def future(self, i): #到底需不需要考虑拓扑顺序呢？？？
if type(self.node_list[i]) != Varnode or self.node_list[i].I == None:
return #第一次future是提前做的，因此现在I还可能是空呢
for item in self.node_list[i].next_dashed_list:
if self.node_list[item].lval != None: #lval != -inf
#e.g. 原来是[j_3, a)，现在有[, 99]
#把lval换成-inf，变成[-inf, a)，然后把这个字符串代入Interval进行初始化
tmp = copy.deepcopy(self.node_list[item])
tmp.lval = str(self.node_list[i].I.lower)
s = str(tmp).replace('-inf', '').replace('inf', '')
if tmp._type == 'int':
e = IntInterval.from_string(s)
else:
e = FloatInterval.from_string(s)
else: #rval != inf
#e.g. 原来是(a,j_3)，现在有[, 99]
#把rval=j_3换成99，变成(a, 99)，然后把这个字符串代入Interval进行初始化
tmp = copy.deepcopy(self.node_list[item])
tmp.rval = str(self.node_list[i].I.upper)
s = str(tmp).replace('-inf',
'').replace('inf',
'') #用字符串来初始化不能有inf，而要留空
if tmp._type == 'int':
e = IntInterval.from_string(s)
else:
e = FloatInterval.from_string(s)
self.node_list[item].I = e
def load_chosen_path(self):
path = []
all_msg_tree_view = self._view.all_msg_scrolled_win.get_child()
focus_segment = all_msg_tree_view.get_selection()
model, _iter = focus_segment.get_selected()
depth = model.iter_depth(_iter)
current__id = model.get_value(_iter, 0)
current_segment_lower_num = model.get_value(_iter, 1)
current_segment_upper_num = model.get_value(_iter, 2)
current_segment = FloatInterval.closed(current_segment_lower_num,
current_segment_upper_num)
path.insert(0, [current__id, current_segment])
parent_iter = _iter
for i in range(depth):
parent_iter = model.iter_parent(parent_iter)
parent_id = model.get_value(parent_iter, 0)
parent_segment_lower_num = model.get_value(parent_iter, 1)
parent_segment_upper_num = model.get_value(parent_iter, 2)
parent_segment = FloatInterval.closed(parent_segment_lower_num,
parent_segment_upper_num)
path.insert(0, [parent_id, parent_segment])
calibrate_parameter = self.load_chosen_parameter()
path.insert(0, [calibrate_parameter, None])
return path
def __init__(self, var_type, interval=None):
super(Var, self).__init__()
self.var_type = var_type
self.interval = {}
if var_type == 'int':
if interval == None:
self.interval['int'] = IntInterval.all()
else:
self.interval['int'] = IntInterval(interval)
elif var_type == 'float':
if interval == None:
self.interval['float'] = FloatInterval.all()
else:
self.interval['float'] = FloatInterval(interval)
def calculate_beta_0_interval(self, alpha=0.05, precision=5):
x_mean = self.x.mean()
delta = x_mean ** 2 / ((self.x - x_mean) ** 2).sum()
delta = math.sqrt(delta + self.x.count() ** -1)
delta = self.calculate_t_distribute().isf(alpha / 2) * delta * self.calculate_std_e()
b0 = self.calculate_Beta0()
return FloatInterval.closed(round(b0 - delta, precision), round(b0 + delta, precision))
def calculate_mean_confidence_interval_large(series, confidence_interval=0.90):
mean = series.mean()
s = math.sqrt(series.var())
count = series.count()
z = norm.isf((1 - confidence_interval) / 2)
delta = round(z * (s / math.sqrt(count)), 1)
return FloatInterval.closed(mean - delta, mean + delta)
def roleta(populacao, start, target):
#Calcula um vetor de fitness dos individuos da populacao
fitPop = []
porcFitness = []
sumFitness = 0
for individuo in populacao:
fitIndividuo = fitness(individuo, start, target)
fitPop.append(fitIndividuo)
sumFitness += fitIndividuo
#Calcula a soma total das fitness
#sumFitness = sum(fitPop)
#Calcula a porcentagem de cada fitness individual em relacao a fitness total
for fit in fitPop:
porcFitness.append(round(fit / sumFitness, 2))
