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Martin Bubel 2024-05-19 20:23:01 +02:00
parent 741bb0b0da
commit e1fa04bb04
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GPy/plotting/matplot_dep/defaults.py
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@ -1,4 +1,4 @@
#=============================================================================== # ===============================================================================
# Copyright (c) 2015, Max Zwiessele # Copyright (c) 2015, Max Zwiessele
# All rights reserved. # All rights reserved.
# #
@ -26,12 +26,12 @@
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#=============================================================================== # ===============================================================================
from matplotlib import cm from matplotlib import cm
from .. import Tango from .. import Tango
''' """
This file is for defaults for the gpy plot, specific to the plotting library. This file is for defaults for the gpy plot, specific to the plotting library.
Create a kwargs dictionary with the right name for the plotting function Create a kwargs dictionary with the right name for the plotting function
@ -40,36 +40,55 @@ the plotting library will be used.
In the code, always ise plotting.gpy_plots.defaults to get the defaults, as In the code, always ise plotting.gpy_plots.defaults to get the defaults, as
it gives back an empty default, when defaults are not defined. it gives back an empty default, when defaults are not defined.
''' """
# Data plots: # Data plots:
data_1d = dict(lw=1.5, marker='x', color='k') data_1d = dict(lw=1.5, marker="x", color="k")
data_2d = dict(s=35, edgecolors='none', linewidth=0., cmap=cm.get_cmap('hot'), alpha=.5) data_2d = dict(
inducing_1d = dict(lw=0, s=500, color=Tango.colorsHex['darkRed']) s=35, edgecolors="none", linewidth=0.0, cmap=cm.get_cmap("hot"), alpha=0.5
inducing_2d = dict(s=17, edgecolor='k', linewidth=.4, color='white', alpha=.5, marker='^') )
inducing_3d = dict(lw=.3, s=500, color=Tango.colorsHex['darkRed'], edgecolor='k') inducing_1d = dict(lw=0, s=500, color=Tango.colorsHex["darkRed"])
xerrorbar = dict(color='k', fmt='none', elinewidth=.5, alpha=.5) inducing_2d = dict(
yerrorbar = dict(color=Tango.colorsHex['darkRed'], fmt='none', elinewidth=.5, alpha=.5) s=17, edgecolor="k", linewidth=0.4, color="white", alpha=0.5, marker="^"
)
inducing_3d = dict(lw=0.3, s=500, color=Tango.colorsHex["darkRed"], edgecolor="k")
xerrorbar = dict(color="k", fmt="none", elinewidth=0.5, alpha=0.5)
yerrorbar = dict(
color=Tango.colorsHex["darkRed"], fmt="none", elinewidth=0.5, alpha=0.5
)
# GP plots: # GP plots:
meanplot_1d = dict(color=Tango.colorsHex['mediumBlue'], linewidth=2) meanplot_1d = dict(color=Tango.colorsHex["mediumBlue"], linewidth=2)
meanplot_2d = dict(cmap='hot', linewidth=.5) meanplot_2d = dict(cmap="hot", linewidth=0.5)
meanplot_3d = dict(linewidth=0, antialiased=True, cstride=1, rstride=1, cmap='hot', alpha=.3) meanplot_3d = dict(
samples_1d = dict(color=Tango.colorsHex['mediumBlue'], linewidth=.3) linewidth=0, antialiased=True, cstride=1, rstride=1, cmap="hot", alpha=0.3
samples_3d = dict(cmap='hot', alpha=.1, antialiased=True, cstride=1, rstride=1, linewidth=0) )
confidence_interval = dict(edgecolor=Tango.colorsHex['darkBlue'], linewidth=.5, color=Tango.colorsHex['lightBlue'],alpha=.2) samples_1d = dict(color=Tango.colorsHex["mediumBlue"], linewidth=0.3)
density = dict(alpha=.5, color=Tango.colorsHex['lightBlue']) samples_3d = dict(
cmap="hot", alpha=0.1, antialiased=True, cstride=1, rstride=1, linewidth=0
)
confidence_interval = dict(
edgecolor=Tango.colorsHex["darkBlue"],
linewidth=0.5,
color=Tango.colorsHex["lightBlue"],
alpha=0.2,
)
density = dict(alpha=0.5, color=Tango.colorsHex["lightBlue"])
# GPLVM plots: # GPLVM plots:
data_y_1d = dict(linewidth=0, cmap='RdBu', s=40) data_y_1d = dict(linewidth=0, cmap="RdBu", s=40)
data_y_1d_plot = dict(color='k', linewidth=1.5) data_y_1d_plot = dict(color="k", linewidth=1.5)
# Kernel plots: # Kernel plots:
ard = dict(edgecolor='k', linewidth=1.2) ard = dict(edgecolor="k", linewidth=1.2)
# Input plots: # Input plots:
latent = dict(aspect='auto', cmap='Greys', interpolation='bicubic') latent = dict(aspect="auto", cmap="Greys", interpolation="bicubic")
gradient = dict(aspect='auto', cmap='RdBu', interpolation='nearest', alpha=.7) gradient = dict(aspect="auto", cmap="RdBu", interpolation="nearest", alpha=0.7)
magnification = dict(aspect='auto', cmap='Greys', interpolation='bicubic') magnification = dict(aspect="auto", cmap="Greys", interpolation="bicubic")
latent_scatter = dict(s=20, linewidth=.2, edgecolor='k', alpha=.9) latent_scatter = dict(s=20, linewidth=0.2, edgecolor="k", alpha=0.9)
annotation = dict(fontdict=dict(family='sans-serif', weight='light', fontsize=9), zorder=.3, alpha=.7) annotation = dict(
fontdict=dict(family="sans-serif", weight="light", fontsize=9),
zorder=0.3,
alpha=0.7,
)