def __init__(self):
if(isinstance(self.__class__.KeysetDict, set)):
self.KeysetDict = {i: None for i in
self.__class__.KeysetDict}
elif(isinstance(self.__class__.KeysetDict, dict)):
self.KeysetDict = ccopy(self.__class__.KeysetDict)
else:
try:
# Attempt to coerce to iterable.
self.KeysetDict = {i: None for i in
list(self.__class__.KeysetDict)}
except TypeError:
raise IllegalArgumentError("KeysetDict not iterable...")
def concat(*args, dim=None, copy=True, inplace=False, reset_dim=True):
"""Concatenate InferenceData objects.
Concatenates over `group`, `chain` or `draw`.
By default concatenates over unique groups.
To concatenate over `chain` or `draw` function
needs identical groups and variables.
The `variables` in the `data` -group are merged if `dim` are not found.
Parameters
----------
*args : InferenceData
Variable length InferenceData list or
Sequence of InferenceData.
dim : str, optional
If defined, concatenated over the defined dimension.
Dimension which is concatenated. If None, concatenates over
unique groups.
copy : bool
If True, groups are copied to the new InferenceData object.
Used only if `dim` is None.
inplace : bool
If True, merge args to first object.
reset_dim : bool
Valid only if dim is not None.
Returns
-------
InferenceData
A new InferenceData object by default.
When `inplace==True` merge args to first arg and return `None`
Examples
--------
Use ``concat`` method to concatenate InferenceData objects. This will concatenates over
unique groups by default. We first create an ``InferenceData`` object:
.. ipython::
In [1]: import arviz as az
...: import numpy as np
...: data = {
...: "a": np.random.normal(size=(4, 100, 3)),
...: "b": np.random.normal(size=(4, 100)),
...: }
...: coords = {"a_dim": ["x", "y", "z"]}
...: dataA = az.from_dict(data, coords=coords, dims={"a": ["a_dim"]})
...: dataA
We have created an ``InferenceData`` object with default group 'posterior'. Now, we will
create another ``InferenceData`` object:
.. ipython::
In [1]: dataB = az.from_dict(prior=data, coords=coords, dims={"a": ["a_dim"]})
...: dataB
We have created another ``InferenceData`` object with group 'prior'. Now, we will concatenate
these two ``InferenceData`` objects:
.. ipython::
In [1]: az.concat(dataA, dataB)
Now, we will concatenate over chain (or draw). It requires identical groups and variables.
Here we are concatenating two identical ``InferenceData`` objects over dimension chain:
.. ipython::
In [1]: az.concat(dataA, dataA, dim="chain")
It will create an ``InferenceData`` with the original group 'posterior'. In similar way,
we can also concatenate over draws.
"""
# pylint: disable=undefined-loop-variable, too-many-nested-blocks
if len(args) == 0:
if inplace:
return
return InferenceData()
if len(args) == 1 and isinstance(args[0], Sequence):
args = args[0]
# assert that all args are InferenceData
for i, arg in enumerate(args):
if not isinstance(arg, InferenceData):
raise TypeError(
"Concatenating is supported only"
"between InferenceData objects. Input arg {} is {}".format(
i, type(arg)))
if dim is not None and dim.lower() not in {"group", "chain", "draw"}:
msg = "Invalid `dim`: {}. Valid `dim` are {}".format(
dim, '{"group", "chain", "draw"}')
raise TypeError(msg)
dim = dim.lower() if dim is not None else dim
if len(args) == 1 and isinstance(args[0], InferenceData):
if inplace:
return None
else:
if copy:
return deepcopy(args[0])
else:
return args[0]
current_time = str(datetime.now())
if not inplace:
# Keep order for python 3.5
inference_data_dict = OrderedDict()
if dim is None:
arg0 = args[0]
arg0_groups = ccopy(arg0._groups_all)
args_groups = dict()
# check if groups are independent
# Concat over unique groups
for arg in args[1:]:
for group in arg._groups_all:
if group in args_groups or group in arg0_groups:
msg = (
"Concatenating overlapping groups is not supported unless `dim` is defined."
