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moonso/loqusdb | loqusdb/plugins/mongo/variant.py | VariantMixin.add_variants | def add_variants(self, variants):
"""Add a bulk of variants
This could be used for faster inserts
Args:
variants(iterable(dict))
"""
operations = []
nr_inserted = 0
for i,variant in enumerate(variants, 1):
... | python | def add_variants(self, variants):
"""Add a bulk of variants
This could be used for faster inserts
Args:
variants(iterable(dict))
"""
operations = []
nr_inserted = 0
for i,variant in enumerate(variants, 1):
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moonso/loqusdb | loqusdb/plugins/mongo/variant.py | VariantMixin.search_variants | def search_variants(self, variant_ids):
"""Make a batch search for variants in the database
Args:
variant_ids(list(str)): List of variant ids
Returns:
res(pymngo.Cursor(variant_obj)): The result
"""
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"""Make a batch search for variants in the database
Args:
variant_ids(list(str)): List of variant ids
Returns:
res(pymngo.Cursor(variant_obj)): The result
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moonso/loqusdb | loqusdb/plugins/mongo/variant.py | VariantMixin.get_variants | def get_variants(self, chromosome=None, start=None, end=None):
"""Return all variants in the database
If no region is specified all variants will be returned.
Args:
chromosome(str)
start(int)
end(int)
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"""Return all variants in the database
If no region is specified all variants will be returned.
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chromosome(str)
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moonso/loqusdb | loqusdb/plugins/mongo/variant.py | VariantMixin.delete_variant | def delete_variant(self, variant):
"""Delete observation in database
This means that we take down the observations variable with one.
If 'observations' == 1 we remove the variant. If variant was homozygote
we decrease 'homozygote' with one.
Also remove the family from array 'fam... | python | def delete_variant(self, variant):
"""Delete observation in database
This means that we take down the observations variable with one.
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moonso/loqusdb | loqusdb/plugins/mongo/variant.py | VariantMixin.get_chromosomes | def get_chromosomes(self, sv=False):
"""Return a list of all chromosomes found in database
Args:
sv(bool): if sv variants should be choosen
Returns:
res(iterable(str)): An iterable with all chromosomes in the database
"""
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... | python | def get_chromosomes(self, sv=False):
"""Return a list of all chromosomes found in database
Args:
sv(bool): if sv variants should be choosen
Returns:
res(iterable(str)): An iterable with all chromosomes in the database
"""
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moonso/loqusdb | loqusdb/plugins/mongo/variant.py | VariantMixin.get_max_position | def get_max_position(self, chrom):
"""Get the last position observed on a chromosome in the database
Args:
chrom(str)
Returns:
end(int): The largest end position found
"""
res = self.db.variant.find({'chrom':chrom}, {'_id':0,... | python | def get_max_position(self, chrom):
"""Get the last position observed on a chromosome in the database
Args:
chrom(str)
Returns:
end(int): The largest end position found
"""
res = self.db.variant.find({'chrom':chrom}, {'_id':0,... | [
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moonso/loqusdb | loqusdb/commands/restore.py | restore | def restore(ctx, filename):
"""Restore the database from a zipped file.
Default is to restore from db dump in loqusdb/resources/
"""
filename = filename or background_path
if not os.path.isfile(filename):
LOG.warning("File {} does not exist. Please point to a valid file".format(filename... | python | def restore(ctx, filename):
"""Restore the database from a zipped file.
Default is to restore from db dump in loqusdb/resources/
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filename = filename or background_path
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yjzhang/uncurl_python | uncurl/sampling.py | downsample | def downsample(data, percent):
"""
downsample the data by removing a given percentage of the reads.
Args:
data: genes x cells array or sparse matrix
percent: float between 0 and 1
"""
n_genes = data.shape[0]
n_cells = data.shape[1]
new_data = data.copy()
total_count = fl... | python | def downsample(data, percent):
"""
downsample the data by removing a given percentage of the reads.
Args:
data: genes x cells array or sparse matrix
percent: float between 0 and 1
"""
n_genes = data.shape[0]
n_cells = data.shape[1]
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yjzhang/uncurl_python | uncurl/nb_state_estimation.py | _create_w_objective | def _create_w_objective(m, X, R):
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m (array): genes x clusters
X (array): genes x cells
R (array): 1 x genes
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m (array): genes x clusters
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R (array): 1 x genes
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yjzhang/uncurl_python | uncurl/nb_state_estimation.py | nb_estimate_state | def nb_estimate_state(data, clusters, R=None, init_means=None, init_weights=None, max_iters=10, tol=1e-4, disp=True, inner_max_iters=400, normalize=True):
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yjzhang/uncurl_python | uncurl/lightlda_utils.py | poisson_objective | def poisson_objective(X, m, w):
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w (array): clusters x cells
X (array): genes x cells
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yjzhang/uncurl_python | uncurl/lightlda_utils.py | lightlda_estimate_state | def lightlda_estimate_state(data, k, input_folder="data1/LightLDA_input", threads=8, max_iters=250, prepare_data=True, init_means=None, init_weights=None, lightlda_folder=None, data_capacity=1000):
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moonso/loqusdb | scripts/load_files.py | cli | def cli(ctx, directory, uri, verbose, count):
"""Load all files in a directory."""
