diff --git a/docs/algorithms/Adjusted_usage.md b/docs/algorithms/Adjusted_usage.md
index 4db49e5c509c398b8aa61f6675dd28781a47326e..7388add068a673800182bfe890fd687fa9612a89 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 = adj(
+ 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..e3f46068de20dadc9188a71c1ebfb62ef2d0e8f3
--- /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
+++ b/etc/adjusted/adjbou_state_.json
@@ -1 +1,24 @@
-{"M32": -0.011809351484171948, "M41": -0.0, "M44": 1.0, "PC": -22, "M24": -0.84581925813504188, "M43": 0.0, "M34": 905.38008857968441, "M33": 0.99618690124939757, "M21": 0.16680172992706568, "M22": 0.98791620101212796, "M23": -0.0049868332295851525, "M11": 0.98342757670906167, "M42": -0.0, "M13": 0.027384986324932026, "M12": -0.15473074200902157, "M14": -1276.1646811919759, "M31": -0.006725053082782385}
\ No newline at end of file
+{
+ "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
+}
\ No newline at end of file
diff --git a/etc/adjusted/adjbou_state_HE_.json b/etc/adjusted/adjbou_state_HE_.json
index 91ed3625c0bbb0ecd40e770c3ac7202797783df8..f9c0e3d9d3ba643259f9619554ecf9e2894d3f90 100644
--- a/etc/adjusted/adjbou_state_HE_.json
+++ b/etc/adjusted/adjbou_state_HE_.json
@@ -1 +1,8 @@
-{"PC": -22.0, "M11": -1.0, "M12": -0.0, "M13": -0.0, "M21": -0.0, "M22": -1.0, "M23": -0.0, "M31": 0.0, "M32": 0.0, "M33": 1.0}
\ No newline at end of file
+{
+ "matrix": [
+ [-1, 0, 0],
+ [0, -1, 0],
+ [0, 0, 1]
+ ],
+ "pier_correction": -22.0
+}
\ No newline at end of file
diff --git a/etc/adjusted/synthetic.json b/etc/adjusted/synthetic.json
new file mode 100644
index 0000000000000000000000000000000000000000..c3dd5594f10c4ed6cc19c9a9256c02e72e81634f
--- /dev/null
+++ b/etc/adjusted/synthetic.json
@@ -0,0 +1,398 @@
+{
+ "variables": {
+ "h_ord": [
+ 1.79289322,
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diff --git a/geomagio/adjusted/AdjustedMatrix.py b/geomagio/adjusted/AdjustedMatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8b8dcc649c14a3008389c0438b49edf5400c0eb
--- /dev/null
+++ b/geomagio/adjusted/AdjustedMatrix.py
@@ -0,0 +1,30 @@
+from obspy import UTCDateTime
+from pydantic import BaseModel
+from typing import (
+ Any,
+ List,
+ Optional,
+)
+
+from .. import pydantic_utcdatetime
+from .Metric import 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: Any
+ pier_correction: float
+ metrics: Optional[List[Metric]] = None
+ starttime: Optional[UTCDateTime] = None
+ endtime: Optional[UTCDateTime] = None
diff --git a/geomagio/adjusted/Affine.py b/geomagio/adjusted/Affine.py
new file mode 100644
index 0000000000000000000000000000000000000000..3189ef60b95fb241da3550700cdee756744eb2e0
--- /dev/null
+++ b/geomagio/adjusted/Affine.py
@@ -0,0 +1,413 @@
+from functools import reduce
+import numpy as np
+from obspy import UTCDateTime
+from pydantic import BaseModel
+from typing import List, Optional, Tuple
+
+from .AdjustedMatrix import AdjustedMatrix
+from .. import ChannelConverter
+from .. import pydantic_utcdatetime
+from .Metric import Metric
+from ..residual import Reading
+from .Transform import Transform, TranslateOrigins, RotationTranslationXY
+
+
+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)
+
+
+def filter_iqr(
+ series: List[float], threshold: int = 6, 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
+
+
+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
+ acausal: when True, utilizes readings from after set endtime
+ update_interval: window of time a matrix is representative of
+ transforms: list of methods for matrix calculations
+ """
+
+ observatory: str = None
+ starttime: UTCDateTime = UTCDateTime() - (86400 * 7)
+ endtime: UTCDateTime = UTCDateTime()
+ acausal: bool = False
+ update_interval: Optional[int] = 86400 * 7
+ transforms: List[Transform] = [
+ RotationTranslationXY(memory=(86400 * 100)),
+ TranslateOrigins(memory=(86400 * 10)),
+ ]
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ def calculate(self, readings: List[Reading]) -> List[AdjustedMatrix]:
+ """Calculates affine matrices for a range of times
+
+ Attributes
+ ----------
+ readings: list of readings containing absolutes
+
+ Outputs
+ -------
+ Ms: list of 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
+ epoch_start = None
+ epoch_end = None
+ # search for "bad" H values
