diff --git a/docs/algorithms/Adjusted_usage.md b/docs/algorithms/Adjusted_usage.md
index 4db49e5c509c398b8aa61f6675dd28781a47326e..b3d9db952d05dd4fce24e6b05be167736bdcaddd 100644
--- a/docs/algorithms/Adjusted_usage.md
+++ b/docs/algorithms/Adjusted_usage.md
@@ -8,7 +8,7 @@ the [Adjusted Algorithm](./Adjusted.md).
`geomag.py --algorithm sqdist`
-### Example
+### Command Line Example
This example uses a state file to produce X, Y, Z and F channels
from raw H, E, Z and F channels using the EDGE channel naming
@@ -27,7 +27,6 @@ contained in the statefile.
--outchannels X Y Z F \
--output-iaga-stdout
-
### Statefile Example Content
This is the content of /etc/adjusted/adjbou_state_.json:
@@ -41,6 +40,56 @@ This is the content of /etc/adjusted/adjbou_state_.json:
"M12": -0.15473074200902157, "M14": -1276.1646811919759,
"M31": -0.006725053082782385}
+### API Example
+
+This example uses an AdjustedMatrix object to produce X, Y, Z and F channels
+from raw H, E, Z and F channels using the EDGE channel naming
+convention. Absolutes were used to compute the transform matrix and pier correction
+contained in the object.
+
+```python
+from geomagio.algorithm import AdjustedAlgorithm
+from geomagio.adjusted.AdjustedMatrix import AdjustedMatrix
+from geomagio.iaga2002 import IAGA2002Factory
+
+with open("etc/adjusted/BOU201601vmin.min") as f:
+ raw = IAGA2002Factory().parse_string(f.read())
+
+a = AdjustedAlgorithm(
+ matrix=AdjustedMatrix(
+ matrix=[
+ [
+ 0.9834275767090617,
+ -0.15473074200902157,
+ 0.027384986324932026,
+ -1276.164681191976,
+ ],
+ [
+ 0.16680172992706568,
+ 0.987916201012128,
+ -0.0049868332295851525,
+ -0.8458192581350419,
+ ],
+ [
+ -0.006725053082782385,
+ -0.011809351484171948,
+ 0.9961869012493976,
+ 905.3800885796844,
+ ],
+ [0, 0, 0, 1],
+ ],
+ pier_correction=-22,
+ )
+ )
+# definition can also use statefiles
+# a = adj(statefile="etc/adjusted/adjbou_state_.json")
+
+result = adjusted.process(raw)
+```
+
+
+
+
### Library Notes
> Note: this library internally represents data gaps as NaN, and
diff --git a/etc/adjusted/BOU_expected.json b/etc/adjusted/BOU_expected.json
new file mode 100644
index 0000000000000000000000000000000000000000..ebe0a6c9bab156e2e39fc6ca9f158ee514421096
--- /dev/null
+++ b/etc/adjusted/BOU_expected.json
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diff --git a/etc/adjusted/adjbou_state_.json b/etc/adjusted/adjbou_state_.json
index 365af0fa1f48d98474ce7642cc270227bc4c92c1..7492efd366c258ee9392814efb53c9ed2451f204 100644
--- a/etc/adjusted/adjbou_state_.json
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\ No newline at end of file
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\ No newline at end of file
diff --git a/etc/adjusted/adjbou_state_HE_.json b/etc/adjusted/adjbou_state_HE_.json
index 91ed3625c0bbb0ecd40e770c3ac7202797783df8..4aa26dd352c076490f79b6941af7676c6d132e9b 100644
--- a/etc/adjusted/adjbou_state_HE_.json
+++ b/etc/adjusted/adjbou_state_HE_.json
@@ -1 +1,12 @@
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\ No newline at end of file
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+}
\ No newline at end of file
diff --git a/etc/adjusted/synthetic.json b/etc/adjusted/synthetic.json
new file mode 100644
index 0000000000000000000000000000000000000000..29f466f5c21eac383ac25d2567effa0def98e6d9
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\ No newline at end of file
diff --git a/etc/residual/BOU20200101.json b/etc/residual/BOU20200101.json
new file mode 100644
index 0000000000000000000000000000000000000000..89697e4f9b53fd1a60c0fc499fdfb1500c42d580
--- /dev/null
+++ b/etc/residual/BOU20200101.json
@@ -0,0 +1 @@
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diff --git a/geomagio/adjusted/AdjustedMatrix.py b/geomagio/adjusted/AdjustedMatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5513877fdd621d4cd33755d78c59d7da5b7d599
--- /dev/null
+++ b/geomagio/adjusted/AdjustedMatrix.py
@@ -0,0 +1,81 @@
+import numpy as np
+from obspy import Stream, UTCDateTime
+from pydantic import BaseModel
+from typing import Any, List, Optional
+
+from ..residual.Reading import Reading, get_absolutes_xyz, get_ordinates
+from .. import ChannelConverter
+from .. import pydantic_utcdatetime
+from .Metric import Metric, get_metric
+
+
+class AdjustedMatrix(BaseModel):
+ """Attributes pertaining to adjusted(affine) matrices, applied by the AdjustedAlgorithm
+
+ Attributes
+ ----------
+ matrix: affine matrix generated by Affine's calculate method
+ pier_correction: pier correction generated by Affine's calculate method
+ starttime: beginning of interval that matrix is valid for
+ endtime: end of interval that matrix is valid for
+ NOTE: valid intervals are only generated when bad data is encountered.
+ Matrix is non-constrained otherwise
+ """
+
+ matrix: Optional[Any] = None
+ pier_correction: Optional[float] = None
+ metrics: Optional[List[Metric]] = None
+ starttime: Optional[UTCDateTime] = None
+ endtime: Optional[UTCDateTime] = None
+ time: Optional[UTCDateTime] = None
+
+ def process(
+ self,
+ stream: Stream,
+ inchannels=["H", "E", "Z", "F"],
+ outchannels=["X", "Y", "Z", "F"],
+ ):
+ """ Apply matrix to raw data. Apply pier correction to F when necessary """
+ raws = np.vstack(
+ [
+ stream.select(channel=channel)[0].data
+ for channel in inchannels
+ if channel != "F"
+ ]
+ + [np.ones_like(stream[0].data)]
+ )
+ adjusted = self.matrix @ raws
+ if "F" in inchannels and "F" in outchannels:
+ f = stream.select(channel="F")[0].data + self.pier_correction
+ adjusted[-1] = f
+ return adjusted
+
+ def get_metrics(self, readings: List[Reading]) -> List[Metric]:
+ """Computes mean absolute error and standard deviation between expected and predicted values
+ Metrics are computed for X, Y, Z, and dF values
+
+ Attributes
+ ----------
+ readings: list of valid readings
+ matrix: composed matrix
+
+ Outputs
+ -------
+ metrics: list of Metric objects
+ """
+ absolutes = get_absolutes_xyz(readings=readings)
+ ordinates = get_ordinates(readings=readings)
+ stacked_ordinates = np.vstack((ordinates, np.ones_like(ordinates[0])))
+ predicted = self.matrix @ stacked_ordinates
+ metrics = []
+ elements = ["X", "Y", "Z", "dF"]
+ expected = list(absolutes) + [
+ ChannelConverter.get_computed_f_using_squares(*absolutes)
+ ]
+ predicted = list(predicted[0:3]) + [
+ ChannelConverter.get_computed_f_using_squares(*predicted[0:3])
+ ]
+ return [
+ get_metric(element=elements[i], expected=expected[i], actual=predicted[i])
+ for i in range(len(elements))
+ ]
diff --git a/geomagio/adjusted/Affine.py b/geomagio/adjusted/Affine.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a7a979c3a8b9629113d7df6b88e2e3261be90d2
--- /dev/null
+++ b/geomagio/adjusted/Affine.py
@@ -0,0 +1,156 @@
+from functools import reduce
+import numpy as np
+from obspy import UTCDateTime
+from pydantic import BaseModel, Field
+from typing import List, Optional, Tuple
+
+from ..residual.Reading import (
+ Reading,
+ get_absolutes_xyz,
+ get_ordinates,
+)
+from .. import pydantic_utcdatetime
+from .AdjustedMatrix import AdjustedMatrix
+from .transform import RotationTranslationXY, TranslateOrigins, Transform
+
+
+class Affine(BaseModel):
+ """Creates AdjustedMatrix objects from readings
+
+ Attributes
+ ----------
+ observatory: 3-letter observatory code
+ starttime: beginning time for matrix creation
+ endtime: end time for matrix creation
+ update_interval: window of time(in seconds) a matrix is representative of
+ transforms: methods for matrix calculations
+ """
+
+ observatory: str = None
+ starttime: UTCDateTime = Field(default_factory=lambda: UTCDateTime() - (86400 * 7))
+ endtime: UTCDateTime = Field(default_factory=lambda: UTCDateTime())
+ update_interval: Optional[int] = 86400 * 7
+ transforms: List[Transform] = [
+ RotationTranslationXY(memory=(86400 * 100), acausal=True),
+ TranslateOrigins(memory=(86400 * 10), acausal=True),
+ ]
+
+ def calculate(
+ self, readings: List[Reading], epochs: Optional[List[UTCDateTime]] = None
+ ) -> List[AdjustedMatrix]:
+ """Calculates affine matrices for a range of times
+
+ Attributes
+ ----------
+ readings: readings containing absolutes
+ epochs: optional time markers for unreliable observations
+
+ Outputs
+ -------
+ Ms: AdjustedMatrix objects created from calculations
+ """
+ # default set to create one matrix between starttime and endtime
+ update_interval = self.update_interval or (self.endtime - self.starttime)
+ all_readings = [r for r in readings if r.valid]
+ Ms = []
+ time = self.starttime
+ # search for "bad" H values
+ epochs = epochs or [
+ r.time for r in all_readings if r.get_absolute("H").absolute == 0
+ ]
+ while time < self.endtime:
+ # update epochs for current time
+ epoch_start, epoch_end = get_epochs(epochs=epochs, time=time)
+ # utilize readings that occur after or before a bad reading
+ readings = [
+ r
+ for r in all_readings
+ if (epoch_start is None or r.time > epoch_start)
+ or (epoch_end is None or r.time < epoch_end)
+ ]
+ M = self.calculate_matrix(time, readings)
+ M.starttime = epoch_start
+ M.endtime = epoch_end
+ time += update_interval
+
+ Ms.append(M)
+
+ return Ms
+
+ def calculate_matrix(
+ self, time: UTCDateTime, readings: List[Reading]
+ ) -> AdjustedMatrix:
+ """Calculates affine matrix for a given time
+
+ Attributes
+ ----------
+ time: time within calculation interval
+ readings: list of valid readings
+
+ Outputs
+ -------
+ AdjustedMatrix object containing result
+ """
+ absolutes = get_absolutes_xyz(readings)
+ ordinates = get_ordinates(readings)
+ Ms = []
+ weights = []
+ inputs = ordinates
+
+ for transform in self.transforms:
+ weights = transform.get_weights(
+ readings=readings,
+ time=time.timestamp,
+ )
+ # raise ValueError if no valid observations
