def view_limits(self, dmin, dmax):
if self._symmetric:
maxabs = max(abs(dmin), abs(dmax))
dmin = -maxabs
dmax = maxabs
dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander = 0.05)
return np.take(self.bin_boundaries(dmin, dmax), [0,-1])
def autoscale(self):
'Try to choose the view limits intelligently'
b = self._transform.base
vmin, vmax = self.axis.get_data_interval()
if vmax < vmin:
vmin, vmax = vmax, vmin
if not is_decade(abs(vmin), b):
if vmin < 0:
vmin = -decade_up(-vmin, b)
else:
vmin = decade_down(vmin, b)
if not is_decade(abs(vmax), b):
if vmax < 0:
vmax = -decade_down(-vmax, b)
else:
vmax = decade_up(vmax, b)
if vmin == vmax:
if vmin < 0:
vmin = -decade_up(-vmin, b)
vmax = -decade_down(-vmax, b)
else:
vmin = decade_down(vmin, b)
vmax = decade_up(vmax, b)
result = mtransforms.nonsingular(vmin, vmax)
return result
def view_limits(self, vmin, vmax):
"""
Try to choose the view limits intelligently. In contrast to the stock
LogLocator, this version always pads by one decade so that the contents
of the largest and smallest histogram bins can be seen even if they
fall on a decade.
"""
b = self._base
if vmax < vmin:
vmin, vmax = vmax, vmin
if self.axis.axes.name == 'polar':
vmax = math.ceil(math.log(vmax) / math.log(b))
vmin = b**(vmax - self.numdecs)
return vmin, vmax
minpos = self.axis.get_minpos()
if minpos <= 0 or not np.isfinite(minpos):
raise ValueError(
"Data has no positive values, and therefore can not be "
"log-scaled.")
if vmin <= minpos:
vmin = minpos
vmin = mticker.decade_down(vmin * (1 - 1e-10), self._base)
vmax = mticker.decade_up(vmax * (1 + 1e-10), self._base)
if vmin == vmax:
vmin = mticker.decade_down(vmin, self._base)
vmax = mticker.decade_up(vmax, self._base)
result = mtransforms.nonsingular(vmin, vmax)
return result
def autoscale(self):
"Try to choose the view limits intelligently"
b = self._transform.base
vmin, vmax = self.axis.get_data_interval()
if vmax < vmin:
vmin, vmax = vmax, vmin
if not is_decade(abs(vmin), b):
if vmin < 0:
vmin = -decade_up(-vmin, b)
else:
vmin = decade_down(vmin, b)
if not is_decade(abs(vmax), b):
if vmax < 0:
vmax = -decade_down(-vmax, b)
else:
vmax = decade_up(vmax, b)
if vmin == vmax:
if vmin < 0:
vmin = -decade_up(-vmin, b)
vmax = -decade_down(-vmax, b)
else:
vmin = decade_down(vmin, b)
vmax = decade_up(vmax, b)
result = mtransforms.nonsingular(vmin, vmax)
return result
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
b = self._transform.base
if vmax<vmin:
vmin, vmax = vmax, vmin
if not is_decade(abs(vmin), b):
if vmin < 0:
vmin = -decade_up(-vmin, b)
else:
vmin = decade_down(vmin, b)
if not is_decade(abs(vmax), b):
if vmax < 0:
vmax = -decade_down(-vmax, b)
else:
vmax = decade_up(vmax, b)
if vmin == vmax:
if vmin < 0:
vmin = -decade_up(-vmin, b)
vmax = -decade_down(-vmax, b)
else:
vmin = decade_down(vmin, b)
vmax = decade_up(vmax, b)
result = mtransforms.nonsingular(vmin, vmax)
return result
def zoom(self, direction):
"Zoom in/out on axis; if direction is >0 zoom in, else zoom out"
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
interval = abs(vmax-vmin)
step = 0.1*interval*direction
self.axis.set_view_interval(vmin + step, vmax - step, ignore=True)
def view_limits(self, dmin, dmax):
if self._symmetric:
maxabs = max(abs(dmin), abs(dmax))
dmin = -maxabs
dmax = maxabs
dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander=0.05)
return np.take(self.bin_boundaries(dmin, dmax), [0, -1])
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
b = self._transform.base
if vmax < vmin:
vmin, vmax = vmax, vmin
if not is_decade(abs(vmin), b):
if vmin < 0:
vmin = -decade_up(-vmin, b)
else:
vmin = decade_down(vmin, b)
if not is_decade(abs(vmax), b):
if vmax < 0:
vmax = -decade_down(-vmax, b)
else:
vmax = decade_up(vmax, b)
if vmin == vmax:
if vmin < 0:
vmin = -decade_up(-vmin, b)
vmax = -decade_down(-vmax, b)
else:
vmin = decade_down(vmin, b)
vmax = decade_up(vmax, b)
result = mtransforms.nonsingular(vmin, vmax)
return result
def test_nonsingular():
# test for zero-expansion type cases; other cases may be added later
zero_expansion = np.array([-0.001, 0.001])
cases = [(0, np.nan), (0, 0), (0, 7.9e-317)]
for args in cases:
out = np.array(mtransforms.nonsingular(*args))
assert_array_equal(out, zero_expansion)
def autoscale(self):
'Try to choose the view limits intelligently'
vmin, vmax = self.axis.get_data_interval()
if vmax < vmin:
vmin, vmax = vmax, vmin
minpos = self.axis.get_minpos()
if minpos <= 0:
raise ValueError(
"Data has no positive values, and therefore can not be log-scaled."
