def main():
'''
Run with at least:
1 argument, defining the number of minima to be found
optional last argument is file name of data to read into memory (if it hasn't alreay been read)
'''
if sys.argv[1] == 'help':
print(main.__doc__)
return
srch_val = int(sys.argv[1])
global np_init_array
try:
np_init_array
except NameError:
flat.print_log_msg('Reading data')
np_init_array, np_init_array_x = rd.read_data(sys.argv[len(sys.argv)-1])
found_width = custom_binary_search_with_trackback(np_init_array, filt.apply_filter_get_minima, srch_val, trackback_delta=200, trackback_step=20, init_search_location=1000)
print('found_width: ', found_width)
flat.print_log_msg('Done')
def trackback(wrapper, srch_val, start_search, delta_coarse, step_coarse, step_fine=1):
found_more = True # just to enter loop
flat.print_log_msg('Starting coarse search')
while found_more: # whenever we find more, search continues looking as far as delta width from newly found location
found_more = False
for i in range(start_search+step_coarse, start_search+delta_coarse, step_coarse):
if i>=len(wrapper):
break
if wrapper[i] == srch_val:
found_more = True
start_search = i
break
# By default, step_fine = 1, therefore fine-grained search will happen. It can be disabled by setting step_fine to 0.
if step_fine > 0:
if step_fine > step_coarse:
raise Exception('Error: step_fine is greater than step_coarse')
delta_fine = step_coarse
found_more = True # just to enter loop
flat.print_log_msg('Starting fine search')
while found_more: # whenever we find more, search continues looking as far as delta width from there
found_more = False
for i in range(start_search+step_fine, start_search+delta_fine, step_fine):
if i>=len(wrapper):
break
if wrapper[i] == srch_val:
found_more = True
start_search = i
break
return start_search
def main():
# def __init__(self, name, snp_first, snp_last, input_config, breakpoints):
#
begin = 9411243
end = 48119216
# begin = 46287140
# end = 48119216
breakpoints1 = [10148322, 15250019, 15864313, 16491839, 17748811, 18252127, 18912106, 19637870, 20332293, 20929869, 21190923, 21649595, 22318833, 23231365, 24271200, 24774771, 25035980, 26088085, 27431612, 27666047, 28290149, 28485200, 28761470, 29335757, 29790442, 30972911, 32778127, 33370496, 34413058, 35253882, 35614394, 36328018, 37283402, 38078491, 39227880, 39908770, 40259482, 40965403, 41448115, 41676786, 42689700, 43100808, 43345207, 43799567, 44748107, 45265729, 45789905, 46336509, 46883153, 47465743]
# metric = Metric('chr21', cnst.const['orig_data'], breakpoints1, begin, end)
metric = Metric('chr21', cnst.return_conf('/nethome/jkpickrell/1kG_data/covariance_matrix/'), breakpoints1, begin, end)
out = metric.calc_metric()
print(out)
print(out['sum']/out['N_zero'])
breakpoints2 = [i for i in range(begin, end+1, int((end-begin)/(len(breakpoints1)-1)))]
metric = Metric('chr21', cnst.return_conf('/nethome/jkpickrell/1kG_data/covariance_matrix/'), breakpoints2, begin, end)
out = metric.calc_metric()
print(out)
print(out['sum']/out['N_zero'])
flat.print_log_msg('Done')
def run_local_search_single(chr_name, breakpoint_loci, locus_index, start,
stop, total_sum, total_N, input_config,
metric_out):
print("---- Running local search single")
try:
print("----", start, stop, locus_index, breakpoint_loci, total_sum,
total_N)
local_search_run = local_search.LocalSearch(chr_name, start, stop,
locus_index,
breakpoint_loci, total_sum,
total_N, input_config)
new_breakpoint, new_metric = local_search_run.search()
print_breakpoint_comparison(new_breakpoint, new_metric,
breakpoint_loci[locus_index], metric_out)
return new_breakpoint, new_metric
except Exception as e:
flat.print_log_msg('Error!')
flat.print_log_msg(str(e))
flat.print_log_msg('start: ' + repr(start))
flat.print_log_msg('stop: ' + repr(stop))
# flat.print_log_msg('local_search.__dict__: '+repr(local_search.__dict__))
flat.print_log_msg('Continuing...')
return breakpoint_loci[locus_index], None
def custom_binary_search_with_trackback(np_init_array,
f,
srch_val,
trackback_delta=200,
trackback_step=20,
init_search_location=1000):
flat.print_log_msg('Starting custom_binary_search_with_trackback')
# One-sided binary (i.e., exponential) search first
"apply f to np_init_array and check if init_search_location is smaller than srch_val"
"if not, double init search val and try again"
print('search_val: ', srch_val)
end_v = find_end(np_init_array, f, init_search_location, srch_val)
print('end_v: ', end_v)
wrapper = FlexibleBoundedAccessor(np_init_array, f, 0, end_v, True)
# Search with deferred detection of equality
found_width_raw = binsrch.find_le_ind(wrapper, srch_val)
print('found_width_raw: ', found_width_raw)
found_width = end_v - found_width_raw
print('found_width: ', found_width)
# Find any remaining "noisy" minima
found_width_trackback_raw = trackback(wrapper, srch_val, found_width_raw,
trackback_delta, trackback_step)
print("found_width_trackback_raw", found_width_trackback_raw)
found_width_trackback = end_v - found_width_trackback_raw
# Final result
found_width = found_width_trackback
print('found_width final: ', found_width)
return found_width
def pipeline_lean(dataset_path,
name,
out_fname,
begin=-1,
end=-1,
img='no',
orient='diag',
red='sum',
dataset_name='NONAME'):
'''
pipeline_lean(dataset_path, name, begin=-1, end=-1, img='no', orient='diag', red='sum')
'''
# analysis = matrix_to_vector.MatrixAnalysis(name, cnst.const[dataset], begin, end)
analysis = matrix_to_vector.MatrixAnalysis(name,
cnst.return_conf(dataset_path),
begin, end)
print(analysis.snp_first)
print(analysis.snp_last)
t = datetime.datetime.now()
t_formatted = t.strftime('%Y_%m_%d_%H_%M_%S')
# out_fname = 'vector-'+dataset_name+'-'+name+'-'+str(analysis.snp_first)+'-'+str(analysis.snp_last)+'-'+orient+'-'+red+'-img_'+img+'-'+t_formatted
# out_fname += '.txt.gz'
flat.print_log_msg('out_fname: ' + out_fname)
if (img == 'yes'):
generate_img = True
elif (img == 'no'):
generate_img = False
else:
raise Exception('Error: Unknown argument: ' + img)
if (orient == 'vert'):
analysis.calc_vert(not generate_img)
elif (orient == 'diag'):
analysis.calc_diag_lean(out_fname, cnst.const['out_delim'],
not generate_img)
else:
raise Exception('Error: Unknown argument: ' + orient)
if (red == 'avg'):
avg = True
raise Exception(
'Average used, but its output is not always consistent - especially for diag!'
