StereoNet tensor method added

src/apsg/config.py

@@ -110,6 +110,16 @@
         pivot="mid",
         units="dots",
     ),
+    stereonet_default_tensor_kwargs=dict(
+        planes=True,
+        alpha=None,
+        color=None,
+        ls="-",
+        lw=1.5,
+        marker="o",
+        mew=1,
+        ms=9,
+    ),
     stereonet_default_contour_kwargs=dict(
         alpha=None,
         antialiased=True,

src/apsg/database/_alchemy.py

@@ -8,7 +8,7 @@
 from datetime import datetime
 import contextlib
 
-from apsg.feature._geodata import Lineation, Foliation, Pair, Fault
+from apsg.feature._geodata import Lineation, Foliation, Pair
 from apsg.feature._container import LineationSet, FoliationSet
 
 from sqlalchemy import create_engine, event

src/apsg/decorator/_decorator.py

@@ -20,7 +20,8 @@ def wrapper(self, *args, **kwargs):
                 return method(self, *nargs, **kwargs)
             else:
                 raise TypeError(
-                    f'Unsupported arguments for {method.__name__}. Must be {" or ".join([dt.__name__ for dt in datatypes])}'
+                    f'Unsupported arguments for {method.__name__}. \
+                      Must be {" or ".join([dt.__name__ for dt in datatypes])}'
                 )
 
         return wrapper

src/apsg/feature/__init__.py

@@ -49,6 +49,7 @@
     "Stress2",
     "Ellipse",
     "OrientationTensor2",
+    "Vector2Set",
     "FeatureSet",
     "Vector3Set",
     "LineationSet",

src/apsg/feature/_container.py

@@ -6,7 +6,6 @@
 from scipy.cluster.hierarchy import linkage, fcluster, dendrogram
 
 from apsg.config import apsg_conf
-from apsg.helpers._math import sind, cosd, atan2d
 from apsg.math._vector import Vector2, Vector3
 from apsg.helpers._math import acosd
 from apsg.feature._geodata import Lineation, Foliation, Pair, Fault, Cone
@@ -75,7 +74,7 @@ def __getitem__(self, key):
             return type(self)([self.data[ix] for ix in idxs])
         else:
             raise TypeError(
-                "Wrong index. Only slice, int and numpy array for fancy indexing are allowed."
+                "Wrong index. Only slice, int and np.array are allowed for indexing."
             )
 
     def __iter__(self):
@@ -103,9 +102,9 @@ def bootstrap(self, n=100, size=None):
           >>> l = Vector3Set.random_fisher(n=100, position=lin(120,40))
           >>> sm = [lb.R() for lb in l.bootstrap()]
           >>> l.fisher_statistics()
-          {'k': 19.912361106049794, 'a95': 3.2490273703993973, 'csd': 18.151964734256303}
+          {'k': 19.91236110604979, 'a95': 3.249027370399397, 'csd': 18.15196473425630}
           >>> Vector3Set(sm).fisher_statistics()
-          {'k': 1735.3602067018592, 'a95': 0.33932243564473413, 'csd': 1.9444205467798013}
+          {'k': 1735.360206701859, 'a95': 0.3393224356447341, 'csd': 1.944420546779801}
         """
         if size is None:
             size = len(self)
@@ -198,7 +197,7 @@ def transform(self, F, **kwargs):
         """Return affine transformation of all features ``FeatureSet`` by matrix 'F'.
 
         Args:
-          F: Transformation matrix. Should be array-like value e.g. ``DeformationGradient3``
+          F: Transformation matrix. Array-like value e.g. ``DeformationGradient3``
 
         Keyword Args:
           norm: normalize transformed features. True or False. Default False
@@ -333,7 +332,8 @@ def from_xy(cls, x, y, name="Default"):
 
     @classmethod
     def random(cls, n=100, name="Default"):
-        """Method to create ``FeatureSet`` of features with uniformly distributed random orientation.
+        """Method to create ``FeatureSet`` of features with uniformly distributed
+        random orientation.
 
         Keyword Args:
           n: number of objects to be generated
@@ -473,7 +473,7 @@ def transform(self, F, **kwargs):
         """Return affine transformation of all features ``FeatureSet`` by matrix 'F'.
 
         Args:
-          F: Transformation matrix. Should be array-like value e.g. ``DeformationGradient3``
+          F: Transformation matrix. Array-like value e.g. ``DeformationGradient3``
 
         Keyword Args:
           norm: normalize transformed features. True or False. Default False
@@ -745,8 +745,8 @@ def random_normal(cls, n=100, position=Vector3(0, 0, 1), sigma=20, name="Default
 
     @classmethod
     def random_fisher(cls, n=100, position=Vector3(0, 0, 1), kappa=20, name="Default"):
-        """Return ``FeatureSet`` of random vectors sampled from von Mises Fisher distribution
-        around center position with concentration kappa.
+        """Return ``FeatureSet`` of random vectors sampled from von Mises Fisher
+        distribution around center position with concentration kappa.
 
