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Reformat cluster_analysis
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@jbeilstenedmands
jbeilstenedmands committed Oct 18, 2024
1 parent ebda946 commit 79224fd
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Showing 3 changed files with 246 additions and 334 deletions.
261 changes: 140 additions & 121 deletions src/xia2/Modules/MultiCrystal/ScaleAndMerge.py
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Original file line number Diff line number Diff line change
Expand Up @@ -316,6 +316,88 @@
)


def clusters_and_types(cos_angle_clusters, cc_clusters, methods):
if "cos_angle" in methods and "correlation" not in methods:
clusters = cos_angle_clusters
ctype = ["cos"] * len(clusters)
elif "correlation" in methods and "cos_angle" not in methods:
clusters = cc_clusters
ctype = ["cc"] * len(clusters)
elif "cos_angle" in methods and "correlation" in methods:
clusters = cos_angle_clusters + cc_clusters
ctype = ["cos"] * len(cos_angle_clusters) + ["cc"] * len(cc_clusters)
else:
raise ValueError("Invalid cluster method: %s" % methods)

clusters.reverse()
ctype.reverse()
return clusters, ctype


def get_subclusters(params, ids_to_identifiers_map, cos_angle_clusters, cc_clusters):
subclusters = []

min_completeness = params.min_completeness
min_multiplicity = params.min_multiplicity
max_clusters = params.max_output_clusters
min_cluster_size = params.min_cluster_size
max_cluster_height_cos = params.hierarchical.max_cluster_height_cos
max_cluster_height_cc = params.hierarchical.max_cluster_height_cc
max_cluster_height = params.hierarchical.max_cluster_height

clusters, ctype = clusters_and_types(
cos_angle_clusters, cc_clusters, params.hierarchical.method
)

n_processed_cos = 0
n_processed_cc = 0

for c, cluster in zip(ctype, clusters):
# This simplifies max_cluster_height into cc and cos angle versions
# But still gives the user the option of just selecting max_cluster_height
# Which makes more sense when they only want one type of clustering

if c == "cc" and max_cluster_height != 100 and max_cluster_height_cc == 100:
max_cluster_height_cc = max_cluster_height
# if user has weirdly set both max_cluster_height and max_cluster_height_cc
# will still default to max_cluster_height_cc as intended
if c == "cos" and max_cluster_height != 100 and max_cluster_height_cos == 100:
max_cluster_height_cos = max_cluster_height

if n_processed_cos == max_clusters and c == "cos":
continue
if n_processed_cc == max_clusters and c == "cc":
continue
if cluster.completeness < min_completeness:
continue
if cluster.multiplicity < min_multiplicity:
continue
if (
len(cluster.labels)
== len(ids_to_identifiers_map) # was len(data_manager_original.experiments)
and not params.hierarchical.distinct_clusters
):
continue
if cluster.height > max_cluster_height_cc and c == "cc":
continue
if cluster.height > max_cluster_height_cos and c == "cos":
continue
if len(cluster.labels) < min_cluster_size:
continue

cluster_identifiers = [ids_to_identifiers_map[l] for l in cluster.labels]
subclusters.append((c, cluster_identifiers, cluster))
if (
not params.hierarchical.distinct_clusters
): # increment so that we only get up to N clusters
if c == "cos":
n_processed_cos += 1
elif c == "cc":
n_processed_cc += 1

return subclusters


class MultiCrystalScale:
def __init__(self, experiments, reflections, params):
self._data_manager = DataManager(experiments, reflections)
Expand Down Expand Up @@ -472,119 +554,40 @@ def __init__(self, experiments, reflections, params):
self._mca = self.multi_crystal_analysis()
self.cluster_analysis()

min_completeness = self._params.clustering.min_completeness
min_multiplicity = self._params.clustering.min_multiplicity
max_clusters = self._params.clustering.max_output_clusters
min_cluster_size = self._params.clustering.min_cluster_size
max_cluster_height_cos = self._params.clustering.max_cluster_height_cos
max_cluster_height_cc = self._params.clustering.max_cluster_height_cc
max_cluster_height = self._params.clustering.max_cluster_height

if (
"cos_angle" in params.clustering.method
and "correlation" not in params.clustering.method
):
clusters = self._cos_angle_clusters
ctype = ["cos" for i in clusters]
elif (
"correlation" in params.clustering.method
and "cos_angle" not in params.clustering.method
):
clusters = self._cc_clusters
ctype = ["cc" for i in clusters]
elif (
"cos_angle" in params.clustering.method
and "correlation" in params.clustering.method
):
clusters = self._cos_angle_clusters + self._cc_clusters
ctype = ["cos" for i in self._cos_angle_clusters] + [
"cc" for i in self._cc_clusters
]
else:
raise ValueError(
"Invalid cluster method: %s" % self._params.clustering.method
)

clusters.reverse()
ctype.reverse()
self.cos_clusters = []
self.cc_clusters = []
self.cos_cluster_ids = {}
self.cc_cluster_ids = {}
# now do the interesting cluster identification algorithm from xia2.cluster_analysis.
# but don't repeat the code.