)
elif (red == 'sum'):
avg = False
else:
raise Exception('Error: Unknown argument: ' + red)
# Output is done step-by-step
# analysis.write_output_to_file(out_fname+'.txt.gz', cnst.const['out_delim'], avg)
if generate_img:
analysis.generate_img(out_fname + cnst.const['img_out_ext'])
flat.print_log_msg('Done')
def standard_run(np_init_array, np_init_array_x, start, stop, step):
# Interactive plots
graphs = filt.apply_filters(np_init_array, start, stop, step)
# graphs = filt.apply_filters(np_init_array, int(sys.argv[1]), int(sys.argv[2])+1, int(sys.argv[3]))
for g in graphs:
flat.print_log_msg('indices' + repr(g['width']) +
repr(g['filtered_minima_ind']))
loci = filt.get_minima_loc(g, np_init_array_x)
flat.print_log_msg('loci' + repr(g['width']) + repr(loci))
plot_all(np_init_array, graphs, np_init_array_x)
def print_breakpoint_comparison(breakpoint1, metric1, breakpoint2, metric2):
flat.print_log_msg('Breakpoint 1: ' + repr(breakpoint1))
flat.print_log_msg('Metric 1:')
print_metric(metric1)
flat.print_log_msg('Breakpoint 2: ' + repr(breakpoint2))
flat.print_log_msg('Metric 2:')
print_metric(metric2)
def main():
'''
Run with at least:
a) 1 argument, defining the central filter width of analysis area
b) 3 arguments, defining the start, stop, and step of series of filter widths
optional last argument is file name of data to read into memory (if it hasn't alreay been read)
'''
if sys.argv[1] == 'help':
print(main.__doc__)
return
# max_w = 10000
# vals = []
# for width in range(1, max_w):
# vals.append(filt.apply_filter_get_minima(np_init_array, width))
# print(width)
# np_temp_array = np.array(vals)
# minima = sig.argrelextrema(np_temp_array, np.greater)[0]
# print(minima)
global np_init_array, np_init_array_x
try:
np_init_array
np_init_array_x
except NameError:
flat.print_log_msg('Reading data')
np_init_array, np_init_array_x = rd.read_data(sys.argv[len(sys.argv) -
1])
relative_width = 0.5
if len(sys.argv) > 3:
start, stop, step = int(sys.argv[1]), int(sys.argv[2]), int(
sys.argv[3])
else:
center_val = int(sys.argv[1])
start, stop, step = math.floor(
center_val - relative_width *
center_val), math.ceil(center_val +
relative_width * center_val), math.floor(
2 * relative_width * center_val / 6)
standard_run(np_init_array, np_init_array_x, start, stop, step)
flat.print_log_msg('Done')
def write_output_to_file(self, filename, out_delim, avg=False):
if not self.calculation_complete:
raise Exception(
'Error: Calculation has not been completed prior to output file generation'
)
flat.print_log_msg('Writing output to file')
if avg:
flat.write_output(filename, self.locus_list,
self.locus_list_deleted, self.vert_sum,
out_delim, self.vert_sum_len)
else:
flat.write_output(filename, self.locus_list,
self.locus_list_deleted, self.vert_sum,
out_delim)
def run_local_search_complete(chr_name, breakpoint_loci, begin, end, input_config, metric_out):
breakpoint_loci_local_search = {}
breakpoint_loci_local_search['loci'] = []
breakpoint_loci_local_search['metrics'] = []
total_sum = metric_out['sum']
total_N = metric_out['N_zero']
# Search between begin and first midpoint
b_stop = int(midpoint(breakpoint_loci[0], breakpoint_loci[1])) #-1 # -1 so as to not overlap with next region! -> this is taken care of in local search
new_breakpoint, new_metric = run_local_search_single(chr_name, breakpoint_loci, 0, begin, b_stop, total_sum, total_N, input_config, metric_out)
breakpoint_loci_local_search['loci'].append(new_breakpoint)
breakpoint_loci_local_search['metrics'].append(new_metric)
for locus_index in range(1, len(breakpoint_loci)-1):
b_start = int(midpoint(breakpoint_loci[locus_index-1], breakpoint_loci[locus_index]))
b_stop = int(midpoint(breakpoint_loci[locus_index], breakpoint_loci[locus_index+1])) #-1 # -1 so as to not overlap with next region! -> this is taken care of in local search
new_breakpoint, new_metric = run_local_search_single(chr_name, breakpoint_loci, locus_index, b_start, b_stop, total_sum, total_N, input_config, metric_out)
breakpoint_loci_local_search['loci'].append(new_breakpoint)
breakpoint_loci_local_search['metrics'].append(new_metric)
# local_search_run = local_search.LocalSearch(chr_name, breakpoint_loci[locus_index-1], breakpoint_loci[locus_index+1], locus_index, breakpoint_loci, total_sum, total_N, input_config)
#
# new_breakpoint, new_metric = local_search_run.search()
#
# print_breakpoint_comparison(new_breakpoint, new_metric, breakpoint_loci[locus_index], metric_out)
# # print(new_breakpoint, new_metric['sum']/new_metric['N_zero'])
# # print(breakpoint_loci[locus_index], total_sum/total_N)
#
# breakpoint_loci_local_search['loci'].append(new_breakpoint)
# breakpoint_loci_local_search['metrics'].append(new_metric)
# Search between last midpoint and end
b_start = int(midpoint(breakpoint_loci[len(breakpoint_loci)-2], breakpoint_loci[len(breakpoint_loci)-1]))
new_breakpoint, new_metric = run_local_search_single(chr_name, breakpoint_loci, len(breakpoint_loci)-1, b_start, end, total_sum, total_N, input_config, metric_out)
breakpoint_loci_local_search['loci'].append(new_breakpoint)
breakpoint_loci_local_search['metrics'].append(new_metric)
flat.print_log_msg('New breakpoints:')
print(breakpoint_loci_local_search)
return breakpoint_loci_local_search
def main():
name = 'chr1'
flat.print_log_msg('Starting run')
x, y, pairs = flat.read_hotspots(cnst.const['genetic_maps']['root']+cnst.const['genetic_maps']['file_base']+name+cnst.const['genetic_maps']['ext'])
flat.print_log_msg('Plotting')
pt.plot(x,y)
fig = pt.gcf()
fig.set_size_inches((40,30))
pt.xlabel('SNP #')
pt.ylabel('Hotspot val')
pt.title('Hotspots')
pt.savefig('genetic_maps_output.png')
pt.clf()
pt.plot(x[5000:10000],y[5000:10000])
fig = pt.gcf()
fig.set_size_inches((40,30))
pt.xlabel('SNP #')
pt.ylabel('Hotspot val')
pt.title('Hotspots zoomed')
pt.savefig('genetic_maps_zoomed_output.png')
flat.print_log_msg('Done')
def calc_diag_lean(self, out_fname, out_delim, dynamic_delete=True):
# flat.print_log_msg('Removing existing matrix output file')
# try:
# os.remove(cnst.const['out_matrix_delim'])
# except OSError:
# pass
if dynamic_delete == False:
raise Exception(
'Error: Conversion has been run in lean mode, but with dynamically=False.'
)
self.dynamic_delete = dynamic_delete
flat.print_log_msg('Start')
# pre-read all relevant partitions at beginning!
last_p_num = -1
for p_num_init in range(0, len(self.partitions) - 1):
if self.snp_first >= self.partitions[p_num_init + 1][0]:
flat.print_log_msg('Pre-reading partition: ' +
str(self.partitions[p_num_init]))
flat.read_partition_into_matrix_lean(
self.partitions, p_num_init, self.matrix, self.locus_list,
self.name, self.input_config, self.snp_first,
self.snp_last)
last_p_num = p_num_init
else:
break
curr_locus = -1
# for p_num, p in enumerate(self.partitions):
for p_num in range(last_p_num + 1, len(self.partitions)):
p = self.partitions[p_num]
flat.print_log_msg('Reading partition: ' + str(p))
flat.read_partition_into_matrix_lean(self.partitions, p_num,
self.matrix, self.locus_list,
self.name, self.input_config,
self.snp_first, self.snp_last)
# print("self.partitions", self.partitions[:5])
# print("self.locus_list", self.locus_list[:5])
# print("self.matrix", list(self.matrix.items())[:1])
# print("self.snp_first", self.snp_first)
# raise
# Determine first locus
if curr_locus < 0: # Either first partition or not found in first partition
# curr_locus = -1 # <- this should have been set to -1 before entering the main for loop
if len(self.locus_list) > 0:
# Find first locus >= snp_first
for i, locus in enumerate(self.locus_list):
if locus >= self.snp_first:
curr_locus = locus
start_locus = locus
curr_locus_index = i
start_locus_index = i
break
else:
raise Exception('Error: locus_list seems to be empty')
# else:
# if len(self.locus_list)>0:
# curr_locus = self.locus_list[0]
# curr_locus_index = 0
# else:
# raise Exception('Error: locus_list seems to be empty')
else:
try:
curr_locus_index = self.locus_list.index(curr_locus)
# curr_locus is carried from prev iteration, but index has changed since part of matrix (and locus_list) has been deleted
except ValueError:
if len(self.locus_list) > 0:
curr_locus = self.locus_list[0]
curr_locus_index = 0
else:
raise Exception('Error: locus_list seems to be empty')
if curr_locus < 0:
flat.print_log_msg(
'Warning: curr_locus not found! Continuing to next partition.'
)
flat.print_log_msg(
'Comment: This is possibly due to snp_first being very close to end of partition.'
