def render(self, t = 'png'):
canvas = FigureCanvas(self.fig)
output = StringIO.StringIO()
with Timer() as duration:
if t == 'svg':
canvas.print_svg(output)
if t == 'pdf':
canvas.print_pdf(output)
else:
canvas.print_png(output)
app.logger.debug("Generating %s took %d ms", t.upper(), duration.miliseconds())
return output.getvalue()
def plot_3d(request):
fig = Figure(figsize=(12, 7), edgecolor='white', facecolor='white')
fig.subplots_adjust(top=0.95)
fig.subplots_adjust(bottom=0.07)
fig.subplots_adjust(left=0.06)
fig.subplots_adjust(right=0.98)
plot_type = request.GET.get('plot_type', 'png')
available_types = []
available_measurement_types = measurement_type.objects.all()
for item in available_measurement_types:
if item.data_type == 'FLOAT':
type_row = []
type_row.append(item.measurement_type_id)
type_row.append(item.measurement_type_name)
available_types.append(type_row)
type_id_01 = request.GET.get('type_id_01', available_types[0][1])
type_id_02 = request.GET.get('type_id_02', available_types[1][1])
#import pdb; pdb.set_trace() # debugging code
measurement_type_item1 = measurement_type.objects.filter(measurement_type_name = type_id_01)
measurement_type_item2 = measurement_type.objects.filter(measurement_type_name = type_id_02)
unit1 = measurement_type_item1[0].units
unit2 = measurement_type_item2[0].units
# This is the grb subset that passes all the user cuts...
#grb_table = measurement.objects.filter(date__range = (x_min, x_max)) # there can be duplicates here...need to fix this later
grb_set, parameters = get_grb_sample(request)
# both measurements must have same number of rows...
# if one item is missing then the other item should be ignored...
x_log = request.GET.get('x_log', 'False')
y_log = request.GET.get('y_log', 'False')
x = []
y = []
for grb_item in grb_set:
measurement_value1 = grb_item[type_id_01]
measurement_value2 = grb_item[type_id_02]
if (measurement_value1 != '-') and (measurement_value2 != '-'):
if (measurement_value1 > 0) and (x_log == 'True'):
x.append(math.log10(measurement_value1))
else:
x.append(measurement_value1)
if (measurement_value2 > 0) and (y_log == 'True'):
y.append(math.log10(measurement_value2))
else:
y.append(measurement_value2)
#print x
#print y
#import pdb; pdb.set_trace() # debugging code
#import numpy as np
#x = np.random.randn(1000)
if x_log == 'True':
if len(unit1) > 1:
if unit1 != 'None':
x_label = 'log ' + request.GET.get('x_label', type_id_01) + ' (' + unit1 + ')'
else:
x_label = 'log ' + request.GET.get('x_label', type_id_01)
else:
x_label = 'log ' + request.GET.get('x_label', type_id_01)
else:
if len(unit1) > 1:
if unit1 != 'None':
x_label = request.GET.get('x_label', type_id_01) + ' (' + unit1 + ')'
else:
x_label = request.GET.get('x_label', type_id_01)
else:
x_label = request.GET.get('x_label', type_id_01)
if y_log == 'True':
if len(unit2) > 1:
if unit2 != 'None':
y_label = 'log ' + request.GET.get('y_label', type_id_02) + ' (' + unit2 + ')'
else:
y_label = 'log ' + request.GET.get('y_label', type_id_02)
else:
y_label = 'log ' + request.GET.get('y_label', type_id_02)
else:
if len(unit2) > 1:
if unit2 != 'None':
y_label = request.GET.get('y_label', type_id_02) + ' (' + unit2 + ')'
else:
y_label = request.GET.get('y_label', type_id_02)
else:
y_label = request.GET.get('y_label', type_id_02)
num_points = len(x)
sample_size_str = " (Number of GRB: " + str(num_points) + ")"
title = request.GET.get('title', 'Correlation Plot') + sample_size_str
ax=fig.add_subplot(111)
ax.plot(x, y, 'ro')
#n, bins, patches = ax.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
#if (y_log == 'True'):
# n, bins, patches = ax.hist(x, bin_num, facecolor='green', alpha=0.75, log=True)
#else:
# n, bins, patches = ax.hist(x, bin_num, facecolor='green', alpha=0.75)
#import pdb; pdb.set_trace() # debugging code
x_scale_min = int(request.GET.get('x_min', 0))
x_scale_max = int(request.GET.get('x_max', 0))
y_scale_min = int(request.GET.get('y_min', 0))
y_scale_max = int(request.GET.get('y_max', 0))
if (x_scale_min < x_scale_max) and (y_scale_min < y_scale_max):
#import pdb; pdb.set_trace() # debugging code
ax.axis([x_scale_min, x_scale_max, y_scale_min, y_scale_max])
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
ax.set_title(title)
#import ipdb; ipdb.set_trace() # debugging code
statistics_str = "Correlation Coefficient : {0:.3}".format(ds.corr(x, y))
#print statistics_str
ax.text(0.05, 0.9, statistics_str,
fontsize=15,
#color='blue',
bbox={'facecolor':'yellow', 'pad':10, 'alpha':0.65},
horizontalalignment='left',
verticalalignment='center',
transform = ax.transAxes)
canvas=FigureCanvas(fig)
if plot_type == 'png':
response=HttpResponse(content_type='image/png')
canvas.print_png(response)
if plot_type == 'pdf':
response = HttpResponse(mimetype='application/pdf')
response['Content-Disposition'] = 'attachment; filename="histo.pdf"'
#response=django.http.HttpResponse(content_type='image/pdf')
#response=['Content-Disposition'] = 'attachment; filename=plot.pdf'
canvas.print_pdf(response)
if plot_type == 'text':
response = HttpResponse(mimetype='text/csv')
response['Content-Disposition'] = 'attachment; filename="plot.csv"'
writer = csv.writer(response)
if num_points > 0:
writer.writerow(['X Values'])
for i in range(num_points):
writer.writerow([x[i]])
writer.writerow(['Y Values'])
for i in range(num_points):
writer.writerow([y[i]])
else:
writer.writerow(["No data!"])
return response
def histo(request):
fig = Figure(figsize=(12, 7), edgecolor='white', facecolor='white')
fig.subplots_adjust(top=0.95)
fig.subplots_adjust(bottom=0.07)
fig.subplots_adjust(left=0.06)
fig.subplots_adjust(right=0.98)
plot_type = request.GET.get('plot_type', 'png')
bin_num = int(request.GET.get('bin_num', '25'))
m_type = str(request.GET.get('measurement_type', 'T90'))
grb_set, parameters = get_grb_sample(request)
#import ipdb; ipdb.set_trace() # debugging code
# following code will improve the plotting speed...
measurement_type_item = measurement_type.objects.filter(measurement_type_name = m_type)
unit = measurement_type_item[0].units
#measurement_table = measurement.objects.filter(measurement_type=measurement_type_item[0])
#import pdb; pdb.set_trace() # debugging code
x_log = request.GET.get('x_log', 'False')
y_log = request.GET.get('y_log', 'False')
x = []
for item in grb_set:
measurement_value = item[m_type]
if measurement_value != '-':
if (measurement_value > 0) and (x_log == 'True'):
x.append(math.log10(measurement_value))
else:
x.append(measurement_value)
#print x
#import ipdb; ipdb.set_trace() # debugging code
#import numpy as np
#x = np.random.randn(1000)
num_points = len(x)
sample_size_str = " (Number of GRBs: " + str(num_points) + ")"
if (x_log == 'True'):
if len(unit) > 1:
if unit != 'None':
x_label = 'log ' + request.GET.get('x_label', m_type) + ' (' + unit + ')'
else:
x_label = 'log ' + request.GET.get('x_label', m_type)
else:
x_label = 'log ' + request.GET.get('x_label', m_type)
else:
if len(unit) > 1:
if unit != 'None':
x_label = request.GET.get('x_label', m_type) + ' (' + unit + ')'
else:
x_label = request.GET.get('x_label', m_type)
else:
x_label = request.GET.get('x_label', m_type)
y_label = request.GET.get('y_label', 'Counts')
title = request.GET.get('title', 'Histogram') + sample_size_str
ax=fig.add_subplot(111)
#ax.plot(x, y, 'r', linewidth=2.0)
