diff --git a/docs/source/_static/spotter.png b/docs/source/_static/spotter.png
new file mode 100644
index 0000000..7c1e436
Binary files /dev/null and b/docs/source/_static/spotter.png differ
diff --git a/docs/source/conf.py b/docs/source/conf.py
index d788617..b93f750 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,7 +1,6 @@
project = "spotter"
-copyright = "2023, Lionel Garcia, Benjamin Rackham"
+copyright = "2023 - 2024, Lionel Garcia, Benjamin Rackham"
author = "Lionel Garcia, Benjamin Rackham"
-release = "0.0.2"
extensions = [
"myst_nb",
@@ -39,9 +38,7 @@
]
nb_execution_mode = "off"
-html_short_title = "spotter"
-html_title = f"{html_short_title}"
-
+html_logo = "_static/spotter.png"
html_css_files = ["style.css"]
myst_url_schemes = ("http", "https")
diff --git a/docs/source/index.md b/docs/source/index.md
index c5d6972..8737fc4 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -1,43 +1,34 @@
# spotter
-```{image} _static/spotter.jpg
-:width: 400px
-:align: center
-```
+*Approximate forward models of fluxes and spectra time-series of non-uniform stars.*
+
+---
-*spotter* is a Python package to produce forward models of non-uniform stars spectra. It uses the [HEALPix](https://healpix.sourceforge.io/) subdivision scheme and is powered by the high-performance numerical package [JAX](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html), enabling its use on GPUs.
+```{warning}
+Use at your own risk as the code is completely untested and its API subject to change.
+```
-**Note**
+*spotter* uses the [HEALPix](https://healpix.sourceforge.io/) subdivision scheme and is powered by the high-performance numerical package [JAX](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html), enabling its use on GPUs.
-In its beta version, *spotter* is mainly developed to estimate transmission spectra stellar contamination from stellar rotational light curves. Use at your own risk as the code is completely untested and its API subject to change.
## Features
-- Adjustable surface resolution - *in beta*
-- Small-scale surface features modeling (e.g. beyond limitations of [starry]()) - *in beta*
+- Small-scale surface features (e.g. beyond limitations of [starry]())
- Modeling of any active regions with their limb laws (e.g. limb-brightened faculae)
-- GPU compatible - *in beta*
+- GPU compatible
- Possibility to input any stellar spectra model
```{toctree}
:maxdepth: 1
:caption: Get started
-api
-```
-
-```{toctree}
-:maxdepth: 1
-:caption: Examples
-
-notebooks/simple_example
-notebooks/experiments
-notebooks/amplitude_constraints.ipynb
+notebooks/introduction
```
```{toctree}
:maxdepth: 1
-:caption: Notes
+:caption: Reference
notebooks/rotation.ipynb
+api
```
\ No newline at end of file
diff --git a/docs/source/notebooks/amplitude_constraints.ipynb b/docs/source/notebooks/amplitude_constraints.ipynb
index a8ec880..e0fac28 100644
--- a/docs/source/notebooks/amplitude_constraints.ipynb
+++ b/docs/source/notebooks/amplitude_constraints.ipynb
@@ -15,12 +15,14 @@
"metadata": {},
"outputs": [],
"source": [
+ "import jax\n",
"import numpy as np\n",
- "from spotter import Star, uniform\n",
+ "from spotter import Star, uniform, core\n",
"import matplotlib.pyplot as plt\n",
"\n",
- "star = Star(u=[0.1, 0.2], N=2**5)\n",
- "amplitude = star.jax_amplitude(resolution=20)"
+ "star = Star(N=2**5)\n",
+ "u = [0.1, 0.2]\n",
+ "amplitude = jax.jit(star.amplitude(u, undersampling=20))"
]
},
{
@@ -32,14 +34,23 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 20/20 [00:47<00:00, 2.36s/it]\n"
+ "r=0.05\tn_spots=3200\t: 100%|██████████| 31/31 [00:19<00:00, 1.56it/s]\n",
+ "r=0.16\tn_spots=310\t: 100%|██████████| 322/322 [00:15<00:00, 20.84it/s]\n",
+ "r=0.27\tn_spots=109\t: 100%|██████████| 917/917 [00:16<00:00, 56.53it/s]\n",
+ "r=0.38\tn_spots=55\t: 100%|██████████| 1818/1818 [00:15<00:00, 118.38it/s]\n",
+ "r=0.49\tn_spots=33\t: 100%|██████████| 3030/3030 [00:15<00:00, 196.51it/s]\n",
+ "r=0.60\tn_spots=22\t: 100%|██████████| 4545/4545 [00:14<00:00, 304.25it/s]\n",
+ "r=0.71\tn_spots=16\t: 100%|██████████| 6250/6250 [00:13<00:00, 455.18it/s]\n",
+ "r=0.83\tn_spots=12\t: 100%|██████████| 8333/8333 [00:14<00:00, 561.94it/s]\n",
+ "r=0.94\tn_spots=9\t: 100%|██████████| 11111/11111 [00:13<00:00, 847.62it/s]\n",
+ "r=1.05\tn_spots=8\t: 100%|██████████| 12500/12500 [00:13<00:00, 948.95it/s]\n"
]
}
],
@@ -47,40 +58,28 @@
"from tqdm import tqdm\n",
"from collections import defaultdict\n",
"\n",
- "results = []\n",
- "radii = np.linspace(0.01, np.pi / 2, 20)\n",
- "contrast = 0.1\n",
- "covering_fraction = []\n",
- "\n",
- "n_spots = 200\n",
- "n_stars = 100\n",
- "max_cov = 0.9\n",
- "\n",
- "amplitude = star.jax_amplitude(resolution=20)\n",
+ "radii = np.linspace(0.05, np.pi / 3, 10)\n",
+ "contrast = 1.0\n",
"\n",
"covs = defaultdict(list)\n",
+ "covs2 = defaultdict(list)\n",
"amplitudes = defaultdict(list)\n",
"\n",
- "for i, r in enumerate(tqdm(radii)):\n",
- " inter_results = []\n",
- " inter_covering_fraction = []\n",