)
flat.print_log_msg('Details: ')
flat.print_log_msg('Partition: ' + repr(p))
flat.print_log_msg('snp_first: ' + repr(self.snp_first))
flat.print_log_msg('curr_locus: ' + repr(curr_locus))
continue #continue to next partition
# raise Exception('Error: curr_locus not found!')
# Determine end locus
if p_num + 1 < len(self.partitions):
end_locus = int(
(self.partitions[p_num][1] + self.partitions[p_num + 1][0])
/ 2) # diag - specific
print("1 end locus", end_locus, p_num)
else:
# end_locus = self.partitions[p_num][1]
# Find last locus <= snp_last
end_locus_found = False
for i in reversed(range(0, len(self.locus_list))):
# for locus in reversed(locus_list):
if self.locus_list[i] <= self.snp_last:
end_locus = self.locus_list[i]
end_locus_index = i
end_locus_found = True
break
print("2 end locus", end_locus, p_num)
if not end_locus_found:
end_locus_index = 0
end_locus = self.locus_list[end_locus_index]
flat.print_log_msg('Running for partition: ' + str(p))
# This will not include the very last SNP of the complete range, but that shouldn't be too important since the end of the range shouldn't be a defining location for LD
while curr_locus <= end_locus:
print("-----" * 5)
print("curr_locus", curr_locus)
total_iterations = 0
total_additions = 0
x = self.locus_list[curr_locus_index]
y = self.locus_list[curr_locus_index]
print("x_idx", curr_locus_index)
print("y_idx", curr_locus_index)
print("x", x)
print("y", y)
delta = 0
while x >= self.partitions[p_num][0] and y <= self.partitions[
p_num][1]:
print(" x", x)
print(" y", y)
# print(" delta", delta)
# when would x not be in matrix or y not be in matrix[x]?
if x in self.matrix and y in self.matrix[x]:
# print("computing corr coeff for", x, y)
corr_coeff = self.matrix[x][y] / math.sqrt(
self.matrix[x][x] * self.matrix[y][y])
self.add_corr_coeff(corr_coeff, curr_locus)
print(" self.vert_sum[curr_locus]",
self.vert_sum[curr_locus])
total_additions += 1
# Just save it in the matrix ;) - removed for chrom11
# self.matrix[x]['data'][y]['corr_coeff'] = corr_coeff
# else:
# flat.print_log_msg('Condition not satisfied 1!')
# flat.print_log_msg('x: '+repr(x)+' y: '+repr(y))
if delta != 0:
x = self.locus_list[curr_locus_index - delta + 1]
if x in self.matrix and y in self.matrix[x]:
# print("computing corr coeff for", x, y)
corr_coeff = self.matrix[x][y] / math.sqrt(
self.matrix[x][x] * self.matrix[y][y])
self.add_corr_coeff(corr_coeff, curr_locus)
print(" self.vert_sum[curr_locus]",
self.vert_sum[curr_locus])
total_additions += 1
# Just save it in the matrix ;) - removed for chrom11
# self.matrix[x]['data'][y]['corr_coeff'] = corr_coeff
# else:
# flat.print_log_msg('Condition not satisfied 2!')
# flat.print_log_msg('x: '+repr(x)+' y: '+repr(y))
delta += 1
if curr_locus_index - delta >= 0:
print("x_idx", curr_locus_index - delta)
x = self.locus_list[curr_locus_index - delta]
else:
# flat.print_log_msg('X index out of bounds')
flat.print_log_msg('X index out of bounds')
break
if curr_locus_index + delta < len(self.locus_list):
print("y_idx", curr_locus_index + delta)
y = self.locus_list[curr_locus_index + delta]
else:
flat.print_log_msg('Y index out of bounds')
break
total_iterations += 1
print("total_iterations", total_iterations)
print("total_additions", total_additions)
if curr_locus_index + 1 < len(self.locus_list):
curr_locus_index += 1
curr_locus = self.locus_list[curr_locus_index]
else:
flat.print_log_msg('curr_locus_index out of bounds')
break
# flat.print_log_msg('Mem before delete: '+repr(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss))
# flat.delete_loci_smaller_than_and_output_matrix_to_file(end_locus, self.matrix, locus_list, locus_list_deleted, cnst.const['out_matrix_filename'])
if self.dynamic_delete:
flat.print_log_msg('Deleting loci not required any more')
if p_num + 1 < len(self.partitions):
delete_loc = self.partitions[p_num + 1][0]
else:
delete_loc = end_locus
flat.delete_loci_smaller_than_lean(delete_loc, self.matrix,
self.locus_list,
self.locus_list_deleted,
out_fname, self.vert_sum,
out_delim)
else:
flat.print_log_msg('locus_list size: ' +
repr(len(self.locus_list)))
# flat.print_log_msg('Mem after delete: '+repr(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss))
self.start_locus = start_locus
self.start_locus_index = start_locus_index
self.end_locus = end_locus
self.end_locus_index = end_locus_index
self.calculation_complete = True
def pipeline(dataset_path,
name,
out_fname,
begin=-1,
end=-1,
img='no',
orient='diag',
red='sum',
snp=None,
comment='',
dataset_name='NONAME'):
'''
pipeline(dataset_path, name, begin=-1, end=-1, img='no', orient='diag', red='sum', snp=None, comment='')
snp1 and snp2 are loci of two SNPs that need to be converted into ordinal numbers representing row/col in image of matrix
'''
# analysis = matrix_to_vector.MatrixAnalysis(name, cnst.const[dataset], begin, end)
analysis = matrix_to_vector.MatrixAnalysis(name,
cnst.return_conf(dataset_path),
begin, end)
print(analysis.snp_first)
print(analysis.snp_last)
if (img == 'yes'):
generate_img = True
elif (img == 'no'):
generate_img = False
else:
raise Exception('Error: Unknown argument: ' + img)
if (orient == 'vert'):
analysis.calc_vert(not generate_img)
elif (orient == 'diag'):
analysis.calc_diag(not generate_img)
else:
raise Exception('Error: Unknown argument: ' + orient)
if (red == 'avg'):
avg = True
raise Exception(
'Average used, but its output is not always consistent - especially for diag!'
)
elif (red == 'sum'):
avg = False
else:
raise Exception('Error: Unknown argument: ' + red)
t = datetime.datetime.now()
t_formatted = t.strftime('%Y_%m_%d_%H_%M_%S')
# out_fname = 'vector-'+dataset_name+'-'+name+'-'+comment+'-'+str(analysis.snp_first)+'-'+str(analysis.snp_last)+'-'+orient+'-'+red+'-img_'+img+'-'+t_formatted
analysis.write_output_to_file(out_fname, cnst.const['out_delim'], avg)
if generate_img:
# flat.print_log_msg('x_values: '+repr(x_values))
if snp is not None:
analysis.generate_img(
'img-' + out_fname + cnst.const['img_out_ext'], snp)
else:
analysis.generate_img('img-' + out_fname +
cnst.const['img_out_ext'])
flat.print_log_msg('Done')
def calc_metric_full(self):
# flat.print_log_msg('Removing existing matrix output file')
# try:
# os.remove(cnst.const['out_matrix_delim'])
# except OSError:
# pass
if not self.dynamic_delete:
raise Exception('Error: dynamic delete must be True for metric calculation!')
flat.print_log_msg('Start metric')
curr_breakpoint_index = 0
block_height = 0
block_width = 0
total_N_SNPs = decimal.Decimal('0')
block_width_sum = decimal.Decimal('0')
# pre-read all relevant partitions at beginning!
last_p_num = -1
for p_num_init in range(0, len(self.partitions)-1):
if self.snp_first >= self.partitions[p_num_init+1][0]:
flat.print_log_msg('Pre-reading partition: '+str(self.partitions[p_num_init]))
flat.read_partition_into_matrix(self.partitions, p_num_init, self.matrix, self.locus_list, self.name, self.input_config, self.snp_first, self.snp_last)
last_p_num = p_num_init
else:
break
curr_locus = -1
# for p_num, p in enumerate(self.partitions):
for p_num in range(last_p_num+1, len(self.partitions)):
p = self.partitions[p_num]
flat.print_log_msg('Reading partition: '+str(p))
flat.read_partition_into_matrix(self.partitions, p_num, self.matrix, self.locus_list, self.name, self.input_config, self.snp_first, self.snp_last)
# Determine first locus
if curr_locus<0: # Either first partition or not found in first partition
# curr_locus = -1 # <- this should have been set to -1 before entering the main for loop
if len(self.locus_list)>0:
# Find first locus >= snp_first
for i, locus in enumerate(self.locus_list):
if locus >= self.snp_first:
curr_locus = locus
start_locus = locus
curr_locus_index = i
start_locus_index = i
break
else:
raise Exception('Error: locus_list seems to be empty')
# else:
# if len(self.locus_list)>0:
# curr_locus = self.locus_list[0]
# curr_locus_index = 0
# else:
# raise Exception('Error: locus_list seems to be empty')
else:
try:
curr_locus_index = self.locus_list.index(curr_locus)
# curr_locus is carried from prev iteration, but index has changed since part of matrix (and locus_list) has been deleted
except ValueError:
if len(self.locus_list)>0:
curr_locus = self.locus_list[0]
curr_locus_index = 0
else:
raise Exception('Error: locus_list seems to be empty')
if curr_locus<0:
flat.print_log_msg('Warning: curr_locus not found! Continuing to next partition.')
flat.print_log_msg('Comment: This is possibly due to snp_first being very close to end of partition.')
flat.print_log_msg('Details: ')
flat.print_log_msg('Partition: '+repr(p))
flat.print_log_msg('snp_first: '+repr(self.snp_first))
flat.print_log_msg('curr_locus: '+repr(curr_locus))
continue #continue to next partition
# raise Exception('Error: curr_locus not found!')
# Determine last locus
if p_num+1 < len(self.partitions):
end_locus = self.partitions[p_num+1][0]
end_locus_index = -1
else:
# end_locus = self.partitions[p_num][1]
# Find last locus <= snp_last
end_locus_found = False
for i in reversed(range(0, len(self.locus_list))):
# for locus in reversed(locus_list):
if self.locus_list[i] <= self.snp_last:
end_locus = self.locus_list[i]
end_locus_index = i
end_locus_found = True
break
if not end_locus_found:
end_locus_index = 0
end_locus = self.locus_list[end_locus_index]
flat.print_log_msg('Running metric for partition: '+str(p))
# This will not include the very last SNP of the complete range, but that shouldn't be too important since the end of the range shouldn't be a defining location for LD
while curr_locus <= end_locus:
if curr_breakpoint_index<len(self.breakpoints):
if curr_locus > self.breakpoints[curr_breakpoint_index]: # Breakpoint is the last element of the block!