)
msg += " Valid dimensions are `chain` and `draw`."
raise TypeError(msg)
group_data = getattr(arg, group)
args_groups[group] = deepcopy(
group_data) if copy else group_data
# add arg0 to args_groups if inplace is False
# otherwise it will merge args_groups to arg0
# inference data object
if not inplace:
for group in arg0_groups:
group_data = getattr(arg0, group)
args_groups[group] = deepcopy(
group_data) if copy else group_data
other_groups = [
group for group in args_groups if group not in SUPPORTED_GROUPS_ALL
]
for group in SUPPORTED_GROUPS_ALL + other_groups:
if group not in args_groups:
continue
if inplace:
if group.startswith(WARMUP_TAG):
arg0._groups_warmup.append(group)
else:
arg0._groups.append(group)
setattr(arg0, group, args_groups[group])
else:
inference_data_dict[group] = args_groups[group]
if inplace:
other_groups = [
group
for group in arg0_groups if group not in SUPPORTED_GROUPS_ALL
] + other_groups
sorted_groups = [
group for group in SUPPORTED_GROUPS + other_groups
if group in arg0._groups
]
setattr(arg0, "_groups", sorted_groups)
sorted_groups_warmup = [
group for group in SUPPORTED_GROUPS_WARMUP + other_groups
if group in arg0._groups_warmup
]
setattr(arg0, "_groups_warmup", sorted_groups_warmup)
else:
arg0 = args[0]
arg0_groups = arg0._groups_all
for arg in args[1:]:
for group0 in arg0_groups:
if group0 not in arg._groups_all:
if group0 == "observed_data":
continue
msg = "Mismatch between the groups."
raise TypeError(msg)
for group in arg._groups_all:
# handle data groups seperately
if group not in [
"observed_data", "constant_data",
"predictions_constant_data"
]:
# assert that groups are equal
if group not in arg0_groups:
msg = "Mismatch between the groups."
raise TypeError(msg)
# assert that variables are equal
group_data = getattr(arg, group)
group_vars = group_data.data_vars
if not inplace and group in inference_data_dict:
group0_data = inference_data_dict[group]
else:
group0_data = getattr(arg0, group)
group0_vars = group0_data.data_vars
for var in group0_vars:
if var not in group_vars:
msg = "Mismatch between the variables."
raise TypeError(msg)
for var in group_vars:
if var not in group0_vars:
msg = "Mismatch between the variables."
raise TypeError(msg)
var_dims = getattr(group_data, var).dims
var0_dims = getattr(group0_data, var).dims
if var_dims != var0_dims:
msg = "Mismatch between the dimensions."
raise TypeError(msg)
if dim not in var_dims or dim not in var0_dims:
msg = "Dimension {} missing.".format(dim)
raise TypeError(msg)
# xr.concat
concatenated_group = xr.concat((group_data, group0_data),
dim=dim)
if reset_dim:
concatenated_group[dim] = range(
concatenated_group[dim].size)
# handle attrs
if hasattr(group0_data, "attrs"):
group0_attrs = deepcopy(getattr(group0_data, "attrs"))
else:
group0_attrs = OrderedDict()
if hasattr(group_data, "attrs"):
group_attrs = getattr(group_data, "attrs")
else:
group_attrs = dict()
# gather attrs results to group0_attrs
for attr_key, attr_values in group_attrs.items():
group0_attr_values = group0_attrs.get(attr_key, None)
equality = attr_values == group0_attr_values
if hasattr(equality, "__iter__"):
equality = np.all(equality)
if equality:
continue
# handle special cases:
if attr_key in ("created_at", "previous_created_at"):
# check the defaults
if not hasattr(group0_attrs,
"previous_created_at"):
group0_attrs["previous_created_at"] = []
if group0_attr_values is not None:
group0_attrs["previous_created_at"].append(
group0_attr_values)
# check previous values
if attr_key == "previous_created_at":
if not isinstance(attr_values, list):