+ if np.sum(weights) == 0:
+ raise ValueError(f"No valid observations for: {time}")
+
+ M = transform.calculate(
+ ordinates=inputs, absolutes=absolutes, weights=weights
+ )
+
+ # apply latest M matrix to inputs to get intermediate inputs
+ inputs = np.vstack([*inputs, np.ones_like(inputs[0])])
+ inputs = np.dot(M, inputs)[0:3]
+ Ms.append(M)
+
+ # compose affine transform matrices using reverse ordered matrices
+ M_composed = reduce(np.dot, reversed(Ms))
+ pier_correction = np.average(
+ [reading.pier_correction for reading in readings], weights=weights
+ )
+ matrix = AdjustedMatrix(
+ matrix=M_composed,
+ pier_correction=pier_correction,
+ )
+ matrix.metrics = matrix.get_metrics(readings=readings)
+ return matrix
+
+
+def get_epochs(
+ epochs: List[float],
+ time: UTCDateTime,
+) -> Tuple[float, float]:
+ """Updates valid start/end time for a given interval
+
+ Attributes
+ ----------
+ epochs: bad data times
+ time: current time epoch is being evaluated at
+
+ Outputs
+ -------
+ epoch_start: start of current valid interval
+ epoch_end: end of current valid interval
+ """
+ epoch_start = None
+ epoch_end = None
+ for e in epochs:
+ if e > time:
+ if epoch_end is None or e < epoch_end:
+ epoch_end = e
+ if e < time:
+ if epoch_start is None or e > epoch_start:
+ epoch_start = e
+ return epoch_start, epoch_end
diff --git a/geomagio/adjusted/Metric.py b/geomagio/adjusted/Metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb60398f2c5ce4f84083d89881df7b6dafd000dd
--- /dev/null
+++ b/geomagio/adjusted/Metric.py
@@ -0,0 +1,24 @@
+from typing import List
+
+import numpy as np
+from pydantic import BaseModel
+
+
+class Metric(BaseModel):
+ """Mean absolute error and standard deviation for a given element
+
+ Attributes
+ ----------
+ element: Channel that metrics are representative of
+ absmean: mean absolute error
+ stddev: standard deviation
+ """
+
+ element: str
+ absmean: float = None
+ stddev: float = None
+
+
+def get_metric(element: str, expected: List[float], actual: List[float]) -> Metric:
+ diff = np.array(expected) - np.array(actual)
+ return Metric(element=element, absmean=np.average(abs(diff)), stddev=np.std(diff))
diff --git a/geomagio/adjusted/__init__.py b/geomagio/adjusted/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b86d5d92707e5d9bb6fd5b3395d88fa59261c661
--- /dev/null
+++ b/geomagio/adjusted/__init__.py
@@ -0,0 +1,9 @@
+from .AdjustedMatrix import AdjustedMatrix
+from .Affine import Affine
+from .Metric import Metric
+
+__all__ = [
+ "AdjustedMatrix",
+ "Affine",
+ "Metric",
+]
diff --git a/geomagio/adjusted/transform/LeastSq.py b/geomagio/adjusted/transform/LeastSq.py
new file mode 100644
index 0000000000000000000000000000000000000000..0caa4b1a03f47311354aff3a56434cdf11c762cb
--- /dev/null
+++ b/geomagio/adjusted/transform/LeastSq.py
@@ -0,0 +1,136 @@
+import numpy as np
+import scipy.linalg as spl
+from typing import List, Optional, Tuple, Union
+
+from .Transform import Transform
+
+
+class LeastSq(Transform):
+ """Intance of Transform. Applies least squares to generate matrices"""
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ """Calculates matrix with least squares and accompanying methods
+ Defaults to least squares calculation with no constraints
+ """
+ abs_stacked, ord_stacked = self.get_stacked_values(
+ absolutes, ordinates, weights
+ )
+ ord_stacked = self.get_weighted_values(ord_stacked, weights)
+ abs_stacked = self.get_weighted_values(abs_stacked, weights)
+ # regression matrix M that minimizes L2 norm
+ matrix, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if self.valid(rank):
+ return self.get_matrix(matrix, absolutes, ordinates, weights)
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ ordinates: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ """Returns matrix formatted for no constraints
+ NOTE: absolutes, ordinates, and weights are only used by QRFactorization's child function
+ """
+ return np.array(
+ [
+ [matrix[0], matrix[1], matrix[2], matrix[3]],
+ [matrix[4], matrix[5], matrix[6], matrix[7]],
+ [matrix[8], matrix[9], matrix[10], matrix[11]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+ )
+
+ def get_stacked_absolutes(
+ self, absolutes: Tuple[List[float], List[float], List[float]]
+ ) -> List[float]:
+ """Formats absolutes for least squares method
+
+ Attributes
+ ----------
+ absolutes: Rotated X, Y, and Z absolutes
+
+ Output
+ ------
+ X, Y and Z absolutes placed end to end and transposed
+ """
+ return np.vstack([absolutes[0], absolutes[1], absolutes[2]]).T.ravel()
+
+ def get_stacked_ordinates(
+ self, ordinates: Tuple[List[float], List[float], List[float]]
+ ) -> List[List[float]]:
+ """ Formats ordinates for least squares method """
+ # (reduces degrees of freedom by 4:
+ # - 4 for the last row of zeros and a one)
+ ord_stacked = np.zeros((12, len(ordinates[0]) * 3))
+ ord_stacked[0, 0::3] = ordinates[0]
+ ord_stacked[1, 0::3] = ordinates[1]
+ ord_stacked[2, 0::3] = ordinates[2]
+ ord_stacked[3, 0::3] = 1.0
+ ord_stacked[4, 1::3] = ordinates[0]
+ ord_stacked[5, 1::3] = ordinates[1]
+ ord_stacked[6, 1::3] = ordinates[2]
+ ord_stacked[7, 1::3] = 1.0
+ ord_stacked[8, 2::3] = ordinates[0]
+ ord_stacked[9, 2::3] = ordinates[1]
+ ord_stacked[10, 2::3] = ordinates[2]
+ ord_stacked[11, 2::3] = 1.0
+
+ return ord_stacked
+
+ def get_stacked_values(
+ self,
+ absolutes: Tuple[List[float], List[float], List[float]],
+ ordinates: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]] = None,
+ ) -> Tuple[List[float], List[List[float]]]:
+ """Gathers stacked stacked absolutes/ordinates
+ NOTE: weights are only used in QRFactorization's child function
+ """
+ # LHS, or dependent variables
+ # [A[0,0], A[1,0], A[2,0], A[0,1], A[1,1], A[2,1], ...]
+ abs_stacked = self.get_stacked_absolutes(absolutes)
+ # RHS, or independent variables
+ # [
+ # [o[0,0], 0, 0, o[0,1], 0, 0, ...],
+ # [0, o[1,0], 0, 0, o[1,1], 0, ...],
+ # [0, 0, o[2,0], 0, 0, o[2,1], ...],
+ # ...
+ # ]
+ ord_stacked = self.get_stacked_ordinates(ordinates)
+ return abs_stacked, ord_stacked
+
+ def get_weighted_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]] = None,
+ ) -> Union[List[float], List[List[float]]]:
+ """Application of weights for least squares methods, which calls for square roots
+
+ Attributes
+ ----------
+ values: absolutes or ordinates
+
+ Outputs
+ -------
+ tuple of weights applied to each element of values
+
+ """
+ if weights is None:
+ return values
+ weights = np.sqrt(weights)
+ weights = np.vstack((weights, weights, weights)).T.ravel()
+ return values * weights
+
+ def valid(self, rank: float) -> bool:
+ """ validates whether or not a matrix can reliably transform the method's number of dimensions """
+ if rank < self.ndims:
+ return False
+ return True
diff --git a/geomagio/adjusted/transform/QRFactorization.py b/geomagio/adjusted/transform/QRFactorization.py
new file mode 100644
index 0000000000000000000000000000000000000000..8159b070d7c381ca2d2a3f29983671570adbed37
--- /dev/null
+++ b/geomagio/adjusted/transform/QRFactorization.py
@@ -0,0 +1,92 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .LeastSq import LeastSq
+from .SVD import SVD
+
+
+class QRFactorization(LeastSq):
+ """Calculates affine using least squares with QR factorization"""
+
+ ndims: int = 2
+ svd: SVD = SVD(ndims=ndims)
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ ordinates: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ """ performs QR factorization steps and formats result within the returned matrix """
+ # QR fatorization
+ # NOTE: forcing the diagonal elements of Q to be positive
+ # ensures that the determinant is 1, not -1, and is
+ # therefore a rotation, not a reflection
+ Q, R = np.linalg.qr(matrix.T)
+ neg = np.diag(Q) < 0
+ Q[:, neg] = -1 * Q[:, neg]
+ R[neg, :] = -1 * R[neg, :]
+
+ # isolate scales from shear
+ S = np.diag(np.diag(R))
+ H = np.dot(np.linalg.inv(S), R)
+
+ # combine shear and rotation
+ QH = np.dot(Q, H)
+
+ weighted_absolutes = self.svd.get_weighted_values(absolutes, weights)
+ weighted_ordinates = self.svd.get_weighted_values(ordinates, weights)
+
+ # now get translation using weighted centroids and R
+ T = self.svd.get_translation_matrix(QH, weighted_absolutes, weighted_ordinates)
+
+ return [
+ [QH[0, 0], QH[0, 1], 0.0, T[0]],
+ [QH[1, 0], QH[1, 1], 0.0, T[1]],
+ [
+ 0.0,
+ 0.0,
+ 1.0,
+ np.array(weighted_absolutes[self.ndims])
+ - np.array(weighted_ordinates[self.ndims]),
+ ],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_stacked_values(
+ self,
+ absolutes: Tuple[List[float], List[float], List[float]],
+ ordinates: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]] = None,
+ ) -> Tuple[List[List[float]], List[List[float]]]:
+ """ stacks and weights absolutes/ordinates """
+ weighted_absolutes = self.svd.get_weighted_values(
+ values=absolutes, weights=weights
+ )
+ weighted_ordinates = self.svd.get_weighted_values(
+ values=ordinates, weights=weights
+ )
+ # LHS, or dependent variables
+ abs_stacked = self.svd.get_stacked_values(
+ values=absolutes,
+ weighted_values=weighted_absolutes,
+ )
+
+ # RHS, or independent variables
+ ord_stacked = self.svd.get_stacked_values(
+ values=ordinates,
+ weighted_values=weighted_ordinates,
+ )
+ return abs_stacked, ord_stacked
+
+ def get_weighted_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]] = None,
+ ) -> List[List[float]]:
+ """ Applies least squares weights in two dimensions(X and Y)"""
+ if weights is None:
+ return values
+ weights = np.sqrt(weights)
+ return np.array([values[i] * weights for i in range(self.ndims)])
diff --git a/geomagio/adjusted/transform/Rescale3D.py b/geomagio/adjusted/transform/Rescale3D.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa1c58b2f9bfeae68e716c6eb5e5c0f4dfa9a819