# block_height = len(self.locus_list) - curr_locus_index
block_height = 0 - total_N_SNPs # - 1 # ? # this is in accordance with the formula for deferred sum calculation
self.metric['N_zero'] += block_height * block_width
block_width_sum += block_width
curr_breakpoint_index += 1
block_width = 0
if curr_breakpoint_index>=len(self.breakpoints):
break
# found = False
try:
for key, el in self.matrix[curr_locus]['data'].items():
if key > self.breakpoints[curr_breakpoint_index]: # Only add those above the breakpoint!
corr_coeff = self.matrix[curr_locus]['data'][key]['shrink'] / math.sqrt( self.matrix[curr_locus]['data'][curr_locus]['shrink'] * self.matrix[key]['data'][key]['shrink'] )
self.metric['sum'] += decimal.Decimal(corr_coeff**2)
self.metric['N_nonzero'] += 1
# found = True
except IndexError as e:
print('Error!')
print(e)
print(key, el)
print(curr_locus)
print(self.matrix)
print(self.breakpoints)
print(curr_breakpoint_index)
# if found:
block_width += 1 # block_width needs to be increased even if it doesn't have values in the outer part of the matrix!
if curr_locus_index+1 < len(self.locus_list):
curr_locus_index+=1
curr_locus = self.locus_list[curr_locus_index]
total_N_SNPs += 1
else:
flat.print_log_msg('curr_locus_index out of bounds')
break
# if block_width > 0: # If an LD block hasn't finished, but a new partition must be read into memory
# # index_of_breakpoint_in_locus_list = -1
# for ind in range(curr_locus_index, len(self.locus_list)):
# if self.locus_list[ind] >= self.breakpoints[curr_breakpoint_index]:
# # index_of_breakpoint_in_locus_list = ind
# break
#
# num_of_SNPs_to_add = ind - curr_locus_index
#
# # if index_of_breakpoint_in_locus_list < 0:
# # raise Exception('Error: index_of_breakpoint_in_locus_list not found!')
#
# # block_height = len(self.locus_list) - index_of_breakpoint_in_locus_list
# block_height = 0 - (total_N_SNPs+num_of_SNPs_to_add)
# self.metric['N_zero'] += block_height * block_width
#
# block_width_sum += block_width
# block_width = 0
# flat.delete_loci_smaller_than_and_output_matrix_to_file(end_locus, self.matrix, locus_list, locus_list_deleted, cnst.const['out_matrix_filename'])
if self.dynamic_delete:
flat.print_log_msg('Deleting loci not required any more')
flat.delete_loci_smaller_than(end_locus, self.matrix, self.locus_list, self.locus_list_deleted)
self.start_locus = start_locus
self.start_locus_index = start_locus_index
self.end_locus = end_locus
self.end_locus_index = end_locus_index
self.metric['N_zero'] += total_N_SNPs * block_width_sum # this is in accordance with the formula for deferred sum calculation
print('total_N_SNPs, block_width', total_N_SNPs, block_width)
print('total_N_SNPs-block_width', total_N_SNPs-block_width)
print('block_width_sum', block_width_sum)
self.calculation_complete = True
return self.metric
def pipeline(input_fname,
chr_name,
dataset_path,
n_snps_bw_bpoints,
out_fname,
begin=-1,
end=-1,
trackback_delta=200,
trackback_step=20,
init_search_location=1000):
# print("n_snps_bw_bpoints", n_snps_bw_bpoints)
# print("trackback_delta", trackback_delta)
# print("trackback_step", trackback_step)
config = cnst.return_conf(dataset_path)
# begin, end = flat.first_last(chr_name, cnst.const[dataset], begin, end)
"just reads first and last position in partitions"
begin, end = flat.first_last(chr_name, config, begin, end)
# READ DATA
flat.print_log_msg('* Reading data')
"just reads into snp pos and val into first and second list"
init_array, init_array_x = rd.read_data_raw(input_fname)
# print(init_array)
# print(init_array_x)
# Clip the input data to the required range and convert to numpy array
"just a bisect left and bisect right"
begin_ind = binsrch.find_ge_ind(init_array_x,
begin) # = init_array_x.index(begin)
end_ind = binsrch.find_le_ind(init_array_x,
end) # = init_array_x.index(end)
#
# print("len before", len(init_array_x))
np_init_array = np.array(init_array[begin_ind:(end_ind + 1)])
np_init_array_x = np.array(init_array_x[begin_ind:(end_ind + 1)])
# print("len after", len(np_init_array_x))
# DETERMINE NUMBER OF BREAKPOINTS
n_bpoints = int(math.ceil(len(np_init_array_x) / n_snps_bw_bpoints - 1))
# flat.print_log_msg('* Number of breakpoints: '+repr(n_bpoints))
# print("hiya")
# result = [filt.apply_filter_get_minima(np_init_array, width) for width in range(0, 1000)]
# print(result)
# raise
# SEARCH FOR FILTER WIDTH
# flat.print_log_msg('* Starting search...')
found_width = find_minima.custom_binary_search_with_trackback(
np_init_array,
filt.apply_filter_get_minima,
n_bpoints,
trackback_delta=trackback_delta,
trackback_step=trackback_step,
init_search_location=init_search_location)
# flat.print_log_msg('* Found_width: ' + repr(found_width))
# GET MINIMA LOCATIONS
flat.print_log_msg('* Applying filter and getting minima locations...')
"just applies hanning to init_array"
g = filt.apply_filter(np_init_array, found_width)
# print("raise", g)
# print("raise", np_init_array)
# print("raise", np_init_array_x)
breakpoint_loci = filt.get_minima_loc(g, np_init_array_x)
# print("raise", breakpoint_loci)
# raise
# METRIC
# flat.print_log_msg('* Calculating metric for non-uniform breakpoints (minima of filtered data)...')
# metric_out = apply_metric(chr_name, begin, end, cnst.const[dataset], breakpoint_loci)
metric_out = apply_metric(chr_name, begin, end, config, breakpoint_loci)
# flat.print_log_msg('Global metric:')
print("raise", metric_out)
raise
# print_metric(metric_out)
# METRIC FOR UNIFORM BREAKPOINTS
# flat.print_log_msg('* Calculating metric for uniform breakpoints...')
# # step = int((end-begin)/(len(breakpoint_loci)+1))
# # breakpoint_loci_uniform = [l for l in range(begin+step, end-step+1, step)]
# step = int(len(init_array_x)/(len(breakpoint_loci)+1))
# breakpoint_loci_uniform = [init_array_x[i] for i in range(step, len(init_array_x)-step+1, step)]
# # metric_out_uniform = apply_metric(chr_name, begin, end, cnst.const[dataset], breakpoint_loci_uniform)
# metric_out_uniform = apply_metric(chr_name, begin, end, config, breakpoint_loci_uniform)
# flat.print_log_msg('Global metric:')
# print_metric(metric_out_uniform)
# LOCAL SEARCH ON FOURIER - missing N runs
flat.print_log_msg('* Running local search for fourier...')
# breakpoint_loci_local_search = run_local_search_complete(chr_name, breakpoint_loci, begin, end, cnst.const[dataset], metric_out)
breakpoint_loci_local_search = run_local_search_complete(
chr_name, breakpoint_loci, begin, end, config, metric_out)
print(breakpoint_loci_local_search)
raise
# RUN METRIC AGAIN W/ NEW BREAKPOINTS FROM FOURIER LOCAL SEARCH
flat.print_log_msg('* Calculating metric for new fourier breakpoints...')
# metric_out_local_search = apply_metric(chr_name, begin, end, cnst.const[dataset], breakpoint_loci_local_search['loci'])
metric_out_local_search = apply_metric(
chr_name, begin, end, config, breakpoint_loci_local_search['loci'])
flat.print_log_msg('Global metric:')
print_metric(metric_out_local_search)
# LOCAL SEARCH ON UNIFORM - missing N runs
flat.print_log_msg('* Running local search for uniform breakpoints...')