# Calcula a posicao da agulha da roleta - numero entre 0 e 1 randomico
agulha = np.random.uniform(0, max(porcFitness), 1) / sumFitness
#Cria os intervalos da roleta e faz o giro
resultado = 0
a = 0
for porc in porcFitness:
b = a + porc
if float(agulha) in fInterval.closed(a, b):
break
else:
resultado += 1
a = b
return populacao[resultado]
def update_calibrate_parameter_interval_combobox(self, widget):
self.clear_factors_scrolled_win()
self.calibrate_parameter_interval_choose_combobox.clear()
last_segment = self._presenter.load_last_dependency_segment()
default_segment = FloatInterval.closed(2020, 2020)
if last_segment == default_segment:
self.clear_calibrate_parameter_interval_combobox()
else:
try:
parameter_segments = self._presenter.get_calibrate_parameter_segments()
except ValueError:
dialog = Gtk.MessageDialog(parent=self.main_window, flags=0, message_type=Gtk.MessageType.INFO,
buttons=Gtk.ButtonsType.OK, text="提示")
dialog.format_secondary_text("请先选择好各个依赖分段")
dialog.run()
dialog.destroy()
else:
self._parameter_segments = parameter_segments
interval_model = Gtk.ListStore(int, str)
interval_model.append([2020, '--请先选择校正参数区间--'])
count = 0
for segment in parameter_segments:
interval = segment[0]
lower_num = interval.lower
upper_num = interval.upper
interval_model.append([count, '[{}, {}]'.format(lower_num, upper_num)])
count += 1
self.calibrate_parameter_interval_choose_combobox.set_model(interval_model)
cell = Gtk.CellRendererText()
self.calibrate_parameter_interval_choose_combobox.pack_start(cell, True)
self.calibrate_parameter_interval_choose_combobox.add_attribute(cell, 'text', 1)
self.calibrate_parameter_interval_choose_combobox.set_active(0)
def test_floats_with_step(self):
interval = FloatInterval((0, 0.5), step=0.5)
assert interval.lower == 0
assert interval.upper == 0.5
assert not interval.lower_inc
assert not interval.upper_inc
assert interval.step
def calculate_Var_confidence_interval_large(series, confidence_interval=0.95):
count = series.count()
var = series.var()
upper = (count - 1) * var
rv = chi2(count - 1)
alpha = 1 - confidence_interval
return FloatInterval.closed(round(upper / rv.isf(alpha / 2), 2), round(upper / rv.isf(1 - alpha / 2), 2))
def calculate_mean_confidence_interval_small(series, confidence_interval=0.95):
mean = series.mean()
s = math.sqrt(series.var())
count = series.count()
rv = t(count - 1)
z = rv.isf((1 - confidence_interval) / 2)
delta = round(z * (s / math.sqrt(count)), 2)
return FloatInterval.closed(mean - delta, mean + delta)
def calculate_prediction_interval(self, x, alpha=0.05, precision=5):
x_mean = self.x.mean()
n = self.x.count()
delta = (x - x_mean) ** 2 / ((self.x - x_mean) ** 2).sum()
delta = math.sqrt(1 + delta + n ** -1)
delta = self.calculate_t_distribute().isf(alpha / 2) * delta * self.calculate_std_e()
y = x * self.calculate_Beta1() + self.calculate_Beta0()
return FloatInterval.closed(round(y - delta, precision), round(y + delta, precision))
def __init__(self, _type, const):
super(Constnode, self).__init__(_type)
self.const = const
val = float(const)
if val == int(val) and not const.endswith('.0'):
self.I = IntInterval([int(val), int(val)]) #TODO: float
else:
self.I = FloatInterval([val, val]) #TODO: float
def test_string_as_constructor_param(self):
with raises(TypeError) as e:
FloatInterval('(0.2, 0.5)')
assert str(e.value) == (
'First argument should be a list or tuple. If you wish to '
'initialize an interval from string, use from_string factory '
'method.'