# configure root logger to print to STDERR
loglevel = "INFO"
if verbose:
loglevel = "DEBUG"
coloredlogs.install(level=loglevel)
p = Path(directory)
if not p.is_dir():
LOG.warning("{0}... | python | def cli(ctx, directory, uri, verbose, count):
"""Load all files in a directory."""
# configure root logger to print to STDERR
loglevel = "INFO"
if verbose:
loglevel = "DEBUG"
coloredlogs.install(level=loglevel)
p = Path(directory)
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yjzhang/uncurl_python | uncurl/nmf_wrapper.py | nmf_init | def nmf_init(data, clusters, k, init='enhanced'):
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yjzhang/uncurl_python | uncurl/nmf_wrapper.py | log_norm_nmf | def log_norm_nmf(data, k, normalize_w=True, return_cost=True, init_weights=None, init_means=None, write_progress_file=None, **kwargs):
"""
Args:
data (array): dense or sparse array with shape (genes, cells)
k (int): number of cell types
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moonso/loqusdb | loqusdb/build_models/variant.py | check_par | def check_par(chrom, pos):
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Args:
chrom(str)
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... | python | def check_par(chrom, pos):
"""Check if a coordinate is in the PAR region
Args:
chrom(str)
pos(int)
Returns:
par(bool)
"""
par = False
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moonso/loqusdb | loqusdb/build_models/variant.py | get_variant_id | def get_variant_id(variant):
"""Get a variant id on the format chrom_pos_ref_alt"""
variant_id = '_'.join([
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str(variant.POS),
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str(variant.ALT[0])
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return variant_id | python | def get_variant_id(variant):
"""Get a variant id on the format chrom_pos_ref_alt"""
variant_id = '_'.join([
str(variant.CHROM),
str(variant.POS),
str(variant.REF),
str(variant.ALT[0])
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return variant_id | [
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moonso/loqusdb | loqusdb/build_models/variant.py | is_greater | def is_greater(a,b):
"""Check if position a is greater than position b
This will look at chromosome and position.
For example a position where chrom = 2 and pos = 300 is greater than a position where
chrom = 1 and pos = 1000
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moonso/loqusdb | loqusdb/build_models/variant.py | get_coords | def get_coords(variant):
"""Returns a dictionary with position information
Args:
variant(cyvcf2.Variant)
Returns:
coordinates(dict)
"""
coordinates = {
'chrom': None,
'end_chrom': None,
'sv_length': None,
'sv_type': None,
'pos': None,... | python | def get_coords(variant):
"""Returns a dictionary with position information
Args:
variant(cyvcf2.Variant)
Returns:
coordinates(dict)
"""
coordinates = {
'chrom': None,
'end_chrom': None,
'sv_length': None,
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moonso/loqusdb | loqusdb/build_models/variant.py | build_variant | def build_variant(variant, case_obj, case_id=None, gq_treshold=None):
"""Return a Variant object
Take a cyvcf2 formated variant line and return a models.Variant.
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Args:
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"""Return a Variant object
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moonso/loqusdb | loqusdb/commands/migrate.py | migrate | def migrate(ctx,):
"""Migrate an old loqusdb instance to 1.0
"""
adapter = ctx.obj['adapter']
start_time = datetime.now()
nr_updated = migrate_database(adapter)
LOG.info("All variants updated, time to complete migration: {}".format(
datetime.now() - start_time))
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"""Migrate an old loqusdb instance to 1.0
"""
adapter = ctx.obj['adapter']
start_time = datetime.now()
nr_updated = migrate_database(adapter)
LOG.info("All variants updated, time to complete migration: {}".format(
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moonso/loqusdb | loqusdb/commands/update.py | update | def update(ctx, variant_file, sv_variants, family_file, family_type, skip_case_id, gq_treshold,
case_id, ensure_index, max_window):
"""Load the variants of a case
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case_id, ensure_index, max_window):
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moonso/loqusdb | loqusdb/commands/export.py | export | def export(ctx, outfile, variant_type):
"""Export the variants of a loqus db
The variants are exported to a vcf file
"""
adapter = ctx.obj['adapter']
version = ctx.obj['version']
LOG.info("Export the variants from {0}".format(adapter))
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"""Export the variants of a loqus db
The variants are exported to a vcf file
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adapter = ctx.obj['adapter']
version = ctx.obj['version']
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moonso/loqusdb | loqusdb/utils/load.py | load_database | def load_database(adapter, variant_file=None, sv_file=None, family_file=None,
family_type='ped', skip_case_id=False, gq_treshold=None,
case_id=None, max_window = 3000, profile_file=None,
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case_id=None, max_window = 3000, profile_file=None,
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moonso/loqusdb | loqusdb/utils/load.py | load_case | def load_case(adapter, case_obj, update=False):
"""Load a case to the database
Args:
adapter: Connection to database
case_obj: dict
update(bool): If existing case should be updated
Returns:
case_obj(models.Case)
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# Check if the case already exists in database.
