def migrate_settings(cls, settings, version):
if (version is None or version < 2) and \
("limit_lower" in settings and "limit_upper" in settings):
# v2 changed limit_lower, limit_upper to per filter limits stored
# in a single dict
lower = settings.pop("limit_lower")
upper = settings.pop("limit_upper")
settings["thresholds"] = {
(Cells, TotalCounts): (lower, upper),
(Cells, DetectionCount): (lower, upper),
(Genes, TotalCounts): (lower, upper),
(Genes, DetectionCount): (lower, upper),
(Data, -1): (lower, upper),
}
if version == 2:
thresholds = settings["thresholds"]
c = thresholds.pop(Cells)
g = thresholds.pop(Genes)
d = thresholds.pop(Data)
thresholds = {
(Cells, TotalCounts): c,
(Cells, DetectionCount): c,
(Genes, TotalCounts): g,
(Genes, DetectionCount): g,
(Data, -1): d,
}
settings["thresholds"] = thresholds
def migrate_settings(cls, settings, version):
if version < 2:
settings.pop('selected_tree_index', None)
v1_min, v1_max = 20, 150
v2_min, v2_max = 100, 400
ratio = (v2_max - v2_min) / (v1_max - v1_min)
settings['zoom'] = int(ratio * (settings['zoom'] - v1_min) + v2_min)
def migrate_settings(cls, settings, version):
if version < 2:
settings.pop('selected_tree_index', None)
v1_min, v1_max = 20, 150
v2_min, v2_max = 100, 400
ratio = (v2_max - v2_min) / (v1_max - v1_min)
settings['zoom'] = int(ratio * (settings['zoom'] - v1_min) + v2_min)
def migrate_to_version_2():
settings["selected_source"] = settings.pop("selected_db_source",
"")
settings["selected_organism"] = settings.pop("selected_group", "")
if "context_settings" in settings:
for co in settings["context_settings"]:
co.values["selected_genes"] = [
g[0] + g[1] for g in co.values["selected_genes"]
]
def migrate_settings(cls, settings, version):
if version is not None and version < 3:
def st2cl(state: bool) -> Clustering:
return Clustering.OrderedClustering if state else \
Clustering.None_
rc = settings.pop("row_clustering", False)
cc = settings.pop("col_clustering", False)
settings["row_clustering_method"] = st2cl(rc).name
settings["col_clustering_method"] = st2cl(cc).name
def migrate_settings(cls, settings, version):
if (version is None or version < 2) and \
("limit_lower" in settings and "limit_upper" in settings):
# v2 changed limit_lower, limit_upper to per filter limits stored
# in a single dict
lower = settings.pop("limit_lower")
upper = settings.pop("limit_upper")
settings["thresholds"] = {
Cells: (lower, upper),
Genes: (lower, upper),
Data: (lower, upper),
}
def migrate_settings(cls, settings, version):
if "variance_covered" in settings:
# Due to the error in gh-1896 the variance_covered was persisted
# as a NaN value, causing a TypeError in the widgets `__init__`.
vc = settings["variance_covered"]
if isinstance(vc, numbers.Real):
if numpy.isfinite(vc):
vc = int(vc)
else:
vc = 100
settings["variance_covered"] = vc
if settings.get("ncomponents", 0) > MAX_COMPONENTS:
settings["ncomponents"] = MAX_COMPONENTS
# Remove old `decomposition_idx` when SVD was still included
settings.pop("decomposition_idx", None)
def migrate_settings(cls, settings, version):
if "variance_covered" in settings:
# Due to the error in gh-1896 the variance_covered was persisted
# as a NaN value, causing a TypeError in the widgets `__init__`.
vc = settings["variance_covered"]
if isinstance(vc, numbers.Real):
if numpy.isfinite(vc):
vc = int(vc)
else:
vc = 100
settings["variance_covered"] = vc
if settings.get("ncomponents", 0) > MAX_COMPONENTS:
settings["ncomponents"] = MAX_COMPONENTS
# Remove old `decomposition_idx` when SVD was still included
settings.pop("decomposition_idx", None)
# Remove RemotePCA settings
settings.pop("batch_size", None)
settings.pop("address", None)
settings.pop("auto_update", None)
def migrate_settings(cls, settings, version): # pylint: disable=redefined-outer-name
if version is None or version < 2:
# was stored as int indexing Methods (but offset by 1)
default = settings.pop("default_method", 0)
default = Methods(default + 1)
settings["default_method_name"] = default.name
