 read_file (filename, sheet_name=None, dtype=None)

 fix_strain_dtype (x)

 plate_to_list (filename, parameter, save=True)

 list_to_plate (filename, parameter, save=True)

 alphanumeric_sort_key (s)

 map_metadata (filename, data, save=True, sheet_name=None)

 default_plot_name (plot_name, title=None)

 save_plot (fig, save, plot_name, title, close=True)

 outliers_z_score (data, group, columns=None, threshold=1.96, ddof=0)

 outliers_madmedianrule (data, group, columns=None)

 detect_outliers (data, group, method='IQR', columns=None)

 canonical_wells (plate_format)

 normalize_wells (s)

 plot_heatmap (data, columns, plate_format, save=True, annotate=True)

 plot_histogram (data, columns, save=True)

 plot_strip_box (data, columns, exclude_strains=None, save=True)

 plot_qq (data, columns, save=True)

 subtract_background_absorbance (data, blanks, absorbance_column='Abs',
                                 contamination_thr=0.2)

 get_fluorescence_signal (data, fi_bg_col='FI_bg', fi_fp_col='FI_fp')

 signal_biomass_normalisation (data)

 analyse (filename, blanks, absorbance_column='Abs',
          contamination_thr=0.2, fi_bg_col='FI_bg', fi_fp_col='FI_fp',
          outlier_method='IQR', outlier_columns=None, save=True)

 aggregate_per_group (df, value_column)

 merge_methods_data (df_m1, df_m2, m1_column, m2_column)

 PassingBablok_fit (x, y)

 ODR_fit (x, y, sx=None, sy=None, lam=None, alpha=0.05)

 pooled_variance (variance, n)

 WLS_fit (x, y, variance, n, condition, alpha=0.05)

 compute_ci_wls_ols (results, x, alpha=0.05, num=200)

 compute_ci_odr (output, x, alpha=0.05, num=200, scale_x=1.0)

 compute_ci_passing_bablok (x, y, a, b, alpha=0.05, B=1000, num=200,
                            random_state=7, pb_fit=None)

 compute_r2 (x, y, a, b, w=None)

 plot_standard_curve_with_ci (x, y, a, b, ci, method_used, xlabel=None,
                              ylabel=None, title=None, point_labels=None,
                              weights=None, show_r2=True, save=True)

 plot_bland_altman (m1, m2, title=None, point_labels=None, xlabel=None,
                    ylabel=None, save=True, alpha=0.05, agreement=1.96,
                    **kwargs)

 fit_standard_curve (df_method1, df_method2, m1_column, m2_column,
                     method='auto', alpha=0.05, min_levels=3,
                     min_fraction_with_se=0.5, plot=True, save=True)

- `"wls"`: WLS with weights `w = n / variance`.
- `"odr"`: ODR with per-point `sx, sy` if enough groups; else Deming with
pooled variance ratio `lambda = Var_x / Var_y`; else WLS and fall back to Passing–Bablok.
- `"ols"`: OLS.
- `"pb"`: Passing–Bablok directly.
- `"auto"`: Auto tries ODR(sx,sy) → Deming (λ) → WLS(y|x) if y-variance available → PB;
x = (y-a)/b.

	Type	Default	Details
filename	str or os.PathLike		Path to the input file. Supported extensions (case-insensitive): `.csv`, `.tsv`, `.xlsx`.
sheet_name	NoneType	None	Excel sheet to read when `filename` is `.xlsx`. Defaults to the first sheet when `None`.
dtype	NoneType	None	Dtype(s) to enforce. For Excel files, pandas may not be able to enforce all dtypes on read; this function will attempt a best-effort post-cast for columns specified in a dtype dict.
Returns	pandas.DataFrame		DataFrame with the first column used as index.

	Type	Details
x	Any	A single value from the `Strain` column (scalar).
Returns	str or pandas.NA	Canonicalized string label. Missing values (NaN/NA) are returned as `pandas.NA`.

	Type	Default	Details
filename	str, os.PathLike or pandas.DataFrame		Path to a plate-layout table (CSV/TSV/XLSX; rows = letters A.., cols = 1..N), or an in-memory DataFrame already in plate layout. The first column is treated as the row index when reading from file.
parameter	str		Name for the value column in the long table (e.g., `Strain`, `Abs`, `FI_bg`).
save	bool	True	If True and `filename` is a path, write `<base>_<parameter>_long.csv` next to the input.
Returns	pandas.DataFrame		Long-format DataFrame with index `Well` (e.g., 'A1', 'B12') and one column `parameter`.

