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Second draft #38

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merged 16 commits into from
Nov 12, 2020
Merged

Second draft #38

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a29837b
dec: Update layers parametrization
cdalvaro Nov 11, 2020
4b5e9be
thesis: Add Data Mining section
cdalvaro Nov 11, 2020
b2ba6c3
docker: Update docker-compose.yml
cdalvaro Nov 11, 2020
62259bc
doc: Update README.md files
cdalvaro Nov 11, 2020
cf69873
feat: Improve graphics
cdalvaro Nov 11, 2020
e2d82da
python: Improve estimate_n_clusters method
cdalvaro Nov 11, 2020
47c6850
notebook: Add kmeans_analysis.ipynb
cdalvaro Nov 11, 2020
c438b68
thesis: Add Soft Clustering with K-Means section
cdalvaro Nov 11, 2020
0580f5a
thesis: Update Deep Embedded Clustering section
cdalvaro Nov 12, 2020
686a826
doc: Update README.md
cdalvaro Nov 12, 2020
aa7eb44
thesis: Update References chapter
cdalvaro Nov 12, 2020
1a047c7
graphics: Update plots
cdalvaro Nov 12, 2020
c345398
ci: Enable --shell-escape
cdalvaro Nov 12, 2020
2499a42
graphics: Update OpenClust Catalogue figure
cdalvaro Nov 12, 2020
52cc16b
thesis: Expand Introduction chapter
cdalvaro Nov 12, 2020
7410907
thesis: Rearrange figure dec_melotte_22
cdalvaro Nov 12, 2020
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feat: Improve graphics
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@cdalvaro
cdalvaro committed Nov 11, 2020
commit cf69873928705f72e7cdf6897e72c24d7571ac72
2 changes: 1 addition & 1 deletion src/cdalvaro/graphics/color_palette.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,6 @@ def set_color_palette():
sns.set_palette(color_palette(as_cmap=False))


def color_palette(as_cmap: bool = True, **kwargs):
def color_palette(as_cmap: bool = False, **kwargs):
# https://medium.com/@morganjonesartist/color-guide-to-seaborn-palettes-da849406d44f
return sns.color_palette('BrBG_r', as_cmap=as_cmap, **kwargs)
45 changes: 30 additions & 15 deletions src/cdalvaro/graphics/plot.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns

from .color_palette import color_palette
Expand All @@ -10,7 +11,7 @@ def plot_clusters_catalogue_distribution(data: pd.DataFrame,
xlim: tuple = None,
ylim: tuple = None,
hue: str = 'diam'):
fig, ax = plt.subplots(figsize=(12, 6))
fig, ax = plt.subplots(figsize=(12, 6), tight_layout=True)
if title is not None:
ax.set_title(title)

Expand All @@ -23,11 +24,12 @@ def plot_clusters_catalogue_distribution(data: pd.DataFrame,
ax.set_ylim(ylim)

palette = None
hue_order = None
if hue is not None:
n_colors = len(pd.unique(data[hue]))
palette = color_palette(n_colors=n_colors)
hue_order = np.sort(pd.unique(data[hue]))
palette = color_palette(n_colors=len(hue_order))

g = sns.scatterplot(data=data, x="ra", y="dec", hue=hue, size=hue, palette=palette, ax=ax)
g = sns.scatterplot(data=data, x="ra", y="dec", hue=hue, hue_order=hue_order, size=hue, palette=palette, ax=ax)

plt.legend().set_title("Diameter (arcmin)")

Expand All @@ -40,7 +42,7 @@ def plot_cluster_proper_motion(data: pd.DataFrame,
ylim: tuple = None,
hue: str = 'cluster_g',
legend: bool = True):
fig, ax = plt.subplots(figsize=(6, 6))
fig, ax = plt.subplots(figsize=(6, 6), tight_layout=True)
if title is not None:
ax.set_title(title)

Expand All @@ -53,16 +55,25 @@ def plot_cluster_proper_motion(data: pd.DataFrame,
ax.set_ylim(ylim)

palette = None
hue_order = None
if hue is not None:
n_colors = len(pd.unique(data[hue]))
palette = color_palette(n_colors=n_colors)
hue_order = np.sort(pd.unique(data[hue]))
palette = color_palette(n_colors=len(hue_order))

g = sns.scatterplot(data=data, x="pmra", y="pmdec", hue=hue, palette=palette, s=12, ax=ax, legend=legend)
g = sns.scatterplot(data=data,
x="pmra",
y="pmdec",
hue=hue,
hue_order=hue_order,
palette=palette,
s=12,
ax=ax,
legend=legend)

return fig, ax, g


def plot_cluster_parallax_histogram(df_cluster,
def plot_cluster_parallax_histogram(data,
title: str = None,
xlim: tuple = None,
ylim: tuple = None,
Expand All @@ -83,13 +94,15 @@ def plot_cluster_parallax_histogram(df_cluster,
ax.set_ylim(ylim)

palette = None
hue_order = None
if hue is not None:
n_colors = len(pd.unique(df_cluster[hue]))
palette = color_palette(n_colors=n_colors)
hue_order = np.sort(pd.unique(data[hue]))
palette = color_palette(n_colors=len(hue_order))

g = sns.histplot(data=df_cluster,
g = sns.histplot(data=data,
x='parallax',
hue=hue,
hue_order=hue_order,
palette=palette,
legend=legend,
bins=bins,
Expand All @@ -106,7 +119,7 @@ def plot_cluster_isochrone_curve(data: pd.DataFrame,
ylim: tuple = None,
hue: str = 'cluster_g',
legend: bool = True):
fig, ax = plt.subplots(figsize=(6, 6))
fig, ax = plt.subplots(figsize=(6, 6), tight_layout=True)
if title is not None:
ax.set_title(title)

Expand All @@ -119,14 +132,16 @@ def plot_cluster_isochrone_curve(data: pd.DataFrame,
ax.set_ylim(ylim)

palette = None
hue_order = None
if hue is not None:
n_colors = len(pd.unique(data[hue]))
palette = color_palette(n_colors=n_colors)
hue_order = np.sort(pd.unique(data[hue]))
palette = color_palette(n_colors=len(hue_order))

g = sns.scatterplot(data=data,
x="bp_rp",
y="phot_g_mean_mag",
hue=hue,
hue_order=hue_order,
size='parallax',
sizes=(2, 20),
palette=palette,
Expand Down