Python matplotlib.transforms.nonsingular() Examples
The following are 30
code examples of matplotlib.transforms.nonsingular().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
matplotlib.transforms
, or try the search function
.
.
Example #1
| Source File: ticker.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
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)
Example #2
| Source File: ticker.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
def __call__(self, x, pos=None):
"""
Return the format for tick val *x*.
"""
if x == 0.0: # Symlog
return '0'
x = abs(x)
b = self._base
# only label the decades
fx = math.log(x) / math.log(b)
is_x_decade = is_close_to_int(fx)
exponent = np.round(fx) if is_x_decade else np.floor(fx)
coeff = np.round(x / b ** exponent)
if self.labelOnlyBase and not is_x_decade:
return ''
if self._sublabels is not None and coeff not in self._sublabels:
return ''
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)
Example #3
| Source File: ticker.py From Computable with MIT License | 6 votes |
|
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)
Example #4
| Source File: ticker.py From Computable with MIT License | 6 votes |
|
def scale_range(vmin, vmax, n=1, threshold=100):
dv = abs(vmax - vmin)
if dv == 0: # maxabsv == 0 is a special case of this.
return 1.0, 0.0
# Note: this should never occur because
# vmin, vmax should have been checked by nonsingular(),
# and spread apart if necessary.
meanv = 0.5 * (vmax + vmin)
if abs(meanv) / dv < threshold:
offset = 0
elif meanv > 0:
ex = divmod(math.log10(meanv), 1)[0]
offset = 10 ** ex
else:
ex = divmod(math.log10(-meanv), 1)[0]
offset = -10 ** ex
ex = divmod(math.log10(dv / n), 1)[0]
scale = 10 ** ex
return scale, offset
Example #5
| Source File: converter.py From Computable with MIT License | 6 votes |
|
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)
#####-------------------------------------------------------------------------
#---- --- Formatter ---
#####-------------------------------------------------------------------------
Example #6
| Source File: ticker.py From Computable with MIT License | 6 votes |
|
def tick_values(self, vmin, vmax):
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)
Example #7
| Source File: _converter.py From vnpy_crypto with MIT License | 6 votes |
|
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)
# -------------------------------------------------------------------------
# --- Formatter ---
# -------------------------------------------------------------------------
Example #8
| Source File: _converter.py From recruit with Apache License 2.0 | 6 votes |
|
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)
# -------------------------------------------------------------------------
# --- Formatter ---
# -------------------------------------------------------------------------
Example #9
| Source File: ticker.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
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)
Example #10
| Source File: ticker.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
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
if rcParams['axes.autolimit_mode'] == 'round_numbers':
exponent, remainder = divmod(
math.log10(vmax - vmin), math.log10(max(self.numticks - 1, 1)))
exponent -= (remainder < .5)
scale = max(self.numticks - 1, 1) ** (-exponent)
vmin = math.floor(scale * vmin) / scale
vmax = math.ceil(scale * vmax) / scale
return mtransforms.nonsingular(vmin, vmax)
Example #11
| Source File: ticker.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
def view_limits(self, dmin, dmax):
"""
Set the view limits to the nearest multiples of base that
contain the data.
"""
if rcParams['axes.autolimit_mode'] == 'round_numbers':
vmin = self._edge.le(dmin) * self._edge.step
vmax = self._edge.ge(dmax) * self._edge.step
if vmin == vmax:
vmin -= 1
vmax += 1
else:
vmin = dmin
vmax = dmax
return mtransforms.nonsingular(vmin, vmax)
Example #12
| Source File: ticker.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
def tick_values(self, vmin, vmax):
if self._symmetric:
vmax = max(abs(vmin), abs(vmax))
vmin = -vmax
vmin, vmax = mtransforms.nonsingular(
vmin, vmax, expander=1e-13, tiny=1e-14)
locs = self._raw_ticks(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)
Example #13
| Source File: ticker.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
def nonsingular(self, vmin, vmax):
if not np.isfinite(vmin) or not np.isfinite(vmax):
return 1, 10 # initial range, no data plotted yet
if vmin > vmax:
vmin, vmax = vmax, vmin
if vmax <= 0:
cbook._warn_external(
"Data has no positive values, and therefore cannot be "
"log-scaled.")
return 1, 10
minpos = self.axis.get_minpos()
if not np.isfinite(minpos):
minpos = 1e-300 # This should never take effect.
if vmin <= 0:
vmin = minpos
if vmin == vmax:
vmin = _decade_less(vmin, self._base)
vmax = _decade_greater(vmax, self._base)
return vmin, vmax
Example #14
| Source File: ticker.py From matplotlib-4-abaqus with MIT License | 6 votes |
|
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)
Example #15
| Source File: ticker.py From matplotlib-4-abaqus with MIT License | 6 votes |
|
def tick_values(self, vmin, vmax):
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)
Example #16
| Source File: ticker.py From neural-network-animation with MIT License | 6 votes |
|
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 = ''
elif abs(fx) > 10000:
s = '%1.0g' % fx
elif abs(fx) < 1:
s = '%1.0g' % fx
else:
s = self.pprint_val(fx, d)
if sign == -1:
s = '-%s' % s
return self.fix_minus(s)
Example #17
| Source File: ticker.py From neural-network-animation with MIT License | 6 votes |
|
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)
Example #18
| Source File: ticker.py From neural-network-animation with MIT License | 6 votes |
|
def tick_values(self, vmin, vmax):
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)
Example #19
| Source File: ticker.py From neural-network-animation with MIT License | 6 votes |
|
def scale_range(vmin, vmax, n=1, threshold=100):
dv = abs(vmax - vmin)
if dv == 0: # maxabsv == 0 is a special case of this.