)
def link_parameter_nodes_factors(self, repeat_read):
all_factors = self._current_calibrate_msg[6]
for node in self._parameter_nodes:
for segment in node.parameter_segments:
left_num = segment[0][0]
right_num = segment[0][1]
if not repeat_read:
interval = FloatInterval.closed(left_num, right_num)
else:
try:
interval = FloatInterval.closed(left_num, right_num)
except RangeBoundsException:
interval = FloatInterval.closed(left_num, left_num + 2)
segment[0] = interval
transfer_num = segment[1]
factors = all_factors[transfer_num]
segment[1] = factors
def show_two_curves_in_senior(self):
_id, lower_num, upper_num = self.load_chosen_segment()
current_interval = FloatInterval.closed(lower_num, upper_num)
current_factors_str = self.load_chosen_factors()
current_factors = eval(current_factors_str)
modified_factors = self.load_factors_entry()
modified_segment = [current_interval, modified_factors]
current_segment = [current_interval, current_factors]
self._editor.show_two_factors_curve(modified_segment, current_segment)
def load_current_entry(self):
interval_list = []
for entry in self._view.entries:
num = float(entry.get_text())
interval_list.append(num)
lower_num = interval_list[0]
upper_num = interval_list[1]
interval = FloatInterval.closed(lower_num, upper_num)
return interval
def create_dependency_node(segment, dependency_id):
dependency_node = entity.CalibrateFile.CalibrateDependencyNode()
segment_upper = segment[0][1]
segment_lower = segment[0][0]
transfer_num = segment[1]
dependency_node.parameter_id = dependency_id
dependency_node.transfer_num = transfer_num
dependency_node.parameter_segment = FloatInterval.closed(
segment_lower, segment_upper)
return dependency_node
def add_depend_segment_node(self,
parent_node,
child_id,
segment=FloatInterval.closed(
float('-inf'), float('inf'))):
# 当父节点在分支中的位置不是参数节点的前一级时，需考虑添加多级的分段
child_node = CalibrateDependencyNode()
child_node.parameter_segment = segment
child_node.parameter_id = child_id
child_node.parent = parent_node
self._child_node = child_node
def modify_depend_segment(lower_num, upper_num, depend_node):
if not isinstance(depend_node, CalibrateDependencyNode):
raise TypeError
check_lower = isinstance(lower_num, int) or isinstance(
lower_num, float)
check_upper = isinstance(upper_num, int) or isinstance(
upper_num, float)
if not check_upper and not check_lower:
raise ValueError('upper bound or lower bound type error')
new_segment = FloatInterval.closed(lower_num, upper_num)
depend_node.parameter_segment = new_segment
def load_last_dependency_segment(self):
viewport = self._view.dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
boxes = main_box.get_children()
last_box = boxes[-1]
last_combobox = last_box.get_children()[1]
segment_activated = last_combobox.get_active()
model = last_combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
last_segment = FloatInterval.from_string(segment_str)
return last_segment
def add_depend_behind(self, depend_in, new_dependency):
for node in self._root_node.descendants:
if depend_in == node.parameter_id:
children_nodes = node.children
node.children = []
depend_node = CalibrateDependencyNode()
depend_node.parameter_id = new_dependency
default_segment = FloatInterval.closed(float('-inf'),
float('inf'))
depend_node.parameter_segment = default_segment
depend_node.parent = node
depend_node.children = children_nodes
def link_dependency_roots(root_node, entry_dependency):
for segment in entry_dependency[1]:
dependency_segment_node = entity.CalibrateFile.CalibrateDependencyNode(
)
parameter_id = entry_dependency[0]
dependency_segment_node.parameter_id = parameter_id
segment_upper = segment[0][1]
segment_lower = segment[0][0]
transfer_num = segment[1]
interval = FloatInterval.closed(segment_lower, segment_upper)
dependency_segment_node.parameter_segment = interval
dependency_segment_node.transfer_num = transfer_num
dependency_segment_node.parent = root_node
def modify_factors_in_senior(self):
path = self.load_chosen_path()
path = path[:-1]
calibrate_parameter_id, lower_num, upper_num = self.load_chosen_segment(
)
current_factors_str = self.load_chosen_factors()
current_factors = eval(current_factors_str)
current_interval = FloatInterval.closed(lower_num, upper_num)
segment = [current_interval, current_factors]
modified_factors = self.load_factors_entry()
self._editor.modify_parameter_factors_in_senior(
self._channel, calibrate_parameter_id, path, segment,
modified_factors)
def parse_resistivity(model):
"""
Given a Boteler resitivity specification *model* (list of depth
[m] / resistivity [Ohm/m] tuples), return a conductivity map
suitable for func:`conductivity.surface_impedance_1D`.
