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QueueGenerator_Targeted.py
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QueueGenerator_Targeted.py
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from pandas import read_excel
from pandas import DataFrame
from math import ceil
from math import floor
from random import shuffle
from os import path
def main():
inputs = get_inputs()
inj_info, max_sample, plate_num = make_samples(inputs)
queue = assign_injection_names(sample_queue(max_sample, inputs, inj_info))
queue_csv = DataFrame(data=queue, dtype=str)
save_to_file(inputs, queue_csv)
def assign_injection_names(queue: list):
"""
After sample randomization and adding pools, generates dictionary as basis for dataframe
:param queue: output of sample_queue()
:return: dict consisting of lists for creating dataframe
"""
i = 1
final_queue = {'Sample Name': [],
'Well': [],
'Plate': [],
'Injection Name': [],
'InjWell': [],
'Non-random index': []}
for sample in queue:
sample_name = sample[0]
final_queue['Sample Name'].append(sample_name)
well = sample[1]
final_queue['Well'].append(well)
plate = sample[2]
final_queue['Plate'].append(plate)
final_queue['InjWell'].append(make_address(well, plate))
# Conditional for use in Excel
if len(well) == 2:
well = well[0] + '0' + well[1]
final_queue['Injection Name'].append("{}-{}.{}-{}".format(str(i).zfill(3), plate, well, sample_name))
final_queue['Non-random index'].append(sample[3])
i += 1
return final_queue
def make_address(well: str, plate: str):
"""
Generate Vanquish Well ID from the plate ID and Well
:param well: str, eg "C10"
:param plate: str, If standard plate, will assign Blue plate, else use Green, Red, Yellow
:return: str
"""
plate_list = ['G', 'R', 'Y']
try:
plate = int(plate) % 3 - 1
address = plate_list[plate] + ':' + well
return address
except ValueError:
return 'B:' + well
def get_inputs():
""" Returns dictionary with the following:
List of samples
Client name
MX number
Sample matrix
Platform
Random flag
"""
inputs = dict()
inputs['filepath'] = input("Please enter sample list filepath: ").strip().strip('"')
df = read_excel(inputs['filepath'], header=None)
inputs['client'] = df.at[2, 0][18:].split()[1]
samples = list()
inputs['samples'] = samples
row_num = df.shape[0]
for ind in range(10, row_num):
samples.append(str(df.at[ind, 4]).strip())
inputs['batch'] = get_batch_size()
inputs['minix'] = input("Please enter MX id: ").strip()
inputs['platform'] = get_platform()
inputs['matrix'] = df.at[8, 0].replace(
"Specimen Type: (e.g.plasma, serum, stool…)",
"").strip()
inputs['random'] = get_bool('Do you want to randomize samples? (y/n):')
return inputs
def get_bool(prompt: str):
"""
transforms a yes/no or y/n user input into a boolean
:param prompt: text asking for
:return:
"""
while True:
rand = input(prompt).strip().lower()
if rand == 'y' or rand == 'yes':
return True
elif rand == 'n' or rand == 'no':
return False
else:
print('Input invalid')
def get_platform():
while True:
try:
print("Please indicate the study's platform:\n(1) Bile Acids\n(2) Steroids\n(3) Oxylipins")
indicator = int(input("").strip()) - 1
return ['BA', 'Ster', 'Oxy'][indicator]
except (ValueError, IndexError):
print('Invalid input. Please try again.')
def get_batch_size():
"""
Fetches size of batches for use in generate_partition().
Leaving blank returns 0
Else returns int
"""
while True:
print('Please enter the number of samples between standards desired.')
try:
batch = input('(Leave blank for auto-calculation): ').strip()
return int(batch)
except ValueError:
if batch == '':
return 0
else:
print('Invalid response. Please enter an integer.')
def gen_wells():
"""
Generates tuple with well names starting at A9 - H12
:return: tuple of strings
"""
letters = list('ABCDEFGH')
wells = list()
for row in letters:
for col in range(1, 13):
wells.append(row + str(col))
return tuple(wells[8:])
def make_samples(inputs: dict):
"""
:param inputs: dictionary output of get_inputs()
:return:
inj_info: list of tuples consisting of sample name, well, and plate they will end up in
max_sample: int, length of samples
plate_num: integer, # of plates needed for samples
"""
samples = inputs['samples']
well_list = gen_wells()
max_sample = len(samples)
inj_info = list()
plate = list()
plate_num = 1
index = 0
num = 1
for sample in samples:
plate.append(tuple([sample, well_list[index], plate_num, num]))
index += 1
num += 1
if index >= 88:
plate_num += 1
index = 0
if inputs['random']:
shuffle(plate)
inj_info.extend(plate)
plate = list()
if inputs['random']:
shuffle(plate)
inj_info.extend(plate)
return inj_info, max_sample, plate_num
def find_cal_curve_number(sample_no: int):
"""
:param sample_no: number of samples in the study
:return: int, the number of cal_curves required from 0-8
"""
x = sample_no + 25
return ceil(x / 75)
def make_sample_partition(sample_no: int, batch_size: int):
"""
:param sample_no: Number of samples in study
:param batch_size: desired # of samples between cal curve instances
:return: partition: tuple of int, distribution of the samples between cal curve instances
If batch size was not specified, batch sizes will be roughly even (20-25 samples)
