def setUvFormat(self):
self.subplot.xaxis.set_major_formatter(PL.FuncFormatter(self.uv_format))
self.subplot.xaxis.set_major_locator(PL.MultipleLocator(self.parent.M/6))
self.subplot.yaxis.set_major_formatter(PL.FuncFormatter(self.uv_format))
self.subplot.yaxis.set_major_locator(PL.MultipleLocator(self.parent.M/6))
self.subplot.set_xlim(100., 500.)
self.subplot.set_ylim(100., 500.)
def Pace_vs_Time(data):
fig = pl.figure()
ax4 = fig.add_subplot(111)
ax4.plot(data['MovingTime'], data['Pace'], color='b')
ax4 = pl.gca()
ax4.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
ax4.yaxis.set_major_formatter(pl.FuncFormatter(HMS))
pl.xlabel("Time")
pl.ylabel("Pace")
pl.xlim([0, data['MovingTime'][-1]])
pl.ylim([240, 720])
### GRAPH ORIGINAL/SMOOTHED DATA ###
# fig = pl.figure()
# pl.title("Moving Triangle Smoothing")
# ax5 = fig.add_subplot(111)
pl.plot(data['MovingTime'],
smoothTriangle(data['Pace'], 10),
label="smoothed d=10",
color='r')
# ax5 = pl.gca()
# ax5.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
# ax5.yaxis.set_major_formatter(pl.FuncFormatter(HMS))
# pl.xlabel("Time")
# pl.ylabel("Pace")
# pl.xlim([0,data['ElapsedTime'][-1]])
# pl.ylim([240,720])
pl.legend()
def setArcFormat(self):
self.subplot.xaxis.set_major_formatter(PL.FuncFormatter(self.arcsec_format))
self.subplot.xaxis.set_major_locator(PL.MultipleLocator(self.parent.M/10))
self.subplot.yaxis.set_major_formatter(PL.FuncFormatter(self.arcsec_format))
self.subplot.yaxis.set_major_locator(PL.MultipleLocator(self.parent.M/10))
self.subplot.set_xlim(200., 400.)
self.subplot.set_ylim(200., 400.)
def set_minor_ticks(ax, x=True, y=True):
"""
Labels first minor tick in the case that there is only a single major
tick label visible.
"""
def even(x, pos):
if int(str(int(x * 10**8))[0]) % 2:
return ""
elif pylab.rcParams["text.usetex"]:
return "$%s$" % float_to_latex(x)
else:
return str(int(x))
# xticks
if x:
ticks = list(ax.get_xticks())
xlim = ax.get_xlim()
for i, tick in enumerate(ticks[::-1]):
if not xlim[0] <= tick <= xlim[1]:
ticks.pop(-1)
if len(ticks) <= 1:
ax.xaxis.set_minor_formatter(pylab.FuncFormatter(even))
# yticks
if y:
ticks = list(ax.get_yticks())
ylim = ax.get_ylim()
for i, tick in enumerate(ticks[::-1]):
if not ylim[0] <= tick <= ylim[1]:
ticks.pop(-1)
if len(ticks) <= 1:
ax.yaxis.set_minor_formatter(pylab.FuncFormatter(even))
return
def drawhist(self):
from matplotlib import pyplot as plt
import pandas as pd
df = pd.read_csv(r'fishMorganMACCS.csv')
log = df['logTox']
val = df['toxValue']
plt.rcParams['font.family'] = 'Times New Roman'
plt.rcParams['axes.xmargin'] = 0
plt.rcParams['axes.ymargin'] = 0
#inch settings
dpi = 300
w = 1063 / dpi
h = 532 / dpi
fig = plt.figure(figsize=(w, h), dpi=dpi)
#fig = plt.figure(figsize=(5, 2.5))
#ax = fig.add_subplot(111)
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.xaxis.set_major_formatter(
plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax1.yaxis.set_major_formatter(
plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax2.xaxis.set_major_formatter(
plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
#normal
ax1.hist(val, bins=200, range=(-20, 120))
ax1.set_ylim([0, 500])
ax1.set_xlabel("Ecotoxicity [mg/L]", fontsize=5)
#log
ax2.hist(log, bins=200, range=(-6, 6))
ax2.set_ylim([0, 60])
ax2.set_xlabel("Log Ecotoxicity [mg/L]", fontsize=5)
#common label
ax1.set_ylabel("Count", fontsize=5)
ax1.tick_params(labelsize=4, width=0.3)
ax2.tick_params(labelsize=4, width=0.3)
#axis width
axis = ['top', 'bottom', 'left', 'right']
line_width = [0.3, 0.3, 0.3, 0.3]
for a, w in zip(axis, line_width): # change axis width
ax1.spines[a].set_linewidth(w)
ax2.spines[a].set_linewidth(w)
#plt.subplots_adjust(left=0.045, bottom=0.03, right=0.75, top=0.55, wspace=0.1, hspace=0.15)
plt.savefig('log.jpg', bbox_inches='tight', dpi=dpi)
#plt.savefig('log.jpg', bbox_inches='tight',dpi=dpi)
plt.show()
def tempo_analyzer(data, wanted, pace):
fig = pl.figure()
ax = fig.add_subplot(111)
ax.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
ax.yaxis.set_major_formatter(pl.FuncFormatter(HMS))
pl.title("Tempo Analyzer")
pl.xlabel("Time")
pl.ylabel("Pace")
indices = np.where(data['Lap'] == int(wanted))
x = data['MovingTime'][indices]
y = data['Pace'][indices]
pl.xlim([x[0], x[-1]])
ax.plot(x, y, color='r')
pl.axhline(y=pace, linewidth=2, color='k')
def barGraph(data, **kw):
"""Draws a bar graph for the given data"""
from pylab import bar
kw.setdefault('barw', 0.5)
kw.setdefault('log', 0)
kw.setdefault('color', 'blue')
xs = [i + 1 for i, row in enumerate(data)]
names, ys = zip(*data)
names = [n.replace('_', '\n') for n in names]
#print 'got xs %s, ys %s, names %s' % (xs, ys, names)
bar(xs,
ys,
width=kw['barw'],
color=kw['color'],
align='center',
log=kw['log'])
ax = pylab.gca()
def f(x, pos=None):
n = int(x) - 1
if n + 1 != x: return ''
if n < 0: return ''
try:
return names[n]
except IndexError:
return ''
ax.xaxis.set_major_formatter(pylab.FuncFormatter(f))
ax.xaxis.set_major_locator(pylab.MultipleLocator(1))
ax.set_xlim(0.5, len(names) + 0.5)
for l in ax.get_xticklabels():
pylab.setp(l, rotation=90)
start = 0.08, 0.18
pos = (start[0], start[1], 0.99 - start[0], 0.95 - start[1])
ax.set_position(pos)
def Pace_Dist(data):
fig = pl.figure()
ax9 = fig.add_subplot(111)
num_bins = 50
ax9.hist(data['Pace'], num_bins)
ax9.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
pl.xlabel("Pace")
def draw_heat_graph(getgraph, opts):
# from pyevolve_graph script
stage_points = getgraph()
fg = pl.figure()
ax = fg.add_subplot(111)
pl.imshow(stage_points,
aspect="auto",
interpolation="gaussian",
cmap=matplotlib.cm.__dict__["jet"])
pl.title("Population scores along the generations")
def labelfmt(x, pos=0):
# there is surely a better way to do that
return (float(x) == int(x)) and '%d' % (x) or ''
ax.xaxis.set_major_formatter(pl.FuncFormatter(labelfmt))
pl.xlabel('Generations -->')
pl.ylabel('Sorted Population Results')
pl.grid(True)
pl.colorbar()
if opts.outfile:
fg.savefig(opts.outfile)
if opts.show:
pl.show()
def draw_correl_graph(getgraph, opts):
# http://matplotlib.sourceforge.net/index.html
fg = pl.figure()
ax = fg.add_subplot(111)
bars = getgraph()
for (values, attrs) in bars:
indexes, width = pl.arange(len(values)), 1.0 / len(bars)
yvalues = [x.speedup for x in values]
xoffset = width * bars.index((values, attrs))
ax.bar(indexes + xoffset, yvalues, width, picker=4000, **attrs)
ax.legend(loc='lower left')
fg.canvas.draw()
# dynamic annotations
def on_pick(event):
ind = int(event.mouseevent.xdata)
point = bars[0][0][ind]
tooltip.set_position((event.mouseevent.xdata, event.mouseevent.ydata))
tooltip.set_text(point_descr(point))
tooltip.set_visible(True)
fg.canvas.draw()
tooltip = ax.text(0,
0,
"undef",
bbox=dict(facecolor='white', alpha=0.8),
verticalalignment='bottom',
visible=False)
# graph title
try:
title = 'Correlation Graph for %s' % (opts.id or opts.targets
or bars[0][0][0].target)
except:
title = 'Correlation Graph'
