diff --git a/deeplc-todo/align.py b/deeplc-todo/align.py deleted file mode 100644 index 037e5c4..0000000 --- a/deeplc-todo/align.py +++ /dev/null @@ -1,845 +0,0 @@ -"""Align retention times across LC-MS runs from a Sage results TSV. - -This script ports the end-to-end logic from `align.ipynb` into a runnable CLI: - -* Read `results.plasma_proteomics.tsv` using psm_utils: - `psm_list = read_file(path, filetype="sage")` -* Build a peptidoform × run RT matrix (median aggregation), ignoring charge. -* Rank runs by summed pairwise overlap (# shared peptidoforms). -* Iteratively align runs (rank order) onto a growing reference scale using a - spline regression model (scikit-learn). -* Emit calibrated RT matrix + reference table + diagnostic table. -* Optional plots: - - run overlap bar chart - - per-alignment diagnostic plots - - before/after SD across runs -* Optional filtering by occurrence (%) and SD thresholds (absolute and/or - relative to gradient length). - -Outputs are written into the directory given by `--outdir`. - -Notes ------ -RT units are whatever Sage reports (often minutes). Ensure `--gradient-length` -uses the same units. -""" - -from __future__ import annotations - -import argparse -from dataclasses import dataclass -from pathlib import Path -from typing import Any, cast - -import numpy as np -import pandas as pd - - -# ------------------------- -# I/O (psm_utils -> DataFrame) -# ------------------------- - - -def load_psm_list(tsv_path: str | Path): - """Load Sage TSV results into a psm_utils PSMList.""" - - try: - from psm_utils.io import read_file - except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "psm_utils is not installed. Install dependencies from requirements.txt and retry." - ) from e - - tsv_path = Path(tsv_path) - if not tsv_path.exists(): - raise FileNotFoundError(f"Input TSV not found: {tsv_path}") - - return read_file(str(tsv_path), filetype="sage") - - -def psm_list_to_long_df(psm_list) -> pd.DataFrame: - """Convert psm_utils PSMList into a long table with (run, peptidoform, rt). - - We ignore charge state by keeping only the string form of peptidoform/peptide. - """ - - rows: list[dict[str, Any]] = [] - for psm in psm_list: - run = ( - getattr(psm, "filename", None) - or getattr(psm, "run", None) - or getattr(psm, "spectrum_id", None) - ) - pep = ( - getattr(psm, "peptidoform", None) - or getattr(psm, "peptide", None) - or getattr(psm, "sequence", None) - ) - rt = getattr(psm, "retention_time", None) or getattr(psm, "rt", None) - - if run is None or pep is None or rt is None: - continue - - rows.append({"run": str(run), "peptidoform": str(pep), "rt": float(rt)}) - - long_df = pd.DataFrame(rows) - if long_df.empty: - raise ValueError( - "No usable PSM rows were extracted. Check that your Sage TSV contains RT and run identifiers " - "and that psm_utils mapped them correctly." - ) - return long_df - - -def build_rt_matrix(long_df: pd.DataFrame) -> pd.DataFrame: - """Pivot to peptidoform×run matrix of retention times (median aggregation).""" - - return long_df.pivot_table( - index="peptidoform", - columns="run", - values="rt", - aggfunc="median", - observed=True, - dropna=False, - ) - - -# ------------------------- -# Run overlap ranking -# ------------------------- - - -def rank_runs_by_overlap(rt_matrix: pd.DataFrame) -> pd.DataFrame: - """Rank runs by summed pairwise overlap (shared peptidoforms).""" - - present = rt_matrix.notna() - X = present.astype("uint8") - overlap = X.T.dot(X) # run x run - - scores = overlap.sum(axis=1) - pd.Series( - overlap.values.diagonal(), index=overlap.index - ) - n_peptidoforms = present.sum(axis=0) - avg_shared = scores / (len(overlap.index) - 1) if len(overlap.index) > 1 else scores - - run_ranking = ( - pd.DataFrame( - { - "run": overlap.index, - "overlap_score": scores.astype(int).values, - "avg_shared": avg_shared.values, - "n_peptidoforms": n_peptidoforms.loc[overlap.index].astype(int).values, - } - ) - .sort_values(["overlap_score", "n_peptidoforms"], ascending=False) - .reset_index(drop=True) - ) - run_ranking.insert(0, "rank", run_ranking.index + 1) - return run_ranking - - -# ------------------------- -# Alignment (spline regression) -# ------------------------- - - -def fit_calibration_model( - ref_df: pd.DataFrame, - run_series: pd.Series, - *, - n_knots: int = 12, - alpha: float = 1e-3, - min_overlap: int | None = None, -): - """Fit a spline model mapping rt_run -> rt_ref on overlapping peptidoforms.""" - - from sklearn.linear_model import Ridge - from sklearn.pipeline import make_pipeline - from sklearn.preprocessing import SplineTransformer - - merged = ref_df.merge( - run_series.rename("rt_run"), - left_on="peptidoform", - right_index=True, - how="inner", - ) - n_overlap = len(merged) - - required = max(50, n_knots * 5) if min_overlap is None else int(min_overlap) - if n_overlap < required: - raise ValueError( - f"Not enough overlap ({n_overlap}) to fit spline (required={required}, n_knots={n_knots})." - ) - - x = merged[["rt_run"]].to_numpy(dtype=float) - y = merged["rt_ref"].to_numpy(dtype=float) - - model = make_pipeline( - SplineTransformer(n_knots=n_knots, degree=3, include_bias=False), - Ridge(alpha=alpha), - ) - model.fit(x, y) - y_hat = model.predict(x) - mae = float(np.mean(np.abs(y - y_hat))) - - return model, merged, y_hat, mae - - -def calibrate_run(model, run_series: pd.Series) -> pd.Series: - """Apply a fitted model to calibrate a run's RTs onto the reference scale.""" - - run_all = run_series.dropna().astype(float) - x_all = run_all.to_numpy(dtype=float).reshape(-1, 1) - return pd.Series(model.predict(x_all), index=run_all.index, name="rt_cal") - - -def update_reference(ref_df: pd.DataFrame, rt_cal: pd.Series) -> pd.DataFrame: - """Weighted reference update with new calibrated RT series (weight=1 for the new run).""" - - out = ref_df.set_index("peptidoform").copy() - out = out.join(rt_cal, how="outer") - out["weight"] = out["weight"].fillna(0.0) - - has_ref = out["rt_ref"].notna() - has_new = out["rt_cal"].notna() - both = has_ref & has_new - only_new = (~has_ref) & has_new - - out.loc[both, "rt_ref"] = ( - out.loc[both, "rt_ref"] * out.loc[both, "weight"] + out.loc[both, "rt_cal"] - ) / (out.loc[both, "weight"] + 1.0) - out.loc[both, "weight"] = out.loc[both, "weight"] + 1.0 - - out.loc[only_new, "rt_ref"] = out.loc[only_new, "rt_cal"] - out.loc[only_new, "weight"] = 1.0 - - return out.drop(columns=["rt_cal"]).reset_index() - - -def maybe_plot_alignment( - x_run: np.ndarray, - y_ref: np.ndarray, - y_hat: np.ndarray, - *, - run_name: str, - rank: int, - max_points: int = 2000, -) -> None: - """Diagnostic plots for one alignment (subsamples if large).""" - - try: - import matplotlib.pyplot as plt - except ModuleNotFoundError: - return - - x_run = np.asarray(x_run, dtype=float) - y_ref = np.asarray(y_ref, dtype=float) - y_hat = np.asarray(y_hat, dtype=float) - - n = x_run.shape[0] - if n == 0: - return - - if n > max_points: - idx = np.random.default_rng(0).choice(n, size=max_points, replace=False) - x_run_s = x_run[idx] - y_ref_s = y_ref[idx] - y_hat_s = y_hat[idx] - else: - x_run_s, y_ref_s, y_hat_s = x_run, y_ref, y_hat - - order = np.argsort(x_run_s) - x_sorted = x_run_s[order] - yhat_sorted = y_hat_s[order] - - resid = y_ref_s - y_hat_s - - fig, axes = plt.subplots(1, 3, figsize=(15, 4)) - fig.suptitle(f"Alignment diagnostics (rank {rank}): {run_name}", y=1.05) - - axes[0].scatter(x_run_s, y_ref_s, s=6, alpha=0.35, label="overlap points") - axes[0].plot(x_sorted, yhat_sorted, color="black", linewidth=2, label="spline fit") - axes[0].set_xlabel("Run RT") - axes[0].set_ylabel("Reference RT") - axes[0].set_title("Overlap + fitted mapping") - axes[0].legend(loc="best", frameon=False) - - axes[1].scatter(x_run_s, resid, s=6, alpha=0.35) - axes[1].axhline(0, color="black", linewidth=1) - axes[1].set_xlabel("Run RT") - axes[1].set_ylabel("Ref - Pred") - axes[1].set_title("Residuals vs RT") - - axes[2].hist(resid, bins=40, color="#4C72B0", alpha=0.9) - axes[2].axvline(0, color="black", linewidth=1) - axes[2].set_xlabel("Residual") - axes[2].set_ylabel("Count") - axes[2].set_title("Residual distribution") - - plt.tight_layout() - plt.show() - - -@dataclass -class AlignmentResult: - run_ranking: pd.DataFrame - ref: pd.DataFrame - diag_df: pd.DataFrame - calibrated_matrix: pd.DataFrame - - -@dataclass -class AlignmentInputs: - """Convenience container for intermediate inputs/outputs. - - This is mainly useful when calling the library-style API. - """ - - long_df: pd.DataFrame - rt_matrix: pd.DataFrame - - -@dataclass -class AlignmentOutputs: - """Full outputs for programmatic use (includes intermediates).""" - - inputs: AlignmentInputs - result: AlignmentResult - - -def align_from_psm_list( - psm_list, - *, - n_knots: int = 12, - alpha: float = 1e-3, - min_overlap: int | None = None, - plot_diagnostics: bool = False, - plot_max_runs: int | None = 5, - make_sd_plot: bool = False, -) -> AlignmentOutputs: - """Run the full alignment pipeline starting from an in-memory PSMList. - - Parameters - ---------- - psm_list: - A psm_utils PSMList (or any iterable of PSM-like objects) with run id, - peptidoform/peptide, and retention time fields. - n_knots, alpha, min_overlap: - Spline calibration model hyperparameters. - plot_diagnostics, plot_max_runs: - If enabled, show per-run alignment diagnostic plots (requires matplotlib). - make_sd_plot: - If enabled, show the before/after SD plot after alignment. - - Returns - ------- - AlignmentOutputs - Includes the long table and raw RT matrix (inputs) and the alignment - outputs (run ranking, reference, diagnostics, calibrated matrix). - """ - - long_df = psm_list_to_long_df(psm_list) - rt_matrix = build_rt_matrix(long_df) - run_ranking = rank_runs_by_overlap(rt_matrix) - - if min_overlap == 0: - min_overlap = None - - result = align_runs( - rt_matrix, - run_ranking, - n_knots=int(n_knots), - alpha=float(alpha), - min_overlap=min_overlap, - plot_diagnostics=bool(plot_diagnostics), - plot_max_runs=plot_max_runs, - ) - - if make_sd_plot: - plot_sd_before_after(rt_matrix, result.calibrated_matrix) - - return AlignmentOutputs( - inputs=AlignmentInputs(long_df=long_df, rt_matrix=rt_matrix), - result=result, - ) - - -def align_runs( - rt_matrix: pd.DataFrame, - run_ranking: pd.DataFrame, - *, - n_knots: int = 12, - alpha: float = 1e-3, - min_overlap: int | None = None, - plot_diagnostics: bool = False, - plot_max_runs: int | None = 5, -) -> AlignmentResult: - """Iteratively align runs (rank order) onto a growing weighted reference.""" - - ranked_runs = run_ranking["run"].tolist() - if len(ranked_runs) < 2: - raise ValueError("Need at least 2 runs to start alignment.") - - def get_run_series(run_name: str) -> pd.Series: - return rt_matrix[run_name].dropna().astype(float) - - run1 = ranked_runs[0] - s1 = get_run_series(run1) - ref = pd.DataFrame({"peptidoform": s1.index, "rt_ref": s1.values, "weight": 1.0}) - - calibrated_rts: dict[str, pd.Series] = {str(run1): s1.rename("rt_cal")} - - diags: list[dict[str, Any]] = [] - for i, run_name in enumerate(ranked_runs[1:], start=2): - run_series = get_run_series(run_name) - model, merged, y_hat, mae = fit_calibration_model( - ref, - run_series, - n_knots=n_knots, - alpha=alpha, - min_overlap=min_overlap, - ) - y_hat = np.asarray(y_hat) - - if plot_diagnostics and (plot_max_runs is None or i <= plot_max_runs): - maybe_plot_alignment( - merged["rt_run"].to_numpy(), - merged["rt_ref"].to_numpy(), - y_hat, - run_name=str(run_name), - rank=i, - ) - - rt_cal = calibrate_run(model, run_series) - calibrated_rts[str(run_name)] = rt_cal - ref = update_reference(ref, rt_cal) - - diags.append( - { - "rank": i, - "run": str(run_name), - "n_overlap": int(len(merged)), - "mae_overlap": float(mae), - "n_run": int(run_series.shape[0]), - "ref_size": int(ref.shape[0]), - } - ) - - diag_df = pd.DataFrame(diags) - calibrated_matrix = pd.concat(calibrated_rts, axis=1) - calibrated_matrix.columns = calibrated_matrix.columns.astype(str) - - return AlignmentResult( - run_ranking=run_ranking, - ref=ref, - diag_df=diag_df, - calibrated_matrix=calibrated_matrix, - ) - - -# ------------------------- -# SD plots and filtering -# ------------------------- - - -def per_peptidoform_std(rt_df: pd.DataFrame, min_runs: int = 2) -> pd.Series: - n = rt_df.notna().sum(axis=1) - std = rt_df.std(axis=1, skipna=True) - std[n < min_runs] = np.nan - return std - - -def plot_sd_before_after( - rt_matrix: pd.DataFrame, calibrated_matrix: pd.DataFrame -) -> None: - """Plot per-peptidoform SD across runs before vs after calibration.""" - - try: - import matplotlib.pyplot as plt - except ModuleNotFoundError: - print("matplotlib not installed; skipping SD plots") - return - - raw_sd = per_peptidoform_std(rt_matrix) - cal_sd = per_peptidoform_std(calibrated_matrix) - cmp = pd.DataFrame({"raw_sd": raw_sd, "cal_sd": cal_sd}).dropna() - - print(f"Peptidoforms with SD in both (>=2 runs each): {cmp.shape[0]:,}") - print("Raw SD median:", float(cmp["raw_sd"].median())) - print("Cal SD median:", float(cmp["cal_sd"].median())) - print( - "Median fold-change (cal/raw):", float((cmp["cal_sd"] / cmp["raw_sd"]).median()) - ) - - fig, axes = plt.subplots(1, 3, figsize=(15, 4)) - fig.suptitle("Per-peptidoform RT variability across runs (SD)", y=1.05) - - axes[0].hist(cmp["raw_sd"], bins=60, alpha=0.75, label="raw", color="#DD8452") - axes[0].hist( - cmp["cal_sd"], bins=60, alpha=0.75, label="calibrated", color="#4C72B0" - ) - axes[0].set_xlabel("SD (RT units)") - axes[0].set_ylabel("Count") - axes[0].set_title("Histogram") - axes[0].legend(frameon=False) - - axes[1].set_title("ECDF") - for col, color in [("raw_sd", "#DD8452"), ("cal_sd", "#4C72B0")]: - x = np.sort(cmp[col].to_numpy()) - y = np.linspace(0, 1, x.size, endpoint=True) - axes[1].plot(x, y, label=col.replace("_", " "), color=color) - axes[1].set_xlabel("SD (RT units)") - axes[1].set_ylabel("Fraction ≤ x") - axes[1].legend(frameon=False) - - n = cmp.shape[0] - cmp_plot = cmp.sample(20000, random_state=0) if n > 20000 else cmp - axes[2].scatter(cmp_plot["raw_sd"], cmp_plot["cal_sd"], s=6, alpha=0.25) - lim = float(np.nanmax([cmp_plot["raw_sd"].max(), cmp_plot["cal_sd"].max()])) - axes[2].plot([0, lim], [0, lim], color="black", linewidth=1) - axes[2].set_xlabel("Raw SD") - axes[2].set_ylabel("Calibrated SD") - axes[2].set_title("Paired: raw vs calibrated") - - plt.tight_layout() - plt.show() - - -def filter_by_occurrence_and_sd( - rt_df: pd.DataFrame, - min_occurrence_pct: float = 50.0, - max_sd: float | None = None, - *, - gradient_length: float | None = None, - max_sd_rel: float | None = None, - min_runs: int = 2, - return_stats: bool = False, - keep: bool = True, - sd_ddof: int = 1, - verbose: bool = True, -) -> pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame]: - """Filter a peptidoform×run RT matrix by occurrence (%) and SD thresholds. - - Supports two SD thresholds: - - Absolute: sd <= max_sd - - Relative-to-gradient: (sd / gradient_length) <= max_sd_rel - - If both SD gates are provided, both must pass. - """ - - if rt_df.shape[1] == 0: - raise ValueError("rt_df has no run columns.") - if not (0.0 <= float(min_occurrence_pct) <= 100.0): - raise ValueError("min_occurrence_pct must be between 0 and 100.") - if max_sd_rel is not None: - if gradient_length is None: - raise ValueError("gradient_length must be provided when max_sd_rel is set.") - if float(gradient_length) <= 0: - raise ValueError("gradient_length must be > 0.") - if float(max_sd_rel) < 0: - raise ValueError("max_sd_rel must be >= 0.") - if max_sd is not None and float(max_sd) < 0: - raise ValueError("max_sd must be >= 0.") - if int(min_runs) < 0: - raise ValueError("min_runs must be >= 0.") - - n_runs = int(rt_df.shape[1]) - n_obs = rt_df.notna().sum(axis=1).astype(int) - occurrence_pct = (n_obs / n_runs) * 100.0 - - sd = rt_df.std(axis=1, skipna=True, ddof=sd_ddof) - sd = sd.where(n_obs >= int(min_runs), np.nan) - - sd_rel = None - if gradient_length is not None: - sd_rel = sd / float(gradient_length) - - pass_occ = occurrence_pct >= float(min_occurrence_pct) - - pass_sd_abs = pd.Series(True, index=rt_df.index) - if max_sd is not None: - pass_sd_abs = sd.notna() & (sd <= float(max_sd)) - - pass_sd_rel = pd.Series(True, index=rt_df.index) - if max_sd_rel is not None: - if sd_rel is None: - raise ValueError("gradient_length must be provided when max_sd_rel is set.") - pass_sd_rel = sd_rel.notna() & (sd_rel <= float(max_sd_rel)) - - pass_sd = pass_sd_abs & pass_sd_rel - - pass_all = pass_occ & pass_sd - if not keep: - pass_all = ~pass_all - - filtered = rt_df.loc[pass_all].copy() - - stats_dict: dict[str, Any] = { - "occurrence_pct": occurrence_pct, - "n_obs": n_obs, - "sd": sd, - } - if sd_rel is not None: - stats_dict["sd_rel"] = sd_rel - stats = pd.DataFrame(stats_dict, index=rt_df.index) - - if verbose: - n_total = int(rt_df.shape[0]) - n_pass_occ = int(pass_occ.sum()) - n_pass_all = int((pass_occ & pass_sd).sum()) - print(f"Total peptidoforms: {n_total:,}") - print( - f"Pass occurrence >= {min_occurrence_pct:g}%: {n_pass_occ:,} ({n_pass_occ / max(n_total,1):.1%})" - ) - if max_sd is not None: - n_pass_abs = int(pass_sd_abs.sum()) - print( - f"Pass SD <= {max_sd:g} (min_runs={min_runs}): {n_pass_abs:,} ({n_pass_abs / max(n_total,1):.1%})" - ) - if max_sd_rel is not None: - n_pass_rel = int(pass_sd_rel.sum()) - gl = float(gradient_length) if gradient_length is not None else float("nan") - print( - f"Pass SD/gradient <= {max_sd_rel:g} (gradient_length={gl:g}, min_runs={min_runs}): " - f"{n_pass_rel:,} ({n_pass_rel / max(n_total,1):.1%})" - ) - print(f"Pass all gates: {n_pass_all:,} ({n_pass_all / max(n_total,1):.1%})") - - return (filtered, stats) if return_stats else filtered - - -# ------------------------- -# CLI glue -# ------------------------- - - -def parse_args(argv: list[str] | None = None) -> argparse.Namespace: - p = argparse.ArgumentParser(description=__doc__) - - p.add_argument( - "--input", - type=str, - default=str(Path(__file__).resolve().parent / "results.plasma_proteomics.tsv"), - help="Path to Sage results TSV (default: results.plasma_proteomics.tsv next to this script).", - ) - p.add_argument( - "--outdir", - type=str, - default=str(Path(__file__).resolve().parent / "out"), - help="Output directory (default: ./out).", - ) - - # Alignment model params - p.add_argument( - "--n-knots", type=int, default=12, help="Spline knots for calibration." - ) - p.add_argument( - "--alpha", type=float, default=1e-3, help="Ridge alpha for spline model." - ) - p.add_argument( - "--min-overlap", - type=int, - default=0, - help="Minimum overlap required to fit model (0 = auto heuristic).", - ) - - # Plotting - p.add_argument( - "--plots", action="store_true", help="Enable plots (requires matplotlib)." - ) - p.add_argument( - "--plot-max-runs", - type=int, - default=5, - help="Max number of per-alignment diagnostic plots to show (ignored if --plots is off).", - ) - - # Filtering - p.add_argument( - "--filter", - action="store_true", - help="Also produce a filtered calibrated matrix based on occurrence and SD gates.", - ) - p.add_argument( - "--min-occurrence-pct", - type=float, - default=0.0, - help="Filter: minimum occurrence percentage across runs.", - ) - p.add_argument( - "--max-sd", - type=float, - default=-1.0, - help="Filter: maximum absolute SD across runs (RT units). Use <0 to disable.", - ) - p.add_argument( - "--gradient-length", - type=float, - default=-1.0, - help="For relative SD filtering: gradient length in RT units. Use <0 to auto-estimate from calibrated RT range.", - ) - p.add_argument( - "--max-sd-rel", - type=float, - default=-1.0, - help="Filter: maximum SD/gradient_length (fraction). Use <0 to disable.", - ) - p.add_argument( - "--min-runs", - type=int, - default=2, - help="Filter: min runs observed to evaluate SD.", - ) - - p.add_argument( - "--no-write-long", - action="store_true", - help="Skip writing long_df parquet (saves disk).", - ) - - return p.parse_args(argv) - - -def main(argv: list[str] | None = None) -> int: - args = parse_args(argv) - in_path = Path(args.input) - outdir = Path(args.outdir) - outdir.mkdir(parents=True, exist_ok=True) - - min_overlap = None if int(args.min_overlap) <= 0 else int(args.min_overlap) - - print(f"Reading PSMs from: {in_path}") - psm_list = load_psm_list(in_path) - print(f"Loaded PSMs: {len(psm_list):,}") - - long_df = psm_list_to_long_df(psm_list) - rt_matrix = build_rt_matrix(long_df) - print(f"RT matrix shape (peptidoforms x runs): {rt_matrix.shape}") - - run_ranking = rank_runs_by_overlap(rt_matrix) - print("\nTop runs by overlap:") - print(run_ranking.head(10).to_string(index=False)) - - if args.plots: - try: - import matplotlib.pyplot as plt - - top = run_ranking.head(15).sort_values("overlap_score", ascending=True) - plt.figure(figsize=(10, 6)) - plt.barh(top["run"].astype(str), top["overlap_score"].astype(float)) - plt.title("Top runs by summed pairwise overlap") - plt.xlabel("Overlap score (sum of shared peptidoforms vs all other runs)") - plt.tight_layout() - plt.show() - except ModuleNotFoundError: - print("matplotlib not installed; skipping overlap plot") - - result = align_runs( - rt_matrix, - run_ranking, - n_knots=int(args.n_knots), - alpha=float(args.alpha), - min_overlap=min_overlap, - plot_diagnostics=bool(args.plots), - plot_max_runs=None if args.plot_max_runs is None else int(args.plot_max_runs), - ) - - print(f"\nCalibrated matrix shape: {result.calibrated_matrix.shape}") - - # Persist outputs - run_ranking_path = outdir / "run_ranking.tsv" - diag_path = outdir / "alignment_diagnostics.tsv" - ref_path = outdir / "reference.parquet" - cal_path = outdir / "calibrated_rts.parquet" - raw_path = outdir / "raw_rts.parquet" - - result.run_ranking.to_csv(run_ranking_path, sep="\t", index=False) - result.diag_df.to_csv(diag_path, sep="\t", index=False) - result.ref.to_parquet(ref_path) - result.calibrated_matrix.to_parquet(cal_path) - rt_matrix.to_parquet(raw_path) - - if not args.no_write_long: - long_df.to_parquet(outdir / "psms_long.parquet", index=False) - - print("\nWrote:") - print(f"- {run_ranking_path}") - print(f"- {diag_path}") - print(f"- {ref_path}") - print(f"- {cal_path}") - print(f"- {raw_path}") - if not args.no_write_long: - print(f"- {outdir / 'psms_long.parquet'}") - - if args.plots: - plot_sd_before_after(rt_matrix, result.calibrated_matrix) - - if args.filter: - max_sd = None if float(args.max_sd) < 0 else float(args.max_sd) - max_sd_rel = None if float(args.max_sd_rel) < 0 else float(args.max_sd_rel) - - if max_sd_rel is None: - gradient_length: float | None = None - else: - if float(args.gradient_length) < 0: - # Auto-estimate from calibrated RT range if user didn't provide one - s = result.calibrated_matrix.stack(dropna=True) - if len(s): - arr = s.to_numpy(dtype=float) - gradient_length = float(np.nanmax(arr) - np.nanmin(arr)) - else: - gradient_length = 0.0 - else: - gradient_length = float(args.gradient_length) - - filtered, stats = filter_by_occurrence_and_sd( - result.calibrated_matrix, - min_occurrence_pct=float(args.min_occurrence_pct), - max_sd=max_sd, - gradient_length=gradient_length, - max_sd_rel=max_sd_rel, - min_runs=int(args.min_runs), - return_stats=True, - verbose=True, - ) - - filtered = cast(pd.DataFrame, filtered) - stats = cast(pd.DataFrame, stats) - - filtered_path = outdir / "calibrated_rts.filtered.parquet" - stats_path = outdir / "calibrated_rts.filter_stats.tsv" - filtered.to_parquet(filtered_path) - stats.to_csv(stats_path, sep="\t") - - print("\nFiltered outputs:") - print(f"- {filtered_path}") - print(f"- {stats_path}") - - return 0 - - -if __name__ == "__main__": - psm_list = load_psm_list("results.plasma_proteomics.tsv") - - out = align_from_psm_list( - psm_list, - n_knots=12, - alpha=1e-3, - min_overlap=None, - plot_diagnostics=False, - make_sd_plot=False, - ) - - ref = out.result.ref - - filtered, stats = filter_by_occurrence_and_sd( - out.result.calibrated_matrix, - min_occurrence_pct=10, - gradient_length=ref["rt_ref"].max() - ref["rt_ref"].min(), - max_sd_rel=0.03, - min_runs=2, - return_stats=True, - verbose=True, - ) - - print(filtered.median(axis=1)) diff --git a/deeplc-todo/gui.py b/deeplc-todo/gui.py deleted file mode 100644 index 7e78c1e..0000000 --- a/deeplc-todo/gui.py +++ /dev/null @@ -1,404 +0,0 @@ -"""NiceGUI-based web interface for DeepLC.""" - -# TODO: Update to new API - -from __future__ import annotations - -import io -import logging -import tempfile -from pathlib import Path - -import pandas as pd -from nicegui import app, ui -from psm_utils.io import FILETYPES, read_file - -from deeplc import __version__ -from deeplc.calibration import PiecewiseLinearCalibration, SplineTransformerCalibration -from deeplc.core import predict, predict_and_calibrate - -logger = logging.getLogger(__name__) - - -def _get_file_format_options() -> dict[str, str]: - """Get file format options from psm_utils FILETYPES.""" - formats = {"auto": "Auto-detect"} - - # Add all formats that have readers - for filetype, properties in FILETYPES.items(): - if properties["reader"] is not None: - # Create friendly display names - display_name = filetype.replace("_", " ").title() - formats[filetype] = display_name - - return formats - - -class DeepLCGUI: - """DeepLC graphical user interface.""" - - def __init__(self): - self.prediction_file = None - self.calibration_file = None - self.results_df = None - self.use_calibration = False - self.calibration_type = "piecewise" - self.file_format = "auto" # Auto-detect by default - self.cal_file_format = "auto" - self.format_options = _get_file_format_options() - - self._build_ui() - - def _build_ui(self): - """Build the user interface.""" - # Header - with ui.header().classes("items-center justify-between"): - ui.label("DeepLC").classes("text-2xl font-bold") - ui.label(f"v{__version__}").classes("text-sm text-gray-400") - - # Main content - with ui.column().classes("w-full max-w-4xl mx-auto p-4 gap-4"): - # Info card - with ui.card().classes("w-full"): - ui.markdown( - """ - ### Deep Learning Retention Time Prediction - - Upload your peptide identification file (PSM file) to predict retention times. - Supports formats: MaxQuant, mzTab, Percolator, and more via psm_utils. - """ - ) - - # Input section - with ui.card().classes("w-full"): - ui.label("Input Files").classes("text-xl font-semibold mb-2") - - # Prediction file upload - ui.label("Peptide identification file (required)").classes("font-medium") - with ui.row().classes("w-full gap-2 items-start"): - self.pred_upload = ui.upload( - label="Choose file", - on_upload=self._handle_prediction_upload, - auto_upload=True, - ).classes("flex-grow") - ui.select( - label="Format", - options=self.format_options, - value="auto", - on_change=lambda e: setattr(self, "file_format", e.value), - ).classes("w-48") - self.pred_file_label = ui.label("No file selected").classes( - "text-sm text-gray-500" - ) # Calibration toggle - self.calibration_switch = ui.switch( - "Use calibration (requires observed retention times)", - on_change=self._toggle_calibration, - ).classes("mb-2") - - # Calibration file upload (hidden by default) - with ( - ui.column() - .classes("w-full") - .bind_visibility_from(self, "use_calibration") as self.calibration_section - ): - ui.label( - "Calibration file (optional - uses prediction file if not provided)" - ).classes("font-medium") - with ui.row().classes("w-full gap-2 items-start"): - self.cal_upload = ui.upload( - label="Choose calibration file", - on_upload=self._handle_calibration_upload, - auto_upload=True, - ).classes("flex-grow") - ui.select( - label="Format", - options=self.format_options, - value="auto", - on_change=lambda e: setattr(self, "cal_file_format", e.value), - ).classes("w-48") - self.cal_file_label = ui.label( - "Using prediction file for calibration" - ).classes("text-sm text-gray-500") - - # Calibration type selector - ui.label("Calibration method").classes("font-medium mt-2") - ui.radio( - ["piecewise", "spline"], - value="piecewise", - on_change=lambda e: setattr(self, "calibration_type", e.value), - ).props("inline").classes("mb-2") - ui.label( - "Piecewise: faster, good for linear relationships | " - "Spline: more flexible, better for complex patterns" - ).classes("text-xs text-gray-500") - - # Run button - with ui.row().classes("w-full justify-center"): - self.run_button = ( - ui.button( - "Predict Retention Times", - on_click=self._run_prediction, - icon="play_arrow", - ) - .classes("text-lg") - .props("color=primary size=lg") - ) - - # Results section - self.results_container = ui.column().classes("w-full") - - def _toggle_calibration(self, e): - """Toggle calibration section visibility.""" - self.use_calibration = e.value - - async def _handle_prediction_upload(self, e): - """Handle prediction file upload.""" - try: - # Store the file content as bytes - content = await e.file.read() - self.prediction_file = content - logger.info(f"Uploaded prediction file: {e.file.name}, size: {len(content)} bytes") - self.pred_file_label.set_text(f"✓ {e.file.name}") - self.pred_file_label.classes(replace="text-sm text-green-600") - ui.notify(f"File uploaded: {e.file.name}", type="positive") - except Exception as ex: - logger.exception("Error reading uploaded file") - self.pred_file_label.set_text(f"Error: {str(ex)}") - self.pred_file_label.classes(replace="text-sm text-red-600") - ui.notify(f"Error uploading file: {str(ex)}", type="negative") - - async def _handle_calibration_upload(self, e): - """Handle calibration file upload.""" - try: - content = await e.file.read() - self.calibration_file = content - logger.info(f"Uploaded calibration file: {e.file.name}, size: {len(content)} bytes") - self.cal_file_label.set_text(f"✓ {e.file.name}") - self.cal_file_label.classes(replace="text-sm text-green-600") - ui.notify(f"Calibration file uploaded: {e.file.name}", type="positive") - except Exception as ex: - logger.exception("Error reading calibration file") - self.cal_file_label.set_text(f"Error: {str(ex)}") - self.cal_file_label.classes(replace="text-sm text-red-600") - ui.notify(f"Error uploading calibration file: {str(ex)}", type="negative") - - async def _run_prediction(self): - """Run DeepLC prediction.""" - # Validation - logger.info(f"Run prediction clicked. prediction_file: {self.prediction_file is not None}") - if not self.prediction_file: - ui.notify("Please upload a peptide identification file", type="negative") - logger.warning("No prediction file uploaded") - return - - # Clear previous results - self.results_container.clear() - - # Show progress - with self.results_container: - progress_card = ui.card().classes("w-full") - with progress_card: - ui.label("Running DeepLC...").classes("text-lg font-semibold") - spinner = ui.spinner(size="lg") - - try: - # Parse input files - logger.info("Reading prediction file...") - file_format = None if self.file_format == "auto" else self.file_format - - # psm_utils requires a file path, so write to temp file - with tempfile.NamedTemporaryFile(mode="wb", delete=False, suffix=".psm") as tmp_file: - tmp_file.write(self.prediction_file) - tmp_pred_path = tmp_file.name - - try: - psm_list = read_file(tmp_pred_path, filetype=file_format) - finally: - Path(tmp_pred_path).unlink(missing_ok=True) - - if self.use_calibration: - # Determine calibration source - if self.calibration_file: - logger.info("Reading calibration file...") - cal_format = None if self.cal_file_format == "auto" else self.cal_file_format - - # Write calibration file to temp file - with tempfile.NamedTemporaryFile( - mode="wb", delete=False, suffix=".psm" - ) as tmp_file: - tmp_file.write(self.calibration_file) - tmp_cal_path = tmp_file.name - - try: - psm_list_cal = read_file(tmp_cal_path, filetype=cal_format) - finally: - Path(tmp_cal_path).unlink(missing_ok=True) - else: - logger.info("Using prediction file for calibration...") - psm_list_cal = psm_list - - # Select calibration method - if self.calibration_type == "spline": - calibration = SplineTransformerCalibration() - else: - calibration = PiecewiseLinearCalibration(number_of_splits=50) - - # Run with calibration - logger.info("Running prediction with calibration...") - predictions = await self._run_in_background( - predict_and_calibrate, - psm_list=psm_list, - psm_list_reference=psm_list_cal, - calibration=calibration, - ) - else: - # Run without calibration - logger.info("Running prediction without calibration...") - predictions = await self._run_in_background(predict, psm_list=psm_list) - - # Prepare results DataFrame - self.results_df = pd.DataFrame( - { - "peptidoform": [psm.peptidoform.proforma for psm in psm_list], - "predicted_rt": predictions, - } - ) - - # Add observed RT if available - observed_rts = [ - psm.retention_time for psm in psm_list if psm.retention_time is not None - ] - if observed_rts and len(observed_rts) == len(psm_list): - self.results_df["observed_rt"] = observed_rts - - # Clear progress and show results - progress_card.delete() - self._show_results() - - except Exception as e: - logger.exception("Error during prediction") - progress_card.delete() - with self.results_container: - with ui.card().classes("w-full bg-red-50"): - ui.label("Error").classes("text-xl font-semibold text-red-600") - ui.label(str(e)).classes("text-red-800") - - async def _run_in_background(self, func, **kwargs): - """Run prediction function in background to avoid blocking UI.""" - import asyncio - - loop = asyncio.get_event_loop() - return await loop.run_in_executor(None, lambda: func(**kwargs)) - - def _show_results(self): - """Display prediction results.""" - with self.results_container: - # Success message - with ui.card().classes("w-full bg-green-50"): - ui.label("✓ Prediction Complete").classes("text-xl font-semibold text-green-600") - ui.label(f"Predicted retention times for {len(self.results_df)} peptides").classes( - "text-green-800" - ) - - # Results table - with ui.card().classes("w-full"): - ui.label("Results Preview").classes("text-xl font-semibold mb-2") - - # Create columns for the table - columns = [ - {"name": col, "label": col.replace("_", " ").title(), "field": col} - for col in self.results_df.columns - ] - - # Convert DataFrame to list of dictionaries for the table - rows = self.results_df.to_dict("records") - - # Display paginated table - ui.table( - columns=columns, - rows=rows, - row_key="peptidoform", - pagination={"rowsPerPage": 10, "sortBy": None, "descending": False}, - ).classes("w-full") - - # Plot if observed RT is available - if "observed_rt" in self.results_df.columns: - with ui.card().classes("w-full"): - ui.label("Observed vs Predicted").classes("text-xl font-semibold mb-2") - self._plot_correlation() - - # Download button - with ui.card().classes("w-full"): - ui.label("Download Results").classes("text-xl font-semibold mb-2") - csv_data = self.results_df.to_csv(index=False) - ui.button( - "Download CSV", - on_click=lambda: ui.download(csv_data.encode(), "deeplc_predictions.csv"), - icon="download", - ).props("color=primary") - - def _plot_correlation(self): - """Plot observed vs predicted retention times using Plotly.""" - import numpy as np - import plotly.graph_objects as go - - # Calculate metrics - observed = self.results_df["observed_rt"].values - predicted = self.results_df["predicted_rt"].values - mae = np.mean(np.abs(observed - predicted)) - r2 = np.corrcoef(observed, predicted)[0, 1] ** 2 - - # Create scatter plot - fig = go.Figure() - - fig.add_trace( - go.Scatter( - x=observed, - y=predicted, - mode="markers", - name="Predictions", - marker=dict(size=5, opacity=0.5, color="blue"), - hovertemplate="Observed: %{x:.2f}