         Args:
           n: number of objects to be generated
@@ -766,8 +766,8 @@ def random_fisher2(cls, n=100, position=Vector3(0, 0, 1), kappa=20, name="Defaul
         """Method to create ``FeatureSet`` of vectors distributed according to
         Fisher distribution.
 
-        Note: For proper von Mises Fisher distrinbution implementation use ``random.fisher``
-        method.
+        Note: For proper von Mises Fisher distrinbution implementation use
+        ``random.fisher`` method.
 
         Args:
           n: number of objects to be generated
@@ -1527,14 +1527,16 @@ def aMAD(self) -> np.ndarray:
     @property
     def MAD_l(self) -> np.ndarray:
         """
-        Return maximum angular deviation (MAD) of linearly distributed vectors. Kirschvink 1980
+        Return maximum angular deviation (MAD) of linearly distributed vectors.
+        Kirschvink 1980
         """
         return np.array([e.MAD_l for e in self])
 
     @property
     def MAD_p(self) -> np.ndarray:
         """
-        Return maximum angular deviation (MAD) of planarly distributed vectors. Kirschvink 1980
+        Return maximum angular deviation (MAD) of planarly distributed vectors.
+        Kirschvink 1980
         """
         return np.array([e.MAD_p for e in self])
 

src/apsg/feature/_paleomag.py

@@ -7,7 +7,6 @@
 
 import numpy as np
 
-from apsg.config import apsg_conf
 from apsg.helpers._helper import eformat
 from apsg.math._vector import Vector3
 from apsg.feature._geodata import Lineation, Foliation, Pair

src/apsg/feature/_tensor3.py

@@ -26,8 +26,8 @@ class DeformationGradient3(Matrix3):
 
     @classmethod
     def from_ratios(cls, Rxy=1, Ryz=1):
-        """Return isochoric ``DeformationGradient3`` tensor with axial stretches defined by strain ratios.
-        Default is identity tensor.
+        """Return isochoric ``DeformationGradient3`` tensor with axial stretches
+        defined by strain ratios. Default is identity tensor.
 
         Keyword Args:
           Rxy (float): XY strain ratio
@@ -122,8 +122,8 @@ def from_vectors_axis(cls, v1, v2, a):
         Return ``DeformationGradient3`` representing rotation of vector v1 to v2 around
         axis a.
 
-        If v1.angle(a) is not equal to v2.angle(b), the minimum adjustment of rotation axis
-        is done automatically.
+        If v1.angle(a) is not equal to v2.angle(b), the minimum adjustment of rotation
+        axis is done automatically.
 
         Args:
           v1: ``Vector3`` like object
@@ -200,13 +200,15 @@ def R(self):
 
     @property
     def U(self):
-        """Return stretching part of ``DeformationGradient3`` from right polar decomposition."""
+        """Return stretching part of ``DeformationGradient3`` from right polar
+        decomposition."""
         _, U = spla.polar(self, "right")
         return type(self)(U)
 
     @property
     def V(self):
-        """Return stretching part of ``DeformationGradient3`` from left polar decomposition."""
+        """Return stretching part of ``DeformationGradient3`` from left polar
+        decomposition."""
         _, V = spla.polar(self, "left")
         return type(self)(V)
 
@@ -231,7 +233,8 @@ def axisangle(self):
 
     def velgrad(self, time=1):
         """
-        Return ``VelocityGradient3`` calculated as matrix logarithm divided by given time.
+        Return ``VelocityGradient3`` calculated as matrix logarithm divided by given
+        time.
 
         Keyword Args:
             time (float): total time. Default 1
@@ -498,8 +501,8 @@ def I3(self):
     @property
     def diagonalized(self):
         """
-        Returns diagonalized Stress tensor and orthogonal matrix R, which transforms actual
-        coordinate system to the principal one.
+        Returns diagonalized Stress tensor and orthogonal matrix R, which transforms
+        actual coordinate system to the principal one.
 
