labelleft=0,
labelright=1,
)
ax1.tick_params(labelsize=plot_utils.TICK_SIZE)
ax2.tick_params(labelsize=plot_utils.TICK_SIZE)
ax2.tick_params(labelsize=plot_utils.TEXT_SIZE, which="minor")
figure.set_size_inches(6.4, 2.6)
figure.subplots_adjust(left=0.09,
bottom=0.16,
right=0.93,
top=0.9,
wspace=0.04,
hspace=0.2)
filename = "newton_linear_converge.pdf"
path = plot_utils.get_path("compensated-newton", filename)
figure.savefig(path)
print("Saved {}".format(filename))
plt.close(figure)
def main():
image1()
image2()
if __name__ == "__main__":
plot_utils.set_styles()
main()
from mpl_toolkits.axes_grid1 import make_axes_locatable
import moleculetools as mt
from mpl_toolkits.mplot3d import Axes3D
import plot_utils as pu
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
#rc('font', **{'family':'sans-serif', 'sans-serif':['Helvetica Neue'], #'weight':'light', 'size':12})
##plt.rcParams['pdf.fonttype'] = 42
#plt.rcParams['lines.linewidth'] = 1
#plt.rcParams['lines.markeredgewidth'] = 0
#plt.rcParams['lines.markersize'] = 4.5
#plt.rcParams['lines.markeredgecolor'] = (0, 0, 0, 0)
pu.set_styles()
def make_colormap(seq):
"""Return a LinearSegmentedColormap
seq: a sequence of floats and RGB-tuples. The floats should be increasing
and in the interval (0,1).
"""
seq = [(None,) * 3, 0.0] + list(seq) + [1.0, (None,) * 3]
cdict = {'red': [], 'green': [], 'blue': []}
for i, item in enumerate(seq):
if isinstance(item, float):
r1, g1, b1 = seq[i - 1]
r2, g2, b2 = seq[i + 1]
cdict['red'].append([item, r1, r2])
cdict['green'].append([item, g1, g2])
cdict['blue'].append([item, b1, b2])
