alecarones · ggalloni · Jul 1, 2025 · Jul 2, 2025 · Jul 2, 2025 · Jul 2, 2025
diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml
@@ -24,8 +24,11 @@ jobs:
     - name: Install dependencies
       run: |
         sudo apt install build-essential python3-dev libfftw3-dev libcfitsio-dev libgsl-dev
-        pip install -e .[pymaster]
-        pip install pytest pytest-cov
+        pip install -e .[pymaster,dev]
+    - name: Check code style
+      run: |
+        ruff format --diff . --config=./pyproject.toml
+        ruff check . --config=./pyproject.toml
     - name: Run tests
       run: |
         pytest --durations=5 -vv
diff --git a/README.md b/README.md
@@ -1,4 +1,4 @@
-<img src="logo_broom.png" width="200"> 
+<img src="broom_logo_new.png" width="200"> 
 
 # BROOM: Blind Reconstruction Of signals from Observations in the Microwaves
 
@@ -69,14 +69,14 @@ Paper on **broom** package is in preparation.
 
 If you use the following methodologies please cite the corresponding papers:
 
-- ILC or NILC: [Delabrouille et al., 2019](https://arxiv.org/abs/0807.0773), [Bennett et al., 2003](https://arxiv.org/abs/astro-ph/0302207)
+- ILC or NILC: [Bennett et al., 2003](https://arxiv.org/abs/astro-ph/0302207), [Delabrouille et al., 2009](https://arxiv.org/abs/0807.0773)
 - cMILC: [Remazeilles et al., 2021](https://arxiv.org/abs/2006.08628), [Carones et al., 2024](https://arxiv.org/abs/2402.17579)
 - MC-ILC or MC-NILC: [Carones et al., 2023](https://arxiv.org/abs/2212.04456)
 - PILC: [Fernández-Cobos et al., 2016](https://arxiv.org/abs/1601.01515)
 - cPILC: [Adak, 2021](https://arxiv.org/abs/2104.13778)
-- GILC, GNILC, GPILC: [Planck Collaboration, 2016](https://arxiv.org/abs/1605.09387), [Remazeilles et al., 2011](https://arxiv.org/abs/1103.1166)
+- GILC, GNILC, GPILC: [Remazeilles et al., 2011](https://arxiv.org/abs/1103.1166), [Planck Collaboration, 2016](https://arxiv.org/abs/1605.09387)
 - foreground diagnostic: [Carones et al., 2024](https://arxiv.org/abs/2402.17579)
-- power spectrum computation: [Gorski et al., 2025](https://arxiv.org/abs/astro-ph/0409513), [Zonca et al., 2019](https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1298Z/abstract), [Alonso et al., 2019](https://arxiv.org/abs/1809.09603)
+- power spectrum computation: [Gorski et al., 2005](https://arxiv.org/abs/astro-ph/0409513), [Zonca et al., 2019](https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1298Z/abstract), [Alonso et al., 2019](https://arxiv.org/abs/1809.09603)
 
 ## 📦 Dependencies
 

diff --git a/broom/__init__.py b/broom/__init__.py
@@ -1,60 +1,3 @@
-"""
-
-# BROOM: Blind Reconstruction Of signals from Observations in the Microwaves
-
-**BROOM** is a Python package for blind component separation and Cosmic Microwave Background (CMB) data analysis.
-
----
-
-## 📦 Installation
-
-You can install the base package using:
-
-```
-pip install cmbroom
-```
-
-This installs the core functionality.  
-If you plan to use the few functions that depend on `pymaster`, **you must install it separately** (version `>=2.4`).
-
----
-
-### 🔧 To include `pymaster` automatically:
-
-You can install `cmbroom` along with its optional `pymaster` dependency by running:
-
-```
-pip install cmbroom[pymaster]
-```
-
-However, `pymaster` requires some additional system libraries to be installed **before** running the above command.
-
-#### ✅ On Ubuntu/Debian:
-```
-sudo apt update
-sudo apt install build-essential python3-dev libfftw3-dev libcfitsio-dev
-```
-
-#### ✅ On macOS (using Homebrew):
-```
-brew install fftw cfitsio
-```
-
-### 📦 Dependencies
-
-This package relies on several scientific Python libraries:
-
-- [astropy>=6.0.1](https://www.astropy.org/)
-- [numpy>1.18.5](https://numpy.org/)
-- [scipy>=1.8](https://scipy.org/)
-- [healpy>=1.15](https://healpy.readthedocs.io/)
-- [pysm3>=3.3.2](https://pysm3.readthedocs.io/en/latest/#)
-- [mtneedlet>=0.0.5](https://javicarron.github.io/mtneedlet/)
-
-
-
-"""
-
 