+ epochs = [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 = self.get_epochs(
+ epoch_start=epoch_start, epoch_end=epoch_end, 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)
+ # if readings are trimmed by bad data, mark the vakid interval
+ M.starttime = epoch_start
+ M.endtime = epoch_end
+ time += update_interval
+
+ Ms.append(M)
+
+ return Ms
+
+ def get_epochs(
+ self,
+ epoch_start: float,
+ epoch_end: float,
+ epochs: List[float],
+ time: UTCDateTime,
+ ) -> Tuple[float, float]:
+ """Updates valid start/end time for a given interval
+
+ Attributes
+ ----------
+ epoch_start: float value signifying start of last valid interval
+ epoch_end: float value signifying end of last valid interval
+ epochs: list of floats signifying bad data times
+ time: current time epoch is being evaluated at
+
+ Outputs
+ -------
+ epoch_start: float value signifying start of current valid interval
+ epoch_end: float value signifying end of current valid interval
+ """
+ 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
+
+ def valid_reading(self, reading: Reading) -> bool:
+ """ensures that readings used in calculations have been marked as valid
+
+ Attributes
+ ----------
+ readings: list containing valid and invalid readings
+ """
+ if (
+ reading.get_absolute("D").valid == True
+ and reading.get_absolute("H").valid == True
+ and reading.get_absolute("Z").valid == True
+ ):
+ return True
+
+ def get_times(self, readings: List[UTCDateTime]) -> np.array:
+ """Returns times from H absolutes"""
+ return np.array([reading.get_absolute("H").endtime for reading in readings])
+
+ def get_ordinates(
+ self, readings: List[Reading]
+ ) -> Tuple[List[float], List[float], List[float]]:
+ """Calculates ordinates from absolutes and baselines"""
+ h_abs, d_abs, z_abs = self.get_absolutes(readings)
+ h_bas, d_bas, z_bas = self.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
+
+ # WebAbsolutes defines/generates h differently than USGS residual
+ # method spreadsheets. The following should ensure that ordinate
+ # values are converted back to their original raw measurements:
+ e_o = h_abs * d_ord * 60 / 3437.7468
+ # TODO: is this handled in residual package?
+ if self.observatory in ["DED", "CMO"]:
+ h_o = np.sqrt(h_ord ** 2 - e_o ** 2)
+ else:
+ h_o = h_ord
+ z_o = z_ord
+ return (h_o, e_o, z_o)
+
+ def get_baselines(
+ self, 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_absolutes(
+ self, 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(
+ self, 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 = self.get_absolutes(readings)
+
+ # convert from cylindrical to Cartesian coordinates
+ x_a = h_abs * np.cos(d_abs * np.pi / 180)
+ y_a = h_abs * np.sin(d_abs * np.pi / 180)
+ z_a = z_abs
+ return (x_a, y_a, z_a)
+
+ 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 = self.get_absolutes_xyz(readings)
+ baselines = self.get_baselines(readings)
+ ordinates = self.get_ordinates(readings)
+ times = self.get_times(readings)
+ Ms = []
+ weights = []
+ inputs = ordinates
+
+ for transform in self.transforms:
+ weights = self.get_weights(
+ time=time,
+ times=times,
+ transform=transform,
+ )
+ # return NaNs if no valid observations
+ if np.sum(weights) == 0:
+ return AdjustedMatrix(
+ matrix=np.nan * np.ones((4, 4)),
+ pier_correction=np.nan,
+ )
+ # zero out statistically 'bad' baselines
+ weights = self.weight_baselines(baselines=baselines, weights=weights)
+
+ M = transform.calculate(
+ ordinates=inputs, absolutes=absolutes, weights=weights
+ )
+
+ # apply latest M matrix to inputs to get intermediate inputs
+ inputs = np.dot(
+ M, np.vstack([inputs[0], inputs[1], inputs[2], np.ones_like(inputs[0])])
+ )[0:3]
+ Ms.append(M)
+
+ # compose affine transform matrices using reverse ordered matrices
+ M_composed = reduce(np.dot, np.flipud(Ms))
+ pier_correction = np.average(
+ [reading.pier_correction for reading in readings], weights=weights
+ )
+ metrics = self.get_metrics(
+ absolutes=absolutes, ordinates=ordinates, matrix=M_composed
+ )
+ return AdjustedMatrix(
+ matrix=M_composed,
+ pier_correction=pier_correction,
+ metrics=metrics,
+ )
+
+ def get_metrics(
+ self, absolutes: List[float], ordinates: List[float], matrix: List[float]
+ ) -> Tuple[List[float], List[float], float, float]:
+ """Computes mean absolute error and standard deviation for X, Y, Z, and dF between expected and predicted values.
