From 5526ec4ad17a34b8eee6ed279b094275bc72dbcc Mon Sep 17 00:00:00 2001 From: Jannis Necker Date: Mon, 2 Oct 2023 10:44:52 +0200 Subject: [PATCH] Nicer plot for positional selection of data (#130) * add jupyter extra * return data_mask when return_all in calculate_position_mask * first try using numbers as markes for visits and colorcode clusters * bugfix: loop over data masks * use markers again not numbers * bump version --- docs/source/conf.py | 2 +- poetry.lock | 1899 +++++++++++++++++++++++++++++++- pyproject.toml | 4 +- timewise/wise_data_base.py | 2 +- timewise/wise_data_by_visit.py | 86 +- 5 files changed, 1935 insertions(+), 58 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 99b7fc5..1705efb 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -23,7 +23,7 @@ author = 'Jannis Necker' # The full version, including alpha/beta/rc tags -release = 'v0.4.5' +release = 'v0.4.6' # -- General configuration --------------------------------------------------- diff --git a/poetry.lock b/poetry.lock index 1901f4f..7a0279a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,9 +1,10 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand. 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interactive widgets for Jupyter Notebook" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"}, + {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"}, +] + [[package]] name = "zipp" version = "3.16.2" description = "Backport of pathlib-compatible object wrapper for zip files" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -1912,4 +3763,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.8, <3.12" -content-hash = "607f75433b009fa95b96f4ef99b2237d5a2530f7c8b1d08b09986ac2430e9cb0" +content-hash = "96c0982d53a9ee6e86935810a3a03b6dc7498ad7f2167d28d50ff7774e48a042" diff --git a/pyproject.toml b/pyproject.toml index c926ea4..dee6e28 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "timewise" -version = "0.4.5" +version = "0.4.6" description = "A small package to download infrared data from the WISE satellite" authors = ["Jannis Necker "] license = "MIT" @@ -30,6 +30,8 @@ seaborn = ">=0.11.2,<0.13.0" urllib3 = "1.26.15" pydantic = ">=1.9.0,<2.0.0" scikit-learn = "^1.3.0" +jupyterlab = {version = "^4.0.6", extras = ["jupyter"]} +jupyter = {version = "^1.0.0", extras = ["jupyter"]} [tool.poetry.dev-dependencies] coveralls = "^3.3.1" diff --git a/timewise/wise_data_base.py b/timewise/wise_data_base.py index 41338ca..c3a3fa8 100644 --- a/timewise/wise_data_base.py +++ b/timewise/wise_data_base.py @@ -1693,7 +1693,7 @@ def calculate_position_mask(lightcurve, ra, dec, whitelist_region, return_all=Fa if return_all: return_closest_allwise_mask = list(closest_allwise_mask) if closest_allwise_mask is not None else None - return list(bad_indices), cluster_res, return_closest_allwise_mask + return list(bad_indices), cluster_res, data_mask, return_closest_allwise_mask else: return list(bad_indices) diff --git a/timewise/wise_data_by_visit.py b/timewise/wise_data_by_visit.py index d9b957f..e3ba737 100644 --- a/timewise/wise_data_by_visit.py +++ b/timewise/wise_data_by_visit.py @@ -4,6 +4,7 @@ import logging from scipy import stats import matplotlib.pyplot as plt +from matplotlib.lines import Line2D from timewise.wise_data_base import WISEDataBase from timewise.utils import get_excess_variance @@ -499,7 +500,7 @@ def plot_diagnostic_binning( # get a mask indicating outliers based on position ra, dec = pos.astype(float) - bad_indices_position, cluster_res, allwise_mask = self.calculate_position_mask( + bad_indices_position, cluster_res, data_mask, allwise_mask = self.calculate_position_mask( lightcurve, ra=ra, dec=dec, return_all=True, whitelist_region=self.whitelist_region.to("arcsec").value ) position_mask = ( @@ -537,8 +538,7 @@ def plot_diagnostic_binning( ) # set markers for visits - markers = ['o', 'v', '^', '<', '>', '1', '2', '3', '4', '8', 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd', '|', - '_', 'P', 'X'] + markers = list(Line2D.filled_markers) + ["$1$", "$2$", "$3$", "$4$", "$5$", "$6$", "$7$", "$8$", "$9$"] # calculate ra and dec relative to center of cutout ra = (lightcurve.ra - pos[self.parent_sample.default_keymap["ra"]]) * 3600 @@ -547,41 +547,65 @@ def plot_diagnostic_binning( # for each visit plot the datapoints on the cutout for visit in np.unique(visit_map): m = visit_map == visit - label = str(visit) - marker = markers[visit] - color = f"C{visit}" + axs[0].plot([], [], marker=markers[visit], label=label, mec="k", mew=1, mfc="none", ls="") - for im, _color, _label, zorder in zip( + for im, mec, zorder in zip( [position_mask, ~position_mask], - [color, "gray"], - [label, ""], + ["k", "none"], [1, 0] ): mask = m & im - datapoints = lightcurve[mask] - if ("sigra" in datapoints.columns) and ("sigdec" in datapoints.columns): - has_sig = ~datapoints.sigra.isna() & ~datapoints.sigdec.isna() - axs[0].errorbar( - ra[mask][has_sig], - dec[mask][has_sig], - xerr=datapoints.sigra[has_sig] / 3600, - yerr=datapoints.sigdec[has_sig] / 3600, - label=_label, - marker=marker, - ls="", - color=_color, - zorder=zorder - ) - axs[0].scatter( - ra[mask][~has_sig], - dec[mask][~has_sig], - marker=marker, - color=_color, - zorder=zorder + + for i_data_mask, i_data in zip([data_mask, ~data_mask], ["data", "other_allwise"]): + datapoints = lightcurve[mask & i_data_mask] + cluster_labels = ( + cluster_res.labels_[mask[i_data_mask]] if i_data == "data" + else np.array([-1] * len(datapoints)) ) - else: - axs[0].scatter(ra[mask], dec[mask], label=_label, marker=marker, color=_color, zorder=zorder) + + for cluster_label in np.unique(cluster_labels): + cluster_label_mask = cluster_labels == cluster_label + datapoints_cluster = datapoints[cluster_label_mask] + # make a marker for the visit and colored by cluster + color = f"C{cluster_label}" if cluster_label != -1 else "grey" + + if ("sigra" in datapoints_cluster.columns) and ("sigdec" in datapoints_cluster.columns): + has_sig = ~datapoints_cluster.sigra.isna() & ~datapoints_cluster.sigdec.isna() + _ra = ra[mask & i_data_mask][cluster_label_mask] + _dec = dec[mask & i_data_mask][cluster_label_mask] + + axs[0].errorbar( + _ra[has_sig], + _dec[has_sig], + xerr=datapoints_cluster.sigra[has_sig] / 3600, + yerr=datapoints_cluster.sigdec[has_sig] / 3600, + marker=markers[visit], + ls="", + color=color, + zorder=zorder, + ms=10, + mec=mec, + mew=0.1 + ) + axs[0].scatter( + _ra[~has_sig], + _dec[~has_sig], + marker=markers[visit], + color=color, + zorder=zorder, + edgecolors=mec, + linewidths=0.1, + ) + else: + axs[0].scatter( + ra[mask], dec[mask], + marker=markers[visit], + color=color, + zorder=zorder, + edgecolors=mec, + linewidths=0.1, + ) # for each band indicate the outliers based on brightness with circles for b, outlier_mask in outlier_masks.items():