	Type	Default	Details
filename	str, os.PathLike or pandas.DataFrame		Either a path to a file loadable by `read_file` or a long-format DataFrame. Required columns: `Well`and a value column named by `parameter`.
parameter	str		Name of the value column to pivot (e.g., `Strain`, `Abs`, `FI_bg`).
save	bool	True	If True and `filename` is a path, write `<base>_<parameter>_list_to_plate.csv` next to the input.
Returns	plate_df: pandas.DataFrame		Plate layout with rows as letters (A..H/P) and columns as integers (1..12/24). Missing wells appear as NaN.

	Type	Details
s	Any	Value to transform into a sort key. Non-strings are converted to string.
Returns	tuple	A tuple of parts where digit substrings are converted to integers and non-digit substrings are case-folded strings. Suitable for use as the `key` in sorting.

	Type	Default	Details
filename	str or os.PathLike		Path to the metadata file accepted by `read_file`. Must contain a Well identifier either as a column named `Well` or as the index (first column in the file).
data	pandas.DataFrame		Data to be merged with metadata. Must also contain Well identifiers, either in a `Well` column or in the index.
save	bool	True	If True and `filename` is a path, write the merged table next to the input as `<base>_w_metadata.csv`.
sheet_name	NoneType	None
Returns	pandas.DataFrame		Merged table, sorted alphanumerically by Well (A1, A2, …, H12 / P24). The output contains a `Well` column and all columns from both inputs.

	Type	Default	Details
plot_name	Any		Base name of the plot (e.g., 'Heatmap').
title	NoneType	None	Extra descriptor appended after an underscore (e.g., condition, method).
Returns	str		A filesystem-friendly filename ending with `.pdf`.

	Type	Default	Details
fig	matplotlib.figure.Figure or matplotlib.axes.Axes		Figure to save. If an `Axes` is provided, its parent `Figure` is used.
save	bool or str or os.PathLike		If `True`, save to the current working directory using `default_plot_name(plot_name, title)`. If `str` or `PathLike`, treat as a file path. If the path has no extension, `.pdf` is appended. If `False`, do not save (early return).
plot_name	Any		Base name for the output file (e.g., `Heatmap`).
title	Any		Extra descriptor appended to the base name (e.g., condition/method).
close	bool	True	If `True`, closes the figure after saving to free memory.
Returns	str or None		Absolute path to the saved file, or `None` if `save` is `False`.

	Type	Default	Details
data	pandas.DataFrame		Input table. Can contain numerical and non-numerical columns.
group	str or list		Grouping variable(s) to apply for Z-score calculations (e.g. [`Condition`,`Strain`])
columns	NoneType	None	List of columns to check for outliers. If None, all numerical columns will be used.
threshold	float	1.96	Z-score threshold for defining an outlier.
ddof	int	0
Returns	tuple of pandas.DataFrame		1. data_wo_outliers: Data with outlier rows removed. 2. outliers: Detected outliers.

	Type	Default	Details
data	pandas.DataFrame		Input table. Required column `Well`. Can contain numerical and non-numerical columns.
columns	str or list of str		Name(s) of column(s) to plot.
plate_format	{'96', '384'}		Plate format used to generate a canonical list of wells (96 or 384).
save	bool	True	If True, save to CWD via `utils.save_plot` function. If a path, save there. If False, only show the plot.
annotate	bool	True	Whether to annotate each cell with its value.
Returns	matplotlib.figure.Figure		Figure containing the heatmaps.

	Type	Default	Details
data	pandas.DataFrame		Input table. Required columns `Condition` and the numeric column(s) to plot.
columns	str or list of str		Name(s) of numeric column(s) to plot.
save	bool	True	If True, save to CWD via `utils.save_plot` function. If a path, save there. If False, only show the plot.
Returns	matplotlib.figure.Figure		Figure containing the histogram plot.

	Type	Default	Details
data	pandas.DataFrame		Input table. Required columns: absorbance column, `Condition` & `Strain` ( only when blank is a string label).
blanks
absorbance_column	str	Abs
contamination_thr	float	0.2	Threshold for flagging/removing contaminated blank wells when using a string label (rows with `Abs >= contamination_thr` are dropped before computing per-condition blank means.

	Type	Default	Details
data	pandas.DataFrame		Input table. Required columns: background fluorescence intensity (e.g. `FI_bg`) & fluorescence intensity of the fluorescent probe (e.g. `FI_fp`).
fi_bg_col	str	FI_bg
fi_fp_col	str	FI_fp
Returns	data: pandas.DataFrame		Copy of `data` with a new column `Fluorescence`, representing subtracted background fluorescence intensity. Non-numeric entries (e.g., 'INVALID') are coerced to missing values.