Predicted: %{y:.2f}", - ) - ) - - # Add diagonal line - min_val = min(observed.min(), predicted.min()) - max_val = max(observed.max(), predicted.max()) - fig.add_trace( - go.Scatter( - x=[min_val, max_val], - y=[min_val, max_val], - mode="lines", - name="y=x", - line=dict(color="red", dash="dash", width=2), - ) - ) - - fig.update_layout( - title=f"Retention Time Prediction Performance (R² = {r2:.3f}, MAE = {mae:.2f})", - xaxis_title="Observed RT", - yaxis_title="Predicted RT", - hovermode="closest", - height=500, - showlegend=True, - ) - - ui.plotly(fig).classes("w-full") - - -def start_gui(host: str = "127.0.0.1", port: int = 8080, reload: bool = False): - """Start the DeepLC NiceGUI interface.""" - DeepLCGUI() - - ui.run( - host=host, - port=port, - title="DeepLC - Retention Time Prediction", - reload=reload, - favicon="🧬", - ) - - -if __name__ in {"__main__", "__mp_main__"}: - start_gui() diff --git a/examples/deeplc_example.py b/examples/deeplc_example.py deleted file mode 100644 index 2a44b2b..0000000 --- a/examples/deeplc_example.py +++ /dev/null @@ -1,78 +0,0 @@ -# Imports -import pandas as pd -from matplotlib import pyplot as plt - -from deeplc import DeepLC, FeatExtractor - -if __name__ == "__main__": - # Input files - peptide_file = "examples/datasets/dia.csv" - - # Read the input data to make predictions for - df = pd.read_csv(peptide_file, sep=",") - - # Unmodified peptides in modifications column should be empty strings, not nan - df = df.fillna("") - - # Generate some identifiers, any kind of identifiers will do - df.index = ["Pep_" + str(dfi) for dfi in df.index] - - # Make a feature extraction object. - # This step can be skipped if you want to use the default feature extraction - # settings. In this example we will use a model that does not use RDKit features - # so we skip the chemical descriptor making procedure. - - # f_extractor = FeatExtractor(chem_descr_feat=False,verbose=False) - f_extractor = FeatExtractor( - add_sum_feat=False, - ptm_add_feat=False, - ptm_subtract_feat=False, - standard_feat=False, - chem_descr_feat=False, - add_comp_feat=False, - cnn_feats=True, - verbose=True, - ) - # Initiate a DeepLC instance that will perform the calibration and predictions - dlc = DeepLC( - path_model=[ - "deeplc/mods/full_hc_hela_hf_psms_aligned_1fd8363d9af9dcad3be7553c39396960.hdf5", - "deeplc/mods/full_hc_hela_hf_psms_aligned_8c22d89667368f2f02ad996469ba157e.hdf5", - "deeplc/mods/full_hc_hela_hf_psms_aligned_cb975cfdd4105f97efa0b3afffe075cc.hdf5", - "deeplc/mods/full_hc_PXD005573_mcp_cb975cfdd4105f97efa0b3afffe075cc.hdf5", - ], - cnn_model=True, - f_extractor=f_extractor, - verbose=True, - write_library=True, - use_library="deeplc/library/library.csv", - ) - - df["tr"] = list(range(len(df.index))) - - # To demonstrate DeepLC's callibration, we'll induce some an artificial - # transformation into the retention times - df["tr"] = df["tr"] ** 0.85 - - # Calibrate the original model based on the new retention times - dlc.calibrate_preds(seq_df=df) - - print("a") - - # Make predictions; calibrated and uncalibrated - preds_cal = dlc._make_preds(seq_df=df) - - print("b") - - preds_uncal = dlc._make_preds(seq_df=df, calibrate=False) - - print("c") - - # Compare calibrated and uncalibrated predictions - # print("Predictions (calibrated): ", preds_cal) - # print("Predictions (uncalibrated): ", preds_uncal) - - plt.scatter(df["tr"], preds_cal, label="Calibrated", s=1) - plt.scatter(df["tr"], preds_uncal, label="Uncalibrated", s=1) - plt.legend() - plt.savefig("deeplc_calibrated_vs_uncalibrated.png") diff --git a/scripts/onnx_model.py b/scripts/onnx_model.py deleted file mode 100644 index bef6e9b..0000000 --- a/scripts/onnx_model.py +++ /dev/null @@ -1,31 +0,0 @@ - -import os -from tensorflow.keras.models import load_model -import tensorflow as tf -import tf2onnx -from onnx2torch import convert -import torch - -deeplc_dir = os.path.dirname(os.path.realpath(__file__)) -DEFAULT_MODELS = [ - "mods/full_hc_PXD005573_pub_1fd8363d9af9dcad3be7553c39396960.keras", - "mods/full_hc_PXD005573_pub_8c22d89667368f2f02ad996469ba157e.keras", - "mods/full_hc_PXD005573_pub_cb975cfdd4105f97efa0b3afffe075cc.keras", -] -DEFAULT_MODELS = [os.path.join(deeplc_dir, dm) for dm in DEFAULT_MODELS] -def _convert_to_onnx(): - for model_path in DEFAULT_MODELS: - if os.path.exists(model_path): - mod = load_model(model_path) - spec = [ - tf.TensorSpec([None, 60, 6], tf.float32, name="input_1"), - tf.TensorSpec([None, 30, 6], tf.float32, name="input_2"), - tf.TensorSpec([None, 55], tf.float32, name="input_3"), - tf.TensorSpec([None, 60, 20], tf.float32, name="input_4"), - ] - onnx_model, _ = tf2onnx.convert.from_keras(mod, input_signature=spec, opset=13) - torch_model = convert(onnx_model) - torch.save(torch_model, model_path.replace(".keras", ".pt")) - -if __name__ == "__main__": - _convert_to_onnx() \ No newline at end of file diff --git a/streamlit/.dockerignore b/streamlit/.dockerignore deleted file mode 100644 index f4bf89e..0000000 --- a/streamlit/.dockerignore +++ /dev/null @@ -1,5 +0,0 @@ -.dockerignore -Dockerfile -__pycache__/ -.venv/ -deeplc_library.txt diff --git a/streamlit/.streamlit/config.toml b/streamlit/.streamlit/config.toml deleted file mode 100644 index 71d187e..0000000 --- a/streamlit/.streamlit/config.toml +++ /dev/null @@ -1,19 +0,0 @@ -[client] -showErrorDetails = false - -[runner] -magicEnabled = false - -[server] -fileWatcherType = "none" -headless = true - -[browser] -gatherUsageStats = false - -[theme] -primaryColor="#763737" -backgroundColor="#FFFFFF" -secondaryBackgroundColor="#f2f2f2" -textColor="#262730" -font="sans serif" diff --git a/streamlit/Dockerfile b/streamlit/Dockerfile deleted file mode 100644 index 583ae9a..0000000 --- a/streamlit/Dockerfile +++ /dev/null @@ -1,7 +0,0 @@ -FROM python:3.10-slim-buster -COPY . /deeplc -WORKDIR /deeplc -RUN pip install --upgrade pip && pip install -r requirements.txt -ENV DEEPLC_LIBRARY_PATH="/tmp/deeplc_library.txt" -EXPOSE 8501 -CMD ["streamlit", "run", "deeplc_streamlit.py"] diff --git a/streamlit/README.md b/streamlit/README.md deleted file mode 100644 index 4fca34f..0000000 --- a/streamlit/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# DeepLC Streamlit-based web server - - -## Usage - -Pull and run with - -```sh -docker run -p 8501 ghcr.io/compomics/deeplc-streamlit -``` - -Streamlit can be further configured using environment variables: - -```sh -docker run \ - -p 8501 \ - -e STREAMLIT_SERVER_MAX_UPLOAD_SIZE=200 \ - ghcr.io/compomics/deeplc-streamlit -``` -See -[Streamlit configuration](https://docs.streamlit.io/en/stable/streamlit_configuration.html) -for more info. diff --git a/streamlit/deeplc_streamlit.py b/streamlit/deeplc_streamlit.py deleted file mode 100644 index fcd574d..0000000 --- a/streamlit/deeplc_streamlit.py +++ /dev/null @@ -1,430 +0,0 @@ -"""Streamlit-based web interface for DeepLC.""" - -import logging -import os -import pathlib -from datetime import datetime -from importlib.metadata import version - -import pandas as pd -import plotly.express as px -from streamlit_utils import hide_streamlit_menu, save_dataframe - -import streamlit as st -from deeplc import DeepLC - -logger = logging.getLogger(__name__) - - -class DeepLCStreamlitError(Exception): - pass - - -class MissingPeptideCSV(DeepLCStreamlitError): - pass - - -class InvalidPeptideCSV(DeepLCStreamlitError): - pass - - -class InvalidCalibrationPeptideCSV(DeepLCStreamlitError): - pass - - -class MissingCalibrationPeptideCSV(DeepLCStreamlitError): - pass - - -class MissingCalibrationColumn(DeepLCStreamlitError): - pass - - -class StreamlitUI: - """DeepLC Streamlit UI.""" - - def __init__(self): - """DeepLC Streamlit UI.""" - self.texts = WebpageTexts - self.user_input = dict() - - self._set_library_path() - - st.set_page_config( - page_title="DeepLC web server", - page_icon=":rocket:", - layout="centered", - initial_sidebar_state="expanded", - ) - - hide_streamlit_menu() - - self._main_page() - self._sidebar() - - def _main_page(self): - """Format main page.""" - st.title("DeepLC") - st.header("Input and configuration") - - with st.form(key="main_form"): - st.subheader("Input files") - self.user_input["input_csv"] = st.file_uploader( - "Peptide CSV", help=self.texts.Help.peptide_csv - ) - self.user_input["input_csv_calibration"] = st.file_uploader( - "Calibration peptide CSV (optional)", - help=self.texts.Help.calibration_peptide_csv, - ) - self.user_input["use_example"] = st.checkbox( - "Use example data", help=self.texts.Help.example_data - ) - - with st.expander("Info about peptide CSV formatting"): - st.markdown(self.texts.Help.csv_formatting) - - st.subheader("Calibration") - self.user_input["calibration_source"] = st.radio( - "Calibration peptides", - [ - "Use calibration peptide CSV", - "Use `tr` column in peptide CSV", - "Do not calibrate predictions", - ], - help=self.texts.Help.calibration_source, - ) - with st.expander("Set advanced calibration options"): - self.user_input["dict_cal_divider"] = st.number_input( - "Dictionary divider", - step=1, - value=100, - help=self.texts.Help.dict_cal_divider, - ) - self.user_input["split_cal"] = st.number_input( - "Split calibration", - step=1, - value=25, - help=self.texts.Help.split_cal, - ) - - st.subheader("Prediction speed boost") - self.user_input["use_library"] = st.checkbox( - "Use prediction library for speed-up", - help=self.texts.Help.use_library, - ) - st.markdown(self.texts.Help.use_library_agreement) - - submit_button = st.form_submit_button("Predict retention times") - - if submit_button: - try: - self._run_deeplc() - except MissingPeptideCSV: - st.error(self.texts.Errors.missing_peptide_csv) - except MissingCalibrationPeptideCSV: - st.error(self.texts.Errors.missing_calibration_peptide_csv) - except MissingCalibrationColumn: - st.error(self.texts.Errors.missing_calibration_column) - except InvalidPeptideCSV: - st.error(self.texts.Errors.invalid_peptide_csv) - except InvalidCalibrationPeptideCSV: - st.error(self.texts.Errors.invalid_calibration_peptide_csv) - - def _sidebar(self): - """Format sidebar.""" - st.sidebar.image( - "https://github.com/compomics/deeplc/raw/master/img/deeplc_logo.png", - width=150, - ) - st.sidebar.markdown(self.texts.Sidebar.badges) - st.sidebar.header("About") - st.sidebar.markdown(self.texts.Sidebar.about, unsafe_allow_html=True) - - def _run_deeplc(self): - """Run DeepLC given user input, and show results.""" - # Parse user config - config = self._parse_user_config(self.user_input) - use_lib = self.user_input["use_library"] - calibrate = isinstance(config["input_df_calibration"], pd.DataFrame) - - logger.info( - "Run requested // %s // peptides %i / use_library %r / calibrate %r", - datetime.now(), - len(config["input_df"]), - use_lib, - calibrate, - ) - - # Run