return 1.0, 0.0
# Note: this should never occur because
# vmin, vmax should have been checked by nonsingular(),
# and spread apart if necessary.
meanv = 0.5 * (vmax + vmin)
if abs(meanv) / dv < threshold:
offset = 0
elif meanv > 0:
ex = divmod(math.log10(meanv), 1)[0]
offset = 10 ** ex
else:
ex = divmod(math.log10(-meanv), 1)[0]
offset = -10 ** ex
ex = divmod(math.log10(dv / n), 1)[0]
scale = 10 ** ex
return scale, offset
Example #20
| Source File: ticker.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def __call__(self, x, pos=None):
"""
Return the format for tick val *x*.
"""
if x == 0.0: # Symlog
return '0'
x = abs(x)
b = self._base
# only label the decades
fx = math.log(x) / math.log(b)
is_x_decade = is_close_to_int(fx)
exponent = np.round(fx) if is_x_decade else np.floor(fx)
coeff = np.round(x / b ** exponent)
if self.labelOnlyBase and not is_x_decade:
return ''
if self._sublabels is not None and coeff not in self._sublabels:
return ''
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)
Example #21
| Source File: ticker.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
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)
Example #22
| Source File: ticker.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def tick_values(self, vmin, vmax):
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)
Example #23
| Source File: ticker.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def view_limits(self, dmin, dmax):
"""
Set the view limits to the nearest multiples of base that
contain the data.
"""
if rcParams['axes.autolimit_mode'] == 'round_numbers':
vmin = self._edge.le(dmin) * self._edge.step
vmax = self._base.ge(dmax) * self._edge.step
if vmin == vmax:
vmin -= 1
vmax += 1
else:
vmin = dmin
vmax = dmax
return mtransforms.nonsingular(vmin, vmax)
Example #24
| Source File: ticker.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def tick_values(self, vmin, vmax):
if self._symmetric:
vmax = max(abs(vmin), abs(vmax))
vmin = -vmax
vmin, vmax = mtransforms.nonsingular(
vmin, vmax, expander=1e-13, tiny=1e-14)
locs = self._raw_ticks(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)
Example #25
| Source File: ticker.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def view_limits(self, vmin, vmax):
'Try to choose the view limits intelligently'
b = self._base
vmin, vmax = self.nonsingular(vmin, vmax)
if self.axis.axes.name == 'polar':
vmax = math.ceil(math.log(vmax) / math.log(b))
vmin = b ** (vmax - self.numdecs)
if rcParams['axes.autolimit_mode'] == 'round_numbers':
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)
return vmin, vmax
Example #26
| Source File: ticker.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def __call__(self, x, pos=None):
"""
Return the format for tick val *x*.
"""
if x == 0.0: # Symlog
return '0'
x = abs(x)
b = self._base
# only label the decades
fx = math.log(x) / math.log(b)
is_x_decade = is_close_to_int(fx)
exponent = np.round(fx) if is_x_decade else np.floor(fx)
coeff = np.round(x / b ** exponent)
if self.labelOnlyBase and not is_x_decade:
return ''
if self._sublabels is not None and coeff not in self._sublabels:
return ''
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)
Example #27
| Source File: ticker.py From matplotlib-4-abaqus with MIT License | 6 votes |
|
def scale_range(vmin, vmax, n=1, threshold=100):
dv = abs(vmax - vmin)
if dv == 0: # maxabsv == 0 is a special case of this.
return 1.0, 0.0
# Note: this should never occur because
# vmin, vmax should have been checked by nonsingular(),
# and spread apart if necessary.
meanv = 0.5 * (vmax + vmin)
if abs(meanv) / dv < threshold:
offset = 0
elif meanv > 0:
ex = divmod(math.log10(meanv), 1)[0]
offset = 10 ** ex
else:
ex = divmod(math.log10(-meanv), 1)[0]
offset = -10 ** ex
ex = divmod(math.log10(dv / n), 1)[0]
scale = 10 ** ex
return scale, offset
Example #28
| Source File: ticker.py From Computable with MIT License | 5 votes |
|
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)
Example #29
| Source File: ticker.py From neural-network-animation with MIT License | 5 votes |
|
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
Example #30
| Source File: ticker.py From neural-network-animation with MIT License | 5 votes |
|
def refresh(self):
'refresh internal information based on current lim'
vmin, vmax = self.axis.get_view_interval()
vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=0.05)
d = abs(vmax - vmin)
self._locator = self.get_locator(d)