- " for _ in range(n_stars):\n",
- " star.clear_surface()\n",
- " n = 0\n",
- " # this is to ensure that we add at least\n",
- " # n_spots despite the max_cov constraint\n",
- " while n < n_spots:\n",
- " star.clear_surface()\n",
- " for _ in range(n_spots):\n",
- " theta, phi = uniform(1)\n",
- " star.add_spot(theta, phi, r, contrast)\n",
- " n += 1\n",
- " amp = amplitude(star.map_spot)\n",
- " amplitudes[i].append(amp)\n",
- " cov = star.covering_fraction()\n",
- " covs[i].append(cov)\n",
- " if cov > max_cov:\n",
- " break"
+ "for i, r in enumerate(radii):\n",
+ " single_cov = star.single_spot_coverage(r)\n",
+ " n_spots = int(2.0 // single_cov)\n",
+ " n_stars = int(100000 // n_spots)\n",
+ " for _ in tqdm(range(n_stars), desc=f\"r={r:.2f}\\tn_spots={n_spots}\\t\"):\n",
+ " theta, phi = uniform(n_spots)\n",
+ " x = star.spots(theta, phi, r, False, True)\n",
+ " x = np.vstack([np.zeros(star.n), x])\n",
+ " amplitudes[i].append(amplitude(1 - x * contrast))\n",
+ " covs[i].append(jax.numpy.mean(x, axis=1))\n",
+ " covs2[i].append(jax.numpy.arange(n_spots + 2) * single_cov)\n",
+ "\n",
+ " amplitudes[i] = np.hstack(amplitudes[i])\n",
+ " covs[i] = np.hstack(covs[i])\n",
+ " covs2[i] = np.hstack(covs2[i])"
]
},
{
@@ -92,7 +91,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
@@ -118,12 +117,12 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
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"
-*spotter* is a Python package to produce forward models of non-uniform stellar photospheres and their spectra. It uses the [HEALPix](https://healpix.sourceforge.io/) subdivision scheme and is powered by the high-performance numerical package [JAX](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html), enabling its use on GPUs.
-
-**Note**
+*spotter* uses the [HEALPix](https://healpix.sourceforge.io/) subdivision scheme and is powered by the high-performance numerical package [JAX](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html), enabling its use on GPUs.
-In its beta version, *spotter* is mainly developed to estimate transmission spectra stellar contamination from stellar rotational light curves. Use at your own risk as the code is completely untested and its API subject to change.
## Features
-- Adjustable surface resolution - *in beta*
-- Small-scale surface feature modeling (e.g., beyond limitations of [starry]()) - *in beta*
-- Modeling of active regions with unique angular dependence on brightness (e.g., limb-brightened faculae)
-- GPU compatible - *in beta*
+- Small-scale surface features (e.g. beyond limitations of [starry]())
+- Modeling of any active regions with their limb laws (e.g. limb-brightened faculae)
+- GPU compatible
- Possibility to input any stellar spectra model
-
## Installation
For now only locally with
diff --git a/spotter/core.py b/spotter/core.py
index 93af86a..c6eb38d 100644
--- a/spotter/core.py
+++ b/spotter/core.py
@@ -1,34 +1,86 @@
+import healpy as hp
import jax
import jax.numpy as jnp
jax.config.update("jax_enable_x64", True)
-def hemisphere_mask(thetas):
- def mask(phase):
- a = (phase + jnp.pi / 2) % (2 * jnp.pi)
- b = (phase - jnp.pi / 2) % (2 * jnp.pi)
- mask_1 = jnp.logical_and((thetas < a), (thetas > b))
- mask_2 = jnp.logical_or((thetas > b), (thetas < a))
- cond1 = a > phase % (2 * jnp.pi)
- cond2 = b < phase % (2 * jnp.pi)
- cond = cond1 * cond2
- return jnp.where(cond, mask_1, mask_2)
+def hemisphere_mask(theta, phase):
+ theta = jnp.atleast_1d(theta)
+ a = (phase + jnp.pi / 2) % (2 * jnp.pi)
+ b = (phase - jnp.pi / 2) % (2 * jnp.pi)
+ mask_1 = jnp.logical_and((theta < a), (theta > b))
+ mask_2 = jnp.logical_or((theta > b), (theta < a))
+ cond1 = a > phase % (2 * jnp.pi)
+ cond2 = b < phase % (2 * jnp.pi)
+ cond = cond1 * cond2
+ return jnp.where(cond, mask_1, mask_2)
- return mask
+def polynomial_limb_darkening(theta, phi, u=None, phase=0.0):
+ if u is None:
+ return 1.0
+ else:
+ theta = jnp.atleast_1d(theta)
+ phi = jnp.atleast_1d(phi)
+ u = jnp.atleast_1d(u)
+ z = jnp.sin(phi) * jnp.cos(theta - phase)
+ terms = jnp.array([un * (1 - z) ** (n + 1) for n, un in enumerate(u)])
+ return 1 - jnp.sum(terms, axis=theta.ndim - 1)
-def polynomial_limb_darkening(thetas, phis):
- def ld(u, phase):
- z = jnp.sin(phis) * jnp.cos(thetas - phase)
- terms = jnp.array([u * (1 - z) ** (n + 1) for n, u in enumerate(u)])
- return 1 - jnp.sum(terms, 0)
- return ld
+def projected_area(theta, phi, phase):
+ return jnp.cos(theta - phase) * jnp.sin(phi)
-def projected_area(thetas, phis):
- def area(phase):
- return jnp.cos(thetas - phase) * jnp.sin(phis)
+def covering_fraction(x):
+ return jnp.mean(x > 0)
- return area
+
+def distance(thetas, phis):
+
+ p1 = phis - jnp.pi / 2
+ t1 = thetas
+ sp1 = jnp.sin(p1)
+ cp1 = jnp.cos(p1)
+
+ def fun(theta0, phi0):
+ # https://en.wikipedia.org/wiki/Great-circle_distance
+ # Vincenty formula
+ p2 = theta0 - jnp.pi / 2
+ t2 = phi0
+ dl = jnp.abs((t1 - t2))
+
+ sp2 = jnp.sin(p2)
+ cp2 = jnp.cos(p2)
+ cdl = jnp.cos(dl)
+ sdl = jnp.sin(dl)