)
if vmin <= minpos:
vmin = minpos
if not is_decade(vmin, self._base):
vmin = decade_down(vmin, self._base)
if not is_decade(vmax, self._base): vmax = decade_up(vmax, self._base)
if vmin == vmax:
vmin = decade_down(vmin, self._base)
vmax = decade_up(vmax, self._base)
result = mtransforms.nonsingular(vmin, vmax)
return result
def __call__(self, x, pos=None):
"Return the format for tick val x at position pos"
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
d = abs(vmax - vmin)
b = self._base
if x == 0:
return "0"
sign = np.sign(x)
# only label the decades
fx = math.log(abs(x)) / math.log(b)
isDecade = self.is_decade(fx)
if not isDecade and self.labelOnlyBase:
s = ""
# if 0: pass
elif fx > 10000:
s = "%1.0e" % fx
# elif x<1: s = '$10^{%d}$'%fx
# elif x<1: s = '10^%d'%fx
elif fx < 1:
s = "%1.0e" % fx
else:
s = self.pprint_val(fx, d)
if sign == -1:
s = "-%s" % s
return self.fix_minus(s)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
b = self._base
if vmax<vmin:
vmin, vmax = vmax, vmin
if self.axis.axes.name == 'polar':
vmax = math.ceil(math.log(vmax) / math.log(b))
vmin = b ** (vmax - self.numdecs)
return vmin, vmax
minpos = self.axis.get_minpos()
if minpos<=0 or not np.isfinite(minpos):
raise ValueError(
"Data has no positive values, and therefore can not be log-scaled.")
if vmin <= minpos:
vmin = minpos
if not is_decade(vmin,self._base): vmin = decade_down(vmin,self._base)
if not is_decade(vmax,self._base): vmax = decade_up(vmax,self._base)
if vmin==vmax:
vmin = decade_down(vmin,self._base)
vmax = decade_up(vmax,self._base)
result = mtransforms.nonsingular(vmin, vmax)
return result
def __call__(self, x, pos=None):
'Return the format for tick val x at position pos'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
d = abs(vmax-vmin)
b=self._base
if x == 0:
return '0'
sign = np.sign(x)
# only label the decades
fx = math.log(abs(x))/math.log(b)
isDecade = self.is_decade(fx)
if not isDecade and self.labelOnlyBase: s = ''
#if 0: pass
elif fx>10000: s= '%1.0e'%fx
#elif x<1: s = '$10^{%d}$'%fx
#elif x<1: s = '10^%d'%fx
elif fx<1: s = '%1.0e'%fx
else : s = self.pprint_val(fx,d)
if sign == -1:
s = '-%s' % s
return self.fix_minus(s)
def test_nonsingular():
# test for zero-expansion type cases; other cases may be added later
zero_expansion = np.array([-0.001, 0.001])
cases = [(0, np.nan), (0, 0), (0, 7.9e-317)]
for args in cases:
out = np.array(mtrans.nonsingular(*args))
assert_array_equal(out, zero_expansion)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
b = self._base
if vmax < vmin:
vmin, vmax = vmax, vmin
if self.axis.axes.name == 'polar':
vmax = math.ceil(math.log(vmax) / math.log(b))
vmin = b**(vmax - self.numdecs)
return vmin, vmax
minpos = self.axis.get_minpos()
if minpos <= 0 or not np.isfinite(minpos):
raise ValueError(
"Data has no positive values, and therefore can not be log-scaled."