"""
conductivity_model = OrderedDict()
last_depth = 0
for depth_i, r in model:
bound = FloatInterval.closed_open(last_depth,
last_depth + depth_i)
conductivity_model[bound] = 1 / r
last_depth += depth_i
return conductivity_model
def load_focus_depend(self):
dependencies_segment_choose_scrolled_win = self._view.dependencies_segment_choose_scrolled_win
viewport = dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
focus_box = main_box.get_focus_child()
focus_box_children = focus_box.get_children()
focus_label = focus_box_children[0]
focus_depend_id = int(focus_label.get_text())
focus_combobox = focus_box_children[1]
segment_activated = focus_combobox.get_active()
model = focus_combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
focus_segment = FloatInterval.from_string(segment_str)
return focus_depend_id, focus_segment
def load_chosen_dependency_segment(self, dependency_id):
viewport = self._view.dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
boxes = main_box.get_children()
for box in boxes:
children = box.get_children()
label = children[0]
depend = int(label.get_text())
if dependency_id == depend:
combobox = children[1]
segment_activated = combobox.get_active()
model = combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
segment = FloatInterval.from_string(segment_str)
return segment
def add_depend_before(self, depend_in, new_dependency):
parent_nodes = []
for node in self._root_node.descendants:
if depend_in == node.parameter_id:
parent_node = node.parent
if parent_node not in parent_nodes:
parent_nodes.append(parent_node)
for node_p in parent_nodes:
parent_children = list(node_p.children)
node_p.children = []
depend_node = CalibrateDependencyNode()
depend_node.parameter_id = new_dependency
default_segment = FloatInterval.closed(float('-inf'), float('inf'))
depend_node.parameter_segment = default_segment
depend_node.parent = node_p
depend_node.children = parent_children
def load_parameter_node_path(self):
viewport = self._view.dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
boxes = main_box.get_children()
calibrate_parameter = self.load_chosen_calibrate_parameter()
parameter_path = [[calibrate_parameter, None]]
for box in boxes:
children = box.get_children()
label = children[0]
depend_id = int(label.get_text())
combobox = children[1]
segment_activated = combobox.get_active()
model = combobox.get_model()
_iter = model.get_iter_from_string('{}'.format(segment_activated))
segment_str = model.get_value(_iter, 0)
focus_segment = FloatInterval.from_string(segment_str)
parameter_path.append([depend_id, focus_segment])
return parameter_path
def update_next_depend_segment(self, widget):
self.clear_calibrate_parameter_interval_combobox()
self.clear_factors_scrolled_win()
viewport = self.dependencies_segment_choose_scrolled_win.get_child()
main_box = viewport.get_child()
boxes = main_box.get_children()
focus_depend_id, focus_segment = self._presenter.load_focus_depend()
default_segment = FloatInterval.closed(2020, 2020)
focus_depend_id_index = self._depends_id.index(focus_depend_id)
if focus_depend_id_index+1 < len(self._depends_id):
next_parameter_id = self._depends_id[focus_depend_id_index+1]
next_model = Gtk.ListStore(str, str)
if focus_segment != default_segment:
depend_path = self._presenter.load_depend_path(focus_depend_id)
next_segments = self._presenter.get_depend_segments(depend_path, next_parameter_id)
next_model.append(['[2020, 2020]', '--请先选择依赖区间--'])
for segment in next_segments:
lower_num = segment.lower
upper_num = segment.upper
next_model.append(['[{}, {}]'.format(lower_num, upper_num), '[{}, {}]'.format(lower_num, upper_num)])
next_box = boxes[focus_depend_id_index+1]
next_label = next_box.get_children()[0]
next_depend_id = int(next_label.get_text())
next_combobox = next_box.get_children()[1]
next_combobox.clear()
next_combobox.set_model(next_model)
current_cell = Gtk.CellRendererText()
next_combobox.pack_start(current_cell, True)
next_combobox.add_attribute(current_cell, 'text', 1)