... | python | def load_case(adapter, case_obj, update=False):
"""Load a case to the database
Args:
adapter: Connection to database
case_obj: dict
update(bool): If existing case should be updated
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case_obj(models.Case)
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moonso/loqusdb | loqusdb/utils/load.py | load_variants | def load_variants(adapter, vcf_obj, case_obj, skip_case_id=False, gq_treshold=None,
max_window=3000, variant_type='snv'):
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adapter (loqusdb.plugins.Adapter): initialized plugin
case_obj(Case): dict with case information
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adapter (loqusdb.plugins.Adapter): initialized plugin
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yjzhang/uncurl_python | uncurl/preprocessing.py | sparse_mean_var | def sparse_mean_var(data):
"""
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Returns:
pair of matrices mean, variance.
"""
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return sparse_means_var_csc(data.data,
data.indices,
... | python | def sparse_mean_var(data):
"""
Calculates the variance for each row of a sparse matrix,
using the relationship Var = E[x^2] - E[x]^2.
Returns:
pair of matrices mean, variance.
"""
data = sparse.csc_matrix(data)
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yjzhang/uncurl_python | uncurl/preprocessing.py | max_variance_genes | def max_variance_genes(data, nbins=5, frac=0.2):
"""
This function identifies the genes that have the max variance
across a number of bins sorted by mean.
Args:
data (array): genes x cells
nbins (int): number of bins to sort genes by mean expression level. Default: 10.
frac (flo... | python | def max_variance_genes(data, nbins=5, frac=0.2):
"""
This function identifies the genes that have the max variance
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data (array): genes x cells
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yjzhang/uncurl_python | uncurl/preprocessing.py | cell_normalize | def cell_normalize(data):
"""
Returns the data where the expression is normalized so that the total
count per cell is equal.
"""
if sparse.issparse(data):
data = sparse.csc_matrix(data.astype(float))
# normalize in-place
sparse_cell_normalize(data.data,
data.i... | python | def cell_normalize(data):
"""
Returns the data where the expression is normalized so that the total
count per cell is equal.
"""
if sparse.issparse(data):
data = sparse.csc_matrix(data.astype(float))
# normalize in-place
sparse_cell_normalize(data.data,
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yjzhang/uncurl_python | uncurl/preprocessing.py | log1p | def log1p(data):
"""
Returns ln(data+1), whether the original data is dense or sparse.
"""
if sparse.issparse(data):
return data.log1p()
else:
return np.log1p(data) | python | def log1p(data):
"""
Returns ln(data+1), whether the original data is dense or sparse.
"""
if sparse.issparse(data):
return data.log1p()
else:
return np.log1p(data) | [
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moonso/loqusdb | loqusdb/build_models/case.py | get_individual_positions | def get_individual_positions(individuals):
"""Return a dictionary with individual positions
Args:
individuals(list): A list with vcf individuals in correct order
Returns:
ind_pos(dict): Map from ind_id -> index position
"""
ind_pos = {}
if individuals:
for i, ind in enu... | python | def get_individual_positions(individuals):
"""Return a dictionary with individual positions
Args:
individuals(list): A list with vcf individuals in correct order
Returns:
ind_pos(dict): Map from ind_id -> index position
"""
ind_pos = {}
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moonso/loqusdb | loqusdb/build_models/case.py | build_case | def build_case(case, vcf_individuals=None, case_id=None, vcf_path=None, sv_individuals=None,
vcf_sv_path=None, nr_variants=None, nr_sv_variants=None, profiles=None,
matches=None, profile_path=None):
"""Build a Case from the given information
Args:
case(ped_parser.Family): ... | python | def build_case(case, vcf_individuals=None, case_id=None, vcf_path=None, sv_individuals=None,
vcf_sv_path=None, nr_variants=None, nr_sv_variants=None, profiles=None,
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yjzhang/uncurl_python | uncurl/simulation.py | generate_poisson_data | def generate_poisson_data(centers, n_cells, cluster_probs=None):
"""
Generates poisson-distributed data, given a set of means for each cluster.
Args:
centers (array): genes x clusters matrix
n_cells (int): number of output cells
cluster_probs (array): prior probability for each clus... | python | def generate_poisson_data(centers, n_cells, cluster_probs=None):
"""
Generates poisson-distributed data, given a set of means for each cluster.
Args:
centers (array): genes x clusters matrix
n_cells (int): number of output cells
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yjzhang/uncurl_python | uncurl/simulation.py | generate_zip_data | def generate_zip_data(M, L, n_cells, cluster_probs=None):
"""
Generates zero-inflated poisson-distributed data, given a set of means and zero probs for each cluster.
Args:
M (array): genes x clusters matrix
L (array): genes x clusters matrix - zero-inflation parameters
n_cells (int)... | python | def generate_zip_data(M, L, n_cells, cluster_probs=None):
"""
Generates zero-inflated poisson-distributed data, given a set of means and zero probs for each cluster.
Args:
M (array): genes x clusters matrix
L (array): genes x clusters matrix - zero-inflation parameters
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yjzhang/uncurl_python | uncurl/simulation.py | generate_state_data | def generate_state_data(means, weights):
"""
Generates data according to the Poisson Convex Mixture Model.
Args:
means (array): Cell types- genes x clusters
weights (array): Cell cluster assignments- clusters x cells
Returns:
data matrix - genes x cells
"""
x_true = np.... | python | def generate_state_data(means, weights):
"""
Generates data according to the Poisson Convex Mixture Model.
Args:
means (array): Cell types- genes x clusters
weights (array): Cell cluster assignments- clusters x cells
Returns:
data matrix - genes x cells
"""
x_true = np.... | [
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yjzhang/uncurl_python | uncurl/simulation.py | generate_zip_state_data | def generate_zip_state_data(means, weights, z):
"""
Generates data according to the Zero-inflated Poisson Convex Mixture Model.