If batch size was specified as n, the last batch size may from vary anywhere from 1-n samples long
"""
# Auto-calculation in case of no inputs
if not batch_size:
cal_curve_number = find_cal_curve_number(sample_no)
batches = cal_curve_number * 3 - 1
min_batch = floor(sample_no / batches)
partition = [min_batch] * batches
i = 0
while i < sample_no % batches:
partition[i] = min_batch + 1
i += 1
return tuple(partition)
# Splitting into set batch size
else:
batches = floor(sample_no / batch_size)
last_batch = sample_no % batch_size
partition = [batch_size] * batches
partition.append(last_batch)
return tuple(partition)
def sample_queue(sample_no: int, inputs: dict, inj_info: list):
"""
:param sample_no: int, number of client samples
:param inputs: dict, user inputs, primarily needed for batch size and platform
:param inj_info: list of sample tuples
:return: list of tuples, represents the queue of injections including blanks, QCs, Stds, and samples
"""
# Setting up parameters
partition = make_sample_partition(sample_no, inputs['batch'])
batch = 0
batch_position = 0
cal_curve_state = 2
queue = list()
platform = inputs['platform']
cal_curve_no = 1
pool_no = 1
wash_no = 1
plate_num = 1
# Begin with solvent washes, Std curves, and test injections
queue.extend(washes(3, wash_no))
wash_no = 4
queue.extend(cal_curve(0, 1, platform, wash_no, pool_no))
queue.extend(qc_blanks(plate_num))
# Iterate variables
wash_no += 1
pool_no += 1
for sample in inj_info:
# Check for adding in blanks and internal std checks at beginning of plate
if type(sample[2]) is int and sample[2] != plate_num:
plate_num += 1
queue.extend(qc_blanks(plate_num))
# Add sample
queue.append(sample)
# At the end of the batch, add cal curves & solvent wash
batch_position += 1
if batch_position == partition[batch]:
batch_position = 0
batch += 1
queue.extend(cal_curve(cal_curve_state, cal_curve_no, platform, wash_no, pool_no))
wash_no += 1
# Add on pool if necessary
if cal_curve_state < 2:
pool_no += 1
# Iterating through cal curve
if cal_curve_state == 3:
cal_curve_state = 1
cal_curve_no += 1
else:
cal_curve_state += 1
# Final solvent wash injections
queue.extend(washes(2, wash_no))
return queue
def qc_blanks(plate_num):
blank_1 = ('Blank{}'.format(str(plate_num*2 - 1)), 'A1', str(plate_num), 'QC')
blank_2 = ('Blank{}'.format(str(plate_num*2)), 'A2', str(plate_num), 'QC')
ist1 = ('IST{}'.format(str(plate_num*2 - 1)), 'A3', str(plate_num), 'QC')
ist2 = ('IST{}'.format(str(plate_num*2)), 'A4', str(plate_num), 'QC')
return [blank_1, blank_2, ist1, ist2]
def cal_curve(state: int, number: int, platform: str, wash_no: int, pool_no: int):
"""
Generates list of cal curve, qc, and blank samples to append in sample_queue()
:param state: int, represents the 0369, 036, 147, or 258 series of cal curve injs
:param number: int, The number of cal curves already constructed, indicates row that it exists in
:param platform: str, (BA Ster or Oxy)
:param wash_no: int, the number of solvent washes already run
:param pool_no: int, pools already run
:return: list of tuples
"""
cal_list = list()
std_plate_name = "SP" + str(ceil(number/7))
row = list('ABCDEFG')[(number-1) % 7]
if state < 2:
wells = [0, 3, 6]
if state == 0 and platform != "Oxy":
wells.append(9)
elif state == 2:
wells = [1, 4, 7]
else:
wells = [2, 5, 8]
for well in wells:
inj_name = "{}Std{}".format(platform, str(well))
well_name = row + str(well + 1)
cal_list.append(tuple([inj_name, well_name, std_plate_name, 'QC']))
cal_list.append(_wash(wash_no))
if state < 2:
cal_list.append(add_pool(pool_no))
return cal_list
def _wash(wash_no: int):
"""
Generates a single solvent wash injection tuple
:param wash_no: int, number of solvent wash injection
:return: tuple, injection
"""
solvent_wells = ['A11', 'A12', 'B11', 'B12', 'C11', 'C12', 'D11', 'D12', 'E11', 'E12', 'F11', 'F12', 'G11', 'G12',
'H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7', 'H8', 'H9', 'H10', 'H11', 'H12'
]
name = "Wash-{}".format(str(wash_no))
well = solvent_wells[wash_no % 26 - 1]
plate = 'SP' + str(ceil(wash_no/26))
return tuple([name, well, plate, 'QC'])
def washes(repl: int, wash_no: int):
"""
Generates repl number of solvent washes
:param repl: number of tuples to return
:param wash_no: index of washes already created
:return: list of solvent wash tuples
"""
washes_list = list()
for i in range(repl):
washes_list.append(_wash(wash_no + i))
return washes_list
def add_pool(pool_no: int):
"""
Generates pool injection tuple
:param pool_no: the number of pools injected
:return: tuple
"""
pool_wells = ['A5', 'A6', 'A7', 'A8']
plasma = 1
if plasma:
name = 'Utak+IST{}'.format(pool_no)
else:
name = 'Pool{}'.format(pool_no)
well = pool_wells[(pool_no-1) % 4]
plate = ceil(pool_no / 4)
return tuple([name, well, plate, 'QC'])
def save_to_file(inputs, queue_df):
"""
Take the file path of the folder and save the new to_csv into the directory the original file is in.
:return: None
"""
save_file_name = "MX{}_{}_{}_Queue.csv".format(inputs['minix'], inputs['client'], inputs['platform'])
directory = path.dirname(inputs['filepath'])
queue_df.to_csv(path_or_buf=directory + "\\" + save_file_name)
print('Queue saved to', directory + "\\" + save_file_name)
main()