--- /dev/null
+++ b/geomagio/adjusted/transform/Rescale3D.py
@@ -0,0 +1,38 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .LeastSq import LeastSq
+
+
+class Rescale3D(LeastSq):
+ """Calculates affine using using least squares, constrained to re-scale each axis"""
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ ordinates: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ return [
+ [matrix[0], 0.0, 0.0, 0.0],
+ [0.0, matrix[1], 0.0, 0.0],
+ [0.0, 0.0, matrix[2], 0.0],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_stacked_ordinates(
+ self, ordinates: Tuple[List[float], List[float], List[float]]
+ ) -> List[List[float]]:
+ # (reduces degrees of freedom by 13:
+ # - 2 for making x independent of y,z;
+ # - 2 for making y,z independent of x;
+ # - 1 for making y independent of z;
+ # - 1 for making z independent of y;
+ # - 3 for not translating xyz
+ # - 4 for the last row of zeros and a one)
+ ord_stacked = np.zeros((3, len(ordinates[0]) * 3))
+ ord_stacked[0, 0::3] = ordinates[0]
+ ord_stacked[1, 1::3] = ordinates[1]
+ ord_stacked[2, 2::3] = ordinates[2]
+ return ord_stacked
diff --git a/geomagio/adjusted/transform/RotationTranslationXY.py b/geomagio/adjusted/transform/RotationTranslationXY.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6a352a15e03147083063b2eae623c18a9f7e420
--- /dev/null
+++ b/geomagio/adjusted/transform/RotationTranslationXY.py
@@ -0,0 +1,37 @@
+import numpy as np
+from typing import List, Tuple
+
+from .SVD import SVD
+
+
+class RotationTranslationXY(SVD):
+ """Calculates affine using singular value decomposition,
+ constrained to rotation and translation in XY(no scale or shear),
+ and only translation in Z"""
+
+ ndims: int = 2
+
+ def get_matrix(
+ self,
+ R: List[List[float]],
+ T: List[List[float]],
+ weighted_absolutes: Tuple[List[float], List[float], List[float]],
+ weighted_ordinates: Tuple[List[float], List[float], List[float]],
+ ) -> np.array:
+ return [
+ [R[0, 0], R[0, 1], 0.0, T[0]],
+ [R[1, 0], R[1, 1], 0.0, T[1]],
+ [
+ 0.0,
+ 0.0,
+ 1.0,
+ np.array(weighted_absolutes[self.ndims])
+ - np.array(weighted_ordinates[self.ndims]),
+ ],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_rotation_matrix(
+ self, U: List[List[float]], Vh: List[List[float]]
+ ) -> List[List[float]]:
+ return np.dot(Vh.T, np.dot(np.diag([1, np.linalg.det(np.dot(Vh.T, U.T))]), U.T))
diff --git a/geomagio/adjusted/transform/SVD.py b/geomagio/adjusted/transform/SVD.py
new file mode 100644
index 0000000000000000000000000000000000000000..9972009cb357c475ec0906e809de4ed7f74d991d
--- /dev/null
+++ b/geomagio/adjusted/transform/SVD.py
@@ -0,0 +1,136 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .Transform import Transform
+
+
+class SVD(Transform):
+ """Instance of Transform. Applies singular value decomposition to generate matrices"""
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ """Calculates matrix with singular value decomposition and accompanying methods
+ Defaults to singular value decomposition constrained for 3D rotation/translation
+ """
+ weighted_ordinates = self.get_weighted_values(values=ordinates, weights=weights)
+ weighted_absolutes = self.get_weighted_values(values=absolutes, weights=weights)
+ # generate weighted "covariance" matrix
+ H = self.get_covariance_matrix(absolutes, ordinates, weights)
+ # Singular value decomposition, then rotation matrix from L&R eigenvectors
+ # (the determinant guarantees a rotation, and not a reflection)
+ U, S, Vh = np.linalg.svd(H)
+ if self.valid(S):
+ R = self.get_rotation_matrix(U, Vh)
+ # now get translation using weighted centroids and R
+ T = self.get_translation_matrix(R, weighted_absolutes, weighted_ordinates)
+ return self.get_matrix(R, T, weighted_absolutes, weighted_ordinates)
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ def get_covariance_matrix(
+ self,
+ absolutes: Tuple[List[float], List[float], List[float]],
+ ordinates: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> List[List[float]]:
+ """ calculate covariance matrix with weighted absolutes/ordinates """
+ weighted_ordinates = self.get_weighted_values(values=ordinates, weights=weights)
+ weighted_absolutes = self.get_weighted_values(values=absolutes, weights=weights)
+ # generate weighted "covariance" matrix
+ H = np.dot(
+ self.get_stacked_values(
+ values=ordinates,
+ weighted_values=weighted_ordinates,
+ ),
+ np.dot(
+ np.diag(weights),
+ self.get_stacked_values(
+ values=absolutes,
+ weighted_values=weighted_absolutes,
+ ).T,
+ ),
+ )
+ return H
+
+ def get_matrix(
+ self,
+ R: List[List[float]],
+ T: List[List[float]],
+ weighted_absolutes: Optional[
+ Tuple[List[float], List[float], List[float]]
+ ] = None,
+ weighted_ordinates: Optional[
+ Tuple[List[float], List[float], List[float]]
+ ] = None,
+ ) -> np.array:
+ """Returns matrix formatted for 3D rotation/translation
+ NOTE: weighted absolutes/ordinates are only used by RotationTranslationXY's child function
+ """
+ return [
+ [R[0, 0], R[0, 1], R[0, 2], T[0]],
+ [R[1, 0], R[1, 1], R[1, 2], T[1]],
+ [R[2, 0], R[2, 1], R[2, 2], T[2]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_rotation_matrix(
+ self, U: List[List[float]], Vh: List[List[float]]
+ ) -> List[List[float]]:
+ """ computes rotation matrix from products of singular value decomposition """
+ return np.dot(
+ Vh.T, np.dot(np.diag([1, 1, np.linalg.det(np.dot(Vh.T, U.T))]), U.T)
+ )
+
+ def get_stacked_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weighted_values: Tuple[List[float], List[float], List[float]],
+ ) -> np.array:
+ """Supports intermediate mathematical steps by differencing and shaping values for SVD
+
+ Attributes
+ ----------
+ values: absolutes or ordinates
+ weighted_values: absolutes or ordinates with weights applied
+ ndims: number of rows desired in return array(case specific). Default set to 3 dimensions(XYZ/HEZ)
+
+ Outputs
+ -------
+ Stacked and differenced values from their weighted counterparts
+ """
+ return np.vstack([[values[i] - weighted_values[i]] for i in range(self.ndims)])
+
+ def get_translation_matrix(
+ self,
+ R: List[List[float]],
+ weighted_absolutes: Tuple[List[float], List[float], List[float]],
+ weighted_ordinates: Tuple[List[float], List[float], List[float]],
+ ) -> List[List[float]]:
+ """ computes translation matrix from rotation matrix and weighted absolutes/ordinates """
+ return np.array([weighted_absolutes[i] for i in range(self.ndims)]) - np.dot(
+ R, [weighted_ordinates[i] for i in range(self.ndims)]
+ )
+
+ def get_weighted_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]],
+ ) -> Tuple[float, float, float]:
+ """Application of weights for SVD methods, which call for weighted averages"""
+ if weights is None:
+ weights = np.ones_like(values[0])
+ return (
+ np.average(values[0], weights=weights),
+ np.average(values[1], weights=weights),
+ np.average(values[2], weights=weights),
+ )
+
+ def valid(self, singular_values: List[float]) -> bool:
+ """ validates whether or not a matrix can reliably transform the method's number of dimensions """
+ if np.sum(singular_values) < self.ndims:
+ return False
+ return True
diff --git a/geomagio/adjusted/transform/ShearYZ.py b/geomagio/adjusted/transform/ShearYZ.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dcc771c59549a77040c898032ea0d6fc52ca27a
--- /dev/null
+++ b/geomagio/adjusted/transform/ShearYZ.py
@@ -0,0 +1,39 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .LeastSq import LeastSq
+
+
+class ShearYZ(LeastSq):
+ """Calculates affine using least squares, constrained to shear y and z, but not x."""
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ ordinates: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ return [
+ [1.0, 0.0, 0.0, 0.0],
+ [matrix[0], 1.0, 0.0, 0.0],
+ [matrix[1], matrix[2], 1.0, 0.0],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_stacked_ordinates(
+ self, ordinates: Tuple[List[float], List[float], List[float]]
+ ) -> List[List[float]]:
+ # (reduces degrees of freedom by 13:
+ # - 2 for making x independent of y,z;
+ # - 1 for making y independent of z;
+ # - 3 for not scaling each axis
+ # - 4 for the last row of zeros and a one)
+ ord_stacked = np.zeros((3, len(ordinates[0]) * 3))
+ ord_stacked[0, 0::3] = 1.0
+ ord_stacked[1, 0::3] = ordinates[0]
+ ord_stacked[1, 1::3] = 1.0
+ ord_stacked[2, 0::3] = ordinates[0]
+ ord_stacked[2, 1::3] = ordinates[1]
+ ord_stacked[2, 2::3] = 1.0
+ return ord_stacked
diff --git a/geomagio/adjusted/transform/Transform.py b/geomagio/adjusted/transform/Transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..0df271c9766491744fd55a429ee4c5c8ad7ae208
--- /dev/null
+++ b/geomagio/adjusted/transform/Transform.py
@@ -0,0 +1,174 @@
+import numpy as np
+from pydantic import BaseModel
+from typing import List, Optional, Tuple
+
+from ...residual.Reading import Reading, get_baselines, get_times
+
+
+class Transform(BaseModel):
+ """Method for generating an affine matrix.
+
+ Attributes
+ ----------
+ acausal: if true, future readings are used in calculations
+ memory: Controls impact of measurements from the past
+ Defaults to infinite(equal weighting)
+ """
+
+ acausal: bool = False
+ memory: Optional[float] = None
+ ndims: int = 3
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ """Type skeleton inherited by any instance of Transform
+
+ Attributes
+ ----------
+ ordinates: H, E and Z ordinates
+ absolutes: X, Y and Z absolutes(NOTE: absolutes must be rotated from original H, E and Z values)
+ weights: time weights to apply during calculations of matrices
+ """
+ # return identity matrix
+ return np.eye(4)
+
+ def get_weights(self, readings: List[Reading], time: int = None) -> List[float]:
+ """
+ Calculate time-dependent weights according to exponential decay.