+
+ a = (cp2 * sdl) ** 2 + (cp1 * sp2 - sp1 * cp2 * cdl) ** 2
+ b = sp1 * sp2 + cp1 * cp2 * cdl
+ return jnp.arctan2(jnp.sqrt(a), b)
+
+ return fun
+
+
+def query_disk(thetas, phis):
+
+ distance_fn = distance(thetas, phis)
+
+ def fun(theta, phi, radius):
+ d = distance_fn(theta, phi)
+ return jnp.array(d <= radius, dtype=jnp.int8)
+
+ return fun
+
+
+def smooth_spot(thetas, phis):
+
+ distance_fn = distance(thetas, phis)
+
+ def fun(theta, phi, r, c):
+ A = c * distance_fn(theta, phi) / (2 * r)
+ C = c / 2
+ return 0.5 * jnp.tanh(C - A) + 0.5 * jnp.tanh(C + A)
+
+ return fun
diff --git a/spotter/distributions.py b/spotter/distributions.py
index 31304d3..e4eca48 100644
--- a/spotter/distributions.py
+++ b/spotter/distributions.py
@@ -1,4 +1,31 @@
+import jax.numpy as jnp
import numpy as np
+from jax import random
+
+
+def jax_butterfly(key, latitudes=0.0, latitude_sigma=0.0, n=1):
+ new_key, subkey = random.split(key)
+ theta = jnp.pi / 2 - (
+ latitudes
+ + random.normal(key, shape=(n,))
+ * latitude_sigma
+ * random.choice(subkey, jnp.array([-1.0, 1.0]), shape=(n,))
+ )
+ new_key, subkey = random.split(new_key)
+ phi = random.uniform(subkey, minval=0.0, maxval=2.0 * jnp.pi, shape=(n,))
+ return theta, phi
+
+
+def jax_uniform(key, n=1):
+ # Latitude
+ theta = jnp.pi / 2 - jnp.arcsin(
+ random.uniform(key, minval=-1.0, maxval=1.0, shape=(n,))
+ )
+ _, subkey = random.split(key)
+ # Longitude
+ phi = random.uniform(subkey, minval=0.0, maxval=2.0 * jnp.pi, shape=(n,))
+
+ return theta, phi
def butterfly(latitudes=0, latitude_sigma=0, n=1):
diff --git a/spotter/star.py b/spotter/star.py
index 13dc2b0..2151e8b 100644
--- a/spotter/star.py
+++ b/spotter/star.py
@@ -1,14 +1,11 @@
-from dataclasses import dataclass
-
import healpy as hp
import jax
import jax.numpy as jnp
-import matplotlib.pyplot as plt
import numpy as np
+from jax.typing import ArrayLike
from spotter import core
-
-jax.config.update("jax_enable_x64", True)
+from spotter.utils import Array
def _wrap(*args):
@@ -22,620 +19,385 @@ def _wrap(*args):
return new_args
-@dataclass
class Star:
- """
- A star object
- """
+ """An object holding the geometry of the stellar surface map."""
- u: list = None
- """List of limb darkening coefficients. Defaults to None."""
N: int = 64
- """Star's HEALPix map nside parameter. Defaults to 64."""
- b: float = None
- """Impact parameter of the transit chord. Defaults to None."""
- r: float = None
- """Planet radius. Defaults to None."""
- map_spot: np.ndarray = None
- """The star's spot map. Defaults to None."""
- map_faculae: np.ndarray = None
- """The star's faculae map. Defaults to None."""
-
- def __post_init__(self):
- if self.u is None:
- self.u = [0.0]
+ """HEALPix map nside"""
+ n: int = None
+ """Number of pixels"""
+ phis: ArrayLike = None # lat
+ """The colatitudes of the pixels"""
+ thetas: ArrayLike = None # lon
+ """The longitudes of the pixels"""
- self.n = hp.nside2npix(self.N)
- self._phis, self._thetas = hp.pix2ang(self.N, np.arange(self.n))
- self._sin_phi = np.sin(self._phis)
+ def __init__(self, N: int = 64):
+ """An object holding the geometry of the stellar surface map.
- # these two maps are subject to different limb laws
- self.clear_surface()
+ Parameters
+ ----------
+ N : int, optional
+ HEALPix map nside, by default 64
+ """
+ self.N = N
+ self.n = hp.nside2npix(self.N)
+ self.thetas, self.phis = jnp.array(hp.pix2ang(self.N, jnp.arange(self.n)))
+ self._smooth_spots = jax.jit(core.smooth_spot(self.phis, self.thetas))
- self.hemisphere_mask = jax.vmap(core.hemisphere_mask(self._thetas))
- self.polynomial_limb_darkening = jax.vmap(
- core.polynomial_limb_darkening(self._thetas, self._phis), in_axes=(None, 0)
- )
- self.projected_area = jax.vmap(core.projected_area(self._thetas, self._phis))
+ def _spots(self, accumulate=False, jit=True):
- # Define transit chord if impact parameter (b) and planet radius (r) provided
- self._map_chord = np.zeros(self.n)
- assert (self.b is None and self.r is None) or (
- self.b is not None and self.r is not None
- ), "Either both b and r must be provided or neither."
- if self.b is not None and self.r is not None:
- self.define_transit_chord(self.b, self.r)
+ if jit:
+ query = jax.jit(
+ jnp.vectorize(
+ core.query_disk(self.phis, self.thetas),
+ signature="(),(),()->(n)",
+ )
+ )
+ else:
- def clear_surface(self):
- """
- Clear the surface of the star by setting the spot and faculae maps to zero.
- """
- self.map_spot = np.zeros(self.n)
- self.map_faculae = np.zeros(self.n)
+ def query(lat, lon, r):
+ lats, long, rs = _wrap(lat, lon, r)
+ x = np.zeros((len(lats), self.n), dtype=np.int8)
+ for i, (la, lo, r) in enumerate(zip(lats, long, rs)):
+ idxs = hp.query_disc(self.N, hp.ang2vec(la, lo), r)
+ x[i, idxs] = 1.0
+ return x
- @property
- def has_chord(self):
- """
- Check if the star has a transit chord defined.
+ def fun(lat, lon, r):
+ x = query(lat, lon, r)
- Returns
- -------
- bool
- True if the star has a transit chord defined, False otherwise.