Args:
means (array): Cell types- genes x clusters
weights (array): Cell cluster assignments- clusters x cells
z (float): zero-inflation parameter
Retu... | python | def generate_zip_state_data(means, weights, z):
"""
Generates data according to the Zero-inflated Poisson Convex Mixture Model.
Args:
means (array): Cell types- genes x clusters
weights (array): Cell cluster assignments- clusters x cells
z (float): zero-inflation parameter
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yjzhang/uncurl_python | uncurl/simulation.py | generate_nb_state_data | def generate_nb_state_data(means, weights, R):
"""
Generates data according to the Negative Binomial Convex Mixture Model.
Args:
means (array): Cell types- genes x clusters
weights (array): Cell cluster assignments- clusters x cells
R (array): dispersion parameter - 1 x genes
R... | python | def generate_nb_state_data(means, weights, R):
"""
Generates data according to the Negative Binomial Convex Mixture Model.
Args:
means (array): Cell types- genes x clusters
weights (array): Cell cluster assignments- clusters x cells
R (array): dispersion parameter - 1 x genes
R... | [
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yjzhang/uncurl_python | uncurl/simulation.py | generate_nb_states | def generate_nb_states(n_states, n_cells, n_genes):
"""
Generates means and weights for the Negative Binomial Mixture Model.
Weights are distributed Dirichlet(1,1,...), means are rand(0, 1).
Returned values can be passed to generate_state_data(M, W).
Args:
n_states (int): number of states o... | python | def generate_nb_states(n_states, n_cells, n_genes):
"""
Generates means and weights for the Negative Binomial Mixture Model.
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yjzhang/uncurl_python | uncurl/simulation.py | generate_poisson_states | def generate_poisson_states(n_states, n_cells, n_genes):
"""
Generates means and weights for the Poisson Convex Mixture Model.
Weights are distributed Dirichlet(1,1,...), means are rand(0, 100).
Returned values can be passed to generate_state_data(M, W).
Args:
n_states (int): number of stat... | python | def generate_poisson_states(n_states, n_cells, n_genes):
"""
Generates means and weights for the Poisson Convex Mixture Model.
Weights are distributed Dirichlet(1,1,...), means are rand(0, 100).
Returned values can be passed to generate_state_data(M, W).
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yjzhang/uncurl_python | uncurl/simulation.py | generate_poisson_lineage | def generate_poisson_lineage(n_states, n_cells_per_cluster, n_genes, means=300):
"""
Generates a lineage for each state- assumes that each state has a common
ancestor.
Returns:
M - genes x clusters
W - clusters x cells
"""
# means...
M = np.random.random((n_genes, n_states))... | python | def generate_poisson_lineage(n_states, n_cells_per_cluster, n_genes, means=300):
"""
Generates a lineage for each state- assumes that each state has a common
ancestor.
Returns:
M - genes x clusters
W - clusters x cells
"""
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yjzhang/uncurl_python | uncurl/simulation.py | generate_nb_data | def generate_nb_data(P, R, n_cells, assignments=None):
"""
Generates negative binomial data
Args:
P (array): genes x clusters
R (array): genes x clusters
n_cells (int): number of cells
assignments (list): cluster assignment of each cell. Default:
random uniform
... | python | def generate_nb_data(P, R, n_cells, assignments=None):
"""
Generates negative binomial data
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P (array): genes x clusters
R (array): genes x clusters
n_cells (int): number of cells
assignments (list): cluster assignment of each cell. Default:
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yjzhang/uncurl_python | uncurl/vis.py | visualize_poisson_w | def visualize_poisson_w(w, labels, filename, method='pca', figsize=(18,10), title='', **scatter_options):
"""
Saves a scatter plot of a visualization of W, the result from Poisson SE.
"""
if method == 'pca':
pca = PCA(2)
r_dim_red = pca.fit_transform(w.T).T
elif method == 'tsne':
... | python | def visualize_poisson_w(w, labels, filename, method='pca', figsize=(18,10), title='', **scatter_options):
"""
Saves a scatter plot of a visualization of W, the result from Poisson SE.
"""
if method == 'pca':
pca = PCA(2)
r_dim_red = pca.fit_transform(w.T).T
elif method == 'tsne':
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yjzhang/uncurl_python | uncurl/vis.py | visualize_dim_red | def visualize_dim_red(r, labels, filename=None, figsize=(18,10), title='', legend=True, label_map=None, label_scale=False, label_color_map=None, **scatter_options):
"""
Saves a scatter plot of a (2,n) matrix r, where each column is a cell.
