fix syntax in docs

README.rst

@@ -13,46 +13,40 @@ maria
 
 *maria blows the stars around / and sends the clouds a-flyin’*
 
-.. raw:: html
-    <audio controls="controls">
-      <source src="./_static/they_call_the_wind_maria.mp4" type="audio/wav">
-      Your browser does not support the <code>audio</code> element.
-    </audio>
-
 ``maria`` is a complete simulator of ground-based millimeter- and submillimeter-wave telescopes. Tutorials for installation and usage can be found in the `documentation <https://www.thomaswmorris.com/maria>`_.
 
-.. Background
-.. ----------
-
-.. Atmospheric modeling is an important step in both experiment design and
-.. subsequent data analysis for ground-based cosmological telescopes
-.. observing the cosmic microwave background (CMB). The next generation of
-.. ground-based CMB experiments will be marked by a huge increase in data
-.. acquisition: telescopes like `AtLAST <https://www.atlast.uio.no>`_ and
-.. `CMB-S4 <https://cmb-s4.org>`_ will consist of hundreds of thousands of
-.. superconducting polarization-sensitive bolometers sampling the sky. This
-.. necessitates new methods of efficiently modeling and simulating
-.. atmospheric emission at small angular resolutions, with algorithms than
-.. can keep up with the high throughput of modern telescopes.
-
-.. maria simulates layers of turbulent atmospheric emission according to a
-.. statistical model derived from observations of the atmosphere in the
-.. Atacama Desert, from the `Atacama Cosmology Telescope
-.. (ACT) <https://lambda.gsfc.nasa.gov/product/act/>`_ and the `Atacama
-.. B-Mode Search (ABS) <https://lambda.gsfc.nasa.gov/product/abs/>`_. It
-.. uses a sparse-precision auto-regressive Gaussian process algorithm that
-.. allows for both fast simulation of high-resolution atmosphere, as well
-.. as the ability to simulate arbitrarily long periods of atmospheric
-.. evolution.
-
-.. Methodology
-.. -----------
-
-.. ``maria`` auto-regressively simulates an multi-layeed two-dimensional
-.. “integrated” atmospheric model that is much cheaper to compute than a
-.. three-dimensional model, which can effectively describe time-evolving
-.. atmospheric emission. maria can thus effectively simulate correlated
-.. atmospheric emission for in excess of 100,000 detectors observing the
-.. sky concurrently, at resolutions as fine as one arcminute. The
-.. atmospheric model used is detailed
-.. `here <https://arxiv.org/abs/2111.01319>`_.
+Background
+----------
+
+Atmospheric modeling is an important step in both experiment design and
+subsequent data analysis for ground-based cosmological telescopes
+observing the cosmic microwave background (CMB). The next generation of
+ground-based CMB experiments will be marked by a huge increase in data
+acquisition: telescopes like `AtLAST <https://www.atlast.uio.no>`_ and
+`CMB-S4 <https://cmb-s4.org>`_ will consist of hundreds of thousands of
+superconducting polarization-sensitive bolometers sampling the sky. This
+necessitates new methods of efficiently modeling and simulating
+atmospheric emission at small angular resolutions, with algorithms than
+can keep up with the high throughput of modern telescopes.
+
+maria simulates layers of turbulent atmospheric emission according to a
+statistical model derived from observations of the atmosphere in the
+Atacama Desert, from the `Atacama Cosmology Telescope
+(ACT) <https://lambda.gsfc.nasa.gov/product/act/>`_ and the `Atacama
+B-Mode Search (ABS) <https://lambda.gsfc.nasa.gov/product/abs/>`_. It
+uses a sparse-precision auto-regressive Gaussian process algorithm that
+allows for both fast simulation of high-resolution atmosphere, as well
+as the ability to simulate arbitrarily long periods of atmospheric
+evolution.
+
+Methodology
+-----------
+
+``maria`` auto-regressively simulates an multi-layeed two-dimensional
+“integrated” atmospheric model that is much cheaper to compute than a
+three-dimensional model, which can effectively describe time-evolving
+atmospheric emission. maria can thus effectively simulate correlated
+atmospheric emission for in excess of 100,000 detectors observing the
+sky concurrently, at resolutions as fine as one arcminute. The
+atmospheric model used is detailed
+`here <https://arxiv.org/abs/2111.01319>`_.

maria/coords/coordinates.py

@@ -111,19 +111,22 @@ def __init__(
                 if (np.ptp(self.time, axis=axis) > 0).any():
                     raise ValueError("Only the last axis can vary in time.")
 