)
if vmin <= minpos:
vmin = minpos
if not is_decade(vmin, self._base):
vmin = decade_down(vmin, self._base)
if not is_decade(vmax, self._base): vmax = decade_up(vmax, self._base)
if vmin == vmax:
vmin = decade_down(vmin, self._base)
vmax = decade_up(vmax, self._base)
result = mtransforms.nonsingular(vmin, vmax)
return result
def __call__(self, x, pos=None):
'Return the format for tick val *x* at position *pos*'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
d = abs(vmax-vmin)
b=self._base
if x == 0:
return '0'
sign = np.sign(x)
# only label the decades
fx = math.log(abs(x))/math.log(b)
isDecade = is_close_to_int(fx)
if not isDecade and self.labelOnlyBase: s = ''
#if 0: pass
elif fx>10000: s= '%1.0e'%fx
#elif x<1: s = '$10^{%d}$'%fx
#elif x<1: s = '10^%d'%fx
elif fx<1: s = '%1.0e'%fx
else : s = self.pprint_val(fx,d)
if sign == -1:
s = '-%s' % s
return self.fix_minus(s)
def view_limits(self, vmin, vmax):
"""
select a scale for the range from vmin to vmax
Normally This will be overridden.
"""
return mtransforms.nonsingular(vmin, vmax)
def view_limits(self, vmin, vmax):
"""
select a scale for the range from vmin to vmax
Normally This will be overridden.
"""
return mtransforms.nonsingular(vmin, vmax)
def view_limits(self, vmin, vmax):
"""
Try to choose the view limits intelligently. In contrast to the stock
LogLocator, this version always pads by one decade so that the contents
of the largest and smallest histogram bins can be seen even if they
fall on a decade.
"""
b = self._base
if vmax < vmin:
vmin, vmax = vmax, vmin
if self.axis.axes.name == 'polar':
vmax = math.ceil(math.log(vmax) / math.log(b))
vmin = b ** (vmax - self.numdecs)
return vmin, vmax
minpos = self.axis.get_minpos()
if minpos <= 0 or not np.isfinite(minpos):
raise ValueError(
"Data has no positive values, and therefore can not be "
"log-scaled.")
if vmin <= minpos:
vmin = minpos
vmin = mticker.decade_down(vmin*(1-1e-10), self._base)
vmax = mticker.decade_up(vmax*(1+1e-10), self._base)
if vmin == vmax:
vmin = mticker.decade_down(vmin, self._base)
vmax = mticker.decade_up(vmax, self._base)
result = mtransforms.nonsingular(vmin, vmax)
return result
def __init__(self, x, y, ids, nx, malicious_ids):
self.malicious_ids = malicious_ids
self.x = np.array(x, float)
self.y = np.array(y, float)
self.ids = ids
self.xmin = np.amin(x)
self.xmax = np.amax(x)
self.ymin = np.amin(y)
self.ymax = np.amax(y)
# to avoid issues with singular data, expand the min/max pairs
self.xmin, self.xmax = mtrans.nonsingular(
self.xmin,
self.xmax,
expander=0.1)
self.ymin, self.ymax = mtrans.nonsingular(
self.ymin,
self.ymax,
expander=0.1)