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yjzhang/uncurl_python | uncurl/experiment_runner.py | run_experiment | def run_experiment(methods, data, n_classes, true_labels, n_runs=10, use_purity=True, use_nmi=False, use_ari=False, use_nne=False, consensus=False):
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yjzhang/uncurl_python | uncurl/experiment_runner.py | PoissonSE.run | def run(self, data):
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Returns:
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markperdue/pyvesync | src/pyvesync/helpers.py | Helpers.calculate_hex | def calculate_hex(hex_string):
"""Credit for conversion to itsnotlupus/vesync_wsproxy"""
hex_conv = hex_string.split(':')
converted_hex = (int(hex_conv[0], 16) + int(hex_conv[1], 16))/8192
return converted_hex | python | def calculate_hex(hex_string):
"""Credit for conversion to itsnotlupus/vesync_wsproxy"""
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converted_hex = (int(hex_conv[0], 16) + int(hex_conv[1], 16))/8192
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fbergmann/libSEDML | examples/python/create_sedml.py | main | def main (args):
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moonso/loqusdb | loqusdb/utils/profiling.py | get_profiles | def get_profiles(adapter, vcf_file):
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moonso/loqusdb | loqusdb/utils/profiling.py | compare_profiles | def compare_profiles(profile1, profile2):
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profile1/2 (str): profile string
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yjzhang/uncurl_python | uncurl/evaluation.py | purity | def purity(labels, true_labels):
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labels (array): 1D array of integers
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purity score - a float bewteen 0 and 1. Closer to 1 is better.
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labels (array): 1D array of integers
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dim_red (array): dimensions (k, cells)
true_labels (array): 1d array of integers
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yjzhang/uncurl_python | uncurl/evaluation.py | mdl | def mdl(ll, k, data):
"""
Returns the minimum description length score of the model given its
log-likelihood and k, the number of cell types.
a lower cost is better...
"""
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N - no. of genes
n - no. of cells
k - no. of cell types
R - sum(Dataset) i.e. total no. of reads
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"""
Returns the minimum description length score of the model given its
log-likelihood and k, the number of cell types.
a lower cost is better...
"""
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yjzhang/uncurl_python | uncurl/nb_clustering.py | find_nb_genes | def find_nb_genes(data):
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Finds the indices of all genes in the dataset that have
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"""
data_means = data.mean(1)
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nb_indices = data_means < 0.9*data_vars
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"""
Finds the indices of all genes in the dataset that have
a mean < 0.9 variance. Returns an array of booleans.
"""
data_means = data.mean(1)
data_vars = data.var(1)
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yjzhang/uncurl_python | uncurl/nb_clustering.py | log_ncr | def log_ncr(a, b):
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"""
val = gammaln(a+1) - gammaln(a-b+1) - gammaln(b+1)
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"""
Returns log(nCr(a,b)), given that b<a. Does not assume that a and b
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"""
val = gammaln(a+1) - gammaln(a-b+1) - gammaln(b+1)
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yjzhang/uncurl_python | uncurl/nb_clustering.py | nb_ll | def nb_ll(data, P, R):
"""
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Args:
data (array): genes x cells
P (array): NB success probability param - genes x clusters
R (array): NB stopping param - genes x clusters
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"""
Returns the negative binomial log-likelihood of the data.
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data (array): genes x cells
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R (array): NB stopping param - genes x clusters
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yjzhang/uncurl_python | uncurl/nb_clustering.py | zinb_ll | def zinb_ll(data, P, R, Z):
"""
Returns the zero-inflated negative binomial log-likelihood of the data.
"""
lls = nb_ll(data, P, R)
clusters = P.shape[1]
for c in range(clusters):
pass
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"""
Returns the zero-inflated negative binomial log-likelihood of the data.
"""
lls = nb_ll(data, P, R)
clusters = P.shape[1]
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yjzhang/uncurl_python | uncurl/nb_clustering.py | nb_ll_row | def nb_ll_row(params, data_row):
"""
returns the negative LL of a single row.
Args:
params (array) - [p, r]
data_row (array) - 1d array of data
Returns:
LL of row
"""
p = params[0]
r = params[1]
n = len(data_row)
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"""
returns the negative LL of a single row.
Args:
params (array) - [p, r]
data_row (array) - 1d array of data
Returns:
LL of row
"""
p = params[0]
r = params[1]
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yjzhang/uncurl_python | uncurl/nb_clustering.py | nb_r_deriv | def nb_r_deriv(r, data_row):
"""
Derivative of log-likelihood wrt r (formula from wikipedia)
Args:
r (float): the R paramemter in the NB distribution
data_row (array): 1d array of length cells
"""
n = len(data_row)
d = sum(digamma(data_row + r)) - n*digamma(r) + n*np.log(r/(r+np... | python | def nb_r_deriv(r, data_row):
"""
Derivative of log-likelihood wrt r (formula from wikipedia)
Args:
r (float): the R paramemter in the NB distribution
data_row (array): 1d array of length cells
"""
n = len(data_row)
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yjzhang/uncurl_python | uncurl/nb_clustering.py | nb_fit | def nb_fit(data, P_init=None, R_init=None, epsilon=1e-8, max_iters=100):
"""
Fits the NB distribution to data using method of moments.
Args:
data (array): genes x cells
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"""
Fits the NB distribution to data using method of moments.
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data (array): genes x cells
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yjzhang/uncurl_python | uncurl/nb_clustering.py | nb_cluster | def nb_cluster(data, k, P_init=None, R_init=None, assignments=None, means=None, max_iters=10):
"""
Performs negative binomial clustering on the given data. If some genes have mean > variance, then these genes are fitted to a Poisson distribution.
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data (array): genes x cells
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yjzhang/uncurl_python | uncurl/nb_clustering.py | fit_cluster | def fit_cluster(data, assignments, k, P_init, R_init, means):
"""
Fits NB/poisson params to a cluster.