if self._params.clustering.output_clusters:
## note this is all hierarchical stuff first - need an if statement
self._data_manager_original = self._data_manager
cwd = os.path.abspath(os.getcwd())
n_processed_cos = 0
n_processed_cc = 0

for c, cluster in zip(ctype, clusters):
# This simplifies max_cluster_height into cc and cos angle versions
# But still gives the user the option of just selecting max_cluster_height
# Which makes more sense when they only want one type of clustering

if (
c == "cc"
and max_cluster_height != 100
and max_cluster_height_cc == 100
):
max_cluster_height_cc = max_cluster_height
# if user has weirdly set both max_cluster_height and max_cluster_height_cc
# will still default to max_cluster_height_cc as intended
if (
c == "cos"
and max_cluster_height != 100
and max_cluster_height_cos == 100
):
max_cluster_height_cos = max_cluster_height

if n_processed_cos == max_clusters and c == "cos":
continue
if n_processed_cc == max_clusters and c == "cc":
continue
if cluster.completeness < min_completeness:
continue
if cluster.multiplicity < min_multiplicity:
continue
if (
len(cluster.labels) == len(self._data_manager_original.experiments)
and not params.clustering.find_distinct_clusters
):
continue
if cluster.height > max_cluster_height_cc and c == "cc":
continue
if cluster.height > max_cluster_height_cos and c == "cos":
continue
if len(cluster.labels) < min_cluster_size:
continue

data_manager = copy.deepcopy(self._data_manager_original)
cluster_identifiers = [
data_manager.ids_to_identifiers_map[l] for l in cluster.labels
]
subclusters = get_subclusters(
self._params.clustering,
data_manager.ids_to_identifiers_map,
self._cos_angle_clusters,
self._cc_clusters,
)

if self._params.clustering.find_distinct_clusters:
if not self._params.clustering.hierarchical.distinct_clusters:
for c, cluster_identifiers, cluster in subclusters:
"""if self._params.clustering.find_distinct_clusters:
# set things up for later, but don't scale now?
if c == "cos":
self.cos_clusters.append(cluster)
self.cos_cluster_ids[cluster.cluster_id] = cluster_identifiers
elif c == "cc":
self.cc_clusters.append(cluster)
self.cc_cluster_ids[cluster.cluster_id] = cluster_identifiers
self.cc_cluster_ids[cluster.cluster_id] = cluster_identifiers"""

else:
if c == "cos":
n_processed_cos += 1
elif c == "cc":
n_processed_cc += 1

if c == "cos":
logger.info("Scaling cos cluster %i:" % cluster.cluster_id)
logger.info(cluster)
cluster_dir = "cos_cluster_%i" % cluster.cluster_id
elif c == "cc":
logger.info("Scaling cc cluster %i:" % cluster.cluster_id)
logger.info(cluster)
cluster_dir = "cc_cluster_%i" % cluster.cluster_id
# else:
# if c == "cos":
# n_processed_cos += 1
# elif c == "cc":
# n_processed_cc += 1
logger.info(f"Scaling {c} cluster {cluster.cluster_id}:")
logger.info(cluster)
cluster_dir = f"{c}_cluster_{cluster.cluster_id}"

if not os.path.exists(cluster_dir):
os.mkdir(cluster_dir)
Expand All @@ -600,28 +603,44 @@ def __init__(self, experiments, reflections, params):
)
os.chdir(cwd)

if self._params.clustering.find_distinct_clusters:
for k, clusters in enumerate([self.cos_clusters, self.cc_clusters]):
if k == 0 and "cos_angle" in self._params.clustering.method:
cty = "cos"
elif k == 1 and "correlation" in self._params.clustering.method:
cty = "cc"
else:
cty = False

if cty:
logger.info("----------------------")
logger.info(f"{cty} cluster analysis")
logger.info("----------------------")
if self._params.clustering.hierarchical.distinct_clusters:
self.cos_clusters = []
self.cc_clusters = []
self.cos_cluster_ids = {}
self.cc_cluster_ids = {}
for c, cluster_identifiers in subclusters:
if c == "cos":
self.cos_clusters.append(cluster)
self.cos_cluster_ids[cluster.cluster_id] = cluster_identifiers
elif c == "cc":
self.cc_clusters.append(cluster)
self.cc_cluster_ids[cluster.cluster_id] = cluster_identifiers

(
file_data,
list_of_clusters,
) = MultiCrystalAnalysis.interesting_cluster_identification(
clusters, self._params
)
for k, clusters in enumerate([self.cos_clusters, self.cc_clusters]):
if (
k == 0
and "cos_angle" in self._params.clustering.hierarchical.method
):
cty = "cos"
elif (
k == 1
and "correlation" in self._params.clustering.hierarchical.method
):
cty = "cc"
else:
cty = False

if cty:
logger.info("----------------------")
logger.info(f"{cty} cluster analysis")
logger.info("----------------------")

_, list_of_clusters = (
MultiCrystalAnalysis.interesting_cluster_identification(
clusters, self._params
)
)

if len(list_of_clusters) > 0:
for item in list_of_clusters:
if k == 0:
cluster_dir = "cos_" + item
Expand Down
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