def plotfitstats(p1a, p2a, p1d, p2d, rmsda, biasa, title, fname):
"""image plot for rmsda with contour lines of bias"""
sns.set_style("white", {"image.cmap": "Greens"})
sns.set_color_palette("pastel")
sns.set_palette("pastel")
x, y = np.meshgrid(p1a, p2a)
fig, ax = plt.subplots()
# im = ax.imshow(rmsda)
# im.set_interpolation('bilinear')
p = ax.pcolor(x, y, rmsda.T)
# p = ax.pcolor(rmsda, cmap = sns.color_palette('pastel', 100, as_cmap = True))
cb = fig.colorbar(p, ax=ax)
cnt = ax.contour(
x,
y,
biasa.T,
colors="black",
# colors = sns.color_palette('bright', 100),
# origin = 'upper',
origin="lower",
linewidth=3,
)
plt.clabel(cnt, inline=1, fontsize=10, fmt="%.2f")
ax.set_title(
"%s, p1_default = %.3g, p2_default = %.3g\nBias (line) and RMSE (image) -- min(RMSE) = %.1f"
% (title, p1d, p2d, np.min(rmsda))
)
ax.set_xlabel("p1")
ax.set_ylabel("p2")
fig.savefig(fname)
def realign_report(target_file, realign_params, displace_params):
"""Create files summarizing the motion correction."""
# Create a DataFrame with the 6 motion parameters
rot = ["rot_" + dim for dim in ["x", "y", "z"]]
trans = ["trans_" + dim for dim in ["x", "y", "z"]]
df = pd.DataFrame(np.loadtxt(realign_params),
columns=rot + trans)
abs, rel = displace_params
df["displace_abs"] = np.loadtxt(abs)
df["displace_rel"] = pd.Series(np.loadtxt(rel), index=df.index[1:])
df.loc[0, "displace_rel"] = 0
motion_file = os.path.abspath("realignment_params.csv")
df.to_csv(motion_file, index=False)
# Write the motion plots
seaborn.set()
seaborn.set_color_palette("husl", 3)
f, (ax_rot, ax_trans) = plt.subplots(2, 1,
figsize=(8, 3.75),
sharex=True)
ax_rot.plot(df[rot] * 100)
ax_rot.axhline(0, c="#444444", ls="--", zorder=1)
ax_trans.plot(df[trans])
ax_trans.axhline(0, c="#444444", ls="--", zorder=1)
ax_rot.set_xlim(0, len(df) - 1)
ax_rot.set_ylabel(r"Rotations (rad $\times$ 100)")
ax_trans.set_ylabel("Translations (mm)")
plt.tight_layout()
plot_file = os.path.abspath("realignment_plots.png")
f.savefig(plot_file, dpi=100, bbox_inches="tight")
plt.close(f)
# Write the example func plot
data = nib.load(target_file).get_data()
n_slices = data.shape[-1]
n_row, n_col = n_slices // 8, 8
start = n_slices % n_col // 2
figsize = (10, 1.375 * n_row)
f, axes = plt.subplots(n_row, n_col, figsize=figsize, facecolor="k")
vmin, vmax = 0, moss.percentiles(data, 99)
for i, ax in enumerate(axes.ravel(), start):
ax.imshow(data[..., i].T, cmap="gray", vmin=vmin, vmax=vmax)
ax.set_xticks([])
ax.set_yticks([])
f.subplots_adjust(hspace=1e-5, wspace=1e-5)
target_file = os.path.abspath("example_func.png")
f.savefig(target_file, dpi=100, bbox_inches="tight",
facecolor="k", edgecolor="k")
plt.close(f)
return [motion_file, plot_file, target_file], motion_file
def histogram(dataset, xlabel, title):
seaborn.set_color_palette("deep", desat=.6)
matplotlib.rc("figure", figsize=(8, 4))
#seaborn.distplot(dataset)
density = stats.gaussian_kde(dataset)
density.covariance_factor = lambda : .25
density._compute_covariance()
xgrid = numpy.linspace(min(dataset), max(dataset), 100)
pyplot.hist(dataset, bins=100, log=True)
#pyplot.plot(xgrid, density.evaluate(xgrid), label='kde', color='r')
#pyplot.plot(xgrid, stats.norm.pdf(xgrid), label='DGP normal', color="g")
pyplot.xlabel(xlabel)
pyplot.ylabel('Frequency (logarithmic)')
pyplot.title(title)
pyplot.show()
def init_graph_style():
sns.set_color_palette("deep", desat=.6)
mpl.rc("figure", figsize=(8, 4))
np.random.seed(9221999)
import pandas
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
import seaborn as sns
from scipy import stats
sns.set_color_palette("deep", desat=.6)
sns.set_axes_style("whitegrid", "talk")
import heroespy
from heroespy.util import times
file = "/Users/schriste/Dropbox/Developer/HEROES/HEROES-Telescope/SAS1_pointing_data.csv"
data = pandas.read_csv(file, parse_dates=True, index_col = 0)
ind = data['ctl el'].index
time_index=ind.indexer_between_time(times.solarobs_target2[0], times.solarobs_target2[1])
sas1_obs = data.iloc[time_index]
# in arcseconds
ctl_el = sas1_obs['ctl el'] * 60 * 60
plt.figure()
ax = ctl_el.plot()
ax.set_title('HEROES/SAS PYAS-F')
ax.set_xlabel('2013-09-21 [UTC]')
ax.set_ylabel('Elevation Offset [arcsec]')
ax.set_ybound(-100,100)
plt.savefig('PYASF_el_timeseries.pdf')
import pandas
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
import seaborn as sns
from scipy import stats
sns.set_color_palette("deep", desat=.6)
sns.set_axes_style("whitegrid", "talk")
import heroespy
from heroespy.util import times
file = "/Users/schriste/Dropbox/Developer/HEROES/HEROES-Telescope/SAS1_pointing_data.csv"
data = pandas.read_csv(file, parse_dates=True, index_col=0)
ind = data['ctl el'].index
time_index = ind.indexer_between_time(times.solarobs_target2[0],
times.solarobs_target2[1])
sas1_obs = data.iloc[time_index]
# in arcseconds
ctl_el = sas1_obs['ctl el'] * 60 * 60
plt.figure()
ax = ctl_el.plot()
ax.set_title('HEROES/SAS PYAS-F')
ax.set_xlabel('2013-09-21 [UTC]')
ax.set_ylabel('Elevation Offset [arcsec]')
ax.set_ybound(-100, 100)
fit_high = []
for i in range(1, 41):
fitness = float(0)
with open('reproduction/pairs/dom-%s.org-B' %i, 'r') as org:
for line in org:
if line.startswith('# Fitness'):
fitness = float(line.split()[2])
fit_high.append(fitness)
fit_high.sort()
fit_high.reverse()
with open('fitness.csv', 'wb') as f:
w = csv.writer(f,delimiter=',', quoting = csv.QUOTE_ALL)
w.writerow('CLH')
for row in zip(fit_comp, fit_low, fit_high):
w.writerow(row)
# must specify that blank space " " is NaN
experimentDF = read_csv("fitness.csv", na_values=[" "])
print experimentDF
sns.set_color_palette("hls")
mpl.rc("figure", figsize=(16, 8))
experimentDF.dropna().plot(kind='kde')
