Streamlines (#2)

* Add inverse transform * Initial commit of streamlines * prebaked v1dd streamline * Integrate datasets * simplify streamline api * fix streamline distance function * pass function correctly * working and efficient streamline depth functions * text change * Fix arbitrary streamlines * improve streamline tests * documentation improvements
CAVEconnectome · Apr 10, 2023 · df995e9 · df995e9
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diff --git a/MANIFEST.in b/MANIFEST.in
@@ -1 +1,2 @@
-include requirements.txt
+include requirements.txt
+include standard_transform/data/*.json
diff --git a/README.md b/README.md
@@ -1,12 +1,14 @@
 # standard_transforms
 
-Orient and scale points in EM datasets the same way!
+Orient and scale points in EM datasets consistently and easily.
 
-When working with EM data, often the orientation of the dataset does not match the desired orientation in space. For example, in cortical data you might want "down" to correspond to the direction orthogonal to the pial surface. This package includes prebaked affine transforms for two datasets, Minnie65 and v1dd, to convert from voxel or nanometer coordinates to a consistent oriented frame in microns.
+When working with EM data, often the orientation of the dataset does not match the desired orientation in space. For example, in cortical data you might want "down" to correspond to the direction orthogonal to the pial surface. This package includes prebaked affine transforms for two datasets, Minnie65 and V1dd, to convert from voxel or nanometer coordinates to a consistent oriented frame in microns.
 
 Install via `pip install standard-transform`.
 
-## Usage
+# Usage
+
+## Transforms
 
 At its simplest, we import the transform we want, initialize and object, and then are ready to rotate, scale, and translate away!
 Let's start with going from nanometer space in Minnie to the oriented space.
@@ -49,9 +51,73 @@ To get the projection functionality with the `apply_dataframe` method, pass it a
 pts_out_x = tform_nm.apply_dataframe(column_name, df, projection='x')
 ```
 
-## Available transforms
+Finally, if you have points in the transformed space and want to back them out to the original coordinates, you can invert the transform of any TransformSequence.
+
+```python
+pts_orig = tform.invert(pts_transformed)
+```
+
+## Streamlines
+
+Because mammalian cortex is strongly organized in a laminar manner, it's often useful to distinguish radial distance from distance along the depth axis.
+However, when comparing the radial distance of points at different depths, the situation is complicated because the orientation of the depth axis, which we call a "streamline" is curvilinear, and changes with depth.
+The Streamline class helps with computing radial distances between arbitrary locations, as well as to store particular streamlines in a consistent way.
+
+Currently we just offer streamlines for the v1dd dataset, but a minnie65 one is forthcoming.
+Like transforms, you must specify if the original data is in nm or voxels, and they are associated with a particular transform in order to use its y-axis orientation.
+```python
+from standard_transform import v1dd_streamline_nm, v1dd_streamline_vx
+```
+
+A Streamline object has a few key functions.
+To get the x,z locations of a streamline passing through a core point `xyz` at a depth or depths `y` in the post-transform space:
+```python
+streamline.streamline_at(xyz, y)
+```
+The argument `return_as_point` can be set to `True` to return an nx3-array instead of the x, z components separately. 
+
+To translate the points defining the streamline to intersect with a point xyz in the pre-transform space:
+```python
+streamline.streamline_points_tform(xyz)
+```
+
+To compute the radial distance in the post-transform space from two points in the pre-transform space:
+```python
+streamline.radial_distance(xyz0, xyz1)
+``` 
+
+One can also get the curvilinear distance, or depth along the streamline of a point in the pre or post-transform space and y=0 in the post-transform space:
+```python
+streamline.depth_along(xyz, depth_from=0)
+```
+
+Similarly, you can put all of these together to get a point in the curvilienar space of the streamline, akin to cylindrical coordinates, where the x axis is radial distance and the y axis is depth along the streamline. For each point, an equivalent location in x,z space is found with the same radial distance and angle as the original x and z.
+```python
+xyz_rad = streamline.radial_points(xyz0, pts)
+```
+
+## Datasets
+
+Datasets are a convenient way to store a transform and streamline together, and are the easiest way to get started.
+There are two datasets currently available, `minnie65` and `v1dd`.
+To get a dataset, simply import it and initialize it.