+
+ Attributes
+ ----------
+ absolutes: X, Y and Z absolutes
+ ordinates: H, E and Z ordinates
+ matrix: composed matrix
+
+ Outputs
+ -------
+ metrics: list of Metric objects
+ """
+ ordinates = np.vstack((ordinates, np.ones_like(ordinates[0])))
+ predicted = matrix @ ordinates
+
+ channels = ["X", "Y", "Z"]
+ metrics = []
+ for i in range(len(channels)):
+ metric = Metric(element=channels[i])
+ metric.calculate(expected=absolutes[i], predicted=predicted[i])
+ metrics.append(metric)
+
+ expected_f = ChannelConverter.get_computed_f_using_squares(
+ absolutes[0], absolutes[1], absolutes[2]
+ )
+ predicted_f = ChannelConverter.get_computed_f_using_squares(
+ predicted[0], predicted[1], predicted[2]
+ )
+ df = ChannelConverter.get_deltaf(expected_f, predicted_f)
+ metrics.append(self.get_metrics_df(predicted=predicted, expected=absolutes))
+ return metrics
+
+ def get_metrics_df(self, predicted, expected):
+ """Computes mean absolute error and standard deviation for dF between expected and predicted values"""
+ expected_f = ChannelConverter.get_computed_f_using_squares(
+ expected[0], expected[1], expected[2]
+ )
+ predicted_f = ChannelConverter.get_computed_f_using_squares(
+ predicted[0], predicted[1], predicted[2]
+ )
+ df = ChannelConverter.get_deltaf(expected_f, predicted_f)
+ return Metric(
+ element="dF",
+ mae=abs(np.nanmean(df)),
+ std=np.nanstd(df),
+ )
+
+ def get_weights(
+ self,
+ time: UTCDateTime,
+ times: List[UTCDateTime],
+ transform: Transform,
+ ) -> np.array:
+ """
+
+ Attributes
+ ----------
+ time: time within calculation interval
+ times: times of valid readings
+ transform: matrix calculation method
+
+ Outputs
+ -------
+ weights: array of weights to apply to absolutes/ordinates within calculations
+ """
+
+ weights = transform.get_weights(time=time.timestamp, times=times)
+ # set weights for future observations to zero if not acausal
+ if not self.acausal:
+ weights[times > time.timestamp] = 0.0
+ return weights
+
+ def weight_baselines(
+ self,
+ baselines: List[float],
+ weights: List[float],
+ threshold=3,
+ ) -> List[float]:
+ """Filters "bad" weights generated by unreliable readings"""
+ good = (
+ filter_iqr(baselines[0], threshold=threshold, weights=weights)
+ & filter_iqr(baselines[1], threshold=threshold, weights=weights)
+ & filter_iqr(baselines[2], threshold=threshold, weights=weights)
+ )
+ return weights * good
diff --git a/geomagio/adjusted/Metric.py b/geomagio/adjusted/Metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee946bb5c88ce17e6d60817b40056592f77dce77
--- /dev/null
+++ b/geomagio/adjusted/Metric.py
@@ -0,0 +1,22 @@
+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
+ mae: mean absolute error
+ std: standard deviation
+ """
+
+ element: str
+ mae: float = None
+ std: float = None
+
+ def calculate(self, expected, predicted):
+ """Calculates mean absolute error and standard deviation between expected and predicted data"""
+ self.mae = abs(np.nanmean(expected - predicted))
+ self.std = np.nanstd(expected - predicted)
diff --git a/geomagio/adjusted/SpreadsheetSummaryFactory.py b/geomagio/adjusted/SpreadsheetSummaryFactory.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbcd1e5b9e5d2bc8d0014558840c5d4bcc3e7aeb
--- /dev/null
+++ b/geomagio/adjusted/SpreadsheetSummaryFactory.py
@@ -0,0 +1,143 @@
+import os
+
+from obspy import UTCDateTime
+import openpyxl
+from typing import List
+
+from ..residual import Absolute, Angle, Reading
+from ..residual.SpreadsheetAbsolutesFactory import parse_relative_time
+
+
+class SpreadsheetSummaryFactory(object):
+ """Read absolutes from summary spreadsheets"""
+
+ def __init__(