	Type	Details
data	pandas.DataFrame	Input table. Required columns: `Absorbance` (biomass proxy with subtracted blank) and `Fluorescence` (background-subtracted fluorescence).
Returns	data: pandas.DataFrame	Copy of `data` with two added columns:`Lipids` = (FI/A), representing biomass normalised fluorescence intensity and `Log(Lipids)`, representing natural logarithm of `Lipids` variable.

	Type	Default	Details
filename	str or os.PathLike		Path to the input file accepted by function `utils.read_file`.
blanks	float or str		Background specification for absorbance subtraction passed to function `subtract_background_absorbance`
absorbance_column	str	Abs
contamination_thr	float	0.2	Threshold for flagging/removing contaminated blank wells when using a string label.
fi_bg_col	str	FI_bg
fi_fp_col	str	FI_fp
outlier_method	str	IQR	Outlier detection method; either 'IQR', 'Z-score' or 'MAD-Median'. Defaults to 'IQR'.
outlier_columns	NoneType	None
save	bool	True	If True, save the normalized + outlier-flagged table and the outliers table next to `filename` (CSV). If False, do not write files.
Returns	tuple of pandas.DataFrames		1. data: Copy of original data with added `Absorbance`, `Fluorescence`,`Lipids`, `Log(Lipids)` and `Outlier` (boolean) columns and removed blank wells. 2. data_wo_outliers: Data with outlier rows removed. 3. outliers: Detected outliers.

	Type	Details
x
y
Returns	float	Intercept (a) and slope (b).

	Type	Default	Details
x
y
sx	NoneType	None
sy	NoneType	None
lam	NoneType	None	Ratio `Var_x / Var_y` for Deming regression. When set (and `sx`/`sy` are not provided), implements Deming by scaling `x` by `s = sqrt(lam)` and performing ODR with equal errors.
alpha	float	0.05	Significance level for the two-sided confidence interval. Must satisfy `0 < alpha < 1`. The returned band has nominal coverage `1 - alpha`.
Returns	float, dict		Intercept (a) and slope (b). Pointwise 95% CI for the mean line with keys: `{"xg", "yline", "lo", "hi"}` as produced by `compute_ci_odr` function.

	Type	Details
variance	array-like	Per-group variances (ideally sample variances with `ddof=1`).
n	array-like	Per-group sample sizes.
Returns	float or None	The pooled variance. Returns `None` if no valid groups are found (i.e., after filtering to finite values with `n >= 2`) or if the denominator is zero.

	Type	Default	Details
x
y
variance	array-like or None		Per-group variance of the response. If provided, WLS is attempted using weights `w = n / variance`.
n	array-like		Per-group replicate counts corresponding to `variance`. Used only to form weights; values can be 1 or larger.
condition	str		Label used in warning messages to identify the subset being fitted.
alpha	float	0.05	Significance level for the two-sided confidence interval. Must satisfy `0 < alpha < 1`. The returned band has nominal coverage `1 - alpha`.
Returns	float, dict, {"wls", "ols"}		Intercept (a) and slope (b). Pointwise 95% CI for the mean line with keys: `{"xg", "yline", "lo", "hi"}` as produced by `compute_ci_wls_ols` function. `method_used` - The fitting method actually used.

	Type	Details
df	pandas.DataFrame	Input table. Required columns `Condition`, `Strain`, and the numeric column specified by `value_column`.
value_column	str	Name of the column to aggregate.
Returns	pandas.DataFrame	Aggregated table with one row per (Condition × Strain) and columns: `<value>_mean`, `<value>_sd`, `<value>_n`, `<value>_se`, `<value>_var`.

	Type	Details
df_m1	pandas.DataFrame	Input table for method 1. Required columns `Condition`, `Strain`, and `m1_column`.
df_m2	pandas.DataFrame	Input table for method 2. Required columns `Condition`, `Strain`, and `m2_column`.
m1_column	str	Name of the numeric value column in `df_m1` to aggregate.
m2_column	str	Name of the numeric value column in `df_m2` to aggregate.
Returns	pandas.DataFrame	Inner join of the two per-group aggregates on (`Condition` and `Strain`). Column names follow the pattern produced by function `aggregate_per_group`.

	Type	Default	Details
results	statsmodels.regression.linear_model.RegressionResults		Fitted results object (e.g., from `statsmodels.api.OLS` or `statsmodels.api.WLS`).
x	array-like		Original predictor values used to determine the plotting grid.
alpha	float	0.05	Significance level for the two-sided confidence interval. Must satisfy `0 < alpha < 1`. The returned band has nominal coverage `1 - alpha`.
num	int	200	Number of grid points between `min(x)` and `max(x)` when `min(x) < max(x)`. If all `x` are equal, a single-point grid is used.
Returns	dict		Pointwise 95% CI for the mean line with keys: `{"xg", "yline", "lo", "hi"}`. xg (grid of x), yline (fitted mean line at xg), lo (lower CI), hi (upper CI).

