Dose Response Curve Fitting (thunor.curve_fit
)
- exception thunor.curve_fit.AAFitWarning
- exception thunor.curve_fit.AUCFitWarning
- exception thunor.curve_fit.DrugCombosNotImplementedError
This function does not support drug combinations yet
- class thunor.curve_fit.HillCurve(popt)
Base class defining Hill/log-logistic curve functionality
- null_response_fn(axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)
Compute the arithmetic mean along the specified axis.
Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.
- Parameters:
a (array_like) – Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
axis (None or int or tuple of ints, optional) –
Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
Added in version 1.7.0.
If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.
dtype (data-type, optional) – Type to use in computing the mean. For integer inputs, the default is float64; for floating point inputs, it is the same as the input dtype.
out (ndarray, optional) – Alternate output array in which to place the result. The default is
None
; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. See ufuncs-output-type for more details.keepdims (bool, optional) –
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.
where (array_like of bool, optional) –
Elements to include in the mean. See ~numpy.ufunc.reduce for details.
Added in version 1.20.0.
- Returns:
m – If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.
- Return type:
ndarray, see dtype parameter above
See also
average
Weighted average
std
,var
,nanmean
,nanstd
,nanvar
Notes
The arithmetic mean is the sum of the elements along the axis divided by the number of elements.
Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.
By default, float16 results are computed using float32 intermediates for extra precision.
Examples
>>> import numpy as np >>> a = np.array([[1, 2], [3, 4]]) >>> np.mean(a) 2.5 >>> np.mean(a, axis=0) array([2., 3.]) >>> np.mean(a, axis=1) array([1.5, 3.5])
In single precision, mean can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.mean(a) 0.54999924
Computing the mean in float64 is more accurate:
>>> np.mean(a, dtype=np.float64) 0.55000000074505806 # may vary
Specifying a where argument:
>>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]]) >>> np.mean(a) 12.0 >>> np.mean(a, where=[[True], [False], [False]]) 9.0
- class thunor.curve_fit.HillCurveLL2(popt)
- classmethod fit_fn(x, b, e)
Two parameter log-logistic function (“Hill curve”)
- Parameters:
x (np.ndarray) – One-dimensional array of “x” values
b (float) – Hill slope
e (float) – EC50 value
- Returns:
Array of “y” values using the supplied curve fit parameters on “x”
- Return type:
np.ndarray
- classmethod initial_guess(x, y)
Heuristic function for initial fit values
Uses the approach followed by R’s drc library: https://cran.r-project.org/web/packages/drc/index.html
- Parameters:
x (np.ndarray) – Array of “x” (dose) values
y (np.ndarray) – Array of “y” (response) values
- Returns:
Four-valued list corresponding to initial estimates of the parameters defined in the
ll4()
function.- Return type:
list
- class thunor.curve_fit.HillCurveLL3u(popt)
Three parameter log logistic curve, for viability data
- classmethod fit_fn(x, b, c, e)
Three parameter log-logistic function (“Hill curve”)
- Parameters:
x (np.ndarray) – One-dimensional array of “x” values
b (float) – Hill slope
c (float) – Maximum response (lower plateau)
e (float) – EC50 value
- Returns:
Array of “y” values using the supplied curve fit parameters on “x”
- Return type:
np.ndarray
- classmethod initial_guess(x, y)
Heuristic function for initial fit values
Uses the approach followed by R’s drc library: https://cran.r-project.org/web/packages/drc/index.html
- Parameters:
x (np.ndarray) – Array of “x” (dose) values
y (np.ndarray) – Array of “y” (response) values
- Returns:
Four-valued list corresponding to initial estimates of the parameters defined in the
ll4()
function.- Return type:
list
- max_fit_evals = None
- static null_response_fn(_)
Compute the arithmetic mean along the specified axis.
Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.
- Parameters:
a (array_like) – Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.
axis (None or int or tuple of ints, optional) –
Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.
Added in version 1.7.0.
If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.
dtype (data-type, optional) – Type to use in computing the mean. For integer inputs, the default is float64; for floating point inputs, it is the same as the input dtype.
out (ndarray, optional) – Alternate output array in which to place the result. The default is
None
; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. See ufuncs-output-type for more details.keepdims (bool, optional) –
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.
where (array_like of bool, optional) –
Elements to include in the mean. See ~numpy.ufunc.reduce for details.
Added in version 1.20.0.
- Returns:
m – If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.
- Return type:
ndarray, see dtype parameter above
See also
average
Weighted average
std
,var
,nanmean
,nanstd
,nanvar
Notes
The arithmetic mean is the sum of the elements along the axis divided by the number of elements.
Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.
By default, float16 results are computed using float32 intermediates for extra precision.