+ self, base_directory: str = r"/Volumes/geomag/pub/Caldata/Checked Baseline Data"
+ ):
+ 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 ".xlsm" not in filename:
+ continue
+ 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, observatory: str) -> 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 {
+ "observatory": observatory,
+ "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, path.split("/")[-1][0:3])
+ 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/adjusted/Transform.py b/geomagio/adjusted/Transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeb33131a0d47b2d8eee290c1af4e4a505d98821
--- /dev/null
+++ b/geomagio/adjusted/Transform.py
@@ -0,0 +1,665 @@
+import numpy as np
+from obspy import UTCDateTime
+from pydantic import BaseModel
+import scipy.linalg as spl
+from typing import List, Tuple, Optional
+
+
+class Transform(BaseModel):
+ """Method for generating an affine matrix.
+
+ Attributes
+ ----------
+ memory: Controls impacts of measurements from the past.
+ Defaults to infinite(equal weighting)
+ """
+
+ memory: Optional[float] = None
+
+ def get_weights(self, times: UTCDateTime, time: int = None) -> List[float]:
+ """
+ Calculate time-dependent weights according to exponential decay.
+
+ Inputs:
+ times: array of times, or any time-like index whose relative values represent spacing between events
+ Output:
+ weights: array of vector distances/metrics
+ """
+
+ # convert to array of floats
+ times = np.asarray(times).astype(float)
+
+ if time is None:
+ time = float(max(times))
+
+ # if memory is actually infinite, return equal weights
+ if np.isinf(self.memory):
+ return 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)
+
+ return weights
+
+ 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
+
+
+class LeastSq(Transform):
+ """Intance of Transform. Applies least squares to generate matrices"""
+
+ def get_stacked_absolutes(self, absolutes):
+ """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_weighted_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]],
+ ) -> Tuple[List[float], List[float], List[float]]:
+ """Application of weights for least squares methods, which calls for square rooting of weights
+
+ 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
+
+
+class SVD(Transform):
+ """Instance of Transform. Applies singular value decomposition to generate matrices"""
+
+ def get_stacked_values(self, values, weighted_values, ndims=3) -> 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(ndims)])
+
+ def get_weighted_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[List[float]],
+ ) -> Tuple[List[float], List[float], List[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),
+ )
+
+
+class NoConstraints(LeastSq):
+ 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 affine with no constraints using least squares."""
+ # 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)
+ # return generate_affine_0(ord_hez, abs_xyz, weights)
+ # RHS, or independent variables
+ # (reduces degrees of freedom by 4:
+ # - 4 for the last row of zeros and a one)
+ # [
+ # [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 = 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
+
+ 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
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+ return np.array(
+ [
+ [M_r[0], M_r[1], M_r[2], M_r[3]],
+ [M_r[4], M_r[5], M_r[6], M_r[7]],
+ [M_r[8], M_r[9], M_r[10], M_r[11]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+ )
+
+
+class ZRotationShear(LeastSq):
+ 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 affine using least squares, constrained to rotate about the Z axis."""