# redraw axis, set labels, legend, grid, ...
def labelfmt(x, pos=0):
return '%.2f%%' % (100.0 * x)
ax.yaxis.set_major_formatter(pl.FuncFormatter(labelfmt))
pl.ylabel('speedup (higher is better) -->')
pl.xlabel('Configurations (ordered by decreasing speedup of ' +
bars[0][1]['label'] + ') -->')
pl.title(title)
pl.axhspan(0.0, 0.0)
pl.axvspan(0.0, 0.0)
pl.grid(True)
if opts.outfile:
fg.savefig(opts.outfile)
if opts.show:
fg.canvas.mpl_connect('pick_event', on_pick)
pl.show()
def draw_search_graph(plots):
import pylab as pl
fg = pl.figure()
ax = fg.add_subplot(111)
ax.yaxis.set_major_formatter(pl.FuncFormatter(labelfmt))
for (values, attrs) in plots:
indexes, width = pl.arange(len(values)), 1.0 / len(plots)
yvalues = [x.result for x in values]
xoffset = width * plots.index((values, attrs))
ax.plot(indexes + xoffset, yvalues, **attrs)
legend = ax.legend(loc='best')
legend.get_frame().set_alpha(0.6)
fg.canvas.draw()
pl.ylabel('tradeoff improvement -->')
pl.xlabel('number of tested configurations -->')
pl.title('Search Graph')
pl.axhspan(0.0, 0.0)
pl.axvspan(0.0, 0.0)
pl.grid(True)
pl.show()
def Distance_vs_Time(data):
fig = pl.figure()
ax3 = fig.add_subplot(111)
ax3.plot(data['MovingTime'], data['Distance'], color='r')
ax3 = pl.gca()
ax3.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
pl.xlabel("Time")
pl.ylabel("Distance (mi)")
pl.xlim([0, data['MovingTime'][-1]])
def Elevation_vs_Time(data):
fig = pl.figure()
ax2 = fig.add_subplot(111)
ax2.plot(data['MovingTime'], data['Altitude'], color='r')
ax2 = pl.gca()
ax2.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
pl.xlabel("Time")
pl.ylabel("Elevation (ft.)")
pl.xlim([0, data['MovingTime'][-1]])
pl.ylim([0, 1500])
def HR_vs_Time(data):
fig = pl.figure()
ax6 = fig.add_subplot(111)
ax6.plot(data['MovingTime'], data['Pulse'], color='r')
ax6 = pl.gca()
ax6.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
pl.xlabel("Time")
pl.ylabel("Heart Rate")
pl.xlim([0, data['MovingTime'][-1]])
pl.ylim([40, 190])
def HR_Pace(data):
fig = pl.figure()
ax7 = fig.add_subplot(111)
ax7.scatter(data['Pace'],
data['Pulse'],
color='blue',
s=5,
edgecolor='none')
ax7.xaxis.set_major_formatter(pl.FuncFormatter(HMS))
#ax7.set_aspect(1./ax7.get_data_ratio()) # make axes square
pl.xlabel("Pace")
pl.ylabel("Heart Rate")
pl.ylim([40, 190])
def set_y_axis(self, plot):
import pylab
def arcmin_formatter(x, pos):
return "{0}'".format(int(x / 60))
ymin, ymax = plot.get_ylim()
numticks = len(plot.get_yticks())
mtscale = max(60, 60 * int(abs(ymax - ymin) / numticks / 60))
plot.set_yticks(np.arange(ymin - ymin % 60, ymax - ymax % 60, mtscale))
plot.set_yticks(np.arange(ymin - ymin % 60, ymax - ymax % 60,
mtscale / 6.0),
minor=True)
plot.yaxis.set_major_formatter(pylab.FuncFormatter(arcmin_formatter))
return plot
def plotScatter(distDict, walkType, numHops, N):
def log_10_product(x, pos):
return '%1i' % (x)
ax = pylab.subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
formatter = pylab.FuncFormatter(log_10_product)
ax.xaxis.set_major_formatter(formatter)
ax.yaxis.set_major_formatter(formatter)
xVals = distDict.keys()
yVals = distDict.values()
ax.scatter(xVals, yVals, s=40, c='b', marker='s', faceted=False)
ax.set_xlim(1e-1, 1e4)
ax.set_ylim(1e-1, 1e4)
pylab.grid(True)
pylab.xlabel(r"Degree", fontsize=12)
pylab.ylabel(r"Frequency", fontsize=12)
def set_axis(axis, linlog, ax=None):
"""Axis setting to linear or logarithmic.
Seems not to work any more (python 3 problem?).
See also make_log_ticks.
"""
if ax is None:
ax = plt.subplot(111, axes='equal')
formatter = plt.FuncFormatter(lin_product)
if axis == 'x':
print(ax)
if linlog == 'log': ax.set_xscale(linlog, nonposx='clip')
ax.xaxis.set_major_formatter(formatter)
if axis == 'y':
if linlog == 'log': ax.set_yscale(linlog, nonposy='clip')
ax.yaxis.set_major_formatter(formatter)
return ax
def plot_classifier(classifier, dataset, target, first_feat, second_feat):
"""
Visualizes the result for two features. Added feature that stores the image
:param classifier: which classifier you wish to use
:param dataset: the complete dataset
:param target: target values. (in our case its the diabetes column)
:param first_feat: the first feature
:param second_feat: the second feature
:return: no return value
"""
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00'])
X = dataset[[first_feat, second_feat]]
y = target.replace(['pos', 'neg'], [1, 0])
lable = ["neg", "pos"]
classifier.fit(X, y)
x_min, x_max = X.iloc[:, 0].min() - .1, X.iloc[:, 0].max() + .1
y_min, y_max = X.iloc[:, 1].min() - .1, X.iloc[:, 1].max() + .1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
np.linspace(y_min, y_max, 100))
Z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
pl.figure()
pl.pcolormesh(xx, yy, Z, cmap=cmap_light)
pl.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, cmap=cmap_bold)
formatter = pl.FuncFormatter(lambda i, *args: lable[int(i)])
pl.colorbar(ticks=[0, 1], format=formatter)
pl.xlabel(first_feat)
pl.ylabel(second_feat)
pl.axis('tight')
fig = pl.gcf()
pl.show()
fig.savefig(
f"../visualization_through_web_app/static/{first_feat}X{second_feat}.png"
)
def plot_dmags(gi,
dmags,
sedcolorkey,
sedtype,
plotfilterlist=filterlist,
titletext=None,
ylims=None,
xlims=None,
newfig=True,
figname=None):
# ylims = dictionary (ylim['u'] = [0, 0])
sedtypes = ['kurucz', 'mlt', 'quasar', 'white_dwarf', 'sn']
# make figure of change in magnitude
if newfig:
pylab.figure()
yplots = 3
xplots = 2
if len(plotfilterlist) == 1:
yplots = 1
xplots = 1
pylab.subplots_adjust(top=0.93,
wspace=0.32,
hspace=0.32,
bottom=0.09,
left=0.12,
right=0.96)
# For Kurucz models
if sedtype == 'kurucz':
print "# looking at kurucz"
# set colors of data points based on their metallicity
metallicity = numpy.array(sedcolorkey[0])
logg = numpy.array(sedcolorkey[1])
metcolors = ['c', 'c', 'b', 'g', 'y', 'r', 'm']
metbinsize = abs(metallicity.min() - metallicity.max()) / 6.0
metbins = numpy.arange(metallicity.min(),
metallicity.max() + metbinsize, metbinsize)
print metbinsize, metbins
i = 1
# for each filter, use a different subplot
plot_logg2 = False
for f in plotfilterlist:
ax = pylab.subplot(yplots, xplots, i)
for metidx in range(len(metbins)):
condition =((metallicity>=metbins[metidx]) & (metallicity<=metbins[metidx]+metbinsize) \
& (logg>3.5))
mcolor = metcolors[metidx]
pylab.plot(gi[condition], dmags[f][condition], mcolor + '.')