+
+ Inputs:
+ -------
+ readings: list of valid readings
+ time: time weights are calculated for
+
+ Output:
+ -------
+ weights: array of vector distances/metrics
+ """
+
+ times = get_times(readings)
+ # convert to array of floats
+ times = np.asarray(times).astype(float)
+
+ if time is None:
+ time = float(max(times))
+
+ baselines = get_baselines(readings)
+
+ # if memory is actually infinite, return equal weights
+ if np.isinf(self.memory):
+ weights = np.ones(times.shape)
+
+ # initialize weights
+ weights = np.zeros(times.shape)
+
+ # calculate exponential decay time-dependent weights
+ weights[times <= time] = np.exp((times[times <= time] - time) / self.memory)
+ weights[times >= time] = np.exp((time - times[times >= time]) / self.memory)
+
+ if not self.acausal:
+ weights[times > time] = 0.0
+
+ weights = filter_iqrs(multiseries=baselines, weights=weights)
+
+ return weights
+
+
+def filter_iqr(
+ series: List[float], threshold: int = 3.0, weights: List[int] = None
+) -> List[int]:
+ """
+ Identify "good" elements in series by calculating potentially weighted
+ 25%, 50% (median), and 75% quantiles of series, the number of 25%-50%
+ quantile ranges below, or 50%-75% quantile ranges above each value of
+ series falls from the median, and finally, setting elements of good to
+ True that fall within these multiples of quantile ranges.
+
+ NOTE: NumPy has a percentile function, but it does not yet handle
+ weights. This algorithm was adapted from the PyPI
+ package wquantiles (https://pypi.org/project/wquantiles/)
+
+ Inputs:
+ series: array of observations to filter
+
+ Options:
+ threshold: threshold in fractional number of 25%-50% (50%-75%)
+ quantile ranges below (above) the median each element of
+ series may fall and still be considered "good"
+ Default set to 6.
+ weights: weights to assign to each element of series. Default set to 1.
+
+ Output:
+ good: Boolean array where True values correspond to "good" data
+
+ """
+
+ if weights is None:
+ weights = np.ones_like(series)
+
+ # initialize good as all True for weights greater than 0
+ good = (weights > 0).astype(bool)
+ if np.size(good) <= 1:
+ # if a singleton is passed, assume it is "good"
+ return good
+
+ good_old = ~good
+ while not (good_old == good).all():
+ good_old = good
+
+ wq25 = weighted_quartile(series[good], weights[good], 0.25)
+ wq50 = weighted_quartile(series[good], weights[good], 0.50)
+ wq75 = weighted_quartile(series[good], weights[good], 0.75)
+
+ # NOTE: it is necessary to include good on the RHS here
+ # to prevent oscillation between two equally likely
+ # "optimal" solutions; this is a common problem with
+ # expectation maximization algorithms
+ good = (
+ good
+ & (series >= (wq50 - threshold * (wq50 - wq25)))
+ & (series <= (wq50 + threshold * (wq75 - wq50)))
+ )
+
+ return good
+
+
+def filter_iqrs(
+ multiseries: List[List[float]],
+ weights: List[float],
+ threshold: float = 3.0,
+) -> List[float]:
+ """Filters "bad" weights generated by unreliable readings"""
+ good = None
+ for series in multiseries:
+ filtered = filter_iqr(series, threshold=threshold, weights=weights)
+ if good is None:
+ good = filtered
+ else:
+ good = good & filtered
+
+ return weights * good
+
+
+def weighted_quartile(data: List[float], weights: List[float], quant: float) -> float:
+ """Get weighted quartile to determine statistically good/bad data
+
+ Attributes
+ ----------
+ data: filtered array of observations
+ weights: array of vector distances/metrics
+ quant: statistical percentile of input data
+ """
+ # sort data and weights
+ ind_sorted = np.argsort(data)
+ sorted_data = data[ind_sorted]
+ sorted_weights = weights[ind_sorted]
+ # compute auxiliary arrays
+ Sn = np.cumsum(sorted_weights)
+ Pn = (Sn - 0.5 * sorted_weights) / Sn[-1]
+ # interpolate to weighted quantile
+ return np.interp(quant, Pn, sorted_data)
diff --git a/geomagio/adjusted/transform/TranslateOrigins.py b/geomagio/adjusted/transform/TranslateOrigins.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ba7ae0c4337b4e766265329f1d48fccb46f36e8
--- /dev/null
+++ b/geomagio/adjusted/transform/TranslateOrigins.py
@@ -0,0 +1,61 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .LeastSq import LeastSq
+
+
+class TranslateOrigins(LeastSq):
+ """Calculates affine using using least squares, constrained to tanslate origins"""
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ ordinates: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ return [
+ [1.0, 0.0, 0.0, matrix[0]],
+ [0.0, 1.0, 0.0, matrix[1]],
+ [0.0, 0.0, 1.0, matrix[2]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_stacked_ordinates(
+ self, ordinates: Tuple[List[float], List[float], List[float]]
+ ) -> List[List[float]]:
+ ord_stacked = np.zeros((3, len(ordinates[0]) * 3))
+ ord_stacked[0, 0::3] = 1.0
+ ord_stacked[1, 1::3] = 1.0
+ ord_stacked[2, 2::3] = 1.0
+ return ord_stacked
+
+ def get_stacked_values(
+ self,
+ absolutes: Tuple[List[float], List[float], List[float]],
+ ordinates: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]] = None,
+ ) -> Tuple[List[float], List[List[float]]]:
+ # LHS, or dependent variables
+ abs_stacked = self.get_stacked_absolutes(absolutes)
+ # subtract ords from abs to force simple translation
+ abs_stacked[0::3] = absolutes[0] - ordinates[0]
+ abs_stacked[1::3] = absolutes[1] - ordinates[1]
+ abs_stacked[2::3] = absolutes[2] - ordinates[2]
+ # RHS, or independent variables
+ ord_stacked = self.get_stacked_ordinates(ordinates)
+ return abs_stacked, ord_stacked
+
+ def get_weighted_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]],
+ ) -> List[float]:
+ """Weights are applied after matrix creation steps,
+ requiring weights to be stacked similar to ordinates and absolutes"""
+ if weights is not None:
+ weights = np.sqrt(weights)
+ weights = np.vstack((weights, weights, weights)).T.ravel()
+ else:
+ weights = 1
+ return values * weights
diff --git a/geomagio/adjusted/transform/ZRotationHScale.py b/geomagio/adjusted/transform/ZRotationHScale.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa3901ef9eb89b6eda6e27b4dabed30167c200bb
--- /dev/null
+++ b/geomagio/adjusted/transform/ZRotationHScale.py
@@ -0,0 +1,42 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .LeastSq import LeastSq
+
+
+class ZRotationHscale(LeastSq):
+ """Calculates affine using least squares, constrained to rotate about the Z axis
+ and apply uniform horizontal scaling."""
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ ordinates: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ return [
+ [matrix[0], matrix[1], 0.0, matrix[2]],
+ [-matrix[1], matrix[0], 0.0, matrix[3]],
+ [0.0, 0.0, matrix[4], matrix[5]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_stacked_ordinates(
+ self, ordinates: Tuple[List[float], List[float], List[float]]
+ ) -> List[List[float]]:
+ # (reduces degrees of freedom by 10:
+ # - 2 for making x,y independent of z;
+ # - 2 for making z independent of x,y
+ # - 2 for not allowing shear in x,y; and
+ # - 4 for the last row of zeros and a one)
+ ord_stacked = np.zeros((6, len(ordinates[0]) * 3))
+ ord_stacked[0, 0::3] = ordinates[0]
+ ord_stacked[0, 1::3] = ordinates[1]
+ ord_stacked[1, 0::3] = ordinates[1]
+ ord_stacked[1, 1::3] = -ordinates[0]
+ ord_stacked[2, 0::3] = 1.0
+ ord_stacked[3, 1::3] = 1.0
+ ord_stacked[4, 2::3] = ordinates[2]
+ ord_stacked[5, 2::3] = 1.0
+ return ord_stacked
diff --git a/geomagio/adjusted/transform/ZRotationHScaleZBaseline.py b/geomagio/adjusted/transform/ZRotationHScaleZBaseline.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9998589f1ac50c2f69caab66b9d65ad17ae65ea
--- /dev/null
+++ b/geomagio/adjusted/transform/ZRotationHScaleZBaseline.py
@@ -0,0 +1,55 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .LeastSq import LeastSq
+
+
+class ZRotationHscaleZbaseline(LeastSq):
+ """Calculates affine using least squares, constrained to rotate about the Z axis,
+ apply a uniform horizontal scaling, and apply a baseline shift for the Z axis."""
