get_BMI_count({'Age': '28', 'Sex': 'female'}){'NA': 3, 'lean': 8, 'overweight': 2, 'underweight': 1}Import csv for reading the file.
In [1]:
import csv
Now write the function. For each row in the file, you need to make sure all the constraints are matching the desired ones. If so, keep track of the BMI group using a dictionary.
In [2]:
def get_BMI_count(dict_constraints):
""" Take as input a dictionary of constraints
for example, {'Age': '28', 'Sex': 'female'}
And return the count of the various groups of BMI
"""
# We use a dictionary to store the results
BMI_count = {}
# Open the file, build a csv DictReader
with open('../data/Lahti2014/Metadata.tab') as f:
csvr = csv.DictReader(f, delimiter = '\t')
# For each row
for row in csvr:
# check that all conditions are met
matching = True
for e in dict_constraints:
if row[e] != dict_constraints[e]:
# The constraint is not met. Move to the next record
matching = False
break
# matching is True only if all the constraints have been met
if matching == True:
# extract the BMI_group
my_BMI = row['BMI_group']
if my_BMI in BMI_count.keys():
# If we've seen it before, add one record to the count
BMI_count[my_BMI] = BMI_count[my_BMI] + 1
else:
# If not, initialize at 1
BMI_count[my_BMI] = 1
return BMI_count
In [3]:
get_BMI_count({'Nationality': 'US', 'Sex': 'female'})
Out[3]:
get_abundance_by_BMI({'Time': '0', 'Nationality': 'US'}, 'Clostridium difficile et rel.')____________________________________________________________________
Abundance of Clostridium difficile et rel. In sub-population:
____________________________________________________________________
Nationality -> US
Time -> 0
____________________________________________________________________
3.08 NA
3.31 underweight
3.84 lean
2.89 overweight
3.31 obese
3.45 severeobese
____________________________________________________________________
In [4]:
import scipy # For log10
def get_abundance_by_BMI(dict_constraints, genus = 'Aerococcus'):
# We use a dictionary to store the results
BMI_IDs = {}
# Open the file, build a csv DictReader
with open('../data/Lahti2014/Metadata.tab') as f:
csvr = csv.DictReader(f, delimiter = '\t')
# For each row
for row in csvr:
# check that all conditions are met
matching = True
for e in dict_constraints:
if row[e] != dict_constraints[e]:
# The constraint is not met. Move to the next record
matching = False
break
# matching is True only if all the constraints have been met
if matching == True:
# extract the BMI_group
my_BMI = row['BMI_group']
if my_BMI in BMI_IDs.keys():
# If we've seen it before, add the SampleID
BMI_IDs[my_BMI] = BMI_IDs[my_BMI] + [row['SampleID']]
else:
# If not, initialize
BMI_IDs[my_BMI] = [row['SampleID']]
# Now let's open the other file, and keep track of the abundance of the genus for each
# BMI group
abundance = {}
with open('../data/Lahti2014/HITChip.tab') as f:
csvr = csv.DictReader(f, delimiter = '\t')
# For each row
for row in csvr:
# check whether we need this SampleID
matching = False
for g in BMI_IDs:
if row['SampleID'] in BMI_IDs[g]:
if g in abundance.keys():
abundance[g][0] = abundance[g][0] + float(row[genus])
abundance[g][1] = abundance[g][1] + 1
else:
abundance[g] = [float(row[genus]), 1]
# we have found it, so move on
break
# Finally, calculate means, and print results
print("____________________________________________________________________")
print("Abundance of " + genus + " In sub-population:")
print("____________________________________________________________________")
for key, value in dict_constraints.items():
print(key, "->", value)
print("____________________________________________________________________")
for ab in ['NA', 'underweight', 'lean', 'overweight',
'obese', 'severeobese', 'morbidobese']:
if ab in abundance.keys():
abundance[ab][0] = scipy.log10(abundance[ab][0] / abundance[ab][1])
print(round(abundance[ab][0], 2), '\t', ab)
print("____________________________________________________________________")
print("")
In [5]:
get_abundance_by_BMI({'Time': '0', 'Nationality': 'US'},
'Clostridium difficile et rel.')
A simple function for extracting all the genera in the database:
In [6]:
def get_all_genera():
with open('../data/Lahti2014/HITChip.tab') as f:
header = f.readline().strip()
genera = header.split('\t')[1:]
return genera
Testing:
In [7]:
get_all_genera()[:6]
Out[7]:
Now use the function we wrote above to print the results for all genera:
In [8]:
for g in get_all_genera()[:5]:
get_abundance_by_BMI({'Time': '0'}, g)