if plot_logg2:
condition =((metallicity>=metbins[metidx]) & (metallicity<=metbins[metidx]+metbinsize) \
& (logg<2.5))
mcolor = metcolors[metidx]
pylab.plot(gi[condition], dmags[f][condition],
mcolor + 'x')
i = i + 1
elif sedtype == 'quasar':
print "# looking at quasars"
# set the colors of data points based on their redshift
redshift = sedcolorkey
redcolors = ['b', 'b', 'g', 'g', 'r', 'r', 'm', 'm']
redbinsize = 0.5
redbins = numpy.arange(0.0, 3.0 + redbinsize, redbinsize)
print redbinsize, redbins, redcolors
i = 1
# for each filter, use a different subplot
for f in plotfilterlist:
ax = pylab.subplot(yplots, xplots, i)
for redidx in range(len(redbins)):
condition = ((redshift >= redbins[redidx]) &
(redshift <= redbins[redidx] + redbinsize))
rcolor = redcolors[redidx]
pylab.plot(gi[condition], dmags[f][condition], rcolor + 'o')
i = i + 1
elif sedtype == 'galaxy':
print "# looking at galaxies"
# set the colors of data points based on their redshift
gallist = sedcolorkey
redcolors = ['b', 'b', 'g', 'g', 'r', 'r', 'm', 'm']
redbinsize = 0.3
redbins = numpy.arange(0.0, 1.7 + redbinsize, redbinsize)
print redbinsize, redbins, redcolors
i = 1
for f in plotfilterlist:
ax = pylab.subplot(yplots, xplots, i)
j = 0
for g in gallist:
galbase, redshift = g.split('_')
redshift = float(redshift)
redidx = int(redshift / redbinsize)
pylab.plot(gi[j], dmags[f][j], redcolors[redidx] + '.')
j = j + 1
i = i + 1
elif sedtype == 'mlt':
print "# looking at MLT"
# set the colors of data points based on their type
mltlist = sedcolorkey[0]
mlist = sedcolorkey[1]
llist = sedcolorkey[2]
tlist = sedcolorkey[3]
i = 1
for f in plotfilterlist:
ax = pylab.subplot(yplots, xplots, i)
for j in range(len(mltlist)):
if (mltlist[j] in mlist):
pylab.plot(gi[j], dmags[f][j], 'bx')
elif (mltlist[j] in llist):
pylab.plot(gi[j], dmags[f][j], 'gx')
elif (mltlist[j] in tlist):
pylab.plot(gi[j], dmags[f][j], 'mx')
i = i + 1
elif sedtype == 'white_dwarf':
print "# looking at White Dwarf"
# set the colors of data points based on their type
wdlist = sedcolorkey[0]
hlist = sedcolorkey[1]
helist = sedcolorkey[2]
i = 1
for f in plotfilterlist:
ax = pylab.subplot(yplots, xplots, i)
for j in range(len(wdlist)):
if (wdlist[j] in hlist):
pylab.plot(gi[j], dmags[f][j], 'y+')
elif (wdlist[j] in helist):
pylab.plot(gi[j], dmags[f][j], 'y+')
i = i + 1
elif sedtype == 'sn':
print "# looking at SN"
# set the color of data points based on their redshift and day
snlist = sedcolorkey[0]
redcolors = ['b', 'b', 'g', 'g', 'r', 'r', 'm', 'm']
redbinsize = 0.18
redbins = numpy.arange(0.0, 1.0 + redbinsize, redbinsize)
print redbinsize, redbins, redcolors
day_symbol = {'0': 's', '20': 's', '40': 's'}
i = 1
for f in plotfilterlist:
ax = pylab.subplot(yplots, xplots, i)
j = 0
for s in snlist:
day, redshift = s.split('_')
redshift = float(redshift)
redidx = int(redshift / redbinsize)
pylab.plot(gi[j], dmags[f][j],
redcolors[redidx] + day_symbol[day])
j = j + 1
i = i + 1
# set up generic items
for i in range(0, len(plotfilterlist)):
f = plotfilterlist[i - 1]
ax = pylab.subplot(yplots, xplots, i + 1)
pylab.xlabel("g-i")
pylab.ylabel(r"$\Delta$ %s (mmag)" % (f))
def axis_formatter(x, pos):
return "%.1f" % (x)
formatter = pylab.FuncFormatter(axis_formatter)
ax.yaxis.set_major_formatter(formatter)
# set axes limits
if ylims == None:
pass
else:
try:
pylab.ylim(ylims[f][0], ylims[f][1])
except KeyError:
pass
if xlims == None:
pass
else:
try:
pylab.xlim(xlims[f][0], xlims[f][1])
except KeyError:
pass
# put a grid in the background
if newfig:
for i in range(0, len(plotfilterlist)):
ax = pylab.subplot(yplots, xplots, i + 1)
pylab.grid(True)
if titletext != None:
pylab.suptitle(titletext)
if figname != None:
pylab.savefig(figname + '.png', format='png')
return
edgecolor='b',
label="Best Vis")
ax1.set_xscale('log')
ax1.legend()
ax2.scatter(abs_vsby,
numpy.array(abs_sknt) * 1.15,
marker='+',
color='r',
label='Best Wind')
ax2.scatter(abv_vsby,
numpy.array(abv_sknt) * 1.15,
marker='o',
facecolor='w',
edgecolor='b',
label='Best Vis')
ax2.set_xscale('log')
ax2.legend()
formatter = pylab.FuncFormatter(log_10_product)
ax1.xaxis.set_major_formatter(formatter)
ax2.xaxis.set_major_formatter(formatter)
ax1.set_title(" \nMax/Min Method: %s/%s Sites Hit" % (len(hits), len(bs_vsby)))
ax2.set_title(" \nAverage Method: %s/%s Sites Hit" %
(len(ahits), len(abs_vsby)))
fig.savefig('test.png')
#import iemplot
#iemplot.makefeature('test')
def draw_graph(getgraph, opts):
# http://matplotlib.sourceforge.net/index.html
fg = pl.figure()
ax = fg.add_subplot(111)
global graph_plots, all_points
graph_plots, all_points = [], []
def draw_tradeoff_plots(ratio, points, attrs):
# select tradeoff given ratio
best = atos_lib.atos_client_results.select_tradeoff(points, ratio)
# graph limits
xmin = min([p.sizered for p in points])
xmax = max([p.sizered for p in points])
ymin = min([p.speedup for p in points])
ymax = max([p.speedup for p in points])
# number of points on ratio line
nbtk = int((ratio >= 1 and 2 or 1 / ratio) * 32)
# ratio line points coordinates
xtk = [xmin + i * ((xmax - xmin) / nbtk) for i in range(nbtk + 1)]
ytk = [best.speedup + (1.0 / ratio) * (best.sizered - x) for x in xtk]
coords = filter(lambda (x, y): y >= ymin and y <= ymax, zip(xtk, ytk))
# first plot: selected tradeoff point
attrs.update({'label': '_nolegend_'})
attrs.update({'markersize': 20, 'linewidth': 0, 'alpha': 0.4})
plots = [(([best.sizered], [best.speedup]), dict(attrs))]
# second plot: ratio line
attrs.update({'marker': '', 'linewidth': 2, 'linestyle': 'solid'})
plots += [((zip(*coords)[0], zip(*coords)[1]), dict(attrs))]
return plots
def draw_all():