         """
         return (
@@ -599,7 +602,8 @@ class Ellipsoid(Tensor3):
     """
 
     def __repr__(self) -> str:
-        return f"{Matrix3.__repr__(self)}\n(S1:{self.S1:.3g}, S2:{self.S2:.3g}, S3:{self.S3:.3g})"
+        return f"{Matrix3.__repr__(self)}\n\
+                 (S1:{self.S1:.3g}, S2:{self.S2:.3g}, S3:{self.S3:.3g})"
 
     @classmethod
     def from_defgrad(cls, F, form="left", **kwargs) -> "Ellipsoid":

src/apsg/math/_matrix.py

@@ -224,7 +224,8 @@ def __init__(self, *args):
 
     @classmethod
     def from_comp(cls, xx=1, xy=0, yx=0, yy=1):
-        """Return ``Matrix2`` defined by individual components. Default is identity tensor.
+        """Return ``Matrix2`` defined by individual components. Default is identity
+        tensor.
 
         Keyword Args:
             xx (float): tensor component M_xx
@@ -310,7 +311,8 @@ def __init__(self, *args):
 
     @classmethod
     def from_comp(cls, xx=1, xy=0, xz=0, yx=0, yy=1, yz=0, zx=0, zy=0, zz=1):
-        """Return ``Matrix3`` defined by individual components. Default is identity tensor.
+        """Return ``Matrix3`` defined by individual components. Default is identity
+        tensor.
 
         Keyword Args:
             xx (float): tensor component M_xx

src/apsg/math/_vector.py

@@ -508,7 +508,9 @@ def random(cls):
 
     @ensure_first_arg_same
     def rotate(self, axis, theta):
-        """Return the vector rotated around axis through angle theta. Right hand rule applies"""
+        """Return the vector rotated around axis through angle theta. Right-hand rule
+        applies
+        """
         v = Vector3(self)  # ensure vector
         k = Vector3(axis.uv())
         return type(self)(

src/apsg/pandas/_pandas_api.py

@@ -7,29 +7,6 @@
 from apsg.feature import FoliationSet, LineationSet, Vector3Set, FaultSet
 from apsg.plotting import StereoNet
 
-"""
-import pandas as pd
-from apsg import *
-from apsg.pandas import *
-df = pd.DataFrame({
-    'name':['A', 'B', 'C'],
-    'azi':[120, 140, 135],
-    'inc':[30, 28, 42]
-})
-
-df.apsg.create_fols().fol.R()
-
-df = pd.DataFrame({
-    'name':['A', 'B', 'C'],
-    'fazi':[120, 140, 135],
-    'finc':[30, 28, 42],
-    'lazi':[170, 120, 95],
-    'linc':[20, 26, 22]
-})
-
-df.apsg.create_fols(columns=['fazi', 'finc']).apsg.create_lins(columns=['lazi', 'linc'])
-"""
-
 
 @pd.api.extensions.register_extension_dtype
 class Vec3Dtype(PandasExtensionDtype):

src/apsg/plotting/_plot_artists.py

@@ -3,7 +3,7 @@
 from apsg.config import apsg_conf
 from apsg.math._vector import Vector3
 from apsg.feature._geodata import Foliation, Pair, Fault, Cone
-from apsg.feature._tensor3 import Ellipsoid
+from apsg.feature._tensor3 import Tensor3, Ellipsoid
 from apsg.feature._container import (
     Vector3Set,
     Vector2Set,
@@ -270,6 +270,20 @@ def parse_kwargs(self, kwargs):
         )
 
 
+class StereoNet_Tensor(StereoNet_Artists):
+    def __init__(self, factory, *args, **kwargs):
+        super().__init__(factory, *args, **kwargs)
+        self.stereonet_method = "_tensor"
+        self.args = args[:1]  # take max 1 args
+        self.parse_kwargs(kwargs)
+
+    def parse_kwargs(self, kwargs):
+        super().update_kwargs("stereonet_default_tensor_kwargs")
+        self.kwargs.update((k, kwargs[k]) for k in self.kwargs.keys() & kwargs.keys())
+        if not isinstance(self.kwargs["label"], str):
+            self.kwargs["label"] = self.args[0].label()
+
+
 class StereoNet_Contour(StereoNet_Artists):
     def __init__(self, factory, *args, **kwargs):
         super().__init__(factory, *args, **kwargs)
@@ -377,6 +391,13 @@ def create_arrow(*args, **kwargs):
         else:
             raise TypeError("Not valid arguments for Stereonet arrow")
 
+    @staticmethod
+    def create_tensor(*args, **kwargs):
+        if all([issubclass(type(arg), Tensor3) for arg in args[:1]]):
+            return StereoNet_Tensor("create_tensor", *args, **kwargs)
+        else:
+            raise TypeError("Not valid arguments for Stereonet arrow")
+
     @staticmethod
     def create_contour(*args, **kwargs):
         if len(args) == 0: