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---
title: BaseGateFitter (v0.29)
description: API reference for qiskit.ignis.characterization.BaseGateFitter in qiskit v0.29
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.ignis.characterization.BaseGateFitter
---
# BaseGateFitter
<Class id="qiskit.ignis.characterization.BaseGateFitter" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-ignis/tree/stable/0.6/qiskit/ignis/characterization/fitters.py" signature="BaseGateFitter(description, backend_result, xdata, qubits, fit_fun, fit_p0, fit_bounds, circuit_names, series=None, expected_state='0')" modifiers="class">
Bases: `qiskit.ignis.characterization.fitters.BaseFitter`
Base class for fitters of gate errors
See BaseFitter \_\_init\_\_
## Methods
<span id="qiskit-ignis-characterization-basegatefitter-add-data" />
### add\_data
<Function id="qiskit.ignis.characterization.BaseGateFitter.add_data" signature="BaseGateFitter.add_data(results, recalc=True, refit=True)">
Add new execution results to previous execution results
**Parameters**
* **results** (`Union`\[`Result`, `List`\[`Result`]]) new execution results
* **recalc** (`bool`) whether tp recalculate the data
* **refit** (`bool`) whether to refit the data
</Function>
<span id="qiskit-ignis-characterization-basegatefitter-fit-data" />
### fit\_data
<Function id="qiskit.ignis.characterization.BaseGateFitter.fit_data" signature="BaseGateFitter.fit_data(qid=- 1, p0=None, bounds=None, series=None)">
Fit the curve.
Compute self.\_params and self.\_params\_err
**Parameters**
* **qid** (`int`) qubit for fitting. If -1 fit for all the qubits
* **p0** (`Optional`\[`List`\[`float`]]) initial guess, equivalent to p0 in scipy.optimize
* **bounds** (`Optional`\[`Tuple`\[`List`\[`float`], `List`\[`float`]]]) bounds, equivalent to bounds in scipy.optimize
* **series** (`Optional`\[`str`]) series to fit (if None fit all)
</Function>
<span id="qiskit-ignis-characterization-basegatefitter-plot" />
### plot
<Function id="qiskit.ignis.characterization.BaseGateFitter.plot" signature="BaseGateFitter.plot(qind, series='0', ax=None, show_plot=True)">
Plot err data.
**Parameters**
* **qind** (*int*) qubit index to plot
* **series** (*str*) the series to plot
* **ax** (*Axes*) plot axes
* **show\_plot** (*bool*) call plt.show()
**Returns**
The axes object
**Return type**
Axes
**Raises**
**ImportError** if matplotlib is not installed
</Function>
## Attributes
### backend\_result
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.backend_result">
Return the execution results
**Return type**
`Union`\[`Result`, `List`\[`Result`]]
</Attribute>
### description
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.description">
Return the fitters purpose, e.g. T1
**Return type**
`str`
</Attribute>
### fit\_fun
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.fit_fun">
Return the function used in the fit, e.g. BaseFitter.\_exp\_fit\_fun
**Return type**
`Callable`
</Attribute>
### measured\_qubits
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.measured_qubits">
Return the indices of the qubits to be characterized
**Return type**
`List`\[`int`]
</Attribute>
### params
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.params">
Return the fit function parameters that were calculated by curve\_fit
**Return type**
`List`\[`float`]
</Attribute>
### params\_err
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.params_err">
Return the error of the fit function parameters
**Return type**
`List`\[`float`]
</Attribute>
### series
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.series">
Return the list of series for the data
**Return type**
`Optional`\[`List`\[`str`]]
</Attribute>
### xdata
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.xdata">
Return the data points on the x-axis, the independenet parameter which is fit against
**Return type**
`Union`\[`List`\[`float`], `array`]
</Attribute>
### ydata
<Attribute id="qiskit.ignis.characterization.BaseGateFitter.ydata">
Return the data points on the y-axis
The data points are returning in the form of a list of dictionaries:
> * **ydata\[i]\[mean] is a list, where item**
>
> no. j is the probability of success of qubit i for a circuit that lasts xdata\[j].
>
> * **ydata\[i]\[std] is a list, where ydata\[std]\[j] is the**
>
> standard deviation of the success of qubit i.
**Return type**
`List`\[`Dict`]
</Attribute>
</Class>