# next_combobox.set_active(0) # 这里的set_active又会跳回去，陷入无限循环，有毒..
if self._is_update_all_dependencies_segment_choose:
if next_depend_id == self._depends_id[-1]:
next_combobox.connect('changed', self.update_calibrate_parameter_interval_combobox)
self._is_update_all_dependencies_segment_choose = False
else:
next_combobox.connect('changed', self.update_next_depend_segment)
else:
for box in boxes[focus_depend_id_index+1:]:
combobox = box.get_children()[1]
combobox.clear()
empty_model = Gtk.ListStore()
combobox.set_model(empty_model)
def parse_conductivity(fid):
"""
Parse a USGS model conductivity file-like object *fid*. Return an
ordered mapping between layer depth interval, [upper depth,
lower_depth) in [m], and the conductivity [(Ohm-m)^{-1}]
"""
for line in fid:
if line.startswith('*/'):
if 'thicknesses' in line.split('!')[1].lower():
thicknesses = map(float, line[1:].split('/')[1].split(',')[:-1])
elif 'resistivities' in line.split('!')[1].lower():
resistivites = map(float, line[1:].split('/')[1].split(',')[:-1])
else:
break
last_depth = 0
bounds = []
for depth in thicknesses:
bounds.append(FloatInterval.closed_open(last_depth, last_depth + depth))
last_depth += depth
return OrderedDict(zip(bounds,
1 / NP.array(resistivites)))
def parse_conductivity(fid):
"""
Parse a USGS model conductivity file-like object *fid*. Return an
ordered mapping between layer depth interval, [upper depth,
lower_depth) in [m], and the conductivity [(Ohm-m)^{-1}]
"""
for line in fid:
if line.startswith('*/'):
if 'thicknesses' in line.split('!')[1].lower():
thicknesses = map(float,
line[1:].split('/')[1].split(',')[:-1])
elif 'resistivities' in line.split('!')[1].lower():
resistivites = map(float,
line[1:].split('/')[1].split(',')[:-1])
else:
break
last_depth = 0
bounds = []
for depth in thicknesses:
bounds.append(FloatInterval.closed_open(last_depth,
last_depth + depth))
last_depth += depth
return OrderedDict(zip(bounds, 1 / NP.array(resistivites)))
def spectrogram_by_eeg_bandwidth(sig, sample_rate=125, timeIntervalSec=30):
slow = FloatInterval.from_string('[0.5, 2.0)')
delta = FloatInterval.from_string('[2, 4.0)')
theta = FloatInterval.from_string('[4.0, 8.0)')
alpha = FloatInterval.from_string('[8.0, 16.0)')
beta = FloatInterval.from_string('[16.0, 32.0)')
gamma = FloatInterval.from_string('[32.0, 100.0)')
##above100Hz = FloatInterval.from_string('[100.0,)')
# no signal above100Hz
nperseg = int(sample_rate * timeIntervalSec)
noverlap = None #will take default value
freqs, times, spec = signal.spectrogram(sig,
fs=sample_rate,
window='hann',
nperseg=sample_rate*timeIntervalSec,
noverlap=noverlap,
detrend=False)
#Edelta, Etheta, Ealpha, Ebeta, Egamma, Eabove100Hz = np.zeros(6)
result = pd.DataFrame(np.zeros(spec.shape[1]*6).reshape((6, spec.shape[1])), index=['Slow', 'Delta', 'Theta', 'Alpha', 'Beta', 'Gamma'])
#
for i in range(0, spec.shape[0]):
for j in range(0, spec.shape[1]):
if freqs[i] in slow:
result.loc["Slow", j]= result.loc["Slow", j] + spec[i, j]
if freqs[i] in delta:
result.loc["Delta", j]= result.loc["Delta", j] + spec[i, j]
elif freqs[i] in theta:
result.loc["Theta", j]= result.loc["Theta", j] + spec[i, j]
elif freqs[i] in alpha:
result.loc["Alpha", j]= result.loc["Alpha", j] + spec[i, j]
elif freqs[i] in beta:
result.loc["Beta", j]= result.loc["Beta", j] + spec[i, j]
elif freqs[i] in gamma:
result.loc["Gamma", j]= result.loc["Gamma", j] + spec[i, j]
# else: we are filtering out frequencies lower than 0.5 Hz
# print("error at cell " + str((i, j)))
return result
def spectrogram_by_eeg_bandwidth(sig, sample_rate=125, timeIntervalSec=2):
delta = FloatInterval.from_string('[0, 4.0)')
theta = FloatInterval.from_string('[4.0, 8.0)')
alpha = FloatInterval.from_string('[8.0, 16.0)')
beta = FloatInterval.from_string('[16.0, 32.0)')
gamma = FloatInterval.from_string('[32.0, 100.0)')
above100Hz = FloatInterval.from_string('[100.0,)')
#
nperseg = int(sample_rate * timeIntervalSec)
noverlap = 0
freqs, times, spec = signal.spectrogram(sig,
fs=sample_rate,
window='hann',
nperseg=sample_rate*timeIntervalSec,
noverlap=noverlap,
detrend=False)
#Edelta, Etheta, Ealpha, Ebeta, Egamma, Eabove100Hz = np.zeros(6)
result = pd.DataFrame(np.zeros(spec.shape[1]*6).reshape((6, spec.shape[1])), index=['Delta', 'Theta', 'Alpha', 'Beta', 'Gamma', 'Above100Hz'])
#
for i in range(0, spec.shape[0]):
for j in range(0, spec.shape[1]):
if freqs[i] in delta:
result.loc["Delta", j]= result.loc["Delta", j] + spec[i, j]
elif freqs[i] in theta:
result.loc["Theta", j]= result.loc["Theta", j] + spec[i, j]
elif freqs[i] in alpha:
result.loc["Alpha", j]= result.loc["Alpha", j] + spec[i, j]
elif freqs[i] in beta:
result.loc["Beta", j]= result.loc["Beta", j] + spec[i, j]
elif freqs[i] in gamma:
result.loc["Gamma", j]= result.loc["Gamma", j] + spec[i, j]
elif freqs[i] in above100Hz:
print(str((i, j)) + "added tp above100Hz")
result.loc["Above100Hz", j]= result.loc["Above100Hz", j] + spec[i, j]
else:
print("error at cell " + str((i, j)))
return result
class TestComparisonOperators(object):
@mark.parametrize(('comparison', 'result'), (
(IntInterval([1, 3]) == IntInterval([1, 3]), True),
(IntInterval([1, 3]) == IntInterval([1, 4]), False),
(IntInterval([inf, inf]) == inf, True),
(IntInterval([3, 3]) == 3, True),
(IntInterval([3, 3]) == 5, False),
(IntInterval('(,)') == None, False)
))
def test_eq_operator(self, comparison, result):
assert comparison is result
@mark.parametrize(('comparison', 'result'), (
(IntInterval([1, 3]) != IntInterval([1, 3]), False),
(IntInterval([1, 3]) != IntInterval([1, 4]), True),
(IntInterval([inf, inf]) != inf, False),
(IntInterval([3, 3]) != 3, False),
(IntInterval([3, 3]) != 5, True),
(IntInterval('(,)') != None, True)
))
def test_ne_operator(self, comparison, result):
assert comparison is result
@mark.parametrize(('comparison', 'result'), (
(IntInterval([1, 3]) > IntInterval([0, 2]), True),
(IntInterval((1, 4)) > 1, False),
(IntInterval((1, 6)) > [1, 6], False),
(IntInterval((1, 6)) > 0, True)
))
def test_gt_operator(self, comparison, result):
assert comparison is result
@mark.parametrize(('comparison', 'result'), (
(IntInterval([1, 3]) >= IntInterval([0, 2]), True),
(IntInterval((1, 4)) >= 1, False),
(IntInterval((1, 6)) >= [1, 6], False),
(IntInterval((1, 6)) >= 0, True)
))
def test_ge_operator(self, comparison, result):
assert comparison is result