+Datasets have a transform and streamline, and can be used to transform points or get streamline information.
+For example, to transform a particular location in v1dd 
+```python
+from standard_transform import minnie65_ds, v1dd_ds
+
+v1dd_ds.transform_nm.apply(xyz)
+```
+
+Or to get the streamline at a particular point:
+```python
+v1dd_ds.streamline_nm.radial_distance(xyz0, xyz1)
+```
+
+A particular resolution can be used by passing it as an argument to `dataset.transform_res(voxel_resolution)` or `dataset.streamline_res(voxel_resolution)`.
 
-There are four transforms currently available, two for each dataset.
+Note: Currently a data-driven streamline is only available for v1dd.
+Minnie65 is using a placeholder "identity streamline" that is just a straight line in the y direction.
 
 ### Minnie65
 

diff --git a/setup.py b/setup.py
@@ -43,6 +43,7 @@ def find_version(*file_paths):
     long_description_content_type="text/markdown",
     install_requires=required,
     include_package_data=True,
+    package_data=['standard_transform/data/*.json'],
     dependency_links=dependency_links,
     url="https://github.com/ceesem/standard_transform",
     packages=["standard_transform"],

diff --git a/standard_transform/__init__.py b/standard_transform/__init__.py
@@ -1,3 +1,14 @@
-from .datasets import minnie_transform_nm, minnie_transform_vx, v1dd_transform_nm, v1dd_transform_vx, identity_transform
+from .datasets import (
+    minnie_transform_nm,
+    minnie_transform_vx,
+    v1dd_transform_nm,
+    v1dd_transform_vx,
+    identity_transform,
+    identity_streamline,
+    v1dd_streamline_nm,
+    v1dd_streamline_vx,
+    v1dd_ds,
+    minnie_ds,
+)
 
-__version__ = "1.1.0"
+__version__ = "1.1.0"
diff --git a/standard_transform/base.py b/standard_transform/base.py
@@ -16,6 +16,9 @@ def __init__(self, scaling):
 
     def apply(self, pts):
         return np.atleast_2d(pts) * self._scaling
+
+    def invert(self, pts):
+        return np.atleast_2d(pts) / self._scaling
 
     def __repr__(self):
         return f"Scale by {self._scaling}"
@@ -32,6 +35,9 @@ def __init__(self, translate):
     def apply(self, pts):
         return np.atleast_2d(pts) + self._translate
 
+    def invert(self, pts):
+        return np.atleast_2d(pts) - self._translate
+
     def __repr__(self):
         return f"Translate by {self._translate}"
 
@@ -45,6 +51,9 @@ def __init__(self, *params, **param_kwargs):
     def apply(self, pts):
         return self._transform.apply(np.atleast_2d(pts))
 
+    def invert(self, pts):
+        return self._transform.inv().apply(np.atleast_2d(pts))
+
     def __repr__(self):
         return f"Rotate with params {self._params} and {self._param_kwargs}"
 
@@ -76,6 +85,26 @@ def apply(self, pts, as_int=False):
         else:
             return self.list_apply(pts, as_int=as_int)
 
+    def invert(self, pts_tf, as_int=False):
+        """Invert points post-transform back into the original coordinate system
+
+        Parameters
+        ----------
+        pts_tf : array-like
+            Points in the post-transform coordinate system
+        as_int : bool, optional
+            Return locations as integers, by default False
+
+        Returns
+        -------
+        array-like
+            Points in the original coordinate system
+        """
+        if isinstance(pts_tf, pd.Series):
+            return self.column_invert(pts_tf, as_int=as_int)
+        else:
+            return self.list_invert(pts_tf, as_int=as_int)
+
     def list_apply(self, pts, as_int=False):
         pts = np.array(pts)
         orig_shape = pts.shape
@@ -87,6 +116,17 @@ def list_apply(self, pts, as_int=False):
         else:
             return pts
 
+    def list_invert(self, pts, as_int=False):
+        pts = np.array(pts)
+        orig_shape = pts.shape
+        for t in self._transforms[::-1]:
+            pts = t.invert(pts)
+        pts = np.reshape(pts, orig_shape)
+        if as_int:
+            return pts.astype(int)
+        else:
+            return pts
+
     def column_apply(self, col, return_array=False, as_int=False):
         pts = np.vstack(col)
         out = self.apply(pts)
@@ -95,6 +135,14 @@ def column_apply(self, col, return_array=False, as_int=False):
         else:
             return self.apply(pts, as_int=as_int).tolist()
 
+    def column_invert(self, col, return_array=False, as_int=False):
+        pts = np.vstack(col)
+        out = self.apply(pts)
+        if return_array:
+            return self.invert(pts, as_int=as_int)
+        else:
+            return self.invert(pts, as_int=as_int).tolist()
+
     def apply_project(self, projection, pts, as_int=False):
         """Apply transform and extract one dimension (e.g. depth)
 
@@ -161,3 +209,7 @@ def apply_dataframe(self, col, df, projection=None, return_array=False, as_int=F
             else:
                 return out.tolist()
 
+
+def identity_transform():
+    "Returns the same points provided"
+    return TransformSequence()
diff --git a/standard_transform/data/v1dd_um_streamline.json b/standard_transform/data/v1dd_um_streamline.json
@@ -0,0 +1 @@
+[[838.8385131370085,-70.4369110927448,139.57027435166526],[838.8017573771021,-61.54823965900002,137.94152324819842],[838.7650615777368,-52.64752242093272,136.35612990070393],[838.7193658898686,-43.746760384015275,134.77073655320945],[838.682670090503,-34.846043145947974,133.18534320571496],[838.6459742911377,-25.945325907880672,131.59994985822047],[838.6002786032694,-17.044563870963117,130.01455651072598],[838.5635228433631,-8.155892437218455,128.38580540725917],[838.5178271554948,0.7448695996990428,126.80041205976467],[838.4811313561295,9.645586837766343,125.2150187122702],[838.4444355567639,18.546304075833586,123.62962536477572],[838.4077397573985,27.447021313901,122.0442320172812],[838.3709839974922,36.335692747645666,120.41548091381439],[838.3432880866297,45.23636518686294,118.8300875663199],[838.3245321037291,54.124947022907556,117.20133646285309],[838.3416557537579,62.9892580549065,115.48586984744166],[838.403778846301,71.8533450926551,113.77040323203023],[838.5286613162003,80.66893532116245,111.88150559272957],[838.7164230845377,89.46012034907413,109.90589244148424],[838.9670641513131,98.2269001763899,107.84356377829431],[839.2356450345534,106.98154460168287,105.73787735913209],[839.4861062197064,115.71218701603078,103.54547542802521],[839.7096276998917,124.45500963125151,101.39643125289068],[839.8971496260659,133.1980114418727,99.24738707775612],[840.1297909158358,141.9648808668885,97.18505841456617],[840.4074316481197,150.73152629765409,95.12272975137624],[840.7571914095075,159.5219049462643,93.14711660013093],[841.1610704229943,168.33610641041918,91.25821896083026],[841.6100088395356,177.1621296846462,89.41267907750192],[842.1130065476347,185.99992997009534,87.61049695014586],[842.6791833568758,194.87355407656176,85.93838809070674],[843.317599116303,203.7950030095181,84.43971025515687],[844.0281938653749,212.75223096464174,83.07110568752391],[844.8200274531355,221.75723894740486,81.87593214378025],[845.7111596171312,230.82198316443018,80.89754737989813],[846.6925904688591,239.94650841456797,80.13595139587757],[847.7911997527459,249.10658869262244,79.50442867977395],[848.9709879147807,258.302403193994,79.00297923158725],[850.2048353683731,267.5099947065877,78.54488753937288],[851.4838021855611,276.74145383357626,78.17351135910316],[852.7808287402956,285.9848691671872,77.8454929348057],[854.1048549605385,295.228150104248,77.51747451050828],[855.4560007673715,304.4953882534038,77.27617159815547],[856.8792056427679,313.7743138160816,77.07822644177497],[858.4104691407389,323.0647475968807,76.92363904136677],[860.09491062488,332.39055721019656,76.89912490887552