	Type	Default	Details
output	scipy.odr.Output		Fitted results object (e.g., from `statsmodels.api.OLS` or `statsmodels.api.WLS`).
x	array-like		Original predictor values used to determine the plotting grid.
alpha	float	0.05	Significance level for the two-sided confidence interval. Must satisfy `0 < alpha < 1`. The returned band has nominal coverage `1 - alpha`.
num	int	200	Number of grid points between `min(x)` and `max(x)` when `min(x) < max(x)`. If all `x` are equal, a single-point grid is used.
scale_x	float	1.0
Returns	dict		Pointwise 95% CI for the mean line with keys: `{"xg", "yline", "lo", "hi"}`. xg (grid of x), yline (fitted mean line at xg), lo (lower CI), hi (upper CI).

	Type	Default	Details
x
y
a
b
w	NoneType	None	Non-negative sample weights.
Returns	float		R² in `[-inf, 1]` for valid inputs; `np.nan` when undefined.

	Type	Default	Details
x
y
a
b
ci	dict		Confidence-band dictionary with keys: `{"xg", "yline", "lo", "hi"}`.
method_used	str		Label of the regression method shown in the plot title.
xlabel	NoneType	None
ylabel	NoneType	None
title	NoneType	None	Additional title line placed under the method label.
point_labels	NoneType	None	Text labels to annotate points.
weights	NoneType	None	Sample weights for R² (passed to `compute_r2`).
show_r2	bool	True	If True, append `R²` to the line legend.
save	bool	True	If True, save via `utils.save_plot` to the CWD. If a path, save there. If False, show the figure and do not save.
Returns	matplotlib.axes.Axes		The Axes object containing the plot.

	Type	Default	Details
df_method1
df_method2
m1_column
m2_column
method	str	auto	Fitting strategy.
alpha	float	0.05	Significance level for the two-sided confidence interval. Must satisfy `0 < alpha < 1`. The returned band has nominal coverage `1 - alpha`.
min_levels	int	3	Minimum number of control levels (rows) required to fit a model in a Condition.
min_fraction_with_se	float	0.5	Minimum fraction of groups with per-point SE available (both methods) to allow ODR with `sx`/`sy` (when `method='odr'` or `'auto'`).
plot	bool	True	If True, produce the calibration/standard curve plot and Bland–Altman plot per Condition.
save	bool	True
Returns	pandas.DataFrames		1. Copy of `df_method1` with added columns: `Calibration_Method`, `a`, `b`, `Lipids_predicted`. 2. Per-Condition agreement metrics (MSE, MAE, RMSE, R²).

API¶

00_Documentation.ipynb¶

utils.py¶

read_file¶

read_file¶

fix_strain_dtype¶

fix_strain_dtype¶

plate_to_list()¶

plate_to_list¶

list_to_plate¶

list_to_plate¶

alphanumeric_sort_key¶

alphanumeric_sort_key¶

map_metadata¶

map_metadata¶

default_plot_name¶

default_plot_name¶

save_plot¶

save_plot¶

qc.py¶

outliers_z_score¶

outliers_z_score¶

outliers_madmedianrule¶

outliers_madmedianrule¶

detect_outliers¶

detect_outliers¶

canonical_wells¶

canonical_wells¶

normalize_wells¶

normalize_wells¶

plot_heatmap¶

plot_heatmap¶

plot_histogram¶

plot_histogram¶

plot_strip_box¶

plot_strip_box¶

plot_qq¶

plot_qq¶

analyse.py¶

subtract_background_absorbance¶

subtract_background_absorbance¶

get_fluorescence_signal¶

get_fluorescence_signal¶

signal_biomass_normalisation¶

signal_biomass_normalisation¶

analyse¶

analyse¶

standard_curve.py¶

aggregate_per_group¶

aggregate_per_group¶

merge_methods_data()¶

merge_methods_data¶

PassingBablok_fit¶

PassingBablok_fit¶

ODR_fit¶

ODR_fit¶

pooled_variance¶

pooled_variance¶

WLS_fit¶

WLS_fit¶

compute_ci_wls_ols¶

compute_ci_wls_ols¶

compute_ci_odr¶

compute_ci_odr¶

compute_ci_passing_bablok¶

compute_ci_passing_bablok¶

compute_r2¶

compute_r2¶

plot_standard_curve_with_ci¶

plot_standard_curve_with_ci¶

plot_bland_altman¶

plot_bland_altman¶

fit_standard_curve¶

fit_standard_curve¶