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ ordinates: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ return [
+ [matrix[0], matrix[1], 0.0, 0.0],
+ [-matrix[1], matrix[0], 0.0, 0.0],
+ [0.0, 0.0, 1.0, matrix[2]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_stacked_ordinates(
+ self, ordinates: Tuple[List[float], List[float], List[float]]
+ ) -> List[List[float]]:
+ # (reduces degrees of freedom by 13:
+ # - 2 for making x,y independent of z;
+ # - 2 for making z independent of x,y;
+ # - 2 for not allowing shear in x,y;
+ # - 2 for not allowing translation in x,y;
+ # - 1 for not allowing scaling in z; and
+ # - 4 for the last row of zeros and a one)
+ ord_stacked = np.zeros((3, len(ordinates[0]) * 3))
+ ord_stacked[0, 0::3] = ordinates[0]
+ ord_stacked[0, 1::3] = ordinates[1]
+ ord_stacked[1, 0::3] = ordinates[1]
+ ord_stacked[1, 1::3] = -ordinates[0]
+ ord_stacked[2, 2::3] = 1.0
+ return ord_stacked
+
+ def get_stacked_values(
+ self,
+ absolutes: Tuple[List[float], List[float], List[float]],
+ ordinates: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]] = None,
+ ) -> Tuple[List[float], List[float]]:
+ # LHS, or dependent variables
+ abs_stacked = self.get_stacked_absolutes(absolutes)
+ # subtract z_o from z_a to force simple z translation
+ abs_stacked[2::3] = absolutes[2] - ordinates[2]
+ # RHS, or independent variables
+ ord_stacked = self.get_stacked_ordinates(ordinates)
+ return abs_stacked, ord_stacked
diff --git a/geomagio/adjusted/transform/ZRotationShear.py b/geomagio/adjusted/transform/ZRotationShear.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0f55783cf6bd28a4aed2e5dd73ba30e56d7dbce
--- /dev/null
+++ b/geomagio/adjusted/transform/ZRotationShear.py
@@ -0,0 +1,40 @@
+import numpy as np
+from typing import List, Optional, Tuple
+
+from .LeastSq import LeastSq
+
+
+class ZRotationShear(LeastSq):
+ """Calculates affine using least squares, constrained to rotate about the Z axis """
+
+ def get_matrix(
+ self,
+ matrix: List[List[float]],
+ absolutes: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ ordinates: Optional[Tuple[List[float], List[float], List[float]]] = None,
+ weights: Optional[List[float]] = None,
+ ) -> np.array:
+ return [
+ [matrix[0], matrix[1], 0.0, matrix[2]],
+ [matrix[3], matrix[4], 0.0, matrix[5]],
+ [0.0, 0.0, matrix[6], matrix[7]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+ def get_stacked_ordinates(
+ self, ordinates: Tuple[List[float], List[float], List[float]]
+ ) -> List[List[float]]:
+ # (reduces degrees of freedom by 8:
+ # - 2 for making x,y independent of z;
+ # - 2 for making z independent of x,y
+ # - 4 for the last row of zeros and a one)
+ ord_stacked = np.zeros((8, len(ordinates[0]) * 3))
+ ord_stacked[0, 0::3] = ordinates[0]
+ ord_stacked[1, 0::3] = ordinates[1]
+ ord_stacked[2, 0::3] = 1.0
+ ord_stacked[3, 1::3] = ordinates[0]
+ ord_stacked[4, 1::3] = ordinates[1]
+ ord_stacked[5, 1::3] = 1.0
+ ord_stacked[6, 2::3] = ordinates[2]
+ ord_stacked[7, 2::3] = 1.0
+ return ord_stacked
diff --git a/geomagio/adjusted/transform/__init__.py b/geomagio/adjusted/transform/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fa99ab9aca8f355ff40cd866cc7f3b515e2a7f6
--- /dev/null
+++ b/geomagio/adjusted/transform/__init__.py
@@ -0,0 +1,26 @@
+from .LeastSq import LeastSq
+from .QRFactorization import QRFactorization
+from .Rescale3D import Rescale3D
+from .RotationTranslationXY import RotationTranslationXY
+from .ShearYZ import ShearYZ
+from .Transform import Transform
+from .TranslateOrigins import TranslateOrigins
+from .SVD import SVD
+from .ZRotationHScale import ZRotationHscale
+from .ZRotationHScaleZBaseline import ZRotationHscaleZbaseline
+from .ZRotationShear import ZRotationShear
+
+__all__ = [
+ "LeastSq",
+ "QRFactorization",
+ "Rescale3D",
+ "RotationTranslation3D",
+ "RotationTranslationXY",
+ "ShearYZ",
+ "Transform",
+ "TranslateOrigins",
+ "SVD",
+ "ZRotationHscale",
+ "ZRotationHscaleZbaseline",
+ "ZRotationShear",
+]
diff --git a/geomagio/algorithm/AdjustedAlgorithm.py b/geomagio/algorithm/AdjustedAlgorithm.py
index 601bad2444246efeee5b0f81f4551f245e3a9406..9782e9713097912371865078503536ac00811204 100644
--- a/geomagio/algorithm/AdjustedAlgorithm.py
+++ b/geomagio/algorithm/AdjustedAlgorithm.py
@@ -1,23 +1,22 @@
-"""Algorithm that converts from one geomagnetic coordinate system to a
- related geographic coordinate system, by using transformations generated
- from absolute, baseline measurements.
-"""
-from __future__ import absolute_import
+import sys
-from .Algorithm import Algorithm
import json
import numpy as np
from obspy.core import Stream, Stats
-import sys
+
+from ..adjusted import AdjustedMatrix
+from .Algorithm import Algorithm
class AdjustedAlgorithm(Algorithm):
- """Adjusted Data Algorithm"""
+ """Algorithm that converts from one geomagnetic coordinate system to a
+ related geographic coordinate system, by using transformations generated
+ from absolute, baseline measurements.
+ """
def __init__(
self,
- matrix=None,
- pier_correction=None,
+ matrix: AdjustedMatrix = None,
statefile=None,
data_type=None,
location=None,
@@ -33,7 +32,6 @@ class AdjustedAlgorithm(Algorithm):
)
# state variables
self.matrix = matrix
- self.pier_correction = pier_correction
self.statefile = statefile
self.data_type = data_type
self.location = location
@@ -45,25 +43,28 @@ class AdjustedAlgorithm(Algorithm):
"""Load algorithm state from a file.
File name is self.statefile.
"""
- # Adjusted matrix defaults to identity matrix
- matrix_size = len([c for c in self.get_input_channels() if c != "F"]) + 1
- self.matrix = np.eye(matrix_size)
- self.pier_correction = 0
if self.statefile is None:
return
- data = None
try:
with open(self.statefile, "r") as f:
data = f.read()
data = json.loads(data)
except IOError as err:
- sys.stderr.write("I/O error {0}".format(err))
- if data is None or data == "":
- return
- for row in range(matrix_size):
- for col in range(matrix_size):
- self.matrix[row, col] = np.float64(data[f"M{row+1}{col+1}"])
- self.pier_correction = np.float64(data["PC"])
+ raise FileNotFoundError("statefile not found")
+ # Adjusted matrix defaults to identity matrix
+ matrix_size = len([c for c in self.get_input_channels() if c != "F"]) + 1
+ if "pier_correction" in data:
+ self.matrix = AdjustedMatrix(**data)
+ elif "PC" in data:
+ matrix = np.eye(matrix_size)
+ # read data from legacy format
+ for row in range(matrix_size):
+ for col in range(matrix_size):
+ matrix[row, col] = np.float64(data[f"M{row+1}{col+1}"])
+ pier_correction = np.float64(data["PC"])
+ self.matrix = AdjustedMatrix(matrix=matrix, pier_correction=pier_correction)
+ else:
+ raise ValueError("pier correction not found in statefile")
def save_state(self):
"""Save algorithm state to a file.
@@ -71,14 +72,10 @@ class AdjustedAlgorithm(Algorithm):
"""
if self.statefile is None:
return
- data = {"PC": self.pier_correction}
- length = len(self.matrix[0, :])
- for i in range(0, length):
- for j in range(0, length):
- key = "M" + str(i + 1) + str(j + 1)
- data[key] = self.matrix[i, j]
+ json_string = self.matrix.json()
+ json_dict = json.loads(json_string)
with open(self.statefile, "w") as f:
- f.write(json.dumps(data))
+ f.write(json.dumps(json_dict))
def create_trace(self, channel, stats, data):
"""Utility to create a new trace object.
@@ -126,15 +123,11 @@ class AdjustedAlgorithm(Algorithm):
out = None
inchannels = self.get_input_channels()
outchannels = self.get_output_channels()
- raws = np.vstack(
- [
- stream.select(channel=channel)[0].data
- for channel in inchannels
- if channel != "F"
- ]
- + [np.ones_like(stream[0].data)]
+ adjusted = self.matrix.process(
+ stream,
+ inchannels=inchannels,
+ outchannels=outchannels,
)
- adjusted = np.matmul(self.matrix, raws)
out = Stream(
[
self.create_trace(
@@ -142,12 +135,9 @@ class AdjustedAlgorithm(Algorithm):
stream.select(channel=inchannels[i])[0].stats,
adjusted[i],
)
- for i in range(len(adjusted) - 1)
+ for i in range(len(outchannels))
]
)
- if "F" in inchannels and "F" in outchannels:
- f = stream.select(channel="F")[0]
- out += self.create_trace("F", f.stats, f.data + self.pier_correction)
return out
def can_produce_data(self, starttime, endtime, stream):
diff --git a/geomagio/processing/adjusted.py b/geomagio/processing/adjusted.py
new file mode 100644
index 0000000000000000000000000000000000000000..97aa9629e9c4f3ce610c1d282777609ff0c24550
--- /dev/null
+++ b/geomagio/processing/adjusted.py
@@ -0,0 +1,52 @@
+import json
+from obspy import UTCDateTime
+from typing import Optional
+import typer
+
+from ..adjusted.Affine import Affine
+from ..residual import SpreadsheetSummaryFactory, WebAbsolutesFactory
+
+
+def main():
+ typer.run(generate_matrix)
+
+
+def generate_matrix(
+ observatory: str,
+ starttime: str,
+ endtime: str,
+ readings_starttime: str,
+ readings_endtime: str,
+ output_file: str,
+ input_factory: str = "webabsolutes",
+ spreadsheet_directory: Optional[str] = None,
+ webabsolutes_url: Optional[
+ str
+ ] = "https://geomag.usgs.gov/baselines/observation.json.php",
+):
+ if input_factory == "spreadsheet":
+ readings = SpreadsheetSummaryFactory(
+ base_directory=spreadsheet_directory
+ ).get_readings(
+ observatory=observatory,
+ starttime=UTCDateTime(readings_starttime),
+ endtime=UTCDateTime(readings_endtime),
+ )
+ elif input_factory == "webabsolutes":
+ readings = WebAbsolutesFactory(url=webabsolutes_url).get_readings(
+ observatory=observatory,
+ starttime=UTCDateTime(readings_starttime),
+ endtime=UTCDateTime(readings_endtime),
+ )
+ else:
+ readings = []
+
+ result = Affine(
+ observatory=observatory,
+ starttime=UTCDateTime(starttime),
+ endtime=UTCDateTime(endtime),
+ ).calculate(readings=readings)[0]
+ result.matrix = result.matrix.tolist()
+
+ with open(output_file, "w") as file:
+ json.dump(result.dict(), file)
diff --git a/geomagio/residual/Calculation.py b/geomagio/residual/Calculation.py
index 4001c5f1115f55e81396290440a44a203babe7b3..0d1cec2156a4c0cc5d6bcd60b5f353ef71227ff5 100644
--- a/geomagio/residual/Calculation.py
+++ b/geomagio/residual/Calculation.py
@@ -2,10 +2,8 @@ from typing import List, Tuple
from typing_extensions import Literal
import numpy as np
-from pydantic import BaseModel
from .Absolute import Absolute
-from .Angle import from_dms, to_dms
from .MeasurementType import (
MeasurementType as mt,
DECLINATION_TYPES,
diff --git a/geomagio/residual/Reading.py b/geomagio/residual/Reading.py
index 902780422cf0d60e2c4e16a60096ab839d80e5be..c91fcd6cc91e3c365bacdb1f65ae52cbe37a8c79 100644
--- a/geomagio/residual/Reading.py
+++ b/geomagio/residual/Reading.py
@@ -1,14 +1,14 @@
-import collections
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
from typing_extensions import Literal
+import numpy as np
from obspy import Stream, UTCDateTime
from pydantic import BaseModel
from .. import TimeseriesUtility
from ..TimeseriesFactory import TimeseriesFactory
from .Absolute import Absolute
-from .Measurement import AverageMeasurement, Measurement, average_measurement
+from .Measurement import Measurement, average_measurement
from .MeasurementType import MeasurementType
@@ -41,6 +41,15 @@ class Reading(BaseModel):
"""
return [m for m in self.measurements if m.measurement_type == measurement_type]
+ def get_absolute(
+ self,
+ element: str,
+ ) -> Optional[Absolute]:
+ for absolute in self.absolutes:
+ if absolute.element == element:
+ return absolute
+ return None
+
def load_ordinates(
self,
observatory: str,
@@ -120,11 +129,54 @@ class Reading(BaseModel):
):
return True
- def get_absolute(
- self,
- element: str,
- ) -> Optional[Absolute]:
- for absolute in self.absolutes:
- if absolute.element == element:
- return absolute
- return None
+
+def get_absolutes(
+ readings: List[Reading],
+) -> Tuple[List[float], List[float], List[float]]:
+ """Get H, D and Z absolutes"""
+ h_abs = np.array([reading.get_absolute("H").absolute for reading in readings])
+ d_abs = np.array([reading.get_absolute("D").absolute for reading in readings])
+ z_abs = np.array([reading.get_absolute("Z").absolute for reading in readings])
+
+ return (h_abs, d_abs, z_abs)
+
+
+def get_absolutes_xyz(
+ readings: List[Reading],
+) -> Tuple[List[float], List[float], List[float]]:
+ """Get X, Y and Z absolutes from H, D and Z baselines"""
+ h_abs, d_abs, z_abs = get_absolutes(readings)
+ # convert from cylindrical to Cartesian coordinates
+ x_a = h_abs * np.cos(np.radians(d_abs))
+ y_a = h_abs * np.sin(np.radians(d_abs))
+ z_a = z_abs
+ return (x_a, y_a, z_a)
+
+
+def get_baselines(
+ readings: List[Reading],
+) -> Tuple[List[float], List[float], List[float]]:
+ """Get H, D and Z baselines"""
+ h_bas = np.array([reading.get_absolute("H").baseline for reading in readings])
+ d_bas = np.array([reading.get_absolute("D").baseline for reading in readings])
+ z_bas = np.array([reading.get_absolute("Z").baseline for reading in readings])
+ return (h_bas, d_bas, z_bas)
+
+
+def get_ordinates(
+ readings: List[Reading],
+) -> Tuple[List[float], List[float], List[float]]:
+ """Calculates ordinates from absolutes and baselines"""
+ h_abs, d_abs, z_abs = get_absolutes(readings)
+ h_bas, d_bas, z_bas = get_baselines(readings)
+ # recreate ordinate variometer measurements from absolutes and baselines
+ h_ord = h_abs - h_bas
+ d_ord = d_abs - d_bas
+ z_ord = z_abs - z_bas
+ e_ord = h_abs * np.radians(d_ord)
+ h_ord = np.sqrt(h_ord ** 2 - e_ord ** 2)
+ return (h_ord, e_ord, z_ord)
+
+
+def get_times(readings: List[UTCDateTime]):
+ return np.array([reading.get_absolute("H").endtime for reading in readings])
diff --git a/geomagio/residual/SpreadsheetSummaryFactory.py b/geomagio/residual/SpreadsheetSummaryFactory.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a1f5082c11e88c3fd26f5637eb3251e25f049e9
--- /dev/null
+++ b/geomagio/residual/SpreadsheetSummaryFactory.py
@@ -0,0 +1,141 @@
+import os
+
+from obspy import UTCDateTime
+import openpyxl
+from typing import List
+
+from .Absolute import Absolute
+from . import Angle
+from .Reading import Reading
+from .SpreadsheetAbsolutesFactory import parse_relative_time
+
+
+class SpreadsheetSummaryFactory(object):
+ """Read absolutes from summary spreadsheets"""
+
+ def __init__(self, base_directory: str):
+ self.base_directory = base_directory
+
+ def get_readings(
+ self, observatory: str, starttime: UTCDateTime, endtime: UTCDateTime
+ ) -> List[Reading]:
+ """Gathers readings from factory's base directory
+
+ Attributes
+ ----------
+ observatory: 3-letter observatory code
+ starttime: beginning date of readings
+ endtime: end date of readings
+ """
+ readings = []
+ start_filename = f"{observatory}{starttime.datetime:%Y%j%H%M}.xlsm"
+ end_filename = f"{observatory}{endtime.datetime:%Y%j%H%M}.xlsm"
+ for year in range(starttime.year, endtime.year + 1):
+ observatory_directory = os.path.join(
+ self.base_directory, observatory, f"{year}"
+ )
+ for (dirpath, _, filenames) in os.walk(observatory_directory):
+ filenames.sort()
+ for filename in filenames:
+ if start_filename <= filename < end_filename:
+ rs = self.parse_spreadsheet(
+ os.path.join(dirpath, filename),
+ )
+ for r in rs:
+ readings.append(r)
+ return readings
+
+ def parse_spreadsheet(self, path: str) -> List[Reading]:
+ sheet = openpyxl.load_workbook(path, data_only=True)["Sheet1"]
+ readings = self._parse_readings(sheet, path)
+ return readings
+
+ def _parse_metadata(self, sheet: openpyxl.worksheet) -> dict:
+ """gather metadata from spreadsheet
+
+ Attributes
+ ----------
+ sheet: excel sheet containing residual summary values
+ observatory: 3-letter observatory code
+ """
+ date = sheet["I1"].value
+ date = f"{date.year}{date.month:02}{date.day:02}"
+ return {
+ "station": sheet["D49"].value[0:3],
+ "pier_correction": sheet["C5"].value,
+ "instrument": sheet["B3"].value,
+ "date": date,
+ "observer": sheet["I10"].value,
+ }
+
+ def _parse_readings(self, sheet: openpyxl.worksheet, path: str) -> List[Reading]:
+ """get list of readings from spreadsheet
+
+ Attributes
+ ----------
+ sheet: excel sheet containing residual summary values
+ path: spreadsheet's filepath
+
+ Outputs
+ -------
+ List of valid readings from spreadsheet.
+ If all readings are valid, 4 readings are returned
+ """
+ metadata = self._parse_metadata(sheet)
+ date = sheet["I1"].value
+ base_date = f"{date.year}{date.month:02}{date.day:02}"
+ readings = []
+ for d_n in range(10, 14):
+ h_n = d_n + 14
+ v_n = d_n + 28
+ absolutes = [
+ Absolute(
+ element="D",
+ absolute=Angle.from_dms(
+ degrees=sheet[f"C{d_n}"].value, minutes=sheet[f"D{d_n}"].value
+ ),
+ baseline=sheet[f"H{d_n}"].value / 60,
+ starttime=parse_relative_time(
+ base_date, "{0:04d}".format(sheet[f"B{v_n}"].value)
+ ),
+ endtime=parse_relative_time(
+ base_date, "{0:04d}".format(sheet[f"B{d_n}"].value)
+ ),
+ ),
+ Absolute(
+ element="H",
+ absolute=sheet[f"D{h_n}"].value,
+ baseline=sheet[f"H{h_n}"].value,
+ starttime=parse_relative_time(
+ base_date, "{0:04d}".format(sheet[f"B{v_n}"].value)
+ ),
+ endtime=parse_relative_time(
+ base_date, "{0:04d}".format(sheet[f"B{h_n}"].value)
+ ),
+ ),
+ Absolute(
+ element="Z",
+ absolute=sheet[f"D{v_n}"].value,
+ baseline=sheet[f"H{v_n}"].value,
+ starttime=parse_relative_time(
+ base_date, "{0:04d}".format(sheet[f"B{v_n}"].value)
+ ),
+ endtime=parse_relative_time(
+ base_date, "{0:04d}".format(sheet[f"B{v_n}"].value)
+ ),
+ ),
+ ]
+ valid = [
+ sheet[f"J{d_n}"].value,
+ sheet[f"J{h_n}"].value,
+ sheet[f"J{d_n}"].value,
+ ]
+ if valid == [None, None, None]:
+ readings.append(
+ Reading(
+ metadata=metadata,
+ absolutes=absolutes,
+ pier_correction=metadata["pier_correction"],
+ ),
+ )
+ return readings
diff --git a/geomagio/residual/__init__.py b/geomagio/residual/__init__.py
index 59e03297abe1d279223f1a63e34a096e1322ebf6..ec2de5e8adb9e2855ba2675a368f5a358f525154 100644
--- a/geomagio/residual/__init__.py
+++ b/geomagio/residual/__init__.py
@@ -20,13 +20,15 @@ from .MeasurementType import (
)
from .Reading import Reading
from .SpreadsheetAbsolutesFactory import SpreadsheetAbsolutesFactory
+from .SpreadsheetSummaryFactory import SpreadsheetSummaryFactory
from .WebAbsolutesFactory import WebAbsolutesFactory
__all__ = [
"Absolute",
"Angle",
"AverageMeasurement",
- "average_measurement" "CalFileFactory",
+ "average_measurement",
+ "CalFileFactory",
"calculate",
"calculate_D_absolute",
"calculate_HZ_absolutes",
@@ -39,5 +41,6 @@ __all__ = [
"MeasurementType",
"Reading",
"SpreadsheetAbsolutesFactory",
+ "SpreadsheetSummaryFactory",
"WebAbsolutesFactory",
]
diff --git a/setup.py b/setup.py
index a92451cdf5762c924da69c7c343f1b73a92efab8..8f1ff47f4d8290489c96edf7283b8e9c5dc3f873 100644
--- a/setup.py
+++ b/setup.py
@@ -27,6 +27,7 @@ setuptools.setup(
"console_scripts": [
"magproc-prepfiles=geomagio.processing.magproc:main",
"filter-realtime=geomagio.processing.obsrio:main",
+ "generate-matrix=geomagio.processing.adjusted:main",
],
},
)
diff --git a/test/adjusted_test/adjusted_test.py b/test/adjusted_test/adjusted_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ac3747660af93332bdfc4cd17c6fe29e106d666
--- /dev/null
+++ b/test/adjusted_test/adjusted_test.py
@@ -0,0 +1,511 @@
+import json
+
+import numpy as np
+from numpy.testing import assert_equal, assert_array_almost_equal
+from obspy.core import UTCDateTime
+import pytest
+
+from geomagio.adjusted import AdjustedMatrix
+from geomagio.adjusted.Affine import Affine, get_epochs
+from geomagio.adjusted.transform import (
+ LeastSq,
+ QRFactorization,
+ Rescale3D,
+ RotationTranslationXY,
+ SVD,
+ ShearYZ,
+ TranslateOrigins,
+ ZRotationHscale,
+ TranslateOrigins,
+ ZRotationHscaleZbaseline,
+ ZRotationShear,
+)
+from test.residual_test.residual_test import (