 from .compsep import (
     component_separation, estimate_residuals, _combine_products, _load_outputs
@@ -65,4 +8,4 @@
 from .clusters import get_and_save_real_tracers_B
 
 
-__version__ = "0.1.1"
+__version__ = "0.1.2"
diff --git a/broom/clusters.py b/broom/clusters.py
@@ -86,7 +86,6 @@ def get_and_save_real_tracers_B(
             Saves the generated tracers to the configured output path.
     """
     from broom import component_separation
-    print('new_version')
     if "tracers_inputs_path" not in config.real_mc_tracers[0]:
         config.real_mc_tracers[0]["tracers_inputs_path"] = f"inputs_mc_tracers/{config.experiment}"
 
@@ -95,7 +94,7 @@ def get_and_save_real_tracers_B(
 
     _log("Generating input simulations for MC-ILC tracers", verbose=config_mc.verbose)
 
-    mc_data = get_mc_data(config_mc, foregrounds=foregrounds, **kwargs)
+    mc_data = get_mc_data(config_mc, foregrounds=foregrounds, systematics=systematics, **kwargs)
 
 #    if systematics is not None:
 #        print("Adding systematic effect to the data")

diff --git a/broom/gpilcs.py b/broom/gpilcs.py
@@ -379,7 +379,7 @@ def _fgd_P_diagnostic_pixel(
     def alm_to_polmap(E=None, B=None):
         T = np.zeros_like(E if E is not None else B)
         return hp.alm2map([T, E if E is not None else T, B if B is not None else T],
-                          config.nside, lmax=lmax, pol=True)[1:]
+                          config.nside, lmax=config.lmax, pol=True)[1:]
 
     for n, channel in enumerate(compsep_run["good_channels"]):
         for c in range(input_alms.shape[-1]):

diff --git a/broom/masking.py b/broom/masking.py
@@ -441,7 +441,7 @@ def threshold_P_tracer(
     """
 
     mask_spectra = np.ones_like(map_[0])
-    map_ = hp.smoothing([0.*map_,map_[0],map_[1]],fwhm=np.radians(deg_smooth_tracer),lmax=lmax,pol=True)[1:]
+    map_ = hp.smoothing([0.*map_,map_[0],map_[1]],fwhm=np.radians(smooth_tracer),lmax=lmax,pol=True)[1:]
     map_P = np.sqrt((map_[0])**2 + (map_[1])**2)
     idx_mask = np.argsort(map_P * mask_in)[-npix_mask:]  #
     mask_spectra[idx_mask] = 0.

diff --git a/broom/pilcs.py b/broom/pilcs.py
@@ -593,7 +593,7 @@ def get_pilc_cov(
             fallback_cov_qq_uu = np.mean(np.einsum('ik,jk->ijk', input_maps[:, 0, valid], input_maps[:, 0, valid]), axis=-1) + \
                            np.mean(np.einsum('ik,jk->ijk', input_maps[:, 1, valid], input_maps[:, 1, valid]), axis=-1)
             cov_qq_uu[..., mask == 0.0] = fallback_cov_qq_uu
-            fallback_qu_uq = np.mean(np.einsum('ik,jk->ijk', input_maps[:, 0, valid], input_maps[:, 1, valid]), axis=-1) - \
+            fallback_cov_qu_uq = np.mean(np.einsum('ik,jk->ijk', input_maps[:, 0, valid], input_maps[:, 1, valid]), axis=-1) - \
                            np.mean(np.einsum('ik,jk->ijk', input_maps[:, 1, valid], input_maps[:, 0, valid]), axis=-1)
             cov_qu_uq[..., mask == 0.0] = np.repeat(fallback_cov_qu_uq[..., np.newaxis], np.sum(mask == 0.0), axis=-1)
 

diff --git a/broom/templates.py b/broom/templates.py
@@ -387,7 +387,7 @@ def _get_fres_pixel(
 
     # Convert alms to maps
     input_maps = np.zeros((good_channels.shape[0], 12 * config.nside**2, input_alms.shape[-1]))
-    for n, channel in enumerate(good_channels_nl):
+    for n, channel in enumerate(good_channels):
         input_maps[n] = np.array([
             hp.alm2map(np.ascontiguousarray(input_alms[channel, :, c]), config.nside, lmax=config.lmax, pol=False)
             for c in range(input_alms.shape[-1])
@@ -556,7 +556,7 @@ def _get_fres_P_needlet_j(
     good_channels_nl = _get_good_channels_nl(config, b_ell)
 
     # Load PILC weights
-    weights_path = os.path.join(config.path_outputs,
+    weights_filename = os.path.join(config.path_outputs,
         compsep_run["compsep_path"],
         f"weights/weights_{compsep_run['field']}_{config.fwhm_out}acm_ns{nside_}_lmax{config.lmax}_nl{nl_scale}"
     )

diff --git a/broom_logo_new.png b/broom_logo_new.png
diff --git a/docs/broom.html b/docs/broom.html
@@ -147,14 +147,14 @@ <h2 id="references">References</h2>
 <p>If you use the following methodologies please cite the corresponding papers:</p>
 
 <ul>
-<li>ILC or NILC: <a href="https://arxiv.org/abs/0807.0773">Delabrouille et al., 2019</a>, <a href="https://arxiv.org/abs/astro-ph/0302207">Bennett et al., 2003</a></li>
+<li>ILC or NILC: <a href="https://arxiv.org/abs/astro-ph/0302207">Bennett et al., 2003</a>, <a href="https://arxiv.org/abs/0807.0773">Delabrouille et al., 2009</a></li>
 <li>cMILC: <a href="https://arxiv.org/abs/2006.08628">Remazeilles et al., 2021</a>, <a href="https://arxiv.org/abs/2402.17579">Carones et al., 2024</a></li>
 <li>MC-ILC or MC-NILC: <a href="https://arxiv.org/abs/2212.04456">Carones et al., 2023</a></li>
 <li>PILC: <a href="https://arxiv.org/abs/1601.01515">Fernández-Cobos et al., 2016</a></li>
 <li>cPILC: <a href="https://arxiv.org/abs/2104.13778">Adak, 2021</a></li>
-<li>GILC, GNILC, GPILC: <a href="https://arxiv.org/abs/1605.09387">Planck Collaboration, 2016</a>, <a href="https://arxiv.org/abs/1103.1166">Remazeilles et al., 2011</a></li>
+<li>GILC, GNILC, GPILC: <a href="https://arxiv.org/abs/1103.1166">Remazeilles et al., 2011</a>, <a href="https://arxiv.org/abs/1605.09387">Planck Collaboration, 2016</a></li>
 <li>foreground diagnostic: <a href="https://arxiv.org/abs/2402.17579">Carones et al., 2024</a></li>
-<li>power spectrum computation: <a href="https://arxiv.org/abs/astro-ph/0409513">Gorski et al., 2025</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1298Z/abstract">Zonca et al., 2019</a>, <a href="https://arxiv.org/abs/1809.09603">Alonso et al., 2019</a></li>
+<li>power spectrum computation: <a href="https://arxiv.org/abs/astro-ph/0409513">Gorski et al., 2005</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1298Z/abstract">Zonca et al., 2019</a>, <a href="https://arxiv.org/abs/1809.09603">Alonso et al., 2019</a></li>
 </ul>
 