attr_values = [attr_values]
group0_attrs["previous_created_at"].extend(
attr_values)
continue
# update "created_at"
if group0_attr_values != current_time:
group0_attrs[attr_key] = current_time
group0_attrs["previous_created_at"].append(
attr_values)
elif attr_key in group0_attrs:
combined_key = "combined_{}".format(attr_key)
if combined_key not in group0_attrs:
group0_attrs[combined_key] = [
group0_attr_values
]
group0_attrs[combined_key].append(attr_values)
else:
group0_attrs[attr_key] = attr_values
# update attrs
setattr(concatenated_group, "attrs", group0_attrs)
if inplace:
setattr(arg0, group, concatenated_group)
else:
inference_data_dict[group] = concatenated_group
else:
# observed_data, "constant_data", "predictions_constant_data",
if group not in arg0_groups:
setattr(arg0, group,
deepcopy(group_data) if copy else group_data)
arg0._groups.append(group)
continue
# assert that variables are equal
group_data = getattr(arg, group)
group_vars = group_data.data_vars
group0_data = getattr(arg0, group)
if not inplace:
group0_data = deepcopy(group0_data)
group0_vars = group0_data.data_vars
for var in group_vars:
if var not in group0_vars:
var_data = getattr(group_data, var)
getattr(arg0, group)[var] = var_data
else:
var_data = getattr(group_data, var)
var0_data = getattr(group0_data, var)
if dim in var_data.dims and dim in var0_data.dims:
concatenated_var = xr.concat(
(group_data, group0_data), dim=dim)
group0_data[var] = concatenated_var
# handle attrs
if hasattr(group0_data, "attrs"):
group0_attrs = getattr(group0_data, "attrs")
else:
group0_attrs = OrderedDict()
if hasattr(group_data, "attrs"):
group_attrs = getattr(group_data, "attrs")
else:
group_attrs = dict()
# gather attrs results to group0_attrs
for attr_key, attr_values in group_attrs.items():
group0_attr_values = group0_attrs.get(attr_key, None)
equality = attr_values == group0_attr_values
if hasattr(equality, "__iter__"):
equality = np.all(equality)
if equality:
continue
# handle special cases:
if attr_key in ("created_at", "previous_created_at"):
# check the defaults
if not hasattr(group0_attrs,
"previous_created_at"):
group0_attrs["previous_created_at"] = []
if group0_attr_values is not None:
group0_attrs["previous_created_at"].append(
group0_attr_values)
# check previous values
if attr_key == "previous_created_at":
if not isinstance(attr_values, list):
attr_values = [attr_values]
group0_attrs["previous_created_at"].extend(
attr_values)
continue
# update "created_at"
if group0_attr_values != current_time:
group0_attrs[attr_key] = current_time
group0_attrs["previous_created_at"].append(
attr_values)
elif attr_key in group0_attrs:
combined_key = "combined_{}".format(attr_key)
if combined_key not in group0_attrs:
group0_attrs[combined_key] = [
group0_attr_values
]
group0_attrs[combined_key].append(attr_values)
else:
group0_attrs[attr_key] = attr_values
# update attrs
setattr(group0_data, "attrs", group0_attrs)
if inplace:
setattr(arg0, group, group0_data)
else:
inference_data_dict[group] = group0_data
return None if inplace else InferenceData(**inference_data_dict)
def concat(*args, copy=True, inplace=False):
"""Concatenate InferenceData objects on a group level.
Supports only concatenating with independent unique groups.
Parameters
----------
*args : InferenceData
Variable length InferenceData list or
Sequence of InferenceData.
copy : bool
If True, groups are copied to the new InferenceData object.
inplace : bool
If True, merge args to first object.
Returns
-------
InferenceData
A new InferenceData object by default.