"""
for c in range(k):
if data[:,assignments==c].shape[1] == 0:
_, assignments = kmeans_pp(data, k)
genes, cells = data.shape
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"""
Fits NB/poisson params to a cluster.
"""
for c in range(k):
if data[:,assignments==c].shape[1] == 0:
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yjzhang/uncurl_python | uncurl/zip_utils.py | zip_ll | def zip_ll(data, means, M):
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Args:
data (array): genes x cells
means (array): genes x k
M (array): genes x k - this is the zero-inflation parameter.
Returns:
cells x k array of log-likelihood for each cell/cluster ... | python | def zip_ll(data, means, M):
"""
Calculates the zero-inflated Poisson log-likelihood.
Args:
data (array): genes x cells
means (array): genes x k
M (array): genes x k - this is the zero-inflation parameter.
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yjzhang/uncurl_python | uncurl/zip_utils.py | zip_ll_row | def zip_ll_row(params, data_row):
"""
Returns the negative log-likelihood of a row given ZIP data.
Args:
params (list): [lambda zero-inf]
data_row (array): 1d array
Returns:
negative log-likelihood
"""
l = params[0]
pi = params[1]
d0 = (data_row==0)
likeliho... | python | def zip_ll_row(params, data_row):
"""
Returns the negative log-likelihood of a row given ZIP data.
Args:
params (list): [lambda zero-inf]
data_row (array): 1d array
Returns:
negative log-likelihood
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l = params[0]
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moonso/loqusdb | loqusdb/utils/migrate.py | migrate_database | def migrate_database(adapter):
"""Migrate an old loqusdb instance to 1.0
Args:
adapter
Returns:
nr_updated(int): Number of variants that where updated
"""
all_variants = adapter.get_variants()
nr_variants = all_variants.count()
nr_updated = 0
with progressb... | python | def migrate_database(adapter):
"""Migrate an old loqusdb instance to 1.0
Args:
adapter
Returns:
nr_updated(int): Number of variants that where updated
"""
all_variants = adapter.get_variants()
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fbergmann/libSEDML | examples/python/create_sedml2.py | main | def main (args):
"""Usage: create_sedml2 output-filename
"""
if (len(args) != 2):
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sys.exit(1);
# create the document
doc = libsedml.SedDocument();
doc.setLevel(1);
doc.setVersion(3);
# create a data description
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ddesc.setId('data1')... | python | def main (args):
"""Usage: create_sedml2 output-filename
"""
if (len(args) != 2):
print(main.__doc__)
sys.exit(1);
# create the document
doc = libsedml.SedDocument();
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yjzhang/uncurl_python | uncurl/gap_score.py | preproc_data | def preproc_data(data, gene_subset=False, **kwargs):
"""
basic data preprocessing before running gap score
Assumes that data is a matrix of shape (genes, cells).
Returns a matrix of shape (cells, 8), using the first 8 SVD
components. Why 8? It's an arbitrary selection...
"""
import uncurl
... | python | def preproc_data(data, gene_subset=False, **kwargs):
"""
basic data preprocessing before running gap score
Assumes that data is a matrix of shape (genes, cells).
Returns a matrix of shape (cells, 8), using the first 8 SVD
components. Why 8? It's an arbitrary selection...
"""
import uncurl
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yjzhang/uncurl_python | uncurl/gap_score.py | calculate_bounding_box | def calculate_bounding_box(data):
"""
Returns a 2 x m array indicating the min and max along each
dimension.
"""
mins = data.min(0)
maxes = data.max(0)
return mins, maxes | python | def calculate_bounding_box(data):
"""
Returns a 2 x m array indicating the min and max along each
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"""
mins = data.min(0)
maxes = data.max(0)
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yjzhang/uncurl_python | uncurl/gap_score.py | calculate_gap | def calculate_gap(data, clustering, km, B=50, **kwargs):
"""
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https://web.stanford.edu/~hastie/Papers/gap.pdf
Returns two results: the gap score, and s_k.
"""
k = len(set(clustering))
Wk = km.inertia_
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"""
See: https://datasciencelab.wordpress.com/2013/12/27/finding-the-k-in-k-means-clustering/
https://web.stanford.edu/~hastie/Papers/gap.pdf
Returns two results: the gap score, and s_k.
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yjzhang/uncurl_python | uncurl/gap_score.py | run_gap_k_selection | def run_gap_k_selection(data, k_min=1, k_max=50, B=5,
skip=5, **kwargs):
"""
Runs gap score for all k from k_min to k_max.
"""
if k_min == k_max:
return k_min
gap_vals = []
sk_vals = []
k_range = list(range(k_min, k_max, skip))
min_k = 0
min_i = 0
for i, k in enum... | python | def run_gap_k_selection(data, k_min=1, k_max=50, B=5,
skip=5, **kwargs):
"""
Runs gap score for all k from k_min to k_max.