+ # LHS, or dependent variables
+ #
+ abs_stacked = self.get_stacked_absolutes(absolutes)
+ # return generate_affine_1(ord_hez, abs_xyz, weights)
+ # RHS, or independent variables
+ # (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
+
+ 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
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ return [
+ [M_r[0], M_r[1], 0.0, M_r[2]],
+ [M_r[3], M_r[4], 0.0, M_r[5]],
+ [0.0, 0.0, M_r[6], M_r[7]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+
+class ZRotationHscale(LeastSq):
+ """Calculates affine using least squares, constrained to rotate about the Z axis
+ and apply uniform horizontal scaling."""
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ # LHS, or dependent variables
+ #
+ abs_stacked = self.get_stacked_absolutes(absolutes)
+ # RHS, or independent variables
+ # (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
+
+ 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
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ return [
+ [M_r[0], M_r[1], 0.0, M_r[2]],
+ [-M_r[1], M_r[0], 0.0, M_r[3]],
+ [0.0, 0.0, M_r[4], M_r[5]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+
+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 calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ # RHS, or independent variables
+ # (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
+
+ # subtract z_o from z_a to force simple z translation
+ abs_stacked = self.get_stacked_absolutes(absolutes)
+ abs_stacked[2::3] = absolutes[2] - ordinates[2]
+
+ abs_stacked = self.get_weighted_values(values=abs_stacked, weights=weights)
+ ord_stacked = self.get_weighted_values(values=ord_stacked, weights=weights)
+
+ # regression matrix M that minimizes L2 norm
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ return [
+ [M_r[0], M_r[1], 0.0, 0.0],
+ [-M_r[1], M_r[0], 0.0, 0.0],
+ [0.0, 0.0, 1.0, M_r[2]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+
+class RotationTranslation3D(SVD):
+ """Calculates affine using using singular value decomposition,
+ constrained to 3D scaled rotation and translation(no shear)"""
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ 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,
+ ndims=3,
+ ),
+ np.dot(
+ np.diag(weights),
+ self.get_stacked_values(
+ values=absolutes,
+ weighted_values=weighted_absolutes,
+ ndims=3,
+ ).T,
+ ),
+ )
+ # 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 np.sum(S) < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ R = np.dot(Vh.T, np.dot(np.diag([1, 1, np.linalg.det(np.dot(Vh.T, U.T))]), U.T))
+
+ # now get translation using weighted centroids and R
+ T = np.array(
+ [weighted_absolutes[0], weighted_absolutes[1], weighted_absolutes[2]]
+ ) - np.dot(
+ R, [weighted_ordinates[0], weighted_ordinates[1], weighted_ordinates[2]]
+ )
+
+ 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],
+ ]
+
+
+class Rescale3D(LeastSq):
+ """Calculates affine using using least squares, constrained to re-scale each axis"""
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ abs_stacked = self.get_stacked_absolutes(absolutes=absolutes)
+ # RHS, or independent variables
+ # (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]
+
+ abs_stacked = self.get_weighted_values(values=abs_stacked, weights=weights)
+ ord_stacked = self.get_weighted_values(values=ord_stacked, weights=weights)
+
+ # regression matrix M that minimizes L2 norm
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ return [
+ [M_r[0], 0.0, 0.0, 0.0],
+ [0.0, M_r[1], 0.0, 0.0],
+ [0.0, 0.0, M_r[2], 0.0],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+
+class TranslateOrigins(LeastSq):
+ """Calculates affine using using least squares, constrained to tanslate origins"""
+
+ def get_weighted_values(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[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
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+
+ abs_stacked = self.get_stacked_absolutes(absolutes)
+ 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
+
+ # 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]
+
+ # apply weights
+ ord_stacked = self.get_weighted_values(values=ord_stacked, weights=weights)
+ abs_stacked = self.get_weighted_values(values=abs_stacked, weights=weights)
+
+ # regression matrix M that minimizes L2 norm
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ return [
+ [1.0, 0.0, 0.0, M_r[0]],
+ [0.0, 1.0, 0.0, M_r[1]],
+ [0.0, 0.0, 1.0, M_r[2]],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+
+class ShearYZ(LeastSq):
+ """Calculates affine using least squares, constrained to shear y and z, but not x."""