global graph_plots, all_points, selected_points, similar_points # :(
# remove old plots
old_points = [(p.sizered, p.speedup, p.variant) for p in all_points]
for x in list(graph_plots):
graph_plots.remove(x)
x.remove()
# get graph values
scatters, frontiers = getgraph()
all_points = sum([x[0] for x in scatters + frontiers], [])
# draw scatters
for (points, attrs) in scatters:
attrsmap = {
's': 20,
'label': '_nolegend_',
'zorder': 2,
'color': 'r',
'edgecolor': 'k'
}
attrsmap.update(attrs)
xy = zip(*[(p.sizered, p.speedup) for p in points])
gr = ax.scatter(*xy, **attrsmap)
graph_plots.append(gr)
# draw frontiers (line plots)
for (points, attrs) in frontiers:
attrsmap = {
'color': 'r',
'marker': 'o',
'label': '_nolegend_',
'zorder': 2,
'markersize': 7,
'linestyle': 'dashed',
'linewidth': 2
}
attrsmap.update(attrs)
xy = zip(*sorted([(p.sizered, p.speedup) for p in points]))
gr, = ax.plot(xy[0], xy[1], **attrsmap)
graph_plots.append(gr)
# show tradeoffs for each frontier
for ratio in opts.tradeoffs or []:
for ((xcrd, ycrd), attrs) in \
draw_tradeoff_plots(ratio, points, dict(attrsmap)):
graph_plots.append(ax.plot(xcrd, ycrd, **attrs)[0])
# draw selected points (hidden)
if opts.show and all_points:
# workaround pb with pick_event event ind (4000)
attrsmap = {
'color': 'b',
'marker': 'o',
'markersize': 20,
'linewidth': 0,
'alpha': 0.4
}
xy = zip(*sorted([(p.sizered, p.speedup) for p in all_points]))
selected_points, = \
ax.plot(xy[0], xy[1], visible=False, picker=4000, **attrsmap)
graph_plots.append(selected_points)
# similar point plot
attrsmap.update({'color': 'g'})
similar_points, = ax.plot(None, None, visible=False, **attrsmap)
graph_plots.append(similar_points)
# highlight new points
if opts.follow and old_points:
new_points = [
p for p in all_points
if ((p.sizered, p.speedup, p.variant) not in old_points)
]
attrsmap = {
'color': 'r',
'marker': 'o',
'markersize': 20,
'linewidth': 0,
'alpha': 0.4,
'zorder': 1
}
if new_points:
xy = zip(*[(p.sizered, p.speedup) for p in new_points])
new_points, = ax.plot(*xy, **attrsmap)
graph_plots.append(new_points)
# redraw legend and figure
if opts.xlim: pl.xlim([float(l) for l in opts.xlim.split(",")])
if opts.ylim: pl.ylim([float(l) for l in opts.ylim.split(",")])
ax.legend(loc='lower left')
fg.canvas.draw()
# dynamic annotations
def on_pick(event):
def closest(x, y):
dp = [(math.hypot(p.sizered - x, p.speedup - y), p)
for p in all_points]
return sorted(dp)[0][1]
def highlight(p):
# print point on console
print '-' * 40 + '\n' + point_str(p)
# highlight point
selected_points.set_visible(True)
selected_points.set_data(p.sizered, p.speedup)
# highlight similar points (same variant)
sim = zip(*([(c.sizered, c.speedup) for c in all_points
if c.variant == p.variant and c != p]))
similar_points.set_visible(True)
similar_points.set_data(sim and sim[0], sim and sim[1])
# selected point legend
main_legend = ax.legend_
lg = point_str(p, short=True, no_id=opts.anonymous)
lp = pl.legend([selected_points], [lg],
loc='lower right',
numpoints=1)
pl.setp(lp.get_texts(), fontsize='medium')
lp.get_frame().set_alpha(0.5)
pl.gca().add_artist(main_legend)
fg.canvas.draw()
ax.legend_ = main_legend
highlight(closest(event.mouseevent.xdata, event.mouseevent.ydata))
# live plotting
def on_timer():
draw_all()
# draw graph for the first time
draw_all()
# graph title
title = 'Optimization Space for %s' % (opts.id or opts.targets or
(all_points
and all_points[0].target))
if opts.refid: title += ' [ref=%s]' % opts.refid
if opts.filter: title += ' [filter=%s]' % opts.filter
# redraw axis, set labels, legend, grid, ...
def labelfmt(x, pos=0):
return '%.2f%%' % (100.0 * x)
ax.xaxis.set_major_formatter(pl.FuncFormatter(labelfmt))
ax.yaxis.set_major_formatter(pl.FuncFormatter(labelfmt))
pl.axhspan(0.0, 0.0)
pl.axvspan(0.0, 0.0)
pl.title(title)
pl.xlabel('size reduction (higher is better) -->')
pl.ylabel('speedup (higher is better) -->')
if opts.xlim: pl.xlim([float(l) for l in opts.xlim.split(",")])
if opts.ylim: pl.ylim([float(l) for l in opts.ylim.split(",")])
pl.grid(True)
if opts.outfile:
fg.savefig(opts.outfile)
if opts.show:
fg.canvas.mpl_connect('pick_event', on_pick)
if opts.follow: timer = atos_lib.repeatalarm(on_timer, 5.0).start()
pl.show()
if opts.follow: timer.stop()
def plot_colorcolor(mags,
sedkeylist,
sedcolorkey,
sedtype,
filterlist,
titletext=None,
newfig=True):
if newfig:
pylab.figure()
gi = calc_stdcolors(mags)
magcolors = {}
colorlabels = []
colorslabels[0] = None
for i in range(1 - 7):
colorlabels[i] = filterlist[i - 1] + '-' + filterlist[i]
magscolors[colorlabels[i]] = mags[filterlist[i -
1]] - mags[filterlist[i]]
# colors = ug, gr, ri, iz, zy
if sedtype == 'kurucz':
print "# plotting kurucz"
# set colors of data points based on their metallicity
metallicity = sedcolorkey
metcolors = ['c', 'c', 'b', 'g', 'y', 'r', 'm']
metbinsize = abs(sedcolorkey.min() - sedcolorkey.max()) / 6.0
metbins = numpy.arange(sedcolorkey.min(),
sedcolorkey.max() + metbinsize, metbinsize)
print metbinsize, metbins
i = 1
# use a different subplot for each color/color combo
for i in range(len(colorlabels - 1)):
ax = pylab.subplot(3, 2, i)
for metidx in range(len(metbins)):
condition = ((metallicity >= metbins[metidx]) &
(metallicity <= metbins[metidx] + metbinsize))
mcolor = metcolors[metidx]
pylab.plot(magscolors[colorlabels[i]][condition],
magscolors[colorlabels[i + 1]][f][condition],
mcolor + '.')
i = i + 1
ax = pylab.subplot(3, 2, 7)
for metidx in range(len(metbins)):
condition = ((metallicity >= metbins[metidx]) &
(metallicity <= metbins[metidx] + metbinsize))
mcolor = metcolors[metidx]
pylab.plot(gi[condition],
magscolors[colorlabels[i + 1]][f][condition],
mcolor + '.')
