def create_classification_linking_form():
""" Create a new ClassificationLinkingForm instance.
"""
workspace_pid = settings.ONTOLOGY_DUMMY_PROJECT_ID
root_links = get_classification_links_qs(workspace_pid, [], True)
# Make sure we use no classification graph more than once
known_roots = []
root_ids = []
for link in root_links:
if link.class_instance_b.id not in known_roots:
known_roots.append(link.class_instance_b.id)
root_ids.append(link.id)
class ClassificationLinkingForm(forms.Form):
# A check-box for each project, checked by default
classification_graph_suggestions = forms.MultipleChoiceField(
required=False,
widget=forms.CheckboxSelectMultiple(),
help_text=
"Only selected classification graphs will be linked to the new projects."
)
additional_links = forms.ModelMultipleChoiceField(
required=False,
widget=FilteredSelectMultiple('Classification roots',
is_stacked=False),
queryset=ClassInstanceClassInstanceProxy.objects.filter(
id__in=root_ids))
return ClassificationLinkingForm
def create_classification_linking_form():
""" Create a new ClassificationLinkingForm instance.
"""
workspace_pid = settings.ONTOLOGY_DUMMY_PROJECT_ID
root_links = get_classification_links_qs(workspace_pid, [], True)
# Make sure we use no classification graph more than once
known_roots = []
root_ids = []
for link in root_links:
if link.class_instance_b.id not in known_roots:
known_roots.append(link.class_instance_b.id)
root_ids.append(link.id)
class ClassificationLinkingForm(forms.Form):
# A check-box for each project, checked by default
classification_graph_suggestions = forms.MultipleChoiceField(
required=False,
widget=forms.CheckboxSelectMultiple(),
help_text="Only selected classification graphs will be linked to the new projects.",
)
additional_links = forms.ModelMultipleChoiceField(
required=False,
widget=FilteredSelectMultiple("Classification roots", is_stacked=False),
queryset=ClassInstanceClassInstanceProxy.objects.filter(id__in=root_ids),
)
return ClassificationLinkingForm
def generate_tag_groups(add_supersets=True, respect_superset_graphs=False):
""" This creates a tag sets dictionary. It ignores projects without any
tags.
"""
project_ids, tag_sets, tag_supersets = get_tag_sets(add_supersets)
# Get a query set that retrieves all CiCi links for all project ids at once
workspace = settings.ONTOLOGY_DUMMY_PROJECT_ID
links_qs = get_classification_links_qs(workspace, project_ids)
# Make sure the the project ids and the classification root ids are
# prefetched
links_qs = links_qs.select_related('class_instance_a__project__id',
'class_instance_b__id')
# Execute the query set to build a look up table
projects_to_cls_links = {}
for cici in links_qs:
if cici.class_instance_a.project.id not in projects_to_cls_links:
projects_to_cls_links[cici.class_instance_a.project.id] = set()
projects_to_cls_links[cici.class_instance_a.project.id].add(
cici.class_instance_b.id)
# Test which groups of projects belonging to a particular tag group,
# have non-uniform classification graph links
# TODO: Test for independent *and* dependent workspace
available_tag_groups = {}
for tags, projects in tag_sets.items():
differs = False
cg_roots = set()
projects_cgroots = {}
# Collect all classification roots in this tag group
for p in projects:
try:
# Get set of CiCi links for current project
croots = projects_to_cls_links[p.id]
except KeyError:
# Use an empty set if there are no CiCi links for the
# current project.
croots = set()
# Remember roots for this projects
projects_cgroots[p] = {
'linked': croots,
'missing': [],
'workspace': workspace,
}
# Add classification graphs introduced by supersets to the expected
# graphs in this tag set.
if p not in tag_supersets[tags] or respect_superset_graphs:
cg_roots.update(croots)
# Check if there are updates needed for some projects
num_differing = 0
meta = []
for p in projects_cgroots:
croots = projects_cgroots[p]['linked']
diff = cg_roots - croots
if len(diff) > 0:
differs = True
projects_cgroots[p]['missing'] = diff
num_differing = num_differing + 1
strdiff = ", ".join([str(cg) for cg in diff])
meta.append("[PID: %s Missing: %s]" % (p.id, strdiff))
# If there is a difference, offer this tag group
# for selection.
if differs:
# Generate a string representation of the tags and use
# it as index for a project classification.
taglist = list(tags)
taglist.sort(key=lambda x: x.id)
name = ", ".join([t.name for t in taglist])
# Fill data structure for later use
available_tag_groups[name] = {
'project_cgroots': projects_cgroots,
'all_cgroots': cg_roots,
'num_differing': num_differing,
'meta': meta,
}
return available_tag_groups
def get_form(self, step=None, data=None, files=None):
form = super(ImportingWizard, self).get_form(step, data, files)
current_step = step or self.steps.current
if current_step == 'pathsettings':
pass
# Pre-populate base URL field with settings variable, if available
if hasattr(settings, base_url_setting):
form.fields['base_url'].initial = getattr(
settings, base_url_setting)
elif current_step == 'projectselection':