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ # RHS, or independent variables
+ # (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
+ abs_stacked = self.get_stacked_absolutes(absolutes=absolutes)
+ ord_stacked = self.get_weighted_values(values=ord_stacked, weights=weights)
+ abs_stacked = self.get_weighted_values(values=abs_stacked, weights=weights)
+ # regression matrix M that minimizes L2 norm
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 3:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ return [
+ [1.0, 0.0, 0.0, 0.0],
+ [M_r[0], 1.0, 0.0, 0.0],
+ [M_r[1], M_r[2], 1.0, 0.0],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+
+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"""
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+ if weights is None:
+ weights = np.ones_like(ordinates[0])
+ weighted_ordinates = self.get_weighted_values(values=ordinates, weights=weights)
+ weighted_absolutes = self.get_weighted_values(values=absolutes, weights=weights)
+ # return generate_affine_8(ord_hez, abs_xyz, weights)
+ # generate weighted "covariance" matrix
+ H = np.dot(
+ self.get_stacked_values(
+ values=ordinates,
+ weighted_values=weighted_ordinates,
+ ndims=2,
+ ),
+ np.dot(
+ np.diag(weights),
+ self.get_stacked_values(
+ values=absolutes,
+ weighted_values=weighted_absolutes,
+ ndims=2,
+ ).T,
+ ),
+ )
+
+ # 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 np.sum(S) < 2:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ R = np.dot(Vh.T, np.dot(np.diag([1, np.linalg.det(np.dot(Vh.T, U.T))]), U.T))
+
+ # now get translation using weighted centroids and R
+ T = np.array([weighted_absolutes[0], weighted_absolutes[1]]) - np.dot(
+ R, [weighted_ordinates[0], weighted_ordinates[1]]
+ )
+
+ 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[2]) - np.array(weighted_ordinates[2]),
+ ],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
+
+
+class QRFactorization(SVD):
+ """Calculates affine using singular value decomposition with QR factorization"""
+
+ def get_weighted_values_lstsq(
+ self,
+ values: Tuple[List[float], List[float], List[float]],
+ weights: Optional[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[0] * weights,
+ values[1] * weights,
+ ]
+ )
+
+ def calculate(
+ self,
+ ordinates: Tuple[List[float], List[float], List[float]],
+ absolutes: Tuple[List[float], List[float], List[float]],
+ weights: List[float],
+ ) -> np.array:
+
+ if weights is None:
+ weights = np.ones_like(ordinates[0])
+
+ weighted_absolutes = self.get_weighted_values(values=absolutes, weights=weights)
+ weighted_ordinates = self.get_weighted_values(values=ordinates, weights=weights)
+
+ # return generate_affine_9(ord_hez, abs_xyz, weights)
+ # LHS, or dependent variables
+ abs_stacked = self.get_stacked_values(
+ values=absolutes,
+ weighted_values=weighted_absolutes,
+ ndims=2,
+ )
+
+ # RHS, or independent variables
+ ord_stacked = self.get_stacked_values(
+ values=ordinates,
+ weighted_values=weighted_ordinates,
+ ndims=2,
+ )
+
+ abs_stacked = self.get_weighted_values_lstsq(
+ values=abs_stacked,
+ weights=weights,
+ )
+ ord_stacked = self.get_weighted_values_lstsq(
+ values=ord_stacked,
+ weights=weights,
+ )
+
+ # regression matrix M that minimizes L2 norm
+ M_r, res, rank, sigma = spl.lstsq(ord_stacked.T, abs_stacked.T)
+ if rank < 2:
+ print("Poorly conditioned or singular matrix, returning NaNs")
+ return np.nan * np.ones((4, 4))
+
+ # 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(M_r.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)
+
+ # now get translation using weighted centroids and R
+ T = np.array([weighted_absolutes[0], weighted_absolutes[1]]) - np.dot(
+ QH, [weighted_ordinates[0], weighted_ordinates[1]]
+ )
+
+ 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[2]) - np.array(weighted_ordinates[2]),
+ ],
+ [0.0, 0.0, 0.0, 1.0],
+ ]
diff --git a/geomagio/algorithm/AdjustedAlgorithm.py b/geomagio/algorithm/AdjustedAlgorithm.py
index 601bad2444246efeee5b0f81f4551f245e3a9406..2a5b647e3f205721359cfdaf92dc2f3910395d82 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.AdjustedMatrix 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
@@ -45,12 +43,12 @@ 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
+ pier_correction = 0
if self.statefile is None:
return
+ # Adjusted matrix defaults to identity matrix
+ matrix_size = len([c for c in self.get_input_channels() if c != "F"]) + 1
+ matrix = np.eye(matrix_size)
data = None
try:
with open(self.statefile, "r") as f:
@@ -60,10 +58,9 @@ class AdjustedAlgorithm(Algorithm):
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"])
+ self.matrix = AdjustedMatrix(
+ matrix=np.array(data["matrix"]), pier_correction=data["pier_correction"]
+ )
def save_state(self):
"""Save algorithm state to a file.