],[861.9504699116562,341.73960725400593,76.9613262883289],[863.9500873750183,351.09998632053663,77.06688542375456],[866.0398236444901,360.4840090072116,77.25916007112492],[868.2015590219845,369.867673303086,77.45143471849522],[870.4264135400799,379.2751156156551,77.73042487781014],[872.6963274612305,388.69437973829645,78.0527727930974],[874.9842411593866,398.11355426323746,78.37512070838469],[877.2631549690399,407.53277358702854,78.6974686236719],[879.5600685556988,416.9519033131197,79.01981653895918],[881.8929217358282,426.3588080394879,79.29880669827406],[884.3427734664941,435.76513038080486,79.57779685758902],[886.9906227442218,445.17046714741997,79.85678701690394],[889.926648335662,454.6105077638003,80.26585044413582],[893.231789276799,464.07280323597223,80.76162938331233],[896.9512848523103,473.6053127869763,81.51755485832271],[901.0401356196818,483.20826041106255,82.53362686916698],[905.4535819785629,492.93005331977207,83.98327643973431],[910.11956486039,502.75900409958285,85.82314581405251],[914.9572051897196,512.7196075487907,88.13995050406612],[919.8762642393602,522.7641284386063,90.76025948588592],[924.7415038396065,532.8450555344904,93.51064173562267],[929.4535054931417,542.8785609935352,96.08759296147018],[933.9489102172012,552.7926835817563,98.23096662759451],[938.3087170083102,562.587020109502,99.94076273399578],[942.6231046331194,572.2972600012396,101.3470545485908],[946.955252192771,581.9592270779868,102.57991533929662],[951.350518893024,591.6449721714287,103.89949164194708],[955.8090846155005,601.3906326945329,105.43585672445909],[960.3761286843375,611.2321220660173,107.31908385474958],[965.1055305094702,621.1450798841358,109.46245752087391],[969.9699906226857,631.0694115238255,111.64918894297061],[974.9784489519462,640.9930263819143,113.83592036506728],[979.8964886723929,650.8327685982453,115.71914749535777],[984.5802314890624,660.613446563065,117.38558584578672],[988.7236212323181,670.3245376329545,118.79187766038177],[992.4527163017406,679.977460428335,119.98138069511529],[995.8396357264336,689.5959481670506,121.04081046193186],[999.0284976435241,699.2033756761451,122.05688247277604],[1002.1183008265427,708.7992501682679,123.02959672764804],[1005.1540447180037,718.383347649169,123.95895322654762],[1008.1718487929999,727.9795805320924,124.93166748141957],[1011.1805930189524,737.5638124095436,125.8610239803192],[1014.1893972054459,747.1600900913177,126.83373823519113],[1017.1891415428955,756.7443667676185,127.76309473409071],[1020.197885768848,766.3285986450699,128.6924512329904],[1023.1976900668385,775.9249211256937,129.6651654878623],[1026.2064342927913,785.5091530031448,130.59452198676195],[1029.206178630241,795.0934296794458,131.52387848566156],[1032.2149828167342,804.6897073612195,132.49659274053343]]
diff --git a/standard_transform/datasets.py b/standard_transform/datasets.py
@@ -1,12 +1,19 @@
-from .base import TransformSequence, R
+from .base import TransformSequence, R, identity_transform
+from .streamlines import Streamline, identity_streamline
 import numpy as np
+import os
+
+V1DD_STREAMLINE_POINT_FILE = os.path.join(
+    os.path.dirname(__file__),
+    'data',
+    'v1dd_um_streamline.json',
+)
+MINNIE_VOXEL_RESOLUTION = np.array([4, 4, 40])
+V1DD_VOXEL_RESOLUTION = np.array([9, 9, 45])
 
 MINNIE_PIA_POINT_NM = np.array([183013, 83535, 21480]) * [4,4,45]
 V1DD_PIA_POINT_NM = np.array([101249, 32249, 9145]) * [9,9,45]
 
-def identity_transform():
-    "Returns the same points provided"
-    return TransformSequence()
 
 def _minnie_transforms( tform, pia_point ):
     angle_offset = 5
@@ -19,7 +26,6 @@ def _minnie_transforms( tform, pia_point ):
     return tform
 
 def _v1dd_transforms( tform, pia_point):
-    # ctr = np.array([[910302.55274889, 273823.89004458, 411543.78900446]])
     up = np.array([-0.00497765, 0.96349375, 0.26768454])
