# Save the plot
f.tight_layout()
png_template = os.path.dirname(__file__) + "/%s.png"
f.savefig(png_template % "example_plot")
# Carousel images
plots = [
"lmplot", "tsplot", "violinplot", "interactplot", "corrplot",
"distplot"
]
styles = {p: "darkgrid" for p in plots}
styles["violinplot"] = "whitegrid"
styles["distplot"] = "nogrid"
figsize = (6, 4.2)
for plot in plots:
sns.set_axes_style(styles[plot], "notebook")
if plot == "distplot":
f, (ax1, ax2) = plt.subplots(2, 1, sharey=True, figsize=figsize)
distplot_hist(ax1)
distplot_kde(ax2)
else:
plot_func = locals()[plot]
f, ax = plt.subplots(figsize=figsize)
plot_func(ax)
f.tight_layout()
f.savefig(png_template % plot)
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 collections
import seaborn as sns
import math
import warnings
import FGE_MISC.code.config as cfg
import mypymvpa.utilities.stats as mus
import plotly.plotly as plotly
import os
# #general
try:
sns.set_style('white')
sns.set_context('notebook')
except:
sns.set_axes_style("white", "notebook")
def savefigure(f, fname, figdir, figsave='local', plotlyprivate=False):
if figsave == 'local':
f.savefig(os.path.join(figdir, fname + '.png'), bbox_inches='tight', dpi=400)
elif figsave == 'plotly':
plotly.sign_in('askerry', 'tn0zp19ph1')
plotly.plot_mpl(f, filename=fname, auto_open=False, world_readable=plotlyprivate)
def nonnancorr(array1, array2):
if np.isnan(np.sum(array1)) or np.isnan(np.sum(array2)):
array1, array2 = np.array(array1), np.array(array2)
arrays = np.array(zip(array1, array2))
nonnans = np.where(~np.isnan(arrays))[0]
def simplebar(values, width=.9, bars=True, elinewidth=2, markersize=None, fmt=None, xtickrotation=None, figsize=[4, 4],
yerr=None, xlabel=None, xlim=None, xticklabels=None, ylabel=None, yticklabels=None, title=None, ylim=None,
colors=['b'], chanceline=None, despine=False, show=True):
'''takes various optional parameters and makes simple bar chart. at minumim requires set of values to plot, nothing else.
example of more complex (and strange) usage: simplebar(values, yerr=[.2, .4, .5, .3, .6, .1], xlabel="x", ylabel="y", yticklabels=['1','2','6','7', 'g', 'h', 'j', 'v', '4'], xticklabels=['a','b','c','d','e','f'], chanceline={'chance':3, 'linetype':'solid', 'linecolor':'r'}, ylim=[-3,10])'''
#deal with some sns version issues
try:
sns.set_style('white')
sns.set_context('notebook')
except:
sns.set_axes_style("white", "notebook")
f, ax = plt.subplots(figsize=figsize)
if bars:
ax.bar(range(len(values)), values, color=colors, width=width)
if any(yerr):
if len(colors) > 1:
while len(colors) < len(values):
colors.extend(colors)
for vn, v in enumerate(values):
baseval = [0 for el in values]
basesem = [0 for el in values]
baseval[vn] = v
basesem[vn] = yerr[vn]
ax.errorbar(np.arange(len(baseval)) + width / 2, baseval, yerr=basesem, color=colors[vn],
ecolor=colors[vn], linestyle='None', fmt=fmt, elinewidth=elinewidth, markersize=markersize)
else:
ax.errorbar(np.arange(len(values)) + width / 2, values, yerr=yerr, color=colors, linestyle='None', fmt=fmt,
elinewidth=elinewidth, markersize=markersize)
if xlabel:
ax.set_xlabel(xlabel)
if ylabel:
ax.set_ylabel(ylabel)
if title:
ax.set_title(title)
if xticklabels:
if xtickrotation != None:
rotation = xtickrotation
else:
if len(xticklabels[0]) > 3:
rotation = 90
else:
rotation = 0
ax.set_xticks(np.arange(len(xticklabels)) + width / 2)
ax.set_xticklabels(xticklabels, rotation=rotation)
else:
ax.set_xticklabels([])
if yticklabels:
ax.set_yticklabels(yticklabels)
if ylim:
ax.set_ylim(ylim)
if not xlim:
xlim = [-width / 8, len(values)]
ax.set_xlim(xlim)
if chanceline:
chance = chanceline['chance']
try:
linetype = chanceline['linetype']
except:
linetype = 'dashed'
try:
linecolor = chanceline['linecolor']
except:
linecolor = 'orange'
ax.plot(xlim, [chance for el in xlim], ls=linetype, color=linecolor)