# In the x-direction, the hexagons exactly cover the region from
# xmin to xmax. Need some padding to avoid roundoff errors.
padding = 1.e-9 * (self.xmax - self.xmin)
self.xmin -= padding
self.xmax += padding
## Deal with very small values
if self.xmax - self.xmin < epsilon:
self.xmin -= epsilon / 2
self.xmax += epsilon / 2
if self.ymax - self.ymin < epsilon:
self.ymin -= epsilon / 2
self.ymax += epsilon / 2
self.nx = nx
self.size = (self.xmax - self.xmin) / self.nx
self.nx = int(math.ceil((self.xmax - self.xmin) /
(math.sqrt(3) * self.size))) + 1
self.ny = int(math.ceil((self.ymax - self.ymin) /
self.size)) + 1
self.x_scale = (self.x - self.xmin) / (self.size * math.sqrt(3))
self.y_scale = (self.y - self.ymin) / self.size
def zoom(self, direction):
"Zoom in/out on axis; if direction is >0 zoom in, else zoom out"
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
interval = abs(vmax-vmin)
step = 0.1*interval*direction
self.axis.set_view_interval(vmin + step, vmax - step, ignore=True)
def autoscale(self):
dmin, dmax = self.axis.get_data_interval()
if self._symmetric:
maxabs = max(abs(dmin), abs(dmax))
dmin = -maxabs
dmax = maxabs
dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander=0.05)
return np.take(self.bin_boundaries(dmin, dmax), [0, -1])
def autoscale(self):
dmin, dmax = self.axis.get_data_interval()
if self._symmetric:
maxabs = max(abs(dmin), abs(dmax))
dmin = -maxabs
dmax = maxabs
dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander=0.05)
return np.take(self.bin_boundaries(dmin, dmax), [0, -1])
def tick_values(self, vmin, vmax):
vmin, vmax = mtransforms.nonsingular(vmin,
vmax,
expander=1e-7,
tiny=1e-13)
self.ndays = float(abs(vmax - vmin))
utime = netcdftime.utime(self.date_unit, self.calendar)
lower = utime.num2date(vmin)
upper = utime.num2date(vmax)
resolution, n = self.compute_resolution(vmin, vmax, lower, upper)
if resolution == 'YEARLY':
# TODO START AT THE BEGINNING OF A DECADE/CENTURY/MILLENIUM as
# appropriate.
years = self._max_n_locator.tick_values(lower.year, upper.year)
ticks = [netcdftime.datetime(int(year), 1, 1) for year in years]
elif resolution == 'MONTHLY':
# TODO START AT THE BEGINNING OF A DECADE/CENTURY/MILLENIUM as
# appropriate.
months_offset = self._max_n_locator.tick_values(0, n)
ticks = []
for offset in months_offset:
year = lower.year + np.floor((lower.month + offset) / 12)
month = ((lower.month + offset) % 12) + 1
ticks.append(netcdftime.datetime(int(year), int(month), 1))
elif resolution == 'DAILY':
# TODO: It would be great if this favoured multiples of 7.
days = self._max_n_locator_days.tick_values(vmin, vmax)
ticks = [utime.num2date(dt) for dt in days]
elif resolution == 'HOURLY':
hour_unit = 'hours since 2000-01-01'
hour_utime = netcdftime.utime(hour_unit, self.calendar)
in_hours = hour_utime.date2num([lower, upper])
hours = self._max_n_locator.tick_values(in_hours[0], in_hours[1])
ticks = [hour_utime.num2date(dt) for dt in hours]
elif resolution == 'MINUTELY':
minute_unit = 'minutes since 2000-01-01'
minute_utime = netcdftime.utime(minute_unit, self.calendar)
in_minutes = minute_utime.date2num([lower, upper])
minutes = self._max_n_locator.tick_values(in_minutes[0],
in_minutes[1])
ticks = [minute_utime.num2date(dt) for dt in minutes]
elif resolution == 'SECONDLY':
second_unit = 'seconds since 2000-01-01'
second_utime = netcdftime.utime(second_unit, self.calendar)
in_seconds = second_utime.date2num([lower, upper])
seconds = self._max_n_locator.tick_values(in_seconds[0],
in_seconds[1])
ticks = [second_utime.num2date(dt) for dt in seconds]
else:
msg = 'Resolution {} not implemented yet.'.format(resolution)
raise ValueError(msg)
return utime.date2num(ticks)
def get_view_interval(self):
vmin, vmax = self.axis.get_view_interval()
if self.epoch:
vmin -= float(self.epoch.gps)
vmax -= float(self.epoch.gps)
if self._scale:
vmin /= self._scale
vmax /= self._scale
return mtransforms.nonsingular(vmin, vmax, expander = 0.05)
def __call__(self):
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = nonsingular(vmin, vmax, expander = 0.05)
vmin = vmin ** self.exponent
vmax = vmax ** self.exponent
if vmax<vmin:
vmin, vmax = vmax, vmin
ticklocs = np.linspace(vmin, vmax, num=self.numticks, endpoint=True)
return self.raise_if_exceeds(ticklocs ** (1.0 / self.exponent))
def __call__(self):
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = nonsingular(vmin, vmax, expander=0.05)
vmin = vmin**self.exponent
vmax = vmax**self.exponent
if vmax < vmin:
vmin, vmax = vmax, vmin
ticklocs = np.linspace(vmin, vmax, num=self.numticks, endpoint=True)
return self.raise_if_exceeds(ticklocs**(1.0 / self.exponent))
def tick_values(self, vmin, vmax):
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=1e-7,
tiny=1e-13)
self.ndays = float(abs(vmax - vmin))
utime = cftime.utime(self.date_unit, self.calendar)
lower = utime.num2date(vmin)
upper = utime.num2date(vmax)
resolution, n = self.compute_resolution(vmin, vmax, lower, upper)
if resolution == 'YEARLY':