elif sedtype == 'quasar':
print "# looking at quasars"
# set the colors of data points based on their redshift
redshift = sedcolorkey
redcolors = ['b', 'b', 'g', 'g', 'r', 'r', 'm', 'm']
redbinsize = 0.5
redbins = numpy.arange(0.0, 3.0 + redbinsize, redbinsize)
print redbinsize, redbins, redcolors
i = 1
# for each filter, use a different subplot
for f in filterlist:
ax = pylab.subplot(3, 2, i)
for redidx in range(len(redbins)):
condition = ((redshift >= redbins[redidx]) &
(redshift <= redbins[redidx] + redbinsize))
rcolor = redcolors[redidx]
pylab.plot(magscolors[colorlabels[i]][condition],
magscolors[colorlabels[i + 1]][f][condition],
rcolor + 'o')
i = i + 1
ax = pylab.subplot(3, 2, 7)
for redidx in range(len(redbins)):
condition = ((redshift >= redbins[redidx]) &
(redshift <= redbins[redidx] + redbinsize))
rcolor = redcolors[redidx]
pylab.plot(gi[condition],
magscolors[colorlabels[i + 1]][f][condition],
rcolor + 'o')
elif sedtype == 'galaxy':
print "# looking at galaxies"
# set the colors of data points based on their redshift
gallist = sedcolorkey
redcolors = ['b', 'b', 'g', 'g', 'r', 'r', 'm', 'm']
redbinsize = 0.3
redbins = numpy.arange(0.0, 1.7 + redbinsize, redbinsize)
print redbinsize, redbins, redcolors
i = 1
# for each filter, use a different subplot
for f in filterlist:
ax = pylab.subplot(3, 2, i)
j = 0
for g in gallist:
galbase, redshift = g.split('_')
redshift = float(redshift)
redidx = int(redshift / redbinsize)
pylab.plot(gi[j], dmags[f][j], redcolors[redidx])
j = j + 1
i = i + 1
ax = pylab.subplot(3, 2, 7)
j = 0
for g in gallist:
galbase, redshift = g.split('_')
redshift = float(redshift)
redidx = int(redshift / redbinsize)
pylab.plot(gi[j], dmags[f][j], redcolors[redidx])
j = j + 1
elif sedtype == 'mlt':
print "# looking at MLT"
# set the colors of data points based on their type
mltlist = sedcolorkey[0]
mlist = sedcolorkey[1]
llist = sedcolorkey[2]
tlist = sedcolorkey[3]
i = 1
for f in filterlist:
ax = pylab.subplot(3, 2, i)
for j in range(len(mltlist)):
if (mltlist[j] in mlist):
pylab.plot(magscolors[colorlabels[i]][condition],
magscolors[colorlabels[i + 1]][f][condition],
'bx')
elif (mltlist[j] in llist):
pylab.plot(magscolors[colorlabels[i]][condition],
magscolors[colorlabels[i + 1]][f][condition],
'gx')
elif (mltlist[j] in tlist):
pylab.plot(magscolors[colorlabels[i]][condition],
magscolors[colorlabels[i + 1]][f][condition],
'mx')
i = i + 1
ax = pylab.subplot(3, 2, 7)
for j in range(len(mltlist)):
if (mltlist[j] in mlist):
pylab.plot(gi[condition],
magscolors[colorlabels[i + 1]][f][condition], 'bx')
elif (mltlist[j] in llist):
pylab.plot(gi[condition],
magscolors[colorlabels[i + 1]][f][condition], 'gx')
elif (mltlist[j] in tlist):
pylab.plot(gi[condition],
magscolors[colorlabels[i + 1]][f][condition], 'mx')
elif sedtype == 'white_dwarf':
print "# looking at White Dwarf"
# set the colors of data points based on their type
wdlist = sedcolorkey[0]
hlist = sedcolorkey[1]
helist = sedcolorkey[2]
i = 1
for f in filterlist:
ax = pylab.subplot(3, 2, i)
for j in range(len(wdlist)):
if (wdlist[j] in hlist):
pylab.plot(magscolors[colorlabels[i]][condition],
magscolors[colorlabels[i + 1]][f][condition],
'y+')
elif (wdlist[j] in helist):
pylab.plot(magscolors[colorlabels[i]][condition],
magscolors[colorlabels[i + 1]][f][condition],
'y+')
i = i + 1
ax = pylab.subplot(3, 2, 7)
for j in range(len(wdlist)):
if (wdlist[j] in hlist):
pylab.plot(gi[condition],
magscolors[colorlabels[i + 1]][f][condition], 'y+')
elif (wdlist[j] in helist):
pylab.plot(gi[condition],
magscolors[colorlabels[i + 1]][f][condition], 'y+')
# set up generic items
for i in range(1, 7):
f = filterlist[i - 1]
ax = pylab.subplot(3, 2, i)
#pylab.xlabel("g-i")
#pylab.ylabel(r"$\Delta$ %s (mmag)" %(f))
def axis_formatter(x, pos):
return "%.1f" % (x)
formatter = pylab.FuncFormatter(axis_formatter)
ax.yaxis.set_major_formatter(formatter)
# set axes limits
if ylims == None:
pass
else:
try:
pylab.ylim(ylims[f][0], ylims[f][1])
except KeyError:
pass
# put a grid in the background
if newfig:
for i in range(1, 7):
ax = pylab.subplot(3, 2, i)
pylab.grid(True)
#pylab.suptitle(titletext)
#pylab.savefig("delta_mags2.eps", format='eps')
return
def __init__(self,
in_file,
vg_files=[1],
data_type=1,
projection='cyl',
color_map='jet',
time_zone=0,
plot_contours=False,
plot_center='t',
plot_meridians=True,
plot_parallels=True,
plot_terminator=True,
resolution='c',
points_of_interest=[],
save_file='',
run_quietly=False,
dpi=150,
parent=None):
self.run_quietly = run_quietly
self.dpi = float(dpi)
plot_parameters = VOAFile((in_file + '.voa'))
plot_parameters.parse_file()
if (plot_parameters.get_projection() != 'cyl'):
print _("Error: Only lat/lon (type 1) input files are supported")
sys.exit(1)
grid = plot_parameters.get_gridsize()
self.image_defs = VOAAreaPlot.IMG_TYPE_DICT[int(data_type)]
# TODO This needs a little more work... what if the pcenter card is not specified
if plot_center == 'p':
plot_centre_location = plot_parameters.get_location(
plot_parameters.P_CENTRE)
else:
plot_centre_location = plot_parameters.get_location(
plot_parameters.TX_SITE)
self.points_of_interest = [plot_centre_location]
if len(points_of_interest) > 0:
self.points_of_interest.extend(points_of_interest)
imageBuf = P.zeros([grid, grid], float)
area_rect = plot_parameters.get_area_rect()
points = P.zeros([grid, grid], float)
lons = P.zeros(grid * grid, float)
lats = P.zeros(grid * grid, float)
lons = P.arange(area_rect.get_sw_lon(),
area_rect.get_ne_lon() + 0.001,
(area_rect.get_ne_lon() - area_rect.get_sw_lon()) /
float(grid - 1))
lats = P.arange(area_rect.get_sw_lat(),
area_rect.get_ne_lat() + 0.001,
(area_rect.get_ne_lat() - area_rect.get_sw_lat()) /
float(grid - 1))
colString = 'P.cm.' + color_map
colMap = eval(colString)
self.subplots = []
matplotlib.rcParams['axes.edgecolor'] = 'gray'
matplotlib.rcParams['axes.facecolor'] = 'white'
matplotlib.rcParams['figure.facecolor'] = 'white'
#matplotlib.rcParams['figure.figsize'] = (6, 10)
# matplotlib.rcParams['figure.subplot.hspace'] = 0.45
# matplotlib.rcParams['figure.subplot.wspace'] = 0.35
# matplotlib.rcParams['figure.subplot.right'] = 0.85
colorbar_fontsize = 8
self.num_rows = 1
self.main_title_fontsize = 18
matplotlib.rcParams['legend.fontsize'] = 12
matplotlib.rcParams['axes.labelsize'] = 10
matplotlib.rcParams['axes.titlesize'] = 10
matplotlib.rcParams['xtick.labelsize'] = 8
matplotlib.rcParams['ytick.labelsize'] = 8
# matplotlib.rcParams['figure.subplot.top'] = 0.8 # single figure plots have a larger title so require more space at the top.
self.fig = Figure(figsize=(9, 5))
# self.main_title_label = self.fig.suptitle(unicode(self.image_defs['title'],'utf-8'), fontsize=self.main_title_fontsize)
ax = self.fig.add_subplot(111, axisbg='white')
self.subplots.append(ax)
ax.label_outer()
#print "opening: ",(in_file+'.vg'+str(vg_files[plot_ctr]))
plot_ctr = 0
# dir_name = os.path.dirname(in_file)
# if not os.path.exists(dir_name):
# os.makedirs(dir_name)
vgFile = open(in_file + '.vg' + str(vg_files[plot_ctr]))
pattern = re.compile(r"[a-z]+")
for line in vgFile:
match = pattern.search(line)
if not match:
value = float(line[int(self.image_defs['first_char']
):int(self.image_defs['last_char'])])