@mark.parametrize(('comparison', 'result'), (
(IntInterval([0, 2]) < IntInterval([1, 3]), True),
(IntInterval([2, 3]) < IntInterval([2, 3]), False),
(IntInterval([2, 5]) < 6, True),
(IntInterval([2, 5]) < 5, False),
(IntInterval([2, 5]) < inf, True)
))
def test_lt_operator(self, comparison, result):
assert comparison is result
@mark.parametrize(('comparison', 'result'), (
(IntInterval([0, 2]) <= IntInterval([1, 3]), True),
(IntInterval([1, 3]) <= IntInterval([1, 3]), True),
(IntInterval([1, 7]) <= 8, True),
(IntInterval([1, 6]) <= 5, False),
(IntInterval([1, 5]) <= inf, True)
))
def test_le_operator(self, comparison, result):
assert comparison is result
def test_integer_comparison(self):
assert IntInterval([2, 2]) <= 3
assert IntInterval([1, 3]) >= 0
assert IntInterval([2, 2]) == 2
assert IntInterval([2, 2]) != 3
@mark.parametrize('value', (
IntInterval([0, 2]),
1,
(-1, 1),
))
def test_contains_operator_for_inclusive_interval(self, value):
assert value in IntInterval([-1, 2])
@mark.parametrize('value', (
IntInterval([0, 2]),
2,
'[-1, 1]',
))
def test_contains_operator_for_non_inclusive_interval(self, value):
assert value not in IntInterval((-1, 2))
@mark.parametrize(('interval1', 'interval2', 'expected'), (
(IntInterval((0, 2)), IntInterval((0, 2)), True),
(IntInterval([0, 2]), IntInterval([0, 2]), True),
(IntInterval('[0, 2)'), IntInterval('[0, 2)'), True),
(IntInterval('(0, 2]'), IntInterval('(0, 2]'), True),
(IntInterval((0, 2)), IntInterval((1, 2)), False),
(IntInterval((0, 2)), IntInterval((0, 1)), False),
(IntInterval((0, 2)), IntInterval([0, 1]), False),
(IntInterval((0, 2)), FloatInterval((0, 1)), False),
))
def test_hash_operator_with_interval_attributes(self, interval1, interval2, expected):
actual = (interval1.__hash__() == interval2.__hash__())
assert actual == expected
@mark.parametrize(('contains_check', 'expected'), (
(IntInterval([0, 2]) in {IntInterval([0, 2]): ''}, True),
(IntInterval([0, 2]) in {IntInterval((0, 2)): ''}, False),
(IntInterval([0, 2]) in set([IntInterval([0, 2])]), True),
))
def test_hash_operator_with_collections(self, contains_check, expected):
assert contains_check is expected
def calculate_beta_1_interval(self, alpha=0.05, precision=5):
delta = math.sqrt(((self.x - self.x.mean()) ** 2).sum())
delta = self.calculate_std_e() / delta
delta = self.calculate_t_distribute().isf(alpha / 2) * delta
b1 = self.calculate_Beta1()
return FloatInterval.closed(round(b1 - delta, precision), round(b1 + delta, precision))
class TestIntervalProperties(object):
@mark.parametrize(('number_range', 'length'), (
([1, 4], 3),
([-1, 1], 2),
((-inf, inf), inf),
((1, inf), inf),
))
def test_length(self, number_range, length):
assert IntInterval(number_range).length == length
@mark.parametrize(('number_range', 'radius'), (
([1, 4], 1.5),
([-1, 1], 1.0),
([-4, -1], 1.5),
((-inf, inf), inf),
((1, inf), inf),
))
def test_radius(self, number_range, radius):
assert IntInterval(number_range).radius == radius
@mark.parametrize(('number_range', 'centre'), (
([1, 4], 2.5),
([-1, 1], 0),
([-4, -1], -2.5),
((1, inf), inf),
))
def test_centre(self, number_range, centre):
assert IntInterval(number_range).centre == centre