# breakpoint_loci_uniform_local_search = run_local_search_complete(chr_name, breakpoint_loci_uniform, begin, end, cnst.const[dataset], metric_out_uniform)
breakpoint_loci_uniform_local_search = run_local_search_complete(
chr_name, breakpoint_loci_uniform, begin, end, config,
metric_out_uniform)
# RUN METRIC AGAIN W/ NEW BREAKPOINTS FROM UNIFORM
flat.print_log_msg('* Calculating metric for new uniform breakpoints...')
# metric_out_uniform_local_search = apply_metric(chr_name, begin, end, cnst.const[dataset], breakpoint_loci_uniform_local_search['loci'])
metric_out_uniform_local_search = apply_metric(
chr_name, begin, end, config,
breakpoint_loci_uniform_local_search['loci'])
flat.print_log_msg('Global metric:')
print_metric(metric_out_uniform_local_search)
# DUMP DATA INTO PICKLE SO IT CAN BE ANALYZED AND LOOKED AT WITHOUT RE-RUNNING EVERYTHING
pickle_out = {}
pickle_out['argv'] = sys.argv
pickle_out['n_bpoints'] = n_bpoints
pickle_out['found_width'] = found_width
pickle_out['fourier'] = {}
pickle_out['fourier']['loci'] = breakpoint_loci
pickle_out['fourier']['metric'] = metric_out
pickle_out['uniform'] = {}
pickle_out['uniform']['loci'] = breakpoint_loci_uniform
pickle_out['uniform']['metric'] = metric_out_uniform
pickle_out[
'fourier_ls'] = breakpoint_loci_local_search # Yes, breakpoint_loci_local_search is already a dict with 'loci' and 'metrics' keys
pickle_out['fourier_ls']['metric'] = metric_out_local_search
pickle_out['uniform_ls'] = breakpoint_loci_uniform_local_search
pickle_out['uniform_ls']['metric'] = metric_out_uniform_local_search
t = datetime.datetime.now()
t_formatted = t.strftime('%Y_%m_%d_%H_%M_%S')
# pickle_dump_fname = 'pickle-'+dataset+'-'+chr_name+'-'+str(n_bpoints)+'-'+str(begin)+'-'+str(end)+'-'+t_formatted+'.pickle'
with open(out_fname, 'wb') as f_out:
pickle.dump(pickle_out, f_out)
flat.print_log_msg('Done')
def calc_diag(self, dynamic_delete=True):
# flat.print_log_msg('Removing existing matrix output file')
# try:
# os.remove(cnst.const['out_matrix_delim'])
# except OSError:
# pass
self.dynamic_delete = dynamic_delete
flat.print_log_msg('Start')
# pre-read all relevant partitions at beginning!
last_p_num = -1
for p_num_init in range(0, len(self.partitions) - 1):
if self.snp_first >= self.partitions[p_num_init + 1][0]:
flat.print_log_msg('Pre-reading partition: ' +
str(self.partitions[p_num_init]))
flat.read_partition_into_matrix(self.partitions, p_num_init,
self.matrix, self.locus_list,
self.name, self.input_config,
self.snp_first, self.snp_last)
last_p_num = p_num_init
else:
break
curr_locus = -1
# for p_num, p in enumerate(self.partitions):
for p_num in range(last_p_num + 1, len(self.partitions)):
p = self.partitions[p_num]
flat.print_log_msg('Reading partition: ' + str(p))
flat.read_partition_into_matrix(self.partitions, p_num,
self.matrix, self.locus_list,
self.name, self.input_config,
self.snp_first, self.snp_last)
# Determine first locus
if curr_locus < 0: # Either first partition or not found in first partition
# curr_locus = -1 # <- this should have been set to -1 before entering the main for loop
if len(self.locus_list) > 0:
# Find first locus >= snp_first
for i, locus in enumerate(self.locus_list):
if locus >= self.snp_first:
curr_locus = locus
start_locus = locus
curr_locus_index = i
start_locus_index = i
break
else:
raise Exception('Error: locus_list seems to be empty')
# else:
# if len(self.locus_list)>0:
# curr_locus = self.locus_list[0]
# curr_locus_index = 0
# else:
# raise Exception('Error: locus_list seems to be empty')
else:
try:
curr_locus_index = self.locus_list.index(curr_locus)
# curr_locus is carried from prev iteration, but index has changed since part of matrix (and locus_list) has been deleted
except ValueError:
if len(self.locus_list) > 0:
curr_locus = self.locus_list[0]
curr_locus_index = 0
else:
raise Exception('Error: locus_list seems to be empty')
if curr_locus < 0:
flat.print_log_msg(
'Warning: curr_locus not found! Continuing to next partition.'
)
flat.print_log_msg(
'Comment: This is possibly due to snp_first being very close to end of partition.'
)
flat.print_log_msg('Details: ')
flat.print_log_msg('Partition: ' + repr(p))
flat.print_log_msg('snp_first: ' + repr(self.snp_first))
flat.print_log_msg('curr_locus: ' + repr(curr_locus))
continue #continue to next partition
# raise Exception('Error: curr_locus not found!')
# Determine end locus
if p_num + 1 < len(self.partitions):
end_locus = int(
(self.partitions[p_num][1] + self.partitions[p_num + 1][0])
/ 2)
else:
# end_locus = self.partitions[p_num][1]
# Find last locus <= snp_last
end_locus_found = False
for i in reversed(range(0, len(self.locus_list))):
# for locus in reversed(locus_list):
if self.locus_list[i] <= self.snp_last:
end_locus = self.locus_list[i]
end_locus_index = i
end_locus_found = True
break
if not end_locus_found:
end_locus_index = 0
end_locus = self.locus_list[end_locus_index]
flat.print_log_msg('Running for partition: ' + str(p))
# This will not include the very last SNP of the complete range, but that shouldn't be too important since the end of the range shouldn't be a defining location for LD
while curr_locus <= end_locus:
x = self.locus_list[curr_locus_index]
y = self.locus_list[curr_locus_index]
delta = 0
while x >= self.partitions[p_num][0] and y <= self.partitions[
p_num][1]:
if x in self.matrix and y in self.matrix[x]['data']:
corr_coeff = self.matrix[x]['data'][y][
'shrink'] / math.sqrt(
self.matrix[x]['data'][x]['shrink'] *
self.matrix[y]['data'][y]['shrink'])
self.add_corr_coeff(corr_coeff, curr_locus)
# Just save it in the matrix ;) ...for img
self.matrix[x]['data'][y]['corr_coeff'] = corr_coeff
if delta != 0:
x = self.locus_list[curr_locus_index - delta + 1]
if x in self.matrix and y in self.matrix[x]['data']:
corr_coeff = self.matrix[x]['data'][y][
'shrink'] / math.sqrt(
self.matrix[x]['data'][x]['shrink'] *
self.matrix[y]['data'][y]['shrink'])
self.add_corr_coeff(corr_coeff, curr_locus)
# Just save it in the matrix ;) ...for img
self.matrix[x]['data'][y][
'corr_coeff'] = corr_coeff
delta += 1
if curr_locus_index - delta >= 0:
x = self.locus_list[curr_locus_index - delta]
else:
# flat.print_log_msg('X index out of bounds')
break
if curr_locus_index + delta < len(self.locus_list):
y = self.locus_list[curr_locus_index + delta]
else:
# flat.print_log_msg('Y index out of bounds')
break
if curr_locus_index + 1 < len(self.locus_list):
curr_locus_index += 1
curr_locus = self.locus_list[curr_locus_index]
else:
flat.print_log_msg('curr_locus_index out of bounds')
break
# flat.delete_loci_smaller_than_and_output_matrix_to_file(end_locus, self.matrix, locus_list, locus_list_deleted, cnst.const['out_matrix_filename'])
if self.dynamic_delete:
flat.print_log_msg('Deleting loci not required any more')
if p_num + 1 < len(self.partitions):
delete_loc = self.partitions[p_num +
1][0] # diag - specific
else:
delete_loc = end_locus
flat.delete_loci_smaller_than(delete_loc, self.matrix,
self.locus_list,
self.locus_list_deleted)
else:
flat.print_log_msg('locus_list size: ' +
repr(len(self.locus_list)))
self.start_locus = start_locus
self.start_locus_index = start_locus_index
self.end_locus = end_locus
self.end_locus_index = end_locus_index
self.calculation_complete = True
def generate_img(self, img_full_path, marked_snp=None):
import numpy as np
import matplotlib as mpl
# mpl.use('svg')
mpl.use('Agg')
import matplotlib.pyplot as pt
mpl.rcParams.update({'font.size': 22})
# import svgwrite
# if center is None:
# first = self.snp_first
# last = self.snp_last
# else:
# if distance_in_snps is None:
# raise Exception('Error: center is defined, but distance_in_snps is not!')
# else:
# first, last = self.query_locus_list(center, distance_in_snps)
if not self.calculation_complete:
raise Exception(
'Error: Calculation has not been completed prior to image generation'
)
if self.dynamic_delete:
raise Exception(
'Error: The matrix was dynamically deleted - cannot generate full image!'