+        ref_time = ttime.monotonic()
         self.compute_transforms()
+        duration_ms = 1e3 * (ttime.monotonic() - ref_time)
+        logger.debug(
+            f"Initialized coordinates with shape {self.shape} in {int(duration_ms)} ms."
+        )  # noqa
 
     def compute_transforms(self):
-        ref_time = ttime.monotonic()
-
         self.shaped_time = np.atleast_1d(self.time)
         keep_dims = (-1,) if hasattr(self.time, "__len__") else ()
         time_ordered_center_phi_theta = np.c_[
             get_center_phi_theta(self._phi, self._theta, keep_dims=keep_dims)
         ]
 
         # (nt) time samples on which to explicitly compute the transformation from astropy
-        time_samples_min_res_seconds = 5
+        time_samples_min_res_seconds = 10
         time_samples_min = np.min(self.time) - 1e0
         time_samples_max = np.max(self.time) + 1e0
         n_time_samples = int(
@@ -214,11 +217,6 @@ def compute_transforms(self):
             setattr(self, frames[frame]["phi"], frame_phi)
             setattr(self, frames[frame]["theta"], frame_theta)
 
-        duration_ms = 1e3 * (ttime.monotonic() - ref_time)
-        logger.debug(
-            f"Initialized coordinates with shape {self.shape} in {int(duration_ms)} ms."
-        )
-
     def downsample(self, timestep: float = None, factor: int = None):
         if timestep is None and factor is None:
             raise ValueError("You must supply either 'timestep' or 'factor'.")

maria/instrument/array/configs/so.yml

@@ -1,6 +1,6 @@
 sat-wafer:
+  n: 631
   primary_size: 0.5
-  beam_spacing: 1.2
   array_offset: [5.2, 9.0]
   field_of_view: 10.0
   array_shape: hex

maria/map/map.py

@@ -246,14 +246,14 @@ def to_fits(self, filepath):
         self.header["comment"] = "Made Synthetic observations via maria code"
         self.header["comment"] = "Overwrote resolution and size of the output map"
 
-        self.header["CDELT1"] = np.rad2deg(self.resolution)
-        self.header["CDELT2"] = np.rad2deg(self.resolution)
+        self.header["CDELT1"] = np.radians(self.resolution)
+        self.header["CDELT2"] = np.radians(self.resolution)
 
         self.header["CRPIX1"] = self.n_x / 2
         self.header["CRPIX2"] = self.n_y / 2
 
-        self.header["CRVAL1"] = np.rad2deg(self.center[0])
-        self.header["CRVAL2"] = np.rad2deg(self.center[1])
+        self.header["CRVAL1"] = np.radians(self.center[0])
+        self.header["CRVAL2"] = np.radians(self.center[1])
 
         self.header["CTYPE1"] = "RA---SIN"
         self.header["CTYPE2"] = "DEC--SIN"

maria/tod/tod.py

@@ -68,8 +68,6 @@ def __init__(
         if self.weight is None:
             self.weight = da.ones_like(self.signal)
 
-        self.boresight = self.coords.boresight()
-
     def get_field(self, field: str):
         if field not in self.fields:
             raise ValueError(f"Field '{field}' not found.")
@@ -84,6 +82,12 @@ def get_field(self, field: str):
 
         return d
 
+    @property
+    def boresight(self):
+        if not hasattr(self, "_boresight"):
+            self._boresight = self.coords.boresight()
+        return self._boresight
+
     def to(self, units: str):
         cal = self.dets.cal(f"{self.units} -> {units}")