+ get_json_readings,
+ get_spreadsheet_directory_readings,
+)
+
+
+def format_result(result) -> dict:
+ Ms = []
+ for M in result:
+ m = []
+ for row in M:
+ m.append(list(row))
+ Ms.append(m)
+ return Ms
+
+
+def get_excpected_matrices(observatory, key):
+ with open(f"etc/adjusted/{observatory}_expected.json", "r") as file:
+ expected = json.load(file)
+ return expected[key]
+
+
+def get_expected_synthetic_result(key):
+ with open("etc/adjusted/synthetic.json") as file:
+ expected = json.load(file)
+ return expected["results"][key]
+
+
+def get_sythetic_variables():
+ with open("etc/adjusted/synthetic.json") as file:
+ data = json.load(file)
+ variables = data["variables"]
+ ordinates = np.array([variables["h_ord"], variables["e_ord"], variables["z_ord"]])
+ absolutes = np.array([variables["x_abs"], variables["y_abs"], variables["z_abs"]])
+ weights = np.arange(0, len(ordinates[0]))
+ return ordinates, absolutes, weights
+
+
+def test_BOU201911202001_infinite_one_interval():
+ readings = get_json_readings("etc/residual/BOU20191001.json")
+ result = Affine(
+ observatory="BOU",
+ starttime=UTCDateTime("2019-11-01T00:00:00Z"),
+ endtime=UTCDateTime("2020-01-31T23:59:00Z"),
+ transforms=[
+ RotationTranslationXY(memory=np.inf, acausal=True),
+ TranslateOrigins(memory=np.inf, acausal=True),
+ ],
+ update_interval=None,
+ ).calculate(
+ readings=readings,
+ )
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("BOU", "inf_one_interval")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+ assert_equal(len(matrices), 1)
+
+
+def test_BOU201911202001_infinite_weekly():
+ readings = get_json_readings("etc/residual/BOU20191001.json")
+
+ starttime = UTCDateTime("2019-11-01T00:00:00Z")
+ endtime = UTCDateTime("2020-01-31T23:59:00Z")
+
+ update_interval = 86400 * 7
+
+ result = Affine(
+ observatory="BOU",
+ starttime=starttime,
+ endtime=endtime,
+ update_interval=update_interval,
+ transforms=[
+ RotationTranslationXY(memory=np.inf, acausal=True),
+ TranslateOrigins(memory=np.inf, acausal=True),
+ ],
+ ).calculate(
+ readings=readings,
+ )
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("BOU", "inf_weekly")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+ assert_equal(len(matrices), ((endtime - starttime) // update_interval) + 1)
+
+
+def test_BOU201911202001_invalid_readings():
+ starttime = UTCDateTime("2019-11-01T00:00:00Z")
+ with pytest.raises(
+ ValueError, match=f"No valid observations for: {starttime}"
+ ) as error:
+ readings = []
+ result = Affine(
+ observatory="BOU",
+ starttime=starttime,
+ endtime=UTCDateTime("2020-01-31T23:59:00Z"),
+ transforms=[
+ RotationTranslationXY(memory=np.inf, acausal=True),
+ TranslateOrigins(memory=np.inf, acausal=True),
+ ],
+ update_interval=None,
+ ).calculate(
+ readings=readings,
+ )
+
+
+def test_BOU201911202001_short_acausal():
+ readings = get_json_readings("etc/residual/BOU20191001.json")
+
+ starttime = UTCDateTime("2019-11-01T00:00:00Z")
+ endtime = UTCDateTime("2020-01-31T23:59:00Z")
+
+ update_interval = 86400 * 7
+
+ result = Affine(
+ observatory="BOU",
+ starttime=starttime,
+ endtime=endtime,
+ update_interval=update_interval,
+ transforms=[
+ RotationTranslationXY(memory=(86400 * 100), acausal=True),
+ TranslateOrigins(memory=(86400 * 10), acausal=True),
+ ],
+ ).calculate(
+ readings=readings,
+ )
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("BOU", "short_acausal")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+ assert_equal(len(matrices), ((endtime - starttime) // update_interval) + 1)
+
+
+def test_BOU201911202001_short_causal():
+ readings = get_json_readings("etc/residual/BOU20191001.json")
+
+ starttime = UTCDateTime("2019-11-01T00:00:00Z")
+ endtime = UTCDateTime("2020-01-31T23:59:00Z")
+
+ update_interval = 86400 * 7
+
+ result = Affine(
+ observatory="BOU",
+ starttime=starttime,
+ endtime=endtime,
+ update_interval=update_interval,
+ transforms=[
+ RotationTranslationXY(memory=(86400 * 100), acausal=False),
+ TranslateOrigins(memory=(86400 * 10), acausal=False),
+ ],
+ ).calculate(readings=readings)
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("BOU", "short_causal")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+ assert_equal(len(matrices), ((endtime - starttime) // update_interval) + 1)
+
+
+def test_CMO2015_infinite_one_interval():
+ readings = get_spreadsheet_directory_readings(
+ observatory="CMO",
+ starttime=UTCDateTime("2015-01-01T00:00:00Z"),
+ endtime=UTCDateTime("2015-12-31T23:59:00Z"),
+ path="etc/residual/Caldata/",
+ )
+
+ assert len(readings) == 146
+
+ result = Affine(
+ observatory="CMO",
+ starttime=UTCDateTime("2015-02-01T00:00:00Z"),
+ endtime=UTCDateTime("2015-11-27T23:59:00Z"),
+ transforms=[
+ RotationTranslationXY(memory=np.inf, acausal=True),
+ TranslateOrigins(memory=np.inf, acausal=True),
+ ],
+ acausal=True,
+ update_interval=None,
+ ).calculate(
+ readings=readings,
+ )
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("CMO", "inf_one_interval")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+
+ assert_equal(len(matrices), 1)
+
+
+def test_CMO2015_infinite_weekly():
+ readings = get_spreadsheet_directory_readings(
+ observatory="CMO",
+ starttime=UTCDateTime("2015-01-01T00:00:00Z"),
+ endtime=UTCDateTime("2015-12-31T23:59:00Z"),
+ path="etc/residual/Caldata/",
+ )
+ assert len(readings) == 146
+
+ starttime = UTCDateTime("2015-02-01T00:00:00Z")
+ endtime = UTCDateTime("2015-11-27T23:59:00Z")
+
+ update_interval = 86400 * 7
+
+ result = Affine(
+ observatory="CMO",
+ starttime=starttime,
+ endtime=endtime,
+ transforms=[
+ RotationTranslationXY(memory=np.inf, acausal=True),
+ TranslateOrigins(memory=np.inf, acausal=True),
+ ],
+ update_interval=update_interval,
+ acausal=True,
+ ).calculate(
+ readings=readings,
+ )
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("CMO", "inf_weekly")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+ assert_equal(len(matrices), ((endtime - starttime) // update_interval) + 1)
+
+
+def test_CMO2015_short_acausal():
+ readings = get_spreadsheet_directory_readings(
+ observatory="CMO",
+ starttime=UTCDateTime("2015-01-01T00:00:00Z"),
+ endtime=UTCDateTime("2015-12-31T23:59:00Z"),
+ path="etc/residual/Caldata/",
+ )
+ assert len(readings) == 146
+
+ starttime = UTCDateTime("2015-02-01T00:00:00Z")
+ endtime = UTCDateTime("2015-11-27T23:59:00Z")
+
+ update_interval = 86400 * 7
+
+ result = Affine(
+ observatory="CMO",
+ starttime=starttime,
+ endtime=endtime,
+ update_interval=update_interval,
+ transforms=[
+ RotationTranslationXY(memory=(86400 * 100), acausal=True),
+ TranslateOrigins(memory=(86400 * 10), acausal=True),
+ ],
+ ).calculate(
+ readings=readings,
+ )
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("CMO", "short_acausal")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+ assert_equal(len(matrices), ((endtime - starttime) // update_interval) + 1)
+
+
+def test_CMO2015_short_causal():
+ readings = get_spreadsheet_directory_readings(
+ observatory="CMO",
+ starttime=UTCDateTime("2015-01-01T00:00:00Z"),
+ endtime=UTCDateTime("2015-12-31T23:59:00Z"),
+ path="etc/residual/Caldata/",
+ )
+ assert len(readings) == 146
+
+ starttime = UTCDateTime("2015-02-01T00:00:00Z")
+ endtime = UTCDateTime("2015-11-27T23:59:00Z")
+
+ update_interval = 86400 * 7
+
+ result = Affine(
+ observatory="CMO",
+ starttime=starttime,
+ endtime=endtime,
+ update_interval=update_interval,
+ transforms=[
+ RotationTranslationXY(memory=(86400 * 100), acausal=False),
+ TranslateOrigins(memory=(86400 * 10), acausal=False),
+ ],
+ ).calculate(
+ readings=readings,
+ )
+
+ matrices = format_result([adjusted_matrix.matrix for adjusted_matrix in result])
+ expected_matrices = get_excpected_matrices("CMO", "short_causal")
+ for i in range(len(matrices)):
+ assert_array_almost_equal(
+ matrices[i],
+ expected_matrices[i],
+ decimal=3,
+ err_msg=f"Matrix {i} not equal",
+ )
+ assert_equal(len(matrices), ((endtime - starttime) // update_interval) + 1)
+
+
+def test_get_epochs():
+ readings = get_json_readings("etc/residual/BOU20200101.json")
+ # force a bad measurement for second reading
+ readings[2].absolutes[1].absolute = 0
+ epochs = [r.time for r in readings if r.get_absolute("H").absolute == 0]
+ assert len(epochs) == 1
+ epoch_start, epoch_end = get_epochs(
+ epochs=epochs,
+ time=UTCDateTime("2019-11-01T00:00:00Z"),
+ )
+ assert epoch_start is None
+ assert epoch_end == readings[2].time
+ epoch_start, epoch_end = get_epochs(
+ epochs=epochs,
+ time=UTCDateTime("2020-01-07T00:00:00Z"),
+ )
+ assert epoch_start == readings[2].time
+ assert epoch_end is None
+ epoch_start, epoch_end = get_epochs(
+ epochs=epochs,
+ time=UTCDateTime("2020-01-07T00:00:00Z"),
+ )
+
+
+def test_LeastSq_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ LeastSq().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("LeastSq"),
+ decimal=3,
+ )
+
+
+def test_QRFactorization_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ QRFactorization().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("QRFactorization"),
+ decimal=3,
+ )
+
+
+def test_Rescale3D_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ Rescale3D().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("Rescale3D"),
+ decimal=3,
+ )
+
+
+def test_RotationTranslationXY_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ RotationTranslationXY().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("RotationTranslationXY"),
+ decimal=3,
+ )
+
+
+def test_ShearYZ_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ ShearYZ().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("ShearYZ"),
+ decimal=3,
+ )
+
+
+def test_SVD_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ SVD().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("SVD"),
+ decimal=3,
+ )
+
+
