From 8b0e2e8e0628d11eae19b146007434563f2b84b4 Mon Sep 17 00:00:00 2001 From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com> Date: Mon, 23 Mar 2026 15:48:38 +0100 Subject: [PATCH] fpod_utils few tweaks --- pyproject.toml | 1 + src/post_processing/utils/fpod_utils.py | 51 +++++++++----------- uv.lock | 64 +++++++++++++++++++++++++ 3 files changed, 88 insertions(+), 28 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index ee3e533..96e3f32 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,7 @@ dependencies = [ "tqdm >=4.67.1", "gpxpy >=1.6.2", "notebook>=7.4.5", + "scikit-learn>=1.8.0", ] [dependency-groups] diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py index 776aac3..c2cdfcf 100644 --- a/src/post_processing/utils/fpod_utils.py +++ b/src/post_processing/utils/fpod_utils.py @@ -81,7 +81,7 @@ def pod2aplose( An APLOSE formatted DataFrame. """ - fpod_start_dt = [tz.localize(entry) for entry in df["Datetime"]] + fpod_start_dt = [tz.localize(entry.floor(bin_size)) for entry in df["Datetime"]] data = { "dataset": [dataset_name] * len(df), @@ -92,11 +92,9 @@ def pod2aplose( "end_frequency": [0] * len(df), "annotation": [annotation] * len(df), "annotator": [annotator] * len(df), - "start_datetime": [ - strftime_osmose_format(entry.floor(bin_size)) for entry in fpod_start_dt - ], + "start_datetime": [strftime_osmose_format(entry) for entry in fpod_start_dt], "end_datetime": [ - strftime_osmose_format(entry.ceil(bin_size)) for entry in fpod_start_dt + strftime_osmose_format(entry + bin_size) for entry in fpod_start_dt ], "type": ["WEAK"] * len(df), "deploy": df["Deploy"].tolist(), @@ -145,8 +143,8 @@ def load_pod_folder( df = read_csv( file, sep=sep, - dtype={"microsec": "Int32"}, - usecols=lambda col: col not in ["SmoothedICI", "ICIslope"], + dtype={"microsecond": "Int32"}, + usecols=lambda col: col not in {"SmoothedICI", "ICIslope"}, ).dropna() df["Deploy"] = file.stem.strip().lower().replace(" ", "_") @@ -281,7 +279,7 @@ def process_feeding_buzz( def compute_ici( df: DataFrame, -) -> Series[Any] | None: +) -> Series | None: """Calculate Inter-Click Intervals from feeding buzz timestamps. The Inter-Click Intervals are expressed in minutes. @@ -297,7 +295,6 @@ def compute_ici( A DataFrame containing non-negative Timestamps in minutes representing ICIs. """ - df = df.copy() df["ICI_minutes"] = df["Datetime"].diff().dt.total_seconds() / 60 return df[df["ICI_minutes"] > 0].dropna(subset=["ICI_minutes"]) @@ -305,7 +302,7 @@ def compute_ici( def log_ici( df: DataFrame, ) -> tuple[DataFrame, Any]: - """Convert ICI time deltas into minutes in order to process GMM. + """Convert ICI time deltas into minutes to process GMM. Parameters ---------- @@ -346,10 +343,12 @@ def gmm_ici( """ df, ici = log_ici(df) - gmm = mixture.GaussianMixture(n_components=comp, - covariance_type="full", - random_state=42, - n_init=20) + gmm = mixture.GaussianMixture( + n_components=comp, + covariance_type="full", + random_state=42, + n_init=20, + ) labels = gmm.fit_predict(ici) rank = argsort(argsort(gmm.means_.flatten())) @@ -415,7 +414,7 @@ def cluster_ici( gmm = mixture.GaussianMixture(n_components=comp, covariance_type="full") gmm.fit(ar_ici) - component_names = ["Buzz ICIs", "Regular ICIs", "Long ICIs",] + component_names = ["Buzz ICIs", "Regular ICIs", "Long ICIs"] cluster_info = [] for i in range(comp): means = sort(gmm.means_, axis=0)[i][0] @@ -474,11 +473,6 @@ def plot_gmm_ici( comp: int Number of GMM components. - Returns - ------- - tuple[plt.Figure, plt.Axes] - The matplotlib Figure and Axes objects for further customization. - """ _, log_ar = log_ici(df) _, gmm_icis = gmm_ici(df, comp) @@ -511,10 +505,12 @@ def plot_gmm_ici( x_axis, gmm_icis.weights_[idx] * stats.norm.pdf( - x_axis, gmm_icis.means_[idx, 0], sqrt(gmm_icis.covariances_[idx, 0, 0]), + x_axis, + gmm_icis.means_[idx, 0], + sqrt(gmm_icis.covariances_[idx, 0, 0]), ).ravel(), label=f"(μ={gmm_icis.means_[idx, 0]:.2f}," - f"σ={sqrt(gmm_icis.covariances_[idx, 0, 0]):.2f})", + f"σ={sqrt(gmm_icis.covariances_[idx, 0, 0]):.2f})", ) lines += [line] (mixture_line,) = ax.plot( @@ -639,8 +635,7 @@ def percent_calc( # Aggregate and compute metrics df = ( - data - .groupby(group_cols) + data.groupby(group_cols) .agg( { "DPh": "sum", @@ -1172,7 +1167,7 @@ def hist_mean_m( ax.set_ylim(0, max_value * 1.1) ax.set_ylabel(y_lab or metric_mean, fontsize=10) - # Only set x-label on last subplot + # Only set x-label on the last subplot if i == n_sites - 1: ax.set_xlabel("Mois", fontsize=10) ax.set_xticks( @@ -1257,7 +1252,7 @@ def hist_mean_h( ax.set_ylabel(y_lab or metric_mean, fontsize=10) ax.set_xticks(range(24)) - # Only set x-label on last subplot + # Only set x-label on the last subplot if i == n_sites - 1: ax.set_xlabel("Heure", fontsize=10) if metric_mean in {"%buzzes_mean", "FBR_mean"}: @@ -1293,7 +1288,7 @@ def hist_mean_s( Suffix for the title. 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