#n, bins, patches = ax.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
if (y_log == 'True'):
n, bins, patches = ax.hist(x, bin_num, facecolor='green', alpha=0.75, log=True)
else:
n, bins, patches = ax.hist(x, bin_num, facecolor='green', alpha=0.75)
x_scale_min = int(request.GET.get('x_min', 0))
x_scale_max = int(request.GET.get('x_max', 0))
y_scale_min = int(request.GET.get('y_min', 0))
y_scale_max = int(request.GET.get('y_max', 0))
if (x_scale_min < x_scale_max) and (y_scale_min < y_scale_max):
#import pdb; pdb.set_trace() # debugging code
ax.axis([x_scale_min, x_scale_max, y_scale_min, y_scale_max])
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
ax.set_title(title)
#import ipdb; ipdb.set_trace() # debugging code
statistics_str = "Mean : {0:.3} \nStd. Deviation : {1:.3}".format(ds.mean(x), ds.std(x))
#print statistics_str
ax.text(0.05, 0.9, statistics_str,
fontsize=15,
#color='blue',
bbox={'facecolor':'yellow', 'pad':10, 'alpha':0.65},
horizontalalignment='left',
verticalalignment='center',
transform = ax.transAxes)
canvas=FigureCanvas(fig)
if plot_type == 'png':
response=HttpResponse(content_type='image/png')
canvas.print_png(response)
if plot_type == 'pdf':
response = HttpResponse(mimetype='application/pdf')
response['Content-Disposition'] = 'attachment; filename="histo.pdf"'
#response=django.http.HttpResponse(content_type='image/pdf')
#response=['Content-Disposition'] = 'attachment; filename=plot.pdf'
canvas.print_pdf(response)
if plot_type == 'text':
response = HttpResponse(mimetype='text/csv')
response['Content-Disposition'] = 'attachment; filename="histo.csv"'
writer = csv.writer(response)
if len(n) > 0:
writer.writerow(['Counts'])
for i in range(len(n)):
writer.writerow([n[i]])
writer.writerow(['Bins'])
for i in range(len(bins)):
writer.writerow([bins[i]])
else:
writer.writerow(["No data!"])
return response
def produce(self,local_options=None):
global panel_j
global _ACCOUNTING
_trn = {
'figsize' : (tuple_of_floats, tuple),
'dpi' : int,
'facecolor' : str,
'edgecolor' : str,
'linewidth' : float,
'frameon' : (boolean, int)
}
# Get Local Defaults
l_defaults = self.get_defaults(local_options)
# logdict(LOGGER.debug,'{PRODUCE} FIGURE LOCAL OPTIONS',l_defaults)
# logdict(LOGGER.debug,'{PRODUCE} self keys',self)
figure_options = extract_dict_from_key('OPTIONS',
self,
self._genspec,
l_defaults,
klass=ldict)
figure_options['frameon']=True
# reduce vertical size if footnote are present
#
if self._footnote_text_len()>0:
lines = int((self._footnote_text_len() / 180)+1)
w,h = tuple_of_floats(figure_options['figsize'])
h-=.19*lines
figure_options['figsize']=u"(%f,%f)"%(w,h)
ex = cast_dict(figure_options,_trn)[0]
# logdict(LOGGER.debug,'{PRODUCE} FIGURE with OPTIONS',ex)
_ACCOUNTING['figure_options']=ex
# fig = plt.figure(**ex)
# fig = plt.figure()
# fig = plt.figure(dpi=300,figsize=(12,7))
# fig = mpl.figure.Figure(**ex)
# backend_name = 'matplotlib.backends.backend_pdf'
# self.backend_mod = __import__(backend_name,
# globals(),locals(),
# [backend_name])
# self.new_figure_manager = self.backend_mod.new_figure_manager
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
fig = Figure(**ex)
cvs = FigureCanvas(fig)
# Create panels
for j,vb in enumerate(self._elems):
p_j = int(vb.spec.xget('POS',j+1)) # panel id [1->...]
panel_j = p_j
fig = self.produce_panel(fig,
vb,
p_j,
{ 'OPTIONS': figure_options })
# Save fig
_format = 'pdf'
# plt.subplots_adjust(left=0.1, right=0.1, top=0.1, bottom=0.1)
if fig:
LOGGER.debug('Salvataggio della figura %s',self._imgfile)
cvs.print_pdf(self._imgfile)
fig.savefig(self._imgfile,format=_format,
orientation='landscape',
trasparent=True,
bbox_inches='tight')
return self._unplotted
