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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Solution of 4.10.2, Lahti et al. 2014
Write a function that takes as input a dictionary of constraints (i.e., selecting a specific group of records) and returns a dictionary tabulating the BMI group for all the records matching the constraints. For example, calling
Step2:  For each row in the file, you need to make sure all the constraints are matching the desired ones. If so, keep count of the BMI group using a dictionary.
Step3:  Write a function that takes as input the constraints (as above), and a bacterial "genus". The function returns the average abundance (in logarithm base 10) of the genus for each group of BMI in the sub-population. For example, calling
Step4:  Repeat this analysis for all genera, and for the records having Time = 0.
A function to extract all the genera in the database
Step5:  Testing
Step6:  Now use this function to print the results for all genera at Time = 0
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Python Code:
import csv # Import csv module for reading the file
Explanation: Solution of 4.10.2, Lahti et al. 2014
Write a function that takes as input a dictionary of constraints (i.e., selecting a specific group of records) and returns a dictionary tabulating the BMI group for all the records matching the constraints. For example, calling:
get_BMI_count({'Age': '28', 'Sex': 'female'}) 
should return:
{'NA': 3, 'lean': 8, 'overweight': 2, 'underweight': 1}
End of explanation
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']
                BMI_count[my_BMI] = BMI_count.get(my_BMI, 0) + 1
    return BMI_count
get_BMI_count({'Nationality': 'US', 'Sex': 'female'})
Explanation: For each row in the file, you need to make sure all the constraints are matching the desired ones. If so, keep count of the BMI group using a dictionary.
End of explanation
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("")
get_abundance_by_BMI({'Time': '0', 'Nationality': 'US'}, 
                     'Clostridium difficile et rel.')
Explanation: Write a function that takes as input the constraints (as above), and a bacterial "genus". The function returns the average abundance (in logarithm base 10) of the genus for each group of BMI in the sub-population. For example, calling:
get_abundance_by_BMI({'Time': '0', 'Nationality': 'US'}, 'Clostridium difficile et rel.')
should return:
```
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
```
End of explanation
def get_all_genera():
    with open('../data/Lahti2014/HITChip.tab') as f:
        header = f.readline().strip()
    genera = header.split('\t')[1:]
    return genera
Explanation: Repeat this analysis for all genera, and for the records having Time = 0.
A function to extract all the genera in the database:
End of explanation
get_all_genera()[:6]
Explanation: Testing:
End of explanation
for g in get_all_genera()[:5]:
    get_abundance_by_BMI({'Time': '0'}, g)
Explanation: Now use this function to print the results for all genera at Time = 0:
End of explanation
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