# TODO START AT THE BEGINNING OF A DECADE/CENTURY/MILLENIUM as
# appropriate.
years = self._max_n_locator.tick_values(lower.year, upper.year)
ticks = [cftime.datetime(int(year), 1, 1) for year in years]
elif resolution == 'MONTHLY':
# TODO START AT THE BEGINNING OF A DECADE/CENTURY/MILLENIUM as
# appropriate.
months_offset = self._max_n_locator.tick_values(0, n)
ticks = []
for offset in months_offset:
year = lower.year + np.floor((lower.month + offset) / 12)
month = ((lower.month + offset) % 12) + 1
ticks.append(cftime.datetime(int(year), int(month), 1))
elif resolution == 'DAILY':
# TODO: It would be great if this favoured multiples of 7.
days = self._max_n_locator_days.tick_values(vmin, vmax)
ticks = [utime.num2date(dt) for dt in days]
elif resolution == 'HOURLY':
hour_unit = 'hours since 2000-01-01'
hour_utime = cftime.utime(hour_unit, self.calendar)
in_hours = hour_utime.date2num([lower, upper])
hours = self._max_n_locator.tick_values(in_hours[0], in_hours[1])
ticks = [hour_utime.num2date(dt) for dt in hours]
elif resolution == 'MINUTELY':
minute_unit = 'minutes since 2000-01-01'
minute_utime = cftime.utime(minute_unit, self.calendar)
in_minutes = minute_utime.date2num([lower, upper])
minutes = self._max_n_locator.tick_values(in_minutes[0],
in_minutes[1])
ticks = [minute_utime.num2date(dt) for dt in minutes]
elif resolution == 'SECONDLY':
second_unit = 'seconds since 2000-01-01'
second_utime = cftime.utime(second_unit, self.calendar)
in_seconds = second_utime.date2num([lower, upper])
seconds = self._max_n_locator.tick_values(in_seconds[0],
in_seconds[1])
ticks = [second_utime.num2date(dt) for dt in seconds]
else:
msg = 'Resolution {} not implemented yet.'.format(resolution)
raise ValueError(msg)
return utime.date2num(ticks)
def view_limits(self, dmin, dmax):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
vmin = self._base.le(dmin)
vmax = self._base.ge(dmax)
if vmin==vmax:
vmin -=1
vmax +=1
return mtransforms.nonsingular(vmin, vmax)
def __call__(self):
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
locs = self.bin_boundaries(vmin, vmax)
prune = self._prune
if prune == "lower":
locs = locs[1:]
elif prune == "upper":
locs = locs[:-1]
elif prune == "both":
locs = locs[1:-1]
return locs
def view_limits(self, dmin, dmax):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
vmin = self._base.le(dmin)
vmax = self._base.ge(dmax)
if vmin == vmax:
vmin -= 1
vmax += 1
return mtransforms.nonsingular(vmin, vmax)
def view_limits(self, dmin, dmax):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
vmin = self._base.le(math.copysign(math.sqrt(abs(dmin)), dmin))
vmax = self._base.ge(math.sqrt(dmax))
if vmin == vmax:
vmin -= 1
vmax += 1
return mtransforms.nonsingular(math.copysign(vmin**2, vmin), vmax**2)
def __call__(self):
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
locs = self.bin_boundaries(vmin, vmax)
prune = self._prune
if prune=='lower':
locs = locs[1:]
elif prune=='upper':
locs = locs[:-1]
elif prune=='both':
locs = locs[1:-1]
return self.raise_if_exceeds(locs)
def __call__(self):
'Return the locations of the ticks'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
if vmax<vmin:
vmin, vmax = vmax, vmin
if (vmin, vmax) in self.presets:
return self.presets[(vmin, vmax)]
if self.numticks is None:
self._set_numticks()
if self.numticks==0: return []
ticklocs = np.linspace(vmin, vmax, self.numticks)
return self.raise_if_exceeds(ticklocs)
def autoscale(self):
"""
Sets the view limits to the nearest multiples of base that contain the data.