@mark.parametrize(('number_range', 'is_open'),
(((2, 3), True), ('(2, 5)', True), ('[3, 4)', False),
('(4, 5]', False), ('3 - 4', False), ([4, 5], False),
('[4, 5]', False)))
def test_open(self, number_range, is_open):
assert IntInterval(number_range).open == is_open
@mark.parametrize(
('number_range', 'is_closed'),
(((2, 3), False), ('(2, 5)', False), ('[3, 4)', False),
('(4, 5]', False), ('3 - 4', True), ([4, 5], True), ('[4, 5]', True)))
def test_closed(self, number_range, is_closed):
assert IntInterval(number_range).closed == is_closed
@mark.parametrize(('number_range', 'empty'), (
((2, 3), True),
([2, 3], False),
([2, 2], False),
((2, 2), True),
('[2, 2)', True),
('(2, 2]', True),
('[2, 3)', False),
((2, 10), False),
))
def test_empty(self, number_range, empty):
assert IntInterval(number_range).empty == empty
@mark.parametrize(('number_range', 'degenerate'), (
((2, 4), False),
('(2, 2)', True),
('[0, 0)', True),
))
def test_degenerate(self, number_range, degenerate):
assert IntInterval(number_range).degenerate == degenerate
@mark.parametrize(
('interval', 'discrete'),
((IntInterval((2, 3)), True), (IntInterval(5), True),
(FloatInterval(3.5), False), (DecimalInterval(Decimal('2.4')), False),
(DateTimeInterval(datetime(2002, 1, 1)), False),
(DateInterval(date(2002, 1, 1)), True)))
def test_discrete(self, interval, discrete):
assert interval.discrete == discrete
def calculate_percent_confidence_interval_large(p, n, confidence_interval=0.95):
z = norm.isf((1 - confidence_interval) / 2)
delta = round(z * math.sqrt(((p * (1 - p)) / n)), 4)
return FloatInterval.closed(p - delta, p + delta)
def testCalculateMeanConfidenceIntervalSmall(self):
data = pandas.read_excel('mean_smaller.xlsx').age
expected = FloatInterval.closed(1476.8, 1503.2)
self.assertEqual(expected, sampler.calculate_mean_confidence_interval_small(data))
def testCalculatePercentageConfidenceIntervalLarge(self):
expected = FloatInterval.closed(0.5565, 0.7435)
self.assertEqual(expected, sampler.calculate_percent_confidence_interval_large(0.65,100))
def testCalculatePercentageConfidenceIntervalLarge(self):
data = pandas.read_excel('var_test.xlsx').weight
expected = FloatInterval.closed(56.83, 180.39)
self.assertEqual(expected, sampler.calculate_Var_confidence_interval_large(data))
def testCalculateMeanConfidenceIntervalLarge(self):
data = pandas.read_excel('mean_large.xlsx').age
expected = FloatInterval.closed(37.3689326757, 41.6310673243)
self.assertEqual(expected, sampler.calculate_mean_confidence_interval_large(data))
def test_interval(self):
self.assertEqual(FloatInterval.closed(4.1317,6.13001), self.target.calculate_beta_1_interval())
self.assertEqual(FloatInterval.closed(-4.9130, 554.01347), self.target.calculate_beta_0_interval())
self.assertEqual(FloatInterval.closed(260.13241, 750.745), self.target.calculate_confidence_interval(45))
self.assertEqual(FloatInterval.closed(-293.41195,1304.28935), self.target.calculate_prediction_interval(45))
def test_floats(self):
interval = FloatInterval((0.2, 0.5))
assert interval.lower == 0.2
assert interval.upper == 0.5
assert not interval.lower_inc
assert not interval.upper_inc