)
if len(self.matrix) <= 0:
raise Exception('Error: The matrix is emmpty or erroneous')
flat.print_log_msg('Image init')
# svg_document = svgwrite.Drawing(filename = cnst.const['svg_out_fname'],
# size = (self.end_locus_index-self.start_locus_index, self.end_locus_index-self.start_locus_index))
# Draw background
# svg_document.add(svg_document.rect(insert=(0, 0), size=('100%', '100%'), rx=None, ry=None, fill='rgb(0,0,0)'))
plot_mtrx_size = self.end_locus_index - self.start_locus_index + 1
plot_mtrx = [[0 for x in range(plot_mtrx_size)]
for x in range(plot_mtrx_size)]
flat.print_log_msg('Plot matrix size: ' + str(plot_mtrx_size))
flat.print_log_msg('Matrix size: ' + str(len(self.matrix)))
flat.print_log_msg('locus_list size: ' + str(len(self.locus_list)))
flat.print_log_msg('locus_list_deleted size: ' +
str(len(self.locus_list_deleted)))
x_values = [0 for x in range(plot_mtrx_size)]
flat.print_log_msg('Generating image data')
for loc_i in self.matrix:
if loc_i >= self.snp_first and loc_i <= self.snp_last:
x_values[self.locus_list.index(loc_i) -
self.start_locus_index] = loc_i
for loc_j in self.matrix[loc_i]['data']:
if loc_j >= self.snp_first and loc_j <= self.snp_last:
# if len(svg_loci)<svg_length:
if 'corr_coeff' in self.matrix[loc_i]['data'][loc_j]:
# color = 255* ( 1- ( self.matrix[loc_i]['data'][loc_j]['corr_coeff'] ** 2 ) )
try:
plot_mtrx[self.locus_list.index(loc_i) -
self.start_locus_index][
self.locus_list.index(loc_j) -
self.start_locus_index] = (
(self.matrix[loc_i]['data']
[loc_j]['corr_coeff'])**2)
except IndexError:
print(
self.locus_list.index(loc_i) -
self.start_locus_index)
print(len(plot_mtrx))
print(
self.locus_list.index(loc_j) -
self.start_locus_index)
print(
len(plot_mtrx[self.locus_list.index(loc_i)
- self.start_locus_index]))
# svg_document.add(svg_document.rect(insert = (self.locus_list.index(loc_i)-self.start_locus_index, self.locus_list.index(loc_j)-self.start_locus_index),
# size = ('1', '1'),
# fill = 'rgb(255,'+str(int(color))+','+str(int(color))+')'))
# svg_loci.add(curr_locus)
else:
flat.print_log_msg("No 'corr_coef' key at: " +
str(loc_i) + ' ' + str(loc_j))
# raise Exception('WTF')
flat.print_log_msg('Writing image file...')
fig = pt.gcf()
dpi = fig.get_dpi()
fig_size = fig.get_size_inches()
# pt.pcolor(np.array(plot_mtrx), cmap='Reds', vmin=0, vmax=1)
pt.pcolormesh(np.array(plot_mtrx), cmap='binary', vmin=0, vmax=1)
pt.colorbar()
# x_values = np.array(x_values) # needs to be numpy array for pcolormesh()
# X, Y = np.meshgrid(x_values, x_values)
# pt.pcolormesh(X, Y, np.array(plot_mtrx), cmap='Reds', vmin=0, vmax=1)
if marked_snp is not None:
bpoint_loc = x_values.index(marked_snp)
pt.scatter((bpoint_loc), (bpoint_loc), marker='x', color='green')
flat.print_log_msg('SNP: ' + repr(marked_snp) + ' @ index: ' +
repr(bpoint_loc) + ' in graph')
fig = pt.gcf()
fig.set_size_inches((40, 30))
pt.xlabel('SNP #')
pt.ylabel('SNP #')
pt.title('Correlation coefficient squared matrix')
pt.savefig(img_full_path)
def calc_vert(self, dynamic_delete=True, sum_both_sides=True):
# flat.print_log_msg('Removing existing matrix output file')
# try:
# os.remove(cnst.const['out_matrix_delim'])
# except OSError:
# pass
raise Exception('calc_vert is deprecated - check code before running!')
self.dynamic_delete = dynamic_delete
flat.print_log_msg('Start')
for p_num, p in enumerate(self.partitions):
flat.print_log_msg('Reading partition: ' + str(p))
flat.read_partition_into_matrix(self.partitions, p_num,
self.matrix, self.locus_list,
self.name, self.input_config,
self.snp_first, self.snp_last)
# Determine first locus
curr_locus = -1
if p_num == 0:
if len(self.locus_list) > 0:
# Find first locus >= snp_first
for i, locus in enumerate(self.locus_list):
if locus >= self.snp_first:
curr_locus = locus
start_locus = locus
curr_locus_index = i
start_locus_index = i
break
else:
raise Exception('Error: locus_list seems to be empty')
else:
if len(self.locus_list) > 0:
curr_locus = self.locus_list[0]
curr_locus_index = 0
else:
raise Exception('Error: locus_list seems to be empty')
if curr_locus < 0:
raise Exception('Error: curr_locus not found!')
if p_num + 1 < len(self.partitions):
end_locus = self.partitions[p_num + 1][0]
end_locus_index = -1
else:
# end_locus = partitions[p_num][1]
# Find last locus <= snp_last
for i in reversed(range(0, len(self.locus_list))):
# for locus in reversed(locus_list):
if self.locus_list[i] <= self.snp_last:
end_locus = self.locus_list[i]
end_locus_index = i
break
flat.print_log_msg('Running for partition: ' + str(p))
# This will not include the very last SNP of the complete range, but that shouldn't be too important since the end of the range shouldn't be a defining location for LD
while curr_locus < end_locus:
for key, el in self.matrix[curr_locus]['data'].items():
corr_coeff = self.matrix[curr_locus]['data'][key][
'shrink'] / math.sqrt(
self.matrix[curr_locus]['data'][curr_locus]
['shrink'] *
self.matrix[key]['data'][key]['shrink'])
self.add_corr_coeff(corr_coeff, curr_locus)
if sum_both_sides:
self.add_corr_coeff(corr_coeff, key)
# Just save it in the matrix ;)
self.matrix[curr_locus]['data'][key][
'corr_coeff'] = corr_coeff
if curr_locus_index + 1 < len(self.locus_list):
curr_locus_index += 1
curr_locus = self.locus_list[curr_locus_index]
else:
flat.print_log_msg('curr_locus_index out of bounds')
break
# flat.delete_loci_smaller_than_and_output_matrix_to_file(end_locus, self.matrix, locus_list, locus_list_deleted, cnst.const['out_matrix_filename'])
if self.dynamic_delete:
flat.print_log_msg('Deleting loci not required any more')
flat.delete_loci_smaller_than(end_locus, self.matrix,
self.locus_list,
self.locus_list_deleted)
self.start_locus = start_locus
self.start_locus_index = start_locus_index
self.end_locus = end_locus
self.end_locus_index = end_locus_index
self.calculation_complete = True
def print_metric(metric_out):
flat.print_log_msg('Sum: ' + repr(metric_out['sum']))
flat.print_log_msg('N (w/ zero\'s): ' + repr(metric_out['N_zero']))
flat.print_log_msg('Metric: ' +
repr(metric_out['sum'] / metric_out['N_zero']))
def search(self):
print("----- Running search")
if not self.init_complete:
flat.print_log_msg('init_search() must be run before search(). Starting automatically...')
self.init_search()
flat.print_log_msg('Starting local search...')
print("addy", len(self.precomputed['data']))
print("len(locus_list)", len(self.precomputed["locus_list"]))
print("locus_list", self.precomputed["locus_list"][:5], self.precomputed["locus_list"][-5:])
# In case the value itself is not in the list:
try:
print("hiihihih", self.snp_bottom, self.snp_top)
snp_bottom_ind = binsrch.find_ge_ind(self.precomputed['locus_list'], self.snp_bottom)
snp_top_ind = binsrch.find_le_ind(self.precomputed['locus_list'], self.snp_top)
except Exception as e:
flat.print_log_msg('Error2!')
flat.print_log_msg(repr(e))
flat.print_log_msg('self.precomputed[\'locus_list\']: '+repr(self.precomputed['locus_list']))
flat.print_log_msg('self.snp_bottom: '+repr(self.snp_bottom))
flat.print_log_msg('self.snp_first: '+repr(self.snp_first))
flat.print_log_msg('self.snp_last: '+repr(self.snp_last))
flat.print_log_msg('self.snp_top: '+repr(self.snp_top))
flat.print_log_msg('self.__dict__: '+repr(self.__dict__))
flat.print_log_msg('Continuing...')