"""
if k_min == k_max:
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bachya/py17track | py17track/client.py | Client._request | async def _request(
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method: str,
url: str,
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headers: dict = None,
params: dict = None,
json: dict = None) -> dict:
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if not headers:
headers = {}
... | python | async def _request(
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method: str,
url: str,
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params: dict = None,
json: dict = None) -> dict:
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markperdue/pyvesync | src/pyvesync/vesync.py | VeSync.get_devices | def get_devices(self) -> list:
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return None
self.in_process = True
response, _ = helpers.call_api(
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"""Return list of VeSync devices"""
if not self.enabled:
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markperdue/pyvesync | src/pyvesync/vesync.py | VeSync.login | def login(self) -> bool:
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user_check = isinstance(self.username, str) and len(self.username) > 0
pass_check = isinstance(self.password, str) and len(self.password) > 0
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response, _ = helpers.call_api(
... | python | def login(self) -> bool:
"""Return True if log in request succeeds"""
user_check = isinstance(self.username, str) and len(self.username) > 0
pass_check = isinstance(self.password, str) and len(self.password) > 0
if user_check and pass_check:
response, _ = helpers.call_api(
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markperdue/pyvesync | src/pyvesync/vesync.py | VeSync.update | def update(self):
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markperdue/pyvesync | src/pyvesync/vesync.py | VeSync.update_energy | def update_energy(self, bypass_check=False):
"""Fetch updated energy information about devices"""
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outlet.update_energy(bypass_check) | python | def update_energy(self, bypass_check=False):
"""Fetch updated energy information about devices"""
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yjzhang/uncurl_python | uncurl/fit_dist_data.py | DistFitDataset | def DistFitDataset(Dat):
"""
Given a data matrix, this returns the per-gene fit error for the
Poisson, Normal, and Log-Normal distributions.
Args:
Dat (array): numpy array with shape (genes, cells)
Returns:
d (dict): 'poiss', 'norm', 'lognorm' give the fit error for each distributi... | python | def DistFitDataset(Dat):
"""
Given a data matrix, this returns the per-gene fit error for the
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Dat (array): numpy array with shape (genes, cells)
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moonso/loqusdb | loqusdb/utils/delete.py | delete | def delete(adapter, case_obj, update=False, existing_case=False):
"""Delete a case and all of it's variants from the database.
Args:
adapter: Connection to database
case_obj(models.Case)
update(bool): If we are in the middle of an update
existing_case(models.Case): If someth... | python | def delete(adapter, case_obj, update=False, existing_case=False):
"""Delete a case and all of it's variants from the database.
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adapter: Connection to database
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moonso/loqusdb | loqusdb/utils/delete.py | delete_variants | def delete_variants(adapter, vcf_obj, case_obj, case_id=None):
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Args:
adapter(loqusdb.plugins.Adapter)
vcf_obj(iterable(dict))
ind_positions(dict)
case_id(str)
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nr_deleted (int): Number of deleted variants... | python | def delete_variants(adapter, vcf_obj, case_obj, case_id=None):
"""Delete variants for a case in the database
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moonso/loqusdb | loqusdb/commands/annotate.py | annotate | def annotate(ctx, variant_file, sv):
"""Annotate the variants in a VCF
"""
adapter = ctx.obj['adapter']
variant_path = os.path.abspath(variant_file)
expected_type = 'snv'
if sv:
expected_type = 'sv'
if 'sv':
nr_cases = adapter.nr_cases(sv_cases=True)
else:
nr_... | python | def annotate(ctx, variant_file, sv):
"""Annotate the variants in a VCF
"""
adapter = ctx.obj['adapter']
variant_path = os.path.abspath(variant_file)
expected_type = 'snv'
if sv:
expected_type = 'sv'
if 'sv':
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bachya/py17track | py17track/track.py | Track.find | async def find(self, *tracking_numbers: str) -> list:
"""Get tracking info for one or more tracking numbers."""
data = {'data': [{'num': num} for num in tracking_numbers]}
tracking_resp = await self._request('post', API_URL_TRACK, json=data)
print(tracking_resp)
if not tracking... | python | async def find(self, *tracking_numbers: str) -> list:
"""Get tracking info for one or more tracking numbers."""
data = {'data': [{'num': num} for num in tracking_numbers]}
tracking_resp = await self._request('post', API_URL_TRACK, json=data)
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moonso/loqusdb | loqusdb/commands/cli.py | cli | def cli(ctx, database, username, password, authdb, port, host, uri, verbose, config, test):
"""loqusdb: manage a local variant count database."""
loglevel = "INFO"
if verbose:
loglevel = "DEBUG"
coloredlogs.install(level=loglevel)
LOG.info("Running loqusdb version %s", __version__)
conf... | python | def cli(ctx, database, username, password, authdb, port, host, uri, verbose, config, test):
"""loqusdb: manage a local variant count database."""
loglevel = "INFO"
if verbose:
loglevel = "DEBUG"
coloredlogs.install(level=loglevel)
LOG.info("Running loqusdb version %s", __version__)
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yjzhang/uncurl_python | uncurl/qual2quant.py | binarize | def binarize(qualitative):
"""
binarizes an expression dataset.
"""
thresholds = qualitative.min(1) + (qualitative.max(1) - qualitative.min(1))/2.0
binarized = qualitative > thresholds.reshape((len(thresholds), 1)).repeat(8,1)
return binarized.astype(int) | python | def binarize(qualitative):
"""
binarizes an expression dataset.