"""
# requires matplotlib >= 0.98.0
(vmin, vmax) = self.axis.get_data_interval()
locs = self._get_default_locs(vmin, vmax)
(vmin, vmax) = locs[[0, -1]]
if vmin == vmax:
vmin -= 1
vmax += 1
return nonsingular(vmin, vmax)
def autoscale(self):
"""
Sets the view limits to the nearest multiples of base that contain the data.
"""
# requires matplotlib >= 0.98.0
(vmin, vmax) = self.axis.get_data_interval()
locs = self._get_default_locs(vmin, vmax)
(vmin, vmax) = locs[[0, -1]]
if vmin == vmax:
vmin -= 1
vmax += 1
return nonsingular(vmin, vmax)
def __call__(self, x, pos=None):
"""
Return the format for tick val *x*.
"""
if x == 0.0: # Symlog
return '0'
x = abs(x)
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
s = self._num_to_string(x, vmin, vmax)
return self.fix_minus(s)
def __call__(self):
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
locs = self.bin_boundaries(vmin, vmax)
#print 'locs=', locs
prune = self._prune
if prune == 'lower':
locs = locs[1:]
elif prune == 'upper':
locs = locs[:-1]
elif prune == 'both':
locs = locs[1:-1]
return self.raise_if_exceeds(locs)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
if vmax<vmin:
vmin, vmax = vmax, vmin
if vmin==vmax:
vmin-=1
vmax+=1
exponent, remainder = divmod(math.log10(vmax - vmin), 1)
if remainder < 0.5:
exponent -= 1
scale = 10**(-exponent)
vmin = math.floor(scale*vmin)/scale
vmax = math.ceil(scale*vmax)/scale
return mtransforms.nonsingular(vmin, vmax)
def tick_values(self, vmin, vmax):
"""Return the ticks for this axis
"""
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=1e-13,
tiny=1e-14)
locs = self.bin_boundaries(vmin, vmax)
prune = self._prune
if prune == 'lower':
locs = locs[1:]
elif prune == 'upper':
locs = locs[:-1]
elif prune == 'both':
locs = locs[1:-1]
return self.raise_if_exceeds(locs)
def pan(self, numsteps):
'Pan numticks (can be positive or negative)'
ticks = self()
numticks = len(ticks)
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
if numticks>2:
step = numsteps*abs(ticks[0]-ticks[1])
else:
d = abs(vmax-vmin)
step = numsteps*d/6.
vmin += step
vmax += step
self.axis.set_view_interval(vmin, vmax, ignore=True)
def autoscale(self):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
dmin, dmax = self.axis.get_data_interval()
vmin = self._base.le(dmin)
vmax = self._base.ge(dmax)
if vmin == vmax:
vmin -= 1
vmax += 1
return mtransforms.nonsingular(vmin, vmax)
def autoscale(self):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
dmin, dmax = self.axis.get_data_interval()
vmin = self._base.le(dmin)
vmax = self._base.ge(dmax)
if vmin == vmax:
vmin -= 1
vmax += 1
return mtransforms.nonsingular(vmin, vmax)
def autoscale(self):
self.verify_intervals()
dmin, dmax = self.axis.get_data_interval()
if dmin > dmax:
dmin, dmax = dmax, dmin
if dmin == dmax:
dmin -= 1
dmax += 1
exp, rem = divmod(math.log(dmax - dmin, 2), 1)
if rem > 0.5:
exp -= 1
scale = 2**(-exp)
dmin = math.floor(scale*dmin)/scale
dmax = math.ceil(scale*dmax)/scale
return nonsingular(dmin, dmax)
def plot(self, experiment, **kwargs):
"""Plot a faceted histogram view of a channel"""
#kwargs.setdefault('histtype', 'stepfilled')
#kwargs.setdefault('alpha', 0.5)
kwargs.setdefault('edgecolor', 'none')
#kwargs.setdefault('mincnt', 1)
#kwargs.setdefault('bins', 'log')
kwargs.setdefault('antialiased', True)
if not self.subset:
x = experiment.data
else:
x = experiment.query(self.subset)
xmin, xmax = (np.amin(x[self.xchannel]), np.amax(x[self.xchannel]))
ymin, ymax = (np.amin(x[self.ychannel]), np.amax(x[self.ychannel]))
# to avoid issues with singular data, expand the min/max pairs
xmin, xmax = mtrans.nonsingular(xmin, xmax, expander=0.1)
ymin, ymax = mtrans.nonsingular(ymin, ymax, expander=0.1)
extent = (xmin, xmax, ymin, ymax)
kwargs.setdefault('extent', extent)
xbins = num_hist_bins(experiment[self.xchannel])
ybins = num_hist_bins(experiment[self.ychannel])
bins = np.mean([xbins, ybins])
kwargs.setdefault('bins', bins) # Do not move above. don't ask.