# TODO Does this need to be normalised here if it's also being done in the plot?
value = max(self.image_defs['min'], value)
value = min(self.image_defs['max'], value)
#if value < self.image_defs[2] : value = self.image_defs[2]
#if value > self.image_defs[3] : value = self.image_defs[3]
points[int(line[3:6]) - 1][int(line[0:3]) - 1] = value
vgFile.close()
map = Basemap(\
llcrnrlon=area_rect.get_sw_lon(), llcrnrlat=area_rect.get_sw_lat(),\
urcrnrlon=area_rect.get_ne_lon(), urcrnrlat=area_rect.get_ne_lat(),\
projection=projection,\
lat_0=plot_centre_location.get_latitude(),\
lon_0=plot_centre_location.get_longitude(),\
resolution=resolution,
ax=ax)
map.drawcoastlines(color='black')
map.drawcountries(color='grey')
map.drawmapboundary(color='black', linewidth=1.0)
warped = ma.zeros((grid, grid), float)
warped, warped_lon, warped_lat = map.transform_scalar(
points,
lons,
lats,
grid,
grid,
returnxy=True,
checkbounds=False,
masked=True)
warped = warped.filled(self.image_defs['min'] - 1.0)
colMap.set_under(color='k', alpha=0.0)
im = map.imshow(warped,
cmap=colMap,
extent=(-180, 180, -90, 90),
origin='lower',
norm=P.Normalize(clip=False,
vmin=self.image_defs['min'],
vmax=self.image_defs['max']))
#######################
# Plot greyline
#######################
if plot_terminator:
the_sun = Sun()
the_month = plot_parameters.get_month(vg_files[plot_ctr] - 1)
the_day = plot_parameters.get_day(vg_files[plot_ctr] - 1)
the_hour = plot_parameters.get_utc(vg_files[plot_ctr] - 1)
if (the_day == 0):
the_day = 15
the_year = datetime.date.today().year
num_days_since_2k = the_sun.daysSince2000Jan0(
the_year, the_month, the_day)
res = the_sun.sunRADec(num_days_since_2k)
declination = res[1]
if (declination == 0.0):
declination = -0.001
tau = the_sun.computeGHA(the_day, the_month, the_year, the_hour)
if declination > 0:
terminator_end_lat = area_rect.get_sw_lat()
else:
terminator_end_lat = area_rect.get_ne_lat()
terminator_lat = [terminator_end_lat]
terminator_lon = [area_rect.get_sw_lon()]
for i in range(int(area_rect.get_sw_lon()),
int(area_rect.get_ne_lon()),
1) + [int(area_rect.get_ne_lon())]:
longitude = i + tau
tan_lat = -the_sun.cosd(longitude) / the_sun.tand(declination)
latitude = the_sun.atand(tan_lat)
latitude = max(latitude, area_rect.get_sw_lat())
latitude = min(latitude, area_rect.get_ne_lat())
xpt, ypt = map(i, latitude)
terminator_lon.append(xpt)
terminator_lat.append(ypt)
terminator_lon.append(area_rect.get_ne_lon())
terminator_lat.append(terminator_end_lat)
#This is a little simplistic and doesn't work for ortho plots....
ax.plot(terminator_lon, terminator_lat, color='grey', alpha=0.75)
ax.fill(terminator_lon,
terminator_lat,
facecolor='grey',
alpha=0.5)
tau = -tau
if (tau > 180.0):
tau = tau - 360.0
if (tau < -180.0):
tau = tau + 360.0
#Plot the position of the sun (if it's in the coverage area)
if area_rect.contains(declination, tau):
xpt, ypt = map(tau, declination)
#sbplt_ax.plot([xpt],[ypt],'yh')
ax.plot([xpt], [ypt], 'yh')
##########################
# Points of interest
##########################
for location in self.points_of_interest:
if area_rect.contains(location.get_latitude(),
location.get_longitude()):
xpt, ypt = map(location.get_longitude(),
location.get_latitude())
ax.plot([xpt], [ypt], 'ro')
ax.text(xpt + 100000, ypt + 100000, location.get_name())
if plot_meridians:
if (area_rect.get_lon_delta() <= 90.0):
meridians = P.arange(-180, 190.0, 10.0)
elif (area_rect.get_lon_delta() <= 180.0):
meridians = P.arange(-180.0, 210.0, 30.0)
else:
meridians = P.arange(-180, 240.0, 60.0)
if ((projection == 'ortho') or (projection == 'vandg')):
map.drawmeridians(meridians)
else:
map.drawmeridians(meridians, labels=[1, 1, 0, 1])
if plot_parallels:
if (area_rect.get_lat_delta() <= 90.0):
parallels = P.arange(-90.0, 120.0, 60.0)
else:
parallels = P.arange(-90.0, 120.0, 30.0)
if ((projection == 'ortho') or (projection == 'vandg')):
map.drawparallels(parallels)
else:
map.drawparallels(parallels, labels=[1, 0, 0, 1])
if plot_contours:
map.contour(warped_lon,
warped_lat,
warped,
self.image_defs['y_labels'],
linewidths=1.0,
colors='k',
alpha=0.5)
#add a title
title_str = plot_parameters.get_plot_description_string(
vg_files[plot_ctr] - 1, self.image_defs['plot_type'], time_zone)
title_str = title_str + "\n" + plot_parameters.get_detailed_plot_description_string(
vg_files[plot_ctr] - 1)
self.subplot_title_label = ax.set_title(title_str)
# Add a colorbar on the right hand side, aligned with the
# top of the uppermost plot and the bottom of the lowest
# plot.
pos = [0.91, 0.19, 0.02, 0.62]
self.cb_ax = self.fig.add_axes(pos)
cb = self.fig.colorbar(im,
cax=self.cb_ax,
orientation='vertical',
ticks=self.image_defs['y_labels'],
format=P.FuncFormatter(
eval('self.' +
self.image_defs['formatter'])))
cb.set_label(unicode(self.image_defs['title'], 'utf-8'))
#print self.image_defs['y_labels']
for t in self.cb_ax.get_yticklabels():
t.set_fontsize(colorbar_fontsize)
canvas = FigureCanvasAgg(self.fig)
if save_file:
self.save_plot(canvas, save_file)
def main():
if os.name == 'nt':
dataDir = os.path.expanduser('~') + '\\Appdata\\Roaming\\Freeorion'
elif os.name == 'posix':
dataDir = os.environ.get(
'XDG_DATA_HOME',
os.environ.get('HOME', "") + "/.local/share") + "/freeorion"
else:
dataDir = os.environ.get('HOME', "") + "/.freeorion"
graphDir = dataDir
fileRoot = "game1"
saveFile = True
turnsP = None
rankings = []
doPlotTypes = ["PP + 2RP"] + ["PP"] + ["RP"] + ["RP_Ratio"
] # +[ "ShipCount"]
allData = {}
species = {}
empireColors = {}
playerName = "Player"
logfiles = sorted(glob(dataDir + os.sep + "A*.log"))
A1log = glob(dataDir + os.sep + "AI_1.log")
if not os.path.exists(dataDir + os.sep + "freeorion.log"):
print "can't find freeorion.log"
elif A1log and (A1log[0] in logfiles) and (
os.path.getmtime(dataDir + os.sep + "freeorion.log") <
os.path.getmtime(A1log[0]) - 300):
print "freeorion.log file is stale ( more than 5 minutes older than AI_1.log) and will be skipped"
else:
data, details = parse_file(dataDir + os.sep + "freeorion.log", False)
if len(data.get('PP', [])) > 0:
allData[playerName] = data
empireColors[playerName] = details['color']
logfiles = sorted(glob(dataDir + os.sep + "A*.log"))
A1log = glob(dataDir + os.sep + "AI_1.log")
if A1log and A1log[0] in logfiles:
A1Time = os.path.getmtime(A1log[0])
for path in logfiles[::-1]:
logtime = os.path.getmtime(path)
# print "path ", path, "logtime diff: %.1f"%(A1Time -logtime)
if logtime < A1Time - 300:
del logfiles[logfiles.index(path)]
print "skipping stale logfile ", path
for lfile in logfiles:
try:
data, details = parse_file(lfile, True)
allData[details['name']] = data
empireColors[details['name']] = details['color']
species[details['name']] = details['species']
except:
print "error processing %s" % lfile
print "Error: exception triggered and caught: ", traceback.format_exc(
)
print
for plotType in doPlotTypes:
print "--------------------"
if plotType == "PP":
caption = "Production"
elif plotType == "RP":
caption = "Research"
elif plotType == "RP_Ratio":
caption = "Res:Prod Ratio"
elif plotType == "RP_Ratio":
caption = "Res:Prod Ratio"
else:
caption = plotType
pylab.figure(figsize=(10, 6))
ax = pylab.gca()
ymin = 9999
ymax = 0
rankings = []
turns = []
pdata = allData.get(playerName, {}).get(plotType, [])
if pdata:
ymin = min(ymin, min(pdata))
ymax = max(ymax, max(pdata))
turns = allData.get(playerName, {}).get('turnsP', [])
for empireName, data in allData.items():
if empireName == playerName:
continue
adata = data.get(plotType, [])
if adata:
rankings.append((adata[-1], empireName))
thisMin = min(adata)
if thisMin > 0:
ymin = min(ymin, thisMin)
ymax = max(ymax, max(adata))
if not turns:
turns = data.get('turnsP', [])
if not turns:
if len(allData) == 0:
break
turns = range(1, len(allData.values()[0].get(plotType, [])) + 1)
rankings.sort()
pylab.xlabel('Turn')
pylab.ylabel(plotType)
pylab.title(caption + ' Progression')
if ymax >= 100:
ax.set_yscale('log', basey=10)
if playerName not in allData:
print "\t\t\tcan't find playerData in allData\n"
else:
if playerName in empireColors:
turnsP = allData[playerName].get("turnsP", [])
thisData = allData.get(playerName, {}).get(plotType, [])
print "plotting with color for player: ", playerName, "data min/max: ", min(
allData[playerName].get(plotType, [])), ' | ', max(