return self.breakpoints[self.initial_breakpoint_index], None
print("self.snp_bottom", self.snp_bottom) #, len(self.precomputed["locus_list"]))
print("self.snp_top", self.snp_top)
print("self.initial_breakpoint_index", self.initial_breakpoint_index)
print("snp_bottom_ind", snp_bottom_ind)
print("snp_top_ind", snp_top_ind)
# Old:
# snp_first_ind = self.precomputed['locus_list'].index(self.snp_first) # This should be snp_bottom
# snp_top_ind = self.precomputed['locus_list'].index(self.snp_top)
# Start from init breakpoint and search left. Then start from init_breakpoint again and search right.
# We start from init_breakpoint because that's the initial sum and N that we have -> so we can use the precomputed data to incrementally check for
# Find the closest locus to the breakpoint value, because a breakpoint doesn't necessarily have to be in the locus_list
breakpoint_index_in_locus_list = binsrch.find_le_ind(self.precomputed['locus_list'], self.breakpoints[self.initial_breakpoint_index])
# print("breakpoint_index_in_locus_list", breakpoint_index_in_locus_list)
# print("breakpoint_index_in_locus_list", self.precomputed["locus_list"])
print("breakpoint_index_in_locus_list", len(self.precomputed["locus_list"]))
init_breakpoint_locus = self.precomputed['locus_list'][breakpoint_index_in_locus_list]
# Old:
# breakpoint_index_in_locus_list = self.precomputed['locus_list'].index(self.breakpoints[self.initial_breakpoint_index])
curr_sum = self.total_sum
curr_N = self.total_N
print("curr_sum", curr_sum)
print("curr_N", curr_N)
min_metric = decimal.Decimal(self.total_sum) / decimal.Decimal(self.total_N)
min_breakpoint = None
min_metric_details = {}
min_metric_details['sum'] = self.total_sum
min_metric_details['N_zero'] = self.total_N
min_distance_right = 0 # because the initial distance of the minimum actually is 0! (until we find a new minima to the RIGHT, or we don't in which case it doesn't matter)
# print("pre", self.precomputed['data'][39967768]['sum_horiz'], self.precomputed['data'][39967768]['sum_vert'])
# Go RIGHT!
flat.print_log_msg('Searching right...')
if breakpoint_index_in_locus_list+1 < len(self.precomputed['locus_list']):
curr_loc_ind = breakpoint_index_in_locus_list+1
curr_loc = self.precomputed['locus_list'][curr_loc_ind]
# counter = 0
# print("self.snp_last", self.snp_last)
while curr_loc <= self.snp_last:
# print("curr_loc", curr_loc)
# print(curr_loc, "curr_sum", curr_sum, self.precomputed['data'][curr_loc]['sum_horiz'], self.precomputed['data'][curr_loc]['sum_vert'])
curr_sum = curr_sum - self.precomputed['data'][curr_loc]['sum_horiz'] + self.precomputed['data'][curr_loc]['sum_vert']
# counter += 1
# print("_N curr_loc_ind", curr_loc_ind, snp_top_ind)
horiz_N = curr_loc_ind-snp_bottom_ind-1
vert_N = snp_top_ind-curr_loc_ind
curr_N = curr_N - horiz_N + vert_N
# print("horiz_N", horiz_N)
# print("vert_N", vert_N)
# print("curr_N", curr_N)
curr_metric = decimal.Decimal(curr_sum) / decimal.Decimal(curr_N)
# print("curr_loc", curr_loc, "curr_metric", curr_metric)
if curr_metric < min_metric:
min_metric = curr_metric
min_breakpoint = curr_loc
min_metric_details['sum'] = curr_sum
min_metric_details['N_zero'] = curr_N
min_distance_right = curr_loc - init_breakpoint_locus
# print("min_metric", min_metric, min_breakpoint)
# print("min_metric", min_metric, min_breakpoint, min_distance_right)
if curr_loc_ind+1 < len(self.precomputed['locus_list']):
curr_loc_ind += 1
curr_loc = self.precomputed['locus_list'][curr_loc_ind]
else:
flat.print_log_msg('curr_locus_index out of bounds') # The possibility of this happening is only at the end of the chromosome (end of last partition)
break
else:
flat.print_log_msg('Warning: breakpoint_index_in_locus_list+1 < len(self.precomputed["locus_list"]) not satisfied!')
flat.print_log_msg('Breakpoints: '+repr(self.breakpoints))
flat.print_log_msg('Locus_list: '+repr(self.precomputed['locus_list']))
flat.print_log_msg('breakpoint_index_in_locus_list: '+ repr(breakpoint_index_in_locus_list))
print("min_metric", min_metric, min_breakpoint, min_distance_right)
# print("counter", counter)
# Reset search for left
curr_sum = self.total_sum
curr_N = self.total_N
# Go LEFT!
flat.print_log_msg('Searching left...')
if breakpoint_index_in_locus_list-1 >= 0:
curr_loc_ind = breakpoint_index_in_locus_list-1
curr_loc = self.precomputed['locus_list'][curr_loc_ind]
curr_sum = self.total_sum
curr_N = self.total_N
while curr_loc > self.snp_first: # Don't include previous breakpoint!
curr_sum = curr_sum + self.precomputed['data'][curr_loc]['sum_horiz'] - self.precomputed['data'][curr_loc]['sum_vert']
horiz_N = curr_loc_ind-snp_bottom_ind-1
vert_N = snp_top_ind-curr_loc_ind
curr_N = curr_N + horiz_N - vert_N
curr_metric = decimal.Decimal(curr_sum) / decimal.Decimal(curr_N)
if (curr_metric < min_metric) or (curr_metric == min_metric and (init_breakpoint_locus - curr_loc)<min_distance_right): # min_distance_right is used to compare to RIGHT metric, not within LEFT metric!
min_metric = curr_metric
min_breakpoint = curr_loc
min_metric_details['sum'] = curr_sum
min_metric_details['N_zero'] = curr_N
if curr_loc_ind-1 >= 0:
curr_loc_ind -= 1
curr_loc = self.precomputed['locus_list'][curr_loc_ind]
else:
flat.print_log_msg('curr_locus_index out of bounds') # The possibility of this happening is only at the beginning of the chromosome (start of first partition)
break
else:
flat.print_log_msg('Warning: breakpoint_index_in_locus_list-1 >=0 not satisfied!')
flat.print_log_msg('Breakpoints: '+repr(self.breakpoints))
flat.print_log_msg('Locus_list: '+repr(self.precomputed['locus_list']))
flat.print_log_msg('breakpoint_index_in_locus_list: '+ repr(breakpoint_index_in_locus_list))
self.search_complete = True
flat.print_log_msg('Search done')
return min_breakpoint, min_metric_details
def __init__(self, name, start_search, stop_search, initial_breakpoint_index, breakpoints, total_sum, total_N, input_config):
decimal.getcontext().prec=50
print(" *** start_search", start_search)
print(" *** stop_search", stop_search)
self.name = name
self.start_search = start_search
self.stop_search = stop_search
self.initial_breakpoint_index = initial_breakpoint_index
self.breakpoints = breakpoints
self.total_sum = total_sum
self.total_N = total_N
self.input_config = input_config
self.matrix = {}
self.locus_list = []
self.locus_list_deleted = []
self.precomputed = {}
self.precomputed['locus_list'] = [] # keep the ordering of loci -> allow for efficient iterating
self.precomputed['data'] = {} # allow ~O(1) access to each element by it's locus
self.dynamic_delete = True
self.init_complete = False
self.search_complete = False
if start_search >= stop_search:
raise Exception('Error: start_search >= stop_search')
if initial_breakpoint_index>=len(breakpoints) or initial_breakpoint_index<0:
raise Exception('Error: initial_breakpoint_index index out of bounds')
if breakpoints[initial_breakpoint_index] >= stop_search:
raise Exception('Error: breakpoint >= stop_search')
if breakpoints[initial_breakpoint_index] <= start_search:
raise Exception('Error: breakpoint <= start_search')
# tmp_partitions = flat.read_partitions(self.name, self.input_config)
tmp_partitions = flat.get_final_partitions(self.input_config, self.name, start_search, stop_search)
if start_search < tmp_partitions[0][0] or start_search > tmp_partitions[len(tmp_partitions)-1][1]:
raise Exception('Error: start_search is out of bounds')
if stop_search < tmp_partitions[0][0] or stop_search > tmp_partitions[len(tmp_partitions)-1][1]:
raise Exception('Error: stop_search is out of bounds')
if initial_breakpoint_index > 0:
if start_search < breakpoints[initial_breakpoint_index-1]:
raise Exception('Error: start_search cannot be further than a neighboring breakpoint')
else:
pass # this is just to emphasize that this has been thought through and covered. It's taken care of when testing for start_search < tmp_partitions[0][0]
if initial_breakpoint_index < (len(breakpoints)-1):
if stop_search > breakpoints[initial_breakpoint_index+1]:
raise Exception('Error: stop_search cannot be further than a neighboring breakpoint')
else:
pass # this is just to emphasize that this has been thought through and covered. It's taken care of when testing for stop_search > tmp_partitions[len(tmp_partitions)-1][1]
# work out snp_first, snp_last - watch out if it's the first or last breakpoint
# # snp_first defines where to start reading data
# if initial_breakpoint_index > 0:
# self.snp_first = breakpoints[initial_breakpoint_index-1]
# else:
# self.snp_first = tmp_partitions[0][0] # this gets the first SNP in the chromosome (setting it just to 1 would cause flat.relevant_subpartitions() and consequently flat.get_final_partitions() to fail)