     rot, _ = R.align_vectors(np.array([[0, 1, 0]]), [up])
 
@@ -32,7 +38,7 @@ def _v1dd_transforms( tform, pia_point):
     tform.add_scaling(1 / 1000)
     return tform
 
-def minnie_transform_vx(voxel_resolution=[4,4,40]):
+def minnie_transform_vx(voxel_resolution=MINNIE_VOXEL_RESOLUTION):
     "Transform for minnie65 dataset from voxels to oriented microns"
     column_transform = TransformSequence()
     column_transform.add_scaling(voxel_resolution)
@@ -44,7 +50,7 @@ def minnie_transform_nm():
     column_transform = TransformSequence()
     return _minnie_transforms(column_transform, MINNIE_PIA_POINT_NM)
 
-def v1dd_transform_vx(voxel_resolution=[9,9,45]):
+def v1dd_transform_vx(voxel_resolution=V1DD_VOXEL_RESOLUTION):
     "Transform for v1dd dataset from voxelsto oriented microns"
     v1dd_transform = TransformSequence()
     v1dd_transform.add_scaling(voxel_resolution)
@@ -55,3 +61,100 @@ def v1dd_transform_nm():
     "Transform for v1dd dataset from nanometers to oriented microns"
     v1dd_transform = TransformSequence()
     return _v1dd_transforms(v1dd_transform, V1DD_PIA_POINT_NM)
+
+#### STREAMLINES
+
+
+def v1dd_streamline_nm():
+    "Streamline for v1dd dataset for nm coordinates"
+    import json
+    with open(V1DD_STREAMLINE_POINT_FILE, 'r') as f:
+        points = np.array(json.load(f))
+    return Streamline(points, tform=v1dd_transform_nm(), transform_points=False)
+
+def v1dd_streamline_vx(voxel_resolution=V1DD_VOXEL_RESOLUTION):
+    "Streamline for v1dd dataset for voxel coordinates"
+    import json
+    with open(V1DD_STREAMLINE_POINT_FILE, 'r') as f:
+        points = np.array(json.load(f))
+    return Streamline(points, tform=v1dd_transform_vx(voxel_resolution), transform_points=False)
+
+def minnie_streamline_nm():
+    "Streamline for minnie65 dataset for nm coordinates"
+    return Streamline(
+        np.array([[0, 0, 0], [0, 1, 0]]),
+        tform=minnie_transform_nm(),
+        transform_points=False,
+    )
+
+def minnie_streamline_vx(voxel_resolution=MINNIE_VOXEL_RESOLUTION):
+    "Streamline for minnie65 dataset for voxel coordinates"
+    return Streamline(
+        np.array([[0, 0, 0], [0, 1000, 0]]),
+        tform=minnie_transform_vx(voxel_resolution),
+        transform_points=False,
+    )
+
+## Dataset object
+class Dataset(object):
+    def __init__(
+        self,
+        name,
+        transform_nm,
+        transform_vx,
+        streamline_nm,
+        streamline_vx,
+    ):
+        self.name = name
+        self._transform_arbitrary = transform_vx
+        self.transform_nm = transform_nm()
+        self.trasnform_vx = transform_vx()
+
+        self._streamline_arbitrary = streamline_vx
+        self.streamline_nm = streamline_nm()
+        self.streamline_vx = streamline_vx() 
+
+    def transform_res(self, resolution):
+        """Transform from arbitrary resolution to oriented microns
+
+        Parameters
+        ----------
+        resolution : list-like
+            Resolution of original data
+
+        Returns
+        -------
+        Transform object
+        """
+        return self._transform_arbitrary(resolution)
+
+    def streamline_res(self, resolution):
+        """Streamline for dataset at arbitrary resolution
+
+        Parameters
+        ----------
+        resolution : list-like
+            Resolution of original data
+
+        Returns
+        -------
+        Streamline object
+        """
+        return self._streamline_arbitrary(resolution)
+
+
+v1dd_ds = Dataset(
+    'v1dd',
+    v1dd_transform_nm,
+    v1dd_transform_vx,
+    v1dd_streamline_nm,
+    v1dd_streamline_vx,
+)
+
+minnie_ds = Dataset(
+    'minnie65',
+    minnie_transform_nm,
+    minnie_transform_vx,
+    minnie_streamline_nm,
+    minnie_streamline_vx,
+)
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1 @@
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