allData[playerName].get(plotType, []))
pylab.plot(turnsP,
thisData,
'o-',
color=empireColors[playerName],
label="%s - %.1f" % (playerName, sum(thisData)),
linewidth=2.0)
else:
print "plotting withOUT color for player: ", playerName, "data min/max: ", min(
allData[playerName].get(plotType, [])), ' | ', max(
allData[playerName].get(plotType, []))
pylab.plot(turnsP,
allData[playerName].get(plotType, []),
'bx-',
label=playerName,
linewidth=2.0)
print "Ranked by ", plotType
for rank, name in rankings[::-1]:
print name
if name in empireColors:
adata = allData[name].get(plotType, [])
pylab.plot(range(turns[0], turns[0] + len(adata)),
adata,
color=empireColors[name],
label="%s: %s - %.1f" %
(name, species[name], sum(adata)),
linewidth=2.0)
else:
print "can't find empire color for ", name
# pylab.plot(range(turns[0], turns[0]+len(allData[name])), allData[name].get(plotType, []), label="(%d) "%(empires.index(name)+1)+name+" : "+species[name], linewidth=2.0)
# legend(loc='upper left',prop={"size":'medium'})
pylab.legend(loc='upper left', prop={"size": 9}, labelspacing=0.2)
pylab.xlabel('Turn')
pylab.ylabel(plotType)
pylab.title(caption + ' Progression ')
x1, x2, y1, y2 = pylab.axis()
newY2 = y2
if plotType in ["PP + 2RP", "PP", "RP", "ShipCount"]:
for yi in range(1, 10):
if 1.05 * ymax < yi * y2 / 10:
newY2 = yi * y2 / 10
break
print "y1: %.1f ; ymin: %.1f ; newY2/100: %.1f" % (y1, ymin,
newY2 / 100)
y1 = max(y1, 4, ymin, newY2 / 100)
pylab.axis((x1, x2, y1, newY2))
pylab.grid(b=True, which='major', color='0.25', linestyle='-')
pylab.grid(b=True, which='minor', color='0.1', linestyle='--')
ax.yaxis.set_minor_formatter(pylab.FuncFormatter(show_only_some))
ax.yaxis.set_ticks_position('right')
ax.yaxis.set_ticks_position('both')
ax.tick_params(labelleft='on'
) # for matplotlib versions where 'both' doesn't work
# ax.tick_params(labelright='on')
if saveFile:
pylab.savefig(graphDir + os.sep + plotType + "_" + fileRoot +
".png")
pylab.show()
def analyze_interactions(df):
print("\n=== Interactions table, original shape={} ===\n".format(df.shape))
df = df.copy()
df['ITEM_ID'] = df['ITEM_ID'].astype(str)
df['USER_ID'] = df['USER_ID'].astype(str)
df.index = df["TIMESTAMP"].values.astype("datetime64[s]")
na_rate = df[INTERACTIONS_REQUIRED_FIELDS].isnull().any(axis=1).mean()
print("missing rate in fields", INTERACTIONS_REQUIRED_FIELDS, na_rate)
if na_rate > NA_RATE_THRESHOLD:
warnings.warn(
"High data missing rate for required fields ({:.1%})!".format(
na_rate))
df = df.dropna(subset=INTERACTIONS_REQUIRED_FIELDS)
print("dropna shape", df.shape)
dup_rate = (df.groupby(INTERACTIONS_REQUIRED_FIELDS).size() -
1.0).sum() / df.shape[0]
print("duplication rate", dup_rate)
if dup_rate > DUP_RATE_THRESHOLD:
warnings.warn("""
High duplication rate ({:.1%})!
Only one event can be taken at the same (user,item,timestamp) index.
""".format(dup_rate))
df = df.drop_duplicates(subset=INTERACTIONS_REQUIRED_FIELDS)
print("drop_duplicates shape", df.shape)
repeat_rate = (df.groupby(["USER_ID", "ITEM_ID"]).size() -
1.0).sum() / df.shape[0]
print("user item repeat rate", repeat_rate)
if repeat_rate > REPEAT_RATE_THRESHOLD:
warnings.warn("""
High rate of repeated consumptions ({:.1%})!
We would not do anything, but it may beneficial to
(1) consider keeping only the last interaction between the same user-item pair,
(2) consider if the ITEM_IDs have collisions, and/or
(3) use high-order hierarchical models.
""".format(repeat_rate))
summary = describe_dataframe(df, 'interactions table')
print("\n=== Hourly activity pattern ===")
print(df.groupby(df.index.hour).size())
print("\n=== Day of week activity pattern ===")
print(df.groupby(df.index.dayofweek).size())
plot_patterns = {
"date": df.index.date,
"hour": df.index.hour,
"dayofweek": df.index.dayofweek
}
for k, v in plot_patterns.items():
pl.plot(df.groupby(v).size(), '.-')
pl.gcf().autofmt_xdate()
pl.title("Activity pattern by %s" % k)
pl.grid()
pl.show()
print("\n=== Temporal shift analysis ===\n")
print(
"Sorting and removing repeated user-items for temporal shift analysis..."
)
df.sort_index(inplace=True, kind='mergesort')
df_dedup = df.drop_duplicates(['USER_ID', 'ITEM_ID'], keep='last')
print("\n=== Temporal shift - retrain frequency ===\n")
for method in TEMPORAL_LOSS_METHODS:
bootstrap_avg = []
past_fut_avg = []
for freq in RETRAIN_FREQUENCY:
_, _, _bs_avg, loss_fmt = compute_bootstrap_loss(
df_dedup, freq, method)
_, _, _ts_avg, loss_fmt = compute_temporal_loss(
df_dedup, freq, method, 1)
bootstrap_avg.append(_bs_avg)
past_fut_avg.append(_ts_avg)
pl.plot(RETRAIN_FREQUENCY,
bootstrap_avg,
'.--',
label='same-period bootstrap')
pl.plot(RETRAIN_FREQUENCY,
past_fut_avg,
'.-',
label='lagged popularity')
pl.legend()
pl.xlabel('retrain frequency')
pl.title(method + ' loss at different frequencies')
pl.grid()
pl.gca().yaxis.set_major_formatter(
pl.FuncFormatter(lambda y, _: loss_fmt.format(y)))
pl.show()
print("\n=== Temporal shift - history cutoffs ===\n")
for method in TEMPORAL_LOSS_METHODS:
for freq in TEMPORAL_FREQUENCY:
bootstrap_loss, _, avg_loss, loss_fmt = compute_bootstrap_loss(
df_dedup, freq, method)
pl.plot(bootstrap_loss.iloc[-TEMPORAL_PLOT_LIMIT:],
'.--',
label='boostrap baseline={}'.format(
loss_fmt.format(avg_loss)))
for hist_len in ROLLING_HISTORY_LEN:
temporal_loss, df_wgt, avg_loss, loss_fmt = compute_temporal_loss(
df_dedup, freq, method, hist_len)
pl.plot(temporal_loss.iloc[-TEMPORAL_PLOT_LIMIT:],
'.-',
label='hist={} * {}, avg={}'.format(
hist_len, freq, loss_fmt.format(avg_loss)))
pl.gca().yaxis.set_major_formatter(
pl.FuncFormatter(lambda y, _: loss_fmt.format(y)))
pl.title('{} {} from rolling history (lower is better)'.format(
freq, method))
pl.grid()
pl.gcf().autofmt_xdate()
pl.legend(loc='upper left')
pl.twinx()
pl.plot(df_wgt.iloc[-TEMPORAL_PLOT_LIMIT:],
color='grey',
lw=3,
ls='--',
alpha=0.5)
pl.legend(['activity density'], loc='upper right')
pl.show()
print("\n=== session time delta describe ===")
user_time_delta = df.groupby('USER_ID')["TIMESTAMP"].transform(
pd.Series.diff).dropna()
user_time_delta.sort_values(ascending=False, inplace=True)
print(user_time_delta.describe())
plot_loglog(user_time_delta, 'session time delta', show=False)
for k, v in TIMEDELTA_REFERENCES:
if pl.ylim()[0] < v < pl.ylim()[1]:
pl.plot(pl.xlim(), [v, v], '--')
pl.text(pl.xlim()[0], v, k)
pl.show()
user_time_span = df.groupby('USER_ID')["TIMESTAMP"].apply(
lambda x: max(x) - min(x))
user_time_span.sort_values(ascending=False, inplace=True)
print("=== user time span describe ===")
print(user_time_span.describe())
plot_loglog(user_time_span, 'user time span', show=False)
for k, v in TIMEDELTA_REFERENCES:
if pl.ylim()[0] < v < pl.ylim()[1]:
pl.plot(pl.xlim(), [v, v], '--')
pl.text(pl.xlim()[0], v, k)
pl.show()
def __init__(self,
data_file,
plot_groups=[1],
data_type=2,
color_map='jet',
plot_contours=False,
plot_label="",
plot_bands=None,
time_zone=0,
plot_max_freq=30.0,
run_quietly=False,
save_file='',
dpi=150,
parent=None):
self.data_type = data_type
self.run_quietly = run_quietly
self.dpi = dpi
self.df = VOAOutFile(data_file,
time_zone=time_zone,
data_type=self.data_type,
quiet=run_quietly)
self.image_defs = self.IMG_TYPE_DICT[self.data_type]
color_map = eval('P.cm.' + color_map)
if plot_groups[0] == 'a':
num_grp = self.df.get_number_of_groups()
plot_groups = range(0, num_grp)
self.subplots = []
number_of_subplots = len(plot_groups)
matplotlib.rcParams['axes.edgecolor'] = 'gray'
matplotlib.rcParams['axes.facecolor'] = 'white'
matplotlib.rcParams['axes.grid'] = True
matplotlib.rcParams['figure.facecolor'] = 'white'
matplotlib.rcParams['legend.fancybox'] = True
matplotlib.rcParams['legend.shadow'] = True
matplotlib.rcParams['figure.subplot.hspace'] = 0.45
matplotlib.rcParams['figure.subplot.wspace'] = 0.35
matplotlib.rcParams['figure.subplot.right'] = 0.85
colorbar_fontsize = 12
if number_of_subplots <= 1:
self.num_rows = 1
self.main_title_fontsize = 24
matplotlib.rcParams['legend.fontsize'] = 12
matplotlib.rcParams['axes.labelsize'] = 12
matplotlib.rcParams['axes.titlesize'] = 8
matplotlib.rcParams['xtick.labelsize'] = 10
matplotlib.rcParams['ytick.labelsize'] = 10
matplotlib.rcParams[
'figure.subplot.top'] = 0.79 # single figure plots have a larger title so require more space at the top.