When `inplace==True` merge args to first arg and return `None`
"""
if len(args) == 0:
return InferenceData()
if len(args) == 1 and isinstance(args[0], Sequence):
args = args[0]
elif len(args) == 1:
if isinstance(args[0], InferenceData):
if inplace:
return None
else:
if copy:
return deepcopy(args[0])
else:
return args[0]
# assert that all args are InferenceData
for i, arg in enumerate(args):
if not isinstance(arg, InferenceData):
raise TypeError(
"Concatenating is supported only"
"between InferenceData objects. Input arg {} is {}".format(
i, type(arg)))
# assert that groups are independent
first_arg = args[0]
first_arg_groups = ccopy(first_arg._groups)
args_groups = dict()
for arg in args[1:]:
for group in arg._groups:
if group in args_groups or group in first_arg_groups:
raise NotImplementedError(
"Concatenating with overlapping groups is not supported.")
group_data = getattr(arg, group)
args_groups[group] = deepcopy(group_data) if copy else group_data
# add first_arg to args_groups if inplace is False
if not inplace:
for group in first_arg_groups:
group_data = getattr(first_arg, group)
args_groups[group] = deepcopy(group_data) if copy else group_data
basic_order = [
"posterior",
"posterior_predictive",
"sample_stats",
"prior",
"prior_predictive",
"sample_stats_prior",
"observed_data",
]
other_groups = [group for group in args_groups if group not in basic_order]
if not inplace:
# Keep order for python 3.5
inference_data_dict = OrderedDict()
for group in basic_order + other_groups:
if group not in args_groups:
continue
if inplace:
first_arg._groups.append(group)
setattr(first_arg, group, args_groups[group])
else:
inference_data_dict[group] = args_groups[group]
if inplace:
other_groups = [
group for group in first_arg_groups if group not in basic_order
] + other_groups
sorted_groups = [
group for group in basic_order + other_groups
if group in first_arg._groups
]
setattr(first_arg, "_groups", sorted_groups)
return None
return InferenceData(**inference_data_dict)
def concat(*args, dim=None, copy=True, inplace=False, reset_dim=True):
"""Concatenate InferenceData objects.
Concatenates over `group`, `chain` or `draw`.
By default concatenates over unique groups.
To concatenate over `chain` or `draw` function
needs identical groups and variables.
The `variables` in the `data` -group are merged if `dim` are not found.
Parameters
----------
*args : InferenceData
Variable length InferenceData list or
Sequence of InferenceData.
dim : str, optional
If defined, concatenated over the defined dimension.
Dimension which is concatenated. If None, concatenates over
unique groups.
copy : bool
If True, groups are copied to the new InferenceData object.
Used only if `dim` is None.
inplace : bool
If True, merge args to first object.
reset_dim : bool
Valid only if dim is not None.
Returns
-------
InferenceData
A new InferenceData object by default.
When `inplace==True` merge args to first arg and return `None`
"""
# pylint: disable=undefined-loop-variable, too-many-nested-blocks
if len(args) == 0:
if inplace:
return
return InferenceData()
if len(args) == 1 and isinstance(args[0], Sequence):
args = args[0]
# assert that all args are InferenceData
for i, arg in enumerate(args):
if not isinstance(arg, InferenceData):
raise TypeError(
"Concatenating is supported only"
"between InferenceData objects. Input arg {} is {}".format(
i, type(arg)))
if dim is not None and dim.lower() not in {"group", "chain", "draw"}:
msg = "Invalid `dim`: {}. Valid `dim` are {}".format(
dim, '{"group", "chain", "draw"}')
raise TypeError(msg)
dim = dim.lower() if dim is not None else dim
if len(args) == 1 and isinstance(args[0], InferenceData):
if inplace:
return None
else:
if copy:
return deepcopy(args[0])
else:
return args[0]
current_time = str(datetime.now())
if not inplace:
# Keep order for python 3.5
inference_data_dict = OrderedDict()
if dim is None:
arg0 = args[0]
arg0_groups = ccopy(arg0._groups)
args_groups = dict()
# check if groups are independent
# Concat over unique groups
for arg in args[1:]:
for group in arg._groups:
if group in args_groups or group in arg0_groups:
msg = (
"Concatenating overlapping groups is not supported unless `dim` is defined."
