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Replace triple-single-quotes with triple-double-quotes for doc strings

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Johannes Schönberger
2013-05-02 18:30:07 +02:00
parent 80827d3959
commit aa309fd926
1 changed files with 34 additions and 34 deletions
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skimage/measure/fit.py
+34 -34
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@@ -16,7 +16,7 @@ class BaseModel(object):
class LineModel(BaseModel):
'''Total least squares estimator for 2D lines.
"""Total least squares estimator for 2D lines.
Lines are parameterized using polar coordinates as functional model:
@@ -35,17 +35,17 @@ class LineModel(BaseModel):
A minimum number of 2 points is required to solve for the parameters.
'''
"""
def estimate(self, data):
'''Estimate line model from data using total least squares.
"""Estimate line model from data using total least squares.
Parameters
----------
data : (N, 2) array
N points with `(x, y)` coordinates, respectively.
'''
"""
_check_data_dim(data, dim=2)
@@ -69,7 +69,7 @@ class LineModel(BaseModel):
self._params = (dist, theta)
def residuals(self, data):
'''Determine residuals of data to model.
"""Determine residuals of data to model.
For each point the shortest distance to the line is returned.
@@ -83,7 +83,7 @@ class LineModel(BaseModel):
residuals : (N, ) array
Residual for each data point.
'''
"""
_check_data_dim(data, dim=2)
@@ -96,7 +96,7 @@ class LineModel(BaseModel):
@classmethod
def is_degenerate(cls, data):
'''Check whether set of points is degenerate.
"""Check whether set of points is degenerate.
Parameters
----------
@@ -108,12 +108,12 @@ class LineModel(BaseModel):
flag : bool
Flag indicating if data is degenerate.
'''
"""
return data.shape[0] < 2
def predict_x(self, y, params=None):
'''Predict x-coordinates using the estimated model.
"""Predict x-coordinates using the estimated model.
Parameters
----------
@@ -127,7 +127,7 @@ class LineModel(BaseModel):
x : array
Predicted x-coordinates.
'''
"""
if params is None:
params = self._params
@@ -135,7 +135,7 @@ class LineModel(BaseModel):
return (dist - y * math.sin(theta)) / math.cos(theta)
def predict_y(self, x, params=None):
'''Predict y-coordinates using the estimated model.
"""Predict y-coordinates using the estimated model.
Parameters
----------
@@ -149,7 +149,7 @@ class LineModel(BaseModel):
y : array
Predicted y-coordinates.
'''
"""
if params is None:
params = self._params
@@ -159,7 +159,7 @@ class LineModel(BaseModel):
class CircleModel(BaseModel):
'''Total least squares estimator for 2D circles.
"""Total least squares estimator for 2D circles.
The functional model of the circle is:
@@ -176,17 +176,17 @@ class CircleModel(BaseModel):
A minimum number of 3 points is required to solve for the parameters.
'''
"""
def estimate(self, data):
'''Estimate circle model from data using total least squares.
"""Estimate circle model from data using total least squares.
Parameters
----------
data : (N, 2) array
N points with `(x, y)` coordinates, respectively.
'''
"""
_check_data_dim(data, dim=2)
@@ -221,7 +221,7 @@ class CircleModel(BaseModel):
self._params = params
def residuals(self, data):
'''Determine residuals of data to model.
"""Determine residuals of data to model.
For each point the shortest distance to the circle is returned.
@@ -235,7 +235,7 @@ class CircleModel(BaseModel):
residuals : (N, ) array
Residual for each data point.
'''
"""
_check_data_dim(data, dim=2)
@@ -248,7 +248,7 @@ class CircleModel(BaseModel):
@classmethod
def is_degenerate(cls, data):
'''Check whether set of points is degenerate.
"""Check whether set of points is degenerate.
Parameters
----------
@@ -260,12 +260,12 @@ class CircleModel(BaseModel):
flag : bool
Flag indicating if data is degenerate.
'''
"""
return data.shape[0] < 3
def predict_xy(self, t, params=None):
'''Predict x- and y-coordinates using the estimated model.
"""Predict x- and y-coordinates using the estimated model.
Parameters
----------
@@ -280,7 +280,7 @@ class CircleModel(BaseModel):
xy : (..., 2) array
Predicted x- and y-coordinates.
'''
"""
if params is None:
params = self._params
xc, yc, r = params
@@ -293,7 +293,7 @@ class CircleModel(BaseModel):
class EllipseModel(BaseModel):
'''Total least squares estimator for 2D ellipses.
"""Total least squares estimator for 2D ellipses.
The functional model of the ellipse is:
@@ -318,17 +318,17 @@ class EllipseModel(BaseModel):
A minimum number of 5 points is required to solve for the parameters.
'''
"""
def estimate(self, data):
'''Estimate circle model from data using total least squares.
"""Estimate circle model from data using total least squares.
Parameters
----------
data : (N, 2) array
N points with `(x, y)` coordinates, respectively.
'''
"""
_check_data_dim(data, dim=2)
@@ -389,7 +389,7 @@ class EllipseModel(BaseModel):
self._params = params[:5]
def residuals(self, data):
'''Determine residuals of data to model.
"""Determine residuals of data to model.
For each point the shortest distance to the ellipse is returned.
@@ -403,7 +403,7 @@ class EllipseModel(BaseModel):
residuals : (N, ) array
Residual for each data point.
'''
"""
_check_data_dim(data, dim=2)
@@ -452,7 +452,7 @@ class EllipseModel(BaseModel):
@classmethod
def is_degenerate(cls, data):
'''Check whether set of points is degenerate.
"""Check whether set of points is degenerate.
Parameters
----------
@@ -464,12 +464,12 @@ class EllipseModel(BaseModel):
flag : bool
Flag indicating if data is degenerate.
'''
"""
return data.shape[0] < 5
def predict_xy(self, t, params=None):
'''Predict x- and y-coordinates using the estimated model.
"""Predict x- and y-coordinates using the estimated model.
Parameters
----------
@@ -484,7 +484,7 @@ class EllipseModel(BaseModel):
xy : (..., 2) array
Predicted x- and y-coordinates.
'''
"""
if params is None:
params = self._params
@@ -503,7 +503,7 @@ class EllipseModel(BaseModel):
def ransac(data, model_class, min_samples, residual_threshold,
max_trials=100):
'''Fits a model to data with the RANSAC (random sample consensus) algorithm.
"""Fits a model to data with the RANSAC (random sample consensus) algorithm.
Parameters
----------
@@ -574,7 +574,7 @@ def ransac(data, model_class, min_samples, residual_threshold,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])
'''
"""
best_model = None
best_inlier_num = 0