g = sns.FacetGrid(x,
col = (self.xfacet if self.xfacet else None),
row = (self.yfacet if self.yfacet else None),
hue = (self.huefacet if self.huefacet else None))
g.map(plt.hexbin, self.xchannel, self.ychannel, **kwargs)
def autoscale(self):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
self.verify_intervals()
dmin, dmax = self.dataInterval.get_bounds()
vmin = self._base.le(dmin)
vmax = self._base.ge(dmax)
if vmin == vmax:
vmin -= 1
vmax += 1
return mtrans.nonsingular(vmin, vmax)
def pan(self, numsteps):
'Pan numticks (can be positive or negative)'
ticks = self()
numticks = len(ticks)
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05)
if numticks>2:
step = numsteps*abs(ticks[0]-ticks[1])
else:
d = abs(vmax-vmin)
step = numsteps*d/6.
vmin += step
vmax += step
self.axis.set_view_interval(vmin, vmax, ignore=True)
def autoscale(self):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
self.verify_intervals()
dmin, dmax = self.dataInterval.get_bounds()
vmin = self._base.le(dmin)
vmax = self._base.ge(dmax)
if vmin==vmax:
vmin -=1
vmax +=1
return mtrans.nonsingular(vmin, vmax)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
if vmax<vmin:
vmin, vmax = vmax, vmin
minpos = self.axis.get_minpos()
if minpos<=0:
raise ValueError(
"Data has no positive values, and therefore can not be log-scaled.")
if vmin <= minpos:
vmin = minpos
if not is_decade(vmin,self._base): vmin = decade_down(vmin,self._base)
if not is_decade(vmax,self._base): vmax = decade_up(vmax,self._base)
if vmin==vmax:
vmin = decade_down(vmin,self._base)
vmax = decade_up(vmax,self._base)
result = mtransforms.nonsingular(vmin, vmax)
return result
def view_limits(self, dmin, dmax):
"""
Set the view limits to the nearest multiples of base that
contain the data
"""
base = self._select_base(dmin, dmax)
if mpl.rcParams['axes.autolimit_mode'] == 'round_numbers':
vmin = base.le(dmin)
vmax = base.ge(dmax)
if vmin == vmax:
vmin -= 1
vmax += 1
else:
vmin = dmin
vmax = dmax
return mtransforms.nonsingular(vmin, vmax)
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
b = self._base
if vmax < vmin:
vmin, vmax = vmax, vmin
if self.axis.axes.name == 'polar':
vmax = math.ceil(math.log(vmax - self.zero) / math.log(b))
vmin = b ** (vmax - self.numdecs)
return vmin, vmax
minpos = self.axis.get_minpos()
if (self.sign > 0.0) :
if vmax <= self.zero or not np.isfinite(minpos):
raise ValueError(
"Data has no positive values, and therefore can not be "
"log-scaled.")
else:
if vmin >= self.zero or not np.isfinite(minpos):
raise ValueError(
"Data has no positive values, and therefore can not be "
"log-scaled.")