Examples
>>> import numpy as np >>> a = np.array([[1, 2], [3, 4]]) >>> np.mean(a) 2.5 >>> np.mean(a, axis=0) array([2., 3.]) >>> np.mean(a, axis=1) array([1.5, 3.5])
In single precision, mean can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.mean(a) 0.54999924
Computing the mean in float64 is more accurate:
>>> np.mean(a, dtype=np.float64) 0.55000000074505806 # may vary
Specifying a where argument:
>>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]]) >>> np.mean(a) 12.0 >>> np.mean(a, where=[[True], [False], [False]]) 9.0
- class thunor.curve_fit.HillCurveLL4(popt)
- aa(min_conc, max_conc)
Find the activity area (area over the curve)
- Parameters:
min_conc (float) – Minimum concentration to consider for fitting the curve
max_conc (float) – Maximum concentration to consider for fitting the curve
- Returns:
Activity area value
- Return type:
float
- auc(min_conc)
Find the area under the curve
- Parameters:
min_conc (float) – Minimum concentration to consider for fitting the curve
- Returns:
Area under the curve (AUC) value
- Return type:
float
- ec(ec_num=50)
Find the effective concentration value (e.g. IC50)
- Parameters:
ec_num (int) – EC number between 0 and 100 (response level)
- Returns:
Effective concentration value for requested response value
- Return type:
float
- classmethod fit_fn(x, b, c, d, e)
Four parameter log-logistic function (“Hill curve”)
- Parameters:
x (np.ndarray) – One-dimensional array of “x” values
b (float) – Hill slope
c (float) – Maximum response (lower plateau)
d (float) – Minimum response (upper plateau)
e (float) – EC50 value
- Returns:
Array of “y” values using the supplied curve fit parameters on “x”
- Return type:
np.ndarray
- ic(ic_num=50)
Find the inhibitory concentration value (e.g. IC50)
- Parameters:
ic_num (int) – IC number between 0 and 100 (response level)
- Returns:
Inhibitory concentration value for requested response value
- Return type:
float
- classmethod initial_guess(x, y)
Heuristic function for initial fit values
Uses the approach followed by R’s drc library: https://cran.r-project.org/web/packages/drc/index.html
- Parameters:
x (np.ndarray) – Array of “x” (dose) values
y (np.ndarray) – Array of “y” (response) values
- Returns:
Four-valued list corresponding to initial estimates of the parameters defined in the
ll4()
function.- Return type:
list
- class thunor.curve_fit.HillCurveNull(popt)
- exception thunor.curve_fit.ValueWarning
- thunor.curve_fit.aa_obs(responses, doses=None)
Activity Area (observed)
- Parameters:
responses (np.array or pd.Series) – Response values, with dose values in the Index if a Series is supplied
doses (np.array or None) – Dose values - only required if responses is not a pd.Series
- Returns:
Activity area (observed)
- Return type:
float
- thunor.curve_fit.fit_drc(doses, responses, response_std_errs=None, fit_cls=<class 'thunor.curve_fit.HillCurveLL4'>, null_rejection_threshold=0.05, ctrl_dose_test=False)
Fit a dose response curve
- Parameters:
doses (np.ndarray) – Array of dose values
responses (np.ndarray) – Array of response values, e.g. viability, DIP rates
response_std_errs (np.ndarray, optional) – Array of fit standard errors for the response values
fit_cls (Class) – Class to use for fitting (default: 4 parameter log logistic “Hill” curve)
null_rejection_threshold (float, optional) – p-value for rejecting curve fit against no effect “flat” response model by F-test (default: 0.05). Set to None to skip test.
ctrl_dose_test (boolean) – If True, the minimum dose is assumed to represent control values (in DIP rate curves), and will reject fits where E0 is greater than a standard deviation higher than the mean of the control response values. Leave as False to skip the test.
- Returns:
A HillCurve object containing the fit parameters
- Return type:
- thunor.curve_fit.fit_params(ctrl_data, expt_data, fit_cls=<class 'thunor.curve_fit.HillCurveLL4'>, ctrl_dose_fn=<function <lambda>>)
Fit dose response curves to DIP rates or viability data
This method computes parameters including IC50, EC50, AUC, AA, Hill coefficient, and Emax. For a faster version, see
fit_params_minimal()
.- Parameters:
ctrl_data (pd.DataFrame or None) – Control DIP rates from
dip_rates()
orctrl_dip_rates()
. Set to None to not use control data.expt_data (pd.DataFrame) – Experiment (non-control) DIP rates from
dip_rates()
orexpt_dip_rates()
, or viability data fromviability()
fit_cls (Class) – Class to use for curve fitting (default:
HillCurveLL4()
)ctrl_dose_fn (function) – Function to use to set an effective “dose” (non-zero) for controls. Takes the list of experiment doses as an argument.
- Returns:
DataFrame containing DIP rate curve fits and parameters
- Return type:
pd.DataFrame
- thunor.curve_fit.fit_params_from_base(base_params, ctrl_resp_data=None, expt_resp_data=None, ctrl_dose_fn=<function <lambda>>, custom_ic_concentrations=frozenset({}), custom_ec_concentrations=frozenset({}), custom_e_values=frozenset({}), custom_e_rel_values=frozenset({}), include_aa=False, include_auc=False, include_hill=False, include_emax=False, include_einf=False, include_response_values=True)
Attach additional parameters to basic set of fit parameters
- thunor.curve_fit.fit_params_minimal(ctrl_data, expt_data, fit_cls=<class 'thunor.curve_fit.HillCurveLL4'>, ctrl_dose_fn=<function <lambda>>)
Fit dose response curves to DIP or viability, and calculate statistics
This function only fits curves and stores basic fit parameters. Use
fit_params()
for more statistics and parameters.- Parameters:
ctrl_data (pd.DataFrame or None) – Control DIP rates from
dip_rates()
orctrl_dip_rates()
. Set to None to not use control data.expt_data (pd.DataFrame) – Experiment (non-control) DIP rates from
dip_rates()
orexpt_dip_rates()
fit_cls (Class) – Class to use for curve fitting (default:
HillCurveLL4()
)ctrl_dose_fn (function) – Function to use to set an effective “dose” (non-zero) for controls. Takes the list of experiment doses as an argument.
- Returns:
DataFrame containing DIP rate curve fits and parameters
- Return type:
pd.DataFrame
- thunor.curve_fit.is_param_truncated(df_params, param_name)
Checks if parameter values are truncated at boundaries of measured range
- Parameters:
df_params (pd.DataFrame) – DataFrame of DIP curve fits with parameters from
fit_params()
param_name (str) – Name of a parameter, e.g. ‘ic50’
- Returns:
Array of booleans showing whether each entry in the DataFrame is truncated
- Return type:
np.ndarray