)
msg += " Valid dimensions are `chain` and `draw`."
raise TypeError(msg)
group_data = getattr(arg, group)
args_groups[group] = deepcopy(group_data) if copy else group_data
# add arg0 to args_groups if inplace is False
if not inplace:
for group in arg0_groups:
group_data = getattr(arg0, group)
args_groups[group] = deepcopy(
group_data) if copy else group_data
basic_order = [
"posterior",
"posterior_predictive",
"sample_stats",
"prior",
"prior_predictive",
"sample_stats_prior",
"observed_data",
]
other_groups = [
group for group in args_groups if group not in basic_order
]
for group in basic_order + other_groups:
if group not in args_groups:
continue
if inplace:
arg0._groups.append(group)
setattr(arg0, group, args_groups[group])
else:
inference_data_dict[group] = args_groups[group]
if inplace:
other_groups = [
group for group in arg0_groups if group not in basic_order
] + other_groups
sorted_groups = [
group for group in basic_order + other_groups
if group in arg0._groups
]
setattr(arg0, "_groups", sorted_groups)
else:
arg0 = args[0]
arg0_groups = arg0._groups
for arg in args[1:]:
for group0 in arg0_groups:
if group0 not in arg._groups:
if group0 == "observed_data":
continue
msg = "Mismatch between the groups."
raise TypeError(msg)
for group in arg._groups:
if group != "observed_data":
# assert that groups are equal
if group not in arg0_groups:
msg = "Mismatch between the groups."
raise TypeError(msg)
# assert that variables are equal
group_data = getattr(arg, group)
group_vars = group_data.data_vars
if not inplace and group in inference_data_dict:
group0_data = inference_data_dict[group]
else:
group0_data = getattr(arg0, group)
group0_vars = group0_data.data_vars
for var in group0_vars:
if var not in group_vars:
msg = "Mismatch between the variables."
raise TypeError(msg)
for var in group_vars:
if var not in group0_vars:
msg = "Mismatch between the variables."
raise TypeError(msg)
var_dims = getattr(group_data, var).dims
var0_dims = getattr(group0_data, var).dims
if var_dims != var0_dims:
msg = "Mismatch between the dimensions."
raise TypeError(msg)
if dim not in var_dims or dim not in var0_dims:
msg = "Dimension {} missing.".format(dim)
raise TypeError(msg)
# xr.concat
concatenated_group = xr.concat((group_data, group0_data),
dim=dim)
if reset_dim:
concatenated_group[dim] = range(
concatenated_group[dim].size)
# handle attrs
if hasattr(group0_data, "attrs"):
group0_attrs = deepcopy(getattr(group0_data, "attrs"))
else:
group0_attrs = OrderedDict()
if hasattr(group_data, "attrs"):
group_attrs = getattr(group_data, "attrs")
else:
group_attrs = dict()
# gather attrs results to group0_attrs
for attr_key, attr_values in group_attrs.items():
group0_attr_values = group0_attrs.get(attr_key, None)
equality = attr_values == group0_attr_values
if hasattr(equality, "__iter__"):
equality = np.all(equality)
if equality:
continue
# handle special cases:
if attr_key in ("created_at", "previous_created_at"):
# check the defaults
if not hasattr(group0_attrs,
"previous_created_at"):
group0_attrs["previous_created_at"] = []
if group0_attr_values is not None:
group0_attrs["previous_created_at"].append(
group0_attr_values)
# check previous values
if attr_key == "previous_created_at":
if not isinstance(attr_values, list):
attr_values = [attr_values]
group0_attrs["previous_created_at"].extend(
attr_values)
continue
# update "created_at"
if group0_attr_values != current_time:
group0_attrs[attr_key] = current_time
group0_attrs["previous_created_at"].append(