@@ -71,12 +68,11 @@ 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]
+ data = {
+ "matrix": list(self.matrix.matrix),
+ "pier_correction": self.matrix.pier_correction,
+ }
+
with open(self.statefile, "w") as f:
f.write(json.dumps(data))
@@ -134,7 +130,7 @@ class AdjustedAlgorithm(Algorithm):
]
+ [np.ones_like(stream[0].data)]
)
- adjusted = np.matmul(self.matrix, raws)
+ adjusted = np.matmul(self.matrix.matrix, raws)
out = Stream(
[
self.create_trace(
@@ -147,7 +143,7 @@ class AdjustedAlgorithm(Algorithm):
)
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)
+ out += self.create_trace("F", f.stats, f.data + self.matrix.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..03eab2ab98b23444f76b79aa8811e9c2cd281954
--- /dev/null
+++ b/geomagio/processing/adjusted.py
@@ -0,0 +1,64 @@
+import json
+from obspy import UTCDateTime
+from typing import Optional, Literal
+import typer
+
+from ..adjusted.Affine import Affine
+from ..adjusted.SpreadsheetSummaryFactory import SpreadsheetSummaryFactory
+from ..residual import WebAbsolutesFactory
+
+
+def main():
+ typer.run(generate_matrix)
+
+
+def generate_matrix(
+ observatory: str,
+ starttime: UTCDateTime,
+ endtime: UTCDateTime,
+ readings_starttime: UTCDateTime,
+ readings_endtime: UTCDateTime,
+ output_file: str,
+ input_factory: str = "webabsolutes",
+ acausal: bool = False,
+ spreadsheet_directory: Optional[
+ str
+ ] = r"/Volumes/geomag/pub/Caldata/Checked Baseline Data",
+ 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=readings_starttime,
+ endtime=readings_endtime,
+ )
+ elif input_factory == "webabsolutes":
+ readings = WebAbsolutesFactory(url=webabsolutes_url).get_readings(
+ observatory=observatory,
+ starttime=readings_starttime,
+ endtime=readings_endtime,
+ )
+ else:
+ readings = []
+
+ result = Affine(
+ observatory=observatory,
+ starttime=starttime,
+ endtime=endtime,
+ acausal=acausal,
+ ).calculate(readings=readings)[0]
+
+ matrix = [
+ list(result.matrix[0]),
+ list(result.matrix[1]),
+ list(result.matrix[2]),
+ list(result.matrix[3]),
+ ]
+ result.matrix = matrix
+
+ with open(output_file, "w") as file:
+ json.dump(result.dict(), file)
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..adcd74112dcf2f80618f7b0aeaaedc72e91dc23c
--- /dev/null
+++ b/test/adjusted_test/adjusted_test.py
@@ -0,0 +1,503 @@
+import json
+import numpy as np
+from numpy.testing import assert_equal, assert_array_almost_equal, assert_array_equal
+from obspy.core import UTCDateTime
+
+from geomagio.adjusted.SpreadsheetSummaryFactory import SpreadsheetSummaryFactory
+from geomagio.adjusted.Affine import Affine
+from geomagio.adjusted.Transform import (
+ NoConstraints,
+ ZRotationShear,
+ ZRotationHscale,
+ ZRotationHscaleZbaseline,
+ RotationTranslation3D,
+ Rescale3D,
+ TranslateOrigins,
+ ShearYZ,
+ RotationTranslationXY,
+ QRFactorization,
+)
+
+from geomagio.residual.WebAbsolutesFactory import WebAbsolutesFactory
+
+
+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/adjusted/Caldata",
+ observatory="CMO",
+ starttime=starttime,
+ endtime=endtime,
+ )
+ for reading in readings:
+ assert_equal(reading.metadata["observatory"], "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 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 get_expected_synthetic_result(key):
+ with open("etc/adjusted/synthetic.json") as file:
+ expected = json.load(file)
+ return expected["results"][key]
+
+
+def test_NoConstraints_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ NoConstraints().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("NoConstraints"),
+ 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,
+ )
+
+
+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_RotationTranslation3D_synthetic():
+ ordinates, absolutes, weights = get_sythetic_variables()
+ assert_array_almost_equal(
+ RotationTranslation3D().calculate(
+ ordinates=ordinates,
+ absolutes=absolutes,
+ weights=weights,
+ ),
+ get_expected_synthetic_result("RotationTranslation3D"),