# The previous (above) was not taking into account start_search, but just assumed where search started!
self.snp_first = start_search
flat.print_log_msg('snp_first: '+repr(self.snp_first))
# snp_last defined where to stop reading data
self.snp_last = stop_search
flat.print_log_msg('snp_last: '+repr(self.snp_last))
# This is the upper bound for the search space (upper border)
if initial_breakpoint_index+1 < len(breakpoints):
self.snp_top = breakpoints[initial_breakpoint_index+1]
else:
self.snp_top = tmp_partitions[len(tmp_partitions)-1][1]
flat.print_log_msg('snp_top: '+repr(self.snp_top))
# This is the bottom bound for the search space (bottom border)
if initial_breakpoint_index-1 >= 0:
self.snp_bottom = breakpoints[initial_breakpoint_index-1]
else:
self.snp_bottom = tmp_partitions[0][0]
flat.print_log_msg('snp_bottom: '+repr(self.snp_bottom))
# flat.print_log_msg('In local search: ')
# flat.print_log_msg(repr(self.snp_first)+' '+repr(self.snp_last)+' '+repr(self.snp_top))
# Data must be read until snp_top!
self.partitions = flat.get_final_partitions(self.input_config, self.name, self.snp_bottom, self.snp_top)
# flat.print_log_msg('self.partitions: ')
# flat.print_log_msg(repr(self.partitions))
self.start_locus = -1
self.start_locus_index = -1
self.end_locus = -1
self.end_locus_index = -1
def init_search_lean(self):
# flat.print_log_msg('Removing existing matrix output file')
# try:
# os.remove(cnst.const['out_matrix_delim'])
# except OSError:
# pass
if not self.dynamic_delete:
raise Exception('Error: dynamic_delete should be True for local search!')
flat.print_log_msg('Start local search init')
print("self.partitions", self.partitions)
# pre-read all relevant partitions at beginning!
last_p_num = -1
for p_num_init in range(0, len(self.partitions)-1):
print("p_num_init", p_num_init)
if self.snp_bottom >= self.partitions[p_num_init+1][0]:
flat.print_log_msg('Pre-reading partition: '+str(self.partitions[p_num_init]))
flat.read_partition_into_matrix_lean(self.partitions, p_num_init, self.matrix, self.locus_list, self.name, self.input_config, self.snp_bottom, self.snp_top)
last_p_num = p_num_init
else:
break
print("===", self.snp_bottom, self.snp_top, self.snp_first, self.snp_last)
# print("after reading, len is", len(self.locus_list))
curr_locus = -1
# for p_num, p in enumerate(self.partitions):
for p_num in range(last_p_num+1, len(self.partitions)):
print("p_num", p_num)
p = self.partitions[p_num]
flat.print_log_msg('Reading partition: '+str(p))
# Data must be read until snp_top!
flat.read_partition_into_matrix_lean(self.partitions, p_num, self.matrix, self.locus_list, self.name, self.input_config, self.snp_bottom, self.snp_top)
# Determine first locus
if curr_locus<0: # Either first partition or not found in first partition
# curr_locus = -1 # <- this should have been set to -1 before entering the main for loop
if len(self.locus_list)>0:
# Find first locus >= snp_bottom
for i, locus in enumerate(self.locus_list):
if locus >= self.snp_bottom:
curr_locus = locus
start_locus = locus
curr_locus_index = i
start_locus_index = i
break
else:
raise Exception('Error: locus_list seems to be empty')
# else:
# if len(self.locus_list)>0:
# curr_locus = self.locus_list[0]
# curr_locus_index = 0
# else:
# raise Exception('Error: locus_list seems to be empty')
else:
try:
curr_locus_index = self.locus_list.index(curr_locus)
# curr_locus is carried from prev iteration, but index has changed since part of matrix (and locus_list) has been deleted
except ValueError:
if len(self.locus_list)>0:
curr_locus = self.locus_list[0]
curr_locus_index = 0
else:
raise Exception('Error: locus_list seems to be empty')
if curr_locus<0:
flat.print_log_msg('Warning: curr_locus not found! Continuing to next partition.')
flat.print_log_msg('Comment: This is possibly due to snp_bottom being very close to end of partition.')
flat.print_log_msg('Details: ')
flat.print_log_msg('Partition: '+repr(p))
flat.print_log_msg('snp_bottom: '+repr(self.snp_bottom))
flat.print_log_msg('curr_locus: '+repr(curr_locus))
continue #continue to next partition
# raise Exception('Error: curr_locus not found!')
# print("start locus is", curr_locus)
print("self.snp_last", self.snp_last)
if p_num+1 < len(self.partitions):
end_locus = self.partitions[p_num+1][0]
end_locus_index = -1
else:
# end_locus = self.partitions[p_num][1]
# Find last locus <= snp_last
end_locus_found = False
for i in reversed(range(0, len(self.locus_list))):
# for locus in reversed(locus_list):
if self.locus_list[i] <= self.snp_last:
end_locus = self.locus_list[i]
# print("setting end locus to", end_locus, "snp last was", self.snp_last)
end_locus_index = i
end_locus_found = True
break
if not end_locus_found:
end_locus_index = 0
end_locus = self.locus_list[end_locus_index]
# flat.print_log_msg('self.locus_list control output: '+repr(self.locus_list))
flat.print_log_msg('Running precompute for partition: '+str(p))
flat.print_log_msg('start_locus: '+repr(start_locus)+' end_locus: '+repr(end_locus)+' end_locus_index '+repr(end_locus_index))
# This will not include the very last SNP of the complete range, but that shouldn't be too important since the end of the range shouldn't be a defining location for LD
print("checking that curr_locus is smaller than", curr_locus, end_locus, "oooo")
while curr_locus <= end_locus:
self.add_locus_to_precomputed(curr_locus) # We want snp_bottom to be added here always (for later use). Same thing for snp_top
# flat.print_log_msg('curr_locus: '+repr(curr_locus)+' end_locus: '+repr(end_locus))
if (curr_locus > self.snp_first or self.initial_breakpoint_index == 0) and (curr_locus <= self.snp_last): # Do not include snp_first in the calculation unless the very first block is being taken into account. Do not calculate anything above snp_last, just insert dummies
for key, el in self.matrix[curr_locus].items():
# don't take into account anything over snp_top
if key <= self.snp_top:
# print("adding", curr_locus, key, el)
corr_coeff = self.matrix[curr_locus][key] / math.sqrt( self.matrix[curr_locus][curr_locus] * self.matrix[key][key] )
# print("ijval", curr_locus, key, self.matrix[curr_locus][key], corr_coeff, self.matrix[curr_locus][curr_locus], self.matrix[key][key])
# print("ijval", corr_coeff ** 2)
# if curr_locus != key: # Don't include diagonal! ...although not that important.
self.add_val_to_precomputed(decimal.Decimal(corr_coeff**2), curr_locus, key) # If the diagonal is included, it doesn't matter because later we add and subtract is exactly once when adding and subra
# else:
# self.add_val_to_precomputed(decimal.Decimal(0), curr_locus, key)
else:
self.add_val_to_precomputed(decimal.Decimal(0), curr_locus, curr_locus) # Dummy value for snp_first! ...in order to be consistent for some other future use of these data structures
if curr_locus_index+1 < len(self.locus_list):
curr_locus_index+=1
curr_locus = self.locus_list[curr_locus_index]
else:
flat.print_log_msg('curr_locus_index out of bounds' + str(len(self.precomputed['data'][curr_locus]['sum_horiz']))) # The possibility of this happening is only at the end of the range [usually chromosome] (end of last partition)
break
print("len(self.locus_list)", len(self.locus_list))
# flat.delete_loci_smaller_than_and_output_matrix_to_file(end_locus, self.matrix, locus_list, locus_list_deleted, cnst.const['out_matrix_filename'])
if self.dynamic_delete:
flat.print_log_msg('Deleting loci not required any more')
flat.delete_loci_smaller_than_leanest(end_locus, self.matrix, self.locus_list)
self.start_locus = start_locus
self.start_locus_index = start_locus_index
self.end_locus = end_locus
self.end_locus_index = end_locus_index
self.init_complete = True