attr_values)
elif attr_key in group0_attrs:
combined_key = "combined_{}".format(attr_key)
if combined_key not in group0_attrs:
group0_attrs[combined_key] = [
group0_attr_values
]
group0_attrs[combined_key].append(attr_values)
else:
group0_attrs[attr_key] = attr_values
# update attrs
setattr(concatenated_group, "attrs", group0_attrs)
if inplace:
setattr(arg0, group, concatenated_group)
else:
inference_data_dict[group] = concatenated_group
else:
# observed_data
if group not in arg0_groups:
setattr(arg0, group,
deepcopy(group_data) if copy else group_data)
arg0._groups.append(group)
continue
# assert that variables are equal
group_data = getattr(arg, group)
group_vars = group_data.data_vars
group0_data = getattr(arg0, group)
if not inplace:
group0_data = deepcopy(group0_data)
group0_vars = group0_data.data_vars
for var in group_vars:
if var not in group0_vars:
var_data = getattr(group_data, var)
arg0.observed_data[var] = var_data
else:
var_data = getattr(group_data, var)
var0_data = getattr(group0_data, var)
if dim in var_data.dims and dim in var0_data.dims:
concatenated_var = xr.concat(
(group_data, group0_data), dim=dim)
group0_data[var] = concatenated_var
# handle attrs
if hasattr(group0_data, "attrs"):
group0_attrs = getattr(group0_data, "attrs")
else:
group0_attrs = OrderedDict()
if hasattr(group_data, "attrs"):
group_attrs = getattr(group_data, "attrs")
else:
group_attrs = dict()
# gather attrs results to group0_attrs
for attr_key, attr_values in group_attrs.items():
group0_attr_values = group0_attrs.get(attr_key, None)
equality = attr_values == group0_attr_values
if hasattr(equality, "__iter__"):
equality = np.all(equality)
if equality:
continue
# handle special cases:
if attr_key in ("created_at", "previous_created_at"):
# check the defaults
if not hasattr(group0_attrs,
"previous_created_at"):
group0_attrs["previous_created_at"] = []
if group0_attr_values is not None:
group0_attrs["previous_created_at"].append(
group0_attr_values)
# check previous values
if attr_key == "previous_created_at":
if not isinstance(attr_values, list):
attr_values = [attr_values]
group0_attrs["previous_created_at"].extend(
attr_values)
continue
# update "created_at"
if group0_attr_values != current_time:
group0_attrs[attr_key] = current_time
group0_attrs["previous_created_at"].append(
attr_values)
elif attr_key in group0_attrs:
combined_key = "combined_{}".format(attr_key)
if combined_key not in group0_attrs:
group0_attrs[combined_key] = [
group0_attr_values
]
group0_attrs[combined_key].append(attr_values)
else:
group0_attrs[attr_key] = attr_values
# update attrs
setattr(group0_data, "attrs", group0_attrs)
if inplace:
setattr(arg0, group, group0_data)
else:
inference_data_dict[group] = group0_data
return None if inplace else InferenceData(**inference_data_dict)
def copy_header(self):
ne = ccopy(self)
ne.matchedtrades = [] #清除trades
ne.datemap = {} #清除datemap
return ne
def copy(self):
return ccopy(self)
def flush(self):
"""Return the whole stack and delete it afterwards"""
old = ccopy(self._stack)
del self._stack[:]
return Stack(old)
def lookup(self):
"""
Return the stack without touch the stack itself.
That way you can use it to lookup the current stack.
"""
return Stack(ccopy(self._stack))
def flush(self):
"""Return the whole stack and delete it afterwards"""
old = ccopy(self._stack)
del self._stack[:]
return Stack(old)
def lookup(self):
"""
Return the stack without touch the stack itself.
That way you can use it to lookup the current stack.