+def test_TranslateOrigins_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ TranslateOrigins().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("TranslateOrigins"),
+ decimal=3,
+ )
+
+
+def test_ZRotationHscale_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ ZRotationHscale().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("ZRotationHscale"),
+ decimal=3,
+ )
+
+
+def test_ZRotationHscaleZbaseline_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ ZRotationHscaleZbaseline().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("ZRotationHscaleZbaseline"),
+ decimal=3,
+ )
+
+
+def test_ZRotationShear_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ ZRotationShear().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("ZRotationShear"),
+ decimal=3,
+ )
diff --git a/test/algorithm_test/AdjustedAlgorithm_test.py b/test/algorithm_test/AdjustedAlgorithm_test.py
index f37f93f78893e6f13b22e50923724bca0b4ee260..98eba78b433567c27b5c879f4e0883dfe11b74c0 100644
--- a/test/algorithm_test/AdjustedAlgorithm_test.py
+++ b/test/algorithm_test/AdjustedAlgorithm_test.py
@@ -1,4 +1,5 @@
-from geomagio.algorithm import AdjustedAlgorithm as adj
+from geomagio.adjusted import AdjustedMatrix
+from geomagio.algorithm import AdjustedAlgorithm
import geomagio.iaga2002 as i2
from numpy.testing import assert_almost_equal, assert_equal
@@ -6,21 +7,55 @@ from numpy.testing import assert_almost_equal, assert_equal
def test_construct():
"""algorithm_test.AdjustedAlgorithm_test.test_construct()"""
# load adjusted data transform matrix and pier correction
- a = adj(statefile="etc/adjusted/adjbou_state_.json")
+ a = AdjustedAlgorithm(statefile="etc/adjusted/adjbou_state_.json")
- assert_almost_equal(actual=a.matrix[0, 0], desired=9.83427577e-01, decimal=6)
+ assert_almost_equal(actual=a.matrix.matrix[0][0], desired=9.83427577e-01, decimal=6)
- assert_equal(actual=a.pier_correction, desired=-22)
+ assert_equal(actual=a.matrix.pier_correction, desired=-22)
-def test_process_XYZF():
+def assert_streams_almost_equal(adjusted, expected, channels):
+ for channel in channels:
+ assert_almost_equal(
+ actual=adjusted.select(channel=channel)[0].data,
+ desired=expected.select(channel=channel)[0].data,
+ decimal=2,
+ )
+
+
+def test_process_XYZF_AdjustedMatrix():
"""algorithm_test.AdjustedAlgorithm_test.test_process()
Check adjusted data processing versus files generated from
original script
"""
- # load adjusted data transform matrix and pier correction
- a = adj(statefile="etc/adjusted/adjbou_state_.json")
+ # Initiate algorithm with AdjustedMatrix object
+ a = AdjustedAlgorithm(
+ matrix=AdjustedMatrix(
+ matrix=[
+ [
+ 0.9834275767090617,
+ -0.15473074200902157,
+ 0.027384986324932026,
+ -1276.164681191976,
+ ],
+ [
+ 0.16680172992706568,
+ 0.987916201012128,
+ -0.0049868332295851525,
+ -0.8458192581350419,
+ ],
+ [
+ -0.006725053082782385,
+ -0.011809351484171948,
+ 0.9961869012493976,
+ 905.3800885796844,
+ ],
+ [0, 0, 0, 1],
+ ],
+ pier_correction=-22,
+ )
+ )
# load boulder Jan 16 files from /etc/ directory
with open("etc/adjusted/BOU201601vmin.min") as f:
@@ -31,37 +66,80 @@ def test_process_XYZF():
# process hezf (raw) channels with loaded transform
adjusted = a.process(raw)
- # compare channels from adjusted and expected streams
- assert_almost_equal(
- actual=adjusted.select(channel="X")[0].data,
- desired=expected.select(channel="X")[0].data,
- decimal=2,
+ assert_streams_almost_equal(
+ adjusted=adjusted, expected=expected, channels=["X", "Y", "Z", "F"]
)
- assert_almost_equal(
- actual=adjusted.select(channel="Y")[0].data,
- desired=expected.select(channel="Y")[0].data,
- decimal=2,
+
+
+def test_process_reverse_polarity_AdjustedMatrix():
+ """algorithm_test.AdjustedAlgorithm_test.test_process()
+
+ Check adjusted data processing versus files generated from
+ original script. Tests reverse polarity martix.
+ """
+ # Initiate algorithm with AdjustedMatrix object
+ a = AdjustedAlgorithm(
+ matrix=AdjustedMatrix(
+ matrix=[
+ [-1, 0, 0],
+ [0, -1, 0],
+ [0, 0, 1],
+ ],
+ pier_correction=-22,
+ ),
+ inchannels=["H", "E"],
+ outchannels=["H", "E"],
)
- assert_almost_equal(
- actual=adjusted.select(channel="Z")[0].data,
- desired=expected.select(channel="Z")[0].data,
- decimal=2,
+
+ # load boulder May 20 files from /etc/ directory
+ with open("etc/adjusted/BOU202005vmin.min") as f:
+ raw = i2.IAGA2002Factory().parse_string(f.read())
+ with open("etc/adjusted/BOU202005adj.min") as f:
+ expected = i2.IAGA2002Factory().parse_string(f.read())
+
+ # process he(raw) channels with loaded transform
+ adjusted = a.process(raw)
+
+ assert_streams_almost_equal(
+ adjusted=adjusted, expected=expected, channels=["H", "E"]
)
- assert_almost_equal(
- actual=adjusted.select(channel="F")[0].data,
- desired=expected.select(channel="F")[0].data,
- decimal=2,
+
+
+def test_process_XYZF_statefile():
+ """algorithm_test.AdjustedAlgorithm_test.test_process()
+
+ Check adjusted data processing versus files generated from
+ original script
+
+ Uses statefile to generate AdjustedMatrix
+ """
+ # load adjusted data transform matrix and pier correction
+ a = AdjustedAlgorithm(statefile="etc/adjusted/adjbou_state_.json")
+
+ # load boulder Jan 16 files from /etc/ directory
+ with open("etc/adjusted/BOU201601vmin.min") as f:
+ raw = i2.IAGA2002Factory().parse_string(f.read())
+ with open("etc/adjusted/BOU201601adj.min") as f:
+ expected = i2.IAGA2002Factory().parse_string(f.read())
+
+ # process hezf (raw) channels with loaded transform
+ adjusted = a.process(raw)
+
+ assert_streams_almost_equal(
+ adjusted=adjusted, expected=expected, channels=["X", "Y", "Z", "F"]
)
-def test_process_reverse_polarity():
+def test_process_reverse_polarity_statefile():
"""algorithm_test.AdjustedAlgorithm_test.test_process()
Check adjusted data processing versus files generated from
original script. Tests reverse polarity martix.
+
+ Uses statefile to generate AdjustedMatrix
"""
# load adjusted data transform matrix and pier correction
- a = adj(
+ a = AdjustedAlgorithm(
statefile="etc/adjusted/adjbou_state_HE_.json",
inchannels=["H", "E"],
outchannels=["H", "E"],
@@ -76,14 +154,6 @@ def test_process_reverse_polarity():
# process he(raw) channels with loaded transform
adjusted = a.process(raw)
- # compare channels from adjusted and expected streams
- assert_almost_equal(
- actual=adjusted.select(channel="H")[0].data,
- desired=expected.select(channel="H")[0].data,
- decimal=2,
- )
- assert_almost_equal(
- actual=adjusted.select(channel="E")[0].data,
- desired=expected.select(channel="E")[0].data,
- decimal=2,
+ assert_streams_almost_equal(
+ adjusted=adjusted, expected=expected, channels=["H", "E"]
)
diff --git a/test/residual_test/residual_test.py b/test/residual_test/residual_test.py
index 5679f2ba990f2b79e262a576e2cc85449be8c43c..e1fe36f2b6708130d5bd2c3ef6a139bc2061b795 100644
--- a/test/residual_test/residual_test.py
+++ b/test/residual_test/residual_test.py
@@ -1,11 +1,16 @@
-from numpy.testing import assert_almost_equal
+import json
+
+from numpy.testing import assert_almost_equal, assert_equal
+from pydantic import parse_obj_as
import pytest
+from typing import List
from obspy.core import UTCDateTime
from geomagio.residual import (
calculate,
Reading,
SpreadsheetAbsolutesFactory,
+ SpreadsheetSummaryFactory,
WebAbsolutesFactory,
)
@@ -43,14 +48,10 @@ def assert_readings_equal(expected: Reading, actual: Reading, decimal: int):
)
-def get_null_absolutes(observatory, starttime, endtime):
- """
- Tests functionality of WebAbsolutesFactory and recalculation of absolutes
- """
- # establish SpreadsheetAbsolutesFactory for reading test data from Excel
- waf = WebAbsolutesFactory()
- # Read spreadsheet containing test data
- readings = waf.get_readings(observatory, starttime, endtime)
+def get_json_readings(filename: str):
+ with open(filename, "r") as file:
+ readings = json.load(file)
+ readings = parse_obj_as(List[Reading], readings)
return readings
@@ -65,6 +66,33 @@ def get_spreadsheet_absolutes(path):
return reading
+def get_spreadsheet_directory_readings(path, observatory, starttime, endtime):
+ ssf = SpreadsheetSummaryFactory(base_directory=path)
+ readings = ssf.get_readings(
+ observatory=observatory, starttime=starttime, endtime=endtime
+ )
+ return readings
+
+
+def test_CMO_summaries():
+ starttime = UTCDateTime("2015-04-01")
+ endtime = UTCDateTime("2015-06-15")
+ readings = get_spreadsheet_directory_readings(
+ path="etc/residual/Caldata",
+ observatory="CMO",
+ starttime=starttime,
+ endtime=endtime,
+ )
+ for reading in readings:
+ assert_equal(reading.metadata["station"], "CMO")
+ assert_equal(reading.metadata["instrument"], 200803)
+ assert_equal(reading.pier_correction, 10.5)
+ assert_equal(len(readings), 26)
+
+ assert readings[0].time > starttime
+ assert readings[-1].time < endtime
+
+
def test_DED_20140952332():
"""
Compare calulations to original absolutes obejct from Spreadsheet.
@@ -101,11 +129,7 @@ def test_BOU_20190702():
Tests gathering of BOU's metadata for use by calculations.
Tests calculations for null method measurements in units of DM.
"""
- readings = get_null_absolutes(
- observatory="BOU",
- starttime=UTCDateTime("2019-07-02T00:00:00Z"),
- endtime=UTCDateTime("2019-07-03T00:00:00Z"),
- )
+ readings = get_json_readings("etc/residual/BOU20190702.json")
for reading in readings:
assert_readings_equal(
expected=reading,
@@ -120,11 +144,7 @@ def test_BOU_20200422():
Tests gathering of BOU's metadata for use by calculations.
Tests calculations for null method measurements in units of DMS.
"""
- readings = get_null_absolutes(
- observatory="BOU",
- starttime=UTCDateTime("2020-04-22T00:00:00Z"),
- endtime=UTCDateTime("2020-04-23T00:00:00Z"),
- )
+ readings = get_json_readings("etc/residual/BOU20200422.json")
for reading in readings:
assert_readings_equal(
expected=reading,