self.x_axes_ticks = P.arange(0, 25, 2)
elif ((number_of_subplots >= 2) and (number_of_subplots <= 6)):
self.num_rows = 2
self.main_title_fontsize = 18
matplotlib.rcParams['legend.fontsize'] = 10
matplotlib.rcParams['axes.labelsize'] = 10
matplotlib.rcParams['axes.titlesize'] = 11
matplotlib.rcParams['xtick.labelsize'] = 8
matplotlib.rcParams['ytick.labelsize'] = 8
self.x_axes_ticks = P.arange(0, 25, 4)
else:
self.num_rows = 3
self.main_title_fontsize = 16
matplotlib.rcParams['legend.fontsize'] = 8
matplotlib.rcParams['axes.labelsize'] = 8
matplotlib.rcParams['axes.titlesize'] = 10
matplotlib.rcParams['xtick.labelsize'] = 6
matplotlib.rcParams['ytick.labelsize'] = 6
self.x_axes_ticks = P.arange(0, 25, 4)
self.num_cols = int(
math.ceil(float(number_of_subplots) / float(self.num_rows)))
self.fig = Figure(figsize=(7, 6.5))
self.main_title_label = self.fig.suptitle(
plot_label + unicode(self.image_defs['title'], 'utf-8'),
fontsize=self.main_title_fontsize)
for chan_grp in plot_groups:
(group_name, group_info, fot, muf, hpf,
image_buffer) = self.df.get_group_data(chan_grp)
ax = self.fig.add_subplot(self.num_rows, self.num_cols,
plot_groups.index(chan_grp) + 1)
self.subplots.append(ax)
if number_of_subplots > 4:
#save a little space by only labelling the outer edges of the plot
ax.label_outer()
_sign = '+' if (time_zone >= 0) else ''
self.x_label = ax.set_xlabel(
_('Time (UTC%(sig)s%(tz)s)') % {
'sig': _sign,
'tz': time_zone
})
self.y_label = ax.set_ylabel(_('Frequency (MHz)'))
## Autoscale y (frequency axis)
if (plot_max_freq == self.AUTOSCALE):
y_max = math.ceil(max(muf) / 5.0) * 5.0
y_max = min(plot_max_freq, 30.0)
y_max = max(plot_max_freq, 5.0)
else:
y_max = math.ceil(plot_max_freq / 5.0) * 5.0
#resize the image
image_buffer = image_buffer[0:y_max - 1, :]
y_ticks = [2, 5]
for y_tick_value in P.arange(10, y_max + 1, 5):
y_ticks.append(y_tick_value)
ax.plot(range(0, 25), muf, 'r-', range(0, 25), fot, 'g-')
ax.set_ylim([2, y_max])
ax.set_xticks(self.x_axes_ticks)
ax.set_yticks(y_ticks)
self.add_legend(ax)
title_str = group_info.strip()
if number_of_subplots > 1:
title_str = self.get_small_title(title_str)
self.subplot_title_label = ax.set_title(title_str,
multialignment='left',
**self.mono_font)
if (self.data_type > 0):
im = ax.imshow(image_buffer,
interpolation='bicubic',
extent=(0, 24, 2, y_max),
origin='lower',
cmap=color_map,
alpha=0.95,
norm=P.Normalize(clip=False,
vmin=self.image_defs['min'],
vmax=self.image_defs['max']))
if plot_contours:
ax.contour(image_buffer,
self.image_defs['y_labels'],
extent=(0, 24, 2, y_max),
linewidths=1.0,
colors='k',
alpha=0.6)
if plot_bands:
for a, b in plot_bands:
ax.axhspan(a, b, alpha=0.5, ec='k', fc='k')
if (self.data_type > 0):
self.cb_ax = self.fig.add_axes(self.get_cb_axes())
self.fig.colorbar(im,
cax=self.cb_ax,
orientation='vertical',
format=P.FuncFormatter(
eval('self.' +
self.image_defs['formatter'])))
for t in self.cb_ax.get_yticklabels():
t.set_fontsize(colorbar_fontsize)
canvas = FigureCanvasGTKAgg(self.fig)
self.fig.canvas.mpl_connect('draw_event', self.on_draw)
canvas.show()
if save_file:
self.save_plot(canvas, save_file)
if not self.run_quietly:
# TODO consider using a scrolled pane here...
dia = VOAPlotWindow('pythonProp - ' + self.image_defs['title'],
canvas,
parent,
dpi=self.dpi)
return
condition = ((ur >= urbins[urbidx]) & (ur <= urbins[urbidx]+urbinsize))
coloridx = urcolors[urbidx]
pylab.plot(gi[condition], shifts[f][condition], coloridx+'.')
"""
for metidx in range(len(metbins)):
condition = ((metallicity >= metbins[metidx]) &
(metallicity <= metbins[metidx] + metbinsize))
coloridx = urcolors[metidx]
pylab.plot(gi[condition], shifts[f][condition], coloridx + '.')
pylab.xlabel("g-i")
pylab.ylabel(r"$\Delta$ %s (mmag)" % (f))
def axis_formatter(x, pos):
return "%.1f" % (x)
formatter = pylab.FuncFormatter(axis_formatter)
ax.yaxis.set_major_formatter(formatter)
if f == 'u':
pylab.ylim(-1, 1)
#elif f=='g':
# pylab.ylim(-5, 3)
#elif f=='i':
# pylab.ylim(-1, 1)
elif f == 'y':
pylab.ylim(-2, 1)
else:
pylab.ylim(-1, 1)
pylab.grid(True)
i = i + 1
#pylab.figtext(0.2, 0.95, "Change in magnitude for %s atmosphere and filter shift of %s" %(atmo_choice, shift_perc, "%"))
pylab.figtext(
def setYFormatter(self, formatter):
"""
Set the y-axis formatter used by this plot to the given function.
"""
self.yFormatter = pylab.FuncFormatter(formatter)
def setMeterFormat(self):
self.subplot.xaxis.set_major_formatter(
PL.FuncFormatter(self.meter_format))
self.subplot.yaxis.set_major_formatter(
PL.FuncFormatter(self.meter_format))