- """
- return self.r is not None
+ if accumulate is True and x.ndim == 2:
+ x = jnp.cumsum(x, 0)
+ x = jnp.asarray(x > 0, dtype=jnp.float64)
- @property
- def resolution(self):
- """
- Resolution of the star's HEALPix map.
+ return x
- Returns
- -------
- float
- The resolution of the star's HEALPix map.
- """
- return hp.nside2resol(self.N)
+ return fun
- def add_spot(self, theta, phi, radius, contrast):
- """
- Add spot(s) to the star's surface.
+ def spots(
+ self,
+ lat: Array,
+ lon: Array,
+ r: Array,
+ summed: bool = True,
+ cumulative: bool = False,
+ ):
+ """Generate an HEALPix map of spots.
Parameters
----------
- theta : float or list
- The polar angle(s) of the spot(s).
- phi : float or list
- The azimuthal angle(s) of the spot(s).
- radius : float or list
- The radius(es) of the spot(s).
- contrast : float or list
- The contrast(s) of the spot(s).
-
- Examples
- --------
- .. plot::
- :context:
- :nofigs:
+ lat : Array
+ latitude(s) of the spots
+ lon : Array
+ longitude(s) of the spots
+ r : Array
+ radius(ii) of the spots
+ summed : bool, optional
+ wether one map per spot is returned or summed, by default True
+ cumulative : bool, optional
+ wether each map contain a given spot plus all the previous ones,
+ by default False
- import matplotlib.pyplot as plt
- from spotter import Star
- star = Star(u=[0.1, 0.2], N=2**7)
-
- >>> from spotter import Star
- >>> star = Star(u=[0.1, 0.2], N=2**7)
-
- To add spot(s)
-
- >>> star.add_spot([1.5, 1.], [0.2, 0.5], [0.1, 0.3], 0.1)
- >>> star.show()
+ Returns
+ -------
+ Array
+ HEALPix map of the spots
+ """
+ if cumulative:
+ summed = False
+
+ if summed:
+ x = np.zeros(self.n, dtype=np.int8)
+ for t, p, r in zip(*_wrap(lat, lon, r)):
+ idxs = hp.query_disc(self.N, hp.ang2vec(t, p), r)
+ x[idxs] = 1
+ else:
+ lats, lons, rs = _wrap(lat, lon, r)
+ x = np.zeros((len(lats), self.n), dtype=np.int8)
+ for i, (t, p, r) in enumerate(zip(lats, lons, rs)):
+ idxs = hp.query_disc(self.N, hp.ang2vec(t, p), r)
+ x[i, idxs] = 1
- .. plot::
- :context:
+ if cumulative:
+ x = np.cumsum(x, 0)
+ x = (x > 0).astype(np.int8)
- star.clear_surface()
- star.add_spot([1.5, 1.], [0.2, 0.5], [0.1, 0.3], 0.1)
- star.show()
- plt.tight_layout()
+ return x
- """
- for t, p, r, c in zip(*_wrap(theta, phi, radius, contrast)):
- idxs = hp.query_disc(self.N, hp.ang2vec(t, p), r)
- self.map_spot[idxs] = c
+ def smooth_spots(self, lat, lon, r, c=12):
+ return self._smooth_spots(lat, lon, r, c)
- def add_faculae(self, theta, phi, radius_in, radius_out, contrast):
- """
- Add facula(e) to the star's surface.
+ def masked(self, x: Array = None, phase: float = 0.0) -> Array:
+ """Returns a map where pixels outside the visible hemisphere
+ of the star are set to zero.
Parameters
----------
- theta : float or list
- The polar angle(s) of the faculae.
- phi : float or list
- The azimuthal angle(s) of the faculae.
- radius_in : float or list
- The inner radius(es) of the faculae.
- radius_out : float or list
- The outer radius(es) of the faculae.
- contrast : float or list
- The contrast(s) of the faculae.
-
- Examples
- --------
- If we create a stellar map
-
- .. plot::
- :context:
- :include-source:
-
- from spotter import Star
- import numpy as np
- from spotter.distributions import butterfly
-
- # adding faculae
- np.random.seed(15)
- star = Star(u=[0.1, 0.2], N=2**7)
- lat, lon = butterfly(0.25, 0.08, 100)
- star.add_faculae(lat, lon, 0.1, 0.12, 0.1)
- star.show()
- plt.tight_layout()
+ x : Array
+ pixels map
+ phase : float, optional
+ phase in radians, by default 0.0
+ Returns
+ -------
+ Array
+ masked map
"""
- for t, p, ri, ro, c in zip(*_wrap(theta, phi, radius_in, radius_out, contrast)):
- inner_idxs = hp.query_disc(self.N, hp.ang2vec(t, p), ri)
- outer_idxs = hp.query_disc(self.N, hp.ang2vec(t, p), ro)
- idxs = np.setdiff1d(outer_idxs, inner_idxs)
- self.map_faculae[idxs] = c
-
- def add_spot_faculae(
- self, theta, phi, radius_in, radius_out, contrast_spot, contrast_faculae
- ):
- """
- Add both spot(s) and facula(e) to the star's surface.
-
- Parameters
- ----------
- theta : float or list
- The polar angle(s) of the spot(s) and faculae.
- phi : float or list
- The azimuthal angle(s) of the spot(s) and faculae.
- radius_in : float or list
- The inner radius(es) of the faculae.
- radius_out : float or list
- The outer radius(es) of the faculae.
- contrast_spot : float or list
- The contrast(s) of the spot(s).
- contrast_faculae : float or list
- The contrast(s) of the faculae.
-
- Examples
- --------
- If we create a stellar map
-
- .. plot::
- :context:
- :include-source:
-
- from spotter import Star
- import numpy as np
- from spotter.distributions import butterfly
-
- # adding spot and faculae
- np.random.seed(15)
- star = Star(u=[0.1, 0.2], N=2**7)
- lat, lon = butterfly(0.25, 0.08, 200)
- radii = np.random.uniform(0.05, 0.1, len(lat))
- star.add_spot_faculae(lat, lon, radii, radii + 0.02, 0.05, 0.03)
- star.show()
- plt.tight_layout()
-
+ if x is None:
+ x = np.ones(self.n)
+ mask = core.hemisphere_mask(self.phis, phase)
+ return x * mask
- """
- for t, p, ri, ro, cs, cf in zip(
- *_wrap(theta, phi, radius_in, radius_out, contrast_spot, contrast_faculae)
- ):
- inner_idxs = hp.query_disc(self.N, hp.ang2vec(t, p), ri)
- outer_idxs = hp.query_disc(self.N, hp.ang2vec(t, p), ro)
- facuale_idxs = np.setdiff1d(outer_idxs, inner_idxs)
- self.map_faculae[facuale_idxs] = cf
- self.map_spot[inner_idxs] = cs
-
- def define_transit_chord(self, b, r):
- """
- Define the transit chord on the star's surface.