 <h3 id="dependencies">📦 Dependencies</h3>
@@ -247,14 +247,14 @@ <h3 id="dependencies">📦 Dependencies</h3>
 </span><span id="L-70"><a href="#L-70"><span class="linenos"> 70</span></a>
 </span><span id="L-71"><a href="#L-71"><span class="linenos"> 71</span></a><span class="sd">If you use the following methodologies please cite the corresponding papers:</span>
 </span><span id="L-72"><a href="#L-72"><span class="linenos"> 72</span></a>
-</span><span id="L-73"><a href="#L-73"><span class="linenos"> 73</span></a><span class="sd">- ILC or NILC: [Delabrouille et al., 2019](https://arxiv.org/abs/0807.0773), [Bennett et al., 2003](https://arxiv.org/abs/astro-ph/0302207)</span>
+</span><span id="L-73"><a href="#L-73"><span class="linenos"> 73</span></a><span class="sd">- ILC or NILC: [Bennett et al., 2003](https://arxiv.org/abs/astro-ph/0302207), [Delabrouille et al., 2009](https://arxiv.org/abs/0807.0773)</span>
 </span><span id="L-74"><a href="#L-74"><span class="linenos"> 74</span></a><span class="sd">- cMILC: [Remazeilles et al., 2021](https://arxiv.org/abs/2006.08628), [Carones et al., 2024](https://arxiv.org/abs/2402.17579)</span>
 </span><span id="L-75"><a href="#L-75"><span class="linenos"> 75</span></a><span class="sd">- MC-ILC or MC-NILC: [Carones et al., 2023](https://arxiv.org/abs/2212.04456)</span>
 </span><span id="L-76"><a href="#L-76"><span class="linenos"> 76</span></a><span class="sd">- PILC: [Fernández-Cobos et al., 2016](https://arxiv.org/abs/1601.01515)</span>
 </span><span id="L-77"><a href="#L-77"><span class="linenos"> 77</span></a><span class="sd">- cPILC: [Adak, 2021](https://arxiv.org/abs/2104.13778)</span>
-</span><span id="L-78"><a href="#L-78"><span class="linenos"> 78</span></a><span class="sd">- GILC, GNILC, GPILC: [Planck Collaboration, 2016](https://arxiv.org/abs/1605.09387), [Remazeilles et al., 2011](https://arxiv.org/abs/1103.1166)</span>
+</span><span id="L-78"><a href="#L-78"><span class="linenos"> 78</span></a><span class="sd">- GILC, GNILC, GPILC: [Remazeilles et al., 2011](https://arxiv.org/abs/1103.1166), [Planck Collaboration, 2016](https://arxiv.org/abs/1605.09387)</span>
 </span><span id="L-79"><a href="#L-79"><span class="linenos"> 79</span></a><span class="sd">- foreground diagnostic: [Carones et al., 2024](https://arxiv.org/abs/2402.17579)</span>
-</span><span id="L-80"><a href="#L-80"><span class="linenos"> 80</span></a><span class="sd">- power spectrum computation: [Gorski et al., 2025](https://arxiv.org/abs/astro-ph/0409513), [Zonca et al., 2019](https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1298Z/abstract), [Alonso et al., 2019](https://arxiv.org/abs/1809.09603)</span>
+</span><span id="L-80"><a href="#L-80"><span class="linenos"> 80</span></a><span class="sd">- power spectrum computation: [Gorski et al., 2005](https://arxiv.org/abs/astro-ph/0409513), [Zonca et al., 2019](https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1298Z/abstract), [Alonso et al., 2019](https://arxiv.org/abs/1809.09603)</span>
 </span><span id="L-81"><a href="#L-81"><span class="linenos"> 81</span></a>
 </span><span id="L-82"><a href="#L-82"><span class="linenos"> 82</span></a><span class="sd">### 📦 Dependencies</span>
 </span><span id="L-83"><a href="#L-83"><span class="linenos"> 83</span></a>