the current branch (`b9804b5`) and created a follow-up PR via `make_pr`.","repo_file_citation",14,"\n* Expanded `tests/viz/test_utils_quantile_bands.py` with required edge-case coverage for:\n * `quantile_bands` even pairing + odd-length error assertion,\n * `_select_nav_paths` empty input, deterministic sorted index behavior, duplicate-label error,\n * `_select_terminal_values` invalid MultiIndex names error, nonstandard level-order correctness, and string-to-numeric coercion. ",145,90,"\n* Updated `fan.make(...)` so it still works when a median quantile is present by removing the median from band pairing when the quantile count is odd, while still rendering the median trace. ",100,123,"### Summary\n* Updated `quantile_bands(...)` to enforce explicit pairing validation without `zip(..., strict=...)`, including an odd-length mismatch `ValueError` and lower/upper ordering checks for stable Python <3.10-compatible behavior. ",{"_104":314,"_53":96,"_108":314,"_683":684,"_685":686,"_687":688,"_143":-5,"_130":689},[95],"input_items",[709,710],"external_post_config",{"_690":691,"_246":180,"_692":291,"_257":-5,"_341":240,"_693":18,"_694":-5,"_695":-5,"_696":697,"_698":699,"_700":-5,"_701":-5,"_702":703,"_704":18,"_705":70,"_706":65,"_707":18,"_708":-5},"user_model_slug","slug",1770786031.437342,"installation_id",71148695,"pr_number","enable_internal_github_app_routing","gitlab_service_account_project_id","review_trigger","comment_id",3882137765,"comment_type","issue","pr_write_guard_token","pending_pr_reaction_id","pending_comment_reaction_id",329961408,"comment_if_no_findings","comment_on_error","share_url","is_parallel_review","review_summary_id",{"_104":660,"_108":314,"_376":715},{"_298":299,"_300":711,"_712":305,"_713":140,"_310":-5,"_104":714},"### Source Issue #4876: [Follow-up] Create src/trend_analysis/viz/__init__.py with a m (PR #4859)\n\nSource: https://github.com/stranske/Trend_Model_Project/issues/4876\n\n> \n> \n> # Fix trend_analysis.viz import side effects and Python <3.10 compatibility\n> \n> ## Why\n> \n> PR #4859 addressed issue #4856, but verification still failed. The remaining gaps are: (1) ensuring `trend_analysis.viz` is import-side-effect free (no implicit Streamlit/heavy viz imports), (2) removing `zip(..., strict=...)` from `quantile_bands(...)` to retain Python <3.10 compatibility, and (3) adding focused tests that exercise minimal construction/serialization of the viz figures plus edge-case coverage for quantile utilities.\n> \n> **Background (previous attempt context):**\n> - The PR only modified `tests/test_viz_package_import.py`, and introduced no new source or test files related to the viz implementation. This failed because it didn't deliver the required initializer/module changes and the specified new tests.\n> - Usage of `zip(..., strict=...)` was not removed, causing compatibility issues with Python versions lower than 3.10. Replace it with Python <3.10-compatible iteration plus explicit length validation.\n> \n> ## Source\n> \n> - Original PR: #4859\n> - Parent issue: #4856\n> \n> ## Tasks\n> \n> ### Package Structure\n> \n> - [ ] Add a minimal package initializer at `src/trend_analysis/viz/__init__.py` that contains no imports of streamlit, fan, path_dist, or risk_return (keep it import-side-effect free).\n> \n> ### Python Compatibility\n> \n> - [ ] Update `trend_analysis.viz.utils.quantile_bands(...)` to remove any usage of `zip(..., strict=...)` and replace it with Python <3.10-compatible pairing/validation logic (including explicit length checks where needed).\n> \n> ### Visualization Tests\n> \n> - [ ] Create `tests/viz/test_fan_make.py` with a test that calls `trend_analysis.viz.fan.make(...)` using minimal valid inputs, asserts the result is `plotly.graph_objects.Figure`, and validates JSON-serializability via `json.loads(fig.to_json())`.\n> - [ ] Create `tests/viz/test_path_dist_make.py` with a test that calls `trend_analysis.viz.path_dist.make(...)` using minimal valid inputs, asserts `plotly.graph_objects.Figure`, and validates JSON-serializability via `json.loads(fig.to_json())`.\n> - [ ] Create `tests/viz/test_risk_return_make.py` with a test that calls `trend_analysis.viz.risk_return.make(...)` using minimal valid inputs, asserts `plotly.graph_objects.Figure`, and validates JSON-serializability via `json.loads(fig.to_json())`.\n> \n> ### Utility Function Tests - quantile_bands\n> \n> - [ ] Create `tests/viz/test_utils_quantile_bands.py` to cover `quantile_bands(...)`: (1) a normal expected pairing case and (2) an odd-length/mismatched quantiles case asserting a well-defined exception (type + message substring) or documented fallback behavior.\n> \n> ### Utility Function Tests - _select_nav_paths\n> \n> - [ ] Create a unit test for `_select_nav_paths` in `tests/viz/test_utils_quantile_bands.py` that passes empty input and asserts it raises a specific exception type with an expected message substring.\n> - [ ] Create a unit test for `_select_nav_paths` in `tests/viz/test_utils_quantile_bands.py` that passes unsorted quantile labels and asserts the output has correct shape and deterministic index order.\n> - [ ] Create a unit test for `_select_nav_paths` in `tests/viz/test_utils_quantile_bands.py` that passes duplicate quantile labels and asserts it raises a specific exception type with an expected message substring.