+ def limbed(self, x: Array = None, u: Array = None, phase=0.0) -> Array:
+ """Returns a map multiplied by the polynomial limb law.
Parameters
----------
- b : float
- Impact parameter of the transit chord.
- r : float
- Planet radius.
- """
- self.b = b
- self.r = r
- theta1 = np.arccos(b + r)
- theta2 = np.arccos(b - r)
- idx = hp.query_strip(self.N, theta1, theta2)
- self._map_chord[idx] = 1
+ x : Array
+ pixels map
+ u : Array
+ polynomial limb law coefficients
+ phase : float, optional
+ phase in radians, by default 0.0
- def jax_flux(self, phases):
+ Returns
+ -------
+ Array
+ limbed map
"""
- Return a [JAX](https://jax.readthedocs.io/en/latest/) function to compute the star's flux.
+ if x is None:
+ x = np.ones(self.n)
+ limb_darkening = core.polynomial_limb_darkening(
+ self.phis, self.thetas, u, phase
+ )
+ return x * limb_darkening
+
+ def masked_limbed(self, x: Array = None, u: Array = None, phase=0.0) -> Array:
+ """Returns a map where pixels outside the visible hemisphere
+ of the star are set to zero and multiplied by the polynomial limb law.
Parameters
----------
- phases : numpy.ndarray
- Array of phases at which to calculate the flux.
+ x : Array
+ map
+ u : Array
+ polynomial limb law coefficients
+ phase : float, optional
+ phase in radians, by default 0.0
Returns
-------
- function
- A JAX function that calculates the flux of the star at the given phases.
-
- Examples
- --------
-
- If we create a stellar map with random spots
-
- .. plot::
- :context:
- :include-source:
-
- from spotter import Star
- import numpy as np
- import matplotlib.pyplot as plt
- from spotter.distributions import butterfly
-
- # adding spots
- np.random.seed(15)
- star = Star(u=[0.1, 0.2], N=2**6)
- lat, lon = butterfly(0.25, 0.08, 200)
- star.add_spot(lat, lon, 0.05, 0.1)
- star.show()
-
- we can compute the light curve of the star at a given phase with
-
- .. plot::
- :include-source:
- :context: close-figs
-
- phases = np.linspace(0, 4 * np.pi, 1000)
- flux = star.jax_flux(phases)
- y = flux(star.map_spot)
- plt.plot(phases, y)
- plt.tight_layout()
-
- Note the gain from using a pre-computed jax flux compared to the base ``flux`` method
-
- .. code-block:: python
-
- from time import time
- import jax
-
- t0 = time()
- y = star.flux(phases)
- time_base = time() - t0
-
- t0 = time()
- y = jax.block_until_ready(flux(star.map_spot))
- time_jax = time() - t0
-
- print(f"base: {time_base:.3f} s")
- print(f"jax: {time_jax:.3f} s")
-
- .. code-block:: none
+ Array
+ masked and limbed map
+ """
+ if x is None:
+ x = np.ones(self.n)
- base: 1.115 s
- jax: 0.031 s
+ mask = core.hemisphere_mask(self.phis, phase)
+ limb_darkening = core.polynomial_limb_darkening(
+ self.phis, self.thetas, u, phase
+ )
+ return x * limb_darkening * mask
+ def area(self, phase: float = 0.0) -> ArrayLike:
+ """Returns the projected area of each pixels in the map.
+ Parameters
+ ----------
+ phase : float, optional
+ phase in radians, by default 0.0
"""
- mask = self.hemisphere_mask(phases)
- limb_darkening = self.polynomial_limb_darkening(self.u, phases)
- projected_area = self.projected_area(phases)
-
- @jax.jit
- def flux(spot_map):
- _spot = (1 - spot_map) * limb_darkening
- _geometry = mask * projected_area
- return (
- np.pi * (_spot * _geometry).sum(1) / (_geometry * limb_darkening).sum(1)
- )
+ return core.projected_area(self.phis, self.thetas, phase)
- return flux
-
- def jax_amplitude(self, resolution=3):
- """
- Return a [JAX](https://jax.readthedocs.io) function to compute the star's peak to peak amplitude.
+ def flux(self, x: Array, u: Array, phase: float) -> float:
+ """Returns the total flux of the map.
Parameters
----------
- resolution : int, optional
- The resolution parameter for the flux calculation. Defaults to 3.
+ x : Array
+ map
+ u : Array
+ polynomial limb law coefficients
+ phase : Array
+ phase in radians
Returns
-------
- function
- A JAX function that calculates the amplitude of the star's peak to peak amplitude.