if despine:
sns.despine(fig=f, ax=ax, top=True, right=True, left=False, bottom=False, trim=False)
if show:
plt.show()
try:
return f, ax
except: return ax
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_axes_style('nogrid', 'talk', 'Helvetica')
import brewer2mpl
from itertools import cycle
from sklearn.decomposition import PCA
from scipy.spatial.distance import pdist, squareform
from sklearn.cluster import AffinityPropagation, KMeans, DBSCAN, \
spectral_clustering
class Data(object):
def __init__(self, psi, n_components, step=0.1):
self.psi = psi
self.psi_fillna_mean = self.psi.T.fillna(self.psi.mean(axis=1)).T
self.step = step
self.n_components = n_components
self.binify()
self.reduce()
def binify(self):
self.bins = np.arange(0, 1 + self.step, self.step)
ncol = int(1 / self.step)
nrow = self.psi.shape[0]
self.binned = np.zeros((nrow, ncol))
for i, (name, row) in enumerate(self.psi.iterrows()):
self.binned[i, :] = np.histogram(row, bins=self.bins,
normed=True)[0]
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_axes_style('nogrid', 'talk', 'Helvetica')
import brewer2mpl
from itertools import cycle
from sklearn.decomposition import PCA
from scipy.spatial.distance import pdist, squareform
from sklearn.cluster import AffinityPropagation, KMeans, DBSCAN, \
spectral_clustering
class Data(object):
def __init__(self, psi, n_components, step=0.1):
self.psi = psi
self.psi_fillna_mean = self.psi.T.fillna(self.psi.mean(axis=1)).T
self.step = step
self.n_components = n_components
self.binify()
self.reduce()
def binify(self):
self.bins = np.arange(0, 1 + self.step, self.step)
ncol = int(1 / self.step)
nrow = self.psi.shape[0]
self.binned = np.zeros((nrow, ncol))
for i, (name, row) in enumerate(self.psi.iterrows()):
self.binned[i, :] = np.histogram(row, bins=self.bins, normed=True)[
0]
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)
ax = plt.subplot(gs[5, 1], sharey=ax)
distplot_kde(ax)
# Save the plot
f.tight_layout()
png_template = os.path.dirname(__file__) + "/%s.png"
f.savefig(png_template % "example_plot")
# Carousel images
plots = ["lmplot", "tsplot", "violinplot", "interactplot",
"corrplot", "distplot"]
styles = {p: "darkgrid" for p in plots}
styles["violinplot"] = "whitegrid"
styles["distplot"] = "nogrid"
figsize = (6, 4.2)
for plot in plots:
sns.set_axes_style(styles[plot], "notebook")
if plot == "distplot":
f, (ax1, ax2) = plt.subplots(2, 1, sharey=True, figsize=figsize)
distplot_hist(ax1)
distplot_kde(ax2)
else:
plot_func = locals()[plot]
f, ax = plt.subplots(figsize=figsize)
plot_func(ax)
f.tight_layout()
f.savefig(png_template % plot)
for dataset in alldata:
if dataset.roi==roi and dataset.disc==disc and dataset.selector==sel:
string=roi+', '+disc+','+sel
#print string
values=np.array(dataset.accuracies)
mean=np.nanmean(values)
#print mean
SEM=np.nanstd(values)/np.sqrt(len(values))
#print SEM
allplotsmean.append(mean)
allplotssem.append(SEM)
axislabels.append(labeldict[disc])
base=np.array([0 for el in allplotsmean])
colors=['g','b','r']
colors=sns.color_palette(['#3E7C10','#6666C2', '#884433'])
sns.set_axes_style('nogrid', 'notebook')
sns.despine()
fig, axis = plt.subplots(1, figsize=[2.6,4])
for valn,val in enumerate(allplotsmean):
means=[el for el in base]
means[valn]=val
sems=[el for el in base]
sems[valn]=allplotssem[valn]
color=colors[valn]
axis.errorbar(np.arange(len(means)), np.array(means), yerr=np.array(sems),elinewidth=3, markersize=12, fmt='o', color=color)
#axis.errorbar(np.arange(len(means[0:2])), np.array(means[0:2]), yerr=np.array(sems[0:2]),elinewidth=3, markersize=12, fmt='o', color=color)
axis.locator_params(nbins=len(allplotsmean)+2)
#axis.locator_params(nbins=2,axis='x')
#axis.set_xticklabels(axislabels[0:2], visible=0)
axis.set_xticklabels(axislabels, visible=1)
xlim_deets=[-.25, len(allplotsmean)-.75]