\n> \n> ### Utility Function Tests - _select_terminal_values\n> \n> - [ ] Create a unit test for `_select_terminal_values` in `tests/viz/test_utils_quantile_bands.py` that passes a MultiIndex with unexpected level names and asserts it raises a specific exception type with an expected message substring.\n> - [ ] Create a unit test for `_select_terminal_values` in `tests/viz/test_utils_quantile_bands.py` that passes a MultiIndex with unexpected level order and asserts it returns correct numeric outputs with exact values.\n> - [ ] Create a unit test for `_select_terminal_values` in `tests/viz/test_utils_quantile_bands.py` that passes numeric values as strings and asserts they are coerced to numeric dtype with correct values.\n> \n> ## Acceptance Criteria\n> \n> ### Package Structure\n> \n> - [ ] A file exists at `src/trend_analysis/viz/__init__.py`.\n> - [ ] `src/trend_analysis/viz/__init__.py` contains no import statements that reference `streamlit`, `trend_analysis.viz.fan`, `trend_analysis.viz.path_dist`, or `trend_analysis.viz.risk_return` (including `import X` and `from X import Y`).\n> - [ ] A test in `tests/test_viz_package_import.py` verifies that after `import trend_analysis.viz` in a fresh interpreter context, `\"streamlit\" not in sys.modules` evaluates to True.\n> \n> ### Python Compatibility\n> \n> - [ ] The function `trend_analysis.viz.utils.quantile_bands(...)` contains no usage of `zip(..., strict=...)`.\n> - [ ] `trend_analysis.viz.utils.quantile_bands(...)` raises `ValueError` on quantile pairing mismatch, and tests assert both `ValueError` type and a specific message substring (e.g., \"mismatch\" or \"odd length\").\n> - [ ] For an input with N quantiles (where N is even), `quantile_bands(...)` produces N/2 bands, and the test asserts `len(result) == N/2` and validates the band structure (e.g., each band has lower and upper quantile labels).\n> \n> ### Visualization Tests\n> \n> - [ ] A test file exists at `tests/viz/test_fan_make.py` and contains at least one test that calls `trend_analysis.viz.fan.make(...)` with minimal valid inputs.\n> - [ ] The `fan.make(...)` test asserts the returned object is an instance of `plotly.graph_objects.Figure`.\n> - [ ] The `fan.make(...)` test validates JSON serializability by calling `json.loads(fig.to_json())` without raising.\n> - [ ] A test file exists at `tests/viz/test_path_dist_make.py` and contains at least one test that calls `trend_analysis.viz.path_dist.make(...)` with minimal valid inputs.\n> - [ ] The `path_dist.make(...)` test asserts the returned object is an instance of `plotly.graph_objects.Figure` and validates JSON-serializability via `json.loads(fig.to_json())`.\n> - [ ] A test file exists at `tests/viz/test_risk_return_make.py` and contains at least one test that calls `trend_analysis.viz.risk_return.make(...)` with minimal valid inputs.\n> - [ ] The `risk_return.make(...)` test asserts the returned object is an instance of `plotly.graph_objects.Figure` and validates JSON-serializability via `json.loads(fig.to_json())`.\n> \n> ### Utility Function Tests\n> \n> - [ ] `tests/viz/test_utils_quantile_bands.py` exists and includes unit tests for `_select_nav_paths` that cover: (1) empty input raising a specific exception type and asserting a message substring, (2) unsorted quantile labels producing output with a deterministic index/column order (asserted), and (3) duplicate quantile labels raising a specific exception type and asserting a message substring.\n> - [ ] `tests/viz/test_utils_quantile_bands.py` includes unit tests for `_select_terminal_values` that cover: (1) MultiIndex with unexpected level names raising a specific exception type and asserting a message substring, (2) MultiIndex with unexpected level order still returning correct numeric outputs (assert exact values), and (3) numeric values passed as strings are coerced to numeric dtype (assert dtype is numeric) and values match expected.\n> \n> ## Implementation Notes\n> \n> - `src/trend_analysis/viz/__init__.py` should be intentionally minimal and import-side-effect free. Prefer defining `__all__` and/or a module docstring; do not import optional dependencies or submodules at import time.\n> - For the `quantile_bands(...)` refactor: replace `zip(..., strict=...)` with explicit validation (e.g., check lengths are equal where applicable; ensure an even number of quantiles if pairing into lower/upper bounds). Keep behavior consistent; if mismatches should error, raise `ValueError` with a stable message substring used by tests.\n> - For Plotly tests: build minimal valid inputs (small pandas objects) and assert:\n> - `isinstance(fig, plotly.graph_objects.Figure)`\n> - `json.loads(fig.to_json())` succeeds\n> - For `_select_nav_paths` / `_select_terminal_values` tests: assert both exception type and a message substring for error cases; for non-error cases, assert exact ordering/values and (where required) numeric dtype after coercion.\n> \n> ## Deferred Tasks (Requires Human)\n> \n> None.\n\n—\nPR created automatically to engage Codex.","base_ref","head_ref","pull_request_info",[716],{"_670":57,"_57":717},"\nCodex Worker activated for branch `codex/issue-4876`.\n\n@codex start\n\nAutomated belt worker prepared this PR. 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