# Make sure there is data available. This is needed to test whether
# the classification step should be shown with the help of the
# show_classification_suggestions() function.
cleaned_path_data = self.get_cleaned_data_for_step('pathsettings')
if not cleaned_path_data:
return form
# Get the cleaned data from first step
path = cleaned_path_data['relative_path']
filter_term = cleaned_path_data['filter_term']
only_unknown = cleaned_path_data['only_unknown_projects']
base_url = cleaned_path_data['base_url']
# Get all folders that match the selected criteria
data_dir = os.path.join(settings.CATMAID_IMPORT_PATH, path)
if data_dir[-1] != os.sep:
data_dir = data_dir + os.sep
if len(filter_term) == "":
filter_term = "*"
folders, projects, not_readable = find_project_folders(
base_url, data_dir, filter_term, only_unknown)
# Sort the folders (wrt. short name) to be better readable
folders = sorted(folders, key=lambda folder: folder[1])
# Save these settings in the form
form.folders = folders
form.not_readable = not_readable
self.projects = projects
# Update the folder list and select all by default
form.fields['projects'].choices = folders
form.fields['projects'].initial = [f[0] for f in folders]
# Get the available user permissions and update the list
user_permissions = get_element_permissions(UserProxy, Project)
form.user_permissions = user_permissions
user_perm_tuples = get_element_permission_tuples(user_permissions)
form.fields['user_permissions'].choices = user_perm_tuples
# Get the available group permissions and update the list
group_permissions = get_element_permissions(GroupProxy, Project)
form.group_permissions = group_permissions
group_perm_tuples = get_element_permission_tuples(
group_permissions)
form.fields['group_permissions'].choices = group_perm_tuples
elif current_step == 'classification':
# Get tag set and all projects within it
tags = self.get_cleaned_data_for_step('projectselection')['tags']
tags = frozenset([t.strip() for t in tags.split(',')])
# Get all projects that have all those tags
projects = Project.objects.filter(tags__name__in=tags).annotate(
repeat_count=Count("id")).filter(repeat_count=len(tags))
# Get all classification graphs linked to those projects and add
# them to a form for being selected.
workspace = settings.ONTOLOGY_DUMMY_PROJECT_ID
croots = {}
for p in projects:
links_qs = get_classification_links_qs(workspace, p.id)
linked_croots = set(
[cici.class_instance_b for cici in links_qs])
# Build up dictionary with all classifications mapping to their
# linked projects.
for cr in linked_croots:
try:
croots[cr].append(p)
except KeyError:
croots[cr] = []
croots[cr].append(p)
# Remember graphs and projects
form.cls_tags = tags
form.cls_graph_map = croots
self.id_to_cls_graph = {}
# Create data structure for form field and id mapping
cgraphs = []
for cr in croots:
# Create form field tuples
name = "%s (%s)" % (cr.name, cr.id)
cgraphs.append((cr.id, name))
# Create ID to classification graph mapping
self.id_to_cls_graph[cr.id] = cr
form.fields['classification_graph_suggestions'].choices = cgraphs
#form.fields['classification_graph_suggestions'].initial = [cg[0] for cg in cgraphs]
return form
def generate_tag_groups(add_supersets=True, respect_superset_graphs=False):
""" This creates a tag sets dictionary. It ignores projects without any
tags.
"""
project_ids, tag_sets, tag_supersets = get_tag_sets(add_supersets)
# Get a query set that retrieves all CiCi links for all project ids at once
workspace = settings.ONTOLOGY_DUMMY_PROJECT_ID
# We are retrieving the classification links of *all* projects. The query
# will therefore be much faster, if we get the inverse set of the empty
# set instead of explicitely requesting the project_ids to be in
# project_ids..
links_qs = get_classification_links_qs(workspace, [], inverse=True)
# Make sure the the project ids and the classification root ids are
# prefetched
links_qs = links_qs.select_related('class_instance_a__project__id',
'class_instance_b__id')
# Execute the query set to build a look up table
projects_to_cls_links = {}
for cici in links_qs:
cls_links = projects_to_cls_links.get(cici.class_instance_a.project.id)
if not cls_links:
cls_links = set()
projects_to_cls_links[cici.class_instance_a.project.id] = cls_links
cls_links.add(cici.class_instance_b.id)
# Test which groups of projects belonging to a particular tag group,
# have non-uniform classification graph links
# TODO: Test for independent *and* dependent workspace
available_tag_groups = {}
for tags, projects in tag_sets.items():
differs = False
cg_roots = set()
projects_cgroots = {}
# Collect all classification roots in this tag group
for pid in projects:
try:
# Get set of CiCi links for current project ID
croots = projects_to_cls_links[pid]
except KeyError:
# Use an empty set if there are no CiCi links for the
# current project.
croots = set()
# Remember roots for this projects
projects_cgroots[pid] = {
'linked': croots,
'missing': [],
'workspace': workspace,
}
# Add classification graphs introduced by supersets to the expected
# graphs in this tag set.
if pid not in tag_supersets[tags] or respect_superset_graphs:
cg_roots.update(croots)
# Check if there are updates needed for some projects
num_differing = 0
meta = []
for pid in projects_cgroots:
croots = projects_cgroots[pid]['linked']
diff = cg_roots - croots
if len(diff) > 0:
differs = True
projects_cgroots[pid]['missing'] = diff
num_differing = num_differing + 1
strdiff = ", ".join([str(cg) for cg in diff])
meta.append("[PID: %s Missing: %s]" % (pid, strdiff))
# If there is a difference, offer this tag group
# for selection.
if differs:
# Generate a string representation of the tags and use
# it as index for a project classification.
taglist = list(tags)
taglist.sort()
name = ", ".join([t for t in taglist])
# Fill data structure for later use
available_tag_groups[name] = {
'project_cgroots': projects_cgroots,
'all_cgroots': cg_roots,
'num_differing': num_differing,
'meta': meta,
}
return available_tag_groups
def get_form(self, step=None, data=None, files=None):
form = super(ImportingWizard, self).get_form(step, data, files)
current_step = step or self.steps.current
if current_step == 'pathsettings':
pass
# Pre-populate base URL field with settings variable, if available
if hasattr(settings, base_url_setting):
form.fields['base_url'].initial = getattr(settings, base_url_setting)
elif current_step == 'projectselection':
# Make sure there is data available. This is needed to test whether
# the classification step should be shown with the help of the
# show_classification_suggestions() function.
cleaned_path_data = self.get_cleaned_data_for_step('pathsettings')
if not cleaned_path_data:
return form
# Get the cleaned data from first step
path = cleaned_path_data['relative_path']
filter_term = cleaned_path_data['filter_term']
only_unknown = cleaned_path_data['only_unknown_projects']
base_url = cleaned_path_data['base_url']
# Get all folders that match the selected criteria
data_dir = os.path.join(settings.CATMAID_IMPORT_PATH, path)
if data_dir[-1] != os.sep:
data_dir = data_dir + os.sep
if len(filter_term) == "":
filter_term = "*"
folders, projects, not_readable = find_project_folders(
base_url, data_dir, filter_term, only_unknown)
# Sort the folders (wrt. short name) to be better readable
folders = sorted(folders, key=lambda folder: folder[1])
# Save these settings in the form
form.folders = folders
form.not_readable = not_readable
self.projects = projects
# Update the folder list and select all by default
form.fields['projects'].choices = folders
form.fields['projects'].initial = [f[0] for f in folders]
# Get the available user permissions and update the list
user_permissions = get_element_permissions(UserProxy, Project)
form.user_permissions = user_permissions
user_perm_tuples = get_element_permission_tuples(user_permissions)
form.fields['user_permissions'].choices = user_perm_tuples
# Get the available group permissions and update the list
group_permissions = get_element_permissions(GroupProxy, Project)
form.group_permissions = group_permissions
group_perm_tuples = get_element_permission_tuples(group_permissions)
form.fields['group_permissions'].choices = group_perm_tuples
elif current_step == 'classification':
# Get tag set and all projects within it
tags = self.get_cleaned_data_for_step('projectselection')['tags']
tags = frozenset([t.strip() for t in tags.split(',')])
# Get all projects that have all those tags
projects = Project.objects.filter( tags__name__in=tags ).annotate(
repeat_count=Count("id") ).filter( repeat_count=len(tags) )
# Get all classification graphs linked to those projects and add
# them to a form for being selected.
workspace = settings.ONTOLOGY_DUMMY_PROJECT_ID
croots = {}
for p in projects:
links_qs = get_classification_links_qs(workspace, p.id)
linked_croots = set([cici.class_instance_b for cici in links_qs])
# Build up dictionary with all classifications mapping to their
# linked projects.
for cr in linked_croots:
try:
croots[cr].append(p)
except KeyError:
croots[cr] = []
croots[cr].append(p)
# Remember graphs and projects
form.cls_tags = tags
form.cls_graph_map = croots
self.id_to_cls_graph = {}
# Create data structure for form field and id mapping
cgraphs = []
for cr in croots:
# Create form field tuples
name = "%s (%s)" % (cr.name, cr.id)
cgraphs.append( (cr.id, name) )
# Create ID to classification graph mapping
self.id_to_cls_graph[cr.id] = cr
form.fields['classification_graph_suggestions'].choices = cgraphs
#form.fields['classification_graph_suggestions'].initial = [cg[0] for cg in cgraphs]
return form