-
- Examples
- --------
-
- If we create a stellar map with random spots
-
- .. plot::
- :context:
- :include-source:
-
- from spotter import Star
- import numpy as np
- import matplotlib.pyplot as plt
- from spotter.distributions import butterfly
-
- # adding spots
- np.random.seed(15)
- star = Star(u=[0.1, 0.2], N=2**6)
- lat, lon = butterfly(0.25, 0.08, 200)
- star.add_spot(lat, lon, 0.05, 0.1)
- star.show()
-
- We can compute the amplitude of the star at a given phase with
-
- .. plot::
- :include-source:
- :context: close-figs
-
- amplitude = star.jax_amplitude(resolution=3)
- a = amplitude(star.map_spot)
- print(f"Amplitude: {a:.3e}")
-
- .. code-block:: none
-
- Amplitude: 1.279e-03
-
- Note the gain from using a pre-computed jax amplitude compared to the base ``amplitude`` method
-
- .. code-block:: python
-
- from time import time
- import jax
-
- phase = np.arange(0, 2 * np.pi, star.resolution)
- t0 = time()
- a = star.flux(phases).ptp() # assuming this method exists
- time_base = time() - t0
-
- t0 = time()
- a = jax.block_until_ready(amplitude(star.map_spot))
- time_jax = time() - t0
-
- print(f"base: {time_base:.3f} s")
- print(f"jax: {time_jax:.3f} s")
-
- .. code-block:: none
-
- base: 1.210 s
- jax: 0.004 s
+ float
+ integrated flux at the given phase
"""
- hp_resolution = hp.nside2resol(self.N) * resolution
- phases = np.arange(0, 2 * np.pi, hp_resolution)
- flux = self.jax_flux(phases)
-
- @jax.jit
- def amplitude(spot_map):
- f = flux(spot_map)
- return jnp.ptp(f)
+ mask = core.hemisphere_mask(self.phis, phase)
+ limb_darkening = core.polynomial_limb_darkening(
+ self.phis, self.thetas, u, phase
+ )
+ projected_area = core.projected_area(self.phis, self.thetas, phase)
+ limbed = x * limb_darkening
+ geometry = mask * projected_area
+ return jnp.pi * (limbed * geometry).sum() / (geometry * limb_darkening).sum()
- return amplitude
+ @property
+ def resolution(self):
+ """Resolution of the map in radians."""
+ return hp.nside2resol(self.N)
- def flux(self, phases):
- """
- Calculate the flux of the star at given phases.
+ def single_spot_coverage(self, r: float):
+ """Return the coverage of a single spot of radius r.
Parameters
----------
- phases : numpy.ndarray
- Array of phases at which to calculate the flux.
+ r : float
+ radius of the spot in radians
Returns
-------
- numpy.ndarray
- The flux of the star at the given phases.
+ float
+ coverage of the spot
"""
- mask = np.vectorize(core.hemisphere_mask(self._thetas), signature="()->(n)")(
- phases
- )
- projected_area = np.vectorize(
- core.projected_area(self._thetas, self._phis), signature="()->(n)"
- )(phases)
- limb_darkening = (
- np.vectorize(
- core.polynomial_limb_darkening(self._thetas, self._phis),
- signature="()->(n)",
- excluded={0},
- )(self.u, phases)
- if len(self.u) > 0
- else 1
- )
- _spot = (1 - self.map_spot) * limb_darkening
- _geometry = mask * projected_area
- # faculae contribution, with same ld for now (TODO)
- _faculae = 0 # self.map_faculae * limb_darkening
-
- return (
- np.pi
- * ((_spot + _faculae) * _geometry).sum(1)
- / (_geometry * limb_darkening).sum(1)
- )
+ return ((2 * np.pi * (1 - np.cos(r))) / self.resolution**2) / self.n
- def map(self, phase=None, limb_darkening=False):
- """
- Return the pixel elements values of the map.
+ def amplitude(self, u: Array, undersampling: int = 3) -> callable:
+ """Returns a function to compute the amplitude of rotational light
+ curve of a given map.
Parameters
----------
- phase : float, optional
- The rotation phase of the star. Defaults to 0.
+ u : Array
+ polynomial limb law coefficients
+ resolution : int, optional
+ undersampling of the light curve according to the
+ resolution element of the map, by default 3
Returns
-------
- numpy.ndarray
- Pixel elements values of the map.
+ callable
+ signature:
+ - if single map: (map: Array) -> amplitude: float
+ - if multiple maps: (maps: Array[Array]) -> amplitudes: Array
"""
- if phase is None:
- mask = 1
- else:
- mask = self.hemisphere_mask(np.array([phase]))[0].__array__()
+ hp_resolution = self.resolution * undersampling
+ phases = jnp.arange(0, 2 * jnp.pi, hp_resolution)
- if limb_darkening and phase is not None:
- spot_limb_darkening = self.polynomial_limb_darkening(
- self.u, np.array([phase])
- )[0].__array__()
- else:
- spot_limb_darkening = 1
+ mask = jax.vmap(core.hemisphere_mask, in_axes=(None, 0))(self.phis, phases)
+ projected_area = jax.vmap(core.projected_area, in_axes=(None, None, 0))(
+ self.phis, self.thetas, phases
+ )
+ limb_darkening = jax.vmap(
+ core.polynomial_limb_darkening, in_axes=(None, None, None, 0)
+ )(self.phis, self.thetas, u, phases)
+
+ geometry = mask * projected_area
+ norm = (geometry * limb_darkening).sum(1)
+
+ def fun(x):
+ fluxes = (
+ np.pi
+ * jnp.einsum("ij,kj->ik", jnp.atleast_2d(x), limb_darkening * geometry)
+ / norm
+ )
+ return jnp.ptp(fluxes, 1)
- faculae_limb_brightening = 1
- m = (1 - self.map_spot) * mask * spot_limb_darkening
- spots = self.map_spot == 0.0
- if np.any(spots):
- m[spots] = m[spots] + (self.map_faculae * faculae_limb_brightening)[spots]
- return m
+ return fun
- def show(
- self,
- phase: float = 0,
- grid: bool = False,
- return_img: bool = False,
- chord: float = None,
- ax=None,
- **kwargs,
- ):
- """
- Show the stellar disk at a given rotation phase.
+ def render(self, x: Array, u: Array = None, phase=0.0):
+ """Render the map disk at a given rotation phase.
Parameters
----------
- phase : float, optional
- The rotation phase of the stellar disk. Defaults to 0.
- grid : bool, optional
- Whether to display a grid on the plot. Defaults to False.
- return_img : bool, optional
- Whether to return the projected map as an image. Defaults to False.
- chord : float, optional
- An additional contrast applied on the map to visualize the
- position of the transit chord. Defaults to `None`.
+ x : Array
+ map
+ u : Array
+ polynomial limb law coefficients
+ phase : Array
+ phase in radians, by default 0.0
Returns
-------
- numpy.ndarray or None
- If `return_img` is True, returns the projected map as a numpy array.
- Otherwise, returns None.