DeepLC - st.header("Running DeepLC") - status_placeholder = st.empty() - status_placeholder.info(":hourglass_flowing_sand: Running DeepLC...") - try: - dlc = DeepLC( - dict_cal_divider=self.user_input["dict_cal_divider"], - split_cal=self.user_input["split_cal"], - use_library=self.library_path if use_lib else "", - write_library=True if use_lib else False, - reload_library=True if use_lib else False, - ) - if calibrate: - config["input_df_calibration"]["modifications"].fillna("", inplace=True) - dlc.calibrate_preds(seq_df=config["input_df_calibration"]) - config["input_df"]["modifications"].fillna("", inplace=True) - preds = dlc.make_preds(seq_df=config["input_df"], calibrate=calibrate) - except Exception as e: - status_placeholder.error(":x: DeepLC ran into a problem") - st.exception(e) - else: - status_placeholder.success(":heavy_check_mark: Finished!") - - # Add predictions to input DataFrame - result_df = config["input_df"] - result_df["predicted_tr"] = preds - - # Show head of result DataFrame - st.header("Results") - st.subheader("Selection of predicted retention times") - st.dataframe(result_df.head(100)) - - # Plot results - self._plot_results(result_df) - - # Download link - st.subheader("Download predicted retention times") - st.download_button( - label="Download", - data=save_dataframe(result_df), - file_name="deeplc_predictions.csv", - mime="text/csv", - ) - - def _set_library_path(self): - if "DEEPLC_LIBRARY_PATH" in os.environ: - try: - pathlib.Path(os.environ["DEEPLC_LIBRARY_PATH"], exist_ok=True).touch() - self.library_path = os.environ["DEEPLC_LIBRARY_PATH"] - except OSError: - self.library_path = "deeplc_library.txt" - else: - self.library_path = "deeplc_library.txt" - - @staticmethod - def get_example_input(): - """Return example DataFrame for input.""" - if os.path.isfile("example_data.csv"): - example_df = pd.read_csv("example_data.csv") - else: - example_df = pd.DataFrame( - [ - ["AAGPSLSHTSGGTQSK", ""], - ["AAINQKLIETGER", "6|Acetyl"], - ["AANDAGYFNDEMAPIEVKTK", "12|Oxidation|18|Acetyl"], - ], - columns=["seq", "modifications"], - ) - return example_df - - def _parse_user_config(self, user_input): - """Validate and parse user input.""" - config = { - "input_filename": None, - "input_df": None, - "input_df_calibration": None, - } - - # Load example if use_example was selected - if user_input["use_example"]: - config["input_filename"] = "example.csv" - config["input_df"] = self.get_example_input() - config["input_df_calibration"] = config["input_df"] - return config - - # Get peptide dataframe - if user_input["input_csv"]: - config["input_filename"] = user_input["input_csv"].name - try: - config["input_df"] = pd.read_csv(user_input["input_csv"]) - except (ValueError, pd.errors.ParserError) as e: - raise InvalidPeptideCSV(e) from e - else: - raise MissingPeptideCSV - - # Get calibration peptide dataframe - if user_input["calibration_source"] == "Use `tr` column in peptide CSV": - if "tr" not in config["input_df"].columns: - raise MissingCalibrationColumn - else: - config["input_df_calibration"] = config["input_df"] - elif user_input["calibration_source"] == "Use calibration peptide CSV": - if not user_input["input_csv_calibration"]: - raise MissingCalibrationPeptideCSV - else: - try: - config["input_df_calibration"] = pd.read_csv( - user_input["input_csv_calibration"] - ) - except (ValueError, pd.errors.ParserError) as e: - raise InvalidPeptideCSV(e) from e - - return config - - @staticmethod - def _plot_results(result_df): - """Plot results with Plotly Express.""" - if "tr" in result_df.columns: - st.subheader("Input retention times vs predictions") - fig = px.scatter( - result_df, - x="tr", - y="predicted_tr", - hover_data=["seq", "modifications"], - trendline="ols", - opacity=0.25, - color_discrete_sequence=["#763737"], - ) - fig.update_traces(marker=dict(size=4)) - fig.update_layout( - xaxis_title_text="Input retention time", - yaxis_title_text="Predicted retention time", - ) - else: - st.subheader("Predicted retention time distribution") - fig = px.histogram( - result_df, - x="predicted_tr", - marginal="rug", - opacity=0.8, - histnorm="density", - color_discrete_sequence=["#763737"], - ) - fig.update_layout( - xaxis_title_text="Predicted retention time", - yaxis_title_text="Density", - bargap=0.2, - ) - st.plotly_chart(fig, use_container_width=True) - - -class WebpageTexts: - class Sidebar: - badges = """ - [![GitHub release](https://img.shields.io/github/release-pre/compomics/deeplc.svg?style=flat-square)](https://github.com/compomics/deeplc/releases) - [![GitHub](https://img.shields.io/github/license/compomics/deeplc.svg?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) - [![Twitter](https://img.shields.io/twitter/follow/compomics?style=flat-square)](https://twitter.com/compomics) - """ - - about = f""" - DeepLC is a retention time predictor for (modified) peptides that employs - Deep Learning. Its strength lies in the fact that it can accurately predict - retention times for modified peptides, even if hasn't seen said modification - during training. - - DeepLC can be run with a - [graphical user interface](https://github.com/compomics/DeepLC#graphical-user-interface), - as a [Python package](https://github.com/compomics/DeepLC#python-package) - (both CLI and Python API), or through this web application. - - If you use DeepLC for your research, please use the following citation: - >**DeepLC can predict retention times for peptides that carry as-yet unseen - modifications**
- >Robbin Bouwmeester, Ralf Gabriels, Niels Hulstaert, Lennart Martens, Sven Degroeve
- >_Nature Methods 18, 1363–1369 (2021)_
- >[doi:10.1038/s41592-021-01301-5](https://doi.org/10.1038/s41592-021-01301-5) - - --- - - Currently using the following package versions:
- [![DeepLC](https://img.shields.io/badge/deeplc-{version("deeplc")}-blue?style=flat-square&logoColor=white&logo=pypi)](https://github.com/compomics/deeplc) - [![Tensorflow](https://img.shields.io/badge/tensorflow-{version("tensorflow")}-blue?style=flat-square&logoColor=white&logo=pypi)](https://github.com/tensorflow/tensorflow) - [![Streamlit](https://img.shields.io/badge/streamlit-{version("streamlit")}-blue?style=flat-square&logoColor=white&logo=pypi)](https://github.com/streamlit/streamlit) - - Latest DeepLC version:
- ![PyPI](https://img.shields.io/pypi/v/deeplc?style=flat-square&logoColor=white&logo=pypi) - """ - - class Help: - peptide_csv = """CSV with peptides for which to predict retention times. Click - below on _Info about peptide CSV formatting_ for more info. - """ - calibration_peptide_csv = """CSV with peptides with known retention times to be - used for calibration. Click below on _Info about peptide CSV formatting_ for - more info. - """ - example_data = "Use example data instead of uploaded CSV files." - csv_formatting = """ - DeepLC expects comma-separated values (CSV) with the following columns: - - - `seq`: Unmodified peptide sequences - - `modifications`: MS²PIP-style formatted peptide modifications: Each - modification is listed as `location|name`, separated by a pipe (`|`) between - the location, the name, and other modifications. `location` is an integer - counted starting at 1 for the first AA. `0` is reserved for N-terminal - modifications, `-1` for C-terminal modifications. `name` has to correspond - to a Unimod (PSI-MS) name. All supported modifications are listed on - [GitHub](https://github.com/compomics/DeepLC/blob/master/deeplc/unimod/unimod_to_formula.csv) - - `tr`: Retention time (only required for calibration CSV) - - For example: - - ```csv - seq,modifications,tr - AAGPSLSHTSGGTQSK,,12.1645 - AAINQKLIETGER,6|Acetyl,34.095 - AANDAGYFNDEMAPIEVKTK,12|Oxidation|18|Acetyl,37.3765 - ``` - - See - [examples/datasets](https://github.com/compomics/DeepLC/tree/master/examples/datasets) - for more examples. - """ - calibration_source = """DeepLC can calibrate its predictions based on set of - known peptide retention times. Calibration also ensures that the - best-fitting DeepLC model is used. - """ - dict_cal_divider = """This parameter defines the precision to use for - fast-lookup of retention times for calibration. A value of 10 means a - precision of 0.1 (and 100 a precision of 0.01) between the calibration - anchor points. This parameter does not influence the precision of the - calibration, but setting it too high results in mean that there is bad - selection of the models between anchor points. A safe value is usually - higher than 10. - """ - split_cal = """The number of splits for the chromatogram. If the value is set - to 10 the chromatogram is split up into 10 equidistant parts. For each part - the median value of the calibration peptides is selected. These are the - anchor points. Between each anchor point a linear model is fit. - """ - use_library = """DeepLC can fetch previously predicted retention times from a - library, instead predicting retention times for the same (modified) peptide - again. This feature will not change any of the predicted retention time - values. It can, however, significantly speed up DeepLC. - """ - use_library_agreement = """_By selecting this box, you allow us to store the - uploaded peptide sequences and modifications on this server indefinitely._ - """ - - class Errors: - missing_peptide_csv = """ - Upload a peptide CSV file or select the _Use example data_ checkbox. - """ - missing_calibration_peptide_csv = """ - Upload a calibration peptide CSV file or select another _Calibration - peptides_ option. - """ - missing_calibration_column = """ - Upload a peptide CSV file with a `tr` column or select another _Calibration - peptides_ option. - """ - invalid_peptide_csv = """ - Uploaded peptide CSV file could not be read. Click on _Info about peptide - CSV formatting_ for more info on the correct input format. - """ - invalid_calibration_peptide_csv = """ - Uploaded calibration peptide CSV file could not be read. Click on _Info - about peptide CSV formatting_ for more info on the correct input format. - """ - - -if __name__ == "__main__": - StreamlitUI() diff --git a/streamlit/example_data.csv b/streamlit/example_data.csv deleted file mode 100644 index eeafc19..0000000 --- a/streamlit/example_data.csv +++ /dev/null @@ -1,1367 +0,0 @@ -seq,modifications,tr -VSLDDLQQSIEEDEDHVQSTDIAAMQK,,15083.4 -NVIAETGAGQHGVATATACAK,,6098.04 -FATVPTGGASSAAAGAAGAAAGGDAAEEEK,,8934.060000000001 -LTPAANQVEIHPLLPQDELINFCK,,15085.800000000001 -DAGAISGLNVLRIINEPTAAAIAYGLGAGK,,15405.0 -FALAGAIGCGSTHSSMVPIDVVK,,13898.88 -IPAVEYFGGESPVDVQSQVDSSSVSEDSAVFK,,15257.16 -ATVDEFPLCVHLVSNELEQLSSEALEAARICANK,,15430.56 -AANSVSQDSSYTDFSFTIAGTAHNAHSVTQSASK,,11014.800000000001 -LVDNAGSVFVGAYTPESCGDYSSGTNHTLPTYGYAR,,13282.019999999999 -FIAEGSNMGSTPEAIAVFETARSTATGPSEAVWYGPPK,,15313.26 -EISEDGADSLNVAMDCISEAFGFEREAVSGILGK,,15451.44 -NMITGTSQADCAILIIAGGVGEFEAGISK,,15436.68 -RAVDNIPVGPNFDDEEDEGEDLCNCEFSLAYGAK,,14474.28 -GVLGYTEDAVVSSDFLGDSHSSIFDASAGIQLSPK,,15435.48 -AIEMEGLTWGAHQFIPIGFGIK,,15368.34 -AASELRILYGGSANGSNAVTFK,,11455.44 -LGTPSEDTVSAAATANNISTMGDESRK,,9801.900000000001 -TVPFVPISGWNGDNMIEATTNAPWYK,,15372.060000000001 -ISAVSTYFESFPYRVNPETGIIDYDTLEK,,15331.619999999999 -SSYGSSSNDDSYGSSNNDDSYGSSNK,,4800.768 -IEVLEQELVRAEAESLVAEAQLSNITRSK,,15362.22 -SINPDEAVAYGAAVQGAILTGQSTSDETK,,15245.22 -EPNRSINPDEAVAYGAAVQAAILTGDESSK,,15301.02 -SLEYRIIHYDDPSQQLVNTDLDILNNTRAK,,14799.48 -TDTSSSSSSSSASSVSAHQPTQEK,,3464.7960000000003 -VGVQVAAQHNTSVFYGLPQEK,,10565.22 -ELDNSSTISTHDASTNGLINQFK,,10771.5 -SVEMHHEQLEQGVPGDNVGFNVK,,9161.52 -LPLVGGHEGAGVVVGMGENVK,,11390.4 -DGNVGNFNETEQILLAENPESEK,,13930.98 -TEPSEAETTTVNTESLGQAEHEIK,,8812.74 -EAVESADLILSVGALLSDFNTGSFSYSYK,,15478.619999999999 -DEIVLSGNSVEDVSQNAADLQQICR,,13751.699999999999 -LQNTSNSTDAGELFNAQNAGYK,,9458.58 -EVYEGEVTELTPEDAENPLGGYGK,,13126.74 -GNYPINAVTTMAETAVIAEQAIAYLPNYDDMRNCTPK,,15447.78 -HDVPQDSNDNNNEELEAQGQQAQEK,,4989.24 -SLSLEEVEGHLFEIIDEASK,,15426.839999999998 -AYTSSAQVMPEVPQHEPSTTQEFNVDELSNELK,,13857.24 -GYVTDMQHYQEMYQQSINEPEK,,11769.36 -IYEVEGMRWAGNANELNAAYAADGYAR,,13295.22 -HDSVEEAWLCDIDEAVIRLSK,,15416.46 -QGNEGNIASNYITQVPLQK,,11325.72 -HCPSSTYVICNDTNLSK,,6661.38 -VASNEAEQLGDLTTLANPEVVPAIISAVENQFFSQK,,15488.52 -NEDACSFTLYGDGASNQGQVFESFNMAK,,15056.039999999999 -LYDIASGQTQQIGMHSAPIK,,9836.039999999999 -HQVDLSGSGTNNMGSNGAPK,,4970.298000000001 -LEILSHEGSVVETIDMRNK,,11184.3 -TEILANEQGNRITPSYVAFTDDERLIGDAAK,,13566.12 -KNEETDLPVSGLFYAIGHTPATK,,14813.7 -TNEAAGDGTTSATVLGRAIFTESVK,,13008.66 -EAYNYNDEQYQAISLVNYNVEK,,13294.08 -YNIFATTSGGGPTGQAESIMHAIAK,,15274.199999999999 -WIGGHGTTIGGIIVDSGK,,11505.12 -EHAPYTDELEEDVYLDPLARVSK,,14108.22 -NHGNVEGYGLDWSPLIK,,13184.820000000002 -EADNSTSYYIAGHATTVIDGK,,9829.5 -IGSSSAVAQFAAHSDPSVQIEQDK,,10250.04 -HQVCTPGGTTIAGLCVMEEK,,10228.8 -HLNICGTVGSIDNDMSTTDATIGAYSALDRICK,,15013.14 -PTGPGGAASHLPNAMNK,,5913.546 -YSVPIVANGRDLMACAQTGSGK,,11346.18 -VAETALFSGNDGADSNPGDSAQVEEHK,,8554.5 -PQGSIEHLQSGVYYLTNIDDK,,12592.62 -NGTDGTLNVAVDACQAAAHSHHFMGVTK,,10520.76 -YDNIPEHAFYMVGGIEDVVAK,,15324.3 -ELVARVQGGEEVNAEELK,,8477.34 -HALPFEGMLPDFGSPVIEGTK,,15347.52 -TTNDTYEATYAIPMHCENCVNDIK,,10349.58 -IGSGSFGDIYHGTNLISGEEVAIK,,13652.94 -HGFMSSNGDYDGANRFMGSK,,8446.8 -LDYENVPSVIFSHPEAGSIGISEK,,14682.42 -IEETHEDDMVTIDGVQVHLSTFNK,,13033.26 -GLSLLDFTSSDNNYMTQVHDYK,,15121.98 -KAEETISWLDSNTTASK,,9040.619999999999 -LPDAVVACVGGGSNSTGMFSPFEHDTSVK,,14010.72 -TVIFTHGVEPTVVVSSK,,10186.02 -QYLITQNGSIQGLPNVPQELK,,13877.640000000001 -YVNDLSHDETVLLDQRK,,9604.5 -PTYNPYTEPSMEIFSWHEGLQK,,14935.68 -EHDEIEEQGDALQDVEESSEK,,9184.98 -PTSTTETVAASAVAAVFEQK,,15294.9 -IATYQERDPANLPWGSSNVDIAIDSTGVFK,,15299.820000000002 -SADEVINMANDSEYGLAAGIHTSNINTALK,,14079.24 -TQTDRTNIDNSTGQAGQANAQSANTVNNRVNNNSK,,4847.628000000001 -GQVVGSIIALDRQEVVSTDDK,,11666.82 -STSGNTHLGGQDFDTNLLEHFK,,13057.08 -YSGVCHTDLHAWHGDWPLPVK,,11740.26 -GEVVDGTVVSCSQHGFEVQVGPMK,,11490.06 -LSDDIPPSAPMAINCEYGSFDNEHVVLPRTK,,12976.68 -LTGAQDAMGHPVQEIESLCMNCGK,,12539.16 -GIEAAGGSADIYQVEETLSPEVVK,,14746.86 -ATNPNGNLSYDATSTLTLPGHELPLPK,,13560.72 -AGPVVTDNNNFIIDADFGEISDPRK,,14525.460000000001 -HSPDFSAFDACHNGASFFK,,11664.84 -IYEVEGMRWAGNANELNAAYAADGYARIK,,13766.699999999999 -PVTVHCDHLIQAQVGGEK,,6772.14 -DNAEGQGESLADQARDYMGAAK,,11202.9 -LEDVSITHMTFGPEFTK,,13307.34 -ISHVSTGGGASLELLEGK,,9450.84 -SHLQSNQLYSNQLPLDFALGK,,14111.039999999999 -QITVVGATSGDTGSAAIYGLRGK,,10409.94 -SQLLYSHSLAHELAEYCEAK,,11278.14 -VHSGSEQLANASEDTK,,3702.672 -LAVVPQGGNTDLVGASVPVFDEIVLSLRNMNK,,15436.68 -QVEDEDHMEVFPAGSSFPSTK,,10858.8 -AVVESVGAEVDEARINELLSSLEGK,,15356.1 -SSYGSNNDDSYGSNNDDSYGSSNK,,4829.082 -SIVTLDVKPWDDETNLEEMVANVK,,15340.199999999999 -VESTYGHNVSAVEEASVVLLGTK,,14387.82 -SPLTEQSTEYASISNNSDQTIAFK,,11566.14 -PSYYLDPEAAAAGAAAVANQGK,,13273.56 -LSETVLEDVYNQLPELIGGSADLTPSNLTRWK,,15403.800000000001 -LYGTDDNTQEVEAVTNK,,8005.14 -SSYGSSNNDSYGSNNDDSYGSSNK,,4622.682000000001 -EEAAPKPPSSAVFSMFGGK,,12660.66 -ESVANANTDGFDFNLLTINSEHK,,14570.04 -VSISHDDLQAVAVAVSTK,,11486.1 -QDHYGGEAASVTLSQLYEK,,10651.32 -TPCGTVGYTAPEVVK,,7796.7 -RSVSEIEIESVQDQPSVAVGSFFK,,14606.94 -EAIYGEPDHVITVDVVMDNLK,,15245.22 -CIYDQLTEGENSLCGVMIESNINEGRQDIPK,,15255.9 -EHGEPLFSSHMLDLSEETDEENISTCVK,,13493.58 -VYVVATHGIFTGDCLEELEK,,13874.460000000001 -HLNTSCELVNSLMHLSPEDK,,14066.76 -RIDTLAAEFPAQTNYLYTTYNATK,,14280.36 -RLGQEFADDDGELVSAADIVPIVQEK,,15270.06 -GPIYNDQNMVISDSGYLLSK,,13851.54 -MVSQLESSLLNCPDSLDADSPCLYTK,,14770.92 -FNQNETDILASTGSDNSIVLYDLRTNSPTQK,,14203.56 -GLGVGTPSEALLYVITSPVGPYYK,,15408.720000000001 -LTNMPFGGNPTANQSGSGNSLFGTK,,12609.539999999999 -IFVGNSHPELGNMVCQRLGIEPAPCTLK,,13318.5 -GQYLVCMDADLQHPPETVPK,,11370.78 -QGAGSATLSMAFAGAK,,9999.42 -HGADCLEANQDVDSPIWTPSK,,10136.76 -AAAEGVANLHLDEATGEMVSK,,10328.58 -YSSSGTPENITGEADDEDEDIIRNSYGQMIK,,12838.5 -VEGAVGNLGNAVTVTEDGTVVTVVSTAK,,15266.039999999999 -SLATSGDSLGAVQLLTTDPPYSADAPTK,,14781.18 -EPVSDWTDDVEARINFGIEK,,15266.039999999999 -NSDVWFLDHNYIENMNEMCK,,15229.56 -ELYGNIVMSGGTTMFPGIAERMQK,,15283.92 -FDSSVDRGSPFQCNIGVGQVIK,,12057.0 -NYENGFINNPIVISPTTTVGEAK,,13975.619999999999 -IEACGVCGSDIHCAAGHWGNMK,,8505.9 -GSGVAMAMYNTDESIEGFAHSSFK,,14636.460000000001 -YLYENNMLHGNTMTVTGDTLAERAK,,10940.460000000001 -FGLPHADDVLGLPIGQHIVIK,,15299.820000000002 -ELESAAYDHAEPVQPEDAPQDIANDELK,,10986.78 -NVNYAFVEYHQSHDANIALQTLNGK,,11810.64 -DTTPLMADDVADLIVYATSRK,,15414.84 -ENVFIVSPYNHTNLPTVTPTYIK,,14260.74 -GHFQYCFLNENTGIETK,,10707.48 -QFANSNNNNNDSGNNNQGDYVTK,,4825.902 -PVDEADNEDMIATIK,,9590.1 -VACQLIMRGLSGEYK,,10601.699999999999 -FEDTLDNIIQSCQPNAIGINGPNPK,,14487.54 -ATIQVYEETAGLTVGDPVLRTGK,,13018.86 -LIVDPMDAPDQPQDLTIDFERGLPVK,,15331.619999999999 -AIMPIIEGHDVLAQAQSGTGK,,12374.400000000001 -RITSIDQLPEWNFDGSSTNQAPGHDSDIYLK,,13982.640000000001 -GQLTPAQVYNYYPDLTNAHFK,,13307.34 -IGPVVAFTHNSTGPFVTQNK,,11271.6 -GVEEVEGEYELTEAVEDGPRPFK,,12837.54 -EVIQNYGDDGYVFSTGGGIVESAESRK,,12632.220000000001 -NGPNPTSSLEIINIDVGTNTLK,,14930.1 -LAAVPAAGPASAGGAAAASGDAAAEEEK,,8299.26 -FVEPPTTFSLSDLTIIPRVK,,15352.44 -ASDIMHDQGPVNTEDWLTK,,11496.18 -TLVSGNALVDIEPIYGPTYLPRK,,15096.54 -ELDLARPIYLPTASYGHFTNQEYSWEK,,14893.380000000001 -ILPEPTEGFAADDEPTTPAELVGK,,13751.039999999999 -KYETDCPLNTAVITPLK,,10744.68 -IPVLEQELVRAEAESLVAEAQLSNITREK,,15373.26 -NAVSTKPTPPPAPEASAESGLSSK,,6530.22 -LSDDIPPSAPMAINCEYGSFDNEHVVLPR,,14209.32 -NLYVGQEDNEMTIPTCSRSGDIIEPLLK,,14697.960000000001 -DESDAISHINTQSPYIYFK,,13028.16 -FCQEHDILVEAYSPLGPLQK,,13957.26 -HEIDATVAQGVEISK,,7748.4 -VYHIDESLIDDPQECADNYEK,,11235.78 -TYQGTEDVSDARSAITSAQELLDSK,,14287.14 -NWDSGCVEETGAVYPFGADK,,12507.6 -IITHEGLDAATVAGWSTK,,10671.72 -PGDHVIALYTAECGK,,8195.82 -TADNNSMPHGAVSSYAK,,5040.87 -IWCFGPDGNGPNLVIDQTK,,14671.14 -VFQVGNIVHPSVVVSNDEENNELVR,,13054.380000000001 -YNLPNGGPAPESVTNAIWEASK,,14309.22 -HVVFGEVVDGYDIVK,,12546.66 -DHGEGGIIVGSALENK,,8575.74 -GILFVGSGVSGGEEGARYGPSLMPGGSEEAWPHIK,,15273.720000000001 -PVSAFHDIPLYADK,,10921.32 -DGSDTCTSNLAPAYPISPVDNFNTHINVPHQFISK,,14465.16 -DQVALRMPELIPVLSETMWDTK,,15406.26 -NLDDSVDDEKLEEEFAPYGTITSAK,,14030.1 -LSDVYDLHFSPAGNYLSTWER,,15297.359999999999 -EHWSETTVAYQLPK,,9383.16 -HCLVNGLDDIGITLQK,,12981.54 -VDQCNTMFDFNDPSFDIQGK,,14737.2 -SISSNTHGFDLGADTK,,7318.9800000000005 -TCNVLVAIEQQSPDIAQGLHYEK,,14714.820000000002 -LTQNQEHTEENEVDATAK,,4211.994 -YSSSSDAASADDSTPLRDNDEAGNEK,,5450.118 -VFSLDPQYLVDDLRPEFAGYSK,,15365.880000000001 -SHDVVIIGGGPAGYVAAIK,,12107.039999999999 -CLTDDCDTITAFPPGHVYDSK,,11129.58 -ISSFEADNGPAWLQFCEK,,14552.46 -EPVVGVIYNPHINLLVSASK,,14642.28 -EYGADELFDYHDADVIEQIK,,14814.3 -LEDNSASMITNSISDLHITGENNVK,,13881.900000000001 -FTLEPTLIATLAELDVTVPINSDDVK,,15455.16 -HFNDFETPHPVVTIESK,,9622.86 -LVGYVSAGTVEYLYSHDDGK,,12619.5 -DYCDGFSYGCPPHAGGGIGLER,,11000.64 -EIQSVASETLISIVNAVNPVAIK,,15448.98 -MVSYISGNYDTDEGFDELRTQIEK,,13942.019999999999 -NENAVSTDTSTPAAAGAPEGKPPQK,,5121.414 -PVSIASYNHLGNNDGYNLSAPK,,10014.42 -IVDTNGAGDAFAGGFMAGLTK,,14874.119999999999 -HFNDYDFVSAENADDDFK,,11333.7 -NPTGNTVVMQPQFK,,8081.339999999999 -EAIDALNGMLLNGQEIYVAPHLSRK,,15110.52 -CSMASRIDNYSEEPSNVFGSVLK,,13145.16 -QPLESASNNNGQSLFVK,,8751.06 -HLAGGLVNVLNGANQPVPEDLIK,,14731.08 -LNDAEDRVVVCATIDNLLK,,15299.820000000002 -CGNADVVVAGGCESMTNAPYYMPAARAGAK,,10806.900000000001 -HGAVHLSTYLK,,5825.9400000000005 -YIASGENHCFALTK,,7057.56 -TAGIQIVADDLTVTNPK,,12588.48 -DVPANASFNLEDPNIDFERVILNAVESLK,,15465.0 -ALRQLPTILADNAGFDSSELVSK,,14376.6 -DLRQLDANSSTTALDALNK,,11274.0 -PEENALTDEEK,,4514.4 -QLVGETISDANMNTLAGGK,,10942.44 -IAGCSPNISVNVGDFTK,,11566.14 -VESHLLNMSQDDLASK,,8746.2 -FTTEQLEAFNHQFESSNFTAR,,13447.44 -QSISDAELLLSSTIK,,14721.960000000001 -VSDGSSEIFFK,,10040.34 -TLPLDDLIAHLK,,15351.240000000002 -VQGSVIGIDLGTTNSAVAIMEGK,,15320.64 -NGNEYNEFLYSK,,9075.84 -AIGALASNNSGVDEHNQILNK,,7998.18 -DYEAAIELRENAHLK,,9192.66 -SPILHWNEFQALK,,12613.5 -TDAQAPTTGFSFGSQLGGNK,,10954.68 -TFQLEPSMFGLEEHELSK,,14715.84 -KPLESYDEDNK,,3707.382 -ITSSQVREHVK,,3572.034 -IIENAEGSRTTPSVVAFTK,,8747.82 -PAFSFGAKPDEK,,6880.8 -WFGWPGLEIPDDEK,,15394.02 -FNNQFEPCAGNITNYLLEK,,14952.300000000001 -YLNDNQREILLNYGGEK,,9950.82 -ITADTQAVTHAAFLSMGSAWAK,,14383.62 -AQAQVEVAQQEYDYYNDLLK,,14092.26 -ELDTAQKHIDAGAK,,3877.476 -FRAPEALFHPSVLGLESAGIDQTTYNSIMK,,15308.4 -FDDVDGKPLVEK,,6476.28 -PQDFQVDYSSEK,,7087.08 -QVLVASSEVLYAEAADEK,,12291.3 -LSICGEESFGTGSNHIREK,,7405.4400000000005 -LVEDPQVIAPFLGK,,14446.2 -ARIMADDYGWDVTDARK,,9335.460000000001 -DYPINSFYSIDLNK,,14460.480000000001 -YGSANVEGTILK,,7966.200000000001 -HSWNYDENFPNEVDPDQK,,9816.3 -HMDYDGNIVK,,5516.358 -TEQPFYDLSLHLK,,13022.039999999999 -PLLDASGVDPREEDK,,8013.839999999999 -NQRAPTAAELQAPPPPPSSTK,,6436.68 -GVIYDCPNSRLMEQK,,7740.539999999999 -PVTAPLPDIEGQTNEGQIGLMK,,13496.880000000001 -QLIEDVQLSRPLAK,,10087.14 -VSSNVQQPQQQLGDMENNLEK,,9814.98 -NPEDLNIWTSTK,,10723.14 -TGLQCRLCGNDHMTMNCPFK,,8905.02 -TFDEHNAPNSNSNK,,3196.488 -WASQMNNIQPTLFPDHNAK,,11553.9 -ISQDTGAFIEETK,,7778.280000000001 -VIVTSSTAALVTPTDMNK,,10597.26 -SLQDIIAILGMDELSEQDK,,15444.060000000001 -FVQGLLQNAAANAEAKGLDATK,,12610.74 -LLDAVDEYIPTPERDLNK,,13369.08 -GAHAAFVPTLLEAAYK,,13315.86 -LGGTPLETRSTADDSLNNSYK,,7580.82 -CCMVTSSWQVFDK,,12298.14 -NTRETAQAINGWELTK,,8613.119999999999 -GHLIIENAPLIEEVK,,12237.119999999999 -TDVVDSTNLPILELLSLK,,15452.7 -LYGENVTIPMLNPK,,13407.84 -FSGNDTFQTFYK,,10667.220000000001 -ISGAMTNAIFK,,9687.06 -YEYEVHANFK,,6805.56 -VPANEIEAISSPLMGIFEK,,15398.94 -QTEIAQHIFTK,,7122.24 -TGDGDIENIPYGVLVWATGNAPREVSK,,15362.22 -EDIIANNAIEEIDRNLNK,,13948.56 -WGFTNLDRPEYLK,,12278.46 -EYVPLDNAEQSTSSSQETK,,7159.62 -TIAETLAEELINAAK,,15413.579999999998 -IEVQTLQNLTNTLDEQETETK,,14006.58 -QDLPEAMSAAEITEK,,10367.28 -HYAGDVTYDIDGITDK,,9849.78 -PQNDYIERHIK,,4839.858 -TVPYLNSNRNVASYLQSNSSQEK,,10385.58 -ETNPGTDVTVSSVESVLAHL,,15416.46 -FANILESATLNTVQQDGIMTK,,15287.640000000001 -QFGGQHDASRVFLCNSGTEANEAALK,,9281.039999999999 -YFTSVSQQQFAVIK,,11476.259999999998 -DHDANFSSELK,,5716.553999999999 -ELEDQHHVVVK,,3979.9979999999996 -IIQHVALPWDAPCPWDESK,,14361.0 -PVFQDLYESTK,,10401.12 -EPADDYVLEASIK,,10609.44 -FLEDLGYTYHDETDNTVYQK,,11638.92 -APLVIADPTYFPEK,,12762.9 -GLGNPLLYDGVERVSLQDIK,,14511.42 -AGGVVEATVCYSGDMLQPGK,,12097.56 -DIGGSSSTTDFTNEIINK,,11537.94 -VSIGTDAVYAAEK,,8379.48 -LDAPSSPHACPGK,,4114.23 -NPSTLDDHVLAK,,5845.434 -ETCRQLGLGTNIYNAER,,8634.36 -NLKHIDVTFTK,,6666.78 -LYTDSGLIYPPK,,10250.4 -IEAALSDALAALQIEDPSADELRK,,15340.199999999999 -HFLETVVENLK,,12740.58 -VLAESNVSYAEESSSHNVLLLQRFPISEAQK,,13532.46 -IAPAGYLVTGSWSQK,,11712.960000000001 -AFTSYDRTLLIADSRRPEPK,,8807.76 -IADISLAAFGRK,,10630.92 -HEDLVNSIESK,,6279.900000000001 -NQWGNYGFYPVTVGSASK,,13045.619999999999 -VGGVVLGYEGLK,,10330.92 -ESGTHHIDGQAIYDK,,4559.742 -DVDGFGPTNIGELNK,,11659.32 -HAVSEGTRAVTK,,2626.116 -EELYIRPPPHLGMNDK,,9618.6 -HLQLAIRNDDELNK,,7005.54 -GTGVVTCVPSNSPDDYITTK,,9773.46 -KEDIVSNINESK,,5054.910000000001 -STSPAANSADTPNETGK,,3439.662 -ELGLTVVTDEAIEQMRK,,14657.640000000001 -FALMRLGDDYQPPATEGLSGNESLVEK,,14246.94 -VNGLQDIFQIAK,,14781.84 -AHYCGTPYLAK,,5113.374 -QSFTRVGENNLTCISCFQPVLNK,,12898.86 -NGAGDDDGVEGSTPK,,3465.816 -TVLFLPGFDHAGIATQSVVEK,,14955.96 -LGYHFGPSDAEAVMAPK,,11013.36 -NLSDETTSQAFQEPK,,7581.36 -ADVDGFLVGGASLKPEFVDIINSRN,,15336.54 -LLTIFDSVDESARIK,,13555.74 -ALEMVSDSTRETVLK,,8812.74 -APSLFGGMGQTGPK,,10003.86 -PSDPTTAQPQTLK,,4771.326 -KKEEEEQLSEEDAK,,3159.6059999999998 -TMLDASMGEILVTLEK,,15407.519999999999 -EQFSDGVGYSWIDGLK,,14878.14 -AEGATGGLVPHK,,4350.39 -IDANTNNINHVK,,4194.24 -LGGEVFIDFTK,,13501.98 -LSQYSIDEDDDIENRMVMPK,,11336.699999999999 -DDIATVERLLTDSSK,,14872.08 -STLNDGIAANFDGISK,,11241.18 -RFLNPDAMLLMETEGMNQASNPK,,14897.880000000001 -HALIVYDDLSK,,8763.84 -NQTASSLTIEDPAITFTHDK,,11410.44 -EANEPTVDDVADRK,,5092.584 -IPYFNAPIYLENK,,14128.08 -EFQSDLTHQLATTVLK,,13147.08 -AVRLEATDYATRAWPGGVGDK,,10244.64 -THGAPTDEVRHVGDMGNVK,,5717.3099999999995 -LVGPTAEVIGAVSGGVDSTVASK,,14324.1 -LVEEDDMHTFNEWFAK,,13968.960000000001 -LDPSTPVSLINDPRHPYGK,,10366.98 -AISSDQLFGRGSFDEAANREAHDK,,8908.92 -IADQATENVADISNLK,,9695.7 -YFRGSELQNYFTK,,10270.32 -INPDDRIDILGLAELAPGK,,15327.960000000001 -IEELQYDSTLSLPSSEFSK,,13529.58 -ASYPVYVSEYDDVK,,10060.5 -LLQAVFSRVMNASDDK,,13140.84 -PNWDEELPK,,9013.62 -DRPNWGYGTPATGVGK,,7684.800000000001 -MIVPTYGDVLDASNRIK,,13070.1 -NAVSVDMSHFEWAK,,11749.199999999999 -FESYDPVTGTYSYTIDHPVLT,,14337.779999999999 -FYSTQIEELPSDIATLLN,,15429.3 -CTGGIILTASHNPGGPENDMGIK,,9928.62 -KFENLPDLISFK,,14100.599999999999 -TLVGHSGTVYSTSFSPDNK,,7903.02 -ICSGSTTISESQTFK,,6611.5199999999995 -TLTTVQGVPEEYDLK,,10451.16 -IGLDCASSEFFK,,12388.32 -WLYGDSEAESFNVNFTHK,,13068.36 -LNDMSNALLSLNACPMSQVK,,14564.16 -PMCVEAFSEYPPLGRFAVR,,14928.18 -ETIARVTGGMK,,4901.67 -LIAHSVATYADSFEGFPK,,12525.06 -HCTISSDTDSDSGNAK,,2779.536 -PTLETMETVK,,7215.0 -YDHEASSSYK,,2624.64 -IVSEGPAHTGAK,,2999.7360000000003 -AILESVGIEIEDEK,,12924.720000000001 -NVPQGIAVTRYHSLAGTESSLPSCLK,,10718.7 -FGFSGFPVTEDGK,,12869.64 -NLSLLAEEVVDNIVNK,,15433.019999999999 -NILAESNSSLDNIVK,,11633.039999999999 -WLQNEGIPAVYGVDTRSLTK,,13082.039999999999 -NARTFLVSGVAANVHEAK,,8061.78 -RFGLEGLESVVPGIK,,14769.48 -EDGHLEDGLSK,,4453.956 -AEWGSDIPTTK,,7647.96 -GEDSRMYSTSNYSISLVDLENGPK,,13054.380000000001 -ADEGIFTPGVGGGGIGIVK,,13372.92 -VIITGAPAAFSPTCTVSHIPGYINYLDELVK,,15391.560000000001 -VDHFLANEAK,,4898.316 -VIWPTFHYILNPSNEGEQEK,,14972.640000000001 -NANLNNSRNANAPGEAGHQNK,,2955.498 -NVACVLNFDLPTTAK,,13983.84 -TVVAEYAIAQSLK,,11216.82 -FSGTVSSDVFK,,8696.4 -LPNCPRPNMSICIFGDAFDVDRAK,,14486.58 -SIIGERLLQSTQGIPSYIVSSK,,15121.74 -TIATSDNSPYCGIVHETK,,7427.58 -RTDNLDELVQASGFSK,,11702.640000000001 -LTLSDSSTPLTEDLIK,,13638.539999999999 -LDIFDDNAAHLK,,10388.64 -YDAIVKPTTDK,,4859.5380000000005 -LWIDEPFNLDFDPRDVIK,,15397.679999999998 -DSSVLEFDSIEEIPNERK,,12884.16 -YGPVHVVNLLSTK,,10512.359999999999 -PVDDENVILNK,,7202.04 -SYELPDGQVITIGNER,,12901.74 -TLSGNDYINASYVK,,9111.12 -VVGASVVVVK,,6846.54 -SSIEDLNHPLAVEINNIAGVK,,14638.5 -NFDSARPAGQNK,,3211.806 -DLNQEGDDQMFVRK,,7494.4800000000005 -GSQAMVMVEEK,,6284.1 -ITELGLEHEPLRLYWNK,,13054.08 -ELLSLNSQIGELSIENENLK,,15244.14 -FEENCAHSWIK,,6911.4 -SQEVEYVDPTVNRVPNYEVSEK,,9678.9 -SNDDEDLLLTADDSTRLK,,10702.980000000001 -PSQCQICQSK,,2993.73 -HCEYYQCGK,,3211.806 -EHLTFNWPEFK,,13305.42 -SNEFDSAIIQSVQGDSNLVTK,,14184.720000000001 -SHVDINMSTIK,,6660.48 -TAPVSSTAGPQTASTSK,,3843.276 -FDMSLQQDEENNELEAGEK,,10568.16 -ELGAGHYALVK,,6764.219999999999 -EEPEQEEIAPSLPSRNSIPAPK,,9710.1 -ITIQLTRDVEPENLQVTSEK,,11465.04 -LIGNFTLAGIPPAPK,,14351.1 -TSGYADRTAEFK,,4771.062 -QAQLTAATNAK,,3708.582 -FSLNTEYQANPETELINLK,,14548.8 -NSPSDFNKPDLPELIQRLNK,,13323.06 -VAGFSGGLVVDDPESK,,10529.34 -RLENFITNTFRK,,9685.26 -PTLYTDIEIPQLK,,14358.18 -DIYRENNGDITAFWK,,12637.019999999999 -ETGSTENTENNELQSRDDK,,3601.44 -LGEADDGAGTTDEDYMAWK,,10946.1 -NPNWLEEYHPK,,8834.1 -MSASDDTTAIFMTDTPK,,11503.26 -ALDADVVSIEFSK,,12569.820000000002 -ALYDTFSVFGDILSSK,,15435.48 -DEIRASVNEASK,,4412.382 -TLDEFGRSIAVGK,,9013.62 -SFRPPYSDAYVGVRQDSGDPVEYTK,,10155.9 -SLNHPENEPSSVK,,3664.668 -TFTNVGGQNLNNAK,,6076.14 -NNSTTTGNGPNNINNK,,2874.594 -DATESVAVEPSNEDVKPEEK,,6337.5 -HEATFELPQPDK,,7895.579999999999 -PIDSFNEVLTHHLEVIK,,13416.66 -QAPIAANESTIVSEWSK,,11786.64 -VVADDLVLVTPK,,11516.220000000001 -APYNISSLASEYALK,,13516.32 -AGIQNNDPINYGK,,6535.5 -DAVSVFGFRTQFGGGK,,13248.9 -YAGEVSHDDK,,2396.076 -PLFHSAILETSAK,,9384.3 -ADEVDYFHEEDRFFEK,,11794.08 -VLSVLQNAGFDDERDFK,,12131.34 -WTNQLTESASHFEQYTK,,10884.3 -SYNENQCVIISGESGAGK,,7087.08 -NNELHILDEQSK,,7270.5 -EWLVENHPDELK,,9020.76 -FDNLYGCRESLVDGIK,,10859.64 -NIVLHDYDLADK,,8825.64 -SPMPQNNTLGGENK,,4928.712 -GPDSYFLINHPAYVK,,11091.36 -EDDVRDFVIR,,9229.619999999999 -RGQVVSEEQRPGTPLFTVK,,7859.28 -LQNNEMDPEPVEEFQDDIK,,12283.68 -ALAYMGLEPNTPLK,,11895.3 -DLTNSHLVNEVPK,,7919.820000000001 -ESLGTHGYFK,,5762.622 -SNSHDLGYVK,,3888.264 -AYLPVNESFGFTGELR,,14632.92 -FRFDLNEDAMATEK,,11170.68 -AVPHYNRLDSYK,,5242.638 -PSETPATIVK,,5193.33 -STLINVLTGELLPTSGEVYTHENCRIAYIK,,15340.199999999999 -GQHLADILNSASRVPESNK,,9784.199999999999 -ALHISGNKPESNPEK,,3776.676 -ECDIVFSGLDADYAGAIEK,,14757.6 -EEFEHNFIGEESLPLDK,,12319.380000000001 -SLVVDSEGQIRYAK,,7944.24 -KDEDEEADEDMK,,2970.132 -SANHTLTDELTTATTNTNGDK,,7957.74 -HEAYLSILMDRQTK,,10284.539999999999 -NFNGNAEDSSTAGNTTHTEYQK,,4474.056 -QTEQLEAQVLQVNK,,9114.0 -HSNVVAGVGGASLNVLK,,9469.800000000001 -AGLLGVESIELTPHIISK,,14822.34 -SSFGEDALANLCIEK,,13265.939999999999 -YANEVRSQVFAQHPDK,,5705.1720000000005 -SALLEHFNTEDFK,,10714.859999999999 -QVILGYENDARPILYLK,,13395.599999999999 -PFSYQEPLPK,,8650.44 -WDDLNEQLTLLSK,,14109.48 -NSGGNNNTTDLK,,2508.198 -RGDVVAMTGDGVNDAPALK,,8556.900000000001 -DIYDDTERELAFYK,,13487.16 -FELGRQPANTK,,4946.316 -SGIINGVEEAARVASQLGQK,,15290.04 -QAATSVSATASK,,2870.37 -ENDQYDTLDVGHYCEK,,8213.58 -EVGITAVHVK,,5983.4039999999995 -TNFVSQHPSSYYAK,,6080.7 -VASANATAAESDVAK,,4488.455999999999 -YEGSGLSAVTRK,,4830.486 -QAALYNAAEDLDRDFFK,,15123.119999999999 -RLGDTLYEEFGISK,,12962.880000000001 -EQEEEVDDEVVNDMFGGK,,13156.08 -YGFAGFPVTTDGK,,11929.08 -ERLNVYFNEASSGK,,7921.32 -STTTMGLVQALTAHLGK,,15304.74 -SLEQIVNEYSTFSENK,,14606.94 -ASNTDVNVISWSDK,,9732.9 -SVLYHEGVRSQFSK,,5780.25 -TSMPYLISHMPSLIEK,,15281.52 -RRTFTTCADNQTTVQFPVYQGERVNCK,,8626.86 -FSHSLQFNSVPEWSTK,,11201.460000000001 -NGSEVEMITLNNIRVSMNK,,13128.18 -LPPHTQIFQAGTK,,6782.28 -VIDNSGAQLAECIK,,8496.42 -EHFLPTFNTEPLQDAK,,12017.76 -NGTLPPASLENGPTHAPVPIPFSLQHAAEPPVEK,,13856.039999999999 -LTDAEVDELLK,,11580.599999999999 -HIQQPISAMNTK,,5200.932 -IARFLEGQEYYEEALNISPDQDQK,,13836.960000000001 -PVALTSFLGYK,,13932.54 -HEEALEVDSLIK,,10023.300000000001 -EEPTIVTASNLK,,7912.679999999999 -DGVVIVNTARGAVIDEQAMTDALRSGK,,14235.179999999998 -VHSAFCDNLSTPVALK,,9897.84 -GINSSYAVWNDVTGK,,11567.16 -GFTNGAESRSDISQSTITK,,6302.04 -GITIQSAATYCSWDK,,10942.980000000001 -ILHQNGFSEQEIK,,6265.5 -GLEESHPNVK,,3654.414 -INHTEELLNILK,,12243.6 -EFHWEDVRGWSAEGGTNIGTAR,,12218.46 -MVDELENNGLIFVGK,,13799.76 -HMDLPEIAAK,,8027.280000000001 -EHIQQEESTYNDEAEIK,,5774.976 -PVNAPGGLPGLK,,8689.92 -LINDSVPDTIDTRVLNWPK,,14349.18 -IRVTPVDGLEGTVK,,8822.039999999999 -FEQVPDSLDDQIFVCEK,,13465.86 -LDDQWLVEVDDK,,12812.94 -KPFSFGTSQPNNTPSFSFGK,,11765.64 -NDFTNSTSNTQQYSIFK,,10844.7 -TNLDQFATGLVGTRSPYGK,,12294.24 -CEPLSEDDVARLCK,,8097.6 -DIEEEGDSIPLEQSTNGK,,9334.92 -GIDPMSLDVFAK,,15060.480000000001 -TLDVVERQLNAVLEEVK,,15367.140000000001 -VPMENYFPDYTGGSDINK,,12282.72 -KVNQGWGDDK,,3319.572 -YRQAITSAGSGCMAALDAEK,,7820.7 -YFNPNAQLIEDPLDK,,13066.259999999998 -NDYYGDTSATLTVDQIK,,10553.16 -STDVQRNEESSGHFFFTDK,,8075.94 -LTDDSSPIPYPEQPPIK,,10988.46 -YGGVYVGTLSKPEVK,,8028.48 -PSLVDVHLLESK,,11332.86 -AAPSPISHVAEIRSALEK,,10266.12 -ETSMPVIEYFETK,,14657.640000000001 -EADATPDDDRSSISSNSNK,,3583.1040000000003 -PNTSGTSDVEK,,2421.366 -AGRDYWWHEEAAK,,6782.28 -NLHVQFATLGDLK,,11742.960000000001 -SLIANIHRLGCTNTIVCNYDAREFPK,,12268.08 -ATNGVVIATEK,,4850.544 -DGSNSGYDLELIEHVK,,11583.24 -NYAVNVPLRDGIDDATYRSVFEPVIK,,14801.16 -HSNDSIFYPTK,,6243.839999999999 -STGNFMTLEQTVEK,,10773.66 -STSPNEIFSPSYSK,,8698.02 -IVFFTWSPDTAPVRSK,,14128.92 -VCHLMENIEK,,6227.22 -VAEYCGNK,,2693.412 -RSPENESLILNAVHK,,7407.0599999999995 -ALLSGCNDYLTK,,9203.22 -GVDAVCISDNYWLGTK,,13349.94 -NPFYGNDFK,,8070.84 -LVSNLHPSESLK,,5954.142 -PLDVVIPIGVLIK,,15408.720000000001 -VGLVCADTFRAGAFDQLK,,12964.02 -VNAITSQVVSK,,5746.998 -TGTLTTSETAHNMK,,3833.2740000000003 -QVANEAASDVLFGNESVK,,10826.880000000001 -LVTCEGSEEK,,3227.7599999999998 -PESALPLGK,,6247.5 -AEQERSTPNSGGMNK,,2515.866 -LFPWQNVGEFHASGK,,12689.400000000001 -KFEATQEENEQEMEK,,4644.701999999999 -LGPEITVAALK,,11063.46 -YVDEQSKNELK,,3631.104 -NDEDDTTENINESK,,3712.05 -DTTFLLFTSDDK,,14682.72 -HFICFDECK,,7745.34 -SFTSIQIYNHLMEK,,11990.279999999999 -SRTLLSVPGYDEK,,8836.86 -VCDIEYEQLCNVDSK,,9408.779999999999 -VELDAERIETAEK,,7285.08 -SDVAAPQGRIFASPLAK,,9708.119999999999 -KQEEVLDDTK,,3468.096 -DSEASSANSDEARAGLIEHVNFK,,9280.800000000001 -TQYSLSHDATLK,,5647.0380000000005 -AHQQPLDMCK,,4096.302 -GAHTIAVASTDEK,,3788.712 -GISTEQFELQNQEREK,,7471.2 -QEEAETLVQK,,4833.33 -LTFYEEVQPGTINEIYMK,,14838.119999999999 -VDGVDGEVELDDK,,7687.200000000001 -EVLPDLQNLNLNTDK,,13355.52 -RQELEEASQQQQLQQNSK,,4618.451999999999 -CPICDSYVRPK,,5464.074 -DFDIEDEENASLFQHNEK,,11699.94 -YETSILEHSGIRLIEPELFNGYNPEK,,15297.359999999999 -WWINVCEEHNVEPPEECK,,11938.740000000002 -ATHFYVAPTALRLIK,,11459.34 -CQINESAPK,,3544.896 -RIVQPYIEEMCNNGRLK,,8968.8 -ALVAQAIDERMDVDK,,9481.38 -YNHDVYWPALVETAREK,,11211.900000000001 -LAHLVDEEDWNITIK,,12777.18 -YAIDMTEQARQGK,,6110.099999999999 -ITAVIPQFPYSK,,12580.08 -DVLGEDGFWRVK,,12418.98 -HWGGGILGNK,,6744.54 -FEDTLNNSLLSDDDK,,10774.5 -SQGHPCPLYTVK,,5129.358 -EQAIVSVTPLLYYK,,14486.220000000001 -GIAFLEFDADK,,14273.16 -VGVEVEFGGK,,8098.799999999999 -NSEEDTDVITDK,,5107.5 -AIPELNARFLFIAPPSVEDLK,,15353.64 -TRTAASAGNGPTDDGTK,,2433.378 -LLLETDAPWCEIK,,14652.6 -VQDNLDQLNQELVGVK,,11948.88 -LPMEYITQGPDEVK,,10977.0 -LPVMEQYCIVGRK,,9720.0 -LIAKSNLAGK,,3876.864 -AMQFNTIGVSDGISMGTK,,12459.36 -DDEMIAVTSGNDGRLK,,7982.64 -FSSLTSTERDTPPSTTIEK,,7284.84 -NQAAMNPSNTVFDAK,,7852.320000000001 -VYAIEATCHAPK,,5519.4 -QQVVGEGTHFLVPWLQK,,14664.18 -MTSHLFGLK,,7972.679999999999 -EDIDQVFELFK,,15396.48 -DENISSENSEENTSDK,,3559.11 -PVAEHPLPEAEVDDK,,6208.32 -VTDDENINLIYMAK,,11856.240000000002 -ALIVTDPGIAAIGLSGRVQK,,13358.88 -VGLVTPTVMDIHTSEMGIK,,13154.7 -LDPTEIEEDVK,,10070.4 -GSMADVPK,,4051.464 -MSPYELVQHK,,7767.0599999999995 -NEEAAIDENSNVEAAERK,,5519.4 -GLPVLEEVEDEDAK,,12682.019999999999 -NPTNFTGGK,,3715.014 -YRPETAAEK,,2737.53 -LLNPTVASSMRK,,6409.8 -LNEFTDEWFK,,13521.18 -EQLEGSSGNNNIPMK,,6041.76 -ITLQLLDTSNQRFK,,11600.22 -EALQILNLTENTLTK,,15161.220000000001 -TFEQALEEERK,,6680.5199999999995 -LCNESSEVAALSGEISK,,10679.34 -LSMSPWGQLAK,,11739.119999999999 -VLGEVSSRTVDLVMSCGEK,,11832.06 -PGEGGELPAHK,,3267.678 -VCPQEDNRALLVMGEK,,8583.119999999999 -EYLPEMAASYSHPK,,8961.78 -QYAQEQGLLFREVSAK,,10204.619999999999 -PLPVEVMELDPADRSLDK,,13751.4 -ILAEETAAYEGNENEAEK,,7238.34 -YGAEYDLVDNK,,7780.98 -FVEGDNPEEFEK,,7608.6 -ASEFYYGFAGPK,,10716.72 -LDRVVCTNTVPFEEK,,8672.640000000001 -NCYRPLNEVLSTATLK,,11623.2 -ETSLFQGFK,,10662.18 -AYADSPENAIAVIK,,10301.94 -ASNISLGSVEQQQQQSITK,,8631.72 -DLSNAIGSYHVDLK,,11418.0 -VSSASGNAALSSNK,,3243.0899999999997 -PMTVVSQEWK,,8209.26 -IDYENHGVRK,,3282.846 -DAYAVRLGVEK,,7285.38 -TGFLNSAGFTENPTTIK,,12052.320000000002 -IRDIADNEVIELLK,,13550.04 -ANVGNVQQMDAK,,4617.618 -ELVETSEVRK,,4380.156 -LFDNFRSTPEIISLASK,,14645.7 -ANFSEYMNFLK,,14882.58 -EAMSNQTLLLPSDK,,9638.52 -PLDIRNEEQFASIK,,10047.72 -LPMIVVFNK,,13266.3 -TGTAISFVVPLK,,13192.320000000002 -IGLGYTLPELPNPITK,,15321.84 -HLTSYFTFK,,9528.539999999999 -RGYNEEQNYSEARK,,3262.08 -VALPIGIIASGIQYSMYDVK,,15448.98 -KPIDIDETIQK,,6544.74 -LWDVSNGQCVATWK,,11637.18 -SYPISEGPERANELVESYRK,,9889.02 -FSLPAPEAAQNNWK,,11377.679999999998 -QLLLLGNALPTVQSLSERK,,15307.14 -ALYSESAEELGALFHK,,14088.720000000001 -APIGTVVSPYSK,,7633.26 -YRPLTPSSEVYSPIEK,,8939.16 -CSIHLAGLEDSQYK,,8719.32 -ELSPNFSQEQTENK,,6572.22 -VTSSTSSSSSNGSVDVRK,,3086.484 -TAQQPQQFAPSPSNK,,5238.336 -DYSFVHLSAGDLLRAEQGRAGSQYGELIK,,14875.140000000001 -VNTLVRPNGTK,,3889.644 -LADTYISVFDK,,12138.84 -TAMPPTEVLLESIQSK,,15154.98 -EGVFDYVIK,,13072.5 -ANEESAADNLDHK,,3629.808 -SPAQLWAERK,,5945.706 -LVDFVHEHK,,5650.332 -YYNAVNPFEFMEDVATAGK,,15412.38 -LLDHLNPEYGISTQSAAGDFIK,,13473.779999999999 -AGTFLFTSESVGEGHPDK,,9933.0 -GFGFVCFSTPEEATK,,13506.960000000001 -PLFQNVPEQGEEPMK,,9844.02 -NPANFACDVCGK,,6801.36 -LVAPEMPLTEVRK,,9811.2 -SLSIAFENGDYAACEK,,10745.279999999999 -SAATFANQRIDPENK,,6133.62 -WCLSVNALDDVEEK,,13941.0 -PISNDETTLK,,4448.567999999999 -VNNCDTNEAAYK,,3335.688 -EQEVLDVWEK,,10785.06 -EGVSEACPVCEK,,4618.986 -LSLTHMVEAAK,,8235.72 -STTVMDILEK,,11577.6 -VYTEAAEDEIK,,6131.58 -VNVFLADMK,,10997.220000000001 -YEATAREWTK,,4796.994 -FALYTDFHHTVTPLSEK,,11567.16 -HTQDNVIMPLK,,6920.04 -NPQGYACPTHYLREM,,9093.36 -EPVVGDVIEK,,7632.360000000001 -NDANNEQWDSK,,3740.91 -RILNTADNPNTQLSSWK,,8909.460000000001 -SRVDQLNLNLTDDQIK,,10579.74 -SSRHTAADSSMNQMDAK,,3285.7019999999998 -VLELDPHFEK,,9169.44 -NALRSVELSELYLK,,11949.119999999999 -YDLEAWFPYQK,,15264.3 -LGGTTSDFSASSSVEEVK,,8289.78 -QASAAVSEKK,,1741.932 -SDGSAATSGFEFSK,,7647.96 -AQDENEGEFDHSK,,3240.732 -EQCIQIAEFYK,,11210.7 -SFYNNVFNFLMVDSPK,,15416.46 -CDICHYPIQFK,,9295.26 -LGVLIYELGELQDQFIDK,,15431.760000000002 -PSINGVNLELIKD,,12652.5 -VTAEHVLWYIPNK,,11303.039999999999 -DEEVFTLDPIVNK,,13809.36 -VSIGNTDQLTANK,,6716.28 -PMLYMEDRNLILTDGENENK,,12463.86 -EISDAQEIRLK,,6559.44 -VAFVESFGDNVVGVDYK,,14004.66 -RLAAPHHWLLDK,,7322.639999999999 -VFTREELTTLGNICVK,,11702.640000000001 -SGGNWAMTALGLGNSLGGSTK,,15129.42 -NLIYTLQTDELK,,12324.48 -FQSTDPSNSSHREK,,2624.376 -YYHEGIDPTK,,4778.892 -WKPEQENTVDFK,,7097.22 -DLESEYWPRLTK,,12226.68 -LNDCTNEAQQK,,2655.042 -LNTAISNLEVENK,,8696.699999999999 -SDDAANEHSCSGDCK,,1774.5300000000002 -PVRCVNCSK,,2262.594 -YDPWCDEMK,,8854.800000000001 -SRPSTAAAVFTTNK,,5384.88 -AALAASTTK,,2899.482 -RLSSTNIGNSIHEK,,4578.75 -FSTDDEEATSK,,3780.1440000000002 -NLATVEDVAK,,6683.1 -NTLEEYIYTLRGK,,13173.36 -LAYHTTTGQK,,3047.52 -MLVPSELSNASPDYIRSTLNAVPK,,15210.96 -ILTVDSNVNDWK,,10707.48 -ISPPVVAYR,,7817.099999999999 -YFHEDVSEDAY,,7874.219999999999 -TIVWNGPPGVFEFEK,,15320.64 -VVLDVDPAVYMINPK,,15123.9 -FIGVADYVSEETREPISYK,,12899.64 -LYDLLGVSPSANEQELK,,13129.859999999999 -PSTLTWSIK,,9691.74 -YNIQNWDSPEALDFK,,13883.28 -VGTIVGGQTSCK,,4303.206 -LWGDSFFNPK,,13809.66 -IASTWEGIQAAK,,8220.06 -LYTSYLDQCIK,,10222.92 -VVLLVPHCEGDGILK,,11603.28 -VGLSNYPLSASVACSK,,10124.820000000002 -LSIEGTPQVSQK,,6839.700000000001 -PIQDMISRGK,,6216.4800000000005 -QLPTPLVDDMNAK,,10574.16 -KLETLINQDFK,,9146.94 -KNDSDVPELATIPAAK,,8794.98 -DVCSVMNIWTK,,14038.5 -TATEVTPQEPK,,4080.858 -GFRELSDLTHK,,7229.700000000001 -AAASTPEPNLK,,4557.1320000000005 -GTNTHSYNVAQQQK,,3210.606 -ESLVCVNCK,,5635.428 -IEQYLQTYGASA,,9825.84 -ETSDDEEPQDK,,2177.796 -ESGSTPISISNAPTFNPK,,10510.08 -DSTGCVVGLSQPDAK,,7413.06 -LNNLRSTHDQNVK,,3210.258 -GIVVEAYSPFGSANAPLLK,,15129.42 -ITDGHVLMYIK,,9299.1 -ALNLNSESLENSSVEK,,8758.14 -TLGEGILEPTK,,9141.36 -AVASSGQELSVEER,,5657.526 -DTFDPNEVLAENK,,10804.44 -VEVNLAAIPLGK,,12689.16 -AQILENYNALFQQNIK,,13985.52 -TMTSFESGMDQESLPK,,9994.08 -DAEGERSSAINTSPISVVDK,,8900.880000000001 -SDDETINSVITTEENTVK,,11850.179999999998 -GILTTAREHHSSVK,,4163.016 -DLLENEQFLRIQK,,12527.640000000001 -WNAYQLLCK,,12076.92 -FFDHHQRGFFETFNEK,,10389.24 -TETGIDLENDRMAIQR,,8951.4 -YAGILDCFK,,11898.42 -ILGTSPDMIDSAENRYK,,9665.279999999999 -AETFHEMEK,,4078.0619999999994 -VSVATEQTK,,3148.314 -LDNCVMTSQGILK,,9620.52 -EIADNLTERQK,,4814.556 -FGELLYFDK,,13551.240000000002 -TNNVEEIMGNWNK,,11582.039999999999 -DFTYMDYLEK,,13787.34 -TVFHSDPNQLIDSAISK,,11553.66 -VDLPEYLLPPDLKPHPEEENENNDSK,,14271.54 -ESQPTIEEDLTRVYK,,10897.86 -NMTVPNPYDVDLNMEDIPACK,,14629.5 -SLSEVTSFVNENAPK,,11534.94 -HVTFGPYILGSTLGEGEFGK,,15142.56 -PVIDDATVK,,5453.886 -PFSTGDMK,,4693.794 -NGESLEGEEIK,,5388.6179999999995 -TVESGIVEDDK,,4989.24 -PFSTAVLAK,,7415.099999999999 -YSQHAVVDHDNK,,2777.856 -ELVQFAQDTLK,,11007.119999999999 -MHLIDLGIATEAEVK,,13432.56 -PGEAVIIPK,,6784.5 -VDEGSDVLNTWK,,9908.22 -LLQSFDHPNVSTIK,,8954.16 -NGLVLYCGDIITEDGK,,12682.320000000002 -FTYVTENTDPK,,6375.6 -LSSANNFQMDK,,5803.176 -SDVPEDPIYSK,,7094.4 -FGIAFISGTPSSSSEGLTIQK,,14631.960000000001 -HELSPSYFVK,,7777.74 -VPDVLHLNK,,8116.259999999999 -EEQQLLEQFLK,,14429.94 -EGIFQEELIYK,,12795.36 -LEDVHLPFPK,,10603.8 -TETGPESELLPEK,,8089.26 -RSTVAQLVQTLEQHDAMK,,13198.08 -EELVEIAYHK,,7987.92 -DTVTLELSCPSCDK,,9397.26 -ADGAYGVEK,,3463.134 -LSVVEDVSLLK,,12875.34 -LIAEWTRPIIGASDNYRDK,,11599.92 -EAENDEDSEVNAK,,2882.568 -SNDDSSLDATNHNSYK,,3980.532 -AENMIGEDRVNQFK,,7459.5599999999995 -ADILLLDEPTNHLDVSNVK,,13136.58 -IFEGIPPPYDK,,10519.619999999999 -LEINGNRLDEDSDALDLLQSK,,13613.88 -QNLYNAQGATIER,,6552.54 -ELRAQYGIK,,5089.5599999999995 -DAGTIAGLNVLR,,11996.4 -DEEVTTGVSDVAATK,,7054.98 -YIWSPINAANDSK,,11295.36 -ETTPEFWRAK,,7619.759999999999 -LQVANLQVASAK,,8533.26 -TQWEVSGYGDNVVGDNK,,9733.86 -TNNPETLVALR,,8878.14 -FITAPSGSVMDK,,8048.34 -MTPEEMRK,,3347.646 -ILATNPIMEAFGNAK,,15039.66 -LLVDVREPK,,7025.82 -VSQLFEEK,,6231.54 -CNDEQVFAITTK,,8806.38 -TTATAAQTSGTDNK,,2102.772 -EDGVPPTK,,3122.034 -GFTLAEVK,,8205.24 -YGMTLISDQEFEEYHILK,,14852.4 -MDNVISQLDTK,,9277.980000000001 -TIEFDESK,,5598.072 -ALFFPHLIGTAMDGTK,,15267.72 -IGGIGTVPVGRVETGVIK,,10665.480000000001 -ELHNAVSK,,2388.75 -VGYFSMHDINSFPTESGFATK,,13818.6 -YPYFPTK,,7988.459999999999 -FYTEHFGMK,,7293.660000000001 -ACIDEIQPFETTGK,,10327.5 -GFAFIDFK,,14108.22 -GNSALLEYTEK,,7685.28 -LQDLQNQIDEIENSMK,,15229.02 -ICSSEMINEQK,,5133.318 -VIDPIAPRHTAIVNPVK,,8547.960000000001 -NVQDLLILTR,,13593.18 -ELEQLVEFNRK,,9813.119999999999 -LSDSNTPVVEK,,4558.0199999999995 -TQDWRSHATQSLVFNTSDILAVK,,13986.78 -PLDIDYLK,,10750.92 -LIDCNSNPLK,,6865.68 -IAQDIFVVENK,,10116.66 -LEYSINDNEGVDEGK,,7709.400000000001 -IICSLNSLPTIGNGNLSLK,,14347.320000000002 -SRLLDLEQSSAK,,6964.0199999999995 -VLGVNFIEIAVK,,15379.38 -EYEEILAK,,6968.1 -DVTTTSANEGK,,2617.452 -GVVRNNYIGPGPPK,,5849.178 -LTSDDLNYFK,,10094.64 -LIIRFDDTNPSK,,9115.92 -YPSEFYTK,,6562.44 -LCSGVTIRNIK,,5959.782 -KWAAAGVCEK,,4282.259999999999 -SSVALATAQRGSAAIVPSQGK,,7719.360000000001 -FNDDFWEK,,10412.52 -NDAASAGHISK,,2474.3399999999997 -VDDLVLGK,,7175.76 -YSEGYPGER,,4356.834 -YDIWYDK,,9387.24 -YAGFSREINLK,,7927.14 -PLTYTGK,,3901.29 -PLTPADFRFTK,,10345.02 -QLEDGRTLSDYNIQK,,7492.799999999999 -LVFDEPFVK,,12554.88 -IQQPMSINEIK,,8494.08 -VFFLDSMK,,13090.92 -LGNFESPVLK,,9436.74 -STGLNYIDFK,,10501.5 -EYCDLITDLK,,11643.0 -AYRTPQNTITMFRPDK,,8103.18 -EDLIASIDSK,,9478.32 -NLIFQSCK,,7503.78 -SVNIEMGRTQQTK,,4820.226000000001 -GLADNGVFK,,7399.92 -TGEFDFEAAVK,,10761.66 -IIDVNAEHNNK,,3983.982 -QENVPTSYTSGLRERYMGVIEGMQITEAEK,,14298.84 -ESVNTFREMAK,,5878.2 -DWTGYLPERMK,,11433.48 -TEECEKASTK,,1510.296 -QSLDVSSDRRK,,3112.128 -TTGRQAQISNITAAK,,4678.956 -NFNGGVLMVSHDISVIDSVCK,,15199.26 -ALAIFVPVPSLAGFHK,,15351.240000000002 -SLLVFDVDK,,12676.02 -DLRGIIENSSK,,8783.82 -ITDVVNIGIGGSDLGPVMVTEALK,,15362.22 -LSEPFMELVDK,,13610.22 -RLMYDDLLK,,10120.140000000001 -SLNEQEEFEK,,5499.72 -AVDILSDILTK,,15323.099999999999 -LTEQNISQLK,,6453.0 -IEILYNTVALEAK,,13007.1 -TGTLTQNDMQLK,,6643.5 -SPSEWQDIIHQREQELQIK,,12846.3 -EDPDALTPEEVK,,7111.5599999999995 -VTLEYNANSK,,5512.38 -TPQEYESK,,2679.138 -SAVEVIEFVK,,11815.08 -TGLVSVDDDIYQK,,9679.32 -IIWETPLISK,,13209.539999999999 -CAGYDELYGYK,,8560.68 -DGSRAVDITNWNFIK,,13509.119999999999 -CILYDADESK,,6725.88 -FPLDVVYPDNNLK,,13360.02 -LLSDFFDGK,,13108.86 -LAEELGLVSRK,,7691.639999999999 -ELESYPTDK,,4731.066 -GLFISDDTTNPVIVARGYDK,,13076.52 -IEEEEYGEDAILK,,9348.720000000001 -ILVSGSWK,,7819.68 -DSTGIDNTLVLNEK,,9803.52 -EIPISDFINK,,12367.74 -YCEYLGK,,5192.142000000001 -MMNTSDSESTLYK,,6462.900000000001 -DATYTGIK,,4570.7519999999995 -ATETLWSLVK,,13180.380000000001 -CEVLSMK,,5609.603999999999 -SLGNVIDPLDVITGIK,,15403.800000000001 -PSNLLINSNCDLK,,8828.58 -MVINVPAEHVPR,,8595.480000000001 -INQALLGASSPK,,6644.52 -VSNWYHQK,,3879.84 -VDLMREESALEHQK,,7974.06 -TFLALQNFAESRK,,11520.06 -LETQLQENK,,4238.91 -IPAFTYEIGAIENSLLVLDPTYVK,,15467.519999999999 -TYFTLFEK,,12630.179999999998 -FSLSPSIEETRK,,7953.3 -FFADAVTK,,7585.8 -RSSTPQISNIESTGSGGMHK,,5589.102000000001 -ELQFIRVSSILK,,13609.68 -VTALFLPLRELK,,14200.44 -QFANTGANGQTNK,,3141.786 -SGIDEVQGAAAEK,,5525.748 -PYDVPGGK,,4366.884 -GLIMQNSLESSEIAK,,10495.98 -VLNADLDGETGK,,6227.22 -LIDVWYK,,10806.900000000001 -SCGGWADK,,3492.7619999999997 -TLAYGIPIVEK,,11622.119999999999 -DVTVPEPK,,5676.072 -VFSSALDVVK,,9947.880000000001 -GRETMYSVVQK,,5195.712 -DGEDIIFNYVPGPGTK,,14565.66 -SVPFPQAVK,,7473.96 -VSSTLNSK,,2515.5240000000003 -VEEMNNIIAASRRH,,6768.36 -VRALTEMK,,4297.872 -AMLYFTK,,8843.16 -QQLENLLVK,,9377.82 -LLLSAEVESLK,,12043.14 -VSWGPNMK,,6719.16 -LIDVAVEYGK,,9324.539999999999 -NDLYFRQEGVPMENTVRK,,8668.439999999999 -TYLQEHIK,,4859.5380000000005 -SYNQVNLDFVK,,10722.06 -VFHLQPVGNK,,6373.139999999999 -TIEEATPFIK,,9252.42 -PVSAVYIVK,,7573.86 -FDTSTWALPNK,,10976.76 -NTHDLHLRPGTY,,5527.194 -RFAHEIELVRK,,5728.38 -SFARIHETNLK,,4835.34 -YTREQVGCK,,2581.224 -DTPMLEVLK,,11919.720000000001 -DNFADEYVDEK,,8374.14 -ILSNLVYQYK,,10317.24 -LDQLEYQYHYLTK,,9716.220000000001 -GDLLFIDQPTGTGFSVEQNK,,14620.68 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a/streamlit/requirements.txt b/streamlit/requirements.txt deleted file mode 100644 index 99f5d5f..0000000 --- a/streamlit/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -plotly -pandas -statsmodels -streamlit>=0.88 -deeplc diff --git a/streamlit/streamlit_utils.py b/streamlit/streamlit_utils.py deleted file mode 100644 index c3a7df7..0000000 --- a/streamlit/streamlit_utils.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Streamlit utils.""" - -import logging - -import streamlit as st - - -class StreamlitLogger: - """Pickup logger and write to Streamlit front end.""" - - def __init__(self, placeholder, logger_name=None, accumulate=True, persist=True): - """ - Pickup logger and write to Streamlit front end. - - Parameters - ---------- - placeholder: streamlit.empty - Streamlit placeholder object on which to write logs. - logger_name: str, optional - Module name of logger to pick up. Leave to None to pick up root logger. - accumulate: boolean, optional - Whether to accumulate log messages or to overwrite with each new - message, keeping only the last line. (default: True) - persist: boolean, optional - Wheter to keep the log when finished, or empty the placeholder element. - - """ - self.placeholder = placeholder - self.persist = persist - - self.logging_stream = _StreamlitLoggingStream(placeholder, accumulate) - self.handler = logging.StreamHandler(self.logging_stream) - self.logger = logging.getLogger(logger_name) - - def __enter__(self): - self.logger.addHandler(self.handler) - - def __exit__(self, exc_type, exc_val, exc_tb): - self.logger.removeHandler(self.handler) - if not self.persist: - self.placeholder.empty() - - -class _StreamlitLoggingStream: - """Logging stream that writes logs to Streamlit front end.""" - - def __init__(self, placeholder, accumulate=True): - """ - Logging stream that writes logs to Streamlit front end. - - Parameters - ---------- - placeholder: streamlit.empty - Streamlit placeholder object on which to write logs - accumulate: boolean, optional - Whether to accumulate log messages or to overwrite with each new - message, keeping only the last line (default: True) - """ - self.placeholder = placeholder - self.accumulate = accumulate - self.message_list = [] - - def write(self, message): - if self.accumulate: - self.message_list.append(message) - else: - self.message_list = [message] - self.placeholder.markdown("```\n" + "".join(self.message_list) + "\n```") - - -def hide_streamlit_menu(): - hide_menu_style = """ - - """ - st.markdown(hide_menu_style, unsafe_allow_html=True) - - -@st.cache -def save_dataframe(df): - """Save dataframe to file object, with streamlit cache.""" - return df.to_csv().encode("utf-8") diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..2f769c4 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4303 @@ +version = 1 +revision = 3 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 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