+ 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_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_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_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_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 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_readings_BOU201911202001():
+ readings = WebAbsolutesFactory().get_readings(
+ observatory="BOU",
+ starttime=UTCDateTime("2019-10-01T00:00:00Z"),
+ endtime=UTCDateTime("2020-02-29T23:59:00Z"),
+ )
+ return readings
+
+
+def test_BOU201911202001_short_causal():
+ readings = get_readings_BOU201911202001()
+
+ 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,
+ ).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_BOU201911202001_short_acausal():
+ readings = get_readings_BOU201911202001()
+
+ 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,
+ 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_infinite_weekly():
+ readings = get_readings_BOU201911202001()
+
+ 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,
+ acausal=True,
+ transforms=[
+ RotationTranslationXY(memory=np.inf),
+ TranslateOrigins(memory=np.inf),
+ ],
+ ).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_infinite_one_interval():
+ readings = get_readings_BOU201911202001()
+ result = Affine(
+ observatory="BOU",
+ starttime=UTCDateTime("2019-11-01T00:00:00Z"),
+ endtime=UTCDateTime("2020-01-31T23:59:00Z"),
+ acausal=True,
+ transforms=[
+ RotationTranslationXY(memory=np.inf),
+ TranslateOrigins(memory=np.inf),
+ ],
+ 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_invalid_readings():
+ readings = []
+ result = Affine(
+ observatory="BOU",
+ starttime=UTCDateTime("2019-11-01T00:00:00Z"),
+ endtime=UTCDateTime("2020-01-31T23:59:00Z"),
+ acausal=True,
+ transforms=[
+ RotationTranslationXY(memory=np.inf),
+ TranslateOrigins(memory=np.inf),
+ ],
+ update_interval=None,
+ ).calculate(readings=readings,)[0]
+ assert_array_equal(result.matrix, np.nan * np.ones((4, 4)))
+ assert_equal(result.pier_correction, np.nan)
+
+
+def test_CMO2015_causal():
+ readings = get_spreadsheet_directory_readings(
+ observatory="CMO",
+ starttime=UTCDateTime("2015-01-01T00:00:00Z"),
+ endtime=UTCDateTime("2015-12-31T23:59:00Z"),
+ path="etc/adjusted/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,
+ ).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_CMO2015_acausal():
+ readings = get_spreadsheet_directory_readings(
+ observatory="CMO",
+ starttime=UTCDateTime("2015-01-01T00:00:00Z"),
+ endtime=UTCDateTime("2015-12-31T23:59:00Z"),
+ path="etc/adjusted/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,
+ 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_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/adjusted/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),
+ TranslateOrigins(memory=np.inf),
+ ],
+ 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_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/adjusted/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),
+ 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("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)
diff --git a/test/algorithm_test/AdjustedAlgorithm_test.py b/test/algorithm_test/AdjustedAlgorithm_test.py
index f37f93f78893e6f13b22e50923724bca0b4ee260..d44ae09e6d06cd2feca7447c7d50992fceaf0134 100644
--- a/test/algorithm_test/AdjustedAlgorithm_test.py
+++ b/test/algorithm_test/AdjustedAlgorithm_test.py
@@ -1,3 +1,4 @@
+from geomagio.adjusted.AdjustedMatrix import AdjustedMatrix
from geomagio.algorithm import AdjustedAlgorithm as adj
import geomagio.iaga2002 as i2
from numpy.testing import assert_almost_equal, assert_equal
@@ -8,19 +9,53 @@ def test_construct():
# load adjusted data transform matrix and pier correction
a = adj(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 = adj(
+ 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:
@@ -31,34 +66,77 @@ 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 = adj(
+ 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 = adj(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(
@@ -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"]
)