-
- Examples
- --------
- To show the stellar disk
+ Array[Array]
+ Image of the map disk
+ """
+ import matplotlib.pyplot as plt
- >>> from spotter import Star
- >>> star = Star(u=[0.1, 0.2], N=2**7, b=-0.7, r=0.06)
- >>> star.show()
+ limb_darkening = core.polynomial_limb_darkening(self.phis, self.thetas, u, 0.0)
+ rotated = hp.Rotator(rot=[phase, 0], deg=False).rotate_map_pixel(x)
+ limbed = rotated * limb_darkening
- .. plot::
- :context:
+ projected_map = hp.orthview(limbed, half_sky=True, return_projected_map=True)
+ plt.close()
- import matplotlib.pyplot as plt
- from spotter import Star
- star = Star(u=[0.1, 0.2], N=2**7, b=-0.7, r=0.06)
- star.show()
- plt.show()
+ return projected_map
- To visualize the transit chord
+ def show(
+ self, x: Array = None, u: Array = None, phase: float = 0.0, ax=None, **kwargs
+ ):
+ """Show the map disk.
- >>> star.show(chord=0.1)
+ Parameters
+ ----------
+ x : Array
+ map
+ u : Array
+ polynomial limb law coefficients
+ phase : Array
+ phase in radians, by default 0.0
+ ax : matplotlib.pyplot.Axe, optional
+ by default None
+ """
+ import matplotlib.pyplot as plt
- .. plot::
- :context:
+ if u is None:
+ u = ()
- star.show(chord=0.1)
- plt.show()
+ if x is None:
+ x = np.ones(self.n)
- """
kwargs.setdefault("cmap", "magma")
kwargs.setdefault("origin", "lower")
ax = ax or plt.gca()
- # both spot and faculae with same ld for now (TODO)
- if (
- self.map_spot.max() == 0.0
- and self.map_faculae.max() == 0.0
- and self.u == [0.0]
- ):
- rotated_m = self.map()
- else:
- rotated_m = hp.Rotator(rot=[phase, 0], deg=False).rotate_map_pixel(
- self.map()
- )
- if self.has_chord and (chord is not None):
- assert isinstance(chord, float), "chord must be a float (or None)"
- mask = self._map_chord > 0
- rotated_m[mask] = rotated_m[mask] * (1 - chord)
-
- projected_map = hp.orthview(
- rotated_m * self.polynomial_limb_darkening(self.u, np.array([0]))[0],
- half_sky=True,
- return_projected_map=True,
- )
- plt.close()
- if return_img:
- return projected_map
- else:
- ax.axis(False)
- ax.imshow(projected_map, **kwargs)
-
- def covering_fraction(
- self, phase: float = None, vmin: float = 0.01, chord=False, disk=False
- ):
- """Return the covering fraction of active regions
+ img = self.render(x, u, phase)
+ ax.axis(False)
+ ax.imshow(img, **kwargs)
- Either computed for the whole star (`phase=None`) or for the stellar
- disk given a phase
+ def transit_chord(self, r: float, b: float = 0.0):
+ """
+ Returns the map of a transit chord.
Parameters
----------
- phase : float, optional
- stellar rotation phase, by default None
- vmin : float, optional
- minimum contrast value for spots, by default 0.01
- vmax : float, optional
- minimum contrast value for faculae, by default 1.0
- transit_chord : bool, optional
- calculate the covering fraction within the transit chord
-
- Returns
- -------
- float
- full star or disk covering fraction
-
- Examples
- --------
- >>> star = Star(u=[0.1, 0.2], N=2**7, b=-0.7, r=0.06)
- >>> star.covering_fraction()
- 0.0
+ b : float
+ Impact parameter of the transit chord.
+ r : float
+ Planet radius.
"""
- if not chord:
- if phase is None:
- return np.sum(self.map_spot >= vmin) / self.n
- else:
- mask = self._get_mask(phase)
- return np.sum(self.map_spot[mask] >= vmin) / mask.sum()
-
- elif chord:
- in_chord = self._map_chord
- is_spotted = self.map_spot >= vmin
- if phase is None:
- return np.logical_and(in_chord, is_spotted).sum() / in_chord.sum()
- else:
- mask = self._get_mask(phase)
- return (
- np.logical_and(in_chord, is_spotted)[mask].sum()
- / in_chord[mask].sum()
- )
+ x = np.zeros(self.n, dtype=np.int8)
+ theta1 = np.arccos(b + r)
+ theta2 = np.arccos(b - r)
+ x[hp.query_strip(self.N, theta1, theta2)] = 1.0
+ return x
+
+ def video(self, x, u=None, duration=4, fps=10):
+ import matplotlib.animation as animation
+ import matplotlib.pyplot as plt
+ from IPython import display
+
+ fig, ax = plt.subplots(figsize=(3, 3))
+ im = plt.imshow(self.render(x, u), cmap="magma")
+ plt.axis("off")
+ plt.tight_layout()
+ ax.set_frame_on(False)
+ fig.patch.set_alpha(0.0)
+ frames = duration * fps
+
+ def update(frame):
+ a = im.get_array()
+ a = self.render(x, u, phase=np.pi * 2 * frame / frames)
+ im.set_array(a)
+ return [im]
+
+ ani = animation.FuncAnimation(
+ fig=fig, func=update, frames=frames, interval=1000 / fps
+ )
+ video = ani.to_jshtml(embed_frames=True)
+ html = display.HTML(video)
+ plt.close()
+ return display.display(html)
diff --git a/spotter/utils.py b/spotter/utils.py
new file mode 100644
index 0000000..798cbc8
--- /dev/null
+++ b/spotter/utils.py
@@ -0,0 +1,92 @@
+from typing import Any
+
+import healpy as hp
+import numpy as np
+
+from spotter import core
+
+Array = Any
+
+
+def show_map(
+ x,
+ u=None,
+ phase: float = 0,
+ return_img: bool = False,
+ chord: float = None,
+ ax=None,
+ **kwargs,
+):
+ """
+ Show the stellar disk at a given rotation phase.
+
+ Parameters
+ ----------
+ phase : float, optional
+ The rotation phase of the stellar disk. Defaults to 0.
+ grid : bool, optional
+ Whether to display a grid on the plot. Defaults to False.
+ return_img : bool, optional
+ Whether to return the projected map as an image. Defaults to False.
+ chord : float, optional
+ An additional contrast applied on the map to visualize the
+ position of the transit chord. Defaults to `None`.
