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handle not ready (refresh) url

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Tanushree Tunstall 2020-04-21 17:12:18 +01:00
parent 8b1a7fc71c
commit 1d84846789
2 changed files with 60 additions and 48 deletions
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86
mcsm/mcsm_wrapper.py
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@ -62,53 +62,57 @@ out_filename_format = gene.lower() + '_mcsm_processed.csv'
outfile_format = outdir + '/' + out_filename_format
#%%=====================================================================
def submit_mcsm():
my_chain = 'A'
# my_ligand_id = 'DCS' # FIXME
my_ligand_id = 'RMP' # FIXME
my_affinity = 10
my_chain = 'A'
# my_ligand_id = 'DCS' # FIXME
my_ligand_id = 'RMP' # FIXME
my_affinity = 10
print('Result urls and error file (if any) will be written in: ', outdir)
# call function to format data to remove duplicate snps before submitting job
mcsm_muts = format_data(infile_snps)
mut_count = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
infile_snps_len = os.popen('wc -l < %s' % infile_snps).read() # quicker than using Python :-)
print('Total SNPs for', gene, ':', infile_snps_len)
for mcsm_mut in mcsm_muts:
print('Processing mutation: %s of %s' % (mut_count, infile_snps_len), mcsm_mut)
print('Parameters for mcsm_lig:', in_filename_pdb, mcsm_mut, my_chain, my_ligand_id, my_affinity, prediction_url, outdir, gene)
# function call: to request mcsm prediction
# which writes file containing url for valid submissions and invalid muts to respective files
holding_page = request_calculation(infile_pdb, mcsm_mut, my_chain, my_ligand_id, my_affinity, prediction_url, outdir, gene, host)
time.sleep(1)
mut_count += 1
# result_url = write_result_url(holding_page, result_urls, host)
print('Request submitted'
, '\nCAUTION: Processing will take at least ten'
, 'minutes, but will be longer for more mutations.')
print('Result urls and error file (if any) will be written in: ', outdir)
# call function to format data to remove duplicate snps before submitting job
mcsm_muts = format_data(infile_snps)
mut_count = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
infile_snps_len = os.popen('wc -l < %s' % infile_snps).read() # quicker than using Python :-)
print('Total SNPs for', gene, ':', infile_snps_len)
for mcsm_mut in mcsm_muts:
print('Processing mutation: %s of %s' % (mut_count, infile_snps_len), mcsm_mut)
print('Parameters for mcsm_lig:', in_filename_pdb, mcsm_mut, my_chain, my_ligand_id, my_affinity, prediction_url, outdir, gene)
# function call: to request mcsm prediction
# which writes file containing url for valid submissions and invalid muts to respective files
holding_page = request_calculation(infile_pdb, mcsm_mut, my_chain, my_ligand_id, my_affinity, prediction_url, outdir, gene, host)
time.sleep(1)
mut_count += 1
# result_url = write_result_url(holding_page, result_urls, host)
print('Request submitted'
, '\nCAUTION: Processing will take at least ten'
, 'minutes, but will be longer for more mutations.')
#%%=====================================================================
def get_results():
output_df = pd.DataFrame()
url_counter = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
infile_len = os.popen('wc -l < %s' % result_urls).read() # quicker than using Python :-) #FIXME filenme (infile_urls)
output_df = pd.DataFrame()
url_counter = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
success_counter = 1
infile_len = os.popen('wc -l < %s' % result_urls).read() # quicker than using Python :-) #FIXME filenme (infile_urls)
print('Total URLs:', infile_len)
print('Total URLs:', infile_len)
with open(result_urls, 'r') as urlfile:
for line in urlfile:
url_line = line.strip()
# call functions
results_interim = scrape_results(url_line)
result_dict = build_result_dict(results_interim)
print('Processing URL: %s of %s' % (url_counter, infile_len))
df = pd.DataFrame(result_dict, index=[url_counter])
url_counter += 1
output_df = output_df.append(df)
output_df.to_csv(mcsm_output, index = None, header = True)
with open(result_urls, 'r') as urlfile:
for line in urlfile:
url_line = line.strip()
# call functions
results_interim = scrape_results(url_line)
if results_interim is not None:
print('Processing URL: %s of %s' % (url_counter, infile_len))
result_dict = build_result_dict(results_interim)
df = pd.DataFrame(result_dict, index=[url_counter])
output_df = output_df.append(df)
success_counter += 1
url_counter += 1
print('Total URLs: %s Successful: %s Failed: %s' % (url_counter-1, success_counter-1, (url_counter - success_counter)))
output_df.to_csv(mcsm_output, index = None, header = True)
#%%=====================================================================
def format_results():
print('Input file:', mcsm_output