if not is_decade((vmin - self.zero) * self.sign, self._base):
if self.sign > 0.0:
vmin = decade_down((vmin - self.zero) * self.sign, self._base) + self.zero
else:
vmin = decade_up((vmin - self.zero) * self.sign, self._base) * self.sign + self.zero
if not is_decade((vmax - self.zero) * self.sign, self._base):
if self.sign > 0.0:
vmax = decade_up((vmax - self.zero) * self.sign, self._base) + self.zero
else:
vmax = decade_down((vmax - self.zero) * self.sign, self._base) * self.sign + self.zero
if vmin == vmax:
if (self.sign > 0.0):
vmin = decade_down((vmin - self.zero) * self.sign, self._base) + self.zero
vmax = decade_up((vmax - self.zero) * self.sign, self._base) + self.zero
else:
vmin = decade_up((vmin - self.zero) * self.sign, self._base) * self.sign + self.zero
vmax = decade_down((vmax - self.zero) * self.sign, self._base) * self.sign + self.zero
result = mtransforms.nonsingular(vmin, vmax)
return result
def __call__(self):
'Return the locations of the ticks'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
if vmax < vmin:
vmin, vmax = vmax, vmin
if (vmin, vmax) in self.presets:
return self.presets[(vmin, vmax)]
if self.numticks is None:
self._set_numticks()
if self.numticks == 0: return []
ticklocs = np.linspace(vmin, vmax, self.numticks)
return ticklocs
def __call__(self):
"Return the locations of the ticks"
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
if vmax < vmin:
vmin, vmax = vmax, vmin
if self.presets.has_key((vmin, vmax)):
return self.presets[(vmin, vmax)]
if self.numticks is None:
self._set_numticks()
if self.numticks == 0:
return []
ticklocs = np.linspace(vmin, vmax, self.numticks)
return ticklocs
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
if vmax < vmin:
vmin, vmax = vmax, vmin
if vmin == vmax:
vmin -= 1
vmax += 1
exponent, remainder = divmod(math.log10(vmax - vmin), 1)
if remainder < 0.5:
exponent -= 1
scale = 10**(-exponent)
vmin = math.floor(scale * vmin) / scale
vmax = math.ceil(scale * vmax) / scale
return mtransforms.nonsingular(vmin, vmax)
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
vmin, vmax = self.dataInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
minpos = self.dataInterval.minpos()
if minpos<=0:
raise RuntimeError('No positive data to plot')
if vmin<=0:
vmin = minpos
if not is_decade(vmin,self._base): vmin = decade_down(vmin,self._base)
if not is_decade(vmax,self._base): vmax = decade_up(vmax,self._base)
if vmin==vmax:
vmin = decade_down(vmin,self._base)
vmax = decade_up(vmax,self._base)
return mtrans.nonsingular(vmin, vmax)
def view_limits(self, data_min, data_max):
'Try to choose the view limits intelligently'
if data_max < data_min:
data_min, data_max = data_max, data_min
# get the nearest tenth-decade that contains the data
if data_max > 0:
logs = np.ceil(np.log10(data_max))
vmax = np.ceil(data_max / (10**(logs - 1))) * (10**(logs - 1))
else:
vmax = 100
if data_min >= 0:
vmin = 0
else:
logs = np.ceil(np.log10(-1.0 * data_min))
vmin = np.floor(data_min / (10**(logs - 1))) * (10**(logs - 1))
return transforms.nonsingular(vmin, vmax)
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
vmin, vmax = self.dataInterval.get_bounds()
if vmax < vmin:
vmin, vmax = vmax, vmin
minpos = self.dataInterval.minpos()
if minpos <= 0:
raise RuntimeError('No positive data to plot')
if vmin <= 0:
vmin = minpos
if not is_decade(vmin, self._base):
vmin = decade_down(vmin, self._base)
if not is_decade(vmax, self._base): vmax = decade_up(vmax, self._base)
if vmin == vmax:
vmin = decade_down(vmin, self._base)
vmax = decade_up(vmax, self._base)
return mtrans.nonsingular(vmin, vmax)
def view_limits(self, dmin, dmax):
# begin partial duplication of MaxNLocator.view_limits
if self._symmetric:
maxabs = max(abs(dmin), abs(dmax))
dmin = -maxabs
dmax = maxabs
dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander=0.05)
# end duplication
margin = self._margin * (dmax - dmin) # fraction of data range
vmin = dmin - margin # expand the view
vmax = dmax + margin
bin_boundaries = self.bin_boundaries(vmin, vmax)
# locate ticks with MaxNLocator
# Note: If the lines below change vmin or vmax, the bin boundaries
# later calculated by MaxNLocator.__call__ may differ from those
# calculated here.
vmin = min(vmin, max(bin_boundaries[bin_boundaries <= dmin]))
# expand view to the highest tick below or touching the data
vmax = max(vmax, min(bin_boundaries[bin_boundaries >= dmax]))
# expand view to the lowest tick above or touching the data
return np.array([vmin, vmax])
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
vmin, vmax = self.dataInterval.get_bounds()
if vmax < vmin:
vmin, vmax = vmax, vmin
if vmin == vmax:
vmin -= 1
vmax += 1
exponent, remainder = divmod(math.log10(vmax - vmin), 1)
if remainder < 0.5:
exponent -= 1
scale = 10**(-exponent)
vmin = math.floor(scale * vmin) / scale
vmax = math.ceil(scale * vmax) / scale
return mtransforms.nonsingular(vmin, vmax)