"""
return Stack(ccopy(self._stack))
def copy_header(self):
ne = ccopy(self)
ne.matchedtrades = [] #清除trades
ne.datemap = {} #清除datemap
return ne
def copy(self):
return ccopy(self)
def device_selected(device):
close_device()
app.gui_panel.btn_chkaddr.Hide()
app.gui_panel.coins_choice.Disable()
app.gui_panel.coins_choice.SetSelection(0)
app.gui_panel.network_choice.Clear()
app.gui_panel.network_choice.Disable()
app.gui_panel.wallopt_choice.Clear()
erase_info(True)
sel_device = device.GetInt()
device_sel_name = DEVICES_LIST[sel_device - 1]
coins_list = ccopy(SUPPORTED_COINS)
if device_sel_name == "Ledger":
coins_list = [
"ETH",
"BSC",
"MATIC",
"FTM",
"OP",
"METIS",
"CELO",
"GLMR",
"ARB",
"AVAX",
]
if device_sel_name == "OpenPGP":
coins_list.remove("SOL")
if device_sel_name == "Cryptnox":
coins_list.remove("SOL")
app.load_coins_list(coins_list)
if sel_device == 1:
# Seed Watcher
start_seedwatcher(app, cb_open_wallet)
if sel_device > 1:
# Real keys device
the_device = get_device_class(device_sel_name)
try:
device_loaded = the_device()
except Exception as exc:
app.gui_panel.devices_choice.SetSelection(0)
logger.error("Error during device loading : %s",
str(exc),
exc_info=exc,
stack_info=True)
warn_modal(str(exc))
return
pin_left = -1
password_default = device_loaded.default_password
while True:
try:
pwd_pin = the_device.password_name
if the_device.has_password:
if (not the_device.password_retries_inf and pin_left == -1
and device_loaded.is_init()):
# Goto password exception to ask for user PIN
pin_left = device_loaded.get_pw_left()
if the_device.is_HD:
HDwallet_settings = app.hd_setup("")
if HDwallet_settings is None:
app.gui_panel.devices_choice.SetSelection(0)
return
raise pwdException
# Can raise notinit
device_loaded.open_account(password_default)
else:
device_loaded.open_account()
break
except NotinitException:
if the_device.is_HD:
mnemonic = ""
if not the_device.internally_gen_keys:
# Mnemonic generated by the software wallet
mnemonic = device_loaded.generate_mnemonic(
) # mnemonic proposal
# Get settings from the user
HDwallet_settings = app.hd_setup(mnemonic)
if HDwallet_settings is None:
app.gui_panel.devices_choice.SetSelection(0)
return
if the_device.has_admin_password:
set_admin_message = (
f"Choose the {the_device.admin_pass_name} to init "
f"the {device_sel_name} device.\n")
set_admin_message += "\nFor demo, quick insecure setup, it can be left blank,\n"
set_admin_message += (
f"and a default {the_device.admin_pass_name} will be used.\n\n"
)
lenmsg = (
str(device_loaded.admin_pwd_minlen)
if device_loaded.admin_pwd_minlen
== device_loaded.admin_pwd_maxlen else
f"between {device_loaded.admin_pwd_minlen} and {device_loaded.admin_pwd_maxlen}"
)
set_admin_message += (
f"The chosen {the_device.admin_pass_name} must be\n{lenmsg} chars long."
)
while True:
admin_password = get_password(device_sel_name,
set_admin_message)
if admin_password is None:
app.gui_panel.devices_choice.SetSelection(0)
return
if admin_password == "":
admin_password = device_loaded.default_admin_password
if (len(admin_password) >=
device_loaded.admin_pwd_minlen
and len(admin_password) <=
device_loaded.admin_pwd_maxlen):
break
warn_modal(
f"{the_device.admin_pass_name} shall be {lenmsg} chars long.",
True,
)
if the_device.has_password:
inp_message = f"Choose your {pwd_pin} to setup the {device_sel_name} wallet.\n"
inp_message += "\nFor demo, quick insecure setup, it can be left blank,\n"
inp_message += f"and a default {pwd_pin} will be used.\n\n"
lenmsg = (
str(device_loaded.password_min_len)
if device_loaded.password_min_len
== device_loaded.password_max_len else
f"between {device_loaded.password_min_len} and {device_loaded.password_max_len}"
)
pintype = "chars"
if device_loaded.is_pin_numeric:
pintype = "digits"
inp_message += f"The chosen {pwd_pin} must be\n{lenmsg} {pintype} long."