+
+ Returns
+ -------
+ numpy.ndarray or None
+ If `return_img` is True, returns the projected map as a numpy array.
+ Otherwise, returns None.
+
+ Examples
+ --------
+ To show the stellar disk
+
+ >>> from spotter import Star
+ >>> star = Star(u=[0.1, 0.2], N=2**7, b=-0.7, r=0.06)
+ >>> star.show()
+
+ .. plot::
+ :context:
+
+ import matplotlib.pyplot as plt
+ from spotter import Star
+ star = Star(u=[0.1, 0.2], N=2**7, b=-0.7, r=0.06)
+ star.show()
+ plt.show()
+
+ To visualize the transit chord
+
+ >>> star.show(chord=0.1)
+
+ .. plot::
+ :context:
+
+ star.show(chord=0.1)
+ plt.show()
+
+ """
+ import matplotlib.pyplot as plt
+
+ if u is None:
+ u = ()
+
+ kwargs.setdefault("cmap", "magma")
+ kwargs.setdefault("origin", "lower")
+ ax = ax or plt.gca()
+
+ limb_darkening = core.polynomial_limb_darkening(self.phis, self.thetas, u, phase)
+ limbed = x * limb_darkening * mask
+ rotated = hp.Rotator(rot=[phase, 0], deg=False).rotate_map_pixel(limbed)
+
+ projected_map = hp.orthview(
+ rotated * self.polynomial_limb_darkening(self.u, np.array([0]))[0],
+ half_sky=True,
+ return_projected_map=True,
+ )
+ plt.close()
+ if return_img:
+ return projected_map
+ else:
+ ax.axis(False)
+ ax.imshow(projected_map, **kwargs)
diff --git a/tests/starry_comparison/test_flux.py b/tests/starry_comparison/test_flux.py
index 127776e..4f2a370 100644
--- a/tests/starry_comparison/test_flux.py
+++ b/tests/starry_comparison/test_flux.py
@@ -1,11 +1,14 @@
from collections import defaultdict
import healpy as hp
+import jax
import numpy as np
import pytest
from spotter import Star
+jax.config.update("jax_enable_x64", True)
+
@pytest.mark.parametrize("deg", (3, 10))
@pytest.mark.parametrize("u", ([], [0.1, 0.4]))
@@ -68,12 +71,12 @@ def starry2healpy(y):
mh = mh * (np.nanmax(ims) - np.nanmin(ims))
mh = mh + np.nanmin(ims)
- star = Star(N=N, u=u)
- star.map_spot = 1 - mh
+ star = Star(N=N)
+ x = mh
# comparison
phases = np.linspace(0, 2 * np.pi, 100)
expected = np.array(ms.flux(theta=np.rad2deg(phases)))
- calc = star.flux(phases)
+ calc = jax.vmap(star.flux, in_axes=(None, None, 0))(x, u, phases)
np.testing.assert_allclose(calc, expected, atol=1e-4)
diff --git a/tests/test_distributions.py b/tests/test_distributions.py
index 6b2f8fd..43639ca 100644
--- a/tests/test_distributions.py
+++ b/tests/test_distributions.py
@@ -1,17 +1,34 @@
-import numpy as np
+import jax
+import jax.numpy as jnp
from spotter import distributions
+def test_butterfly_jax():
+ key = jax.random.PRNGKey(0)
+ distributions.jax_butterfly(key)
+
+ calc = jnp.array(distributions.jax_butterfly(key, n=20))
+ assert calc.shape == (2, 20)
+
+
+def test_uniform_jax():
+ key = jax.random.PRNGKey(0)
+ distributions.jax_uniform(key)
+
+ calc = jnp.array(distributions.jax_uniform(key, n=20))
+ assert calc.shape == (2, 20)
+
+
def test_butterfly():
distributions.butterfly()
- calc = np.array(distributions.butterfly(n=20))
+ calc = jnp.array(distributions.butterfly(n=20))
assert calc.shape == (2, 20)
def test_uniform():
distributions.uniform()
- calc = np.array(distributions.uniform(n=20))
+ calc = jnp.array(distributions.uniform(n=20))
assert calc.shape == (2, 20)
diff --git a/tests/test_jax_healpy.py b/tests/test_jax_healpy.py
index 1041c1f..160e061 100644
--- a/tests/test_jax_healpy.py
+++ b/tests/test_jax_healpy.py
@@ -5,20 +5,18 @@
from spotter import Star
-@pytest.mark.skip(reason="")
+# @pytest.mark.skip(reason="")
@pytest.mark.parametrize("N", [2**n for n in range(1, 10)])
@pytest.mark.parametrize(
"center", [(0.5, 0.0), (np.pi / 2, 1.0), (1.0, np.pi), (1.0, 1.0)]
)
@pytest.mark.parametrize("radius", [0.1, 0.5, 1.0, 2.0])
def test_query_idxs(N, center, radius):
- from spotter.star import query_idxs_function
+ from spotter.core import query_disk
expected = hp.query_disc(N, hp.ang2vec(*center), radius)
star = Star(N=N)
- computed = np.flatnonzero(
- query_idxs_function(star._thetas, star._phis)(*center, radius)
- )
+ computed = np.flatnonzero(query_disk(star.phis, star.thetas)(*center, radius))
np.testing.assert_array_equal(computed, expected)
diff --git a/tests/test_star.py b/tests/test_star.py
index c221245..4caf84a 100644
--- a/tests/test_star.py
+++ b/tests/test_star.py
@@ -1,3 +1,4 @@
+import jax
import numpy as np
from spotter import Star, uniform
@@ -5,7 +6,6 @@
def test_show_empty_star():
star = Star()
- img = star.show()
def test_flux():
@@ -13,8 +13,6 @@ def test_flux():
np.random.rand(42)
n = 5
radii = np.random.uniform(0.01, 0.3, n)
- star.add_spot(*uniform(n), radii, 0.1)
+ spot_map = star.spots(*uniform(n), radii)
phase = np.linspace(0, 2 * np.pi, 300)
- jaxed = star.jax_flux(phase)(star.map_spot)
- simple = star.flux(phase)
- np.testing.assert_allclose(simple, jaxed)
+ jaxed = jax.vmap(star.flux, in_axes=(None, None, 0))(spot_map, [0.1, 0.2], phase)