"""
thresholds = qualitative.min(1) + (qualitative.max(1) - qualitative.min(1))/2.0
binarized = qualitative > thresholds.reshape((len(thresholds), 1)).repeat(8,1)
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yjzhang/uncurl_python | uncurl/qual2quant.py | qualNorm_filter_genes | def qualNorm_filter_genes(data, qualitative, pval_threshold=0.05, smoothing=1e-5, eps=1e-5):
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Does qualNorm but returns a filtered gene set, based on a p-value threshold.
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yjzhang/uncurl_python | uncurl/qual2quant.py | qualNorm | def qualNorm(data, qualitative):
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OCHA-DAP/hdx-python-country | setup.py | script_dir | def script_dir(pyobject, follow_symlinks=True):
"""Get current script's directory
Args:
pyobject (Any): Any Python object in the script
follow_symlinks (Optional[bool]): Follow symlinks or not. Defaults to True.
Returns:
str: Current script's directory
"""
if getattr(sys, '... | python | def script_dir(pyobject, follow_symlinks=True):
"""Get current script's directory
Args:
pyobject (Any): Any Python object in the script
follow_symlinks (Optional[bool]): Follow symlinks or not. Defaults to True.
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str: Current script's directory
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"""Get current script's directory and then append a filename
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filename (str): Filename to append to directory path
pyobject (Any): Any Python object in the script
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moonso/loqusdb | loqusdb/commands/identity.py | identity | def identity(ctx, variant_id):
"""Check how well SVs are working in the database
"""
if not variant_id:
LOG.warning("Please provide a variant id")
ctx.abort()
adapter = ctx.obj['adapter']
version = ctx.obj['version']
LOG.info("Search variants {0}".format(adapter))
... | python | def identity(ctx, variant_id):
"""Check how well SVs are working in the database
"""
if not variant_id:
LOG.warning("Please provide a variant id")
ctx.abort()
adapter = ctx.obj['adapter']
version = ctx.obj['version']
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ggravlingen/pygleif | pygleif/gleif.py | GLEIFEntity.registration_authority_entity_id | def registration_authority_entity_id(self):
"""
Some entities return the register entity id,
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"""
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"""
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"""
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ggravlingen/pygleif | pygleif/gleif.py | GLEIFEntity.legal_form | def legal_form(self):
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ggravlingen/pygleif | pygleif/gleif.py | DirectChild.valid_child_records | def valid_child_records(self):
child_lei = list()
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if d['att... | python | def valid_child_records(self):
child_lei = list()
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moonso/loqusdb | loqusdb/utils/annotate.py | annotate_variant | def annotate_variant(variant, var_obj=None):
"""Annotate a cyvcf variant with observations
Args:
variant(cyvcf2.variant)
var_obj(dict)
Returns:
variant(cyvcf2.variant): Annotated variant
"""
if var_obj:
variant.INFO['Obs'] = var_obj['observations']
... | python | def annotate_variant(variant, var_obj=None):
"""Annotate a cyvcf variant with observations
Args:
variant(cyvcf2.variant)
var_obj(dict)
Returns:
variant(cyvcf2.variant): Annotated variant
"""
if var_obj:
variant.INFO['Obs'] = var_obj['observations']
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moonso/loqusdb | loqusdb/utils/annotate.py | annotate_snv | def annotate_snv(adpter, variant):
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Args:
adapter(loqusdb.plugin.adapter)
variant(cyvcf2.Variant)
"""
variant_id = get_variant_id(variant)
variant_obj = adapter.get_variant(variant={'_id':variant_id})
annotated_variant = annotated_variant... | python | def annotate_snv(adpter, variant):
"""Annotate an SNV/INDEL variant
Args:
adapter(loqusdb.plugin.adapter)
variant(cyvcf2.Variant)
"""
variant_id = get_variant_id(variant)
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moonso/loqusdb | loqusdb/utils/annotate.py | annotate_svs | def annotate_svs(adapter, vcf_obj):
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Args:
adapter(loqusdb.plugin.adapter)
vcf_obj(cyvcf2.VCF)
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variant(cyvcf2.Variant)
"""
for nr_variants, variant in enumerate(vcf_obj, 1):
variant_info = get_coords(variant)
... | python | def annotate_svs(adapter, vcf_obj):
"""Annotate all SV variants in a VCF
Args:
adapter(loqusdb.plugin.adapter)
vcf_obj(cyvcf2.VCF)
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variant(cyvcf2.Variant)
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moonso/loqusdb | loqusdb/utils/annotate.py | annotate_snvs | def annotate_snvs(adapter, vcf_obj):
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adapter(loqusdb.plugin.adapter)
vcf_obj(cyvcf2.VCF)
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"""
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... | python | def annotate_snvs(adapter, vcf_obj):
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adapter(loqusdb.plugin.adapter)
vcf_obj(cyvcf2.VCF)
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variant(cyvcf2.Variant): Annotated variant
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bosth/plpygis | plpygis/geometry.py | Geometry.from_geojson | def from_geojson(geojson, srid=4326):
"""
Create a Geometry from a GeoJSON. The SRID can be overridden from the
expected 4326.
"""
type_ = geojson["type"].lower()
if type_ == "geometrycollection":
geometries = []
for geometry in geojson["geometries... | python | def from_geojson(geojson, srid=4326):
"""
Create a Geometry from a GeoJSON. The SRID can be overridden from the
expected 4326.
"""
type_ = geojson["type"].lower()
if type_ == "geometrycollection":
geometries = []
for geometry in geojson["geometries... | [
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