while True:
password = get_password(device_sel_name, inp_message)
if password is None:
app.gui_panel.devices_choice.SetSelection(0)
return
if password == "":
password = device_loaded.default_password
if (len(password) >= device_loaded.password_min_len and
len(password) <= device_loaded.password_max_len
and (not device_loaded.is_pin_numeric
or password.isdigit())):
break
wmsg = f"Device {pwd_pin} shall be {lenmsg} {pintype} long."
if device_loaded.is_pin_numeric:
wmsg += f"\n\nThe {pwd_pin} must be {pintype} (0-9) only."
warn_modal(
wmsg,
True,
)
try:
if the_device.has_admin_password:
device_loaded.set_admin(admin_password)
if the_device.is_HD:
if the_device.has_password:
HDwallet_settings["file_password"] = password
device_loaded.initialize_device(HDwallet_settings)
elif the_device.has_password:
device_loaded.initialize_device(password)
else:
device_loaded.initialize_device()
break
except Exception as exc:
app.gui_panel.devices_choice.SetSelection(0)
logger.error(
"Error during device initialization : %s",
{str(exc)},
exc_info=exc,
stack_info=True,
)
warn_modal(str(exc))
return
except pwdException as excp:
if not device_loaded.password_retries_inf:
try:
pin_left = device_loaded.get_pw_left()
except Exception as exc:
device_error(exc)
return
if pin_left == 0:
warn_modal(f"Device {pwd_pin} is locked.")
return
if (the_device.password_softlock > 0
and pin_left == the_device.password_softlock
and str(excp) == "0"):
warn_modal(
f"Device {pwd_pin} is soft locked. Restart it to try again."
)
return
while True:
inp_message = f"Input your {device_sel_name} wallet {pwd_pin}.\n"
if not the_device.password_retries_inf:
inp_message += (
f"{pin_left} {pwd_pin} tr{'ies' if pin_left >=2 else 'y'}"
" left on this device.\n")
lenmsg = (
str(device_loaded.password_min_len)
if device_loaded.password_min_len
== device_loaded.password_max_len else
f"between {device_loaded.password_min_len} and {device_loaded.password_max_len}"
)
pintype = "chars"
if device_loaded.is_pin_numeric:
pintype = "digits"
inp_message += f"\nThe {pwd_pin} to provide\nis {lenmsg} {pintype} long."
password_default = get_password(device_sel_name,
inp_message)
if password_default is None:
app.gui_panel.devices_choice.SetSelection(0)
return
if (len(password_default) >= device_loaded.password_min_len
and len(password_default) <=
device_loaded.password_max_len
and (not device_loaded.is_pin_numeric
or password_default.isdigit())):
break
wmsg = f"Device {pwd_pin} shall be {lenmsg} {pintype} long."
if device_loaded.is_pin_numeric:
wmsg += f"\n\nThe {pwd_pin} must be {pintype} (0-9) only."
warn_modal(
wmsg,
True,
)
except Exception as exc:
return device_error(exc)
wx.MilliSleep(100)
if the_device.has_password and the_device.is_HD and not the_device.password_retries_inf:
# Kind of special for Cryptnox for now
device_loaded.set_path(HDwallet_settings)
app.device = device_loaded
if app.device.created:
info_modal(
"Device created",
f"A new {device_sel_name} device was successfully created.",
)
app.gui_panel.coins_choice.Enable()
app.gui_panel.coins_choice.Hide()
app.gui_panel.coins_choice.ShowWithEffect(wx.SHOW_EFFECT_ROLL_TO_RIGHT,
750)
app.gui_panel.coins_choice.SetFocus()
else:
erase_info(True)
